From 452d4f46b45b21cf5d8f24b76256a3e56cb8fe1c Mon Sep 17 00:00:00 2001 From: gitee Date: Mon, 23 Jun 2025 16:13:07 +0800 Subject: [PATCH 01/79] =?UTF-8?q?=E5=A2=9E=E5=8A=A0whl=E5=8C=85=E4=B8=8D?= =?UTF-8?q?=E5=8C=85=E5=90=ABadump=E5=8A=9F=E8=83=BD=E6=8F=8F=E8=BF=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- debug/accuracy_tools/msprobe/docs/01.installation.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/debug/accuracy_tools/msprobe/docs/01.installation.md b/debug/accuracy_tools/msprobe/docs/01.installation.md index 2610852e2..6f2b1a5ef 100644 --- a/debug/accuracy_tools/msprobe/docs/01.installation.md +++ b/debug/accuracy_tools/msprobe/docs/01.installation.md @@ -38,7 +38,7 @@ sha256sum {name}.whl # 验证whl包，若校验码一致，则whl包在下载中 ```bash pip install ./mindstudio_probe-{version}-py3-none-any.whl # 安装whl包 ``` - +上面提供的whl包链接不包含adump功能，如果需要使用adump功能，请参考[源码安装](#3-从源码安装)下载源码编译whl包。 若覆盖安装，请在命令行末尾添加 `--force-reinstall` 参数。 ## 3 从源码安装 -- Gitee From 74d6118766f37284ca1f6bc069d8fc0e182c08db Mon Sep 17 00:00:00 2001 From: gitee Date: Mon, 23 Jun 2025 16:16:48 +0800 Subject: [PATCH 02/79] =?UTF-8?q?=E6=9B=B4=E6=AD=A3=E6=A0=BC=E5=BC=8F?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- debug/accuracy_tools/msprobe/docs/01.installation.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/debug/accuracy_tools/msprobe/docs/01.installation.md b/debug/accuracy_tools/msprobe/docs/01.installation.md index 6f2b1a5ef..538d32eb6 100644 --- a/debug/accuracy_tools/msprobe/docs/01.installation.md +++ b/debug/accuracy_tools/msprobe/docs/01.installation.md @@ -38,8 +38,8 @@ sha256sum {name}.whl # 验证whl包，若校验码一致，则whl包在下载中 ```bash pip install ./mindstudio_probe-{version}-py3-none-any.whl # 安装whl包 ``` +若覆盖安装，请在命令行末尾添加 `--force-reinstall` 参数。 上面提供的whl包链接不包含adump功能，如果需要使用adump功能，请参考[源码安装](#3-从源码安装)下载源码编译whl包。 -若覆盖安装，请在命令行末尾添加 `--force-reinstall` 参数。 ## 3 从源码安装 -- Gitee From b87449115e6f9535b8147ee461b9fb7273f4a05a Mon Sep 17 00:00:00 2001 From: gitee Date: Mon, 23 Jun 2025 16:17:43 +0800 Subject: [PATCH 03/79] =?UTF-8?q?=E8=B5=84=E6=96=99=E6=9B=B4=E6=AD=A3?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- debug/accuracy_tools/msprobe/docs/01.installation.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/debug/accuracy_tools/msprobe/docs/01.installation.md b/debug/accuracy_tools/msprobe/docs/01.installation.md index 538d32eb6..d24740002 100644 --- a/debug/accuracy_tools/msprobe/docs/01.installation.md +++ b/debug/accuracy_tools/msprobe/docs/01.installation.md @@ -39,7 +39,7 @@ sha256sum {name}.whl # 验证whl包，若校验码一致，则whl包在下载中 pip install ./mindstudio_probe-{version}-py3-none-any.whl # 安装whl包 ``` 若覆盖安装，请在命令行末尾添加 `--force-reinstall` 参数。 -上面提供的whl包链接不包含adump功能，如果需要使用adump功能，请参考[源码安装](#3-从源码安装)下载源码编译whl包。 +上面提供的whl包链接不包含adump功能，如果需要使用adump功能，请参考[从源码安装](#3-从源码安装)下载源码编译whl包。 ## 3 从源码安装 -- Gitee From ecb5103b7dbeae9fdaf36831687fffa6f18a2c73 Mon Sep 17 00:00:00 2001 From: wangchao426 Date: Mon, 23 Jun 2025 20:05:19 +0800 Subject: [PATCH 04/79] =?UTF-8?q?=E4=BF=AE=E6=94=B9=E5=AE=89=E5=85=A8?= =?UTF-8?q?=E9=97=AE=E9=A2=98?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- debug/accuracy_tools/msprobe/nan_analyze/graph.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/debug/accuracy_tools/msprobe/nan_analyze/graph.py b/debug/accuracy_tools/msprobe/nan_analyze/graph.py index 18b313ce3..32012e01b 100644 --- a/debug/accuracy_tools/msprobe/nan_analyze/graph.py +++ b/debug/accuracy_tools/msprobe/nan_analyze/graph.py @@ -17,6 +17,7 @@ from dataclasses import dataclass from msprobe.core.common.const import Const from msprobe.core.common.log import logger from msprobe.nan_analyze.utils import FileCache, RankPath, is_ignore_op, check_item_anomaly, NanAnalyseConst +from mstt.debug.accuracy_tools.msprobe.core.common.exceptions import MsprobeException @dataclass @@ -52,6 +53,8 @@ class DataNode: def find_stack(self, stack_info): for item in stack_info.values(): + if not isinstance(item, list): + raise MsprobeException(4, f'The value\'s type in stack.json should be a list, not {type(item)}!') if len(item) >= 2 and self.op_name in item[0]: return item[1] return {} -- Gitee From 7b66b8d039fce71dfc64ccb1adc925e7de440da9 Mon Sep 17 00:00:00 2001 From: yangxinxian <947098055@qq.com> Date: Mon, 23 Jun 2025 20:10:57 +0800 Subject: [PATCH 05/79] =?UTF-8?q?=E4=BF=AE=E6=94=B9cleancode?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../msprobe/ccsrc/base/DebuggerConfig.cpp | 7 ++-- .../msprobe/ccsrc/core/AclDumper.h | 2 +- .../msprobe/ccsrc/core/AclTensor.cpp | 5 +-- .../msprobe/ccsrc/utils/CPythonUtils.h | 3 +- .../api_accuracy_checker/api_runner.py | 35 ++++++++++++++++++- .../dym_loader/hook_dynamic_loader.cpp | 3 +- 6 files changed, 47 insertions(+), 8 deletions(-) diff --git a/debug/accuracy_tools/msprobe/ccsrc/base/DebuggerConfig.cpp b/debug/accuracy_tools/msprobe/ccsrc/base/DebuggerConfig.cpp index a23f53f03..37f065e97 100644 --- a/debug/accuracy_tools/msprobe/ccsrc/base/DebuggerConfig.cpp +++ b/debug/accuracy_tools/msprobe/ccsrc/base/DebuggerConfig.cpp @@ -51,8 +51,11 @@ DebuggerErrno ParseJsonBaseObj2Var(const nlohmann::json& content, const std::str } template -DebuggerErrno ParseJsonStringAndTrans(const nlohmann::json& content, const std::string& field, - const std::map& enum2name, T& output, bool mandatory = false) +DebuggerErrno ParseJsonStringAndTrans(const nlohmann::json& content, + const std::string& field, + const std::map& enum2name, + T& output, + bool mandatory = false) { DebuggerErrno ret; std::string value; diff --git a/debug/accuracy_tools/msprobe/ccsrc/core/AclDumper.h b/debug/accuracy_tools/msprobe/ccsrc/core/AclDumper.h index b4316b184..8407394e2 100644 --- a/debug/accuracy_tools/msprobe/ccsrc/core/AclDumper.h +++ b/debug/accuracy_tools/msprobe/ccsrc/core/AclDumper.h @@ -29,7 +29,7 @@ namespace MindStudioDebugger { class AclDumper { public: - static AclDumper& GetInstance() + static AclDumper& GetInstance() { static AclDumper dumperInstance; return dumperInstance; diff --git a/debug/accuracy_tools/msprobe/ccsrc/core/AclTensor.cpp b/debug/accuracy_tools/msprobe/ccsrc/core/AclTensor.cpp index 4ddfd33d6..6b1d6ad6b 100644 --- a/debug/accuracy_tools/msprobe/ccsrc/core/AclTensor.cpp +++ b/debug/accuracy_tools/msprobe/ccsrc/core/AclTensor.cpp @@ -273,7 +273,7 @@ size_t SizeOfTensor(const AclTensorInfo& tensor, bool host) return num * eleSize; } -static inline int64_t GetCubeSizeByType(const AclDtype& dtype) +static inline int64_t GetCubeSizeByType(const AclDtype& dtype) { if (dtype == AclDtype::DT_UINT8 || dtype == AclDtype::DT_INT8) { return CUBE_32; @@ -842,7 +842,8 @@ static DebuggerErrno TransBf16ToFp32(const uint8_t* input, size_t num, uint8_t* static DebuggerErrno TransInt4ToInt8(const uint8_t* input, size_t elemNums, uint8_t* output, - size_t bufferSize) { + size_t bufferSize) +{ // 输出缓冲区要能容纳 elemNums 个 int8_t if (bufferSize < elemNums * sizeof(int8_t)) { LOG_ERROR(DebuggerErrno::ERROR_BUFFER_OVERFLOW, diff --git a/debug/accuracy_tools/msprobe/ccsrc/utils/CPythonUtils.h b/debug/accuracy_tools/msprobe/ccsrc/utils/CPythonUtils.h index db5153139..628cf6942 100644 --- a/debug/accuracy_tools/msprobe/ccsrc/utils/CPythonUtils.h +++ b/debug/accuracy_tools/msprobe/ccsrc/utils/CPythonUtils.h @@ -86,7 +86,8 @@ public: Py_INCREF(Py_None); ptr = Py_None; } - PythonObject(PyObject* o) : ptr(o) { + PythonObject(PyObject* o) : ptr(o) + { if (ptr == nullptr) { ptr = Py_None; } diff --git a/debug/accuracy_tools/msprobe/mindspore/api_accuracy_checker/api_runner.py b/debug/accuracy_tools/msprobe/mindspore/api_accuracy_checker/api_runner.py index e1640aab9..3e228bf97 100644 --- a/debug/accuracy_tools/msprobe/mindspore/api_accuracy_checker/api_runner.py +++ b/debug/accuracy_tools/msprobe/mindspore/api_accuracy_checker/api_runner.py @@ -13,6 +13,14 @@ # See the License for the specific language governing permissions and # limitations under the License. +from typing import ( + Any, + Dict, + List, + Tuple, + Union, + TypeAlias, +) import os import numpy as np import mindspore @@ -39,6 +47,26 @@ if torch_mindtorch_importer.is_valid_pt_mt_env: else: import torch +# 类型别名，分别表示前向、后向（MS/MT）和函数总体可能的返回类型 +ReturnForward: TypeAlias = Tuple[ + List[ComputeElement], + Tuple[Any, ...], + Dict[str, Any], + Tuple[Any, ...], +] + +ReturnBackwardMS: TypeAlias = Tuple[ + List[ComputeElement], + Union[Any, Tuple[Any, ...]], + Tuple[Any, ...], +] + +ReturnType: TypeAlias = Union[ + List[ComputeElement], # 非 MS/MT 后向 + ReturnForward, # MS/MT 前向 + ReturnBackwardMS, # MS/MT 后向 +] + class ApiInputAggregation: def __init__(self, inputs, kwargs, gradient_inputs) -> None: @@ -179,7 +207,12 @@ class ApiRunner: return api_instance @staticmethod - def run_api(api_instance, api_input_aggregation, forward_or_backward, api_platform): + def run_api( + api_instance, + api_input_aggregation, + forward_or_backward: str, + api_platform: str, + ) -> ReturnType: inputs = tuple(compute_element.get_parameter(get_origin=False, tensor_platform=api_platform) for compute_element in api_input_aggregation.inputs) kwargs = {key: value.get_parameter(get_origin=False, tensor_platform=api_platform) diff --git a/debug/accuracy_tools/msprobe/mindspore/dym_loader/hook_dynamic_loader.cpp b/debug/accuracy_tools/msprobe/mindspore/dym_loader/hook_dynamic_loader.cpp index 4503b6c6d..435b9b8c2 100644 --- a/debug/accuracy_tools/msprobe/mindspore/dym_loader/hook_dynamic_loader.cpp +++ b/debug/accuracy_tools/msprobe/mindspore/dym_loader/hook_dynamic_loader.cpp @@ -29,7 +29,8 @@ HookDynamicLoader &HookDynamicLoader::GetInstance() return instance; } -bool HookDynamicLoader::LoadFunction(void *handle, const std::string &functionName) { +bool HookDynamicLoader::LoadFunction(void *handle, const std::string &functionName) +{ void *func = dlsym(handle, functionName.c_str()); if (!func) { MS_LOG(WARNING) << "Could not load function: " << functionName << ", error: " << dlerror(); -- Gitee From e3394802b88a9fde62578e83af199ca094c34cc0 Mon Sep 17 00:00:00 2001 From: wangchao426 Date: Mon, 23 Jun 2025 20:19:41 +0800 Subject: [PATCH 06/79] =?UTF-8?q?=E4=BF=AE=E6=94=B9=E5=AE=89=E5=85=A8?= =?UTF-8?q?=E9=97=AE=E9=A2=98?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- debug/accuracy_tools/msprobe/nan_analyze/graph.py | 2 +- .../msprobe/test/nan_analyze_ut/test_nan_analyzer_graph.py | 5 +++++ 2 files changed, 6 insertions(+), 1 deletion(-) diff --git a/debug/accuracy_tools/msprobe/nan_analyze/graph.py b/debug/accuracy_tools/msprobe/nan_analyze/graph.py index 32012e01b..39807306f 100644 --- a/debug/accuracy_tools/msprobe/nan_analyze/graph.py +++ b/debug/accuracy_tools/msprobe/nan_analyze/graph.py @@ -17,7 +17,7 @@ from dataclasses import dataclass from msprobe.core.common.const import Const from msprobe.core.common.log import logger from msprobe.nan_analyze.utils import FileCache, RankPath, is_ignore_op, check_item_anomaly, NanAnalyseConst -from mstt.debug.accuracy_tools.msprobe.core.common.exceptions import MsprobeException +from msprobe.core.common.exceptions import MsprobeException @dataclass diff --git a/debug/accuracy_tools/msprobe/test/nan_analyze_ut/test_nan_analyzer_graph.py b/debug/accuracy_tools/msprobe/test/nan_analyze_ut/test_nan_analyzer_graph.py index 715f9bae0..9bd3777ab 100644 --- a/debug/accuracy_tools/msprobe/test/nan_analyze_ut/test_nan_analyzer_graph.py +++ b/debug/accuracy_tools/msprobe/test/nan_analyze_ut/test_nan_analyzer_graph.py @@ -21,6 +21,8 @@ from unittest.mock import patch from msprobe.nan_analyze.graph import CommunicationNode, DataNode from msprobe.nan_analyze.utils import RankPath + +from msprobe.core.common.exceptions import MsprobeException from test_nan_analyzer import DumpDataBuilder, gen_normal_dump_json, MockedFileCache, json_dict, gen_stack_json, do_nothing @@ -121,6 +123,9 @@ class TestDataNode(unittest.TestCase): data_node = DataNode(op_name, 0, dump_json[0]['data'][op_name]) stack_info = data_node.find_stack(json_dict[os.path.join('./step0', 'rank', 'stack.json')]) self.assertEqual(stack_info[0], 'File /root/example.py, line 10, in test_fcn, \\n test(tensor)') + with self.assertRaises(MsprobeException) as context: + data_node.find_stack({op_name: 'blabla'}) + self.assertEqual(context.exception.code, 4) def test_find_complete_construct(self): with patch('msprobe.nan_analyze.graph.FileCache', MockedFileCache): -- Gitee From ad1bd00a42d15a7d025e91c572a11b1762229ab5 Mon Sep 17 00:00:00 2001 From: yangxinxian <947098055@qq.com> Date: Mon, 23 Jun 2025 20:23:54 +0800 Subject: [PATCH 07/79] Update api_runner.py --- .../api_accuracy_checker/api_runner.py | 17 ++++++----------- 1 file changed, 6 insertions(+), 11 deletions(-) diff --git a/debug/accuracy_tools/msprobe/mindspore/api_accuracy_checker/api_runner.py b/debug/accuracy_tools/msprobe/mindspore/api_accuracy_checker/api_runner.py index 3e228bf97..8ec8125f7 100644 --- a/debug/accuracy_tools/msprobe/mindspore/api_accuracy_checker/api_runner.py +++ b/debug/accuracy_tools/msprobe/mindspore/api_accuracy_checker/api_runner.py @@ -18,8 +18,7 @@ from typing import ( Dict, List, Tuple, - Union, - TypeAlias, + Union ) import os import numpy as np @@ -47,25 +46,21 @@ if torch_mindtorch_importer.is_valid_pt_mt_env: else: import torch -# 类型别名，分别表示前向、后向（MS/MT）和函数总体可能的返回类型 -ReturnForward: TypeAlias = Tuple[ +# 为了可读性，我们先给每种返回形态起个别名 +ForwardResult = Tuple[ List[ComputeElement], Tuple[Any, ...], Dict[str, Any], Tuple[Any, ...], ] -ReturnBackwardMS: TypeAlias = Tuple[ +BackwardResultMT = Tuple[ List[ComputeElement], Union[Any, Tuple[Any, ...]], Tuple[Any, ...], ] -ReturnType: TypeAlias = Union[ - List[ComputeElement], # 非 MS/MT 后向 - ReturnForward, # MS/MT 前向 - ReturnBackwardMS, # MS/MT 后向 -] +PyTorchBackward = List[ComputeElement] class ApiInputAggregation: @@ -212,7 +207,7 @@ class ApiRunner: api_input_aggregation, forward_or_backward: str, api_platform: str, - ) -> ReturnType: + ) -> Union[ForwardResult, BackwardResultMT, PyTorchBackward]: inputs = tuple(compute_element.get_parameter(get_origin=False, tensor_platform=api_platform) for compute_element in api_input_aggregation.inputs) kwargs = {key: value.get_parameter(get_origin=False, tensor_platform=api_platform) -- Gitee From e23395b5a5828f7ce293a91ad2de38ef73585831 Mon Sep 17 00:00:00 2001 From: gitee Date: Mon, 23 Jun 2025 20:29:22 +0800 Subject: [PATCH 08/79] fix bug --- .../msprobe/pytorch/api_accuracy_checker/run_ut/run_ut.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/debug/accuracy_tools/msprobe/pytorch/api_accuracy_checker/run_ut/run_ut.py b/debug/accuracy_tools/msprobe/pytorch/api_accuracy_checker/run_ut/run_ut.py index bf04b3cb2..082f391c9 100644 --- a/debug/accuracy_tools/msprobe/pytorch/api_accuracy_checker/run_ut/run_ut.py +++ b/debug/accuracy_tools/msprobe/pytorch/api_accuracy_checker/run_ut/run_ut.py @@ -293,7 +293,7 @@ def run_torch_api(api_full_name, real_data_path, backward_content, api_info_dict if grad_input_index is not None: grad_index = grad_input_index.get('grad_index') - if need_backward: + if need_backward and out is not None: if need_to_backward(grad_index, out): backward_args = backward_content[api_full_name].get("input") func_options = { -- Gitee From 548cc88f9c102d5597eb08e1608108ad39fab92b Mon Sep 17 00:00:00 2001 From: wangchao426 Date: Mon, 23 Jun 2025 20:43:14 +0800 Subject: [PATCH 09/79] =?UTF-8?q?=E4=BF=AE=E6=94=B9=E5=AE=89=E5=85=A8?= =?UTF-8?q?=E9=97=AE=E9=A2=98?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- debug/accuracy_tools/msprobe/nan_analyze/graph.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/debug/accuracy_tools/msprobe/nan_analyze/graph.py b/debug/accuracy_tools/msprobe/nan_analyze/graph.py index 39807306f..13ec61444 100644 --- a/debug/accuracy_tools/msprobe/nan_analyze/graph.py +++ b/debug/accuracy_tools/msprobe/nan_analyze/graph.py @@ -54,7 +54,8 @@ class DataNode: def find_stack(self, stack_info): for item in stack_info.values(): if not isinstance(item, list): - raise MsprobeException(4, f'The value\'s type in stack.json should be a list, not {type(item)}!') + raise MsprobeException(MsprobeException.UNSUPPORTED_TYPE_ERROR, + f'The value\'s type in stack.json should be a list, not {type(item)}!') if len(item) >= 2 and self.op_name in item[0]: return item[1] return {} -- Gitee From c651ce86e7eb77ac076891c4f27f7ca30f66d2a1 Mon Sep 17 00:00:00 2001 From: z30043230 Date: Mon, 23 Jun 2025 20:51:13 +0800 Subject: [PATCH 10/79] =?UTF-8?q?db=E5=92=8Ctext=E5=9C=BA=E6=99=AF?= =?UTF-8?q?=E4=B8=8Brank=5Fset=E4=BB=8Eparallel=5Fgroup=5Finfo=E4=B8=AD?= =?UTF-8?q?=E8=8E=B7=E5=8F=96?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../communication_group/base_communication_group.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/profiler/msprof_analyze/cluster_analyse/communication_group/base_communication_group.py b/profiler/msprof_analyze/cluster_analyse/communication_group/base_communication_group.py index 2765ba263..31cf1ad9f 100644 --- a/profiler/msprof_analyze/cluster_analyse/communication_group/base_communication_group.py +++ b/profiler/msprof_analyze/cluster_analyse/communication_group/base_communication_group.py @@ -190,18 +190,22 @@ class BaseCommunicationGroup: comm_group_df.loc[comm_group_df.shape[0]] = [Constant.P2P, list(rank_set), group_name] # create parallel group dataframe - parallel_group_cols = ["group_name", "group_id", "pg_name"] + parallel_group_cols = ["group_name", "group_id", "pg_name", "global_ranks"] parallel_group_df = pd.DataFrame(columns=parallel_group_cols) for group_id, parallel_info in self.parallel_group_info.items(): group_name = str(double_hash(group_id)) # group_name is hashed group_id pg_name = parallel_info.get("group_name", "") - if not pg_name: + global_ranks = parallel_info.get("global_ranks", []) + if not pg_name or not global_ranks: continue - parallel_group_df.loc[parallel_group_df.shape[0]] = [group_name, group_id, pg_name] + parallel_group_df.loc[parallel_group_df.shape[0]] = [group_name, group_id, pg_name, global_ranks] # merge by group_name df = pd.merge(comm_group_df, parallel_group_df, on='group_name', how='left') df.fillna("", inplace=True) + if self.parallel_group_info: + df["rank_set"] = df["global_ranks"] + df = df.drop(columns=["global_ranks"]) self.comm_group_parallel_info_df = df -- Gitee From 39ef711498f624d43dd032ac8f48995ae7e9c17f Mon Sep 17 00:00:00 2001 From: l30036321 Date: Tue, 24 Jun 2025 09:44:42 +0800 Subject: [PATCH 11/79] fix bug --- .../msprobe/mindspore/debugger/precision_debugger.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/debug/accuracy_tools/msprobe/mindspore/debugger/precision_debugger.py b/debug/accuracy_tools/msprobe/mindspore/debugger/precision_debugger.py index 05c0bf8e0..182eaf433 100644 --- a/debug/accuracy_tools/msprobe/mindspore/debugger/precision_debugger.py +++ b/debug/accuracy_tools/msprobe/mindspore/debugger/precision_debugger.py @@ -231,7 +231,7 @@ class PrecisionDebugger(BasePrecisionDebugger): @classmethod def _start_kernel_dump(cls): - instance = cls.get_instance() + instance = cls._get_instance() is_graph_config = cls._is_graph_dump(instance.config) instance.config.check_config_with_l2(is_graph_config) if not is_graph_config: -- Gitee From cbc28e120f343011353f08851b911f19ed23090f Mon Sep 17 00:00:00 2001 From: Linwei-Ying Date: Tue, 24 Jun 2025 09:55:08 +0800 Subject: [PATCH 12/79] msprobe exception bugfix --- debug/accuracy_tools/msprobe/pytorch/parse_tool/lib/utils.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/debug/accuracy_tools/msprobe/pytorch/parse_tool/lib/utils.py b/debug/accuracy_tools/msprobe/pytorch/parse_tool/lib/utils.py index 144e886ca..2cdfe6f51 100644 --- a/debug/accuracy_tools/msprobe/pytorch/parse_tool/lib/utils.py +++ b/debug/accuracy_tools/msprobe/pytorch/parse_tool/lib/utils.py @@ -37,8 +37,6 @@ try: from rich.table import Table from rich import print as rich_print from rich.columns import Columns - - install() except ImportError as err: install = None Panel = None -- Gitee From 712bb2cb44fe013a0a1f1bd54dea7e412c786336 Mon Sep 17 00:00:00 2001 From: Mrtutu Date: Tue, 24 Jun 2025 10:41:41 +0800 Subject: [PATCH 13/79] fix dyno passwd --- msmonitor/dynolog_npu/cli/Cargo.toml | 1 - msmonitor/dynolog_npu/cli/src/main.rs | 22 ++++++++++++++++++---- 2 files changed, 18 insertions(+), 5 deletions(-) diff --git a/msmonitor/dynolog_npu/cli/Cargo.toml b/msmonitor/dynolog_npu/cli/Cargo.toml index 2f9703700..65b8d94af 100644 --- a/msmonitor/dynolog_npu/cli/Cargo.toml +++ b/msmonitor/dynolog_npu/cli/Cargo.toml @@ -17,4 +17,3 @@ chrono = "0.4" num-bigint = "0.4" openssl = { version = "0.10", features = ["vendored"] } rpassword = "7.2.0" - diff --git a/msmonitor/dynolog_npu/cli/src/main.rs b/msmonitor/dynolog_npu/cli/src/main.rs index 6df6dfb19..ba02f3a39 100644 --- a/msmonitor/dynolog_npu/cli/src/main.rs +++ b/msmonitor/dynolog_npu/cli/src/main.rs @@ -518,6 +518,20 @@ fn create_dyno_client_with_no_certs( Ok(DynoClient::Insecure(stream)) } +// 安全清除密码的函数 +fn secure_clear_password(password: &mut Vec) { + if !password.is_empty() { + // 使用零覆盖密码数据 + for byte in password.iter_mut() { + *byte = 0; + } + // 清空向量 + password.clear(); + // 收缩向量容量，释放内存 + password.shrink_to_fit(); + } +} + fn create_dyno_client_with_certs( host: &str, port: u16, @@ -597,12 +611,12 @@ fn create_dyno_client_with_certs( // 根据是否加密来加载私钥 let keys = if is_encrypted { // 如果私钥是加密的，请求用户输入密码 - let mut password = prompt_password("Please enter the certificate password: ")?; - let pkey = PKey::private_key_from_pem_passphrase(&key_data, password.as_bytes()) + let mut password = prompt_password("Please enter the certificate password: ")?.into_bytes(); + let pkey = PKey::private_key_from_pem_passphrase(&key_data, &password) .map_err(|e| io::Error::new(io::ErrorKind::InvalidData, format!("Failed to decrypt private key: {}", e)))?; - // 清除密码 - password.clear(); + // 手动清除密码 + secure_clear_password(&mut password); // 返回私钥 vec![pkey.private_key_to_der() -- Gitee From 37324aa5a576c110beb130c799d506e9c8e55c5a Mon Sep 17 00:00:00 2001 From: wuyulong17 <2284273586@qq.com> Date: Mon, 23 Jun 2025 19:30:35 +0800 Subject: [PATCH 14/79] =?UTF-8?q?=E4=BB=8Emaster=E5=88=86=E6=94=AF?= =?UTF-8?q?=E4=B8=8A=E4=B8=8B=E6=9E=B6torch-tb-profiler-ascend=E6=8F=92?= =?UTF-8?q?=E4=BB=B6?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 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plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/static/index.js delete mode 100644 plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/static/trace_embedding.html delete mode 100644 plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/static/trace_script.js delete mode 100644 plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/static/trace_viewer_full.html delete mode 100644 plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/utils.py diff --git a/README.md b/README.md index 96394cb1e..899f32e5c 100644 --- a/README.md +++ b/README.md @@ -46,11 +46,10 @@ mstt包括精度工具（msprobe）和性能工具（msprof-analyze），分析 MindStudio一站式在线监控工具。 -## [Tensorboard](./plugins/tensorboard-plugins/tb_plugin) +## [Tensorboard](./plugins/tensorboard-plugins/tb_graph_ascend) +Tensorboard 支持模型结构进行分级可视化展示的插件 tb-graph-ascend。 -Tensorboard 支持 NPU 性能数据可视化插件 PyTorch Profiler TensorBoard NPU Plugin。 - -支持将 Ascend 平台采集、解析的 PyTorch Profiling 数据可视化呈现，也兼容 GPU 数据采集、解析可视化。 +可将模型的层级关系、精度数据进行可视化，并支持将调试模型和标杆模型进行分视图展示和关联比对，方便用户快速定位精度问题。 ## 分支维护策略 diff --git a/plugins/tensorboard-plugins/tb_plugin/.flake8 b/plugins/tensorboard-plugins/tb_plugin/.flake8 deleted file mode 100644 index 1c5254b9f..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/.flake8 +++ /dev/null @@ -1,3 +0,0 @@ -[flake8] -max-line-length = 120 -per-file-ignores = __init__.py:F401 torch_tb_profiler/io/file.py: F401 diff --git a/plugins/tensorboard-plugins/tb_plugin/.gitignore b/plugins/tensorboard-plugins/tb_plugin/.gitignore deleted file mode 100644 index dc7d4e627..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/.gitignore +++ /dev/null @@ -1,4 +0,0 @@ -/build -/dist -/*.egg-info -__pycache__ diff --git a/plugins/tensorboard-plugins/tb_plugin/.pre-commit-config.yaml b/plugins/tensorboard-plugins/tb_plugin/.pre-commit-config.yaml deleted file mode 100644 index a650ec832..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/.pre-commit-config.yaml +++ /dev/null @@ -1,34 +0,0 @@ -# ------------------------------------------------------------------------- -# Copyright (c) Microsoft Corporation. All rights reserved. -# ------------------------------------------------------------------------- -default_language_version: - python: python3.8 - -ci: - autofix_prs: true - autoupdate_commit_msg: '[pre-commit.ci] pre-commit suggestions' - autoupdate_schedule: quarterly - # submodules: true - -repos: - - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.1.0 - hooks: - - id: end-of-file-fixer - exclude: torch_tb_profiler/static/index.html - - id: trailing-whitespace - - id: double-quote-string-fixer - - - repo: https://github.com/pre-commit/mirrors-autopep8 - rev: v1.6.0 - hooks: - - id: autopep8 - name: Format code - - repo: https://github.com/PyCQA/flake8 - rev: 4.0.1 - hooks: - - id: flake8 - args: - - "--max-line-length=120" - - "--per-file-ignores=__init__.py:F401 tb_plugin/torch_tb_profiler/io/file.py: F401" - name: Check PEP8 diff --git a/plugins/tensorboard-plugins/tb_plugin/LICENSE b/plugins/tensorboard-plugins/tb_plugin/LICENSE deleted file mode 100644 index edb179715..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/LICENSE +++ /dev/null @@ -1,33 +0,0 @@ -BSD License - -For Kineto software - -Copyright (c) Facebook, Inc. and its affiliates. All rights reserved. - -All contributions by Microsoft: -Copyright (c) Microsoft Corporation. (The Azure AI Platform team) - -Redistribution and use in source and binary forms, with or without modification, -are permitted provided that the following conditions are met: - - * Redistributions of source code must retain the above copyright notice, this - list of conditions and the following disclaimer. - - * Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - - * Neither the name Facebook nor the names of its contributors may be used to - endorse or promote products derived from this software without specific - prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND -ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED -WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR -ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES -(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; -LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON -ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS -SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/plugins/tensorboard-plugins/tb_plugin/README.md b/plugins/tensorboard-plugins/tb_plugin/README.md index 6b9a3aee0..f3a629c19 100644 --- a/plugins/tensorboard-plugins/tb_plugin/README.md +++ b/plugins/tensorboard-plugins/tb_plugin/README.md @@ -1,44 +1,21 @@ # PyTorch Profiler TensorBoard NPU Plugin +### 下架说明 +本插件代码已不再维护，推荐使用[MindStudio Insight](https://www.hiascend.com/document/detail/zh/mindstudio/80RC1/GUI_baseddevelopmenttool/msascendinsightug/)工具获取更为强大的调优体验。 + ### 介绍 此工具是PyTorch profiling数据以及可视化的TensorBoard的插件。 \ -它支持将Ascend平台采集、解析的Pytorch Profiling数据可视化呈现，也兼容GPU数据采集、解析可视化，现已支持PyTorch 2.0GPU版本的profiling数据可视化。同时集成了精度比对的功能，支持查看loss曲线和比对两个网络的loss收敛趋势。 +它支持将Ascend平台采集、解析的Pytorch Profiling数据可视化呈现，也兼容GPU数据采集、解析可视化，支持PyTorch 2.0GPU版本的profiling数据可视化。同时集成了精度比对的功能，支持查看loss曲线和比对两个网络的loss收敛趋势。 ### 快速安装说明 * 相关依赖： pandas >= 1.0.0 ，tensorboard >= 2.11.0，protobuf <= 3.20.3 * 安装方式 - 1. pip安装（推荐） \ - * 现本插件已经上传到pypi社区，用户可在python环境下直接通过以下pip指令进行安装：\ + * 本插件已经上传到pypi社区，用户可在python环境下直接通过以下pip指令进行安装：\ `pip install torch-tb-profiler-ascend` * 也可在pypi社区上下载离线whl包，传输到无法访问公网的环境上离线安装使用。访问[下载链接](https://pypi.org/project/torch-tb-profiler-ascend/#files)选择whl包进行下载，之后便可使用指令安装（此处{version}为whl包实际版本）\ `pip install torch-tb-profiler_ascend_{version}_py3_none_any.whl` - 2. 从源代码安装 - * 从仓库下载源码: - - `git clone https://gitee.com/ascend/mstt.git` - - * 进入目录 `/plugins/tensorboard-plugins/tb_plugin` 下. - * 编译前端代码 - ``` - cd fe - # 此步骤为安装前端依赖 - yarn - # 回到上层目录执行构建指令 - cd .. - python setup.py build_fe - ``` - **注意**: 编译前端步骤需要安装[Node.js](https://nodejs.org/zh-cn/download)和[yarn](https://yarn.bootcss.com/docs/install/index.html)环境 - * 执行安装命令可直接安装: - - `pip install .` - * 或： 构建whl包安装 - - `python setup.py build_fe sdist bdist_wheel` - - 在 `/tb_plugins/profiling/tb_plugin/dist` 目录下取出whl包，使用以下指令安装（此处{version}为whl包实际版本） - - `pip install torch-tb-profiler-ascend-{version}-py3-none-any.whl` - ### 解析数据说明 * 准备profiling数据 diff --git a/plugins/tensorboard-plugins/tb_plugin/ci_scripts/install_env.sh b/plugins/tensorboard-plugins/tb_plugin/ci_scripts/install_env.sh deleted file mode 100644 index 04406ae6e..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/ci_scripts/install_env.sh +++ /dev/null @@ -1,17 +0,0 @@ -#!/bin/bash - -set -ex - -# install pytorch -pip install numpy tensorboard typing-extensions pillow pytest -if [ "$PYTORCH_VERSION" = "nightly" ]; then - pip install --pre torch -f "https://download.pytorch.org/whl/nightly/$CUDA_VERSION/torch_nightly.html" - pip install --pre torchvision --no-deps -f "https://download.pytorch.org/whl/nightly/$CUDA_VERSION/torch_nightly.html" -elif [ "$PYTORCH_VERSION" = "1.11rc" ]; then - pip install --pre torch -f "https://download.pytorch.org/whl/test/$CUDA_VERSION/torch_test.html" - pip install --pre torchvision --no-deps -f "https://download.pytorch.org/whl/test/$CUDA_VERSION/torch_test.html" -elif [ "$PYTORCH_VERSION" = "stable" ]; then - pip install torch torchvision -fi - -python -c "import torch; print(torch.__version__, torch.version.git_version); from torch.autograd import kineto_available; print(kineto_available())" diff --git a/plugins/tensorboard-plugins/tb_plugin/docs/gpu_utilization.md b/plugins/tensorboard-plugins/tb_plugin/docs/gpu_utilization.md deleted file mode 100644 index c4f45b880..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/docs/gpu_utilization.md +++ /dev/null @@ -1,22 +0,0 @@ -* GPU Utilization: GPU busy time / all steps time. The higher, the better. All steps time is the total time of all profiler steps(or called as iterations). - GPU busy time is the time during “all steps time” when is at least one GPU kernel running on this GPU. - However, this high-level utilization metric is coarse. It can’t tell how many SMs(Stream Multiprocessors) are in use. - For example, a kernel with a single thread running continuously will get 100% GPU utilization. - -* Est. SM Efficiency: Estimated Stream Multiprocessor Efficiency. The higher, the better. This metric of a kernel, SM_Eff_K = min(blocks of this kernel / SM number of this GPU, 100%). - This overall number is the sum of all kernels' SM_Eff_K weighted by kernel's execution duration, divided by “all steps time”. - It shows GPU Stream Multiprocessors’ utilization. - Although it is finer grained than above “GPU Utilization”, it still can’t tell the whole story. - For example, a kernel with only one thread per block can’t fully utilize each SM. - -* Est. Achieved Occupancy: For most cases such as memory bandwidth bound kernels, a higher value often translates to better performance, especially when the initial value is very low. [Reference](http://developer.download.nvidia.com/GTC/PDF/GTC2012/PresentationPDF/S0514-GTC2012-GPU-Performance-Analysis.pdf). The definition of occupancy is [here](https://docs.nvidia.com/gameworks/content/developertools/desktop/analysis/report/cudaexperiments/kernellevel/achievedoccupancy.htm). - Occupancy is the ratio of active warps on an SM to the maximum number of - active warps supported by the SM. The theoretical occupancy of a kernel is upper limit occupancy of this kernel, limited by multiple - factors such as kernel shape, kernel used resource, and the GPU compute capability. - Est. Achieved Occupancy of a kernel, OCC_K = min(threads of the kernel / SM number / max threads per SM, theoretical occupancy of the kernel). - This overall number is the weighted sum of all kernels OCC_K using kernel's execution duration as weight. It shows fine-grained low-level GPU utilization. - - * Kernel Time using Tensor Cores: Total GPU Time for Tensor Core kernels / Total GPU Time for all kernels. Higher is better. - Tensor Cores are mixed precision floating point operations available for Volta GPUs (Titan V) and beyond. - The cuDNN and cuBLAS libraries contain several Tensor Cores enabled GPU kernels for most Convolution and GEMM operations. - This number shows Tensor Cores usage time ratio among all kernels on a GPU. diff --git "a/plugins/tensorboard-plugins/tb_plugin/docs/\345\205\254\347\275\221URL\350\257\264\346\230\216.xlsx" "b/plugins/tensorboard-plugins/tb_plugin/docs/\345\205\254\347\275\221URL\350\257\264\346\230\216.xlsx" deleted file mode 100644 index de0bb25fe155aa188e5670a377311e96168586e8..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 17513 zcmaJ|19)Cd(@xSjjT$v(W81c!#`Z6}Qz+qUie$=mOL(GPn)c}}i9gPnVy zb9Q!iMq2CxFeJcx3y|F4e!u>IgLwN#&r(;~#?s1`O8Siq@vQ^=53&(nH9#6b000&s z006kZlWAL7Q97BMrABs&exQN#KlMI^vneg4aMzCH=>D zuui74twrLy&jw5xs^?qggs-Dqt5RETlT#&rfiunCQZT(K-{{(EEELX?x9|57=m?HQ 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z9irdM(*NT2fBUKbsr3Jg)PHCHz2^Ea_Pw`t{4e%DE3p5U(QoV2zuLReT5o{AIRXER z;h!jZGXKqM_{PA|JUH(i!Oh)cWaT~ zG7jFX{aXpWjk{$KY0bMi#re7-mP|397ZpT_^5Q23Wd>%X)8m0tM!kbghv_)7pP=^uLkKJWi) z(C?LgzuNnxDZcsrZ-f4uNAYfAh<^$6{x@^}5+yAL^2ZAh82}kT^=$#X<9z4)Ka~-98vp= 4: - break - p.step() - print("done") diff --git a/plugins/tensorboard-plugins/tb_plugin/examples/resnet50_autograd_api.py b/plugins/tensorboard-plugins/tb_plugin/examples/resnet50_autograd_api.py deleted file mode 100644 index 9ff5d89aa..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/examples/resnet50_autograd_api.py +++ /dev/null @@ -1,46 +0,0 @@ -import os -import torch -import torch.nn as nn -import torch.backends.cudnn as cudnn -import torch.optim -import torch.utils.data -import torchvision -import torchvision.transforms as T -import torchvision.models as models - -from torch.autograd.profiler import profile - -model = models.resnet50(pretrained=True) -model.cuda() -cudnn.benchmark = True - -transform = T.Compose([T.Resize(256), T.CenterCrop(224), T.ToTensor()]) -trainset = torchvision.datasets.CIFAR10(root='./data', train=True, - download=True, transform=transform) -trainloader = torch.utils.data.DataLoader(trainset, batch_size=32, - shuffle=True, num_workers=0) - -criterion = nn.CrossEntropyLoss().cuda() -optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9) -device = torch.device("cuda:0") -model.train() - -with profile(use_cuda=True, use_kineto=True, record_shapes=True) as p: - for step, data in enumerate(trainloader, 0): - print("step:{}".format(step)) - inputs, labels = data[0].to(device=device), data[1].to(device=device) - - outputs = model(inputs) - loss = criterion(outputs, labels) - - optimizer.zero_grad() - loss.backward() - optimizer.step() - if step >= 5: - break - -try: - os.mkdir("result") -except Exception: - pass -p.export_chrome_trace("./result/worker0.pt.trace.json") diff --git a/plugins/tensorboard-plugins/tb_plugin/examples/resnet50_ddp_profiler.py b/plugins/tensorboard-plugins/tb_plugin/examples/resnet50_ddp_profiler.py deleted file mode 100644 index 0ebcfe989..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/examples/resnet50_ddp_profiler.py +++ /dev/null @@ -1,95 +0,0 @@ -import os - -import torch -import torch.backends.cudnn as cudnn -import torch.distributed as dist -import torch.multiprocessing as mp -import torch.nn as nn -import torch.optim -import torch.profiler -import torch.utils.data -import torchvision -import torchvision.models as models -import torchvision.transforms as T -from torch.nn.parallel import DistributedDataParallel as DDP - - -def example(rank, use_gpu=True): - if use_gpu: - torch.cuda.set_device(rank) - model = models.resnet50(pretrained=True).to(rank) - model.cuda() - cudnn.benchmark = True - model = DDP(model, device_ids=[rank]) - else: - model = models.resnet50(pretrained=True) - model = DDP(model) - - # Use gradient compression to reduce communication - # model.register_comm_hook(None, default.fp16_compress_hook) - # or - # state = powerSGD_hook.PowerSGDState(process_group=None,matrix_approximation_rank=1,start_powerSGD_iter=2) - # model.register_comm_hook(state, powerSGD_hook.powerSGD_hook) - - transform = T.Compose([T.Resize(256), T.CenterCrop(224), T.ToTensor()]) - trainset = torchvision.datasets.CIFAR10(root='./data', train=True, - download=True, transform=transform) - train_sampler = torch.utils.data.distributed.DistributedSampler(trainset) - trainloader = torch.utils.data.DataLoader(trainset, batch_size=32, sampler=train_sampler, - shuffle=False, num_workers=4) - - if use_gpu: - criterion = nn.CrossEntropyLoss().to(rank) - else: - criterion = nn.CrossEntropyLoss() - optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9) - model.train() - - with torch.profiler.profile( - activities=[ - torch.profiler.ProfilerActivity.CPU, - torch.profiler.ProfilerActivity.CUDA], - schedule=torch.profiler.schedule( - wait=2, - warmup=2, - active=5), - with_stack=False, - on_trace_ready=torch.profiler.tensorboard_trace_handler('./result'), - record_shapes=True - ) as p: - for step, data in enumerate(trainloader, 0): - print("step:{}".format(step)) - if use_gpu: - inputs, labels = data[0].to(rank), data[1].to(rank) - else: - inputs, labels = data[0], data[1] - outputs = model(inputs) - loss = criterion(outputs, labels) - - optimizer.zero_grad() - loss.backward() - optimizer.step() - p.step() - if step + 1 >= 10: - break - - -def init_process(rank, size, fn, backend='nccl'): - """ Initialize the distributed environment. """ - os.environ['MASTER_ADDR'] = '127.0.0.1' - os.environ['MASTER_PORT'] = '29500' - dist.init_process_group(backend, rank=rank, world_size=size) - fn(rank, size) - - -if __name__ == "__main__": - size = 4 - processes = [] - mp.set_start_method("spawn") - for rank in range(size): - p = mp.Process(target=init_process, args=(rank, size, example)) - p.start() - processes.append(p) - - for p in processes: - p.join() diff --git a/plugins/tensorboard-plugins/tb_plugin/examples/resnet50_profiler_api.py b/plugins/tensorboard-plugins/tb_plugin/examples/resnet50_profiler_api.py deleted file mode 100644 index cdfa14aa7..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/examples/resnet50_profiler_api.py +++ /dev/null @@ -1,52 +0,0 @@ -import torch -import torch.nn as nn -import torch.backends.cudnn as cudnn -import torch.optim -import torch.utils.data -import torchvision -import torchvision.transforms as T -import torchvision.models as models - -import torch.profiler - -model = models.resnet50(pretrained=True) -model.cuda() -cudnn.benchmark = True - -transform = T.Compose([T.Resize(256), T.CenterCrop(224), T.ToTensor()]) -trainset = torchvision.datasets.CIFAR10(root='./data', train=True, - download=True, transform=transform) -trainloader = torch.utils.data.DataLoader(trainset, batch_size=32, - shuffle=True, num_workers=4) - -criterion = nn.CrossEntropyLoss().cuda() -optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9) -device = torch.device("cuda:0") -model.train() - -with torch.profiler.profile( - activities=[ - torch.profiler.ProfilerActivity.CPU, - torch.profiler.ProfilerActivity.CUDA], - schedule=torch.profiler.schedule( - wait=1, - warmup=1, - active=2), - on_trace_ready=torch.profiler.tensorboard_trace_handler('./result', worker_name='worker0'), - record_shapes=True, - profile_memory=True, # This will take 1 to 2 minutes. Setting it to False could greatly speedup. - with_stack=True -) as p: - for step, data in enumerate(trainloader, 0): - print("step:{}".format(step)) - inputs, labels = data[0].to(device=device), data[1].to(device=device) - - outputs = model(inputs) - loss = criterion(outputs, labels) - - optimizer.zero_grad() - loss.backward() - optimizer.step() - if step + 1 >= 4: - break - p.step() diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/.gitignore b/plugins/tensorboard-plugins/tb_plugin/fe/.gitignore deleted file mode 100644 index e6a926968..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/.gitignore +++ /dev/null @@ -1,3 +0,0 @@ -/dist -/node_modules -*.log diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/README.md b/plugins/tensorboard-plugins/tb_plugin/fe/README.md deleted file mode 100644 index aa9cf2587..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/README.md +++ /dev/null @@ -1,18 +0,0 @@ -# Pytorch Profiler - -### Install & Build - -1. install [Node.js](https://nodejs.org/) - * ```bash - curl -fsSL https://deb.nodesource.com/setup_16.x | sudo -E bash - - sudo apt-get install -y nodejs``` -2. install [Yarn](https://yarnpkg.com/) - * ```bash - curl -sS https://dl.yarnpkg.com/debian/pubkey.gpg | sudo apt-key add - - echo "deb https://dl.yarnpkg.com/debian/ stable main" | sudo tee /etc/apt/sources.list.d/yarn.list - sudo apt update && sudo apt install yarn - ``` -3. shell `yarn` to prepare JS dependency -4. shell `yarn build:copy` -5. Go to `tb_plugin` folder and install the package using `python setup.py develop` -6. Launch tensorboard diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/index.html b/plugins/tensorboard-plugins/tb_plugin/fe/index.html deleted file mode 100644 index a58ddc088..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/index.html +++ /dev/null @@ -1,10 +0,0 @@ - - - - - - - -
- - diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/package.json b/plugins/tensorboard-plugins/tb_plugin/fe/package.json deleted file mode 100644 index 5edca28fa..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/package.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "name": "fe", - "private": "true", - "version": "1.0.0", - "main": "index.js", - "scripts": { - "build": "cross-env NODE_ENV=production webpack", - "build:copy": "yarn build && node ./update-static.js", - "build:dev": "webpack", - "dev": "webpack serve", - "prettier": "prettier --config ./prettier.json --write ./src/**/*.{ts,tsx} ./*.js" - }, - "dependencies": { - "@ant-design/icons": "^5.2.6", - "@babel/runtime": "^7.13.10", - "@material-ui/core": "^4.11.3", - "@material-ui/icons": "^4.11.2", - "antd": "^4.23.0", - "clsx": "^1.1.1", - "echarts": "^5.4.2", - "portable-fetch": "^3.0.0", - "react": "^16.13.1", - "react-dom": "^16.13.1", - "react-flame-graph": "^1.4.0", - "url": "^0.11.1" - }, - "devDependencies": { - "@types/react": "^17.0.69", - "@types/react-dom": "^18.2.14", - "cross-env": "^7.0.2", - "css-loader": "^5.2.4", - "html-webpack-plugin": "^5.3.1", - "inline-chunk-html-plugin": "^1.1.1", - "prettier": "^2.1.2", - "style-loader": "^2.0.0", - "ts-loader": "^8.0.18", - "typescript": "^4.0.3", - "webpack": "^5.28.0", - "webpack-cli": "^4.5.0", - "webpack-dev-server": "^4.7.4" - }, - "resolutions": { - "portable-fetch/**/node-fetch": "^2.6.1", - "webpack/**/browserslist": "^4.16.5", - "postcss/**/nanoid": "^3.1.31" - } -} diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/prettier.json b/plugins/tensorboard-plugins/tb_plugin/fe/prettier.json deleted file mode 100644 index ef5789da9..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/prettier.json +++ /dev/null @@ -1,12 +0,0 @@ -{ - "parser": "typescript", - "semi": true, - "singleQuote": true, - "jsxSingleQuote": false, - "bracketSpacing": true, - "tabWidth": 2, - "useTabs": false, - "trailingComma": "all", - "proseWrap": "always", - "endOfLine": "lf" -} diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/scripts/add_header.py b/plugins/tensorboard-plugins/tb_plugin/fe/scripts/add_header.py deleted file mode 100644 index 69bc6c055..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/scripts/add_header.py +++ /dev/null @@ -1,51 +0,0 @@ -# ------------------------------------------------------------------------- -# Copyright (c) Microsoft Corporation. -# Copyright(c) 2023 Huawei Technologies. -# All rights reserved -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# Modifications: Add visualization of PyTorch Ascend profiling. -# -------------------------------------------------------------------------- -# !/usr/bin/env python -import glob -import os -import sys - -HEADER = '''/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *--------------------------------------------------------------------------------------------*/ - -''' - - -def add_header(file): - with open(file, 'r') as f: - contents = f.readlines() - - # do nothing if there is already header - if contents and contents[0].startswith('/*-'): - return - - with open(file, 'w') as out: - out.write(HEADER) - out.writelines(contents) - - -if __name__ == '__main__': - directory = sys.argv[1] - if not os.path.isdir(directory): - raise ValueError('{} is not a directory'.format(directory)) - - for ts_file in glob.glob(directory + '/*.ts'): - add_header(ts_file) diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/scripts/build.sh b/plugins/tensorboard-plugins/tb_plugin/fe/scripts/build.sh deleted file mode 100644 index 014a26e26..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/scripts/build.sh +++ /dev/null @@ -1,13 +0,0 @@ -#!/bin/bash -set -e - -current_dir="$( cd "$( dirname "$0" )" && pwd )" -FE_ROOT="$(dirname "$current_dir")" -cd $FE_ROOT/ - -java -jar $FE_ROOT/swagger-codegen-cli.jar generate -i $FE_ROOT/src/api/openapi.yaml -l typescript-fetch -o $FE_ROOT/src/api/generated/ --additional-properties modelPropertyNaming=original -rm $FE_ROOT/src/api/generated/api_test.spec.ts -yarn prettier --end-of-line lf -python $FE_ROOT/scripts/add_header.py $FE_ROOT/src/api/generated/ - -yarn build:copy diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/scripts/setup.sh b/plugins/tensorboard-plugins/tb_plugin/fe/scripts/setup.sh deleted file mode 100644 index fb2680d98..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/scripts/setup.sh +++ /dev/null @@ -1,25 +0,0 @@ -#!/bin/bash -set -e - -current_dir="$( cd "$( dirname "$0" )" && pwd )" -FE_ROOT="$(dirname "$current_dir")" - -# # install nodejs -if ! command -v node &> /dev/null -then - curl -fsSL https://deb.nodesource.com/setup_16.x | sudo -E bash - - sudo apt-get install -y nodejs -fi - -# install yarn -if ! command -v yarn &> /dev/null -then - curl -sS https://dl.yarnpkg.com/debian/pubkey.gpg | sudo apt-key add - - echo "deb https://dl.yarnpkg.com/debian/ stable main" | sudo tee /etc/apt/sources.list.d/yarn.list - sudo apt update && sudo apt install yarn -fi - -# download swagger-codegen-cli -if [[ ! -f "$FE_ROOT/swagger-codegen-cli.jar" ]]; then - wget https://repo1.maven.org/maven2/io/swagger/codegen/v3/swagger-codegen-cli/3.0.25/swagger-codegen-cli-3.0.25.jar -O swagger-codegen-cli.jar -fi diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/api/README.md b/plugins/tensorboard-plugins/tb_plugin/fe/src/api/README.md deleted file mode 100644 index 06208c419..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/api/README.md +++ /dev/null @@ -1,13 +0,0 @@ -# How to generate the api.ts - -## Prerequisites -1. install java -2. run command -```bash - cd fe - wget https://repo1.maven.org/maven2/io/swagger/codegen/v3/swagger-codegen-cli/3.0.25/swagger-codegen-cli-3.0.25.jar -O swagger-codegen-cli.jar - java -jar swagger-codegen-cli.jar generate -i ./src/api/openapi.yaml -l typescript-fetch -o ./src/api/generated/ --additional-properties modelPropertyNaming=original - rm ./src/api/generated/api_test.spec.ts - yarn prettier --end-of-line lf - python ./scripts/add_header.py ./src/api/generated/ -``` diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/api/generated/api.ts b/plugins/tensorboard-plugins/tb_plugin/fe/src/api/generated/api.ts deleted file mode 100644 index 29cde96eb..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/api/generated/api.ts +++ /dev/null @@ -1,4724 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *-------------------------------------------------------------------------------------------- - * Copyright (c) 2023, Huawei Technologies. - * All rights reserved. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * - * Modifications: Add visualization of PyTorch Ascend profiling. - *--------------------------------------------------------------------------------------------*/ - -/// -// tslint:disable -/** - * Pytorch profile API - * No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen) - * - * OpenAPI spec version: 1.0.0 - * - * - * NOTE: This file is auto generated by the swagger code generator program. - * https://github.com/swagger-api/swagger-codegen.git - * Do not edit the file manually. - */ - -import * as url from 'url'; -import * as portableFetch from 'portable-fetch'; -import { Configuration } from './configuration'; - -const BASE_PATH = '.'.replace(/\/+$/, ''); - -/** - * - * @export - */ -export const COLLECTION_FORMATS = { - csv: ',', - ssv: ' ', - tsv: '\t', - pipes: '|', -}; - -/** - * - * @export - * @interface FetchAPI - */ -export interface FetchAPI { - (url: string, init?: any): Promise; -} - -/** - * - * @export - * @interface FetchArgs - */ -export interface FetchArgs { - url: string; - options: any; -} - -/** - * - * @export - * @class BaseAPI - */ -export class BaseAPI { - protected configuration: Configuration; - - constructor( - configuration?: Configuration, - protected basePath: string = BASE_PATH, - protected fetch: FetchAPI = portableFetch - ) { - if (configuration) { - this.configuration = configuration; - this.basePath = configuration.basePath || this.basePath; - } - } -} - -/** - * - * @export - * @class RequiredError - * @extends {Error} - */ -export class RequiredError extends Error { - name: 'RequiredError'; - constructor(public field: string, msg?: string) { - super(msg); - } -} - -/** - * - * @export - * @interface CallStackTableData - */ -export interface CallStackTableData extends Array {} -/** - * - * @export - * @interface CallStackTableDataInner - */ -export interface CallStackTableDataInner { - /** - * - * @type {string} - * @memberof CallStackTableDataInner - */ - name: string; - /** - * - * @type {string} - * @memberof CallStackTableDataInner - */ - input_shape?: string; - /** - * - * @type {number} - * @memberof CallStackTableDataInner - */ - calls: number; - /** - * - * @type {number} - * @memberof CallStackTableDataInner - */ - device_self_duration?: number; - /** - * - * @type {number} - * @memberof CallStackTableDataInner - */ - device_total_duration?: number; - /** - * - * @type {number} - * @memberof CallStackTableDataInner - */ - host_self_duration: number; - /** - * - * @type {number} - * @memberof CallStackTableDataInner - */ - host_total_duration: number; - /** - * - * @type {string} - * @memberof CallStackTableDataInner - */ - call_stack?: string; - /** - * - * @type {string} - * @memberof CallStackTableDataInner - */ - tc_eligible?: string; - /** - * - * @type {number} - * @memberof CallStackTableDataInner - */ - tc_self_ratio?: number; - /** - * - * @type {number} - * @memberof CallStackTableDataInner - */ - tc_total_ratio?: number; -} -/** - * - * @export - * @interface DiffNode - */ -export interface DiffNode { - /** - * - * @type {OpStats} - * @memberof DiffNode - */ - left: OpStats; - /** - * - * @type {OpStats} - * @memberof DiffNode - */ - right: OpStats; - /** - * - * @type {string} - * @memberof DiffNode - */ - path: string; - /** - * - * @type {Array} - * @memberof DiffNode - */ - children: Array; -} -/** - * - * @export - * @interface DistributedGraph - */ -export interface DistributedGraph { - /** - * - * @type {DistributedGraphMetadata} - * @memberof DistributedGraph - */ - metadata: DistributedGraphMetadata; - /** - * - * @type {any} - * @memberof DistributedGraph - */ - data: any; -} -/** - * - * @export - * @interface DistributedGraphMetadata - */ -export interface DistributedGraphMetadata { - /** - * - * @type {string} - * @memberof DistributedGraphMetadata - */ - title: string; - /** - * - * @type {Array} - * @memberof DistributedGraphMetadata - */ - legends: Array; - /** - * - * @type {string} - * @memberof DistributedGraphMetadata - */ - units: string; -} -/** - * - * @export - * @interface Environment - */ -export interface Environment { - /** - * - * @type {string} - * @memberof Environment - */ - title: string; - /** - * - * @type {string} - * @memberof Environment - */ - value: string; -} -/** - * - * @export - * @interface GpuInfo - */ -export interface GpuInfo { - /** - * - * @type {GpuInfoMetadata} - * @memberof GpuInfo - */ - metadata: GpuInfoMetadata; - /** - * - * @type {any} - * @memberof GpuInfo - */ - data: any; -} -/** - * - * @export - * @interface GpuInfoMetadata - */ -export interface GpuInfoMetadata { - /** - * - * @type {string} - * @memberof GpuInfoMetadata - */ - title: string; -} -/** - * - * @export - * @interface GpuMetric - */ -export interface GpuMetric { - /** - * - * @type {string} - * @memberof GpuMetric - */ - title: string; - /** - * - * @type {string} - * @memberof GpuMetric - */ - value: string; -} -/** - * - * @export - * @interface GpuMetrics - */ -export interface GpuMetrics { - /** - * - * @type {Array} - * @memberof GpuMetrics - */ - data: Array; - /** - * - * @type {string} - * @memberof GpuMetrics - */ - tooltip: string; -} -/** - * - * @export - * @interface Graph - */ -export interface Graph { - /** - * - * @type {string} - * @memberof Graph - */ - title?: string; - /** - * - * @type {Array} - * @memberof Graph - */ - columns: Array; - /** - * - * @type {Array>} - * @memberof Graph - */ - rows: Array>; -} -/** - * - * @export - * @interface ValueAndTooltip - */ -export interface ValueAndTooltip { - /** - * - * @type {string | number} - * @memberof ValueAndTooltip - */ - value: string | number; - /** - * - * @type {string} - * @memberof ValueAndTooltip - */ - tooltip?: string; -} -/** - * - * @export - * @interface StepedGraph - */ -export interface StepedGraph { - /** - * - * @type {string} - * @memberof StepedGraph - */ - title?: string; - /** - * - * @type {Array} - * @memberof StepedGraph - */ - columns: Array; - /** - * - * @type {Array>} - * @memberof StepedGraph - */ - rows: Array>; -} -/** - * - * @export - * @interface GraphAscend - */ -export interface GraphAscend { - /** - * - * @type {string} - * @memberof GraphAscend - */ - title?: string; - /** - * - * @type {Array} - * @memberof GraphAscend - */ - columns: Array; - /** - * - * @type {any} - * @memberof GraphAscend - */ - rows: any; -} -/** - * - * @export - * @interface GraphColumn - */ -export interface GraphColumn { - /** - * - * @type {string} - * @memberof GraphColumn - */ - type: string; - /** - * - * @type {string} - * @memberof GraphColumn - */ - name: string; - /** - * - * @type {string} - * @memberof GraphColumn - */ - role?: string; - /** - * - * @type {GraphColumnP} - * @memberof GraphColumn - */ - p?: GraphColumnP; -} -/** - * - * @export - * @interface GraphColumnP - */ -export interface GraphColumnP { - /** - * - * @type {boolean} - * @memberof GraphColumnP - */ - html?: boolean; -} -/** - * - * @export - * @interface InlineResponse200 - */ -export interface InlineResponse200 { - /** - * - * @type {TableMetadata} - * @memberof InlineResponse200 - */ - metadata: TableMetadata; - /** - * - * @type {OperationTableData} - * @memberof InlineResponse200 - */ - data: OperationTableData; -} -/** - * - * @export - * @interface InlineResponse2001 - */ -export interface InlineResponse2001 { - /** - * - * @type {TableMetadata} - * @memberof InlineResponse2001 - */ - metadata: TableMetadata; - /** - * - * @type {CallStackTableData} - * @memberof InlineResponse2001 - */ - data: CallStackTableData; -} -/** - * - * @export - * @interface InlineResponse2002 - */ -export interface InlineResponse2002 { - /** - * - * @type {GpuInfoMetadata} - * @memberof InlineResponse2002 - */ - metadata: GpuInfoMetadata; - /** - * - * @type {any} - * @memberof InlineResponse2002 - */ - data: any; -} -/** - * - * @export - * @interface KernelGraph - */ -export interface KernelGraph { - /** - * - * @type {Graph} - * @memberof KernelGraph - */ - total: Graph; - device_target: string; -} -/** - * - * @export - * @interface KeyedColumn - */ -export interface KeyedColumn { - /** - * - * @type {string} - * @memberof KeyedColumn - */ - type: string; - /** - * - * @type {string} - * @memberof KeyedColumn - */ - name: string; - /** - * - * @type {string} - * @memberof KeyedColumn - */ - key: string; -} -/** - * - * @export - * @interface MemoryCurveDataAll - */ -export interface MemoryCurveDataAll { - /** - * - * @type {string} - * @memberof MemoryCurveDataAll - */ - default_device: string; - /** - * - * @type {Array} - * @memberof MemoryCurveDataAll - */ - devices: Array; - /** - * - * @type {MemoryCurveDataAscend} - * @memberof MemoryCurveDataAll - */ - total: MemoryCurveDataAscend; - /** - * - * @type {MemoryCurveDataAscend} - * @memberof MemoryCurveDataAll - */ - ptaGe: MemoryCurveDataAscend; -} -/** - * - * @export - * @interface MemoryCurveData - */ -export interface MemoryCurveData { - /** - * - * @type {MemoryCurveDataMetadata} - * @memberof MemoryCurveData - */ - metadata: MemoryCurveDataMetadata; - /** - * - * @type {Array} - * @memberof MemoryCurveData - */ - columns: Array; - /** - * - * @type {any} - * @memberof MemoryCurveData - */ - rows: any; -} -/** - * - * @export - * @interface MemoryCurveDataAscend - */ -export interface MemoryCurveDataAscend { - /** - * - * @type {MemoryCurveDataMetadata} - * @memberof MemoryCurveDataAscend - */ - metadata: MemoryCurveDataMetadata; - /** - * - * @type {any} - * @memberof MemoryCurveDataAscend - */ - columns: any; - /** - * - * @type {any} - * @memberof MemoryCurveDataAscend - */ - rows: any; -} -/** - * - * @export - * @interface MemoryCurveDataMetadata - */ -export interface MemoryCurveDataMetadata { - /** - * - * @type {string} - * @memberof MemoryCurveDataMetadata - */ - default_device: string; - /** - * - * @type {Array} - * @memberof MemoryCurveDataMetadata - */ - devices: Array; - /** - * - * @type {any} - * @memberof MemoryCurveDataMetadata - */ - peaks: any; - /** - * - * @type {any} - * @memberof MemoryCurveDataMetadata - */ - totals: any; - /** - * - * @type {number} - * @memberof MemoryCurveDataMetadata - */ - first_ts: number; - /** - * - * @type {string} - * @memberof MemoryCurveDataMetadata - */ - time_metric: string; - /** - * - * @type {string} - * @memberof MemoryCurveDataMetadata - */ - memory_metric: string; - /** - * - * @type {number} - * @memberof MemoryCurveDataMetadata - */ - time_factor: number; - /** - * - * @type {number} - * @memberof MemoryCurveDataMetadata - */ - memory_factor: number; -} -/** - * - * @export - * @interface MemoryEventsData - */ -export interface MemoryEventsData { - /** - * - * @type {MemoryEventsTableMetadata} - * @memberof MemoryEventsData - */ - metadata: MemoryEventsTableMetadata; - /** - * - * @type {Array} - * @memberof MemoryEventsData - */ - columns: Array; - /** - * - * @type {any} - * @memberof MemoryEventsData - */ - rows: any; -} -/** - * - * @exports - * @interface MemoryEventsDataAll - */ -export interface MemoryEventsDataAll { - /** - * - * @type {MemoryEventsData} - * @memberof MemoryEventsDataAll - */ - operator: MemoryEventsData; - /** - * - * @type {MemoryEventsData} - * @memberof MemoryEventsDataAll - */ - component: MemoryEventsData; -} -/** - * - * @export - * @interface MemoryEventsTableMetadata - */ -export interface MemoryEventsTableMetadata { - /** - * - * @type {string} - * @memberof MemoryEventsTableMetadata - */ - title: string; - /** - * - * @type {string} - * @memberof MemoryEventsTableMetadata - */ - default_device: string; - /** - * - * @type {string} - * @memberof MemoryEventsTableMetadata - */ - search?: string; - /** - * - * @type {string} - * @memberof MemoryEventsTableMetadata - */ - sort?: string; -} -/** - * - * @export - * @interface MemoryStatsData - */ -export interface MemoryStatsData { - /** - * - * @type {MemoryStatsTableMetadata} - * @memberof MemoryStatsData - */ - metadata: MemoryStatsTableMetadata; - /** - * - * @type {Array} - * @memberof MemoryStatsData - */ - columns: Array; - /** - * - * @type {any} - * @memberof MemoryStatsData - */ - rows: any; -} -/** - * - * @export - * @interface MemoryStatsTableMetadata - */ -export interface MemoryStatsTableMetadata { - /** - * - * @type {string} - * @memberof MemoryStatsTableMetadata - */ - title: string; - /** - * - * @type {string} - * @memberof MemoryStatsTableMetadata - */ - default_device: string; - /** - * - * @type {string} - * @memberof MemoryStatsTableMetadata - */ - search: string; - /** - * - * @type {string} - * @memberof MemoryStatsTableMetadata - */ - sort: string; -} -/** - * - * @export - * @interface ModuleStats - */ -export interface ModuleStats { - /** - * - * @type {string} - * @memberof ModuleStats - */ - name: string; - /** - * - * @type {string} - * @memberof ModuleStats - */ - id: string; - /** - * - * @type {number} - * @memberof ModuleStats - */ - occurences: number; - /** - * - * @type {number} - * @memberof ModuleStats - */ - operators: number; - /** - * - * @type {number} - * @memberof ModuleStats - */ - host_duration: number; - /** - * - * @type {number} - * @memberof ModuleStats - */ - self_host_duration: number; - /** - * - * @type {number} - * @memberof ModuleStats - */ - device_duration: number; - /** - * - * @type {number} - * @memberof ModuleStats - */ - self_device_duration: number; - /** - * - * @type {number} - * @memberof ModuleStats - */ - avg_duration: number; - /** - * - * @type {Array} - * @memberof ModuleStats - */ - children: Array; -} -/** - * - * @export - * @interface ModuleViewData - */ -export interface ModuleViewData { - /** - * - * @type {Array} - * @memberof ModuleViewData - */ - columns: Array; - /** - * - * @type {Array} - * @memberof ModuleViewData - */ - data: Array; -} -/** - * - * @export - * @interface OpAgg - */ -export interface OpAgg { - /** - * - * @type {string} - * @memberof OpAgg - */ - name: string; - /** - * - * @type {number} - * @memberof OpAgg - */ - calls: number; - /** - * - * @type {number} - * @memberof OpAgg - */ - host_duration: number; - /** - * - * @type {number} - * @memberof OpAgg - */ - device_duration: number; - /** - * - * @type {number} - * @memberof OpAgg - */ - self_host_duration: number; - /** - * - * @type {number} - * @memberof OpAgg - */ - self_device_duration: number; -} -/** - * - * @export - * @interface OpStats - */ -export interface OpStats { - /** - * - * @type {string} - * @memberof OpStats - */ - name: string; - /** - * - * @type {number} - * @memberof OpStats - */ - duration: number; - /** - * - * @type {number} - * @memberof OpStats - */ - device_duration: number; - /** - * - * @type {number} - * @memberof OpStats - */ - total_duration: number; - /** - * - * @type {Array} - * @memberof OpStats - */ - aggs: Array; -} -/** - * - * @export - * @interface OperationTableData - */ -export interface OperationTableData extends Array {} -/** - * - * @export - * @interface OperationTableDataInner - */ -export interface OperationTableDataInner { - /** - * - * @type {string} - * @memberof OperationTableDataInner - */ - name: string; - /** - * - * @type {string} - * @memberof OperationTableDataInner - */ - input_shape?: string; - /** - * - * @type {number} - * @memberof OperationTableDataInner - */ - calls: number; - /** - * - * @type {number} - * @memberof OperationTableDataInner - */ - device_self_duration?: number; - /** - * - * @type {number} - * @memberof OperationTableDataInner - */ - device_total_duration?: number; - /** - * - * @type {number} - * @memberof OperationTableDataInner - */ - host_self_duration: number; - /** - * - * @type {number} - * @memberof OperationTableDataInner - */ - host_total_duration: number; - /** - * - * @type {boolean} - * @memberof OperationTableDataInner - */ - has_call_stack: boolean; - /** - * - * @type {string} - * @memberof OperationTableDataInner - */ - tc_eligible?: string; - /** - * - * @type {number} - * @memberof OperationTableDataInner - */ - tc_self_ratio?: number; - /** - * - * @type {number} - * @memberof OperationTableDataInner - */ - tc_total_ratio?: number; -} -/** - * - * @export - * @interface OperatorGraph - */ -export interface OperatorGraph { - /** - * - * @type {Graph} - * @memberof OperatorGraph - */ - device_total_time: Graph; - /** - * - * @type {Graph} - * @memberof OperatorGraph - */ - device_self_time: Graph; - /** - * - * @type {Graph} - * @memberof OperatorGraph - */ - host_total_time: Graph; - /** - * - * @type {Graph} - * @memberof OperatorGraph - */ - host_self_time: Graph; -} -/** - * - * @export - * @interface OperatorNode - */ -export interface OperatorNode { - /** - * - * @type {string} - * @memberof OperatorNode - */ - name: string; - /** - * - * @type {number} - * @memberof OperatorNode - */ - start_time: number; - /** - * - * @type {number} - * @memberof OperatorNode - */ - end_time: number; - /** - * - * @type {string} - * @memberof OperatorNode - */ - type: string; - /** - * - * @type {number} - * @memberof OperatorNode - */ - tid: number; - /** - * - * @type {Array} - * @memberof OperatorNode - */ - children: Array; -} -/** - * - * @export - * @interface Overview - */ -export interface Overview { - /** - * - * @type {Array} - * @memberof Overview - */ - performance: Array; - /** - * - * @type {Array} - * @memberof Overview - */ - environments: Array; - /** - * - * @type {StepedGraph} - * @memberof Overview - */ - steps: StepedGraph; - /** - * - * @type {string} - * @memberof Overview - */ - recommendations: string; - /** - * - * @type {GpuMetrics} - * @memberof Overview - */ - gpu_metrics?: GpuMetrics; -} -/** - * - * @export - * @interface Performance - */ -export interface Performance { - /** - * - * @type {string} - * @memberof Performance - */ - name: string; - /** - * - * @type {string} - * @memberof Performance - */ - description?: string; - /** - * - * @type {string} - * @memberof Performance - */ - value?: string; - /** - * - * @type {string} - * @memberof Performance - */ - extra?: string; - /** - * - * @type {Array} - * @memberof Performance - */ - children?: Array; -} -/** - * - * @export - * @interface Runs - */ -export interface Runs { - /** - * - * @type {Array} - * @memberof Runs - */ - runs: Array; - /** - * - * @type {boolean} - * @memberof Runs - */ - loading: boolean; -} -/** - * - * @export - * @interface TableData - */ -export interface TableData { - /** - * - * @type {Graph} - * @memberof TableData - */ - data: Graph; - /** - * - * @type {TableMetadata} - * @memberof TableData - */ - metadata: TableMetadata; -} -/** - * - * @export - * @interface TableMetadata - */ -export interface TableMetadata { - /** - * - * @type {string} - * @memberof TableMetadata - */ - sort: string; - /** - * - * @type {any} - * @memberof TableMetadata - */ - tooltips?: any; -} -/** - * - * @export - * @interface TensorCoresGraph - */ -export interface TensorCoresGraph { - /** - * - * @type {Graph} - * @memberof TensorCoresGraph - */ - total: Graph; -} -/** - * - * @export - * @interface ValueAndFormat - */ -export interface ValueAndFormat { - /** - * - * @type {string | number | boolean} - * @memberof ValueAndFormat - */ - v: string | number | boolean; - /** - * - * @type {string} - * @memberof ValueAndFormat - */ - f: string; -} -/** - * - * @exports - * @interface Views - */ -export interface Views { - /** - * - * @type {string} - * @memberof Views - */ - device_target: string; - /** - * - * @type {Array} - * @memberof Views - */ - views: Array; -} -/** - * DefaultApi - fetch parameter creator - * @export - */ -export const DefaultApiFetchParamCreator = function ( - configuration?: Configuration -) { - return { - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {string} exp_run - * @param {string} exp_worker - * @param {string} exp_span - * @param {string} [path] - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - diffnodeGet( - run: string, - worker: string, - span: string, - exp_run: string, - exp_worker: string, - exp_span: string, - path?: string, - options: any = {} - ): FetchArgs { - // verify required parameter 'run' is not null or undefined - if (run === null || run === undefined) { - throw new RequiredError( - 'run', - 'Required parameter run was null or undefined when calling diffnodeGet.' - ); - } - // verify required parameter 'worker' is not null or undefined - if (worker === null || worker === undefined) { - throw new RequiredError( - 'worker', - 'Required parameter worker was null or undefined when calling diffnodeGet.' - ); - } - // verify required parameter 'span' is not null or undefined - if (span === null || span === undefined) { - throw new RequiredError( - 'span', - 'Required parameter span was null or undefined when calling diffnodeGet.' - ); - } - // verify required parameter 'exp_run' is not null or undefined - if (exp_run === null || exp_run === undefined) { - throw new RequiredError( - 'exp_run', - 'Required parameter exp_run was null or undefined when calling diffnodeGet.' - ); - } - // verify required parameter 'exp_worker' is not null or undefined - if (exp_worker === null || exp_worker === undefined) { - throw new RequiredError( - 'exp_worker', - 'Required parameter exp_worker was null or undefined when calling diffnodeGet.' - ); - } - // verify required parameter 'exp_span' is not null or undefined - if (exp_span === null || exp_span === undefined) { - throw new RequiredError( - 'exp_span', - 'Required parameter exp_span was null or undefined when calling diffnodeGet.' - ); - } - const localVarPath = `/diffnode`; - const localVarUrlObj = url.parse(localVarPath, true); - const localVarRequestOptions = Object.assign({ method: 'GET' }, options); - const localVarHeaderParameter = {} as any; - const localVarQueryParameter = {} as any; - - if (run !== undefined) { - localVarQueryParameter.run = run; - } - - if (worker !== undefined) { - localVarQueryParameter.worker = worker; - } - - if (span !== undefined) { - localVarQueryParameter.span = span; - } - - if (exp_run !== undefined) { - localVarQueryParameter.exp_run = exp_run; - } - - if (exp_worker !== undefined) { - localVarQueryParameter.exp_worker = exp_worker; - } - - if (exp_span !== undefined) { - localVarQueryParameter.exp_span = exp_span; - } - - if (path !== undefined) { - localVarQueryParameter.path = path; - } - - localVarUrlObj.query = Object.assign( - {}, - localVarUrlObj.query, - localVarQueryParameter, - options.query - ); - // fix override query string Detail: https://stackoverflow.com/a/7517673/1077943 - delete localVarUrlObj.search; - localVarRequestOptions.headers = Object.assign( - {}, - localVarHeaderParameter, - options.headers - ); - - return { - url: url.format(localVarUrlObj), - options: localVarRequestOptions, - }; - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - distributedCommopsGet( - run: string, - worker: string, - span: string, - options: any = {} - ): FetchArgs { - // verify required parameter 'run' is not null or undefined - if (run === null || run === undefined) { - throw new RequiredError( - 'run', - 'Required parameter run was null or undefined when calling distributedCommopsGet.' - ); - } - // verify required parameter 'worker' is not null or undefined - if (worker === null || worker === undefined) { - throw new RequiredError( - 'worker', - 'Required parameter worker was null or undefined when calling distributedCommopsGet.' - ); - } - // verify required parameter 'span' is not null or undefined - if (span === null || span === undefined) { - throw new RequiredError( - 'span', - 'Required parameter span was null or undefined when calling distributedCommopsGet.' - ); - } - const localVarPath = `/distributed/commops`; - const localVarUrlObj = url.parse(localVarPath, true); - const localVarRequestOptions = Object.assign({ method: 'GET' }, options); - const localVarHeaderParameter = {} as any; - const localVarQueryParameter = {} as any; - - if (run !== undefined) { - localVarQueryParameter.run = run; - } - - if (worker !== undefined) { - localVarQueryParameter.worker = worker; - } - - if (span !== undefined) { - localVarQueryParameter.span = span; - } - - localVarUrlObj.query = Object.assign( - {}, - localVarUrlObj.query, - localVarQueryParameter, - options.query - ); - // fix override query string Detail: https://stackoverflow.com/a/7517673/1077943 - delete localVarUrlObj.search; - localVarRequestOptions.headers = Object.assign( - {}, - localVarHeaderParameter, - options.headers - ); - - return { - url: url.format(localVarUrlObj), - options: localVarRequestOptions, - }; - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - distributedGpuinfoGet( - run: string, - worker: string, - span: string, - options: any = {} - ): FetchArgs { - // verify required parameter 'run' is not null or undefined - if (run === null || run === undefined) { - throw new RequiredError( - 'run', - 'Required parameter run was null or undefined when calling distributedGpuinfoGet.' - ); - } - // verify required parameter 'worker' is not null or undefined - if (worker === null || worker === undefined) { - throw new RequiredError( - 'worker', - 'Required parameter worker was null or undefined when calling distributedGpuinfoGet.' - ); - } - // verify required parameter 'span' is not null or undefined - if (span === null || span === undefined) { - throw new RequiredError( - 'span', - 'Required parameter span was null or undefined when calling distributedGpuinfoGet.' - ); - } - const localVarPath = `/distributed/gpuinfo`; - const localVarUrlObj = url.parse(localVarPath, true); - const localVarRequestOptions = Object.assign({ method: 'GET' }, options); - const localVarHeaderParameter = {} as any; - const localVarQueryParameter = {} as any; - - if (run !== undefined) { - localVarQueryParameter.run = run; - } - - if (worker !== undefined) { - localVarQueryParameter.worker = worker; - } - - if (span !== undefined) { - localVarQueryParameter.span = span; - } - - localVarUrlObj.query = Object.assign( - {}, - localVarUrlObj.query, - localVarQueryParameter, - options.query - ); - // fix override query string Detail: https://stackoverflow.com/a/7517673/1077943 - delete localVarUrlObj.search; - localVarRequestOptions.headers = Object.assign( - {}, - localVarHeaderParameter, - options.headers - ); - - return { - url: url.format(localVarUrlObj), - options: localVarRequestOptions, - }; - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - distributedOverlapGet( - run: string, - worker: string, - span: string, - options: any = {} - ): FetchArgs { - // verify required parameter 'run' is not null or undefined - if (run === null || run === undefined) { - throw new RequiredError( - 'run', - 'Required parameter run was null or undefined when calling distributedOverlapGet.' - ); - } - // verify required parameter 'worker' is not null or undefined - if (worker === null || worker === undefined) { - throw new RequiredError( - 'worker', - 'Required parameter worker was null or undefined when calling distributedOverlapGet.' - ); - } - // verify required parameter 'span' is not null or undefined - if (span === null || span === undefined) { - throw new RequiredError( - 'span', - 'Required parameter span was null or undefined when calling distributedOverlapGet.' - ); - } - const localVarPath = `/distributed/overlap`; - const localVarUrlObj = url.parse(localVarPath, true); - const localVarRequestOptions = Object.assign({ method: 'GET' }, options); - const localVarHeaderParameter = {} as any; - const localVarQueryParameter = {} as any; - - if (run !== undefined) { - localVarQueryParameter.run = run; - } - - if (worker !== undefined) { - localVarQueryParameter.worker = worker; - } - - if (span !== undefined) { - localVarQueryParameter.span = span; - } - - localVarUrlObj.query = Object.assign( - {}, - localVarUrlObj.query, - localVarQueryParameter, - options.query - ); - // fix override query string Detail: https://stackoverflow.com/a/7517673/1077943 - delete localVarUrlObj.search; - localVarRequestOptions.headers = Object.assign( - {}, - localVarHeaderParameter, - options.headers - ); - - return { - url: url.format(localVarUrlObj), - options: localVarRequestOptions, - }; - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - distributedWaittimeGet( - run: string, - worker: string, - span: string, - options: any = {} - ): FetchArgs { - // verify required parameter 'run' is not null or undefined - if (run === null || run === undefined) { - throw new RequiredError( - 'run', - 'Required parameter run was null or undefined when calling distributedWaittimeGet.' - ); - } - // verify required parameter 'worker' is not null or undefined - if (worker === null || worker === undefined) { - throw new RequiredError( - 'worker', - 'Required parameter worker was null or undefined when calling distributedWaittimeGet.' - ); - } - // verify required parameter 'span' is not null or undefined - if (span === null || span === undefined) { - throw new RequiredError( - 'span', - 'Required parameter span was null or undefined when calling distributedWaittimeGet.' - ); - } - const localVarPath = `/distributed/waittime`; - const localVarUrlObj = url.parse(localVarPath, true); - const localVarRequestOptions = Object.assign({ method: 'GET' }, options); - const localVarHeaderParameter = {} as any; - const localVarQueryParameter = {} as any; - - if (run !== undefined) { - localVarQueryParameter.run = run; - } - - if (worker !== undefined) { - localVarQueryParameter.worker = worker; - } - - if (span !== undefined) { - localVarQueryParameter.span = span; - } - - localVarUrlObj.query = Object.assign( - {}, - localVarUrlObj.query, - localVarQueryParameter, - options.query - ); - // fix override query string Detail: https://stackoverflow.com/a/7517673/1077943 - delete localVarUrlObj.search; - localVarRequestOptions.headers = Object.assign( - {}, - localVarHeaderParameter, - options.headers - ); - - return { - url: url.format(localVarUrlObj), - options: localVarRequestOptions, - }; - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {string} group_by Group By - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - kernelGet( - run: string, - worker: string, - span: string, - group_by: string, - options: any = {} - ): FetchArgs { - // verify required parameter 'run' is not null or undefined - if (run === null || run === undefined) { - throw new RequiredError( - 'run', - 'Required parameter run was null or undefined when calling kernelGet.' - ); - } - // verify required parameter 'worker' is not null or undefined - if (worker === null || worker === undefined) { - throw new RequiredError( - 'worker', - 'Required parameter worker was null or undefined when calling kernelGet.' - ); - } - // verify required parameter 'span' is not null or undefined - if (span === null || span === undefined) { - throw new RequiredError( - 'span', - 'Required parameter span was null or undefined when calling kernelGet.' - ); - } - // verify required parameter 'group_by' is not null or undefined - if (group_by === null || group_by === undefined) { - throw new RequiredError( - 'group_by', - 'Required parameter group_by was null or undefined when calling kernelGet.' - ); - } - const localVarPath = `/kernel`; - const localVarUrlObj = url.parse(localVarPath, true); - const localVarRequestOptions = Object.assign({ method: 'GET' }, options); - const localVarHeaderParameter = {} as any; - const localVarQueryParameter = {} as any; - - if (run !== undefined) { - localVarQueryParameter.run = run; - } - - if (worker !== undefined) { - localVarQueryParameter.worker = worker; - } - - if (span !== undefined) { - localVarQueryParameter.span = span; - } - - if (group_by !== undefined) { - localVarQueryParameter.group_by = group_by; - } - - localVarUrlObj.query = Object.assign( - {}, - localVarUrlObj.query, - localVarQueryParameter, - options.query - ); - // fix override query string Detail: https://stackoverflow.com/a/7517673/1077943 - delete localVarUrlObj.search; - localVarRequestOptions.headers = Object.assign( - {}, - localVarHeaderParameter, - options.headers - ); - - return { - url: url.format(localVarUrlObj), - options: localVarRequestOptions, - }; - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {string} [group_by] Group By - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - kernelTableGet( - run: string, - worker: string, - span: string, - group_by?: string, - options: any = {} - ): FetchArgs { - // verify required parameter 'run' is not null or undefined - if (run === null || run === undefined) { - throw new RequiredError( - 'run', - 'Required parameter run was null or undefined when calling kernelTableGet.' - ); - } - // verify required parameter 'worker' is not null or undefined - if (worker === null || worker === undefined) { - throw new RequiredError( - 'worker', - 'Required parameter worker was null or undefined when calling kernelTableGet.' - ); - } - // verify required parameter 'span' is not null or undefined - if (span === null || span === undefined) { - throw new RequiredError( - 'span', - 'Required parameter span was null or undefined when calling kernelTableGet.' - ); - } - const localVarPath = `/kernel/table`; - const localVarUrlObj = url.parse(localVarPath, true); - const localVarRequestOptions = Object.assign({ method: 'GET' }, options); - const localVarHeaderParameter = {} as any; - const localVarQueryParameter = {} as any; - - if (run !== undefined) { - localVarQueryParameter.run = run; - } - - if (worker !== undefined) { - localVarQueryParameter.worker = worker; - } - - if (span !== undefined) { - localVarQueryParameter.span = span; - } - - if (group_by !== undefined) { - localVarQueryParameter.group_by = group_by; - } - - localVarUrlObj.query = Object.assign( - {}, - localVarUrlObj.query, - localVarQueryParameter, - options.query - ); - // fix override query string Detail: https://stackoverflow.com/a/7517673/1077943 - delete localVarUrlObj.search; - localVarRequestOptions.headers = Object.assign( - {}, - localVarHeaderParameter, - options.headers - ); - - return { - url: url.format(localVarUrlObj), - options: localVarRequestOptions, - }; - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - kernelTcPieGet( - run: string, - worker: string, - span: string, - options: any = {} - ): FetchArgs { - // verify required parameter 'run' is not null or undefined - if (run === null || run === undefined) { - throw new RequiredError( - 'run', - 'Required parameter run was null or undefined when calling kernelTcPieGet.' - ); - } - // verify required parameter 'worker' is not null or undefined - if (worker === null || worker === undefined) { - throw new RequiredError( - 'worker', - 'Required parameter worker was null or undefined when calling kernelTcPieGet.' - ); - } - // verify required parameter 'span' is not null or undefined - if (span === null || span === undefined) { - throw new RequiredError( - 'span', - 'Required parameter span was null or undefined when calling kernelTcPieGet.' - ); - } - const localVarPath = `/kernel/tc_pie`; - const localVarUrlObj = url.parse(localVarPath, true); - const localVarRequestOptions = Object.assign({ method: 'GET' }, options); - const localVarHeaderParameter = {} as any; - const localVarQueryParameter = {} as any; - - if (run !== undefined) { - localVarQueryParameter.run = run; - } - - if (worker !== undefined) { - localVarQueryParameter.worker = worker; - } - - if (span !== undefined) { - localVarQueryParameter.span = span; - } - - localVarUrlObj.query = Object.assign( - {}, - localVarUrlObj.query, - localVarQueryParameter, - options.query - ); - // fix override query string Detail: https://stackoverflow.com/a/7517673/1077943 - delete localVarUrlObj.search; - localVarRequestOptions.headers = Object.assign( - {}, - localVarHeaderParameter, - options.headers - ); - - return { - url: url.format(localVarUrlObj), - options: localVarRequestOptions, - }; - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - memoryCurveGet( - run: string, - worker: string, - span: string, - options: any = {} - ): FetchArgs { - // verify required parameter 'run' is not null or undefined - if (run === null || run === undefined) { - throw new RequiredError( - 'run', - 'Required parameter run was null or undefined when calling memoryCurveGet.' - ); - } - // verify required parameter 'worker' is not null or undefined - if (worker === null || worker === undefined) { - throw new RequiredError( - 'worker', - 'Required parameter worker was null or undefined when calling memoryCurveGet.' - ); - } - // verify required parameter 'span' is not null or undefined - if (span === null || span === undefined) { - throw new RequiredError( - 'span', - 'Required parameter span was null or undefined when calling memoryCurveGet.' - ); - } - const localVarPath = `/memory_curve`; - const localVarUrlObj = url.parse(localVarPath, true); - const localVarRequestOptions = Object.assign({ method: 'GET' }, options); - const localVarHeaderParameter = {} as any; - const localVarQueryParameter = {} as any; - - if (run !== undefined) { - localVarQueryParameter.run = run; - } - - if (worker !== undefined) { - localVarQueryParameter.worker = worker; - } - - if (span !== undefined) { - localVarQueryParameter.span = span; - } - - localVarUrlObj.query = Object.assign( - {}, - localVarUrlObj.query, - localVarQueryParameter, - options.query - ); - // fix override query string Detail: https://stackoverflow.com/a/7517673/1077943 - delete localVarUrlObj.search; - localVarRequestOptions.headers = Object.assign( - {}, - localVarHeaderParameter, - options.headers - ); - - return { - url: url.format(localVarUrlObj), - options: localVarRequestOptions, - }; - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {number} [start_ts] - * @param {number} [end_ts] - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - memoryEventsGet( - run: string, - worker: string, - span: string, - start_ts?: number, - end_ts?: number, - options: any = {} - ): FetchArgs { - // verify required parameter 'run' is not null or undefined - if (run === null || run === undefined) { - throw new RequiredError( - 'run', - 'Required parameter run was null or undefined when calling memoryEventsGet.' - ); - } - // verify required parameter 'worker' is not null or undefined - if (worker === null || worker === undefined) { - throw new RequiredError( - 'worker', - 'Required parameter worker was null or undefined when calling memoryEventsGet.' - ); - } - // verify required parameter 'span' is not null or undefined - if (span === null || span === undefined) { - throw new RequiredError( - 'span', - 'Required parameter span was null or undefined when calling memoryEventsGet.' - ); - } - const localVarPath = `/memory_events`; - const localVarUrlObj = url.parse(localVarPath, true); - const localVarRequestOptions = Object.assign({ method: 'GET' }, options); - const localVarHeaderParameter = {} as any; - const localVarQueryParameter = {} as any; - - if (run !== undefined) { - localVarQueryParameter.run = run; - } - - if (worker !== undefined) { - localVarQueryParameter.worker = worker; - } - - if (span !== undefined) { - localVarQueryParameter.span = span; - } - - if (start_ts !== undefined) { - localVarQueryParameter.start_ts = start_ts; - } - - if (end_ts !== undefined) { - localVarQueryParameter.end_ts = end_ts; - } - - localVarUrlObj.query = Object.assign( - {}, - localVarUrlObj.query, - localVarQueryParameter, - options.query - ); - // fix override query string Detail: https://stackoverflow.com/a/7517673/1077943 - delete localVarUrlObj.search; - localVarRequestOptions.headers = Object.assign( - {}, - localVarHeaderParameter, - options.headers - ); - - return { - url: url.format(localVarUrlObj), - options: localVarRequestOptions, - }; - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {number} [start_ts] - * @param {number} [end_ts] - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - memoryGet( - run: string, - worker: string, - span: string, - start_ts?: number, - end_ts?: number, - options: any = {} - ): FetchArgs { - // verify required parameter 'run' is not null or undefined - if (run === null || run === undefined) { - throw new RequiredError( - 'run', - 'Required parameter run was null or undefined when calling memoryGet.' - ); - } - // verify required parameter 'worker' is not null or undefined - if (worker === null || worker === undefined) { - throw new RequiredError( - 'worker', - 'Required parameter worker was null or undefined when calling memoryGet.' - ); - } - // verify required parameter 'span' is not null or undefined - if (span === null || span === undefined) { - throw new RequiredError( - 'span', - 'Required parameter span was null or undefined when calling memoryGet.' - ); - } - const localVarPath = `/memory`; - const localVarUrlObj = url.parse(localVarPath, true); - const localVarRequestOptions = Object.assign({ method: 'GET' }, options); - const localVarHeaderParameter = {} as any; - const localVarQueryParameter = {} as any; - - if (run !== undefined) { - localVarQueryParameter.run = run; - } - - if (worker !== undefined) { - localVarQueryParameter.worker = worker; - } - - if (span !== undefined) { - localVarQueryParameter.span = span; - } - - if (start_ts !== undefined) { - localVarQueryParameter.start_ts = start_ts; - } - - if (end_ts !== undefined) { - localVarQueryParameter.end_ts = end_ts; - } - - localVarUrlObj.query = Object.assign( - {}, - localVarUrlObj.query, - localVarQueryParameter, - options.query - ); - // fix override query string Detail: https://stackoverflow.com/a/7517673/1077943 - delete localVarUrlObj.search; - localVarRequestOptions.headers = Object.assign( - {}, - localVarHeaderParameter, - options.headers - ); - - return { - url: url.format(localVarUrlObj), - options: localVarRequestOptions, - }; - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - moduleGet( - run: string, - worker: string, - span: string, - options: any = {} - ): FetchArgs { - // verify required parameter 'run' is not null or undefined - if (run === null || run === undefined) { - throw new RequiredError( - 'run', - 'Required parameter run was null or undefined when calling moduleGet.' - ); - } - // verify required parameter 'worker' is not null or undefined - if (worker === null || worker === undefined) { - throw new RequiredError( - 'worker', - 'Required parameter worker was null or undefined when calling moduleGet.' - ); - } - // verify required parameter 'span' is not null or undefined - if (span === null || span === undefined) { - throw new RequiredError( - 'span', - 'Required parameter span was null or undefined when calling moduleGet.' - ); - } - const localVarPath = `/module`; - const localVarUrlObj = url.parse(localVarPath, true); - const localVarRequestOptions = Object.assign({ method: 'GET' }, options); - const localVarHeaderParameter = {} as any; - const localVarQueryParameter = {} as any; - - if (run !== undefined) { - localVarQueryParameter.run = run; - } - - if (worker !== undefined) { - localVarQueryParameter.worker = worker; - } - - if (span !== undefined) { - localVarQueryParameter.span = span; - } - - localVarUrlObj.query = Object.assign( - {}, - localVarUrlObj.query, - localVarQueryParameter, - options.query - ); - // fix override query string Detail: https://stackoverflow.com/a/7517673/1077943 - delete localVarUrlObj.search; - localVarRequestOptions.headers = Object.assign( - {}, - localVarHeaderParameter, - options.headers - ); - - return { - url: url.format(localVarUrlObj), - options: localVarRequestOptions, - }; - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {string} group_by Group By - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - operationGet( - run: string, - worker: string, - span: string, - group_by: string, - options: any = {} - ): FetchArgs { - // verify required parameter 'run' is not null or undefined - if (run === null || run === undefined) { - throw new RequiredError( - 'run', - 'Required parameter run was null or undefined when calling operationGet.' - ); - } - // verify required parameter 'worker' is not null or undefined - if (worker === null || worker === undefined) { - throw new RequiredError( - 'worker', - 'Required parameter worker was null or undefined when calling operationGet.' - ); - } - // verify required parameter 'span' is not null or undefined - if (span === null || span === undefined) { - throw new RequiredError( - 'span', - 'Required parameter span was null or undefined when calling operationGet.' - ); - } - // verify required parameter 'group_by' is not null or undefined - if (group_by === null || group_by === undefined) { - throw new RequiredError( - 'group_by', - 'Required parameter group_by was null or undefined when calling operationGet.' - ); - } - const localVarPath = `/operation`; - const localVarUrlObj = url.parse(localVarPath, true); - const localVarRequestOptions = Object.assign({ method: 'GET' }, options); - const localVarHeaderParameter = {} as any; - const localVarQueryParameter = {} as any; - - if (run !== undefined) { - localVarQueryParameter.run = run; - } - - if (worker !== undefined) { - localVarQueryParameter.worker = worker; - } - - if (span !== undefined) { - localVarQueryParameter.span = span; - } - - if (group_by !== undefined) { - localVarQueryParameter.group_by = group_by; - } - - localVarUrlObj.query = Object.assign( - {}, - localVarUrlObj.query, - localVarQueryParameter, - options.query - ); - // fix override query string Detail: https://stackoverflow.com/a/7517673/1077943 - delete localVarUrlObj.search; - localVarRequestOptions.headers = Object.assign( - {}, - localVarHeaderParameter, - options.headers - ); - - return { - url: url.format(localVarUrlObj), - options: localVarRequestOptions, - }; - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {string} group_by Group By - * @param {string} op_name - * @param {string} [input_shape] - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - operationStackGet( - run: string, - worker: string, - span: string, - group_by: string, - op_name: string, - input_shape?: string, - options: any = {} - ): FetchArgs { - // verify required parameter 'run' is not null or undefined - if (run === null || run === undefined) { - throw new RequiredError( - 'run', - 'Required parameter run was null or undefined when calling operationStackGet.' - ); - } - // verify required parameter 'worker' is not null or undefined - if (worker === null || worker === undefined) { - throw new RequiredError( - 'worker', - 'Required parameter worker was null or undefined when calling operationStackGet.' - ); - } - // verify required parameter 'span' is not null or undefined - if (span === null || span === undefined) { - throw new RequiredError( - 'span', - 'Required parameter span was null or undefined when calling operationStackGet.' - ); - } - // verify required parameter 'group_by' is not null or undefined - if (group_by === null || group_by === undefined) { - throw new RequiredError( - 'group_by', - 'Required parameter group_by was null or undefined when calling operationStackGet.' - ); - } - // verify required parameter 'op_name' is not null or undefined - if (op_name === null || op_name === undefined) { - throw new RequiredError( - 'op_name', - 'Required parameter op_name was null or undefined when calling operationStackGet.' - ); - } - const localVarPath = `/operation/stack`; - const localVarUrlObj = url.parse(localVarPath, true); - const localVarRequestOptions = Object.assign({ method: 'GET' }, options); - const localVarHeaderParameter = {} as any; - const localVarQueryParameter = {} as any; - - if (run !== undefined) { - localVarQueryParameter.run = run; - } - - if (worker !== undefined) { - localVarQueryParameter.worker = worker; - } - - if (span !== undefined) { - localVarQueryParameter.span = span; - } - - if (group_by !== undefined) { - localVarQueryParameter.group_by = group_by; - } - - if (op_name !== undefined) { - localVarQueryParameter.op_name = op_name; - } - - if (input_shape !== undefined) { - localVarQueryParameter.input_shape = input_shape; - } - - localVarUrlObj.query = Object.assign( - {}, - localVarUrlObj.query, - localVarQueryParameter, - options.query - ); - // fix override query string Detail: https://stackoverflow.com/a/7517673/1077943 - delete localVarUrlObj.search; - localVarRequestOptions.headers = Object.assign( - {}, - localVarHeaderParameter, - options.headers - ); - - return { - url: url.format(localVarUrlObj), - options: localVarRequestOptions, - }; - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {string} group_by Group By - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - operationTableGet( - run: string, - worker: string, - span: string, - group_by: string, - options: any = {} - ): FetchArgs { - // verify required parameter 'run' is not null or undefined - if (run === null || run === undefined) { - throw new RequiredError( - 'run', - 'Required parameter run was null or undefined when calling operationTableGet.' - ); - } - // verify required parameter 'worker' is not null or undefined - if (worker === null || worker === undefined) { - throw new RequiredError( - 'worker', - 'Required parameter worker was null or undefined when calling operationTableGet.' - ); - } - // verify required parameter 'span' is not null or undefined - if (span === null || span === undefined) { - throw new RequiredError( - 'span', - 'Required parameter span was null or undefined when calling operationTableGet.' - ); - } - // verify required parameter 'group_by' is not null or undefined - if (group_by === null || group_by === undefined) { - throw new RequiredError( - 'group_by', - 'Required parameter group_by was null or undefined when calling operationTableGet.' - ); - } - const localVarPath = `/operation/table`; - const localVarUrlObj = url.parse(localVarPath, true); - const localVarRequestOptions = Object.assign({ method: 'GET' }, options); - const localVarHeaderParameter = {} as any; - const localVarQueryParameter = {} as any; - - if (run !== undefined) { - localVarQueryParameter.run = run; - } - - if (worker !== undefined) { - localVarQueryParameter.worker = worker; - } - - if (span !== undefined) { - localVarQueryParameter.span = span; - } - - if (group_by !== undefined) { - localVarQueryParameter.group_by = group_by; - } - - localVarUrlObj.query = Object.assign( - {}, - localVarUrlObj.query, - localVarQueryParameter, - options.query - ); - // fix override query string Detail: https://stackoverflow.com/a/7517673/1077943 - delete localVarUrlObj.search; - localVarRequestOptions.headers = Object.assign( - {}, - localVarHeaderParameter, - options.headers - ); - - return { - url: url.format(localVarUrlObj), - options: localVarRequestOptions, - }; - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - overviewGet( - run: string, - worker: string, - span: string, - options: any = {} - ): FetchArgs { - // verify required parameter 'run' is not null or undefined - if (run === null || run === undefined) { - throw new RequiredError( - 'run', - 'Required parameter run was null or undefined when calling overviewGet.' - ); - } - // verify required parameter 'worker' is not null or undefined - if (worker === null || worker === undefined) { - throw new RequiredError( - 'worker', - 'Required parameter worker was null or undefined when calling overviewGet.' - ); - } - // verify required parameter 'span' is not null or undefined - if (span === null || span === undefined) { - throw new RequiredError( - 'span', - 'Required parameter span was null or undefined when calling overviewGet.' - ); - } - const localVarPath = `/overview`; - const localVarUrlObj = url.parse(localVarPath, true); - const localVarRequestOptions = Object.assign({ method: 'GET' }, options); - const localVarHeaderParameter = {} as any; - const localVarQueryParameter = {} as any; - - if (run !== undefined) { - localVarQueryParameter.run = run; - } - - if (worker !== undefined) { - localVarQueryParameter.worker = worker; - } - - if (span !== undefined) { - localVarQueryParameter.span = span; - } - - localVarUrlObj.query = Object.assign( - {}, - localVarUrlObj.query, - localVarQueryParameter, - options.query - ); - // fix override query string Detail: https://stackoverflow.com/a/7517673/1077943 - delete localVarUrlObj.search; - localVarRequestOptions.headers = Object.assign( - {}, - localVarHeaderParameter, - options.headers - ); - - return { - url: url.format(localVarUrlObj), - options: localVarRequestOptions, - }; - }, - /** - * - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - runsGet(options: any = {}): FetchArgs { - const localVarPath = `/runs`; - const localVarUrlObj = url.parse(localVarPath, true); - const localVarRequestOptions = Object.assign({ method: 'GET' }, options); - const localVarHeaderParameter = {} as any; - const localVarQueryParameter = {} as any; - - localVarUrlObj.query = Object.assign( - {}, - localVarUrlObj.query, - localVarQueryParameter, - options.query - ); - // fix override query string Detail: https://stackoverflow.com/a/7517673/1077943 - delete localVarUrlObj.search; - localVarRequestOptions.headers = Object.assign( - {}, - localVarHeaderParameter, - options.headers - ); - - return { - url: url.format(localVarUrlObj), - options: localVarRequestOptions, - }; - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - spansGet(run: string, worker: string, options: any = {}): FetchArgs { - // verify required parameter 'run' is not null or undefined - if (run === null || run === undefined) { - throw new RequiredError( - 'run', - 'Required parameter run was null or undefined when calling spansGet.' - ); - } - // verify required parameter 'worker' is not null or undefined - if (worker === null || worker === undefined) { - throw new RequiredError( - 'worker', - 'Required parameter worker was null or undefined when calling spansGet.' - ); - } - const localVarPath = `/spans`; - const localVarUrlObj = url.parse(localVarPath, true); - const localVarRequestOptions = Object.assign({ method: 'GET' }, options); - const localVarHeaderParameter = {} as any; - const localVarQueryParameter = {} as any; - - if (run !== undefined) { - localVarQueryParameter.run = run; - } - - if (worker !== undefined) { - localVarQueryParameter.worker = worker; - } - - localVarUrlObj.query = Object.assign( - {}, - localVarUrlObj.query, - localVarQueryParameter, - options.query - ); - // fix override query string Detail: https://stackoverflow.com/a/7517673/1077943 - delete localVarUrlObj.search; - localVarRequestOptions.headers = Object.assign( - {}, - localVarHeaderParameter, - options.headers - ); - - return { - url: url.format(localVarUrlObj), - options: localVarRequestOptions, - }; - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - traceGet( - run: string, - worker: string, - span: string, - options: any = {} - ): FetchArgs { - // verify required parameter 'run' is not null or undefined - if (run === null || run === undefined) { - throw new RequiredError( - 'run', - 'Required parameter run was null or undefined when calling traceGet.' - ); - } - // verify required parameter 'worker' is not null or undefined - if (worker === null || worker === undefined) { - throw new RequiredError( - 'worker', - 'Required parameter worker was null or undefined when calling traceGet.' - ); - } - // verify required parameter 'span' is not null or undefined - if (span === null || span === undefined) { - throw new RequiredError( - 'span', - 'Required parameter span was null or undefined when calling traceGet.' - ); - } - const localVarPath = `/trace`; - const localVarUrlObj = url.parse(localVarPath, true); - const localVarRequestOptions = Object.assign({ method: 'GET' }, options); - const localVarHeaderParameter = {} as any; - const localVarQueryParameter = {} as any; - - if (run !== undefined) { - localVarQueryParameter.run = run; - } - - if (worker !== undefined) { - localVarQueryParameter.worker = worker; - } - - if (span !== undefined) { - localVarQueryParameter.span = span; - } - - localVarUrlObj.query = Object.assign( - {}, - localVarUrlObj.query, - localVarQueryParameter, - options.query - ); - // fix override query string Detail: https://stackoverflow.com/a/7517673/1077943 - delete localVarUrlObj.search; - localVarRequestOptions.headers = Object.assign( - {}, - localVarHeaderParameter, - options.headers - ); - - return { - url: url.format(localVarUrlObj), - options: localVarRequestOptions, - }; - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - treeGet( - run: string, - worker: string, - span: string, - options: any = {} - ): FetchArgs { - // verify required parameter 'run' is not null or undefined - if (run === null || run === undefined) { - throw new RequiredError( - 'run', - 'Required parameter run was null or undefined when calling treeGet.' - ); - } - // verify required parameter 'worker' is not null or undefined - if (worker === null || worker === undefined) { - throw new RequiredError( - 'worker', - 'Required parameter worker was null or undefined when calling treeGet.' - ); - } - // verify required parameter 'span' is not null or undefined - if (span === null || span === undefined) { - throw new RequiredError( - 'span', - 'Required parameter span was null or undefined when calling treeGet.' - ); - } - const localVarPath = `/tree`; - const localVarUrlObj = url.parse(localVarPath, true); - const localVarRequestOptions = Object.assign({ method: 'GET' }, options); - const localVarHeaderParameter = {} as any; - const localVarQueryParameter = {} as any; - - if (run !== undefined) { - localVarQueryParameter.run = run; - } - - if (worker !== undefined) { - localVarQueryParameter.worker = worker; - } - - if (span !== undefined) { - localVarQueryParameter.span = span; - } - - localVarUrlObj.query = Object.assign( - {}, - localVarUrlObj.query, - localVarQueryParameter, - options.query - ); - // fix override query string Detail: https://stackoverflow.com/a/7517673/1077943 - delete localVarUrlObj.search; - localVarRequestOptions.headers = Object.assign( - {}, - localVarHeaderParameter, - options.headers - ); - - return { - url: url.format(localVarUrlObj), - options: localVarRequestOptions, - }; - }, - /** - * - * @param {string} run - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - viewsGet(run: string, options: any = {}): FetchArgs { - // verify required parameter 'run' is not null or undefined - if (run === null || run === undefined) { - throw new RequiredError( - 'run', - 'Required parameter run was null or undefined when calling viewsGet.' - ); - } - const localVarPath = `/views`; - const localVarUrlObj = url.parse(localVarPath, true); - const localVarRequestOptions = Object.assign({ method: 'GET' }, options); - const localVarHeaderParameter = {} as any; - const localVarQueryParameter = {} as any; - - if (run !== undefined) { - localVarQueryParameter.run = run; - } - - localVarUrlObj.query = Object.assign( - {}, - localVarUrlObj.query, - localVarQueryParameter, - options.query - ); - // fix override query string Detail: https://stackoverflow.com/a/7517673/1077943 - delete localVarUrlObj.search; - localVarRequestOptions.headers = Object.assign( - {}, - localVarHeaderParameter, - options.headers - ); - - return { - url: url.format(localVarUrlObj), - options: localVarRequestOptions, - }; - }, - /** - * - * @param {string} run - * @param {string} view - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - workersGet(run: string, view: string, options: any = {}): FetchArgs { - // verify required parameter 'run' is not null or undefined - if (run === null || run === undefined) { - throw new RequiredError( - 'run', - 'Required parameter run was null or undefined when calling workersGet.' - ); - } - // verify required parameter 'view' is not null or undefined - if (view === null || view === undefined) { - throw new RequiredError( - 'view', - 'Required parameter view was null or undefined when calling workersGet.' - ); - } - const localVarPath = `/workers`; - const localVarUrlObj = url.parse(localVarPath, true); - const localVarRequestOptions = Object.assign({ method: 'GET' }, options); - const localVarHeaderParameter = {} as any; - const localVarQueryParameter = {} as any; - - if (run !== undefined) { - localVarQueryParameter.run = run; - } - - if (view !== undefined) { - localVarQueryParameter.view = view; - } - - localVarUrlObj.query = Object.assign( - {}, - localVarUrlObj.query, - localVarQueryParameter, - options.query - ); - // fix override query string Detail: https://stackoverflow.com/a/7517673/1077943 - delete localVarUrlObj.search; - localVarRequestOptions.headers = Object.assign( - {}, - localVarHeaderParameter, - options.headers - ); - - return { - url: url.format(localVarUrlObj), - options: localVarRequestOptions, - }; - }, - }; -}; - -/** - * DefaultApi - functional programming interface - * @export - */ -export const DefaultApiFp = function (configuration?: Configuration) { - return { - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {string} exp_run - * @param {string} exp_worker - * @param {string} exp_span - * @param {string} [path] - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - diffnodeGet( - run: string, - worker: string, - span: string, - exp_run: string, - exp_worker: string, - exp_span: string, - path?: string, - options?: any - ): (fetch?: FetchAPI, basePath?: string) => Promise { - const localVarFetchArgs = DefaultApiFetchParamCreator( - configuration - ).diffnodeGet( - run, - worker, - span, - exp_run, - exp_worker, - exp_span, - path, - options - ); - return ( - fetch: FetchAPI = portableFetch, - basePath: string = BASE_PATH - ) => { - return fetch( - basePath + localVarFetchArgs.url, - localVarFetchArgs.options - ).then((response) => { - if (response.status >= 200 && response.status < 300) { - return response.json(); - } else { - throw response; - } - }); - }; - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - distributedCommopsGet( - run: string, - worker: string, - span: string, - options?: any - ): (fetch?: FetchAPI, basePath?: string) => Promise { - const localVarFetchArgs = DefaultApiFetchParamCreator( - configuration - ).distributedCommopsGet(run, worker, span, options); - return ( - fetch: FetchAPI = portableFetch, - basePath: string = BASE_PATH - ) => { - return fetch( - basePath + localVarFetchArgs.url, - localVarFetchArgs.options - ).then((response) => { - if (response.status >= 200 && response.status < 300) { - return response.json(); - } else { - throw response; - } - }); - }; - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - distributedGpuinfoGet( - run: string, - worker: string, - span: string, - options?: any - ): (fetch?: FetchAPI, basePath?: string) => Promise { - const localVarFetchArgs = DefaultApiFetchParamCreator( - configuration - ).distributedGpuinfoGet(run, worker, span, options); - return ( - fetch: FetchAPI = portableFetch, - basePath: string = BASE_PATH - ) => { - return fetch( - basePath + localVarFetchArgs.url, - localVarFetchArgs.options - ).then((response) => { - if (response.status >= 200 && response.status < 300) { - return response.json(); - } else { - throw response; - } - }); - }; - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - distributedOverlapGet( - run: string, - worker: string, - span: string, - options?: any - ): (fetch?: FetchAPI, basePath?: string) => Promise { - const localVarFetchArgs = DefaultApiFetchParamCreator( - configuration - ).distributedOverlapGet(run, worker, span, options); - return ( - fetch: FetchAPI = portableFetch, - basePath: string = BASE_PATH - ) => { - return fetch( - basePath + localVarFetchArgs.url, - localVarFetchArgs.options - ).then((response) => { - if (response.status >= 200 && response.status < 300) { - return response.json(); - } else { - throw response; - } - }); - }; - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - distributedWaittimeGet( - run: string, - worker: string, - span: string, - options?: any - ): (fetch?: FetchAPI, basePath?: string) => Promise { - const localVarFetchArgs = DefaultApiFetchParamCreator( - configuration - ).distributedWaittimeGet(run, worker, span, options); - return ( - fetch: FetchAPI = portableFetch, - basePath: string = BASE_PATH - ) => { - return fetch( - basePath + localVarFetchArgs.url, - localVarFetchArgs.options - ).then((response) => { - if (response.status >= 200 && response.status < 300) { - return response.json(); - } else { - throw response; - } - }); - }; - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {string} group_by Group By - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - kernelGet( - run: string, - worker: string, - span: string, - group_by: string, - options?: any - ): (fetch?: FetchAPI, basePath?: string) => Promise { - const localVarFetchArgs = DefaultApiFetchParamCreator( - configuration - ).kernelGet(run, worker, span, group_by, options); - return ( - fetch: FetchAPI = portableFetch, - basePath: string = BASE_PATH - ) => { - return fetch( - basePath + localVarFetchArgs.url, - localVarFetchArgs.options - ).then((response) => { - if (response.status >= 200 && response.status < 300) { - return response.json(); - } else { - throw response; - } - }); - }; - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {string} [group_by] Group By - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - kernelTableGet( - run: string, - worker: string, - span: string, - group_by?: string, - options?: any - ): (fetch?: FetchAPI, basePath?: string) => Promise { - const localVarFetchArgs = DefaultApiFetchParamCreator( - configuration - ).kernelTableGet(run, worker, span, group_by, options); - return ( - fetch: FetchAPI = portableFetch, - basePath: string = BASE_PATH - ) => { - return fetch( - basePath + localVarFetchArgs.url, - localVarFetchArgs.options - ).then((response) => { - if (response.status >= 200 && response.status < 300) { - return response.json(); - } else { - throw response; - } - }); - }; - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - kernelTcPieGet( - run: string, - worker: string, - span: string, - options?: any - ): (fetch?: FetchAPI, basePath?: string) => Promise { - const localVarFetchArgs = DefaultApiFetchParamCreator( - configuration - ).kernelTcPieGet(run, worker, span, options); - return ( - fetch: FetchAPI = portableFetch, - basePath: string = BASE_PATH - ) => { - return fetch( - basePath + localVarFetchArgs.url, - localVarFetchArgs.options - ).then((response) => { - if (response.status >= 200 && response.status < 300) { - return response.json(); - } else { - throw response; - } - }); - }; - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - memoryCurveGet( - run: string, - worker: string, - span: string, - options?: any - ): ( - fetch?: FetchAPI, - basePath?: string - ) => Promise { - const localVarFetchArgs = DefaultApiFetchParamCreator( - configuration - ).memoryCurveGet(run, worker, span, options); - return ( - fetch: FetchAPI = portableFetch, - basePath: string = BASE_PATH - ) => { - return fetch( - basePath + localVarFetchArgs.url, - localVarFetchArgs.options - ).then((response) => { - if (response.status >= 200 && response.status < 300) { - return response.json(); - } else { - throw response; - } - }); - }; - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {number} [start_ts] - * @param {number} [end_ts] - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - memoryEventsGet( - run: string, - worker: string, - span: string, - start_ts?: number, - end_ts?: number, - options?: any - ): ( - fetch?: FetchAPI, - basePath?: string - ) => Promise { - const localVarFetchArgs = DefaultApiFetchParamCreator( - configuration - ).memoryEventsGet(run, worker, span, start_ts, end_ts, options); - return ( - fetch: FetchAPI = portableFetch, - basePath: string = BASE_PATH - ) => { - return fetch( - basePath + localVarFetchArgs.url, - localVarFetchArgs.options - ).then((response) => { - if (response.status >= 200 && response.status < 300) { - return response.json(); - } else { - throw response; - } - }); - }; - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {number} [start_ts] - * @param {number} [end_ts] - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - memoryGet( - run: string, - worker: string, - span: string, - start_ts?: number, - end_ts?: number, - options?: any - ): (fetch?: FetchAPI, basePath?: string) => Promise { - const localVarFetchArgs = DefaultApiFetchParamCreator( - configuration - ).memoryGet(run, worker, span, start_ts, end_ts, options); - return ( - fetch: FetchAPI = portableFetch, - basePath: string = BASE_PATH - ) => { - return fetch( - basePath + localVarFetchArgs.url, - localVarFetchArgs.options - ).then((response) => { - if (response.status >= 200 && response.status < 300) { - return response.json(); - } else { - throw response; - } - }); - }; - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - moduleGet( - run: string, - worker: string, - span: string, - options?: any - ): (fetch?: FetchAPI, basePath?: string) => Promise { - const localVarFetchArgs = DefaultApiFetchParamCreator( - configuration - ).moduleGet(run, worker, span, options); - return ( - fetch: FetchAPI = portableFetch, - basePath: string = BASE_PATH - ) => { - return fetch( - basePath + localVarFetchArgs.url, - localVarFetchArgs.options - ).then((response) => { - if (response.status >= 200 && response.status < 300) { - return response.json(); - } else { - throw response; - } - }); - }; - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {string} group_by Group By - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - operationGet( - run: string, - worker: string, - span: string, - group_by: string, - options?: any - ): (fetch?: FetchAPI, basePath?: string) => Promise { - const localVarFetchArgs = DefaultApiFetchParamCreator( - configuration - ).operationGet(run, worker, span, group_by, options); - return ( - fetch: FetchAPI = portableFetch, - basePath: string = BASE_PATH - ) => { - return fetch( - basePath + localVarFetchArgs.url, - localVarFetchArgs.options - ).then((response) => { - if (response.status >= 200 && response.status < 300) { - return response.json(); - } else { - throw response; - } - }); - }; - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {string} group_by Group By - * @param {string} op_name - * @param {string} [input_shape] - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - operationStackGet( - run: string, - worker: string, - span: string, - group_by: string, - op_name: string, - input_shape?: string, - options?: any - ): (fetch?: FetchAPI, basePath?: string) => Promise { - const localVarFetchArgs = DefaultApiFetchParamCreator( - configuration - ).operationStackGet( - run, - worker, - span, - group_by, - op_name, - input_shape, - options - ); - return ( - fetch: FetchAPI = portableFetch, - basePath: string = BASE_PATH - ) => { - return fetch( - basePath + localVarFetchArgs.url, - localVarFetchArgs.options - ).then((response) => { - if (response.status >= 200 && response.status < 300) { - return response.json(); - } else { - throw response; - } - }); - }; - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {string} group_by Group By - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - operationTableGet( - run: string, - worker: string, - span: string, - group_by: string, - options?: any - ): (fetch?: FetchAPI, basePath?: string) => Promise { - const localVarFetchArgs = DefaultApiFetchParamCreator( - configuration - ).operationTableGet(run, worker, span, group_by, options); - return ( - fetch: FetchAPI = portableFetch, - basePath: string = BASE_PATH - ) => { - return fetch( - basePath + localVarFetchArgs.url, - localVarFetchArgs.options - ).then((response) => { - if (response.status >= 200 && response.status < 300) { - return response.json(); - } else { - throw response; - } - }); - }; - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - overviewGet( - run: string, - worker: string, - span: string, - options?: any - ): (fetch?: FetchAPI, basePath?: string) => Promise { - const localVarFetchArgs = DefaultApiFetchParamCreator( - configuration - ).overviewGet(run, worker, span, options); - return ( - fetch: FetchAPI = portableFetch, - basePath: string = BASE_PATH - ) => { - return fetch( - basePath + localVarFetchArgs.url, - localVarFetchArgs.options - ).then((response) => { - if (response.status >= 200 && response.status < 300) { - return response.json(); - } else { - throw response; - } - }); - }; - }, - /** - * - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - runsGet( - options?: any - ): (fetch?: FetchAPI, basePath?: string) => Promise { - const localVarFetchArgs = - DefaultApiFetchParamCreator(configuration).runsGet(options); - return ( - fetch: FetchAPI = portableFetch, - basePath: string = BASE_PATH - ) => { - return fetch( - basePath + localVarFetchArgs.url, - localVarFetchArgs.options - ).then((response) => { - if (response.status >= 200 && response.status < 300) { - return response.json(); - } else { - throw response; - } - }); - }; - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - spansGet( - run: string, - worker: string, - options?: any - ): (fetch?: FetchAPI, basePath?: string) => Promise> { - const localVarFetchArgs = DefaultApiFetchParamCreator( - configuration - ).spansGet(run, worker, options); - return ( - fetch: FetchAPI = portableFetch, - basePath: string = BASE_PATH - ) => { - return fetch( - basePath + localVarFetchArgs.url, - localVarFetchArgs.options - ).then((response) => { - if (response.status >= 200 && response.status < 300) { - return response.json(); - } else { - throw response; - } - }); - }; - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - traceGet( - run: string, - worker: string, - span: string, - options?: any - ): (fetch?: FetchAPI, basePath?: string) => Promise { - const localVarFetchArgs = DefaultApiFetchParamCreator( - configuration - ).traceGet(run, worker, span, options); - return ( - fetch: FetchAPI = portableFetch, - basePath: string = BASE_PATH - ) => { - return fetch( - basePath + localVarFetchArgs.url, - localVarFetchArgs.options - ).then((response) => { - if (response.status >= 200 && response.status < 300) { - return response.json(); - } else { - throw response; - } - }); - }; - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - treeGet( - run: string, - worker: string, - span: string, - options?: any - ): (fetch?: FetchAPI, basePath?: string) => Promise { - const localVarFetchArgs = DefaultApiFetchParamCreator( - configuration - ).treeGet(run, worker, span, options); - return ( - fetch: FetchAPI = portableFetch, - basePath: string = BASE_PATH - ) => { - return fetch( - basePath + localVarFetchArgs.url, - localVarFetchArgs.options - ).then((response) => { - if (response.status >= 200 && response.status < 300) { - return response.json(); - } else { - throw response; - } - }); - }; - }, - /** - * - * @param {string} run - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - viewsGet( - run: string, - options?: any - ): (fetch?: FetchAPI, basePath?: string) => Promise { - const localVarFetchArgs = DefaultApiFetchParamCreator( - configuration - ).viewsGet(run, options); - return ( - fetch: FetchAPI = portableFetch, - basePath: string = BASE_PATH - ) => { - return fetch( - basePath + localVarFetchArgs.url, - localVarFetchArgs.options - ).then((response) => { - if (response.status >= 200 && response.status < 300) { - return response.json(); - } else { - throw response; - } - }); - }; - }, - /** - * - * @param {string} run - * @param {string} view - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - workersGet( - run: string, - view: string, - options?: any - ): (fetch?: FetchAPI, basePath?: string) => Promise> { - const localVarFetchArgs = DefaultApiFetchParamCreator( - configuration - ).workersGet(run, view, options); - return ( - fetch: FetchAPI = portableFetch, - basePath: string = BASE_PATH - ) => { - return fetch( - basePath + localVarFetchArgs.url, - localVarFetchArgs.options - ).then((response) => { - if (response.status >= 200 && response.status < 300) { - return response.json(); - } else { - throw response; - } - }); - }; - }, - }; -}; - -/** - * DefaultApi - factory interface - * @export - */ -export const DefaultApiFactory = function ( - configuration?: Configuration, - fetch?: FetchAPI, - basePath?: string -) { - return { - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {string} exp_run - * @param {string} exp_worker - * @param {string} exp_span - * @param {string} [path] - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - diffnodeGet( - run: string, - worker: string, - span: string, - exp_run: string, - exp_worker: string, - exp_span: string, - path?: string, - options?: any - ) { - return DefaultApiFp(configuration).diffnodeGet( - run, - worker, - span, - exp_run, - exp_worker, - exp_span, - path, - options - )(fetch, basePath); - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - distributedCommopsGet( - run: string, - worker: string, - span: string, - options?: any - ) { - return DefaultApiFp(configuration).distributedCommopsGet( - run, - worker, - span, - options - )(fetch, basePath); - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - distributedGpuinfoGet( - run: string, - worker: string, - span: string, - options?: any - ) { - return DefaultApiFp(configuration).distributedGpuinfoGet( - run, - worker, - span, - options - )(fetch, basePath); - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - distributedOverlapGet( - run: string, - worker: string, - span: string, - options?: any - ) { - return DefaultApiFp(configuration).distributedOverlapGet( - run, - worker, - span, - options - )(fetch, basePath); - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - distributedWaittimeGet( - run: string, - worker: string, - span: string, - options?: any - ) { - return DefaultApiFp(configuration).distributedWaittimeGet( - run, - worker, - span, - options - )(fetch, basePath); - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {string} group_by Group By - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - kernelGet( - run: string, - worker: string, - span: string, - group_by: string, - options?: any - ) { - return DefaultApiFp(configuration).kernelGet( - run, - worker, - span, - group_by, - options - )(fetch, basePath); - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {string} [group_by] Group By - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - kernelTableGet( - run: string, - worker: string, - span: string, - group_by?: string, - options?: any - ) { - return DefaultApiFp(configuration).kernelTableGet( - run, - worker, - span, - group_by, - options - )(fetch, basePath); - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - kernelTcPieGet(run: string, worker: string, span: string, options?: any) { - return DefaultApiFp(configuration).kernelTcPieGet( - run, - worker, - span, - options - )(fetch, basePath); - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - memoryCurveGet(run: string, worker: string, span: string, options?: any) { - return DefaultApiFp(configuration).memoryCurveGet( - run, - worker, - span, - options - )(fetch, basePath); - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {number} [start_ts] - * @param {number} [end_ts] - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - memoryEventsGet( - run: string, - worker: string, - span: string, - start_ts?: number, - end_ts?: number, - options?: any - ) { - return DefaultApiFp(configuration).memoryEventsGet( - run, - worker, - span, - start_ts, - end_ts, - options - )(fetch, basePath); - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {number} [start_ts] - * @param {number} [end_ts] - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - memoryGet( - run: string, - worker: string, - span: string, - start_ts?: number, - end_ts?: number, - options?: any - ) { - return DefaultApiFp(configuration).memoryGet( - run, - worker, - span, - start_ts, - end_ts, - options - )(fetch, basePath); - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - moduleGet(run: string, worker: string, span: string, options?: any) { - return DefaultApiFp(configuration).moduleGet( - run, - worker, - span, - options - )(fetch, basePath); - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {string} group_by Group By - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - operationGet( - run: string, - worker: string, - span: string, - group_by: string, - options?: any - ) { - return DefaultApiFp(configuration).operationGet( - run, - worker, - span, - group_by, - options - )(fetch, basePath); - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {string} group_by Group By - * @param {string} op_name - * @param {string} [input_shape] - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - operationStackGet( - run: string, - worker: string, - span: string, - group_by: string, - op_name: string, - input_shape?: string, - options?: any - ) { - return DefaultApiFp(configuration).operationStackGet( - run, - worker, - span, - group_by, - op_name, - input_shape, - options - )(fetch, basePath); - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {string} group_by Group By - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - operationTableGet( - run: string, - worker: string, - span: string, - group_by: string, - options?: any - ) { - return DefaultApiFp(configuration).operationTableGet( - run, - worker, - span, - group_by, - options - )(fetch, basePath); - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - overviewGet(run: string, worker: string, span: string, options?: any) { - return DefaultApiFp(configuration).overviewGet( - run, - worker, - span, - options - )(fetch, basePath); - }, - /** - * - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - runsGet(options?: any) { - return DefaultApiFp(configuration).runsGet(options)(fetch, basePath); - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - spansGet(run: string, worker: string, options?: any) { - return DefaultApiFp(configuration).spansGet( - run, - worker, - options - )(fetch, basePath); - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - traceGet(run: string, worker: string, span: string, options?: any) { - return DefaultApiFp(configuration).traceGet( - run, - worker, - span, - options - )(fetch, basePath); - }, - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - treeGet(run: string, worker: string, span: string, options?: any) { - return DefaultApiFp(configuration).treeGet( - run, - worker, - span, - options - )(fetch, basePath); - }, - /** - * - * @param {string} run - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - viewsGet(run: string, options?: any) { - return DefaultApiFp(configuration).viewsGet(run, options)( - fetch, - basePath - ); - }, - /** - * - * @param {string} run - * @param {string} view - * @param {*} [options] Override http request option. - * @throws {RequiredError} - */ - workersGet(run: string, view: string, options?: any) { - return DefaultApiFp(configuration).workersGet( - run, - view, - options - )(fetch, basePath); - }, - }; -}; - -/** - * DefaultApi - object-oriented interface - * @export - * @class DefaultApi - * @extends {BaseAPI} - */ -export class DefaultApi extends BaseAPI { - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {string} exp_run - * @param {string} exp_worker - * @param {string} exp_span - * @param {string} [path] - * @param {*} [options] Override http request option. - * @throws {RequiredError} - * @memberof DefaultApi - */ - public diffnodeGet( - run: string, - worker: string, - span: string, - exp_run: string, - exp_worker: string, - exp_span: string, - path?: string, - options?: any - ) { - return DefaultApiFp(this.configuration).diffnodeGet( - run, - worker, - span, - exp_run, - exp_worker, - exp_span, - path, - options - )(this.fetch, this.basePath); - } - - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {*} [options] Override http request option. - * @throws {RequiredError} - * @memberof DefaultApi - */ - public distributedCommopsGet( - run: string, - worker: string, - span: string, - options?: any - ) { - return DefaultApiFp(this.configuration).distributedCommopsGet( - run, - worker, - span, - options - )(this.fetch, this.basePath); - } - - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {*} [options] Override http request option. - * @throws {RequiredError} - * @memberof DefaultApi - */ - public distributedGpuinfoGet( - run: string, - worker: string, - span: string, - options?: any - ) { - return DefaultApiFp(this.configuration).distributedGpuinfoGet( - run, - worker, - span, - options - )(this.fetch, this.basePath); - } - - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {*} [options] Override http request option. - * @throws {RequiredError} - * @memberof DefaultApi - */ - public distributedOverlapGet( - run: string, - worker: string, - span: string, - options?: any - ) { - return DefaultApiFp(this.configuration).distributedOverlapGet( - run, - worker, - span, - options - )(this.fetch, this.basePath); - } - - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {*} [options] Override http request option. - * @throws {RequiredError} - * @memberof DefaultApi - */ - public distributedWaittimeGet( - run: string, - worker: string, - span: string, - options?: any - ) { - return DefaultApiFp(this.configuration).distributedWaittimeGet( - run, - worker, - span, - options - )(this.fetch, this.basePath); - } - - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {string} group_by Group By - * @param {*} [options] Override http request option. - * @throws {RequiredError} - * @memberof DefaultApi - */ - public kernelGet( - run: string, - worker: string, - span: string, - group_by: string, - options?: any - ) { - return DefaultApiFp(this.configuration).kernelGet( - run, - worker, - span, - group_by, - options - )(this.fetch, this.basePath); - } - - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {string} [group_by] Group By - * @param {*} [options] Override http request option. - * @throws {RequiredError} - * @memberof DefaultApi - */ - public kernelTableGet( - run: string, - worker: string, - span: string, - group_by?: string, - options?: any - ) { - return DefaultApiFp(this.configuration).kernelTableGet( - run, - worker, - span, - group_by, - options - )(this.fetch, this.basePath); - } - - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {*} [options] Override http request option. - * @throws {RequiredError} - * @memberof DefaultApi - */ - public kernelTcPieGet( - run: string, - worker: string, - span: string, - options?: any - ) { - return DefaultApiFp(this.configuration).kernelTcPieGet( - run, - worker, - span, - options - )(this.fetch, this.basePath); - } - - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {*} [options] Override http request option. - * @throws {RequiredError} - * @memberof DefaultApi - */ - public memoryCurveGet( - run: string, - worker: string, - span: string, - options?: any - ) { - return DefaultApiFp(this.configuration).memoryCurveGet( - run, - worker, - span, - options - )(this.fetch, this.basePath); - } - - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {number} [start_ts] - * @param {number} [end_ts] - * @param {*} [options] Override http request option. - * @throws {RequiredError} - * @memberof DefaultApi - */ - public memoryEventsGet( - run: string, - worker: string, - span: string, - start_ts?: number, - end_ts?: number, - options?: any - ) { - return DefaultApiFp(this.configuration).memoryEventsGet( - run, - worker, - span, - start_ts, - end_ts, - options - )(this.fetch, this.basePath); - } - - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {number} [start_ts] - * @param {number} [end_ts] - * @param {*} [options] Override http request option. - * @throws {RequiredError} - * @memberof DefaultApi - */ - public memoryGet( - run: string, - worker: string, - span: string, - start_ts?: number, - end_ts?: number, - options?: any - ) { - return DefaultApiFp(this.configuration).memoryGet( - run, - worker, - span, - start_ts, - end_ts, - options - )(this.fetch, this.basePath); - } - - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {*} [options] Override http request option. - * @throws {RequiredError} - * @memberof DefaultApi - */ - public moduleGet(run: string, worker: string, span: string, options?: any) { - return DefaultApiFp(this.configuration).moduleGet( - run, - worker, - span, - options - )(this.fetch, this.basePath); - } - - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {string} group_by Group By - * @param {*} [options] Override http request option. - * @throws {RequiredError} - * @memberof DefaultApi - */ - public operationGet( - run: string, - worker: string, - span: string, - group_by: string, - options?: any - ) { - return DefaultApiFp(this.configuration).operationGet( - run, - worker, - span, - group_by, - options - )(this.fetch, this.basePath); - } - - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {string} group_by Group By - * @param {string} op_name - * @param {string} [input_shape] - * @param {*} [options] Override http request option. - * @throws {RequiredError} - * @memberof DefaultApi - */ - public operationStackGet( - run: string, - worker: string, - span: string, - group_by: string, - op_name: string, - input_shape?: string, - options?: any - ) { - return DefaultApiFp(this.configuration).operationStackGet( - run, - worker, - span, - group_by, - op_name, - input_shape, - options - )(this.fetch, this.basePath); - } - - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {string} group_by Group By - * @param {*} [options] Override http request option. - * @throws {RequiredError} - * @memberof DefaultApi - */ - public operationTableGet( - run: string, - worker: string, - span: string, - group_by: string, - options?: any - ) { - return DefaultApiFp(this.configuration).operationTableGet( - run, - worker, - span, - group_by, - options - )(this.fetch, this.basePath); - } - - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {*} [options] Override http request option. - * @throws {RequiredError} - * @memberof DefaultApi - */ - public overviewGet(run: string, worker: string, span: string, options?: any) { - return DefaultApiFp(this.configuration).overviewGet( - run, - worker, - span, - options - )(this.fetch, this.basePath); - } - - /** - * - * @param {*} [options] Override http request option. - * @throws {RequiredError} - * @memberof DefaultApi - */ - public runsGet(options?: any) { - return DefaultApiFp(this.configuration).runsGet(options)( - this.fetch, - this.basePath - ); - } - - /** - * - * @param {string} run - * @param {string} worker - * @param {*} [options] Override http request option. - * @throws {RequiredError} - * @memberof DefaultApi - */ - public spansGet(run: string, worker: string, options?: any) { - return DefaultApiFp(this.configuration).spansGet( - run, - worker, - options - )(this.fetch, this.basePath); - } - - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {*} [options] Override http request option. - * @throws {RequiredError} - * @memberof DefaultApi - */ - public traceGet(run: string, worker: string, span: string, options?: any) { - return DefaultApiFp(this.configuration).traceGet( - run, - worker, - span, - options - )(this.fetch, this.basePath); - } - - /** - * - * @param {string} run - * @param {string} worker - * @param {string} span - * @param {*} [options] Override http request option. - * @throws {RequiredError} - * @memberof DefaultApi - */ - public treeGet(run: string, worker: string, span: string, options?: any) { - return DefaultApiFp(this.configuration).treeGet( - run, - worker, - span, - options - )(this.fetch, this.basePath); - } - - /** - * - * @param {string} run - * @param {*} [options] Override http request option. - * @throws {RequiredError} - * @memberof DefaultApi - */ - public viewsGet(run: string, options?: any) { - return DefaultApiFp(this.configuration).viewsGet(run, options)( - this.fetch, - this.basePath - ); - } - - /** - * - * @param {string} run - * @param {string} view - * @param {*} [options] Override http request option. - * @throws {RequiredError} - * @memberof DefaultApi - */ - public workersGet(run: string, view: string, options?: any) { - return DefaultApiFp(this.configuration).workersGet( - run, - view, - options - )(this.fetch, this.basePath); - } -} diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/api/generated/configuration.ts b/plugins/tensorboard-plugins/tb_plugin/fe/src/api/generated/configuration.ts deleted file mode 100644 index 85b77bf65..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/api/generated/configuration.ts +++ /dev/null @@ -1,68 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *--------------------------------------------------------------------------------------------*/ - -// tslint:disable -/** - * Pytorch profile API - * No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen) - * - * OpenAPI spec version: 1.0.0 - * - * - * NOTE: This file is auto generated by the swagger code generator program. - * https://github.com/swagger-api/swagger-codegen.git - * Do not edit the file manually. - */ -export interface ConfigurationParameters { - apiKey?: string | ((name: string) => string); - username?: string; - password?: string; - accessToken?: string | ((name: string, scopes?: string[]) => string); - basePath?: string; -} - -export class Configuration { - /** - * parameter for apiKey security - * @param name security name - * @memberof Configuration - */ - apiKey?: string | ((name: string) => string); - /** - * parameter for basic security - * - * @type {string} - * @memberof Configuration - */ - username?: string; - /** - * parameter for basic security - * - * @type {string} - * @memberof Configuration - */ - password?: string; - /** - * parameter for oauth2 security - * @param name security name - * @param scopes oauth2 scope - * @memberof Configuration - */ - accessToken?: string | ((name: string, scopes?: string[]) => string); - /** - * override base path - * - * @type {string} - * @memberof Configuration - */ - basePath?: string; - - constructor(param: ConfigurationParameters = {}) { - this.apiKey = param.apiKey; - this.username = param.username; - this.password = param.password; - this.accessToken = param.accessToken; - this.basePath = param.basePath; - } -} diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/api/generated/custom.d.ts b/plugins/tensorboard-plugins/tb_plugin/fe/src/api/generated/custom.d.ts deleted file mode 100644 index 992af4688..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/api/generated/custom.d.ts +++ /dev/null @@ -1,6 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *--------------------------------------------------------------------------------------------*/ - -declare module 'portable-fetch'; -declare module 'url'; diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/api/generated/index.ts b/plugins/tensorboard-plugins/tb_plugin/fe/src/api/generated/index.ts deleted file mode 100644 index 7ad784e60..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/api/generated/index.ts +++ /dev/null @@ -1,18 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *--------------------------------------------------------------------------------------------*/ - -// tslint:disable -/** - * Pytorch profile API - * No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen) - * - * OpenAPI spec version: 1.0.0 - * - * - * NOTE: This file is auto generated by the swagger code generator program. - * https://github.com/swagger-api/swagger-codegen.git - * Do not edit the file manually. - */ -export * from './api'; -export * from './configuration'; diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/api/index.ts b/plugins/tensorboard-plugins/tb_plugin/fe/src/api/index.ts deleted file mode 100644 index 98b35abfb..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/api/index.ts +++ /dev/null @@ -1,8 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *--------------------------------------------------------------------------------------------*/ - -import * as api from './generated'; - -export const defaultApi = new api.DefaultApi(undefined, undefined, fetch); -export * from './generated/api'; diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/api/mock.ts b/plugins/tensorboard-plugins/tb_plugin/fe/src/api/mock.ts deleted file mode 100644 index 4b4b447d9..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/api/mock.ts +++ /dev/null @@ -1,6684 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *--------------------------------------------------------------------------------------------*/ - -export class MockAPI { - runsGet() { - return { - runs: ['resnet50_num_workers_0', 'resnet50_num_workers_4'], - loading: false, - }; - } - - viewsGet(run: string) { - return Promise.resolve([ - 'Overview', - 'Operator', - 'Kernel', - 'Trace', - 'Memory', - ]); - } - - spansGet(run: string, view: string): Promise { - return Promise.resolve(['1', '2']); - } - - workersGet(run: string, view: string): Promise { - return Promise.resolve(['worker0']); - } - - overviewGet(run: string, worker: string, span: string) { - return Promise.resolve({ - steps: { - columns: [ - { type: 'string', name: 'Step' }, - { type: 'number', name: 'Kernel' }, - { type: 'string', role: 'tooltip', p: { html: 'true' } }, - { type: 'number', name: 'Memcpy' }, - { type: 'string', role: 'tooltip', p: { html: 'true' } }, - { type: 'number', name: 'Memset' }, - { type: 'string', role: 'tooltip', p: { html: 'true' } }, - { type: 'number', name: 'Runtime' }, - { type: 'string', role: 'tooltip', p: { html: 'true' } }, - { type: 'number', name: 'DataLoader' }, - { type: 'string', role: 'tooltip', p: { html: 'true' } }, - { type: 'number', name: 'CPU Exec' }, - { type: 'string', role: 'tooltip', p: { html: 'true' } }, - { type: 'number', name: 'Other' }, - { type: 'string', role: 'tooltip', p: { html: 'true' } }, - ], - rows: [ - [ - '5', - 98598, - '
Step 5
Total: 187948us
Kernel: 98598us
Percentage: 52.46%
', - 1941, - '
Step 5
Total: 187948us
Memcpy: 1941us
Percentage: 1.03%
', - 90, - '
Step 5
Total: 187948us
Memset: 90us
Percentage: 0.05%
', - 2796, - '
Step 5
Total: 187948us
Runtime: 2796us
Percentage: 1.49%
', - 69317, - '
Step 5
Total: 187948us
DataLoader: 69317us
Percentage: 36.88%
', - 14091, - '
Step 5
Total: 187948us
CPU Exec: 14091us
Percentage: 7.5%
', - 1115, - '
Step 5
Total: 187948us
Other: 1115us
Percentage: 0.59%
', - ], - [ - '6', - 98570, - '
Step 6
Total: 175153us
Kernel: 98570us
Percentage: 56.28%
', - 1947, - '
Step 6
Total: 175153us
Memcpy: 1947us
Percentage: 1.11%
', - 89, - '
Step 6
Total: 175153us
Memset: 89us
Percentage: 0.05%
', - 2762, - '
Step 6
Total: 175153us
Runtime: 2762us
Percentage: 1.58%
', - 57669, - '
Step 6
Total: 175153us
DataLoader: 57669us
Percentage: 32.92%
', - 12968, - '
Step 6
Total: 175153us
CPU Exec: 12968us
Percentage: 7.4%
', - 1148, - '
Step 6
Total: 175153us
Other: 1148us
Percentage: 0.66%
', - ], - [ - '7', - 98596, - '
Step 7
Total: 179733us
Kernel: 98596us
Percentage: 54.86%
', - 1931, - '
Step 7
Total: 179733us
Memcpy: 1931us
Percentage: 1.07%
', - 91, - '
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', - ], - ], - }, - performance: [ - { - name: 'Average Step Time', - description: '', - value: 177592, - extra: 100, - children: [ - { name: 'Kernel', description: '', value: 98589, extra: 55.51 }, - { name: 'Memcpy', description: '', value: 1915, extra: 1.08 }, - { name: 'Memset', description: '', value: 89, extra: 0.05 }, - { name: 'Runtime', description: '', value: 2908, extra: 1.64 }, - { name: 'DataLoader', description: '', value: 59262, extra: 33.37 }, - { name: 'CPU Exec', description: '', value: 13674, extra: 7.7 }, - { name: 'Other', description: '', value: 1156, extra: 0.65 }, - ], - }, - ], - recommendations: - '
  • This run has high time cost on input data loading. 33.4% of the step time is in DataLoader. You could try to set num_workers on DataLoader\'s construction and enable multi-processes on data loading.
  • Kernels with 68% time are launched by Tensor Cores eligible operators. You could enable Automatic Mixed Precision to speedup by using FP16.
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device_duration: 1513, - self_host_duration: 30, - self_device_duration: 1283, - }, - { - name: 'MaxPool2DWithIndicesBackward0', - calls: 1, - host_duration: 83, - device_duration: 1513, - self_host_duration: 10, - self_device_duration: 0, - }, - { - name: 'autograd::engine::evaluate_function: MaxPool2DWithIndicesBackward0', - calls: 1, - host_duration: 106, - device_duration: 1513, - self_host_duration: 23, - self_device_duration: 0, - }, - ], - }, - path: '0-21', - }, - { - left: { - name: 'aten::zeros', - duration: 160, - device_duration: 0, - total_duration: 160, - aggs: [ - { - name: 'aten::empty', - calls: 1, - host_duration: 87, - device_duration: 0, - self_host_duration: 87, - self_device_duration: 0, - }, - { - name: 'aten::zero_', - calls: 1, - host_duration: 4, - device_duration: 0, - self_host_duration: 4, - self_device_duration: 0, - }, - { - name: 'aten::zeros', - calls: 1, - host_duration: 160, - device_duration: 0, - self_host_duration: 69, - self_device_duration: 0, - }, - ], - }, - right: { - name: 'aten::zeros', - duration: 119, - device_duration: 0, - total_duration: 119, - aggs: [ - { - name: 'aten::empty', - calls: 1, - host_duration: 105, - device_duration: 0, - self_host_duration: 105, - self_device_duration: 0, - }, - { - name: 'aten::zero_', - calls: 1, - host_duration: 2, - device_duration: 0, - self_host_duration: 2, - self_device_duration: 0, - }, - { - name: 'aten::zeros', - calls: 1, - host_duration: 119, - device_duration: 0, - self_host_duration: 12, - self_device_duration: 0, - }, - ], - }, - path: '0-22', - }, - { - left: { - name: 'Optimizer.step#SGD.step', - duration: 75435, - device_duration: 1295, - total_duration: 1295, - aggs: [ - { - name: 'aten::empty', - calls: 1, - host_duration: 40, - device_duration: 0, - self_host_duration: 40, - self_device_duration: 0, - }, - { - name: 'aten::mul_', - calls: 161, - host_duration: 11945, - device_duration: 401, - self_host_duration: 9568, - self_device_duration: 401, - }, - { - name: 'aten::add_', - calls: 322, - host_duration: 22480, - device_duration: 894, - self_host_duration: 17805, - self_device_duration: 894, - }, - ], - }, - right: { - name: 'Optimizer.step#SGD.step', - duration: 16687, - device_duration: 1298, - total_duration: 1298, - aggs: [ - { - name: 'aten::empty', - calls: 1, - host_duration: 8, - device_duration: 0, - self_host_duration: 8, - self_device_duration: 0, - }, - { - name: 'aten::mul_', - calls: 161, - host_duration: 3440, - device_duration: 404, - self_host_duration: 1824, - self_device_duration: 404, - }, - { - name: 'aten::add_', - calls: 322, - host_duration: 6161, - device_duration: 894, - self_host_duration: 3186, - self_device_duration: 894, - }, - ], - }, - path: '0-23', - }, - ], - }); - } -} diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/api/openapi.yaml b/plugins/tensorboard-plugins/tb_plugin/fe/src/api/openapi.yaml deleted file mode 100644 index 0218adb9d..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/api/openapi.yaml +++ /dev/null @@ -1,1204 +0,0 @@ -openapi: 3.0.1 -info: - title: Pytorch profile API - version: 1.0.0 -servers: - - url: . -paths: - /runs: - get: - responses: - '200': - description: successful operation - content: - '*/*': - schema: - $ref: '#/components/schemas/Runs' - /views: - get: - parameters: - - in: query - name: run - required: true - schema: - type: string - responses: - '200': - description: successful views - content: - '*/*': - schema: - type: array - items: - type: string - enum: - - Overview - - Operator - - Kernel - - Trace - - Distributed - - Memory - /workers: - get: - parameters: - - in: query - name: run - required: true - schema: - type: string - - in: query - name: view - required: true - schema: - type: string - responses: - '200': - description: successful workers - content: - '*/*': - schema: - type: array - items: - type: string - /spans: - get: - parameters: - - in: query - name: run - required: true - schema: - type: string - - in: query - name: worker - required: true - schema: - type: string - responses: - '200': - description: successful spans - content: - '*/*': - schema: - type: array - items: - type: string - /overview: - get: - parameters: - - in: query - name: run - required: true - schema: - type: string - - in: query - name: worker - required: true - schema: - type: string - - in: query - name: span - required: true - schema: - type: string - responses: - '200': - description: successful operation - content: - '*/*': - schema: - $ref: '#/components/schemas/Overview' - /operation: - get: - parameters: - - in: query - name: run - required: true - schema: - type: string - - in: query - name: worker - required: true - schema: - type: string - - in: query - name: span - required: true - schema: - type: string - - in: query - name: group_by - required: true - schema: - type: string - enum: - - Operation - - OperationAndInputShape - description: Group By - responses: - '200': - description: successful operation - content: - '*/*': - schema: - $ref: '#/components/schemas/OperatorGraph' - /operation/table: - get: - parameters: - - in: query - name: run - required: true - schema: - type: string - - in: query - name: worker - required: true - schema: - type: string - - in: query - name: span - required: true - schema: - type: string - - in: query - name: group_by - required: true - schema: - type: string - enum: - - Operation - - OperationAndInputShape - description: Group By - responses: - '200': - description: successful operation - content: - '*/*': - schema: - type: object - required: - - metadata - - data - properties: - metadata: - $ref: '#/components/schemas/TableMetadata' - data: - $ref: '#/components/schemas/OperationTableData' - /operation/stack: - get: - parameters: - - in: query - name: run - required: true - schema: - type: string - - in: query - name: worker - required: true - schema: - type: string - - in: query - name: span - required: true - schema: - type: string - - in: query - name: group_by - required: true - schema: - type: string - enum: - - Operation - - OperationAndInputShape - description: Group By - - in: query - name: op_name - required: true - schema: - type: string - - in: query - name: input_shape - schema: - type: string - responses: - '200': - description: successful operation - content: - '*/*': - schema: - type: object - required: - - metadata - - data - properties: - metadata: - $ref: '#/components/schemas/TableMetadata' - data: - $ref: '#/components/schemas/CallStackTableData' - /distributed/overlap: - get: - parameters: - - in: query - name: run - required: true - schema: - type: string - - in: query - name: worker - required: true - schema: - type: string - - in: query - name: span - required: true - schema: - type: string - responses: - '200': - description: successful operation - content: - '*/*': - schema: - $ref: '#/components/schemas/DistributedGraph' - /distributed/waittime: - get: - parameters: - - in: query - name: run - required: true - schema: - type: string - - in: query - name: worker - required: true - schema: - type: string - - in: query - name: span - required: true - schema: - type: string - responses: - '200': - description: successful operation - content: - '*/*': - schema: - $ref: '#/components/schemas/DistributedGraph' - /distributed/commops: - get: - parameters: - - in: query - name: run - required: true - schema: - type: string - - in: query - name: worker - required: true - schema: - type: string - - in: query - name: span - required: true - schema: - type: string - responses: - '200': - description: successful operation - content: - '*/*': - schema: - type: object - required: - - metadata - - data - properties: - metadata: - type: object - required: - - title - properties: - title: - type: string - data: - type: object - /distributed/gpuinfo: - get: - parameters: - - in: query - name: run - required: true - schema: - type: string - - in: query - name: worker - required: true - schema: - type: string - - in: query - name: span - required: true - schema: - type: string - responses: - '200': - description: successful operation - content: - '*/*': - schema: - $ref: '#/components/schemas/GpuInfo' - /memory: - get: - parameters: - - in: query - name: run - required: true - schema: - type: string - - in: query - name: worker - required: true - schema: - type: string - - in: query - name: span - required: true - schema: - type: string - - in: query - name: start_ts - required: false - schema: - type: number - - in: query - name: end_ts - required: false - schema: - type: number - responses: - '200': - description: successful operation - content: - '*/*': - schema: - $ref: '#/components/schemas/MemoryStatsData' - /memory_curve: - get: - parameters: - - in: query - name: run - required: true - schema: - type: string - - in: query - name: worker - required: true - schema: - type: string - - in: query - name: span - required: true - schema: - type: string - responses: - '200': - description: successful operation - content: - '*/*': - schema: - $ref: '#/components/schemas/MemoryCurveData' - /memory_events: - get: - parameters: - - in: query - name: run - required: true - schema: - type: string - - in: query - name: worker - required: true - schema: - type: string - - in: query - name: span - required: true - schema: - type: string - - in: query - name: start_ts - required: false - schema: - type: number - - in: query - name: end_ts - required: false - schema: - type: number - responses: - '200': - description: successful operation - content: - '*/*': - schema: - $ref: '#/components/schemas/MemoryEventsData' - /kernel: - get: - parameters: - - in: query - name: run - required: true - schema: - type: string - - in: query - name: worker - required: true - schema: - type: string - - in: query - name: span - required: true - schema: - type: string - - in: query - name: group_by - required: true - schema: - type: string - enum: - - Kernel - - KernelNameAndOpName - description: Group By - responses: - '200': - description: successful operation - content: - '*/*': - schema: - $ref: '#/components/schemas/KernelGraph' - /kernel/table: - get: - parameters: - - in: query - name: run - required: true - schema: - type: string - - in: query - name: worker - required: true - schema: - type: string - - in: query - name: span - required: true - schema: - type: string - - in: query - name: group_by - required: false - schema: - type: string - enum: - - Kernel - - KernelNameAndOpName - description: Group By - responses: - '200': - description: successful kernel - content: - '*/*': - schema: - $ref: '#/components/schemas/TableData' - /kernel/tc_pie: - get: - parameters: - - in: query - name: run - required: true - schema: - type: string - - in: query - name: worker - required: true - schema: - type: string - - in: query - name: span - required: true - schema: - type: string - responses: - '200': - description: successful operation - content: - '*/*': - schema: - $ref: '#/components/schemas/TensorCoresGraph' - /trace: - get: - parameters: - - in: query - name: run - required: true - schema: - type: string - - in: query - name: worker - required: true - schema: - type: string - - in: query - name: span - required: true - schema: - type: string - responses: - '200': - description: successful trace data - content: - '*/*': - schema: - type: object - /module: - get: - parameters: - - in: query - name: run - required: true - schema: - type: string - - in: query - name: worker - required: true - schema: - type: string - - in: query - name: span - required: true - schema: - type: string - responses: - '200': - description: successful operation - content: - '*/*': - schema: - $ref: '#/components/schemas/ModuleViewData' - /tree: - get: - parameters: - - in: query - name: run - required: true - schema: - type: string - - in: query - name: worker - required: true - schema: - type: string - - in: query - name: span - required: true - schema: - type: string - responses: - '200': - description: successful operation - content: - '*/*': - schema: - $ref: '#/components/schemas/OperatorNode' - /diffnode: - get: - parameters: - - in: query - name: run - required: true - schema: - type: string - - in: query - name: worker - required: true - schema: - type: string - - in: query - name: span - required: true - schema: - type: string - - in: query - name: exp_run - required: true - schema: - type: string - - in: query - name: exp_worker - required: true - schema: - type: string - - in: query - name: exp_span - required: true - schema: - type: string - - in: query - name: path - required: false - schema: - type: string - responses: - '200': - description: successful operation - content: - '*/*': - schema: - $ref: '#/components/schemas/DiffNode' -components: - schemas: - Runs: - type: object - required: - - runs - - loading - properties: - runs: - type: array - items: - type: string - loading: - type: boolean - Performance: - type: object - required: - - name - properties: - name: - type: string - description: - type: string - value: - type: string - extra: - type: string - children: - type: array - items: - $ref: '#/components/schemas/Performance' - Environment: - type: object - required: - - title - - value - properties: - title: - type: string - value: - type: string - GraphColumn: - type: object - required: - - type - - name - properties: - type: - type: string - name: - type: string - role: - type: string - p: - type: object - properties: - html: - type: boolean - ValueAndFormat: - type: object - required: - - v - - f - properties: - v: - oneOf: - - type: string - - type: number - - type: boolean - f: - type: string - Graph: - type: object - required: - - columns - - rows - properties: - title: - type: string - columns: - type: array - items: - $ref: '#/components/schemas/GraphColumn' - rows: - type: array - items: - type: array - items: - oneOf: - - type: string - - type: number - - type: boolean - - $ref: '#/components/schemas/ValueAndFormat' - Overview: - type: object - required: - - performance - - environments - - steps - - recommendations - properties: - performance: - type: array - items: - $ref: '#/components/schemas/Performance' - environments: - type: array - items: - $ref: '#/components/schemas/Environment' - steps: - $ref: '#/components/schemas/Graph' - recommendations: - type: string - gpu_metrics: - $ref: '#/components/schemas/GpuMetrics' - OperatorGraph: - type: object - required: - - device_total_time - - device_self_time - - host_total_time - - host_self_time - properties: - device_total_time: - $ref: '#/components/schemas/Graph' - device_self_time: - $ref: '#/components/schemas/Graph' - host_total_time: - $ref: '#/components/schemas/Graph' - host_self_time: - $ref: '#/components/schemas/Graph' - TableMetadata: - type: object - required: - - sort - properties: - sort: - type: string - tooltips: - type: object - TableData: - type: object - required: - - metadata - - data - properties: - data: - $ref: '#/components/schemas/Graph' - metadata: - $ref: '#/components/schemas/TableMetadata' - KernelGraph: - type: object - required: - - total - properties: - total: - $ref: '#/components/schemas/Graph' - TensorCoresGraph: - type: object - required: - - total - properties: - total: - $ref: '#/components/schemas/Graph' - OperationTableData: - type: array - items: - type: object - required: - - name - - calls - - host_self_duration - - host_total_duration - - has_call_stack - properties: - name: - type: string - input_shape: - type: string - calls: - type: number - device_self_duration: - type: number - device_total_duration: - type: number - host_self_duration: - type: number - host_total_duration: - type: number - has_call_stack: - type: boolean - tc_eligible: - type: string - tc_self_ratio: - type: number - tc_total_ratio: - type: number - CallStackTableData: - type: array - items: - type: object - required: - - name - - calls - - host_self_duration - - host_total_duration - properties: - name: - type: string - input_shape: - type: string - calls: - type: number - device_self_duration: - type: number - device_total_duration: - type: number - host_self_duration: - type: number - host_total_duration: - type: number - call_stack: - type: string - tc_eligible: - type: string - tc_self_ratio: - type: number - tc_total_ratio: - type: number - DistributedGraph: - type: object - required: - - metadata - - data - properties: - metadata: - type: object - required: - - title - - legends - - units - properties: - title: - type: string - legends: - type: array - items: - type: string - units: - type: string - data: - type: object - GpuInfo: - type: object - required: - - metadata - - data - properties: - metadata: - type: object - required: - - title - properties: - title: - type: string - data: - type: object - GpuMetrics: - type: object - required: - - data - - tooltip - properties: - data: - type: array - items: - $ref: '#/components/schemas/GpuMetric' - tooltip: - type: string - GpuMetric: - type: object - required: - - title - - value - properties: - title: - type: string - value: - type: string - MemoryStatsData: - type: object - required: - - metadata - - columns - - rows - properties: - metadata: - $ref: '#/components/schemas/MemoryStatsTableMetadata' - columns: - type: array - items: - $ref: '#/components/schemas/GraphColumn' - rows: - type: object - MemoryEventsData: - type: object - required: - - metadata - - columns - - rows - properties: - metadata: - $ref: '#/components/schemas/MemoryEventsTableMetadata' - columns: - type: array - items: - $ref: '#/components/schemas/GraphColumn' - rows: - type: object - MemoryEventsTableMetadata: - type: object - required: - - title - - default_device - - value - properties: - title: - type: string - default_device: - type: string - search: - type: string - sort: - type: string - MemoryStatsTableMetadata: - type: object - required: - - title - - default_device - - search - - sort - - value - properties: - title: - type: string - default_device: - type: string - search: - type: string - sort: - type: string - MemoryCurveDataMetadata: - type: object - required: - - default_device - - devices - - peaks - - totals - - first_ts - - time_metric - - memory_metric - - time_factor - - memory_factor - properties: - default_device: - type: string - devices: - type: array - items: - type: string - peaks: - type: object - totals: - type: object - first_ts: - type: number - time_metric: - type: string - memory_metric: - type: string - time_factor: - type: number - memory_factor: - type: number - MemoryCurveData: - type: object - required: - - metadata - - columns - - rows - properties: - metadata: - $ref: '#/components/schemas/MemoryCurveDataMetadata' - columns: - type: array - items: - $ref: '#/components/schemas/GraphColumn' - rows: - type: object - KeyedColumn: - type: object - required: - - type - - name - - key - properties: - type: - type: string - name: - type: string - key: - type: string - ModuleViewData: - type: object - required: - - columns - - data - properties: - columns: - type: array - items: - $ref: '#/components/schemas/KeyedColumn' - data: - type: array - items: - $ref: '#/components/schemas/ModuleStats' - ModuleStats: - type: object - required: - - name - - id - - occurences - - operators - - host_duration - - self_host_duration - - device_duration - - self_device_duration - - avg_duration - - children - properties: - name: - type: string - id: - type: string - occurences: - type: number - operators: - type: number - host_duration: - type: number - self_host_duration: - type: number - device_duration: - type: number - self_device_duration: - type: number - avg_duration: - type: number - children: - type: array - items: - $ref: '#/components/schemas/ModuleStats' - OperatorNode: - type: object - required: - - name - - start_time - - end_time - - type - - tid - - children - properties: - name: - type: string - start_time: - type: number - end_time: - type: number - type: - type: string - tid: - type: number - children: - type: array - items: - $ref: '#/components/schemas/OperatorNode' - OpAgg: - type: object - required: - - name - - calls - - host_duration - - device_duration - - self_host_duration - - self_device_duration - properties: - name: - type: string - calls: - type: number - host_duration: - type: number - device_duration: - type: number - self_host_duration: - type: number - self_device_duration: - type: number - OpStats: - type: object - required: - - name - - duration - - device_duration - - total_duration - - aggs - properties: - name: - type: string - duration: - type: number - device_duration: - type: number - total_duration: - type: number - aggs: - type: array - items: - $ref: '#/components/schemas/OpAgg' - DiffNode: - type: object - required: - - left - - right - - children - - path - properties: - left: - $ref: '#/components/schemas/OpStats' - right: - $ref: '#/components/schemas/OpStats' - path: - type: string - children: - type: array - items: - $ref: '#/components/schemas/DiffNode' diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/app.tsx b/plugins/tensorboard-plugins/tb_plugin/fe/src/app.tsx deleted file mode 100644 index 19eb4b112..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/app.tsx +++ /dev/null @@ -1,625 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *-------------------------------------------------------------------------------------------- - * Copyright (c) 2023, Huawei Technologies. - * All rights reserved. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * - * Modifications: Add visualization of PyTorch Ascend profiling. - *--------------------------------------------------------------------------------------------*/ - -import Box from '@material-ui/core/Box'; -import Card from '@material-ui/core/Card'; -import CardContent from '@material-ui/core/CardContent'; -import CardHeader from '@material-ui/core/CardHeader'; -import ClickAwayListener from '@material-ui/core/ClickAwayListener'; -import CssBaseline from '@material-ui/core/CssBaseline'; -import Divider from '@material-ui/core/Divider'; -import Drawer from '@material-ui/core/Drawer'; -import Fab from '@material-ui/core/Fab'; -import FormControl from '@material-ui/core/FormControl'; -import IconButton from '@material-ui/core/IconButton'; -import ListSubheader from '@material-ui/core/ListSubheader'; -import MenuItem from '@material-ui/core/MenuItem'; -import Select, { SelectProps } from '@material-ui/core/Select'; -import { makeStyles } from '@material-ui/core/styles'; -import Tab from '@material-ui/core/Tab'; -import Tabs from '@material-ui/core/Tabs'; -import Typography from '@material-ui/core/Typography'; -import ChevronLeftIcon from '@material-ui/icons/ChevronLeft'; -import ChevronRightIcon from '@material-ui/icons/ChevronRight'; -import { message } from 'antd'; -import 'antd/es/button/style/css'; -import 'antd/es/list/style/css'; -import 'antd/es/table/style/css'; -import clsx from 'clsx'; -import * as React from 'react'; -import * as api from './api'; -import { AccuracyLeftPanel } from './components/Accuracy/AccuracyLeftPanel'; -import { FileInfo } from './components/Accuracy/entity'; -import { LossComparison } from './components/Accuracy/LossComparison'; -import { DiffOverview } from './components/DiffOverview'; -import { DistributedView } from './components/DistributedView'; -import { FullCircularProgress } from './components/FullCircularProgress'; -import { Kernel as KernelView } from './components/Kernel'; -import { MemoryView } from './components/MemoryView'; -import { ModuleView } from './components/ModuleView'; -import { Operator as OperatorView } from './components/Operator'; -import { Overview as OverviewPage } from './components/Overview'; -import { TraceView } from './components/TraceView'; -import { setup } from './setup'; -import './styles.css'; -import { firstOrUndefined, sleep } from './utils'; - -export enum Views { - Overview = 'Overview', - Operator = 'Operator', - Kernel = 'Kernel', - Trace = 'Trace', - Distributed = 'Distributed', - Memory = 'Memory', - Module = 'Module', - Lightning = 'Lightning', -} - -const viewNames = { - [Views.Overview]: Views.Overview, - [Views.Operator]: Views.Operator, - [Views.Kernel]: 'Kernel', - [Views.Trace]: Views.Trace, - [Views.Distributed]: Views.Distributed, - [Views.Memory]: Views.Memory, - [Views.Module]: Views.Module, - [Views.Lightning]: Views.Lightning, -}; - -const drawerWidth = 340; -const useStyles = makeStyles((theme) => ({ - root: { - display: 'flex', - height: '100%', - }, - appBar: { - zIndex: theme.zIndex.drawer + 1, - transition: theme.transitions.create(['width', 'margin'], { - easing: theme.transitions.easing.sharp, - duration: theme.transitions.duration.leavingScreen, - }), - }, - appBarShift: { - marginLeft: drawerWidth, - width: `calc(100% - ${drawerWidth}px)`, - transition: theme.transitions.create(['width', 'margin'], { - easing: theme.transitions.easing.sharp, - duration: theme.transitions.duration.enteringScreen, - }), - }, - menuButton: { - marginRight: 36, - }, - hide: { - display: 'none', - }, - drawer: { - width: drawerWidth, - flexShrink: 0, - whiteSpace: 'nowrap', - }, - drawerOpen: { - width: drawerWidth, - zIndex: 999, - transition: theme.transitions.create('width', { - easing: theme.transitions.easing.sharp, - duration: theme.transitions.duration.enteringScreen, - }), - }, - drawerClose: { - transition: theme.transitions.create('width', { - easing: theme.transitions.easing.sharp, - duration: theme.transitions.duration.leavingScreen, - }), - overflowX: 'hidden', - width: 0, - [theme.breakpoints.up('sm')]: { - width: 0, - }, - }, - toolbar: { - display: 'flex', - alignItems: 'center', - justifyContent: 'flex-end', - padding: theme.spacing(0, 1), - // necessary for content to be below app bar - ...theme.mixins.toolbar, - }, - content: { - flexGrow: 1, - padding: theme.spacing(3), - overflowX: 'hidden', - }, - formControl: { - margin: theme.spacing(1), - minWidth: 120, - }, - fab: { - marginLeft: theme.spacing(1), - marginTop: theme.spacing(1), - position: 'absolute', - }, - iconButton: { - padding: '8px', - }, -})); - -export const App = (): JSX.Element => { - const classes = useStyles(); - - // #region - State - const [selectedTab, setSelectedTab] = React.useState(0); - - const [run, setRun] = React.useState(''); - const [runs, setRuns] = React.useState([]); - const [runsLoading, setRunsLoading] = React.useState(true); - - const [workers, setWorkers] = React.useState([]); - const [worker, setWorker] = React.useState(''); - - const [spans, setSpans] = React.useState([]); - const [span, setSpan] = React.useState(''); - - const [views, setViews] = React.useState([]); - const [view, setView] = React.useState(''); - const [loaded, setLoaded] = React.useState(false); - const iframeRef = React.useRef(null); - const [deviceTarget, setDeviceTarget] = React.useState('GPU'); - - const [diffLeftWorkerOptions, setDiffLeftWorkerOptions] = React.useState([]); - const [diffLeftSpansOptions, setDiffLeftSpansOptions] = React.useState([]); - const [diffLeftRun, setDiffLeftRun] = React.useState(''); - const [diffLeftWorker, setDiffLeftWorker] = React.useState(''); - const [diffLeftSpan, setDiffLeftSpan] = React.useState(''); - - const [diffRightWorkerOptions, setDiffRightWorkerOptions] = React.useState([]); - const [diffRightSpansOptions, setDiffRightSpansOptions] = React.useState([]); - const [diffRightRun, setDiffRightRun] = React.useState(''); - const [diffRightWorker, setDiffRightWorker] = React.useState(''); - const [diffRightSpan, setDiffRightSpan] = React.useState(''); - - const [open, setOpen] = React.useState(true); - - const [topTab, setTopTab] = React.useState(0); - const [fileList, setFileList] = React.useState([]); - const [uploadedCount, setUploadedCount] = React.useState(0); // #endregion - - React.useEffect(() => { - setup() - .catch(() => { - message.warning('google chart is not supported offline'); - }) - .finally(() => { - setLoaded(true); - }); - }, []); - - const continuouslyFetchRuns = async (): Promise => { - while (true) { - try { - const result = await api.defaultApi.runsGet(); - setRuns(result.runs); - setRunsLoading(result.loading); - } catch (e) { - message.warning(`Cannot fetch runs: ${e}`); - } - await sleep(5000); - } - }; - - React.useEffect(() => { - continuouslyFetchRuns(); - }, []); - - React.useEffect(() => { - if (!run || !runs.includes(run)) { - setRun(firstOrUndefined(runs) ?? ''); - } - }, [runs]); // #region - Diff Left - - React.useEffect(() => { - if (diffLeftRun) { - api.defaultApi.workersGet(diffLeftRun, Views.Overview).then((data) => { - setDiffLeftWorkerOptions(data); - }); - } - }, [diffLeftRun]); - - React.useEffect(() => { - if (diffLeftRun && diffLeftWorker) { - api.defaultApi.spansGet(diffLeftRun, diffLeftWorker).then((data) => { - setDiffLeftSpansOptions(data); - }); - } - }, [diffLeftRun, diffLeftWorker]); - - // #endregion - // #region - Diff Right - React.useEffect(() => { - if (diffRightRun) { - api.defaultApi.workersGet(diffRightRun, Views.Overview).then((data) => { - setDiffRightWorkerOptions(data); - }); - } - }, [diffRightRun]); - - React.useEffect(() => { - if (diffRightRun && diffRightWorker) { - api.defaultApi.spansGet(diffRightRun, diffRightWorker).then((data) => { - setDiffRightSpansOptions(data); - }); - } - }, [diffRightRun, diffRightWorker]); - - // #endregion - // #region - normal - React.useEffect(() => { - if (run) { - api.defaultApi.viewsGet(run).then((rawViews) => { - const result = rawViews.views.map((v) => Views[Views[v as Views]]).filter(Boolean); - setDeviceTarget(rawViews.device_target); - setViews(result); - }); - } - }, [run]); - - React.useEffect(() => { - setView(firstOrUndefined(views) ?? ''); - }, [views]); - - React.useEffect(() => { - if (run && view) { - api.defaultApi.workersGet(run, view).then((data) => { - setWorkers(data); - }); - } - }, [run, view]); - - React.useEffect(() => { - setWorker(firstOrUndefined(workers) ?? ''); - }, [workers]); - - React.useEffect(() => { - if (run && worker) { - api.defaultApi.spansGet(run, worker).then((data) => { - setSpans(data); - }); - } - }, [run, worker]); - - React.useEffect(() => { - setSpan(firstOrUndefined(spans) ?? ''); - }, [spans]); - - // #endregion - - // #region - Event Handler - const handleTabChange = (event: React.ChangeEvent>, value: any): void => { - setSelectedTab(value as number); - }; - - const handleTopTabChange = (event: React.ChangeEvent>, value: any): void => { - setTopTab(value as number); - }; - - const handleRunChange: SelectProps['onChange'] = (event) => { - setRun(event.target.value as string); - setView(''); - setWorker(''); - setSpan(''); - }; - - const handleViewChange: SelectProps['onChange'] = (event) => { - setView(event.target.value as Views); - setWorker(''); - setSpan(''); - }; - - const handleWorkerChange: SelectProps['onChange'] = (event) => { - setWorker(event.target.value as string); - setSpan(''); - }; - - const handleSpanChange: SelectProps['onChange'] = (event) => { - setSpan(event.target.value as string); - }; - - const handleDiffLeftRunChange: SelectProps['onChange'] = (event) => { - setDiffLeftRun(event.target.value as string); - setDiffLeftWorker(''); - setDiffLeftSpan(''); - }; - - const handleDiffLeftWorkerChange: SelectProps['onChange'] = (event) => { - setDiffLeftWorker(event.target.value as string); - setDiffLeftSpan(''); - }; - - const handleDiffLeftSpanChange: SelectProps['onChange'] = (event) => { - setDiffLeftSpan(event.target.value as string); - }; - - const handleDiffRightRunChange: SelectProps['onChange'] = (event) => { - setDiffRightRun(event.target.value as string); - setDiffRightWorker(''); - setDiffRightSpan(''); - }; - - const handleDiffRightWorkerChange: SelectProps['onChange'] = (event) => { - setDiffRightWorker(event.target.value as string); - setDiffRightSpan(''); - }; - - const handleDiffRightSpanChange: SelectProps['onChange'] = (event) => { - setDiffRightSpan(event.target.value as string); - }; - - const handleDrawerOpen = (): void => { - setOpen(true); - setIframeActive(); - }; - - const handleDrawerClose = (): void => { - setOpen(false); - setIframeActive(); - }; - - const setIframeActive = (): void => { - iframeRef.current?.focus(); - }; - - const _changeFileList = (files: FileInfo[]): void => { - if (JSON.stringify(files) !== JSON.stringify(fileList)) { - setFileList(files); - } - }; - - const _getViews = (viewName: Views): string => { - if (viewName === Views.Kernel) { - return deviceTarget === 'Ascend' ? `NPU ${viewNames[viewName]}` : `GPU ${viewNames[viewName]}`; - } else { - return viewNames[viewName]; - } - }; - - const _changeUploadCount = (count: number): void => { - setUploadedCount(count); - }; // #endregion - - const renderContent = (): JSX.Element => { - if (!runsLoading && runs.length === 0) { - return ( - - - - There are not any runs in the log folder. - - - ); - } - const notReady = !loaded || !run || !worker || !view || !span; - if (notReady) { - return ; - } - - if (selectedTab === 0) { - switch (view) { - case Views.Overview: - return ; - case Views.Operator: - return ; - case Views.Kernel: - return ; - case Views.Trace: - return ; - case Views.Distributed: - return ; - case Views.Memory: - return ; - case Views.Module: - case Views.Lightning: - return ; - default: - return <>; - } - } else { - return ( - - ); - } - }; - - const spanComponent = (): JSX.Element => { - const spanFragment = ( - - Spans - - - - - - - ); - - if (!spans || spans.length <= 1) { - return
{spanFragment}
; - } else { - return spanFragment; - } - }; - - return ( -
- - -
- - - -
- - - - - - - - {topTab === 0 ? ( - <> - - - - - - - {selectedTab === 0 ? ( - <> - Runs - - - - - - Views - - - - - - Workers - - - - - - {spanComponent()} - - ) : ( - <> -   Baseline - Runs - - - - Workers - - - - - Spans - - - - - - -   Experimental - Runs - - - - Workers - - - - Spans - - - - - )} - - ) : ( - - )} - - {!open && ( - - - - )} -
- {topTab === 0 ? renderContent() : } -
-
- ); -}; diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/Accuracy/AccuracyLeftPanel.tsx b/plugins/tensorboard-plugins/tb_plugin/fe/src/components/Accuracy/AccuracyLeftPanel.tsx deleted file mode 100644 index c7b7d7cf0..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/Accuracy/AccuracyLeftPanel.tsx +++ /dev/null @@ -1,334 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *-------------------------------------------------------------------------------------------- - * Copyright (c) 2023, Huawei Technologies. - * All rights reserved. - * - * Licensed under the Apache License, Version 2.0 (the 'License') - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an 'AS IS' BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - *--------------------------------------------------------------------------------------------*/ - -import * as React from 'react'; -import { useState, useEffect, useCallback, useRef } from 'react'; -import { makeStyles } from '@material-ui/core/styles'; -import { Button, Checkbox, Spin, Modal, message } from 'antd'; -import { CheckboxChangeEvent } from 'antd/es/checkbox'; -import { DeleteOutlined, DownloadOutlined, ImportOutlined, SettingOutlined, WarningTwoTone } from '@ant-design/icons'; -import { RegexConfigModal } from './RegexConfigModal'; -import { FileInfo } from './entity'; - -interface IProps { - onChangeCheckedFileList: (files: FileInfo[]) => void; - onChangeUploadedCount: (count: number) => void; -} - -// 匹配数字包括科学计数法 -const LOSS_REG_EXP = /[+-]?\d+(?:\.\d+)?(?:[eE][+-]?\d+)?/; -// 匹配自然数 -const ITER_REG_EXP = /\d+/; -// 单个文件最大大小 -const FILE_MAX_SIZE = 50 * 1024 * 1024; -// 最大文件上传数量 -export const MAX_FILE_COUNT = 6; - -const useStyles = makeStyles(() => ({ - root: { - height: '100%', - }, - btnPanel: { - height: 50, - lineHeight: '50px', - borderBottom: '1px solid #DFE5EF', - display: 'flex', - '& .ant-btn': { - margin: 'auto', - }, - }, - fileContainer: { - height: 54, - padding: '0 24px', - display: 'flex', - alignItems: 'center', - '& .fileNameLabel': { - display: 'inline-block', - marginLeft: 12, - width: 200, - fontSize: 14, - overflow: 'hidden', - textOverflow: 'ellipsis', - whiteSpace: 'nowrap', - }, - '& .btns': { - display: 'inline-block', - marginLeft: 'auto', - '& .icon': { - cursor: 'pointer', - '&:hover': { - color: '#1890ff', - }, - }, - '& .iconLeft': { - marginRight: 8, - }, - }, - }, - deleteModal: { - '& .ant-modal-title': { - fontWeight: 'bold', - }, - '& .deleteModalBody': { - display: 'flex', - alignItems: 'center', - height: 80, - '& .warningIcon': { - display: 'inline-block', - fontSize: 50, - }, - '& .warningText': { - display: 'inline-block', - marginLeft: 16, - overflow: 'hidden', - wordBreak: 'break-all', - flex: 1, - }, - }, - }, -})); - -export const AccuracyLeftPanel: React.FC = (props) => { - const { onChangeCheckedFileList, onChangeUploadedCount } = props; - const classes = useStyles(); - const [configModalVis, setConfigModalVis] = useState(false); - const [deleteModalVis, setDeleteModalVis] = useState(false); - const [fileList, setFileList] = useState([]); - const [importSpin, setImportSpin] = useState(false); - const [selectedFile, setSelectedFile] = useState(undefined); - const downLoadRef = useRef(null); - - const parseFile = (file: FileInfo): FileInfo => { - file.losses = []; - file.iterLosses = {}; - file.iters = []; - const lines = file.fileContent.split(/\r\n|\n|\r/); - for (let i = 0; i < lines.length; i++) { - const iter = parseByTag(lines[i], file.iterTag, false); - const loss = parseByTag(lines[i], file.lossTag, true); - if (iter !== null && loss !== null) { - file.iters.push(iter); - file.losses.push([iter, loss]); - file.iterLosses[iter] = loss; - } - } - return file; - }; - - const parseByTag = (line: string, tag: string, isLoss: boolean): number | null => { - let pos = line.indexOf(tag); - let result: number | null = null; - if (pos !== -1) { - const res = (isLoss ? LOSS_REG_EXP : ITER_REG_EXP).exec( - line - .substring(pos + tag.length) - .trim() - .split(/\s+/)[0] - ); - if (res !== null) { - if (isLoss) { - result = parseFloat(res[0]); - } else { - result = parseInt(res[0]); - } - } else { - console.warn(`Found ${isLoss ? 'loss' : 'iteration'} text, but parse value with error: [${line}]`); - } - } - return result; - }; - - const importFile = (): void => { - document.getElementById('accComparisonSelectFile')?.click(); - }; - - const uploadFile = (e: React.ChangeEvent): void => { - setImportSpin(true); - const file = e.target.files?.[0]; - if (file) { - if (file.size > FILE_MAX_SIZE) { - message.warn('Sorry, the file size cannot be greater than 50MB.'); - setImportSpin(false); - // 防止同名文件不触发事件 - e.target.value = ''; - return; - } - const reader = new FileReader(); - reader.onload = ((loadedFile) => { - return (event) => { - addFile(loadedFile.name.trim(), event.target?.result as string); - setImportSpin(false); - }; - })(file); - reader.readAsText(file); - } - // 防止同名文件不触发事件 - e.target.value = ''; - }; - - const addFile = (fileName: string, fileContent: string): void => { - const fileLength = fileName.length; - const tempList: FileInfo[] = JSON.parse(JSON.stringify(fileList)); - let updatedFileName = fileName; // 新变量用于存储更新后的文件名 - // 上传同名文件加上(1~最大文件数减1)标识 - if (!!tempList.find((item) => item.fileName === fileName)) { - for (let i = 1; i < MAX_FILE_COUNT; i++) { - let temp = `${fileName.slice(0, fileLength - 4)}(${i})${fileName.slice(fileLength - 4)}`; - if (tempList.find((item) => item.fileName === temp) === undefined) { - updatedFileName = temp; - break; - } - } - } - const file: FileInfo = { - id: fileList.length, - fileName: updatedFileName, - fileContent, - checked: true, - lossTag: 'loss:', - iterTag: 'iteration', - iters: [], - losses: [], - iterLosses: {}, - }; - tempList.push(parseFile(file)); - setFileList(tempList); - }; - - const exportCsv = (data: FileInfo): void => { - let csvContent = `data:text/csv;charset=utf-8,${data.iterTag},${data.lossTag}\n`; - data.losses.forEach((item) => { - csvContent += `${item[0]},${item[1]}\n`; - }); - downLoadRef.current?.setAttribute('href', encodeURI(csvContent)); - downLoadRef.current?.setAttribute('download', `${data.fileName}.csv`); - downLoadRef.current?.click(); - }; - - const onCheckChange = (e: CheckboxChangeEvent, index: number): void => { - const tempList: FileInfo[] = JSON.parse(JSON.stringify(fileList)); - tempList[index].checked = e.target.checked; - setFileList(tempList); - }; - - const onConfigIconClick = (data: FileInfo): void => { - setSelectedFile(data); - setConfigModalVis(true); - }; - - const onDeleteIconClick = (data: FileInfo): void => { - setSelectedFile(data); - setDeleteModalVis(true); - }; - - const configModalOk = (data: FileInfo): void => { - const tempList = fileList.map((item) => { - return item.id === data.id ? parseFile(data) : item; - }); - setFileList(tempList); - setConfigModalVis(false); - }; - - const configModalCancel = (): void => { - setConfigModalVis(false); - }; - - const deleteModalOk = (): void => { - const tempList = JSON.parse(JSON.stringify(fileList)); - let founded = false; - let index = 0; - for (let i = 0; i < tempList.length; i++) { - if (founded) { - tempList[i].id -= 1; - continue; - } - if (tempList[i].id === selectedFile?.id) { - founded = true; - index = i; - } - } - tempList.splice(index, 1); - setFileList(tempList); - setSelectedFile(undefined); - setDeleteModalVis(false); - }; - - const renderFileItems = useCallback(() => { - return fileList.map((item) => { - return ( -
- onCheckChange(e, item.id)} /> - - {item.fileName} - -
- onConfigIconClick(item)} /> - exportCsv(item)} /> - onDeleteIconClick(item)} /> -
-
- ); - }); - }, [JSON.stringify(fileList)]); - - useEffect(() => { - onChangeCheckedFileList(fileList.filter((item) => item.checked)); - onChangeUploadedCount(fileList.length); - }, [JSON.stringify(fileList)]); - - return ( -
- -
- - -
- {renderFileItems()} - - {configModalVis && ( - - )} - setDeleteModalVis(false)} - onOk={deleteModalOk} - width={500} - className={classes.deleteModal} - > -
- - - Are you sure to delete "{selectedFile?.fileName}"? - -
- - -
- ); -}; diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/Accuracy/ComparisonPanel.tsx b/plugins/tensorboard-plugins/tb_plugin/fe/src/components/Accuracy/ComparisonPanel.tsx deleted file mode 100644 index 500d29764..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/Accuracy/ComparisonPanel.tsx +++ /dev/null @@ -1,322 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *-------------------------------------------------------------------------------------------- - * Copyright (c) 2023, Huawei Technologies. - * All rights reserved. - * - * Licensed under the Apache License, Version 2.0 (the 'License') - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an 'AS IS' BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - *--------------------------------------------------------------------------------------------*/ - -import * as React from 'react'; -import { useState, useLayoutEffect, useRef, useEffect } from 'react'; -import { makeStyles } from '@material-ui/core/styles'; -import { FileInfo } from './entity'; -import { Empty, Popover, Radio, RadioChangeEvent, Select, Table } from 'antd'; -import { ColumnsType } from 'antd/es/table'; -import * as echarts from 'echarts'; -import { InfoCircleOutlined } from '@ant-design/icons'; - -interface IProps { - fileList: FileInfo[]; -} - -interface ILineDataList { - normal: number[][]; - absolute: number[][]; - relative: number[][]; -} - -const useStyles = makeStyles(() => ({ - root: { - height: '50%', - width: '100%', - padding: '0 24px 24px', - display: 'flex', - flexDirection: 'column', - }, - title: { - height: 24, - lineHeight: '24px', - fontFamily: 'sans-serif', - fontSize: 16, - fontWeight: 700, - }, - filter: { - height: 40, - lineHeight: '40px', - '& .comparisonSelect': { - margin: '0 8px', - }, - '& .comparisonLabel': { - marginRight: 8, - }, - '& .comparisonBtn': { - marginLeft: 20, - }, - '& .infoLabel': { - fontSize: 20, - }, - }, - empty: { - marginTop: 60, - }, - content: { - flex: 1, - display: 'flex', - }, - lossChart: { - height: '100%', - flex: 1, - }, - lossTable: { - height: '100%', - width: '32%', - }, - tableHeader: { - display: 'inline-block', - width: 134, - position: 'absolute', - top: '50%', - transform: 'translateY(-50%)', - overflow: 'hidden', - textOverflow: 'ellipsis', - whiteSpace: 'nowrap', - }, -})); - -export const ComparisonPanel: React.FC = (props) => { - const { fileList } = props; - const classes = useStyles(); - const [selectedFiles, setSelectedFiles] = useState([]); - const [compareWay, setCompareWay] = useState(0); - const [pageSize, setPageSize] = useState(20); - const [lineData, setLineData] = useState(undefined); - const [tableData, setTableData] = useState([]); - const chartRef = useRef(null); - - const getColumns = (): ColumnsType => { - const columns: ColumnsType = [ - { - title: 'Iteration', - key: 'iter', - dataIndex: 'iter', - }, - ]; - selectedFiles.forEach((item, index) => { - columns.push({ - title: () => ( -
- {item} -
- ), - key: index, - dataIndex: item, - width: '40%', - }); - }); - return columns; - }; - - const compareFile = (fileNames: string[]): void => { - if (fileNames.length < 2) { - return; - } - const baseFile = fileList.find((item) => item.fileName === fileNames[0]); - const expFile = fileList.find((item) => item.fileName === fileNames[1]); - if (!!baseFile && !!expFile) { - const commonIters: number[] = []; - const lessIters = baseFile.iters.length <= expFile.iters.length ? baseFile.iters : expFile.iters; - const moreIters = baseFile.iters.length > expFile.iters.length ? baseFile.iters : expFile.iters; - lessIters.forEach((iter) => { - if (moreIters.includes(iter)) { - commonIters.push(iter); - } - }); - commonIters.sort((a, b) => a - b); - const tempTableData: any[] = []; - const tempChartData: ILineDataList = { - normal: [], - absolute: [], - relative: [], - }; - commonIters.forEach((iter, index) => { - const baseLoss = baseFile.iterLosses[iter]; - const expLoss = expFile.iterLosses[iter]; - tempTableData.push({ - key: `${iter}_${index}`, - iter, - [baseFile.fileName]: baseLoss, - [expFile.fileName]: expLoss, - }); - tempChartData.normal.push([iter, expLoss - baseLoss]); - tempChartData.absolute.push([iter, Math.abs(expLoss - baseLoss)]); - tempChartData.relative.push([iter, baseLoss === 0 ? 0 : Math.abs(expLoss - baseLoss) / baseLoss]); - }); - setTableData(tempTableData); - setLineData(tempChartData); - } - }; - - const onSelectChange = (value: string[]): void => { - setSelectedFiles(value); - compareFile(value); - }; - - const onRadioChange = (e: RadioChangeEvent): void => { - setCompareWay(e.target.value); - }; - - const onShowSizeChange = (current: number, size: number): void => { - setPageSize(size); - }; - - useLayoutEffect(() => { - const element = chartRef.current; - if (!element || !lineData) { - return undefined; - } - const echart = echarts.init(element); - let dataSource: number[][] = []; - if (compareWay === 0) { - dataSource = lineData.normal; - } else if (compareWay === 1) { - dataSource = lineData.absolute; - } else { - dataSource = lineData.relative; - } - const option: echarts.EChartsOption = { - title: { - text: 'Comparison Chart', - textStyle: { - fontSize: 12, - color: '#000', - }, - }, - legend: { bottom: 0 }, - xAxis: { - type: 'category', - boundaryGap: false, - name: 'Iteration', - }, - yAxis: { - type: 'value', - name: 'Difference', - scale: true, - }, - tooltip: { - trigger: 'axis', - valueFormatter: (value) => (value as number).toFixed(6), - }, - dataZoom: { - type: 'inside', - }, - dataset: { - source: dataSource, - }, - series: { - type: 'line', - name: 'Difference', - symbol: 'none', - }, - }; - - if (option) { - echart.setOption(option, true); - } - return () => { - echart.dispose(); - }; - }, [compareWay, lineData]); - - useEffect(() => { - const tempValue = selectedFiles.filter((item) => { - return !!fileList.find((file) => file.fileName === item); - }); - if (JSON.stringify(tempValue) === JSON.stringify(selectedFiles)) { - compareFile(tempValue); - } - setSelectedFiles(tempValue); - }, [fileList]); - - return ( -
-
Comparison Data
-
- Comparison objects: - -
-
- Iteration Tag - -
- - ); -}; diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/Accuracy/entity.ts b/plugins/tensorboard-plugins/tb_plugin/fe/src/components/Accuracy/entity.ts deleted file mode 100644 index 270c4cb65..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/Accuracy/entity.ts +++ /dev/null @@ -1,30 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *-------------------------------------------------------------------------------------------- - * Copyright (c) 2023, Huawei Technologies. - * All rights reserved. - * - * Licensed under the Apache License, Version 2.0 (the 'License') - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an 'AS IS' BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - *--------------------------------------------------------------------------------------------*/ - -export interface FileInfo { - id: number; - fileName: string; - fileContent: string; - checked: boolean; - lossTag: string; - iterTag: string; - iters: number[]; - losses: number[][]; - iterLosses: { [iter: number]: number }; -} diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/DataLoading.tsx b/plugins/tensorboard-plugins/tb_plugin/fe/src/components/DataLoading.tsx deleted file mode 100644 index 3c5d353ce..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/DataLoading.tsx +++ /dev/null @@ -1,19 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *--------------------------------------------------------------------------------------------*/ - -import * as React from 'react'; -import { FullCircularProgress } from './FullCircularProgress'; - -interface IProps { - value?: T | null; - children: (t: T) => JSX.Element; -} - -export function DataLoading(props: IProps): JSX.Element { - if (props.value === undefined || props.value === null) { - return ; - } - - return props.children(props.value); -} diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/DiffOverview.tsx b/plugins/tensorboard-plugins/tb_plugin/fe/src/components/DiffOverview.tsx deleted file mode 100644 index ed029d502..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/DiffOverview.tsx +++ /dev/null @@ -1,942 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *--------------------------------------------------------------------------------------------*/ - -import Button from '@material-ui/core/Button'; -import Card from '@material-ui/core/Card'; -import CardContent from '@material-ui/core/CardContent'; -import CardHeader from '@material-ui/core/CardHeader'; -import Grid from '@material-ui/core/Grid'; -import { makeStyles } from '@material-ui/core/styles'; -import Typography from '@material-ui/core/Typography'; -import ChevronLeftIcon from '@material-ui/icons/ChevronLeft'; -import { Select, Table } from 'antd'; -import * as React from 'react'; -import * as api from '../api'; -import { useResizeEventDependency } from '../utils/resize'; -import { FullCircularProgress } from './FullCircularProgress'; -import * as echarts from 'echarts'; - -const { Option } = Select; - -const topGraphHeight = 230; - -const useStyles = makeStyles((theme) => ({ - root: { - flexGrow: 1, - }, - pre: { - '& ul': { - margin: 0, - paddingLeft: theme.spacing(3), - ...theme.typography.body1, - }, - '& li': {}, - '& a': { - color: '#ffa726', - }, - '& a:active': { - color: '#ffa726', - }, - '& p': { - margin: 0, - ...theme.typography.subtitle1, - fontWeight: theme.typography.fontWeightBold, - }, - }, - topGraph: { - height: topGraphHeight + 40, - }, - iconButton: { - padding: '8px', - }, -})); - -const getAngleByDataLength = (data: number): number => { - if (data < 10) { - return 0; - } else { - // 数量越大越趋近于旋转90度 - return 90 * (1 - (10 / data)); - } -}; - -export interface DiffColumnChartIProps { - rawData: any[]; - selectCallback: (row: number, column: number) => void; -} - -export interface DiffStepChartIProps { - rawData: any[]; -} - -const DiffColumnChart: React.FC = (props: DiffColumnChartIProps) => { - const { rawData, selectCallback } = props; - const graphRef = React.useRef(null); - const [resizeEventDependency] = useResizeEventDependency(); - - React.useLayoutEffect(() => { - const element = graphRef.current; - if (!element) { - return undefined; - } - - const chart = echarts.init(element); - - const options: echarts.EChartsOption = { - title: { - text: 'Execution Comparsion', - }, - legend: { - top: 10, - right: 10, - }, - tooltip: { - trigger: 'axis', - formatter: function (params: any) { - const index = params[0].name.indexOf('@'); - const safeName = params[0].name.replace(//g, '>'); - let res = `${index > -1 ? safeName.slice(index + 1) : safeName}
`; - for (const item of params) { - if (typeof item.value[item.encode.y[0]] === 'number') { - res += ` - - ${item.seriesName}: ${item.value[item.encode.y[0]]}
`; - } - } - return res; - }, - }, - series: [ - { - type: 'bar', - itemStyle: { - color: '#3366cc', - }, - yAxisIndex: 0, - }, - { - type: 'bar', - itemStyle: { - color: '#dc3912', - }, - yAxisIndex: 0, - }, - { - type: 'line', - itemStyle: { - color: '#ff9900', - }, - yAxisIndex: 1, - }, - { - type: 'line', - itemStyle: { - color: '#109618', - }, - yAxisIndex: 1, - }, - ], - xAxis: { - type: 'category', - axisLabel: { - interval: 0, - rotate: getAngleByDataLength(rawData.length), - formatter: (name: string) => { - const index = name.indexOf('@'); - const displayName = index > -1 ? name.slice(index + 1) : name; // 创建新变量 - return displayName.length > 16 ? `${displayName.slice(0, 14)}...` : displayName; - }, - }, - }, - yAxis: [ - { - type: 'value', - name: 'Time Difference(us)', - scale: true, - }, - { - type: 'value', - name: 'Accumulated Difference(us)', - scale: true, - }, - ], - dataset: { - source: rawData.map((item, idx) => { - // 添加索引保证x轴刻度不重复 - let param: any[] = [...item]; - param[0] = `${idx}@${param[0]}`; - return param; - }), - }, - }; - - if (options) { - chart.setOption(options, true); - } - chart.on('click', (param) => { - if (param.seriesIndex !== undefined) { - selectCallback(param.dataIndex, param.seriesIndex + 1); - } - }); - - return () => { - chart.dispose(); - }; - }, [rawData, resizeEventDependency]); - - return ( -
-
-
- ); -}; - -const DiffStepChart: React.FC = (props: DiffStepChartIProps) => { - const { rawData } = props; - const graphRef = React.useRef(null); - const [resizeEventDependency] = useResizeEventDependency(); - - React.useLayoutEffect(() => { - const element = graphRef.current; - if (!element) { - return undefined; - } - const chart = echarts.init(element); - const options: echarts.EChartsOption = { - title: { - text: 'Execution Diff', - }, - legend: { - top: 10, - right: 10, - }, - dataset: { - source: rawData.map((item, idx) => { - // 添加索引保证x轴刻度不重复 - let param: any[] = [...item]; - param[0] = `${idx}@${param[0]}`; - return param; - }), - }, - xAxis: { - type: 'category', - axisLabel: { - interval: 0, - rotate: getAngleByDataLength(rawData.length), - formatter: (name: string) => { - const index = name.indexOf('@'); - const displayName = index > -1 ? name.slice(index + 1) : name; // 创建新变量 - return displayName.length > 16 ? `${displayName.slice(0, 14)}...` : displayName; - }, - }, - }, - yAxis: { - type: 'value', - scale: true, - }, - tooltip: { - trigger: 'axis', - formatter: function (params: any) { - const index = params[0].name.indexOf('@'); - const safeName = params[0].name.replace(//g, '>'); - let res = `${index > -1 ? safeName.slice(index + 1) : safeName}
`; - for (const item of params) { - if (typeof item.value[item.encode.y[0]] === 'number') { - res += ` - - ${item.seriesName}: ${item.value[item.encode.y[0]]}
`; - } - } - return res; - }, - }, - series: [ - { - type: 'line', - color: '#3366cc', - symbolSize: 0, - step: 'middle', - areaStyle: { - color: '#c1d1ef', - opacity: 1, - }, - }, - { - type: 'line', - color: '#dc3912', - symbolSize: 0, - step: 'middle', - areaStyle: { - color: '#f4c3b7', - opacity: 1, - }, - }, - ], - }; - - if (options) { - chart.setOption(options, true); - } - return () => { - chart.dispose(); - }; - }, [rawData, resizeEventDependency]); - - return ( -
-
-
- ); -}; - -export interface IProps { - run: string; - worker: string; - span: string; - expRun: string; - expWorker: string; - expSpan: string; -} - -export interface ColumnUnderlyingData { - name: string; - path: string; - leftAggs: any[]; - rightAggs: any[]; -} - -export interface TableRow { - key: number; - - operator: string; - baselineCalls?: number; - expCalls?: number; - deltaCalls?: number; - deltaCallsPercentNumber?: number; - deltaCallsPercent?: string; - - baselineHostDuration: number; - expHostDuration: number; - deltaHostDuration: number; - deltaHostDurationPercentNumber: number; - deltaHostDurationPercent: string; - - baselineSelfHostDuration: number; - expSelfHostDuration: number; - deltaSelfHostDuration: number; - deltaSelfHostDurationPercentNumber: number; - deltaSelfHostDurationPercent: string; - - baselineDeviceDuration: number; - expDeviceDuration: number; - deltaDeviceDuration: number; - deltaDeviceDurationPercentNumber: number; - deltaDeviceDurationPercent: string; - - baselineSelfDeviceDuration: number; - expSelfDeviceDuration: number; - deltaSelfDeviceDuration: number; - deltaSelfDeviceDurationPercentNumber: number; - deltaSelfDeviceDurationPercent: string; -} - -let columnChartDataStack: any[][] = []; -let stepChartDataStack: any[][] = []; -let columnUnderlyingDataStack: ColumnUnderlyingData[][] = []; -let columnTableDataSourceStack: TableRow[][] = []; - -export const DiffOverview: React.FC = (props: IProps) => { - // #region - Constant - const COMPOSITE_NODES_NAME = 'CompositeNodes'; - - const hostDurationColumns = [ - { - title: 'Baseline Host Duration (us)', - dataIndex: 'baselineHostDuration', - key: 'baselineHostDuration', - sorter: (a: TableRow, b: TableRow): number => { - const aBaselineHost = a.baselineHostDuration ?? 0; - const bBaselineHost = b.baselineHostDuration ?? 0; - return aBaselineHost - bBaselineHost; - }, - }, - { - title: 'Exp Host Duration (us)', - dataIndex: 'expHostDuration', - key: 'expHostDuration', - sorter: (a: TableRow, b: TableRow): number => { - const aExpHost = a.expHostDuration ?? 0; - const bExpHost = b.expHostDuration ?? 0; - return aExpHost - bExpHost; - }, - }, - { - title: 'Delta Host Duration (us)', - dataIndex: 'deltaHostDuration', - key: 'deltaHostDuration', - sorter: (a: TableRow, b: TableRow): number => { - const aDeltaHost = a.deltaHostDuration ?? 0; - const bDeltaHost = b.deltaHostDuration ?? 0; - return aDeltaHost - bDeltaHost; - }, - }, - { - title: 'Delta Host Duration%', - dataIndex: 'deltaHostDurationPercent', - key: 'deltaHostDurationPercent', - sorter: (a: TableRow, b: TableRow): number => { - const aPercent = a.deltaHostDurationPercentNumber ?? 0; - const bPercent = b.deltaHostDurationPercentNumber ?? 0; - return aPercent - bPercent; - }, - }, - ]; - - const selfHostDurationColumns = [ - { - title: 'Baseline Self Host Duration (us)', - dataIndex: 'baselineSelfHostDuration', - key: 'baselineSelfHostDuration', - sorter: (a: TableRow, b: TableRow): number => a.baselineSelfHostDuration - b.baselineSelfHostDuration, - }, - { - title: 'Exp Self Host Duration (us)', - dataIndex: 'expSelfHostDuration', - key: 'expSelfHostDuration', - sorter: (a: TableRow, b: TableRow): number => a.expSelfHostDuration - b.expSelfHostDuration, - }, - { - title: 'Delta Self Host Duration (us)', - dataIndex: 'deltaSelfHostDuration', - key: 'deltaSelfHostDuration', - sorter: (a: TableRow, b: TableRow): number => { - const aDeltaSelfHost = a.deltaSelfHostDuration ?? 0; - const bDeltaSelfHost = b.deltaSelfHostDuration ?? 0; - return aDeltaSelfHost - bDeltaSelfHost; - }, - }, - { - title: 'Delta Self Host Duration%', - dataIndex: 'deltaSelfHostDurationPercent', - key: 'deltaSelfHostDurationPercent', - sorter: (a: TableRow, b: TableRow): number => { - const aSelfPercent = a.deltaSelfHostDurationPercentNumber ?? 0; - const bSelfPercent = b.deltaSelfHostDurationPercentNumber ?? 0; - return aSelfPercent - bSelfPercent; - }, - }, - ]; - - const deviceDurationColumns = [ - { - title: 'Baseline Device Duration (us)', - dataIndex: 'baselineDeviceDuration', - key: 'baselineDeviceDuration', - sorter: (a: TableRow, b: TableRow): number => a.baselineDeviceDuration - b.baselineDeviceDuration, - }, - { - title: 'Exp Device Duration (us)', - dataIndex: 'expDeviceDuration', - key: 'expDeviceDuration', - sorter: (a: TableRow, b: TableRow): number => a.expDeviceDuration - b.expDeviceDuration, - }, - { - title: 'Delta Device Duration (us)', - dataIndex: 'deltaDeviceDuration', - key: 'deltaDeviceDuration', - sorter: (a: TableRow, b: TableRow): number => { - const aDeltaDeviceDuration = a.deltaDeviceDuration ?? 0; - const bdeltaDeviceDuration = b.deltaDeviceDuration ?? 0; - return aDeltaDeviceDuration - bdeltaDeviceDuration; - }, - }, - { - title: 'Delta Device Duration%', - dataIndex: 'deltaDeviceDurationPercent', - key: 'deltaDeviceDurationPercent', - sorter: (a: TableRow, b: TableRow): number => { - const aDeltaDeviceDurationPercentNumber = a.deltaDeviceDurationPercentNumber ?? 0; - const bDeltaDeviceDurationPercentNumber = b.deltaDeviceDurationPercentNumber ?? 0; - return aDeltaDeviceDurationPercentNumber - bDeltaDeviceDurationPercentNumber; - }, - }, - ]; - - const selfDeviceDurationColumns = [ - { - title: 'Baseline Self Device Duration (us)', - dataIndex: 'baselineSelfDeviceDuration', - key: 'baselineSelfDeviceDuration', - sorter: (a: TableRow, b: TableRow): number => a.baselineSelfDeviceDuration - b.baselineSelfDeviceDuration, - }, - { - title: 'Exp Self Device Duration (us)', - dataIndex: 'expSelfDeviceDuration', - key: 'expSelfDeviceDuration', - sorter: (a: TableRow, b: TableRow): number => a.expSelfDeviceDuration - b.expSelfDeviceDuration, - }, - { - title: 'Delta Self Device Duration (us)', - dataIndex: 'deltaSelfDeviceDuration', - key: 'deltaSelfDeviceDuration', - sorter: (a: TableRow, b: TableRow): number => { - const aDeltaSelfDeviceDuration = a.deltaSelfDeviceDuration ?? 0; - const bDeltaSelfDeviceDuration = b.deltaSelfDeviceDuration ?? 0; - return aDeltaSelfDeviceDuration - bDeltaSelfDeviceDuration; - }, - }, - { - title: 'Delta Self Device Duration%', - dataIndex: 'deltaSelfDeviceDurationPercent', - key: 'deltaSelfDeviceDurationPercent', - sorter: (a: TableRow, b: TableRow): number => { - const aDeltaSelfDeviceDurationPercentNumber = a.deltaSelfDeviceDurationPercentNumber ?? 0; - const bDeltaSelfDeviceDurationPercentNumber = b.deltaSelfDeviceDurationPercentNumber ?? 0; - return aDeltaSelfDeviceDurationPercentNumber - bDeltaSelfDeviceDurationPercentNumber; - }, - }, - ]; - - interface IColumnMap { - [key: string]: any; - } - type IColumnMapType = IColumnMap; - - const tableSourceColumnMap: IColumnMapType = { - selfHostDuration: selfHostDurationColumns, - hostDuration: hostDurationColumns, - deviceDuration: deviceDurationColumns, - selfDeviceDuration: selfDeviceDurationColumns, - }; - - const baseTableColumns = [ - { - title: 'Operator', - dataIndex: 'operator', - key: 'operator', - sorter: (a: TableRow, b: TableRow) => a.operator.localeCompare(b.operator), - }, - { - title: 'Baseline Calls', - dataIndex: 'baselineCalls', - key: 'baselineCalls', - sorter: (a: TableRow, b: TableRow) => a.baselineCalls ?? 0 - (b.baselineCalls ?? 0), - }, - { - title: 'Exp Calls', - dataIndex: 'expCalls', - key: 'expCalls', - sorter: (a: TableRow, b: TableRow) => a.expCalls ?? 0 - (b.expCalls ?? 0), - }, - { - title: 'Delta Calls', - dataIndex: 'deltaCalls', - key: 'deltaCalls', - sorter: (a: TableRow, b: TableRow) => a.deltaCalls ?? 0 - (b.deltaCalls ?? 0), - }, - { - title: 'Delta Calls%', - dataIndex: 'deltaCallsPercent', - key: 'deltaCallsPercent', - sorter: (a: TableRow, b: TableRow) => a.deltaCallsPercentNumber ?? 0 - (b.deltaCallsPercentNumber ?? 0), - }, - ]; - - // #endregion - - // #region - State - const [tableDataSource, setTableDataSource] = React.useState([]); - const { run, worker, span, expRun, expWorker, expSpan } = props; - - const [columnUnderlyingData, setColumnUnderlyingData] = React.useState([]); - - const [rootUnderlyingData, setRootUnderlyingData] = React.useState(); - - const [columnChartData, setColumnChartData] = React.useState([]); - const [stepChartData, setStepChartData] = React.useState([]); - - const [selectedTableColumnsOptions, setSelectedTableColumnsOptions] = React.useState<[key: string]>(['hostDuration']); - const [selectedTableColumns, setSelectedTableColumns] = React.useState([ - ...baseTableColumns, - ...hostDurationColumns, - ]); - - const [dataStackLevel, setDataStackLevel] = React.useState(0); - const [loading, setLoading] = React.useState(false); - - // #endregion - const classes = useStyles(); - - // #region - Event Handler - const handleChartColumnSelect = (row: number, column: number): void => { - if (columnUnderlyingData.length === 0) { - return; - } - - let selectedUnderlyingData = columnUnderlyingData[row]; - if (!selectedUnderlyingData) { - return; - } - - let tableDataSource1 = generateDataSourceFromUnderlyingData(selectedUnderlyingData); - setTableDataSource(tableDataSource1); - columnTableDataSourceStack.push(tableDataSource1); - - setLoading(true); - - api.defaultApi - .diffnodeGet(run, worker, span, expRun, expWorker, expSpan, selectedUnderlyingData.path) - .then((resp) => handleDiffNodeResp(resp)) - .finally(() => setLoading(false)); - }; - - const handleGoBack = (): void => { - if (columnChartDataStack.length > 1) { - columnChartDataStack.pop(); - let top = columnChartDataStack[columnChartDataStack.length - 1]; - setColumnChartData(top); - } - - if (stepChartDataStack.length > 1) { - stepChartDataStack.pop(); - let top = stepChartDataStack[stepChartDataStack.length - 1]; - setStepChartData(top); - } - - if (columnUnderlyingDataStack.length > 0) { - columnUnderlyingDataStack.pop(); - let top = columnUnderlyingDataStack[columnUnderlyingDataStack.length - 1]; - setColumnUnderlyingData(top); - } - - if (columnTableDataSourceStack.length > 0) { - columnTableDataSourceStack.pop(); - let top = columnTableDataSourceStack[columnTableDataSourceStack.length - 1]; - - if (top) { - setTableDataSource(top); - } else { - let tableDataSource2 = generateDataSourceFromUnderlyingData(rootUnderlyingData); - setTableDataSource(tableDataSource2); - } - } - - setDataStackLevel(dataStackLevel - 1); - }; - - const toPercentString = (percentNumber: number): string => { - if (isNaN(percentNumber)) { - return 'N/A'; - } - - return `${percentNumber.toFixed(2)}%`; - }; - - const handleColumnSelectionChange = (value: [key: string]): void => { - let columns = value.map((x) => tableSourceColumnMap[x]).flat(); - let r = [...baseTableColumns, ...columns]; - setSelectedTableColumnsOptions(value); - setSelectedTableColumns(r); - }; - - const generateDataSourceFromUnderlyingData = (selectedUnderlyingData?: ColumnUnderlyingData): TableRow[] => { - if (!selectedUnderlyingData) { - return []; - } - let newTableDataSource: TableRow[] = []; - - for (let i = 0; i < selectedUnderlyingData.leftAggs.length; i++) { - let left = selectedUnderlyingData.leftAggs[i]; - let right = selectedUnderlyingData.rightAggs[i]; - - let deltaCallsPercentNumber = ((right.calls - left.calls) / left.calls) * 100; - - let deltaHostDurationPercentNumber = ((right.host_duration - left.host_duration) / left.host_duration) * 100; - - let deltaSelfHostDurationPercentNumber = - ((right.self_host_duration - left.self_host_duration) / left.self_host_duration) * 100; - - let deltaDeviceDurationPercentNumber = - ((right.device_duration - left.device_duration) / left.device_duration) * 100; - - let deltaSelfDeviceDurationPercentNumber = - ((right.self_device_duration - left.self_device_duration) / left.self_device_duration) * 100; - - newTableDataSource.push({ - key: i, - operator: left.name, - baselineCalls: left.calls, - expCalls: right.calls, - deltaCalls: right.calls - left.calls, - deltaCallsPercentNumber: deltaCallsPercentNumber, - deltaCallsPercent: toPercentString(deltaCallsPercentNumber), - - baselineHostDuration: left.host_duration, - expHostDuration: right.host_duration, - deltaHostDuration: parseFloat((right.host_duration - left.host_duration).toFixed(3)), - deltaHostDurationPercentNumber: deltaHostDurationPercentNumber, - deltaHostDurationPercent: toPercentString(deltaHostDurationPercentNumber), - - baselineSelfHostDuration: left.self_host_duration, - expSelfHostDuration: right.self_host_duration, - deltaSelfHostDuration: parseFloat((right.self_host_duration - left.self_host_duration).toFixed(3)), - deltaSelfHostDurationPercentNumber: deltaSelfHostDurationPercentNumber, - deltaSelfHostDurationPercent: toPercentString(deltaSelfHostDurationPercentNumber), - - baselineDeviceDuration: left.device_duration, - expDeviceDuration: right.device_duration, - deltaDeviceDuration: parseFloat((right.device_duration - left.device_duration).toFixed(3)), - deltaDeviceDurationPercentNumber: deltaDeviceDurationPercentNumber, - deltaDeviceDurationPercent: toPercentString(deltaDeviceDurationPercentNumber), - - baselineSelfDeviceDuration: left.self_device_duration, - expSelfDeviceDuration: right.self_device_duration, - deltaSelfDeviceDuration: parseFloat((right.self_device_duration - left.self_device_duration).toFixed(3)), - deltaSelfDeviceDurationPercentNumber: deltaSelfDeviceDurationPercentNumber, - deltaSelfDeviceDurationPercent: toPercentString(deltaSelfDeviceDurationPercentNumber), - }); - } - - return newTableDataSource; - }; - - React.useEffect(() => { - const hasData = - run.length > 0 && - worker.length > 0 && - span.length > 0 && - expRun.length > 0 && - expWorker.length > 0 && - expSpan.length > 0; - if (hasData) { - setLoading(true); - - columnChartDataStack = []; - stepChartDataStack = []; - columnUnderlyingDataStack = []; - columnTableDataSourceStack = []; - - api.defaultApi - .diffnodeGet(run, worker, span, expRun, expWorker, expSpan) - .then((resp) => { - handleDiffNodeResp(resp); - let newRootUnderlyingData = { - name: 'rootNode', - path: resp.path, - leftAggs: resp.left.aggs, - rightAggs: resp.right.aggs, - }; - - setRootUnderlyingData(newRootUnderlyingData); - let tableDataSource3 = generateDataSourceFromUnderlyingData(newRootUnderlyingData); - setTableDataSource(tableDataSource3); - }) - .finally(() => setLoading(false)); - - setSelectedTableColumns([...baseTableColumns, ...hostDurationColumns]); - } - }, [run, worker, span, expRun, expWorker, expSpan]); - - const handleDiffNodeResp = (resp: any): void => { - let newColumnChartData: any[] = []; - let newStepChartData: any[] = []; - let underlyingData: ColumnUnderlyingData[] = []; - - newColumnChartData.push(['Call', 'Baseline', 'Experiment', 'Baseline Trend', 'Exp Trend']); - newStepChartData.push(['Call', 'Diff', 'Accumulated Diff']); - - if (resp.children.length > 0) { - let accumulatedLeftDuration = 0; - let accumulatedRightDuration = 0; - let accumulatedStepDiff = 0; - for (let i = 0; i < resp.children.length; i++) { - let left = resp.children[i].left; - let right = resp.children[i].right; - let currColumn: any[] = []; - let currStep: any[] = []; - - let name = left.name; - if (name === COMPOSITE_NODES_NAME) { - continue; - } - - if (name.startsWith('aten::')) { - // Ignore aten operators - continue; - } - - if (name.startsWith('enumerate(DataLoader)')) { - name = name.substring(21); - } - - if (name.startsWith('enumerate(DataPipe)')) { - name = name.substring(19); - } - - if (name.startsWith('nn.Module: ')) { - name = name.substring(11); - } - - if (name.startsWith('Optimizer.zero_grad')) { - name = 'Optimizer.zero_grad'; - } - - if (name.startsWith('Optimizer.step')) { - name = 'Optimizer.step'; - } - - currColumn.push(name); - currColumn.push(left.total_duration); - currColumn.push(right.total_duration); - - accumulatedLeftDuration += left.total_duration; - currColumn.push(accumulatedLeftDuration); - - accumulatedRightDuration += right.total_duration; - currColumn.push(accumulatedRightDuration); - newColumnChartData.push(currColumn); - - underlyingData.push({ - name: name, - path: resp.children[i].path, - leftAggs: left.aggs, - rightAggs: right.aggs, - }); - - currStep.push(name); - let stepDiff = right.total_duration - left.total_duration; - currStep.push(stepDiff); - - accumulatedStepDiff += stepDiff; - currStep.push(accumulatedStepDiff); - - newStepChartData.push(currStep); - } - } else { - let left = resp.left; - let right = resp.right; - let currColumn: any[] = []; - let currStep: any[] = []; - let name = left.name; - - if (name.startsWith('nn.Module: ')) { - name = name.substring(11); - } - - currColumn.push(name); - currColumn.push(left.total_duration); - currColumn.push(right.total_duration); - currColumn.push(left.total_duration); - currColumn.push(right.total_duration); - - newColumnChartData.push(currColumn); - - currStep.push(name); - let stepDiff = right.total_duration - left.total_duration; - currStep.push(stepDiff); - currStep.push(stepDiff); - newStepChartData.push(currStep); - } - - setColumnChartData(newColumnChartData); - columnChartDataStack.push(newColumnChartData); - - setStepChartData(newStepChartData); - stepChartDataStack.push(newStepChartData); - - setColumnUnderlyingData(underlyingData); - columnUnderlyingDataStack.push(underlyingData); - - setDataStackLevel(columnChartDataStack.length); - }; // #endregion - - if (!loading && columnUnderlyingDataStack.length === 0) { - return ( - - - - There is no run selected for diff. - - - ); - } - - if (loading) { - return ; - } - - return ( -
- - - - - - - - {columnChartData.length > 1 && ( - <> - - - - )} - {columnChartData.length === 1 && No more level to show.} - - - - - - - - - - -   - - - - - - - - ); -}; diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/DistributedView.tsx b/plugins/tensorboard-plugins/tb_plugin/fe/src/components/DistributedView.tsx deleted file mode 100644 index 096501b61..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/DistributedView.tsx +++ /dev/null @@ -1,313 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *--------------------------------------------------------------------------------------------*/ - -import Card from '@material-ui/core/Card'; -import CardContent from '@material-ui/core/CardContent'; -import CardHeader from '@material-ui/core/CardHeader'; -import Grid from '@material-ui/core/Grid'; -import InputLabel from '@material-ui/core/InputLabel'; -import MenuItem from '@material-ui/core/MenuItem'; -import Select, { SelectProps } from '@material-ui/core/Select'; -import { makeStyles } from '@material-ui/core/styles'; -import { Table } from 'antd'; -import { ColumnsType } from 'antd/es/table'; -import * as React from 'react'; -import * as api from '../api'; -import { DistributedGraph, GpuInfo, Graph } from '../api'; -import { firstOrUndefined } from '../utils'; -import { ColumnChart } from './charts/ColumnChart'; -import { DataLoading } from './DataLoading'; -import { GpuInfoTable } from './GpuInfoTable'; -import { makeChartHeaderRenderer, useTooltipCommonStyles } from './helpers'; -import { - distributedCommopsTableTooltip, - distributedGpuInfoTableTooltip, - distributedOverlapGraphTooltip, - distributedWaittimeGraphTooltip, -} from './TooltipDescriptions'; - -export interface IProps { - run: string; - worker: string; - span: string; -} - -const useStyles = makeStyles((theme) => ({ - root: { - flexGrow: 1, - }, - verticalInput: { - display: 'flex', - alignItems: 'center', - }, - inputWidth: { - width: '4em', - }, - inputWidthOverflow: { - minWidth: '15em', - whiteSpace: 'nowrap', - }, - description: { - marginLeft: theme.spacing(1), - }, - table: { - height: '100%', - border: '1px solid #efefef', - '& .ant-table-tbody > tr': { - height: 20, - fontSize: '10pt', - '& > td': { - padding: '0 8px!important', - }, - }, - }, -})); - -export const DistributedView: React.FC = (props) => { - const tooltipCommonClasses = useTooltipCommonStyles(); - const chartHeaderRenderer = React.useMemo( - () => makeChartHeaderRenderer(tooltipCommonClasses), - [tooltipCommonClasses] - ); - - let { run, worker, span } = props; - const classes = useStyles(); - - const [overlapGraph, setOverlapGraph] = React.useState(undefined); - const [waittimeGraph, setWaittimeGraph] = React.useState(undefined); - const [commopsTableData, setCommopsTableData] = React.useState(undefined); - const [gpuInfo, setGpuInfo] = React.useState(undefined); - const [commopsTableTitle, setCommopsTableTitle] = React.useState(''); - const [commopsWorkers, setCommopsWorkers] = React.useState([]); - const [overlapSteps, setOverlapSteps] = React.useState([]); - const [waittimeSteps, setWaittimeSteps] = React.useState([]); - const [overlapStep, setOverlapStep] = React.useState(''); - const [waittimeStep, setWaittimeStep] = React.useState(''); - const [commopsWorker, setCommopsWorker] = React.useState(''); - const [columns, setColumns] = React.useState>([]); - const [pageSize, setPageSize] = React.useState(30); - - React.useEffect(() => { - if (waittimeSteps.includes('all')) { - setWaittimeStep('all'); - } else { - setWaittimeStep(firstOrUndefined(waittimeSteps) ?? ''); - } - }, [waittimeSteps]); - - React.useEffect(() => { - if (overlapSteps.includes('all')) { - setOverlapStep('all'); - } else { - setOverlapStep(firstOrUndefined(overlapSteps) ?? ''); - } - }, [overlapSteps]); - - React.useEffect(() => { - setCommopsWorker(firstOrUndefined(commopsWorkers) ?? ''); - }, [commopsWorkers]); - - React.useEffect(() => { - api.defaultApi.distributedOverlapGet(run, 'All', span).then((resp) => { - setOverlapGraph(resp); - setOverlapSteps(Object.keys(resp.data)); - }); - api.defaultApi.distributedWaittimeGet(run, 'All', span).then((resp) => { - setWaittimeGraph(resp); - setWaittimeSteps(Object.keys(resp.data)); - }); - api.defaultApi.distributedCommopsGet(run, 'All', span).then((resp) => { - setCommopsTableData(resp.data); - setCommopsWorkers(Object.keys(resp.data)); - setCommopsTableTitle(resp.metadata.title); - }); - api.defaultApi.distributedGpuinfoGet(run, 'All', span).then((resp) => { - setGpuInfo(resp); - }); - }, [run, worker, span]); - - const onCommopsWorkerChanged: SelectProps['onChange'] = (event) => { - setCommopsWorker(event.target.value as string); - }; - - const onOverlapStepChanged: SelectProps['onChange'] = (event) => { - setOverlapStep(event.target.value as string); - }; - - const onWaittimeStepChanged: SelectProps['onChange'] = (event) => { - setWaittimeStep(event.target.value as string); - }; - - const getColumnChartData = (distributedGraph?: DistributedGraph, step?: string): any => { - if (!distributedGraph || !step) { - return undefined; - } - const barLabels = Object.keys(distributedGraph.data[step]); - return { - legends: distributedGraph.metadata.legends, - barLabels, - barHeights: barLabels.map((label) => distributedGraph.data[step][label]), - }; - }; - const overlapData = React.useMemo(() => getColumnChartData(overlapGraph, overlapStep), [overlapGraph, overlapStep]); - const waittimeData = React.useMemo( - () => getColumnChartData(waittimeGraph, waittimeStep), - [waittimeGraph, waittimeStep] - ); - - const getTableData = (tableData?: any, opsWorker?: string): any[] => { - if (!tableData || !opsWorker) { - return []; - } - let dataInfo: api.Graph = tableData[opsWorker]; - const stringCompare = (a: string, b: string): number => a.localeCompare(b); - const numberCompare = (a: number, b: number): number => a - b; - let column: any[] = dataInfo.columns.map((item) => { - return { - title: item.name, - key: item.name, - dataIndex: item.name, - sorter: - item.type === 'string' - ? (a: any, b: any): number => stringCompare(a[item.name], b[item.name]) - : (a: any, b: any): number => numberCompare(a[item.name], b[item.name]), - }; - }); - setColumns(column); - return dataInfo.rows.map((row, index) => { - if (row.length !== dataInfo.columns.length) { - return null; - } - const dataRow: { [column: string]: number | string } = { key: index }; - dataInfo.columns.forEach((item, idx) => { - dataRow[item.name] = row[idx] as string | number; - }); - return dataRow; - }); - }; - const commopsTable: any[] = React.useMemo(() => { - return getTableData(commopsTableData, commopsWorker); - }, [commopsTableData, commopsWorker]); - - const onShowSizeChange = (current: number, size: number): void => { - setPageSize(size); - }; - - return ( -
- - - - - {gpuInfo && ( - - - - - - - - - )} - - - {(chartData): JSX.Element => ( - - - - - Step - - - - - - - {overlapGraph?.metadata?.title && ( - - )} - - - )} - - - - - {(chartData): JSX.Element => ( - - - - - Step - - - - - - - {waittimeGraph?.metadata?.title && ( - - )} - - - )} - - - - - - - - - - Worker - - - - - - - - -
- - - - - - - - ); -}; diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/FullCircularProgress.tsx b/plugins/tensorboard-plugins/tb_plugin/fe/src/components/FullCircularProgress.tsx deleted file mode 100644 index 3f4c0fbaf..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/FullCircularProgress.tsx +++ /dev/null @@ -1,23 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *--------------------------------------------------------------------------------------------*/ -import CircularProgress from '@material-ui/core/CircularProgress'; -import { makeStyles } from '@material-ui/core/styles'; -import * as React from 'react'; - -const useStyles = makeStyles(() => ({ - root: { - width: '100%', - display: 'flex', - justifyContent: 'center', - }, -})); - -export const FullCircularProgress: React.FC = () => { - const classes = useStyles(); - return ( -
- -
- ); -}; diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/GpuInfoTable.tsx b/plugins/tensorboard-plugins/tb_plugin/fe/src/components/GpuInfoTable.tsx deleted file mode 100644 index 07f6f1d78..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/GpuInfoTable.tsx +++ /dev/null @@ -1,130 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *--------------------------------------------------------------------------------------------*/ - -import { makeStyles } from '@material-ui/core/styles'; -import * as React from 'react'; - -export interface IProps { - gpuInfo: any; -} - -const useStyles = makeStyles((theme) => ({ - root: { - border: '1px solid #E0E0E0', - borderCollapse: 'collapse', - width: '100%', - }, - td: { - borderTop: '1px solid #E0E0E0', - borderBottom: '1px solid #E0E0E0', - borderCollapse: 'collapse', - paddingLeft: 10, - paddingRight: 10, - }, - nodeTd: { - fontWeight: 'bold', - }, - pidTd: { - fontWeight: 'normal', - }, - gpuTd: { - fontWeight: 'normal', - }, - keyTd: { - fontWeight: 'normal', - textAlign: 'right', - }, - valueTd: { - fontWeight: 'bold', - }, -})); - -interface TableCellInfo { - content: string; - rowspan: number; - cellType: 'node' | 'pid' | 'gpu' | 'key' | 'value'; - last?: boolean; -} - -function makeTableCellInfo(gpuInfo: any): TableCellInfo[][] { - const rows: TableCellInfo[][] = []; - let currRow: TableCellInfo[] = []; - rows.push(currRow); - Object.keys(gpuInfo.data).forEach((nodeName) => { - const nodeCell = { - content: nodeName, - rowspan: 0, - cellType: 'node' as const, - }; - const i = rows.length; - currRow.push(nodeCell); - Object.keys(gpuInfo.data[nodeName]).forEach((pid) => { - const pidCell = { content: pid, rowspan: 0, cellType: 'pid' as const }; - const j = rows.length; - currRow.push(pidCell); - Object.keys(gpuInfo.data[nodeName][pid]).forEach((gpu) => { - const gpuCell = { content: gpu, rowspan: 0, cellType: 'gpu' as const }; - const k = rows.length; - currRow.push(gpuCell); - Object.keys(gpuInfo.data[nodeName][pid][gpu]).forEach((keyName) => { - currRow.push({ - content: keyName, - rowspan: 1, - cellType: 'key' as const, - }); - const value: string = gpuInfo.data[nodeName][pid][gpu][keyName]; - currRow.push({ - content: value, - rowspan: 1, - cellType: 'value' as const, - }); - currRow = []; - rows.push(currRow); - }); - gpuCell.rowspan = rows.length - k; - }); - pidCell.rowspan = rows.length - j; - }); - nodeCell.rowspan = rows.length - i; - }); - rows.pop(); - return rows; -} - -export const GpuInfoTable: React.FC = (props) => { - const classes = useStyles(); - interface TableCellInfoNoLast { - content: string; - rowspan: number; - cellType: 'node' | 'pid' | 'gpu' | 'key' | 'value'; - } - - const rows = React.useMemo(() => makeTableCellInfo(props.gpuInfo), [props.gpuInfo]); - - const cellToClass = { - node: classes.nodeTd, - pid: classes.pidTd, - gpu: classes.gpuTd, - key: classes.keyTd, - value: classes.valueTd, - }; - - const renderCell = function (info: TableCellInfoNoLast): JSX.Element { - let cellClass = cellToClass[info.cellType]; - let content = info.cellType === 'key' ? `${info.content}:` : info.content; - return ( - - ); - }; - - return ( -
- {content} -
- {rows.map((row) => ( - {row.map(renderCell)} - ))} -
- ); -}; diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/Kernel.tsx b/plugins/tensorboard-plugins/tb_plugin/fe/src/components/Kernel.tsx deleted file mode 100644 index 66e056951..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/Kernel.tsx +++ /dev/null @@ -1,301 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *-------------------------------------------------------------------------------------------- - * Copyright (c) 2023, Huawei Technologies. - * All rights reserved. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * - * Modifications: Add visualization of PyTorch Ascend profiling. - *--------------------------------------------------------------------------------------------*/ - -import Card from '@material-ui/core/Card'; -import CardContent from '@material-ui/core/CardContent'; -import CardHeader from '@material-ui/core/CardHeader'; -import FormControlLabel from '@material-ui/core/FormControlLabel'; -import Grid from '@material-ui/core/Grid'; -import InputLabel from '@material-ui/core/InputLabel'; -import MenuItem from '@material-ui/core/MenuItem'; -import Radio from '@material-ui/core/Radio'; -import RadioGroup, { RadioGroupProps } from '@material-ui/core/RadioGroup'; -import Select, { SelectProps } from '@material-ui/core/Select'; -import { makeStyles } from '@material-ui/core/styles'; -import TextField, { StandardTextFieldProps, TextFieldProps } from '@material-ui/core/TextField'; -import * as React from 'react'; -import * as api from '../api'; -import { Graph } from '../api'; -import { KernelGroupBy } from '../constants/groupBy'; -import { useSearch } from '../utils/search'; -import { topIsValid, UseTop, useTopN } from '../utils/top'; -import { AntTableChart } from './charts/AntTableChart'; -import { PieChart } from './charts/PieChart'; -import { DataLoading } from './DataLoading'; -import { makeChartHeaderRenderer, useTooltipCommonStyles } from './helpers'; -import { - gpuKernelTotalTimeTooltip, - tensorCoresPieChartTooltip, - tensorCoresPieChartTooltipAscend, -} from './TooltipDescriptions'; - -export interface IProps { - run: string; - worker: string; - span: string; - deviceTarget: string; -} - -const useStyles = makeStyles((theme) => ({ - root: { - flexGrow: 1, - }, - verticalInput: { - display: 'flex', - alignItems: 'center', - }, - inputWidth: { - width: '4em', - }, - inputWidthOverflow: { - minWidth: '15em', - whiteSpace: 'nowrap', - }, - description: { - marginLeft: theme.spacing(1), - }, -})); - -export const Kernel: React.FC = (props) => { - const { run, worker, span, deviceTarget } = props; - const classes = useStyles(); - const tooltipCommonClasses = useTooltipCommonStyles(); - const chartHeaderRenderer = React.useMemo( - () => makeChartHeaderRenderer(tooltipCommonClasses), - [tooltipCommonClasses] - ); - - const [kernelGraph, setKernelGraph] = React.useState(undefined); - const [tcGraph, setTcGraph] = React.useState(undefined); - const [kernelTable, setKernelTable] = React.useState(undefined); - const [groupBy, setGroupBy] = React.useState(KernelGroupBy.KERNEL); - const [searchKernelName, setSearchKernelName] = React.useState(''); - const [searchOpName, setSearchOpName] = React.useState(''); - const [sortColumn, setSortColumn] = React.useState(''); - const [hasStep, setHasStep] = React.useState(false); - - const [topText, actualTop, useTop, setTopText, setUseTop] = useTopN({ - defaultUseTop: UseTop.USE, - defaultTop: 10, - }); - - React.useEffect(() => { - setSearchOpName(''); - }, [groupBy]); - - React.useEffect(() => { - if (kernelGraph) { - setTopText(String(Math.min(kernelGraph.rows?.length, 10))); - } - }, [kernelGraph]); - - React.useEffect(() => { - api.defaultApi.kernelTableGet(run, worker, span, groupBy).then((resp) => { - setSortColumn(resp.metadata.sort); - setKernelTable(resp.data); - const nameColumnIdx = resp.data.columns.findIndex((c) => c.name.toLowerCase() === 'step id'); - setHasStep(nameColumnIdx > -1); - }); - }, [run, worker, span, groupBy]); - - React.useEffect(() => { - api.defaultApi.kernelGet(run, worker, span, KernelGroupBy.KERNEL).then((resp) => { - setKernelGraph(resp.total); - setGroupBy(resp.device_target === 'Ascend' ? KernelGroupBy.KERNEL_NAME_AND_OP_NAME : KernelGroupBy.KERNEL); - }); - }, [run, worker, span]); - - React.useEffect(() => { - api.defaultApi.kernelTcPieGet(run, worker, span).then((resp) => { - setTcGraph(resp.total); - }); - }, [run, worker, span]); - - const [searchedKernelTable] = useSearch(searchKernelName, 'name', kernelTable); - const [searchedOpTable] = useSearch( - searchOpName, - deviceTarget === 'Ascend' ? 'step id' : 'operator', - searchedKernelTable - ); - - const onGroupByChanged: SelectProps['onChange'] = (event) => { - setGroupBy(event.target.value as KernelGroupBy); - }; - - const onSearchKernelChanged: TextFieldProps['onChange'] = (event) => { - setSearchKernelName(event.target.value as string); - }; - - const onSearchOpChanged: TextFieldProps['onChange'] = (event) => { - setSearchOpName(event.target.value as string); - }; - - const onUseTopChanged: RadioGroupProps['onChange'] = (event) => { - setUseTop(event.target.value as UseTop); - }; - - const onTopChanged = (event: React.ChangeEvent): void => { - setTopText(event.target.value); - }; - - const inputProps: StandardTextFieldProps['inputProps'] = { - min: 1, - }; - - const GPUKernelTotalTimeTitle = React.useMemo( - () => chartHeaderRenderer('Total Time (us)', gpuKernelTotalTimeTooltip), - [chartHeaderRenderer] - ); - - const TensorCoresTitle = React.useMemo( - () => - deviceTarget === 'Ascend' - ? chartHeaderRenderer('Accelerator Core Utilization', tensorCoresPieChartTooltipAscend) - : chartHeaderRenderer('Tensor Cores Utilization', tensorCoresPieChartTooltip), - [chartHeaderRenderer, deviceTarget] - ); - - return ( -
- - - - - - - - } label='All kernels' /> - } label='Top kernels to show' /> - - - {useTop === UseTop.USE && ( - - - - )} - - - - {(graph): JSX.Element => ( - - - - - )} - - - - - {(graph): JSX.Element => ( - - - - - )} - - - - - - - Group By - - - - - - - - {deviceTarget === 'Ascend' - ? groupBy === KernelGroupBy.KERNEL && - hasStep && ( - - - - ) - : groupBy === KernelGroupBy.KERNEL_NAME_AND_OP_NAME && ( - - - - )} - - - - - - {(graph): JSX.Element => } - - - - - - - -
- ); -}; diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/MemoryView.tsx b/plugins/tensorboard-plugins/tb_plugin/fe/src/components/MemoryView.tsx deleted file mode 100644 index 225f28a93..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/MemoryView.tsx +++ /dev/null @@ -1,531 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *-------------------------------------------------------------------------------------------- - * Copyright (c) 2023, Huawei Technologies. - * All rights reserved. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * - * Modifications: Add visualization of PyTorch Ascend profiling. - *--------------------------------------------------------------------------------------------*/ - -import Card from '@material-ui/core/Card'; -import CardContent from '@material-ui/core/CardContent'; -import CardHeader from '@material-ui/core/CardHeader'; -import Grid from '@material-ui/core/Grid'; -import InputLabel from '@material-ui/core/InputLabel'; -import MenuItem from '@material-ui/core/MenuItem'; -import Select, { SelectProps } from '@material-ui/core/Select'; -import Slider from '@material-ui/core/Slider'; -import { makeStyles } from '@material-ui/core/styles'; -import TextField, { TextFieldProps } from '@material-ui/core/TextField'; -import * as React from 'react'; -import * as api from '../api'; -import { - Graph, - GraphAscend, - MemoryCurveDataAll, - MemoryCurveData, - MemoryCurveDataAscend, - MemoryEventsData, - MemoryEventsDataAll, - MemoryStatsData, -} from '../api'; -import { useSearchDirectly } from '../utils/search'; -import { AntTableChart } from './charts/AntTableChart'; -import { LineChart } from './charts/NewLineChart'; -import { DataLoading } from './DataLoading'; -import { MemoryStatsTable } from './tables/MemoryStatsTable'; - -const useStyles = makeStyles((theme) => ({ - root: { - flexGrow: 1, - }, - curve: { - marginBottom: 20, - }, - verticalInput: { - display: 'flex', - alignItems: 'center', - }, - inputWidth: { - width: '4em', - }, - inputWidthOverflow: { - minWidth: '15em', - whiteSpace: 'nowrap', - }, - full: { - width: '100%', - }, - description: { - marginLeft: theme.spacing(1), - }, - filterSlider: { - marginTop: 15, - marginRight: 6, - width: 250, - }, - filterInput: { - width: 100, - }, -})); - -export interface IProps { - run: string; - worker: string; - span: string; - deviceTarget: string; -} - -const tags = ['Operator', 'Component']; - -export const MemoryView: React.FC = React.memo((props) => { - interface EventSizeFilter { - [deviceName: string]: Array; - } - - interface MaxEventSize { - [deviceName: string]: number; - } - - const { run, worker, span, deviceTarget } = props; - const classes = useStyles(); - - const [memoryStatsData, setMemoryStatsData] = React.useState(undefined); - - // for backward compatability, old profile do not have events to show - const showEvents = (): boolean | undefined => { - return memoryEventsData && Object.keys(memoryEventsData.rows).length !== 0; - }; - const [memoryEventsData, setMemoryEventsData] = React.useState(undefined); - - // for backward compatability, old profile do not have curve to show - const showCurve = (): boolean | undefined => { - return memoryCurveData && Object.keys(memoryCurveData.rows).length !== 0; - }; - const [memoryCurveData, setMemoryCurveData] = React.useState( - undefined - ); - - const [lineChartData, setLineChartData] = React.useState(undefined); - - const [devices, setDevices] = React.useState([]); - const [device, setDevice] = React.useState(''); - const [tag, setTag] = React.useState('Operator'); - const memoryCurveDataAllRef = React.useRef(undefined); - const memoryEventDataAllRef = React.useRef(undefined); - - interface SelectedRange { - start: number; - end: number; - startTs: number; - endTs: number; - } - const [selectedRange, setSelectedRange] = React.useState(); - const [searchOperatorName, setSearchOperatorName] = React.useState(''); - const [searchEventOperatorName, setSearchEventOperatorName] = React.useState(''); - const [filterEventSize, setFilterEventSize] = React.useState({}); - const [maxSize, setMaxSize] = React.useState({}); - - const getSearchIndex = function (): number { - if (!memoryStatsData) { - return -1; - } - for (let i = 0; i < memoryStatsData.columns.length; i++) { - if (memoryStatsData.columns[i].name === memoryStatsData.metadata.search) { - return i; - } - } - return -1; - }; - - const getStep = (size: number, indexBias: number): number => { - return 10 ** (Math.floor(Math.log10(size !== 0 ? size : 1)) - indexBias); - }; - - const filterByEventSize = (rows: T[] | undefined, size: Array): T[] | undefined => { - const result = React.useMemo(() => { - if (!rows) { - return undefined; - } - - // workaround type system - const field = (row: any): number => { - const sizeColIndex = 1; - return row[sizeColIndex]; - }; - - return rows.filter((row) => { - return field(row) >= size[0] && field(row) <= size[1]; - }); - }, [rows, size]); - - return result; - }; - - const searchIndex = getSearchIndex(); - const getName = React.useCallback((row: any) => row[searchIndex], [searchIndex]); - const getNameAscend = (row: any): any => row[0]; - const [searchedTableDataRows] = useSearchDirectly(searchOperatorName, getName, memoryStatsData?.rows[device] ?? []); - const [searchedEventsTableDataRows] = useSearchDirectly( - searchEventOperatorName, - deviceTarget === 'Ascend' ? getNameAscend : getName, - filterByEventSize(memoryEventsData?.rows[device], filterEventSize[device] ?? [0, Infinity]) ?? [] - ); - - const onSearchOperatorChanged: TextFieldProps['onChange'] = (event) => { - setSearchOperatorName(event.target.value as string); - }; - - const onSearchEventOperatorChanged: TextFieldProps['onChange'] = (event) => { - setSearchEventOperatorName(event.target.value as string); - }; - - const [selectedRecord, setSelectedRecord] = React.useState(); - const onRowSelected = (record?: object, rowIndex?: number): void => { - setSelectedRecord(record); - }; - - const onFilterEventSizeChanged = (event: any, newValue: number | number[]): void => { - setFilterEventSize({ - ...filterEventSize, - [device]: newValue as number[], - }); - }; - - const onFilterEventMinSizeInputChanged = (event: React.ChangeEvent): void => { - setFilterEventSize({ - ...filterEventSize, - [device]: [Number(event.target.value), filterEventSize[device][1]], - }); - }; - - const onFilterEventMaxSizeInputChanged = (event: React.ChangeEvent): void => { - setFilterEventSize({ - ...filterEventSize, - [device]: [filterEventSize[device][0], Number(event.target.value)], - }); - }; - - React.useEffect(() => { - if (deviceTarget !== 'Ascend') { - api.defaultApi.memoryGet(run, worker, span, selectedRange?.startTs, selectedRange?.endTs).then((resp) => { - setMemoryStatsData(resp); - if (!devices || devices.length === 0) { - // setDevices only execute on view load. Since selection on curve - // might filter all events later, some devices might is missing. - setDevices(Object.keys(resp.rows)); - setDevice(resp.metadata.default_device); - } - }); - } - }, [run, worker, span, selectedRange]); - - React.useEffect(() => { - api.defaultApi.memoryEventsGet(run, worker, span, selectedRange?.startTs, selectedRange?.endTs).then((resp) => { - const tempRes = deviceTarget === 'Ascend' ? (resp as MemoryEventsDataAll).operator : (resp as MemoryEventsData); - if (deviceTarget === 'Ascend') { - memoryEventDataAllRef.current = resp as MemoryEventsDataAll; - } - let curMaxSize: MaxEventSize = {}; - let curFilterEventSize: EventSizeFilter = {}; - Object.keys(tempRes.rows).forEach((deviceName) => { - curMaxSize[deviceName] = 0; - for (let i = 0; i < tempRes.rows[deviceName].length; i++) { - curMaxSize[deviceName] = Math.max(curMaxSize[deviceName], tempRes.rows[deviceName][i][1]); - } - curFilterEventSize[deviceName] = [curMaxSize[deviceName] / 4, curMaxSize[deviceName]]; - curMaxSize[deviceName] = curMaxSize[deviceName]; - }); - setMaxSize(curMaxSize); - setFilterEventSize(curFilterEventSize); - setMemoryEventsData(tempRes); - }); - }, [run, worker, span, selectedRange]); - - React.useEffect(() => { - api.defaultApi.memoryCurveGet(run, worker, span).then((resp) => { - // Reset the select range to null whenever run/worker/span changes - setSelectedRange(undefined); - if (deviceTarget === 'Ascend') { - const allCurveData = resp as MemoryCurveDataAll; - memoryCurveDataAllRef.current = allCurveData; - setDevice(allCurveData.default_device); - setDevices(allCurveData.devices); - setMemoryCurveData(allCurveData.total); - setTag('Operator'); - } else { - setMemoryCurveData(resp as MemoryCurveData); - } - }); - }, [run, worker, span]); - - React.useEffect(() => { - if (memoryCurveData !== undefined) { - if (deviceTarget === 'Ascend') { - setLineChartData({ - title: memoryCurveData.metadata.peaks[device] ?? '', - columns: memoryCurveData.columns[device] ?? [], - rows: memoryCurveData.rows[device] ?? {}, - }); - } else { - setLineChartData({ - title: memoryCurveData.metadata.peaks[device], - columns: memoryCurveData.columns, - rows: memoryCurveData.rows[device] ?? [], - }); - } - } - }, [memoryCurveData, device]); - - const onDeviceChanged: SelectProps['onChange'] = (event) => { - setDevice(event.target.value as string); - setSelectedRange(undefined); - }; - - const onTagChanged: SelectProps['onChange'] = (event) => { - setTag(event.target.value as string); - if (event.target.value === 'Operator') { - setMemoryCurveData(memoryCurveDataAllRef.current?.total); - setMemoryEventsData(memoryEventDataAllRef.current?.operator); - setSelectedRange(undefined); - } else { - setMemoryCurveData(memoryCurveDataAllRef.current?.ptaGe); - setMemoryEventsData(memoryEventDataAllRef.current?.component); - } - }; - - const onSelectedRangeChanged = (start: number, end: number): void => { - if (start > end) { - setSelectedRange(undefined); - return; - } - - let allDatas = deviceTarget === 'Ascend' ? memoryCurveData?.rows[device]?.Allocated : memoryCurveData?.rows[device]; - if (allDatas.length <= 1) { - setSelectedRange(undefined); - return; - } - - let startTs = 0; - let endTs = 0; - let realStart = 0; - let realEnd = 0; - let startId = 1; - let endId = 0; - let needLoopStart = true; - for (let i = 1; i < allDatas.length; i++) { - if (startId > start && needLoopStart) { - needLoopStart = false; - realStart = i - 1; - } - if (allDatas[i][0] !== allDatas[i - 1][0]) { - if (startId <= start) { - startId += 1; - } - endId += 1; - } - if (endId > end) { - realEnd = i - 1; - break; - } else { - realEnd = i; - if (needLoopStart) { - realStart = i; - } - } - } - - if (deviceTarget === 'Ascend') { - startTs = allDatas[realStart][0]; - endTs = allDatas[realEnd][0]; - } else { - let bias = memoryCurveData?.metadata.first_ts ?? 0; - let scale = 1 / (memoryCurveData?.metadata.time_factor ?? 1); - startTs = Math.round((allDatas[realStart][0] * scale) + bias); - endTs = Math.round((allDatas[realEnd][0] * scale) + bias); - } - - setSelectedRange({ start, end, startTs, endTs }); - }; - - return ( -
- - - - - - - {(graph): JSX.Element => ( - - - - Device - - - {deviceTarget === 'Ascend' && ( - - Group By - - - )} - - {showCurve() && lineChartData && lineChartData.columns.length > 0 && ( - -
- -
- - )} - - )} - - - {showEvents() && ( - <> - {(deviceTarget !== 'Ascend' || tag === 'Operator') && ( - - - - - - - - - - - - - - - - - - - - - )} - - - {(data): JSX.Element => { - return ( - - ); - }} - - - - )} - {deviceTarget !== 'Ascend' && ( - <> - - - - - - - - {(data): JSX.Element => ( - - )} - - - - )} - - - -
- ); -}); diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/ModuleView.tsx b/plugins/tensorboard-plugins/tb_plugin/fe/src/components/ModuleView.tsx deleted file mode 100644 index a66a82536..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/ModuleView.tsx +++ /dev/null @@ -1,244 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *--------------------------------------------------------------------------------------------*/ -import Card from '@material-ui/core/Card'; -import CardHeader from '@material-ui/core/CardHeader'; -import InputLabel from '@material-ui/core/InputLabel'; -import MenuItem from '@material-ui/core/MenuItem'; -import Select, { SelectProps } from '@material-ui/core/Select'; -import { makeStyles } from '@material-ui/core/styles'; -import { message, Table } from 'antd'; -import * as React from 'react'; -import { FlameGraph } from 'react-flame-graph'; -import { defaultApi, KeyedColumn, ModuleStats, ModuleViewData, OperatorNode } from '../api'; - -const useStyles = makeStyles((theme) => ({ - root: { - flexGrow: 1, - }, - hide: { - display: 'none', - }, -})); - -export interface IProps { - run: string; - worker: string; - span: string; -} - -const getKeyedTableColumns = (columns: KeyedColumn[]): any[] => { - return columns.map((col) => { - return { - dataIndex: col.key, - key: col.key, - title: col.name, - }; - }); -}; - -const getTableRows = (key: number, rows: ModuleStats[]): any[] => { - let initialKey = key; - return rows.map((row) => { - const currentKey = initialKey++; - const data: any = { - key: currentKey, - name: row.name, - occurences: row.occurences, - operators: row.operators, - host_duration: row.host_duration, - self_host_duration: row.self_host_duration, - device_duration: row.device_duration, - self_device_duration: row.self_device_duration, - }; - - if (row.children.length) { - data.children = getTableRows(key, row.children); - } - - return data; - }); -}; - -const getFlameGraphData = (rows: ModuleStats[]): any[] => { - return rows.map((row) => { - const data: any = { - name: row.name, - value: row.avg_duration, - tooltip: `${row.name} (module id: ${row.id}): ${row.avg_duration} us`, - }; - - if (row.children.length) { - data.children = getFlameGraphData(row.children); - } - - return data; - }); -}; - -const getTreeHeight = (row: ModuleStats): number => { - if (row.children?.length) { - return 1 + Math.max(...row.children.map((child) => getTreeHeight(child))); - } else { - return 1; - } -}; - -const getOperatorTree = (level: number, row: OperatorNode, result: object[]): void => { - result.push({ - level: level, - name: row.name, - start: row.start_time, - end: row.end_time, - }); - if (row.children.length) { - row.children.forEach((child) => getOperatorTree(level + 1, child, result)); - } -}; - -export const ModuleView: React.FC = (props) => { - const { run, worker, span } = props; - const classes = useStyles(); - - const [moduleView, setModuleView] = React.useState(undefined); - const [flameData, setFlameData] = React.useState([]); - const [flameHeight, setFlameHeight] = React.useState(0); - const [modules, setModules] = React.useState([]); - const [module, setModule] = React.useState(0); - - const [columns, setColumns] = React.useState([]); - const [rows, setRows] = React.useState([]); - - const cardRef = React.useRef(null); - const [cardWidth, setCardWidth] = React.useState(undefined); - const timelineRef = React.useRef(null); - - React.useEffect(() => { - defaultApi - .moduleGet(run, worker, span) - .then((resp) => { - setModuleView(resp); - if (resp) { - // set the flamegraph data - const flameGraphData: any[] = getFlameGraphData(resp.data); - setFlameData(flameGraphData); - const flameGraphHeight = Math.max(...flameGraphData.map((x) => getTreeHeight(x))); - setFlameHeight(flameGraphHeight * 25); - setModules(Array.from(Array(flameGraphData.length).keys())); - setModule(0); - - // set the tree table data - setColumns(getKeyedTableColumns(resp.columns)); - setRows(getTableRows(1, resp.data)); - } - }) - .catch((e) => { - if (e.status === 404) { - setModules([]); - setFlameData([]); - setRows([]); - } - }); - - if (cardRef.current) { - setCardWidth(cardRef.current.offsetWidth - 10); - } - try { - if (timelineRef.current) { - defaultApi.treeGet(run, worker, span).then((resp) => { - if (resp) { - const data = new google.visualization.DataTable(); - data.addColumn({ type: 'string', id: 'Layer' }); - data.addColumn({ type: 'string', id: 'Name' }); - data.addColumn({ type: 'string', role: 'tooltip' }); - data.addColumn({ type: 'number', id: 'Start' }); - data.addColumn({ type: 'number', id: 'End' }); - - let timelineData: any[] = []; - getOperatorTree(0, resp, timelineData); - timelineData.sort((a, b) => a.level - b.level); - const maxLevel = timelineData[timelineData.length - 1].level; - timelineData.forEach((d) => { - data.addRow([ - d.level.toString(), - d.name, - `${d.name} Duration: ${d.end - d.start} us`, - d.start / 1000.0, // the time unit is us returned from server, but the google charts only accept milliseconds here - d.end / 1000.0, - ]); - }); - - const chart = new google.visualization.Timeline(timelineRef.current); - const options = { - height: (maxLevel + 1) * 50, - tooltip: { - isHtml: true, - }, - timeline: { - showRowLabels: false, - }, - }; - chart.draw(data, options); - } - }); - } - } catch (e) { - message.warning('Timeline in module view is not supported offline.'); - } - }, [run, worker, span]); - - const handleModuleChange: SelectProps['onChange'] = (event) => { - setModule(event.target.value as number); - }; - - const moduleComponent = (): JSX.Element => { - const moduleFragment = ( - - Module - - - ); - - if (!modules || modules.length <= 1) { - return
{moduleFragment}
; - } else { - return moduleFragment; - } - }; - - return ( -
- - - {rows && rows.length > 0 && ( - - )} - - {moduleComponent()} - - {flameData && flameData.length > 0 && ( - {}} - /> - )} - -
- -
- ); -}; diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/Operator.tsx b/plugins/tensorboard-plugins/tb_plugin/fe/src/components/Operator.tsx deleted file mode 100644 index b19bef196..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/Operator.tsx +++ /dev/null @@ -1,282 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *-------------------------------------------------------------------------------------------- - * Copyright (c) 2023, Huawei Technologies. - * All rights reserved. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * - * Modifications: Add visualization of PyTorch Ascend profiling. - *--------------------------------------------------------------------------------------------*/ - -import Card from '@material-ui/core/Card'; -import CardContent from '@material-ui/core/CardContent'; -import CardHeader from '@material-ui/core/CardHeader'; -import FormControlLabel from '@material-ui/core/FormControlLabel'; -import Grid from '@material-ui/core/Grid'; -import GridList from '@material-ui/core/GridList'; -import GridListTile from '@material-ui/core/GridListTile'; -import InputLabel from '@material-ui/core/InputLabel'; -import MenuItem from '@material-ui/core/MenuItem'; -import Radio from '@material-ui/core/Radio'; -import RadioGroup, { RadioGroupProps } from '@material-ui/core/RadioGroup'; -import Select, { SelectProps } from '@material-ui/core/Select'; -import { makeStyles } from '@material-ui/core/styles'; -import TextField, { StandardTextFieldProps, TextFieldProps } from '@material-ui/core/TextField'; -import * as React from 'react'; -import * as api from '../api'; -import { OperationTableData, OperationTableDataInner, OperatorGraph } from '../api'; -import { OperationGroupBy } from '../constants/groupBy'; -import { useSearchDirectly } from '../utils/search'; -import { topIsValid, UseTop, useTopN } from '../utils/top'; -import { PieChart } from './charts/PieChart'; -import { DataLoading } from './DataLoading'; -import { makeChartHeaderRenderer, useTooltipCommonStyles } from './helpers'; -import { OperationTable } from './tables/OperationTable'; -import { - deviceSelfTimeTooltip, - deviceSelfTimeTooltipAscend, - deviceTotalTimeTooltip, - deviceTotalTimeTooltipAscend, - hostSelfTimeTooltip, - hostTotalTimeTooltip, -} from './TooltipDescriptions'; - -const useStyles = makeStyles((theme) => ({ - root: { - flexGrow: 1, - }, - verticalInput: { - display: 'flex', - alignItems: 'center', - }, - inputWidth: { - width: '4em', - }, - inputWidthOverflow: { - minWidth: '15em', - whiteSpace: 'nowrap', - }, - full: { - width: '100%', - }, - description: { - marginLeft: theme.spacing(1), - }, -})); - -export interface IProps { - run: string; - worker: string; - span: string; - deviceTarget: string; -} - -export const Operator: React.FC = (props) => { - const { run, worker, span, deviceTarget } = props; - const classes = useStyles(); - const tooltipCommonClasses = useTooltipCommonStyles(); - const chartHeaderRenderer = React.useMemo( - () => makeChartHeaderRenderer(tooltipCommonClasses), - [tooltipCommonClasses] - ); - - const [operatorGraph, setOperatorGraph] = React.useState(undefined); - const [operatorTable, setOperatorTable] = React.useState(undefined); - const [sortColumn, setSortColumn] = React.useState(''); - const [tableTooltips, setTableTooltips] = React.useState(undefined); - const [groupBy, setGroupBy] = React.useState(OperationGroupBy.OPERATION); - const [searchOperatorName, setSearchOperatorName] = React.useState(''); - const [topText, actualTop, useTop, setTopText, setUseTop] = useTopN({ - defaultUseTop: UseTop.USE, - defaultTop: 10, - }); - - const getName = React.useCallback((row: OperationTableDataInner) => row.name, []); - const [searchedOperatorTable] = useSearchDirectly(searchOperatorName, getName, operatorTable); - - const onSearchOperatorChanged: TextFieldProps['onChange'] = (event) => { - setSearchOperatorName(event.target.value as string); - }; - - React.useEffect(() => { - if (operatorGraph) { - const counts = [ - operatorGraph.device_self_time?.rows.length ?? 0, - operatorGraph.device_total_time?.rows.length ?? 0, - operatorGraph.host_self_time.rows?.length ?? 0, - operatorGraph.host_total_time.rows?.length ?? 0, - ]; - setTopText(String(Math.min(Math.max(...counts), 10))); - } - }, [operatorGraph]); - - React.useEffect(() => { - api.defaultApi.operationTableGet(run, worker, span, groupBy).then((resp) => { - setSortColumn(resp.metadata.sort); - setTableTooltips(resp.metadata.tooltips); - setOperatorTable(resp.data); - }); - }, [run, worker, span, groupBy]); - - React.useEffect(() => { - api.defaultApi.operationGet(run, worker, span, groupBy).then((resp) => { - setOperatorGraph(resp); - }); - }, [run, worker, span, groupBy]); - - const onGroupByChanged: SelectProps['onChange'] = (event) => { - setGroupBy(event.target.value as OperationGroupBy); - }; - - const onUseTopChanged: RadioGroupProps['onChange'] = (event) => { - setUseTop(event.target.value as UseTop); - }; - - const onTopChanged = (event: React.ChangeEvent): void => { - setTopText(event.target.value); - }; - - const inputProps: StandardTextFieldProps['inputProps'] = { - min: 1, - }; - - const renderCharts = (graph: api.OperatorGraph): JSX.Element => { - return ( - - {graph.device_self_time && ( - - - {graph.device_self_time.title && ( - - )} - - - - )} - {graph.device_total_time && ( - - - {graph.device_total_time.title && ( - - )} - - - - )} - - - {graph.host_self_time.title && ( - - )} - - - - - - {graph.host_total_time.title && ( - - )} - - - - - ); - }; - - return ( -
- - - - - - - - } label='All operators' /> - } label='Top operators to show' /> - - - {useTop === UseTop.USE && ( - - - - )} - - - {renderCharts} - - - - - - Group By - - - - - - - - - - {(table): JSX.Element => ( - - )} - - - - - - -
- ); -}; diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/Overview.tsx b/plugins/tensorboard-plugins/tb_plugin/fe/src/components/Overview.tsx deleted file mode 100644 index 6a81c567b..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/Overview.tsx +++ /dev/null @@ -1,225 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *--------------------------------------------------------------------------------------------*/ - -import Card from '@material-ui/core/Card'; -import CardContent from '@material-ui/core/CardContent'; -import CardHeader from '@material-ui/core/CardHeader'; -import Grid from '@material-ui/core/Grid'; -import { makeStyles } from '@material-ui/core/styles'; -import { Table } from 'antd'; -import { ColumnsType } from 'antd/es/table'; -import * as React from 'react'; -import * as api from '../api'; -import { PieChart } from './charts/PieChart'; -import { SteppedAreaChart } from './charts/SteppedAreaChart'; -import { DataLoading } from './DataLoading'; -import { makeChartHeaderRenderer, useTooltipCommonStyles } from './helpers'; -import { TextListItem } from './TextListItem'; -import { stepTimeBreakDownTooltip } from './TooltipDescriptions'; -import { transformPerformanceIntoPie, transformPerformanceIntoTable } from './transform'; - -const topGraphHeight = 230; - -const useStyles = makeStyles((theme) => ({ - root: { - flexGrow: 1, - }, - pre: { - '& ul': { - margin: 0, - paddingLeft: theme.spacing(3), - ...theme.typography.body1, - }, - '& li': {}, - '& a': { - color: '#ffa726', - }, - '& a:active': { - color: '#ffa726', - }, - '& p': { - margin: 0, - ...theme.typography.subtitle1, - fontWeight: theme.typography.fontWeightBold, - }, - }, - topGraph: { - height: topGraphHeight + 40, - }, - table: { - height: '100%', - border: '1px solid #efefef', - '& .ant-table-tbody > tr': { - height: 20, - fontSize: '10pt', - '& > td': { - padding: '0 8px!important', - }, - }, - }, -})); - -export interface IProps { - run: string; - worker: string; - span: string; -} - -export const Overview: React.FC = (props) => { - const { run, worker, span } = props; - - const [steps, setSteps] = React.useState(undefined); - const [performances, setPerformances] = React.useState([]); - const [environments, setEnvironments] = React.useState([]); - const [gpuMetrics, setGpuMetrics] = React.useState(undefined); - const [recommendations, setRecommendations] = React.useState(''); - const [columns, setColumns] = React.useState>([]); - - const tableRows = React.useMemo(() => { - let dataInfo: api.Graph = transformPerformanceIntoTable(performances); - if (dataInfo.columns.length < 3) { - return []; - } - const stringCompare = (a: string, b: string): number => a.localeCompare(b); - const numberCompare = (a: number, b: number): number => a - b; - let column: any[] = dataInfo.columns.map((item) => { - return { - title: item.name, - key: item.name, - dataIndex: item.name, - sorter: - item.type === 'string' - ? (a: any, b: any): number => stringCompare(a[item.name], b[item.name]) - : (a: any, b: any): number => numberCompare(a[item.name], b[item.name]), - }; - }); - setColumns(column); - return dataInfo.rows.map((row, index) => { - if (row.length < 3) { - return null; - } - return { - key: index, - [dataInfo.columns[0].name]: row[0], - [dataInfo.columns[1].name]: row[1], - [dataInfo.columns[2].name]: row[2], - }; - }); - }, [performances]); - - const synthesizedPieGraph = React.useMemo(() => { - return transformPerformanceIntoPie(performances); - }, [performances]); - - React.useEffect(() => { - api.defaultApi.overviewGet(run, worker, span).then((resp) => { - setPerformances(resp.performance); - setEnvironments(resp.environments); - setSteps(resp.steps); - setRecommendations(resp.recommendations); - setGpuMetrics(resp.gpu_metrics); - }); - }, [run, worker, span]); - - const classes = useStyles(); - const tooltipCommonClasses = useTooltipCommonStyles(); - const chartHeaderRenderer = React.useMemo( - () => makeChartHeaderRenderer(tooltipCommonClasses, false), - [tooltipCommonClasses] - ); - - const stepTimeBreakDownTitle = React.useMemo( - () => chartHeaderRenderer('Step Time Breakdown', stepTimeBreakDownTooltip), - [tooltipCommonClasses, chartHeaderRenderer] - ); - - const cardSizes = gpuMetrics ? ([2, 3, 7] as const) : ([4, undefined, 8] as const); - - return ( -
- - - - {React.useMemo( - () => ( - - - - {environments.map((environment) => ( - - ))} - - - ), - [environments] - )} - - {gpuMetrics && ( - - - - - {gpuMetrics.data.map((metric) => ( - - ))} - - - - )} - - - - - - -
- - - - - - - - - - - - - - - - {(graph): JSX.Element => ( - - )} - - - - - - - - - - -
-
-
- - - - - -
- ); -}; diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/TextListItem.tsx b/plugins/tensorboard-plugins/tb_plugin/fe/src/components/TextListItem.tsx deleted file mode 100644 index 59eb79c2a..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/TextListItem.tsx +++ /dev/null @@ -1,82 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *--------------------------------------------------------------------------------------------*/ - -import Grid from '@material-ui/core/Grid'; -import { makeStyles } from '@material-ui/core/styles'; -import * as React from 'react'; - -export interface IStylesProps { - root?: string; - name?: string; -} - -export interface IProps { - name: string; - value?: string; - description?: string; - extra?: string; - classes?: IStylesProps; - dangerouslyAllowHtml?: boolean; -} - -const useStyles = makeStyles((theme) => ({ - label: { - ...theme.typography.subtitle2, - fontWeight: 'bolder', - }, - value: { - textAlign: 'right', - ...theme.typography.subtitle2, - fontWeight: 'bolder', - }, -})); - -export const TextListItem: React.FC = (props) => { - const classes = useStyles(); - - const getSizes = function (): readonly any[] { - if (props.value && props.extra) { - return [4, 4, 4] as const; - } - if (props.value) { - if (props.value.length > props.name.length) { - return [4, 8, undefined] as const; - } - return [8, 4, undefined] as const; - } - return [12, undefined, undefined] as const; - }; - - const sizes = getSizes(); - - const renderSpan = function (content: string, className?: string): React.JSX.Element { - if (props.dangerouslyAllowHtml) { - return ; - } - return {content}; - }; - - return ( - - - - - {renderSpan(props.name, props.classes?.name)} - - {props.description && {renderSpan(props.description)}} - - - {props.value && ( - - {renderSpan(props.value)} - - )} - {props.extra && ( - - {renderSpan(props.extra)} - - )} - - ); -}; diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/TooltipDescriptions.ts b/plugins/tensorboard-plugins/tb_plugin/fe/src/components/TooltipDescriptions.ts deleted file mode 100644 index 6d3631fee..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/TooltipDescriptions.ts +++ /dev/null @@ -1,38 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *--------------------------------------------------------------------------------------------*/ - -export const stepTimeBreakDownTooltip = `The time spent on each step is broken down into multiple categories as follows: -Kernel: Kernels execution time on GPU device; -Memcpy: GPU involved memory copy time (either D2D, D2H or H2D); -Memset: GPU involved memory set time; -Runtime: CUDA runtime execution time on host side; Such as cudaLaunchKernel, cudaMemcpyAsync, cudaStreamSynchronize, ... -DataLoader: The data loading time spent in PyTorch DataLoader object; -CPU Exec: Host compute time, including every PyTorch operator running time; -Other: The time not included in any of the above.`; - -export const deviceSelfTimeTooltip = `The accumulated time spent on GPU, not including this operator’s child operators.`; - -export const deviceSelfTimeTooltipAscend = `The accumulated time spent on NPU, not including this operator’s child operators.`; - -export const deviceTotalTimeTooltip = `The accumulated time spent on GPU, including this operator’s child operators.`; - -export const deviceTotalTimeTooltipAscend = `The accumulated time spent on NPU, including this operator’s child operators.`; - -export const hostSelfTimeTooltip = `The accumulated time spent on Host, not including this operator’s child operators.`; - -export const hostTotalTimeTooltip = `The accumulated time spent on Host, including this operator’s child operators.`; - -export const gpuKernelTotalTimeTooltip = `The accumulated time of all calls of this kernel.`; - -export const tensorCoresPieChartTooltip = `The accumulated time of all kernels using or not using Tensor Cores.`; - -export const tensorCoresPieChartTooltipAscend = `The accumulated time of all kernels group by Accelerator Core.`; - -export const distributedGpuInfoTableTooltip = `Information about GPU hardware used during the run.`; - -export const distributedOverlapGraphTooltip = `The time spent on computation vs communication.`; - -export const distributedWaittimeGraphTooltip = `The time spent waiting vs communicating between devices.`; - -export const distributedCommopsTableTooltip = `Statistics for operations managing communications between nodes.`; diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/TraceView.tsx b/plugins/tensorboard-plugins/tb_plugin/fe/src/components/TraceView.tsx deleted file mode 100644 index be4997949..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/TraceView.tsx +++ /dev/null @@ -1,79 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *--------------------------------------------------------------------------------------------*/ - -import ClickAwayListener from '@material-ui/core/ClickAwayListener'; -import { makeStyles } from '@material-ui/core/styles'; -import * as React from 'react'; -import * as api from '../api'; - -export interface IProps { - run: string; - worker: string; - span: string; - iframeRef: React.RefObject; -} - -const useStyles = makeStyles(() => ({ - root: { - flexGrow: 1, - }, - frame: { - width: '100%', - height: 'calc(100vh - 48px)', - border: 'none', - }, -})); - -export const TraceView: React.FC = (props) => { - const { run, worker, span, iframeRef } = props; - const classes = useStyles(); - - const [traceData, setTraceData] = React.useState | null>(null); - const [traceViewReady, setTraceViewReady] = React.useState(false); - - React.useEffect(() => { - setTraceData( - api.defaultApi.traceGet(run, worker, span).then((resp) => { - return JSON.stringify(resp); - }) - ); - }, [run, worker, span]); - - React.useEffect(() => { - function callback(event: MessageEvent): void { - const data = event.data || {}; - if (data.msg === 'ready') { - setTraceViewReady(true); - } - } - - window.addEventListener('message', callback); - return () => { - window.removeEventListener('message', callback); - }; - }, []); - - React.useEffect(() => { - if (traceData && traceViewReady) { - traceData.then((data) => { - iframeRef.current?.contentWindow?.postMessage({ msg: 'data', data }, window.origin); - }); - } - }, [traceData, traceViewReady]); - const setIframeActive = (): void => { - iframeRef.current?.focus(); - }; - return ( -
- {React.useMemo( - () => ( - - - - ), - [] - )} -
- ); -}; diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/charts/AntTableChart.tsx b/plugins/tensorboard-plugins/tb_plugin/fe/src/components/charts/AntTableChart.tsx deleted file mode 100644 index 83618064b..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/charts/AntTableChart.tsx +++ /dev/null @@ -1,111 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *--------------------------------------------------------------------------------------------*/ - -import { makeStyles } from '@material-ui/core/styles'; -import { Table } from 'antd'; -import * as React from 'react'; -import { Graph } from '../../api'; - -interface IProps { - graph: Graph; - sortColumn?: string; - initialPageSize?: number; - onRowSelected?: (record?: object, rowIndex?: number) => void; -} - -const useStyles = makeStyles((theme) => ({ - tooltip: { - whiteSpace: 'pre-wrap', - }, - row: { - wordBreak: 'break-word', - }, -})); - -const getTableColumns = function (columns: any, sort: string | undefined, tooltipClass: string): any { - let i = 0; - return columns.map((col: any) => { - const key = `col${i++}`; - const stringCompare = (a: any, b: any): number => a[key].localeCompare(b[key]); - const numberCompare = (a: any, b: any): number => (a[key] || 0) - (b[key] || 0); - return { - dataIndex: key, - key: key, - title: col.name, - sorter: col.type === 'string' ? stringCompare : numberCompare, - defaultSortOrder: sort === col.name ? ('descend' as const) : undefined, - showSorterTooltip: col.tooltip ? { title: col.tooltip, overlayClassName: tooltipClass } : true, - }; - }); -}; - -const getTableRows = function (rows: any): any { - return rows.map((row: any) => { - let i = 0; - const res: any = {}; - row.forEach((entry: any) => { - res[`col${i++}`] = entry; - }); - return res; - }); -}; - -export const AntTableChart: React.FC = (props) => { - const { graph, sortColumn, initialPageSize, onRowSelected } = props; - const classes = useStyles(props); - - const rows = React.useMemo(() => getTableRows(graph.rows), [graph.rows]); - - const columns = React.useMemo( - () => getTableColumns(graph.columns, sortColumn, classes.tooltip), - [graph.columns, sortColumn, classes.tooltip] - ); - - // key is used to reset the Table state (page and sort) if the columns change - const key: string = React.useMemo(() => `${Math.random()}`, [graph.columns]); - - const [pageSize, setPageSize] = React.useState(initialPageSize ?? 30); - const onShowSizeChange = (current: number, size: number): void => { - setPageSize(size); - }; - - const onRow = ( - record: object, - rowIndex?: number - ): { - onMouseEnter: (event: any) => void; - onMouseLeave: (event: any) => void; - } => { - return { - onMouseEnter: (event: any): void => { - if (onRowSelected) { - onRowSelected(record, rowIndex); - } - }, - onMouseLeave: (event: any): void => { - if (onRowSelected) { - onRowSelected(undefined, undefined); - } - }, - }; - }; - - return ( -
- ); -}; diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/charts/AreaChart.tsx b/plugins/tensorboard-plugins/tb_plugin/fe/src/components/charts/AreaChart.tsx deleted file mode 100644 index cda12860c..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/charts/AreaChart.tsx +++ /dev/null @@ -1,72 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *--------------------------------------------------------------------------------------------*/ - -import { makeStyles } from '@material-ui/core/styles'; -import * as React from 'react'; -import { Graph } from '../../api'; -import { useResizeEventDependency } from '../../utils/resize'; - -interface IProps { - graph: Graph; - height?: number; - hAxisTitle?: string; -} - -const useStyles = makeStyles(() => ({ - root: { - height: (props: Pick): number | undefined => props.height, - }, -})); - -export const AreaChart: React.FC = (props) => { - const { graph, height = 400, hAxisTitle } = props; - const classes = useStyles({ height }); - const graphRef = React.useRef(null); - const [resizeEventDependency] = useResizeEventDependency(); - - React.useLayoutEffect(() => { - const element = graphRef.current; - if (!element) { - return undefined; - } - - const data = new google.visualization.DataTable(); - data.addColumn('string', 'step'); - graph.columns.forEach((column) => { - data.addColumn({ - type: column.type, - label: column.name, - role: column.role, - p: column.p, - }); - }); - data.addRows(graph.rows.map((x, i) => [(i + 1).toString(), ...x])); - - const options = { - title: graph.title, - isStacked: true, - height, - legend: { position: 'bottom' }, - tooltip: { isHtml: true }, - chartArea: { left: '15%', width: '80%', top: '10%' }, - hAxis: { - title: hAxisTitle, - }, - }; - - const chart = new google.visualization.AreaChart(element); - - chart.draw(data, options); - - return () => { - chart.clearChart(); - }; - }, [graph, height, resizeEventDependency]); - - return ( -
-
-
- ); -}; diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/charts/ColumnChart.tsx b/plugins/tensorboard-plugins/tb_plugin/fe/src/components/charts/ColumnChart.tsx deleted file mode 100644 index ae51dc1a3..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/charts/ColumnChart.tsx +++ /dev/null @@ -1,118 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *-------------------------------------------------------------------------------------------- - * Copyright (c) 2023, Huawei Technologies. - * All rights reserved. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * - * Modifications: Offer offline supporting. - *--------------------------------------------------------------------------------------------*/ - -import * as React from 'react'; -import { useResizeEventDependency } from '../../utils/resize'; -import * as echarts from 'echarts'; - -interface IProps { - title?: string; - units?: string; - colors?: Array; - chartData: ColumnChartData; -} - -export interface ColumnChartData { - legends: Array; - barLabels: Array; - barHeights: Array>; -} - -export const ColumnChart: React.FC = (props) => { - const { title, units, colors, chartData } = props; - const { legends, barLabels, barHeights } = chartData; - const graphRef = React.useRef(null); - const [resizeEventDependency] = useResizeEventDependency(); - - const getAngleByDataLength = (data: number): number => { - if (data < 10) { - return 0; - } else { - // 数量越大越趋近于旋转90度 - return 90 * (1 - (10 / data)); - } - }; - - React.useLayoutEffect(() => { - const element = graphRef.current; - if (!element) { - return undefined; - } - - const chart = echarts.init(element); - const dataSource: Array> = []; - dataSource.push(['worker', ...legends]); - barHeights.forEach((item, index) => { - if (barLabels[index] !== undefined) { - dataSource.push([barLabels[index], ...item]); - } - }); - const options: echarts.EChartsOption = { - title: { - text: title, - }, - legend: { - bottom: 0, - }, - xAxis: { - type: 'category', - axisLabel: { - interval: 0, - rotate: getAngleByDataLength(barLabels.length), - formatter: (name: string) => { - const index = name.indexOf('@'); - const processedName = index > -1 ? name.slice(index + 1) : name; // 使用新变量处理 - return processedName.length > 16 ? `${processedName.slice(0, 14)}...` : processedName; - }, - }, - }, - yAxis: { - type: 'value', - name: units, - nameTextStyle: { - fontSize: 16, - }, - }, - tooltip: { - trigger: 'item', - }, - dataset: { - source: dataSource, - }, - series: Array(legends.length).fill({ - type: 'bar', - stack: 'samesign', - }), - }; - if (colors) { - options.color = colors.slice(0, barLabels.length); - } - - if (options) { - chart.setOption(options, true); - } - return () => { - chart.dispose(); - }; - }, [title, chartData, resizeEventDependency]); - - return
; -}; diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/charts/NewLineChart.tsx b/plugins/tensorboard-plugins/tb_plugin/fe/src/components/charts/NewLineChart.tsx deleted file mode 100644 index a6e222a6c..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/charts/NewLineChart.tsx +++ /dev/null @@ -1,386 +0,0 @@ -/*-------------------------------------------------------------------------------------------- - * Copyright (c) 2023, Huawei Technologies. - * All rights reserved. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - *--------------------------------------------------------------------------------------------*/ - -import * as React from 'react'; -import { Graph, GraphAscend } from '../../api'; -import { useResizeEventDependency } from '../../utils/resize'; -import { binarySearch } from '../../utils/binarysearch'; -import * as echarts from 'echarts'; - -interface IProps { - graph: Graph | GraphAscend; - height?: number; - deviceTarget: string; - tag: string; - hAxisTitle?: string; - vAxisTitle?: string; - onSelectionChanged?: (start: number, end: number) => void; - record?: any; -} - -export const LineChart: React.FC = (props) => { - const { graph, height = 400, deviceTarget, tag, hAxisTitle, vAxisTitle, onSelectionChanged, record } = props; - const graphRef = React.useRef(null); - const [resizeEventDependency] = useResizeEventDependency(); - const [chartObj, setChartObj] = React.useState(); - const selectedPoints = React.useRef>([]); - - React.useLayoutEffect(() => { - const element = graphRef.current; - if (!element) { - return undefined; - } - element.oncontextmenu = (): boolean => { - return false; - }; - - let myChart = echarts.init(element); - - let option: echarts.EChartsOption = { - title: { - text: graph.title, - textStyle: { - fontSize: 16, - }, - }, - tooltip: { trigger: 'axis' }, - legend: { - type: 'scroll', - bottom: 0, - }, - xAxis: { - type: 'category', - boundaryGap: false, - name: hAxisTitle, - }, - yAxis: { - type: 'value', - name: vAxisTitle, - scale: true, - }, - toolbox: { - feature: { - dataZoom: { - yAxisIndex: 'none', - }, - restore: {}, - }, - }, - }; - - if (deviceTarget === 'Ascend') { - if (tag === 'Component') { - const mixedTooltip: echarts.TooltipComponentOption = { - trigger: 'axis', - formatter: function (params: any) { - let res = `${params[0].name}
`; - for (const item of params) { - if (typeof item.value[item.encode.y[0]] === 'number') { - res += ` - - ${item.seriesName}: ${item.value[item.encode.y[0]]}
`; - } - } - return res; - }, - }; - if (graph.columns.length <= 4) { - let finalRows = graph.rows.PTA ?? graph.rows.GE; - if (graph.columns.length === 4) { - const mergedAPPRows = graph.rows.APP.map((item: Array) => { - return [item[0], null, null, item[1]]; - }); - finalRows = finalRows.concat(mergedAPPRows).sort((a: any, b: any) => { - return a[0] - b[0]; - }); - } - option = { - ...option, - tooltip: mixedTooltip, - dataset: { - source: [graph.columns.map((column) => column.name), ...finalRows], - }, - series: Array(graph.columns.length - 1).fill({ - type: 'line', - select: { - itemStyle: { - borderWidth: 5, - shadowBlur: 5, - }, - }, - emphasis: { - itemStyle: { - borderWidth: 5, - shadowBlur: 5, - }, - }, - selectedMode: 'single', - }), - }; - } else if (graph.columns.length <= 6) { - const datasetTitle = graph.columns.map((item) => item.name); - let mergedGERows = graph.rows.GE.map((item: Array) => { - return [item[0], null, null, item[1], item[2]]; - }); - if (graph.columns.length === 6) { - const mergedAPPRows = graph.rows.APP.map((item: Array) => { - return [item[0], null, null, null, null, item[2]]; - }); - mergedGERows = mergedGERows.concat(mergedAPPRows); - } - const finalRows = graph.rows.PTA.concat(mergedGERows).sort((a: any, b: any) => { - return a[0] - b[0]; - }); - option = { - ...option, - tooltip: mixedTooltip, - dataset: { - source: [datasetTitle, ...finalRows], - }, - series: Array(graph.columns.length - 1).fill({ - type: 'line', - connectNulls: true, - select: { - itemStyle: { - borderWidth: 5, - shadowBlur: 5, - }, - }, - emphasis: { - itemStyle: { - borderWidth: 5, - shadowBlur: 5, - }, - }, - selectedMode: 'single', - datasetIndex: 0, - }), - }; - } - } else { - if (graph.columns.length === 3) { - const datasetTitle1: Array = []; - const datasetTitle2: Array = []; - graph.columns.forEach((column, index) => { - if (index === 0 || index < 2) { - datasetTitle1.push(column.name); - } - if (index === 0 || index >= 2) { - datasetTitle2.push(column.name); - } - }); - option = { - ...option, - dataset: [ - { - source: [datasetTitle1, ...graph.rows.Allocated], - }, - { - source: [datasetTitle2, ...graph.rows.Reserved], - }, - ], - series: [ - { - type: 'line', - name: 'Allocated', - emphasis: { - label: { - show: true, - }, - itemStyle: { - borderWidth: 5, - shadowBlur: 5, - }, - }, - select: { - itemStyle: { - borderWidth: 5, - shadowBlur: 5, - }, - }, - datasetIndex: 0, - }, - { - type: 'line', - name: 'Reserved', - select: { - itemStyle: { - borderWidth: 5, - shadowBlur: 5, - }, - }, - emphasis: { - itemStyle: { - borderWidth: 5, - shadowBlur: 5, - }, - }, - selectedMode: 'single', - datasetIndex: 1, - }, - ], - }; - } - } - } else { - option = { - ...option, - dataset: { - source: [graph.columns.map((column) => column.name), ...graph.rows], - }, - series: [ - { - type: 'line', - name: 'Allocated', - select: { - itemStyle: { - borderWidth: 5, - shadowBlur: 5, - }, - }, - emphasis: { - itemStyle: { - borderWidth: 5, - shadowBlur: 5, - }, - }, - selectedMode: 'single', - }, - { - type: 'line', - name: 'Reserved', - select: { - itemStyle: { - borderWidth: 5, - shadowBlur: 5, - }, - }, - emphasis: { - itemStyle: { - borderWidth: 5, - shadowBlur: 5, - }, - }, - selectedMode: 'single', - }, - ], - }; - } - - if (option) { - myChart.setOption(option, true); - } - myChart.dispatchAction({ - type: 'takeGlobalCursor', - key: 'dataZoomSelect', - dataZoomSelectActive: true, - }); - - myChart.on('dataZoom', (param: any) => { - if (onSelectionChanged) { - onSelectionChanged(param.batch[0].startValue, param.batch[0].endValue); - } - }); - - myChart.on('restore', () => { - if (onSelectionChanged) { - // Set startId greater than endId to query all memory events. - onSelectionChanged(0, -1); - } - }); - - myChart.on('click', (param) => { - myChart.dispatchAction({ - type: 'unselect', - seriesId: param.seriesId, - dataIndex: selectedPoints.current, - }); - myChart.dispatchAction({ - type: 'select', - seriesId: param.seriesId, - dataIndex: param.dataIndex, - }); - - selectedPoints.current = [param.dataIndex]; - }); - - myChart.getZr().on('contextmenu', () => { - myChart.dispatchAction({ - type: 'restore', - }); - myChart.dispatchAction({ - type: 'takeGlobalCursor', - key: 'dataZoomSelect', - dataZoomSelectActive: true, - }); - }); - - setChartObj(myChart); - return () => { - myChart.dispose(); - }; - }, [graph, height, resizeEventDependency]); - - React.useEffect(() => { - const compareFn = (key: number, mid: Array): number => key - mid[0]; - if (chartObj && tag === 'Operator') { - if (record) { - let startId = -1; - let endId = -1; - if (deviceTarget === 'Ascend') { - startId = binarySearch(graph.rows.Allocated, record.col2, compareFn); - endId = binarySearch(graph.rows.Allocated, record.col3, compareFn); - } else { - startId = binarySearch(graph.rows, record.col2, compareFn); - endId = binarySearch(graph.rows, record.col3, compareFn); - } - let selection = []; - if (startId >= 0) { - selection.push(startId); - } - if (endId >= 0) { - selection.push(endId); - } - chartObj.dispatchAction({ - type: 'downplay', - seriesName: 'Allocated', - dataIndex: selectedPoints.current, - }); - chartObj.dispatchAction({ - type: 'highlight', - seriesName: 'Allocated', - dataIndex: selection, - }); - selectedPoints.current = selection; - } else { - chartObj.dispatchAction({ - type: 'downplay', - seriesName: 'Allocated', - dataIndex: selectedPoints.current, - }); - selectedPoints.current = []; - } - } - }, [graph, record, chartObj]); - - return
; -}; diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/charts/PieChart.tsx b/plugins/tensorboard-plugins/tb_plugin/fe/src/components/charts/PieChart.tsx deleted file mode 100644 index 49c59ff02..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/charts/PieChart.tsx +++ /dev/null @@ -1,173 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *-------------------------------------------------------------------------------------------- - * Copyright (c) 2023, Huawei Technologies. - * All rights reserved. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * - * Modifications: Offer offline supporting. - *--------------------------------------------------------------------------------------------*/ - -import * as React from 'react'; -import { Graph } from '../../api'; -import { value } from '../../utils'; -import { useResizeEventDependency } from '../../utils/resize'; -import * as echarts from 'echarts'; - -interface IProps { - graph: Graph; - height?: number; - top?: number; - noLegend?: boolean; - title?: string; - colors?: Array; - tooltipMode?: string; -} - -interface IAreaPosition { - left: string; - width: string; - top?: string; - height?: string; -} - -const noLegendArea: IAreaPosition = { - left: '5%', - width: '90%', - top: '5%', - height: '90%', -}; -const normalArea: IAreaPosition = { left: '5%', width: '95%' }; -const noTitleArea: IAreaPosition = { - left: '5%', - width: '95%', - top: '10%', - height: '80%', -}; - -export const PieChart: React.FC = (props) => { - const { graph, height = 300, top, noLegend, title, colors, tooltipMode = 'both' } = props; - const graphRef = React.useRef(null); - - const [resizeEventDependency] = useResizeEventDependency(); - - React.useLayoutEffect(() => { - const element = graphRef.current; - if (!element) { - return undefined; - } - - const chart = echarts.init(element); - - let totalValue = 0; - const rowsWithUniqueName: Array<{ name: string; value: number }> = - top === undefined - ? graph.rows.map((item, index) => { - totalValue += item[1] as number; - return { name: `${index}_${item[0]}`, value: item[1] as number }; - }) - : graph.rows - .sort((a, b) => (value(b[1]) as number) - (value(a[1]) as number)) - .slice(0, top) - .map((item, index) => { - totalValue += item[1] as number; - return { name: `${index}_${item[0]}`, value: item[1] as number }; - }); - - const option: echarts.EChartsOption = { - height, - width: '100%', - title: { - text: title, - }, - tooltip: { - trigger: 'item', - formatter: (data) => { - const typedData = data as echarts.DefaultLabelFormatterCallbackParams; - const index = typedData.name.indexOf('_'); - const safeName = typedData.name.replace(//g, '>'); - return `${index > -1 ? safeName.slice(index + 1) : safeName}
${ - tooltipMode === 'both' ? typedData.value : '' - }(${typedData.percent}%)`; - }, - confine: true, - extraCssText: `max-width: 300px; - word-wrap:break-word; - white-space:pre-wrap; - padding-right: 10px`, - }, - chartArea: ((): IAreaPosition => { - if (noLegend) { - return noLegendArea; - } - if (!title) { - return noTitleArea; - } else { - return normalArea; - } - })(), - legend: { - type: noLegend ? 'plain' : 'scroll', - orient: 'vertical', - left: 'right', - z: 10, - // Display at most 36 characters. - formatter: (name) => { - // Show legends for datas with the same name. - const index = name.indexOf('_'); - const processedName = index > -1 ? name.slice(index + 1) : name; // 使用新变量处理 - return processedName.length > 36 ? `${processedName.slice(0, 34)}...` : processedName; - }, - tooltip: { - show: true, - triggerOn: 'mousemove', - formatter: (data) => { - const currentItem = rowsWithUniqueName.find((item) => item.name === data.name); - const index = data.name.indexOf('_'); - const percent = (((currentItem?.value || 0) * 100) / totalValue).toFixed(2); - const safeName = data.name.replace(//g, '>'); - return `${index > -1 ? safeName.slice(index + 1) : safeName}
${ - tooltipMode === 'both' ? currentItem?.value || 0 : '' - }(${percent}%)`; - }, - }, - }, - sliceVisibilityThreshold: 0, - colors, - series: [ - { - type: 'pie', - radius: ['32%', '80%'], - center: ['32%', '50%'], - label: { - position: 'inside', - formatter: `{d}%`, - color: '#ffffff', - }, - data: rowsWithUniqueName, - }, - ], - }; - - if (option) { - chart.setOption(option, true); - } - - return () => { - chart.dispose(); - }; - }, [graph, height, top, resizeEventDependency]); - - return
; -}; diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/charts/SteppedAreaChart.tsx b/plugins/tensorboard-plugins/tb_plugin/fe/src/components/charts/SteppedAreaChart.tsx deleted file mode 100644 index 3e3b01ccb..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/charts/SteppedAreaChart.tsx +++ /dev/null @@ -1,106 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *-------------------------------------------------------------------------------------------- - * Copyright (c) 2023, Huawei Technologies. - * All rights reserved. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * - * Modifications: Offer offline supporting. - *--------------------------------------------------------------------------------------------*/ - -import { makeStyles } from '@material-ui/core/styles'; -import * as React from 'react'; -import { StepedGraph } from '../../api'; -import { useResizeEventDependency } from '../../utils/resize'; -import * as echarts from 'echarts'; - -interface IProps { - graph: StepedGraph; - height?: number; - hAxisTitle?: string; - vAxisTitle?: string; -} - -const useStyles = makeStyles(() => ({ - root: { - height: (props: Pick): number | undefined => props.height, - }, -})); - -export const SteppedAreaChart: React.FC = (props) => { - const { graph, height = 400, hAxisTitle, vAxisTitle } = props; - const classes = useStyles({ height }); - const graphRef = React.useRef(null); - const [resizeEventDependency] = useResizeEventDependency(); - - React.useLayoutEffect(() => { - const element = graphRef.current; - if (!element) { - return undefined; - } - - const chart = echarts.init(element); - const dataSource: Array> = []; - dataSource.push(graph.columns); - graph.rows.forEach((row) => { - dataSource.push(row.map((item) => item.value)); - }); - const options: echarts.EChartsOption = { - title: { - text: graph.title, - }, - legend: { - bottom: 0, - }, - xAxis: { - type: 'category', - name: hAxisTitle, - axisLabel: { - interval: 0, - }, - }, - yAxis: { - type: 'value', - name: vAxisTitle, - }, - tooltip: { - trigger: 'item', - formatter: (params: any) => { - return graph.rows[params.dataIndex][params.seriesIndex + 1]?.tooltip || ''; - }, - }, - dataset: { - source: dataSource, - }, - series: Array(graph.columns.length - 1).fill({ - type: 'bar', - stack: 'samesign', - }), - }; - - if (options) { - chart.setOption(options, true); - } - - return () => { - chart.dispose(); - }; - }, [graph, height, resizeEventDependency]); - - return ( -
-
-
- ); -}; diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/charts/TableChart.tsx b/plugins/tensorboard-plugins/tb_plugin/fe/src/components/charts/TableChart.tsx deleted file mode 100644 index 444b41b19..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/charts/TableChart.tsx +++ /dev/null @@ -1,85 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *--------------------------------------------------------------------------------------------*/ - -import { makeStyles } from '@material-ui/core/styles'; -import * as React from 'react'; -import { Graph } from '../../api'; -import { useResizeEventDependency } from '../../utils/resize'; - -interface IProps { - graph: Graph; - sortColumn?: number; - height?: number; - allowHtml?: boolean; - setCellProperty?: (row: number, column: number, cb: (key: string, value: any) => void) => void; -} - -const useStyles = makeStyles(() => ({ - root: { - height: (props: IProps): number | undefined => props.height, - }, -})); - -export const TableChart: React.FC = (props) => { - const { graph, sortColumn, setCellProperty, allowHtml } = props; - const classes = useStyles(props); - const graphRef = React.useRef(null); - const [resizeEventDependency] = useResizeEventDependency(); - - React.useLayoutEffect(() => { - const element = graphRef.current; - if (!element || !element.parentElement) { - return; - } - - const data = new google.visualization.DataTable(); - graph.columns.forEach((column) => { - data.addColumn({ - type: column.type, - label: column.name, - role: column.role, - p: column.p, - }); - }); - data.addRows(graph.rows); - - if (setCellProperty) { - for (let row = 0; row < graph.rows.length; ++row) { - for (let column = 0; column < graph.columns.length; ++column) { - setCellProperty(row, column, (key: string, value: any) => { - data.setProperty(row, column, key, value); - }); - } - } - } - - const options = { - width: '100%', - height: '100%', - page: 'enable', - allowHtml, - pageSize: 30, - tooltip: { isHtml: true }, - sortColumn: sortColumn, - sortAscending: false, - }; - - const chart = new google.visualization.Table(element); - - /* `chart.draw()` removes the contents of `element` and rebuilds it. This can cause a jump in the scroll position - * if the height/width change to 0. Since we can't change the code of Google Charts, we temporarily lock the dims - * of the parent container. */ - if (element.offsetHeight > 0) { - element.parentElement.style.height = `${element.offsetHeight}px`; - } - chart.draw(data, options); - element.parentElement.style.height = ''; - }, [graph, resizeEventDependency]); - - return ( -
-
-
- ); -}; diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/helpers.tsx b/plugins/tensorboard-plugins/tb_plugin/fe/src/components/helpers.tsx deleted file mode 100644 index bfbb346e4..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/helpers.tsx +++ /dev/null @@ -1,41 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *--------------------------------------------------------------------------------------------*/ - -import { makeStyles } from '@material-ui/core/styles'; -import Tooltip from '@material-ui/core/Tooltip'; -import HelpOutline from '@material-ui/icons/HelpOutline'; -import clsx from 'clsx'; -import * as React from 'react'; - -export const useTooltipCommonStyles = makeStyles((theme) => ({ - tooltip: { - maxWidth: '600px', - whiteSpace: 'pre-wrap', - fontSize: '14px', - }, - cardTitle: { - display: 'flex', - alignItems: 'center', - }, - titleText: { - marginRight: theme.spacing(0.5), - }, - smallTitleText: { - fontSize: '.8rem', - fontWeight: 'bold', - }, -})); - -export const makeChartHeaderRenderer = - (classes: ReturnType, smallTitleText = true) => - (title: string, tooltip: string): JSX.Element => { - return ( - - {title} - - - - - ); - }; diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/tables/CallFrameList.tsx b/plugins/tensorboard-plugins/tb_plugin/fe/src/components/tables/CallFrameList.tsx deleted file mode 100644 index 0334d29e5..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/tables/CallFrameList.tsx +++ /dev/null @@ -1,35 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *--------------------------------------------------------------------------------------------*/ - -import * as React from 'react'; -import { CallStackFrame } from './transform'; -import { List } from 'antd'; -import { NavToCodeButton } from './NavToCodeButton'; -import { makeStyles } from '@material-ui/core/styles'; - -interface IProps { - callFrames: CallStackFrame[]; -} - -const useStyles = makeStyles(() => ({ - item: { - paddingTop: '1px !important', - paddingBottom: '1px !important', - }, -})); - -export const CallFrameList = (props: IProps): React.JSX.Element => { - const classes = useStyles(); - - const renderItem = React.useCallback( - (item: CallStackFrame) => ( - - - - ), - [classes.item] - ); - - return ; -}; diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/tables/CallStackTable.tsx b/plugins/tensorboard-plugins/tb_plugin/fe/src/components/tables/CallStackTable.tsx deleted file mode 100644 index c3176428d..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/tables/CallStackTable.tsx +++ /dev/null @@ -1,103 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *-------------------------------------------------------------------------------------------- - * Copyright (c) 2023, Huawei Technologies. - * All rights reserved. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * - * Modifications: Add visualization of PyTorch Ascend profiling. - *--------------------------------------------------------------------------------------------*/ - -import * as React from 'react'; -import { makeStyles } from '@material-ui/core/styles'; -import { CallStackTableData, OperationTableDataInner } from '../../api'; -import { Table, TableProps } from 'antd'; - -import * as api from '../../api'; -import { transformTableData, TransformedCallStackDataInner } from './transform'; -import { attachId, getCommonOperationColumns } from './common'; -import { OperationGroupBy } from '../../constants/groupBy'; -import { makeExpandIcon } from './ExpandIcon'; -import { CallFrameList } from './CallFrameList'; - -export interface IProps { - data: OperationTableDataInner; - run: string; - worker: string; - span: string; - groupBy: OperationGroupBy; - deviceTarget: string; -} - -const useStyles = makeStyles((theme) => ({ - tooltip: { - whiteSpace: 'pre-wrap', - }, -})); - -const expandIcon = makeExpandIcon( - 'View call frames', - (record) => !record.callStackFrames.length -); - -const rowExpandable = (record: TransformedCallStackDataInner): boolean => !!record.callStackFrames.length; -const expandedRowRender = (record: TransformedCallStackDataInner): React.JSX.Element => ( - -); - -export const CallStackTable = (props: IProps): React.JSX.Element => { - const { data, run, worker, span, groupBy, deviceTarget } = props; - const { name, input_shape } = data; - const classes = useStyles(props); - - const [stackData, setStackData] = React.useState(undefined); - const [tooltips, setTooltips] = React.useState(); - - React.useEffect(() => { - api.defaultApi.operationStackGet(run, worker, span, groupBy, name, input_shape).then((resp) => { - setTooltips(resp.metadata.tooltips); - setStackData(resp.data); - }); - }, [name, input_shape, run, worker, span, groupBy]); - - const transformedData = React.useMemo(() => stackData && transformTableData(attachId(stackData)), [stackData]); - - const columns = React.useMemo( - () => transformedData && getCommonOperationColumns(transformedData, deviceTarget, undefined, tooltips, classes), - [transformedData] - ); - - const expandIconColumnIndex = columns?.length; - - const expandable: TableProps['expandable'] = React.useMemo( - () => ({ - expandIconColumnIndex, - expandIcon, - expandedRowRender, - rowExpandable, - }), - [expandIconColumnIndex] - ); - - return ( -
- ); -}; diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/tables/ExpandIcon.tsx b/plugins/tensorboard-plugins/tb_plugin/fe/src/components/tables/ExpandIcon.tsx deleted file mode 100644 index 422bb7816..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/tables/ExpandIcon.tsx +++ /dev/null @@ -1,35 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *--------------------------------------------------------------------------------------------*/ - -import * as React from 'react'; -import { Button, TableProps } from 'antd'; -import { OperationTableDataInner, CallStackTableDataInner } from '../../api'; -import { Arguments } from '../../utils/type'; - -type Types = NonNullable['expandable']>['expandIcon']; -type BasePropType = Arguments>>[0]; -type PropType = BasePropType & { text: string; disabled?: boolean }; - -export function ExpandIcon( - props: PropType -): React.JSX.Element { - const onClick = (e: React.MouseEvent): void => { - props.onExpand(props.record, e); - }; - - return ( - - ); -} - -export function makeExpandIcon( - text: string, - disabled?: (v: T) => boolean -) { - return (props: BasePropType): React.JSX.Element => ( - - ); -} diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/tables/MemoryStatsTable.tsx b/plugins/tensorboard-plugins/tb_plugin/fe/src/components/tables/MemoryStatsTable.tsx deleted file mode 100644 index c7e1809a3..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/tables/MemoryStatsTable.tsx +++ /dev/null @@ -1,77 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *--------------------------------------------------------------------------------------------*/ - -import * as React from 'react'; -import { Table } from 'antd'; -import { makeStyles } from '@material-ui/core'; - -export interface IProps { - data: any; - sort: string; -} - -const useStyles = makeStyles((theme) => ({ - tooltip: { - whiteSpace: 'pre-wrap', - }, -})); - -const getMemoryStatsTableColumns = function (columns: any, sort: string, tooltipClass: string): any { - let i = 0; - return columns.map((col: any) => { - const key = `col${i++}`; - const stringCompare = (a: any, b: any): number => a[key].localeCompare(b[key]); - const numberCompare = (a: any, b: any): number => (a[key] || 0) - (b[key] || 0); - return { - dataIndex: key, - key: key, - title: col.name, - sorter: col.type === 'string' ? stringCompare : numberCompare, - defaultSortOrder: sort === col.name ? ('descend' as const) : undefined, - showSorterTooltip: col.tooltip ? { title: col.tooltip, overlayClassName: tooltipClass } : true, - }; - }); -}; - -const getMemoryStatsTableRows = function (rows: any): any { - return rows.map((row: any) => { - let i = 0; - const res: any = {}; - row.forEach((entry: any) => { - res[`col${i++}`] = entry; - }); - return res; - }); -}; - -export const MemoryStatsTable = (props: IProps): React.JSX.Element => { - const { data, sort } = props; - const classes = useStyles(); - - const rows = React.useMemo(() => getMemoryStatsTableRows(data.rows), [data.rows]); - - const columns = React.useMemo( - () => getMemoryStatsTableColumns(data.columns, sort, classes.tooltip), - [data.columns, sort, classes.tooltip] - ); - - const [pageSize, setPageSize] = React.useState(30); - const onShowSizeChange = (current: number, size: number): void => { - setPageSize(size); - }; - - return ( -
- ); -}; diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/tables/NavToCodeButton.tsx b/plugins/tensorboard-plugins/tb_plugin/fe/src/components/tables/NavToCodeButton.tsx deleted file mode 100644 index 2c999aa12..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/tables/NavToCodeButton.tsx +++ /dev/null @@ -1,29 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *--------------------------------------------------------------------------------------------*/ - -import * as React from 'react'; -import { CallStackFrame } from './transform'; -import { Button } from 'antd'; -import { navToCode } from '../../utils/vscode'; - -interface IProps { - frame: CallStackFrame; -} - -export const NavToCodeButton = (props: IProps): React.JSX.Element => { - const { raw, line, file } = props.frame; - const couldNavToFile = line && file; - - const onClick = (): void => { - if (line && file) { - navToCode(file, line - 1); - } - }; - - return ( - - ); -}; diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/tables/OperationTable.tsx b/plugins/tensorboard-plugins/tb_plugin/fe/src/components/tables/OperationTable.tsx deleted file mode 100644 index 1ce77ee81..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/tables/OperationTable.tsx +++ /dev/null @@ -1,105 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *-------------------------------------------------------------------------------------------- - * Copyright (c) 2023, Huawei Technologies. - * All rights reserved. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * - * Modifications: Add visualization of PyTorch Ascend profiling. - *--------------------------------------------------------------------------------------------*/ - -import * as React from 'react'; -import { makeStyles } from '@material-ui/core/styles'; -import { OperationTableData, OperationTableDataInner, TableMetadata } from '../../api'; -import { OperationGroupBy } from '../../constants/groupBy'; -import { attachId, getCommonOperationColumns } from './common'; -import { Table, TableProps } from 'antd'; -import { makeExpandIcon } from './ExpandIcon'; -import { CallStackTable } from './CallStackTable'; - -export interface IProps { - data: OperationTableData; - run: string; - worker: string; - span: string; - groupBy: OperationGroupBy; - sortColumn: string; - tooltips?: any; - deviceTarget: string; -} - -const useStyles = makeStyles((theme) => ({ - tooltip: { - whiteSpace: 'pre-wrap', - }, -})); - -const rowExpandable = (record: OperationTableDataInner): boolean => record.has_call_stack; -const expandIcon = makeExpandIcon('View CallStack', (record) => !record.has_call_stack); -export const OperationTable = (props: IProps): React.JSX.Element => { - const { data, run, worker, span, groupBy, sortColumn, tooltips, deviceTarget } = props; - const classes = useStyles(props); - - const rows = React.useMemo(() => attachId(data), [data]); - - const columns = React.useMemo( - () => getCommonOperationColumns(rows, deviceTarget, sortColumn, tooltips, classes), - [rows] - ); - - const [pageSize, setPageSize] = React.useState(30); - const onShowSizeChange = (current: number, size: number): void => { - setPageSize(size); - }; - - const expandIconColumnIndex = columns.length; - const expandedRowRender = React.useCallback( - (record: OperationTableDataInner) => ( - - ), - [run, worker, span, groupBy] - ); - - const expandable: TableProps['expandable'] = React.useMemo( - () => ({ - expandIconColumnIndex, - expandIcon, - expandedRowRender, - rowExpandable, - }), - [expandIconColumnIndex, expandedRowRender] - ); - - return ( -
- ); -}; diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/tables/common.tsx b/plugins/tensorboard-plugins/tb_plugin/fe/src/components/tables/common.tsx deleted file mode 100644 index a84a1a3bb..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/tables/common.tsx +++ /dev/null @@ -1,150 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *-------------------------------------------------------------------------------------------- - * Copyright (c) 2023, Huawei Technologies. - * All rights reserved. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * - * Modifications: Add visualization of PyTorch Ascend profiling. - *--------------------------------------------------------------------------------------------*/ - -import { firstOrUndefined, isDef } from '../../utils/def'; -import { CallStackTableDataInner, OperationTableDataInner } from '../../api'; -import type { ColumnsType } from 'antd/es/table'; -import { ClassNameMap } from '@material-ui/styles'; - -export function getCommonOperationColumns( - data?: T[], - deviceTarget?: string, - defaultSort?: string, - tooltips?: any, - classes?: ClassNameMap<'tooltip'> -): ColumnsType { - const firstData = firstOrUndefined(data); - - const hasInputShape = !firstData || isDef(firstData.input_shape); - const hasDeviceSelfDuration = !firstData || isDef(firstData.device_self_duration); - const hasDeviceTotalDuration = !firstData || isDef(firstData.device_total_duration); - const hasTcEligible = !firstData || isDef(firstData.tc_eligible); - const hasTcSelfRatio = !firstData || isDef(firstData.tc_self_ratio); - const hasTcTotalRatio = !firstData || isDef(firstData.tc_total_ratio); - - const nameCompare = (a: T, b: T): number => a.name.localeCompare(b.name); - const callsCompare = (a: T, b: T): number => a.calls - b.calls; - const deviceSelfDurationCompare = (a: T, b: T): number => - (a.device_self_duration || 0) - (b.device_self_duration || 0); - const deviceTotalDurationCompare = (a: T, b: T): number => - (a.device_total_duration || 0) - (b.device_total_duration || 0); - const hostSelfDurationCompare = (a: T, b: T): number => (a.host_self_duration || 0) - (b.host_self_duration || 0); - const hostTotalDurationCompare = (a: T, b: T): number => (a.host_total_duration || 0) - (b.host_total_duration || 0); - const tcEligibleCompare = (a: T, b: T): number => (a.tc_eligible ?? '').localeCompare(b.tc_eligible ?? ''); - const tcSelfRatioCompare = (a: T, b: T): number => (a.tc_self_ratio || 0) - (b.tc_self_ratio || 0); - const tcTotalRatioCompare = (a: T, b: T): number => (a.tc_total_ratio || 0) - (b.tc_total_ratio || 0); - - const columns: ColumnsType = [ - { - dataIndex: 'name', - key: 'name', - title: 'Name', - sorter: nameCompare, - }, - hasInputShape - ? { - dataIndex: 'input_shape', - key: 'input_shape', - title: 'Input Shape', - } - : undefined, - { - dataIndex: 'calls', - sorter: callsCompare, - key: 'calls', - title: 'Calls', - }, - hasDeviceSelfDuration - ? { - dataIndex: 'device_self_duration', - key: 'device_self_duration', - title: 'Device Self Duration (us)', - sorter: deviceSelfDurationCompare, - // Use device_self_duration as default sort if defaultSort is unspecified - defaultSortOrder: defaultSort ? undefined : ('descend' as const), - } - : undefined, - hasDeviceTotalDuration - ? { - dataIndex: 'device_total_duration', - key: 'device_total_duration', - title: 'Device Total Duration (us)', - sorter: deviceTotalDurationCompare, - } - : undefined, - { - dataIndex: 'host_self_duration', - key: 'host_self_duration', - title: 'Host Self Duration (us)', - sorter: hostSelfDurationCompare, - }, - { - dataIndex: 'host_total_duration', - key: 'host_total_duration', - title: 'Host Total Duration (us)', - sorter: hostTotalDurationCompare, - }, - hasTcEligible - ? { - dataIndex: 'tc_eligible', - key: 'tc_eligible', - title: deviceTarget === 'Ascend' ? 'AI Cores Eligible' : 'Tensor Cores Eligible', - sorter: tcEligibleCompare, - } - : undefined, - hasTcSelfRatio - ? { - dataIndex: 'tc_self_ratio', - key: 'tc_self_ratio', - title: deviceTarget === 'Ascend' ? 'AI Cores Self(%)' : 'Tensor Cores Self(%)', - sorter: tcSelfRatioCompare, - } - : undefined, - hasTcTotalRatio - ? { - dataIndex: 'tc_total_ratio', - key: 'tc_total_ratio', - title: deviceTarget === 'Ascend' ? 'AI Cores Total(%)' : 'Tensor Cores Total(%)', - sorter: tcTotalRatioCompare, - } - : undefined, - ].filter(isDef); - columns.forEach((column) => { - if (column.key === defaultSort) { - column.defaultSortOrder = 'descend' as const; - } - if (tooltips[column.key as string]) { - column.showSorterTooltip = { - title: tooltips[column.key as string], - overlayClassName: classes?.tooltip, - }; - } - }); - return columns; -} - -let uid = 1; -export function attachId(data: T[]): T[] { - return data.map((d) => ({ - ...d, - key: uid++, - })); -} diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/tables/transform.ts b/plugins/tensorboard-plugins/tb_plugin/fe/src/components/tables/transform.ts deleted file mode 100644 index 5f59728fe..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/tables/transform.ts +++ /dev/null @@ -1,63 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *--------------------------------------------------------------------------------------------*/ - -import { CallStackTableData, CallStackTableDataInner } from '../../api'; - -export interface CallStackFrame { - file?: string; - line?: number; - raw: string; -} - -export interface TransformedCallStackDataInner extends CallStackTableDataInner { - callStackFrames: CallStackFrame[]; -} - -const lineRegex = /\([0-9]+\)$/; - -function parseCallStackLine(raw: string): CallStackFrame { - let rawResult = raw.trim(); - const results = rawResult.split(':'); - const location = results.slice(0, results.length - 1).join(':'); - - const result = lineRegex.exec(location); - if (!result) { - return { raw: rawResult }; - } - - const lineWithParens = result[0].trim(); - const file = rawResult.slice(0, result.index).trim(); - const line = Number( - lineWithParens.substr(1, lineWithParens.length - 2).trim() - ); - - return { - raw: rawResult, - file, - line, - }; -} - -function parseCallStack(callStack?: string): CallStackFrame[] { - const lines = (callStack ?? '') - .trim() - .split(';') - .map((x) => x.trim()); - return lines.map(parseCallStackLine); -} - -function transformCallStackData( - data: CallStackTableDataInner -): TransformedCallStackDataInner { - return { - ...data, - callStackFrames: parseCallStack(data.call_stack), - }; -} - -export function transformTableData( - data: CallStackTableData -): TransformedCallStackDataInner[] { - return data.map(transformCallStackData); -} diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/transform.ts b/plugins/tensorboard-plugins/tb_plugin/fe/src/components/transform.ts deleted file mode 100644 index 94ee9f384..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/components/transform.ts +++ /dev/null @@ -1,83 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *--------------------------------------------------------------------------------------------*/ - -import * as api from '../api'; -import { assertDef, isDef } from '../utils/def'; - -export function transformPerformanceIntoTable(performances: api.Performance[]): api.Graph { - const columns: api.GraphColumn[] = [ - { type: 'string', name: 'Category' }, - { type: 'number', name: 'Time Duration (us)' }, - { type: 'number', name: 'Percentage (%)' }, - ]; - - const rows: api.Graph['rows'] = []; - const queue = [...performances]; - - while (queue.length) { - const first = queue.shift(); - assertDef(first); - - const row: api.Graph['rows'][number] = []; - const { name, value, extra, children } = first; - assertDef(value); - assertDef(extra); - - row.push(name); - row.push(value); - row.push(extra); - - if (isDef(children) && children.length) { - queue.push(...children); - } - - rows.push(row); - } - - return { - columns, - rows, - }; -} - -export function transformPerformanceIntoPie(performances: api.Performance[]): { - columns: api.GraphColumn[]; - rows: Array>; -} { - const columns: api.GraphColumn[] = [ - { type: 'string', name: 'Name' }, - { type: 'number', name: 'Value' }, - ]; - - const rows: api.Graph['rows'] = []; - const queue: api.Performance[] = []; - performances.forEach((topLevel) => { - if (topLevel.children) { - queue.push(...topLevel.children); - } - }); - - while (queue.length) { - const first = queue.shift(); - assertDef(first); - - const row: api.Graph['rows'][number] = []; - const { name, value, children } = first; - assertDef(value); - - row.push(name); - row.push(Number.parseInt(value, 10)); - - if (isDef(children) && children.length) { - queue.push(...children); - } - - rows.push(row); - } - - return { - columns, - rows, - }; -} diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/constants/groupBy.ts b/plugins/tensorboard-plugins/tb_plugin/fe/src/constants/groupBy.ts deleted file mode 100644 index 88ea9e3f4..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/constants/groupBy.ts +++ /dev/null @@ -1,13 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *--------------------------------------------------------------------------------------------*/ - -export enum OperationGroupBy { - OPERATION = 'Operation', - OPERATION_AND_INPUT_SHAPE = 'OperationAndInputShape', -} - -export enum KernelGroupBy { - KERNEL = 'Kernel', - KERNEL_NAME_AND_OP_NAME = 'KernelNameAndOpName', -} diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/gstatic.d.ts b/plugins/tensorboard-plugins/tb_plugin/fe/src/gstatic.d.ts deleted file mode 100644 index 521c5fbb8..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/gstatic.d.ts +++ /dev/null @@ -1,6 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *--------------------------------------------------------------------------------------------*/ - -declare const google: any; -declare module 'react-flame-graph'; diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/index.tsx b/plugins/tensorboard-plugins/tb_plugin/fe/src/index.tsx deleted file mode 100644 index 851474766..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/index.tsx +++ /dev/null @@ -1,10 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *--------------------------------------------------------------------------------------------*/ - -import * as React from 'react'; -import { render } from 'react-dom'; -import { App } from './app'; -import 'antd/dist/antd.css'; - -render(, document.getElementById('app')); diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/setup.tsx b/plugins/tensorboard-plugins/tb_plugin/fe/src/setup.tsx deleted file mode 100644 index c811ae152..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/setup.tsx +++ /dev/null @@ -1,9 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *--------------------------------------------------------------------------------------------*/ - -export async function setup(): Promise { - await google.charts.load('current', { - packages: ['corechart', 'table', 'timeline'], - }); -} diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/styles.css b/plugins/tensorboard-plugins/tb_plugin/fe/src/styles.css deleted file mode 100644 index f7705b54a..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/styles.css +++ /dev/null @@ -1,16 +0,0 @@ -#app { - height: 100%; -} -.goog-tooltip { - display: none !important; -} - -.visualization-tooltip { - padding: 4px 10px; - white-space: nowrap; -} - -div.google-visualization-tooltip { - pointer-events: none; - max-width: 90%; -} diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/utils/binarysearch.ts b/plugins/tensorboard-plugins/tb_plugin/fe/src/utils/binarysearch.ts deleted file mode 100644 index 41382dcdb..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/utils/binarysearch.ts +++ /dev/null @@ -1,20 +0,0 @@ -export function binarySearch( - arr: Array, - key: any, - compareFn: (key: number, mid: Array) => number -): number { - let low = 0; - let high = arr.length - 1; - while (low <= high) { - let mid = Math.round((high + low) / 2); - let cmp = compareFn(key, arr[mid]); - if (cmp > 0) { - low = mid + 1; - } else if (cmp < 0) { - high = mid - 1; - } else { - return mid; - } - } - return -1; -} diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/utils/debounce.ts b/plugins/tensorboard-plugins/tb_plugin/fe/src/utils/debounce.ts deleted file mode 100644 index 82c7f04a9..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/utils/debounce.ts +++ /dev/null @@ -1,21 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *--------------------------------------------------------------------------------------------*/ - -import * as React from 'react'; - -export function useDebounce(value: T, delay: number): T { - const [debouncedValue, setDebouncedValue] = React.useState(value); - - React.useEffect(() => { - const handler = setTimeout(() => { - setDebouncedValue(value); - }, delay); - - return () => { - clearTimeout(handler); - }; - }, [value, delay]); - - return debouncedValue; -} diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/utils/def.ts b/plugins/tensorboard-plugins/tb_plugin/fe/src/utils/def.ts deleted file mode 100644 index df6bef8ea..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/utils/def.ts +++ /dev/null @@ -1,20 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *--------------------------------------------------------------------------------------------*/ - -export function isDef(v?: T | null): v is T { - return v !== null && v !== undefined; -} - -export function assertDef(v?: T | null): asserts v is T { - if (!isDef(v)) { - throw new Error('Must be defined'); - } -} - -export function firstOrUndefined(v?: T[]): T | undefined { - if (!v || !v.length) { - return undefined; - } - return v[0]; -} diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/utils/hooks.ts b/plugins/tensorboard-plugins/tb_plugin/fe/src/utils/hooks.ts deleted file mode 100644 index 473b393d9..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/utils/hooks.ts +++ /dev/null @@ -1,27 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *--------------------------------------------------------------------------------------------*/ - -import * as React from 'react'; - -const cbs: Array<() => void> = []; -export const useOnResize = (cb: () => void): void => { - React.useEffect(() => { - if (cbs.length === 0) { - window.addEventListener('resize', () => { - cbs.forEach((callback) => callback()); - }); - } - cbs.push(cb); - - return (): void => { - const idx = cbs.findIndex(cb); - if (idx > -1) { - cbs.splice(idx, 1); - } - if (cbs.length === 0) { - window.removeEventListener('reset', cb); - } - }; - }, [cb]); -}; diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/utils/index.ts b/plugins/tensorboard-plugins/tb_plugin/fe/src/utils/index.ts deleted file mode 100644 index 5da446721..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/utils/index.ts +++ /dev/null @@ -1,24 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *--------------------------------------------------------------------------------------------*/ - -import { ValueAndFormat } from '../api'; - -export function firstOrUndefined(v?: T[] | null): T | undefined { - if (!v || !v.length) { - return undefined; - } - return v[0]; -} - -export function sleep(delay: number): Promise { - return new Promise((resolve) => setTimeout(resolve, delay)); -} - -export function isValueAndFormat(v: any): v is ValueAndFormat { - return 'f' in v && 'v' in v; -} - -export function value(v: boolean | number | string | ValueAndFormat): boolean | number | string { - return typeof v === 'object' && isValueAndFormat(v) ? v.v : v; -} diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/utils/resize.ts b/plugins/tensorboard-plugins/tb_plugin/fe/src/utils/resize.ts deleted file mode 100644 index 766a10d54..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/utils/resize.ts +++ /dev/null @@ -1,27 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *--------------------------------------------------------------------------------------------*/ - -import * as React from 'react'; -import debounce from '@material-ui/core/utils/debounce'; - -export function useResizeEventDependency(): readonly [number] { - const [version, setVersion] = React.useState(0); - - const increaseVersion = React.useCallback( - debounce(() => { - setVersion((prev) => prev + 1); - }, 100), - [] - ); - - React.useEffect(() => { - window.addEventListener('resize', increaseVersion); - - return (): void => { - window.removeEventListener('resize', increaseVersion); - }; - }, []); - - return [version] as const; -} diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/utils/search.ts b/plugins/tensorboard-plugins/tb_plugin/fe/src/utils/search.ts deleted file mode 100644 index 8a2efc36d..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/utils/search.ts +++ /dev/null @@ -1,68 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *--------------------------------------------------------------------------------------------*/ - -import * as React from 'react'; -import { value } from '.'; -import * as api from '../api'; -import { useDebounce } from './debounce'; - -export function useSearch( - searchName: string, - columnName: string, - table?: api.Graph -): [api.Graph | undefined] { - const searchNameDebounce = useDebounce(searchName.trim(), 500); - - const searchedTable: api.Graph | undefined = React.useMemo(() => { - if (!searchNameDebounce) { - return table; - } - - if (!table) { - return undefined; - } - - const columnNameToFind = columnName.toLowerCase(); - const nameColumnIdx = table.columns.findIndex( - (c) => c.name.toLowerCase() === columnNameToFind - ); - if (nameColumnIdx < 0) { - return table; - } - - return { - ...table, - rows: table.rows.filter((x) => { - const cell = value(x[nameColumnIdx]); - return typeof cell === 'string' && cell.includes(searchNameDebounce); - }), - }; - }, [table, searchNameDebounce]); - return [searchedTable]; -} - -export function useSearchDirectly( - searchName: string, - field: (v: T) => string, - table?: T[] -): [T[] | undefined] { - const searchNameDebounce = useDebounce(searchName.trim(), 500); - - const result = React.useMemo(() => { - if (!searchNameDebounce) { - return table; - } - - if (!table) { - return undefined; - } - - return table.filter((row) => { - return field(row) - .toLowerCase() - .includes(searchNameDebounce.toLowerCase()); - }); - }, [table, field, searchNameDebounce]); - return [result]; -} diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/utils/top.ts b/plugins/tensorboard-plugins/tb_plugin/fe/src/utils/top.ts deleted file mode 100644 index 4af19968d..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/utils/top.ts +++ /dev/null @@ -1,54 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *--------------------------------------------------------------------------------------------*/ - -import debounce from '@material-ui/core/utils/debounce'; -import * as React from 'react'; - -export enum UseTop { - NOT_USE = 'NotUse', - USE = 'Use', -} - -interface IOptions { - defaultTop?: number; - defaultUseTop?: UseTop; - noDebounce?: boolean; - wait?: number; -} - -export function useTopN( - options?: IOptions -): readonly [ - string, - number | undefined, - UseTop, - React.Dispatch>, - React.Dispatch> -] { - let realOptions = options ?? {}; - - const [topText, setTopText] = React.useState(String(realOptions.defaultTop ?? 15)); - const [actualTop, setActualTop] = React.useState(Number(topText)); - const [useTop, setUseTop] = React.useState(realOptions.defaultUseTop ?? UseTop.NOT_USE); - - const setActualDebounce = !realOptions.noDebounce - ? React.useCallback(debounce(setActualTop, realOptions.wait ?? 500), []) - : setActualTop; - React.useEffect(() => { - if (useTop !== UseTop.USE) { - setActualDebounce(undefined); - } else if (topIsValid(topText)) { - setActualDebounce(Number(topText)); - } else { - setActualDebounce(actualTop); - } - }, [topText, useTop]); - - return [topText, actualTop, useTop, setTopText, setUseTop] as const; -} - -export function topIsValid(topText: string): boolean { - const top = Number(topText); - return !Number.isNaN(top) && top > 0 && Number.isInteger(top); -} diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/utils/type.ts b/plugins/tensorboard-plugins/tb_plugin/fe/src/utils/type.ts deleted file mode 100644 index ccd45fd16..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/utils/type.ts +++ /dev/null @@ -1,9 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *--------------------------------------------------------------------------------------------*/ - -export type Arguments void> = T extends ( - ...args: infer A -) => void - ? A - : never; diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/src/utils/vscode.ts b/plugins/tensorboard-plugins/tb_plugin/fe/src/utils/vscode.ts deleted file mode 100644 index 2a763adca..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/src/utils/vscode.ts +++ /dev/null @@ -1,13 +0,0 @@ -/*--------------------------------------------------------------------------------------------- - * Copyright (c) Microsoft Corporation. All rights reserved. - *--------------------------------------------------------------------------------------------*/ - -export function navToCode(filename: string, line: number): void { - window.parent.parent.postMessage( - { - filename, - line, - }, - window.origin - ); -} diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/tsconfig.json b/plugins/tensorboard-plugins/tb_plugin/fe/tsconfig.json deleted file mode 100644 index 182aafbe1..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/tsconfig.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "compilerOptions": { - "target": "es5", - "lib": [ - "dom", - "dom.iterable", - "esnext" - ], - "strict": true, - "strictPropertyInitialization": false, - "module": "esnext", - "moduleResolution": "node", - "forceConsistentCasingInFileNames": true, - "rootDir": "src", - "outDir": "dist", - "jsx": "react", - } -} diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/update-static.js b/plugins/tensorboard-plugins/tb_plugin/fe/update-static.js deleted file mode 100644 index 67c9be6cc..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/update-static.js +++ /dev/null @@ -1,7 +0,0 @@ -const fs = require('fs'); -const path = require('path'); - -fs.copyFileSync( - path.resolve(__dirname, 'dist/index.html'), - path.resolve(__dirname, '../torch_tb_profiler/static/index.html') -); diff --git a/plugins/tensorboard-plugins/tb_plugin/fe/webpack.config.js b/plugins/tensorboard-plugins/tb_plugin/fe/webpack.config.js deleted file mode 100644 index a47f8b319..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/fe/webpack.config.js +++ /dev/null @@ -1,36 +0,0 @@ -const path = require('path'); -const HtmlWebpackPlugin = require('html-webpack-plugin'); -const InlineChunkHtmlPlugin = require('inline-chunk-html-plugin'); - -const isDev = process.env.NODE_ENV !== 'production'; - -/** - * @type {import('webpack').Configuration & import('webpack-dev-server').Configuration} - */ -module.exports = { - mode: isDev ? 'development' : 'production', - entry: './src/index.tsx', - output: { - path: path.resolve(__dirname, 'dist'), - filename: 'index.js', - }, - resolve: { - // Add `.ts` and `.tsx` as a resolvable extension. - extensions: ['.ts', '.tsx', '.js'], - }, - module: { - rules: [ - { test: /\.tsx?$/i, use: 'ts-loader' }, - { test: /\.css$/i, use: ['style-loader', 'css-loader'] }, - ], - }, - plugins: [ - new HtmlWebpackPlugin({ - inject: true, - scriptLoading: 'blocking', - template: 'index.html', - }), - !isDev ? new InlineChunkHtmlPlugin(HtmlWebpackPlugin, [/.*/]) : undefined, - ].filter(Boolean), - devServer: {}, -}; diff --git a/plugins/tensorboard-plugins/tb_plugin/packaging/torch_tb_profiler/meta.yaml b/plugins/tensorboard-plugins/tb_plugin/packaging/torch_tb_profiler/meta.yaml deleted file mode 100644 index ab105fd04..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/packaging/torch_tb_profiler/meta.yaml +++ /dev/null @@ -1,39 +0,0 @@ -{% set data = load_setup_py_data(setup_file='../../setup.py', from_recipe_dir=True) %} - -package: - name: torch_tb_profiler - version: "{{ data.get('version') }}" - -source: - path: "{{ environ.get('SOURCE_ROOT_DIR', '../..') }}" - -requirements: - host: - - python - - setuptools - - pytorch - - run: - - python - - pandas - - tensorboard - -build: - noarch: python - script: python setup.py install --single-version-externally-managed --record=record.txt - -test: - imports: - - torch_tb_profiler - source_files: - - test - requires: - - tensorboard - - pandas - - -about: - home: https://github.com/pytorch/kineto/tree/main/tb_plugin - license: BSD - license_file: LICENSE - summary: 'Tensoboard Plugin that provides visualization of PyTorch profiling' diff --git a/plugins/tensorboard-plugins/tb_plugin/setup.py b/plugins/tensorboard-plugins/tb_plugin/setup.py deleted file mode 100644 index 2d4260b21..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/setup.py +++ /dev/null @@ -1,125 +0,0 @@ -# ------------------------------------------------------------------------- -# Copyright (c) Microsoft Corporation. -# Copyright(c) 2023 Huawei Technologies. -# All rights reserved -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# Modifications: Add visualization of PyTorch Ascend profiling. -# -------------------------------------------------------------------------- -import os -import pathlib -import subprocess -from configparser import ConfigParser - -import setuptools - -config = ConfigParser() -config.read('./torch_tb_profiler/config/config.ini') - - -def read(rel_path): - here = os.path.abspath(os.path.dirname(__file__)) - with open(os.path.join(here, rel_path)) as fp: - return fp.read() - - -def get_version(rel_path): - for line in read(rel_path).splitlines(): - if line.startswith("__version__"): - delim = '"' if '"' in line else "'" - version = line.split(delim)[1] - - if os.getenv('TORCH_TB_PROFILER_BUILD_VERSION'): - version = os.getenv('TORCH_TB_PROFILER_BUILD_VERSION') - return version - - -INSTALL_REQUIRED = [ - "pandas >= 1.0.0", - "tensorboard >= 2.11.0" -] - -TESTS_REQUIRED = INSTALL_REQUIRED + [ - "torch >= 1.8", - "torchvision >= 0.8" -] - -EXTRAS = { - "s3": ["boto3"], - "blob": ["azure-storage-blob"], - "gs": ["google-cloud-storage"] -} - - -class build_fe(setuptools.Command): - """Build the frontend""" - description = "run yarn build on frontend directory" - - user_options = [] - - def initialize_options(self): - pass - - def finalize_options(self): - pass - - def run(self): - cwd = pathlib.Path().absolute() - root = pathlib.Path(__file__).parent.absolute() - os.chdir(root / "fe") - subprocess.run(["yarn", "build:copy"], check=True) - # restore the working directory - os.chdir(cwd) - - -setuptools.setup( - name="torch-tb-profiler-ascend", - version=get_version(os.path.join('torch_tb_profiler', '__init__.py')), - description="PyTorch Ascend Profiler TensorBoard Plugin", - long_description=f"PyTorch Ascend Profiler TensorBoard Plugin: {config.get('URL', 'repository_url')}", - url=config.get('URL', 'repository_url'), - author="Ascend Team", - author_email=config.get('EMAIL', 'author_email'), - cmdclass={ - "build_fe": build_fe - }, - packages=setuptools.find_packages(), - package_data={ - "torch_tb_profiler": ["static/**", "config/**"], - }, - entry_points={ - "tensorboard_plugins": [ - "torch_profiler = torch_tb_profiler.plugin:TorchProfilerPlugin", - ], - }, - python_requires=">=3.6.2", - install_requires=INSTALL_REQUIRED, - tests_require=TESTS_REQUIRED, - classifiers=[ - 'Intended Audience :: Developers', - 'Intended Audience :: Education', - 'Intended Audience :: Science/Research', - 'License :: OSI Approved :: BSD License', - 'Programming Language :: Python :: 3', - 'Topic :: Scientific/Engineering', - 'Topic :: Scientific/Engineering :: Mathematics', - 'Topic :: Scientific/Engineering :: Artificial Intelligence', - 'Topic :: Software Development', - 'Topic :: Software Development :: Libraries', - 'Topic :: Software Development :: Libraries :: Python Modules', - ], - license='BSD-3', - keywords='pytorch tensorboard profile plugin', - extras_require=EXTRAS -) diff --git a/plugins/tensorboard-plugins/tb_plugin/test/gpu_metrics_expected.json b/plugins/tensorboard-plugins/tb_plugin/test/gpu_metrics_expected.json deleted file mode 100644 index 81f03632c..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/test/gpu_metrics_expected.json +++ /dev/null @@ -1,3105 +0,0 @@ - -{ - "schemaVersion": 1, - - "computeProperties": [ - - { - "id": 0, "name": "Tesla V100-DGXS-32GB", "totalGlobalMem": 34084028416, - "major": 7, "minor": 0, - "maxThreadsPerBlock": 1024, "maxThreadsPerMultiProcessor": 2048, - "regsPerBlock": 65536, "regsPerMultiprocessor": 65536, "warpSize": 32, - "sharedMemPerBlock": 49152, "sharedMemPerMultiprocessor": 98304, - "multiProcessorCount": 80, "sharedMemPerBlockOptin": 98304 - }, - - { - "id": 1, "name": "Tesla V100-DGXS-32GB", "totalGlobalMem": 34087305216, - "major": 7, "minor": 0, - "maxThreadsPerBlock": 1024, "maxThreadsPerMultiProcessor": 2048, - "regsPerBlock": 65536, "regsPerMultiprocessor": 65536, "warpSize": 32, - "sharedMemPerBlock": 49152, "sharedMemPerMultiprocessor": 98304, - "multiProcessorCount": 80, "sharedMemPerBlockOptin": 98304 - }, - - { - "id": 2, "name": "Tesla V100-DGXS-32GB", "totalGlobalMem": 34087305216, - "major": 7, "minor": 0, - "maxThreadsPerBlock": 1024, "maxThreadsPerMultiProcessor": 2048, - "regsPerBlock": 65536, "regsPerMultiprocessor": 65536, "warpSize": 32, - "sharedMemPerBlock": 49152, "sharedMemPerMultiprocessor": 98304, - "multiProcessorCount": 80, "sharedMemPerBlockOptin": 98304 - }, - - { - "id": 3, "name": "Tesla V100-DGXS-32GB", "totalGlobalMem": 34087305216, - "major": 7, "minor": 0, - "maxThreadsPerBlock": 1024, "maxThreadsPerMultiProcessor": 2048, - "regsPerBlock": 65536, "regsPerMultiprocessor": 65536, "warpSize": 32, - "sharedMemPerBlock": 49152, "sharedMemPerMultiprocessor": 98304, - "multiProcessorCount": 80, "sharedMemPerBlockOptin": 98304 - } - ], - "traceEvents": [ - - { - "ph": "X", "cat": "Operator", - "name": "aten::empty", "pid": 24572, "tid": "24572", - "ts": 1621401187223197, "dur": 21, - "args": { - "Device": 24572, "External id": 2, - "Trace name": "PyTorch Profiler", "Trace iteration": 0 - - } - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::zero_", "pid": 24572, "tid": "24572", - "ts": 1621401187223264, "dur": 5, - "args": { - "Device": 24572, "External id": 3, - "Trace name": "PyTorch Profiler", "Trace iteration": 0 - - } - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::zeros", "pid": 24572, "tid": "24572", - "ts": 1621401187223182, "dur": 99, - "args": { - "Device": 24572, "External id": 1, - "Trace name": "PyTorch Profiler", "Trace iteration": 0 - - } - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::empty", "pid": 24572, "tid": "24572", - "ts": 1621401187223376, "dur": 19, - "args": { - "Device": 24572, "External id": 5, - "Trace name": "PyTorch Profiler", "Trace iteration": 0 - - } - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::empty", "pid": 24572, "tid": "24572", - "ts": 1621401187223480, "dur": 18, - "args": { - "Device": 24572, "External id": 7, - "Trace name": "PyTorch Profiler", "Trace iteration": 0 - - } - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::zero_", "pid": 24572, "tid": "24572", - "ts": 1621401187223530, "dur": 5, - "args": { - "Device": 24572, "External id": 8, - "Trace name": "PyTorch Profiler", "Trace iteration": 0 - - } - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::zeros", "pid": 24572, "tid": "24572", - "ts": 1621401187223469, "dur": 72, - "args": { - "Device": 24572, "External id": 6, - "Trace name": "PyTorch Profiler", "Trace iteration": 0 - - } - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::empty", "pid": 24572, "tid": "24572", - "ts": 1621401187223622, "dur": 19, - "args": { - "Device": 24572, "External id": 10, - "Trace name": "PyTorch Profiler", "Trace iteration": 0 - - } - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::as_strided", "pid": 24572, "tid": "24572", - "ts": 1621401187223790, "dur": 12, - "args": { - "Device": 24572, "External id": 13, - "Trace name": "PyTorch Profiler", "Trace iteration": 0 - - } - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::unsqueeze", "pid": 24572, "tid": "24572", - "ts": 1621401187223777, "dur": 50, - "args": { - "Device": 24572, "External id": 12, - "Trace name": "PyTorch Profiler", "Trace iteration": 0 - - } - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::as_strided", "pid": 24572, "tid": "24572", - "ts": 1621401187223850, "dur": 7, - "args": { - "Device": 24572, "External id": 15, - "Trace name": "PyTorch Profiler", "Trace iteration": 0 - - } - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::unsqueeze", "pid": 24572, "tid": "24572", - "ts": 1621401187223841, "dur": 24, - "args": { - "Device": 24572, "External id": 14, - "Trace name": "PyTorch Profiler", "Trace iteration": 0 - - } - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::empty", "pid": 24572, "tid": "24572", - "ts": 1621401187223904, "dur": 16, - "args": { - "Device": 24572, "External id": 18, - "Trace name": "PyTorch Profiler", "Trace iteration": 0 - - } - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::resize_", "pid": 24572, "tid": "24572", - "ts": 1621401187223945, "dur": 14, - "args": { - "Device": 24572, "External id": 19, - "Trace name": "PyTorch Profiler", "Trace iteration": 0 - - } - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::_cat", "pid": 24572, "tid": "24572", - "ts": 1621401187223888, "dur": 87, - "args": { - "Device": 24572, "External id": 17, - "Trace name": "PyTorch Profiler", "Trace iteration": 0 - - } - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::cat", "pid": 24572, "tid": "24572", - "ts": 1621401187223876, "dur": 106, - "args": { - "Device": 24572, "External id": 16, - "Trace name": "PyTorch Profiler", "Trace iteration": 0 - - } - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::stack", "pid": 24572, "tid": "24572", - "ts": 1621401187223752, "dur": 245, - "args": { - "Device": 24572, "External id": 11, - "Trace name": "PyTorch Profiler", "Trace iteration": 0 , - "Fwd thread id": 0, "Sequence number": 22 - } - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::as_strided", "pid": 24572, "tid": "24572", - "ts": 1621401187224094, "dur": 12, - "args": { - "Device": 24572, "External id": 22, - "Trace name": "PyTorch Profiler", "Trace iteration": 0 - - } - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::unsqueeze", "pid": 24572, "tid": "24572", - "ts": 1621401187224074, "dur": 43, - "args": { - "Device": 24572, "External id": 21, - "Trace name": "PyTorch Profiler", "Trace iteration": 0 - - } - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::as_strided", "pid": 24572, "tid": "24572", - "ts": 1621401187224137, "dur": 6, - "args": { - "Device": 24572, "External id": 24, - "Trace name": "PyTorch Profiler", "Trace iteration": 0 - - } - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::unsqueeze", "pid": 24572, "tid": "24572", - "ts": 1621401187224128, "dur": 21, - "args": { - "Device": 24572, "External id": 23, - "Trace name": "PyTorch Profiler", "Trace iteration": 0 - - } - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::empty", "pid": 24572, "tid": "24572", - "ts": 1621401187224184, "dur": 15, - "args": { - "Device": 24572, "External id": 27, - "Trace name": "PyTorch Profiler", "Trace iteration": 0 - - } - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::resize_", "pid": 24572, "tid": "24572", - "ts": 1621401187224223, "dur": 12, - 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"ts": 1621401187228962, "dur": 1, - "args": { - "queued": 0, "device": 0, "context": 1, - "stream": 7, "correlation": 440, "external id": 91, - "registers per thread": 16, - "shared memory": 0, - "blocks per SM": 0.0125, - "warps per SM": 0.025, - "grid": [1, 1, 1], - "block": [64, 1, 1], - "theoretical occupancy %": 0 - } - }, - { - "ph": "f", "id": 440, "pid": 0, "tid": "stream 7", "ts": 1621401187228962, - "cat": "async", "name": "launch", "bp": "e" - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 24572, "tid": "24610", - "ts": 1621401187228932, "dur": 30, - "args": { - "cbid": 211, "correlation": 440, - "external id": 91, "external ts": 1621401187228885 - } - }, - { - "ph": "s", "id": 440, "pid": 24572, "tid": 24610, "ts": 1621401187228932, - "cat": "async", "name": "launch" - }, - { - "ph": "X", "cat": "Kernel", - "name": "void at::native::unrolled_elementwise_kernel, OffsetCalculator<3, unsigned int>, at::detail::Array<1, unsigned int>, at::native::memory::LoadWithoutCast, OffsetCalculator::StoreWithoutCast>(int, at::native::mse_backward_cuda_kernel(at::TensorIterator&, c10::Scalar const&)::{lambda()#1}::operator()() const::{lambda()#4}::operator()() const::{lambda(float, float, float)#1}, at::detail::Array, OffsetCalculator<3, unsigned int>, at::detail::Array<1, unsigned int>, at::native::memory::LoadWithoutCast, OffsetCalculator::StoreWithoutCast)", "pid": 0, "tid": "stream 7", - 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"ts": 1621401187229711, "dur": 4, - "args": { - "queued": 0, "device": 0, "context": 1, - "stream": 7, "correlation": 460, "external id": 94, - "registers per thread": 35, - "shared memory": 16, - "blocks per SM": 0.0125, - "warps per SM": 0.00625, - "grid": [1, 1, 1], - "block": [1, 16, 1], - "theoretical occupancy %": 0 - } - }, - { - "ph": "f", "id": 460, "pid": 0, "tid": "stream 7", "ts": 1621401187229711, - "cat": "async", "name": "launch", "bp": "e" - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 24572, "tid": "24610", - "ts": 1621401187229681, "dur": 30, - "args": { - "cbid": 211, "correlation": 460, - "external id": 94, "external ts": 1621401187229459 - } - }, - { - "ph": "s", "id": 460, "pid": 24572, "tid": 24610, "ts": 1621401187229681, - "cat": "async", "name": "launch" - }, - { - "ph": "X", "cat": "Kernel", - "name": "void at::native::vectorized_elementwise_kernel<4, at::native::AddFunctor, at::detail::Array >(int, at::native::AddFunctor, at::detail::Array)", "pid": 0, "tid": "stream 7", - "ts": 1621401187230162, "dur": 1, - "args": { - "queued": 0, "device": 0, "context": 1, - "stream": 7, "correlation": 467, "external id": 98, - "registers per thread": 20, - "shared memory": 0, - "blocks per SM": 0.0125, - "warps per SM": 0.025, - "grid": [1, 1, 1], - "block": [64, 1, 1], - "theoretical occupancy %": 0 - } - }, - { - "ph": "f", "id": 467, "pid": 0, "tid": "stream 7", "ts": 1621401187230162, - "cat": "async", "name": "launch", "bp": "e" - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 24572, "tid": "24610", - "ts": 1621401187230133, "dur": 29, - "args": { - "cbid": 211, "correlation": 467, - "external id": 98, "external ts": 1621401187230059 - } - }, - { - "ph": "s", "id": 467, "pid": 24572, "tid": 24610, "ts": 1621401187230133, - "cat": "async", "name": "launch" - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFlags", "pid": 24572, "tid": "24610", - "ts": 1621401187231063, "dur": 4, - "args": { - 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"ts": 1621401187231073, "dur": 27, - "args": { - "cbid": 211, "correlation": 482, - "external id": 107, "external ts": 1621401187230889 - } - }, - { - "ph": "s", "id": 482, "pid": 24572, "tid": 24610, "ts": 1621401187231073, - "cat": "async", "name": "launch" - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFlags", "pid": 24572, "tid": "24610", - "ts": 1621401187231658, "dur": 3, - "args": { - "cbid": 251, "correlation": 491, - "external id": 116, "external ts": 1621401187231491 - } - }, - { - "ph": "X", "cat": "Kernel", - "name": "void gemmSN_NN_kernel, cublasGemvTensorStridedBatched >(cublasGemmSmallNParams, cublasGemvTensorStridedBatched, float>)", "pid": 0, "tid": "stream 7", - "ts": 1621401187231692, "dur": 2, - "args": { - "queued": 0, "device": 0, "context": 1, - "stream": 7, "correlation": 492, "external id": 116, - "registers per thread": 64, - "shared memory": 12288, - "blocks per SM": 0.05, - "warps per SM": 0.4, - "grid": [1, 4, 1], - "block": [256, 1, 1], - "theoretical occupancy %": 1 - } - }, - { - "ph": "f", "id": 492, "pid": 0, "tid": "stream 7", "ts": 1621401187231692, - "cat": "async", "name": "launch", "bp": "e" - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 24572, "tid": "24610", - "ts": 1621401187231665, "dur": 27, - "args": { - "cbid": 211, "correlation": 492, - "external id": 116, "external ts": 1621401187231491 - } - }, - { - "ph": "s", "id": 492, "pid": 24572, "tid": 24610, "ts": 1621401187231665, - "cat": "async", "name": "launch" - }, - { - "ph": "X", "cat": "Kernel", - "name": "void at::native::vectorized_elementwise_kernel<4, at::native::AddFunctor, at::detail::Array >(int, at::native::AddFunctor, at::detail::Array)", "pid": 0, "tid": "stream 7", - "ts": 1621401187232603, "dur": 1, - "args": { - "queued": 0, "device": 0, "context": 1, - "stream": 7, "correlation": 503, "external id": 126, - "registers per thread": 20, - "shared memory": 0, - "blocks per SM": 0.0125, - "warps per SM": 0.025, - "grid": [1, 1, 1], - "block": [64, 1, 1], - "theoretical occupancy %": 0 - } - }, - { - "ph": "f", "id": 503, "pid": 0, "tid": "stream 7", "ts": 1621401187232603, - "cat": "async", "name": "launch", "bp": "e" - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 24572, "tid": "24610", - "ts": 1621401187232583, "dur": 19, - "args": { - "cbid": 211, "correlation": 503, - "external id": 126, "external ts": 1621401187232535 - } - }, - { - "ph": "s", "id": 503, "pid": 24572, "tid": 24610, "ts": 1621401187232583, - "cat": "async", "name": "launch" - }, - { - "ph": "X", "cat": "Kernel", - "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", "pid": 0, "tid": "stream 7", - "ts": 1621401187232921, "dur": 1, - "args": { - "queued": 0, "device": 0, "context": 1, - "stream": 7, "correlation": 513, "external id": 130, - "registers per thread": 16, - "shared memory": 0, - "blocks per SM": 0.0125, - "warps per SM": 0.025, - "grid": [1, 1, 1], - "block": [64, 1, 1], - "theoretical occupancy %": 0 - } - }, - { - "ph": "f", "id": 513, "pid": 0, "tid": "stream 7", "ts": 1621401187232921, - "cat": "async", "name": "launch", "bp": "e" - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 24572, "tid": "24610", - "ts": 1621401187232901, "dur": 19, - "args": { - "cbid": 211, "correlation": 513, - "external id": 130, "external ts": 1621401187232866 - } - }, - { - "ph": "s", "id": 513, "pid": 24572, "tid": 24610, "ts": 1621401187232901, - "cat": "async", "name": "launch" - }, - { - "ph": "X", "cat": "Kernel", - "name": "void at::native::vectorized_elementwise_kernel<4, at::native::BUnaryFunctor >, at::detail::Array >(int, at::native::BUnaryFunctor >, at::detail::Array)", "pid": 0, "tid": "stream 7", - "ts": 1621401187233342, "dur": 1, - "args": { - "queued": 0, "device": 0, "context": 1, - "stream": 7, "correlation": 526, "external id": 133, - "registers per thread": 16, - "shared memory": 0, - "blocks per SM": 0.0125, - "warps per SM": 0.025, - "grid": [1, 1, 1], - "block": [64, 1, 1], - "theoretical occupancy %": 0 - } - }, - { - "ph": "f", "id": 526, "pid": 0, "tid": "stream 7", "ts": 1621401187233342, - "cat": "async", "name": "launch", "bp": "e" - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 24572, "tid": "24610", - "ts": 1621401187233323, "dur": 18, - "args": { - "cbid": 211, "correlation": 526, - "external id": 133, "external ts": 1621401187233168 - } - }, - { - "ph": "s", "id": 526, "pid": 24572, "tid": 24610, "ts": 1621401187233323, - "cat": "async", "name": "launch" - }, - { - "ph": "X", "cat": "Kernel", - "name": "void at::native::unrolled_elementwise_kernel, OffsetCalculator<3, unsigned int>, at::detail::Array<1, unsigned int>, at::native::memory::LoadWithoutCast, OffsetCalculator::StoreWithoutCast>(int, at::native::(anonymous namespace)::where_kernel_impl(at::TensorIterator&, c10::ScalarType)::{lambda()#1}::operator()() const::{lambda()#8}::operator()() const::{lambda(bool, float, float)#1}, at::detail::Array, OffsetCalculator<3, unsigned int>, at::detail::Array<1, unsigned int>, at::native::memory::LoadWithoutCast, OffsetCalculator::StoreWithoutCast)", "pid": 0, "tid": "stream 7", - "ts": 1621401187233770, "dur": 2, - "args": { - "queued": 0, "device": 0, "context": 1, - "stream": 7, "correlation": 535, "external id": 144, - "registers per thread": 26, - "shared memory": 0, - "blocks per SM": 0.0125, - "warps per SM": 0.025, - "grid": [1, 1, 1], - "block": [64, 1, 1], - "theoretical occupancy %": 0 - } - }, - { - "ph": "f", "id": 535, "pid": 0, "tid": "stream 7", "ts": 1621401187233770, - "cat": "async", "name": "launch", "bp": "e" - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 24572, "tid": "24610", - "ts": 1621401187233751, "dur": 19, - "args": { - "cbid": 211, "correlation": 535, - "external id": 144, "external ts": 1621401187233620 - } - }, - { - "ph": "s", "id": 535, "pid": 24572, "tid": 24610, "ts": 1621401187233751, - "cat": "async", "name": "launch" - }, - { - "ph": "X", "cat": "Kernel", - "name": "void at::native::reduce_kernel<512, 1, at::native::ReduceOp::operator()(at::TensorIterator&)::{lambda(float, float)#1}>, unsigned int, float, 4> >(at::native::ReduceOp::operator()(at::TensorIterator&)::{lambda(float, float)#1}>, unsigned int, float, 4>)", "pid": 0, "tid": "stream 7", - "ts": 1621401187234156, "dur": 3, - "args": { - "queued": 0, "device": 0, "context": 1, - "stream": 7, "correlation": 548, "external id": 147, - "registers per thread": 32, - "shared memory": 16, - "blocks per SM": 0.0125, - "warps per SM": 0.025, - "grid": [1, 1, 1], - "block": [4, 16, 1], - "theoretical occupancy %": 0 - } - }, - { - "ph": "f", "id": 548, "pid": 0, "tid": "stream 7", "ts": 1621401187234156, - "cat": "async", "name": "launch", "bp": "e" - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 24572, "tid": "24610", - "ts": 1621401187234135, "dur": 19, - "args": { - "cbid": 211, "correlation": 548, - "external id": 147, "external ts": 1621401187233990 - } - }, - { - "ph": "s", "id": 548, "pid": 24572, "tid": 24610, "ts": 1621401187234135, - "cat": "async", "name": "launch" - }, - { - "ph": "X", "cat": "Kernel", - "name": "void at::native::vectorized_elementwise_kernel<4, at::native::AddFunctor, at::detail::Array >(int, at::native::AddFunctor, at::detail::Array)", "pid": 0, "tid": "stream 7", - "ts": 1621401187234445, "dur": 1, - "args": { - "queued": 0, "device": 0, "context": 1, - "stream": 7, "correlation": 555, "external id": 151, - "registers per thread": 20, - "shared memory": 0, - "blocks per SM": 0.0125, - "warps per SM": 0.025, - "grid": [1, 1, 1], - "block": [64, 1, 1], - "theoretical occupancy %": 0 - } - }, - { - "ph": "f", "id": 555, "pid": 0, "tid": "stream 7", "ts": 1621401187234445, - "cat": "async", "name": "launch", "bp": "e" - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 24572, "tid": "24610", - "ts": 1621401187234425, "dur": 19, - "args": { - "cbid": 211, "correlation": 555, - "external id": 151, "external ts": 1621401187234378 - } - }, - { - "ph": "s", "id": 555, "pid": 24572, "tid": 24610, "ts": 1621401187234425, - "cat": "async", "name": "launch" - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFlags", "pid": 24572, "tid": "24610", - "ts": 1621401187235000, "dur": 2, - "args": { - "cbid": 251, "correlation": 568, - "external id": 160, "external ts": 1621401187234890 - } - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFlags", "pid": 24572, "tid": "24610", - "ts": 1621401187235004, "dur": 0, - "args": { - "cbid": 251, "correlation": 569, - "external id": 160, "external ts": 1621401187234890 - } - }, - { - "ph": "X", "cat": "Kernel", - "name": "volta_sgemm_128x32_nt", "pid": 0, "tid": "stream 7", - "ts": 1621401187235025, "dur": 3, - "args": { - "queued": 0, "device": 0, "context": 1, - "stream": 7, "correlation": 570, "external id": 160, - "registers per thread": 55, - "shared memory": 16384, - "blocks per SM": 0.0125, - "warps per SM": 0.1, - "grid": [1, 1, 1], - "block": [256, 1, 1], - "theoretical occupancy %": 0 - } - }, - { - "ph": "f", "id": 570, "pid": 0, "tid": "stream 7", "ts": 1621401187235025, - "cat": "async", "name": "launch", "bp": "e" - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 24572, "tid": "24610", - "ts": 1621401187235006, "dur": 17, - "args": { - "cbid": 211, "correlation": 570, - "external id": 160, "external ts": 1621401187234890 - } - }, - { - "ph": "s", "id": 570, "pid": 24572, "tid": 24610, "ts": 1621401187235006, - "cat": "async", "name": "launch" - }, - { - "ph": "X", "cat": "Kernel", - "name": "void at::native::vectorized_elementwise_kernel<4, at::native::AddFunctor, at::detail::Array >(int, at::native::AddFunctor, at::detail::Array)", "pid": 0, "tid": "stream 7", - "ts": 1621401187235555, "dur": 1, - "args": { - "queued": 0, "device": 0, "context": 1, - "stream": 7, "correlation": 579, "external id": 170, - "registers per thread": 20, - "shared memory": 0, - "blocks per SM": 0.0125, - "warps per SM": 0.025, - "grid": [1, 1, 1], - "block": [64, 1, 1], - "theoretical occupancy %": 0 - } - }, - { - "ph": "f", "id": 579, "pid": 0, "tid": "stream 7", "ts": 1621401187235555, - "cat": "async", "name": "launch", "bp": "e" - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 24572, "tid": "24610", - 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"correlation": 440, "external id": 91, - "registers per thread": 16, - "shared memory": 0, - "blocks per SM": 0.0125, - "warps per SM": 0.025, - "grid": [1, 1, 1], - "block": [64, 1, 1], - "theoretical occupancy %": 0 - } - }, - { - "ph": "f", "id": 440, "pid": 0, "tid": "stream 7", "ts": 1621401187228962, - "cat": "async", "name": "launch", "bp": "e" - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 24572, "tid": "24610", - "ts": 1621401187228932, "dur": 30, - "args": { - "cbid": 211, "correlation": 440, - "external id": 91, "external ts": 1621401187228885 - } - }, - { - "ph": "s", "id": 440, "pid": 24572, "tid": 24610, "ts": 1621401187228932, - "cat": "async", "name": "launch" - }, - { - "ph": "X", "cat": "Kernel", - "name": "void at::native::unrolled_elementwise_kernel, OffsetCalculator<3, unsigned int>, at::detail::Array<1, unsigned int>, at::native::memory::LoadWithoutCast, OffsetCalculator::StoreWithoutCast>(int, at::native::mse_backward_cuda_kernel(at::TensorIterator&, c10::Scalar const&)::{lambda()#1}::operator()() const::{lambda()#4}::operator()() const::{lambda(float, float, float)#1}, at::detail::Array, OffsetCalculator<3, unsigned int>, at::detail::Array<1, unsigned int>, at::native::memory::LoadWithoutCast, OffsetCalculator::StoreWithoutCast)", "pid": 0, "tid": "stream 7", - "ts": 1621401187229153, "dur": 2, - "args": { - "queued": 0, "device": 0, "context": 1, - "stream": 7, "correlation": 446, "external id": 92, - "registers per thread": 28, - "shared memory": 0, - "blocks per SM": 0.0125, - "warps per SM": 0.025, - "grid": [1, 1, 1], - "block": [64, 1, 1], - "theoretical occupancy %": 0 - } - }, - { - "ph": "f", "id": 446, "pid": 0, "tid": "stream 7", "ts": 1621401187229153, - "cat": "async", "name": "launch", "bp": "e" - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 24572, "tid": "24610", - "ts": 1621401187229127, "dur": 26, - "args": { - "cbid": 211, 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{ - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 24572, "tid": "24610", - "ts": 1621401187229681, "dur": 30, - "args": { - "cbid": 211, "correlation": 460, - "external id": 94, "external ts": 1621401187229459 - } - }, - { - "ph": "s", "id": 460, "pid": 24572, "tid": 24610, "ts": 1621401187229681, - "cat": "async", "name": "launch" - }, - { - "ph": "X", "cat": "Kernel", - "name": "void at::native::vectorized_elementwise_kernel<4, at::native::AddFunctor, at::detail::Array >(int, at::native::AddFunctor, at::detail::Array)", "pid": 0, "tid": "stream 7", - "ts": 1621401187230162, "dur": 1, - "args": { - "queued": 0, "device": 0, "context": 1, - "stream": 7, "correlation": 467, "external id": 98, - "registers per thread": 20, - "shared memory": 0, - "blocks per SM": 0.0125, - "warps per SM": 0.025, - "grid": [1, 1, 1], - "block": [64, 1, 1], - "theoretical occupancy %": 0 - } - }, - { - "ph": "f", "id": 467, "pid": 0, "tid": "stream 7", "ts": 1621401187230162, - "cat": 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"name": "volta_sgemm_128x32_nt", "pid": 0, "tid": "stream 7", - "ts": 1621401187231100, "dur": 3, - "args": { - "queued": 0, "device": 0, "context": 1, - "stream": 7, "correlation": 482, "external id": 107, - "registers per thread": 55, - "shared memory": 16384, - "blocks per SM": 0.0125, - "warps per SM": 0.1, - "grid": [1, 1, 1], - "block": [256, 1, 1], - "theoretical occupancy %": 0 - } - }, - { - "ph": "f", "id": 482, "pid": 0, "tid": "stream 7", "ts": 1621401187231100, - "cat": "async", "name": "launch", "bp": "e" - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 24572, "tid": "24610", - "ts": 1621401187231073, "dur": 27, - "args": { - "cbid": 211, "correlation": 482, - "external id": 107, "external ts": 1621401187230889 - } - }, - { - "ph": "s", "id": 482, "pid": 24572, "tid": 24610, "ts": 1621401187231073, - "cat": "async", "name": "launch" - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFlags", "pid": 24572, "tid": "24610", - "ts": 1621401187231658, "dur": 3, - "args": { - "cbid": 251, "correlation": 491, - "external id": 116, "external ts": 1621401187231491 - } - }, - { - "ph": "X", "cat": "Kernel", - "name": "void gemmSN_NN_kernel, cublasGemvTensorStridedBatched >(cublasGemmSmallNParams, cublasGemvTensorStridedBatched, float>)", "pid": 0, "tid": "stream 7", - "ts": 1621401187231692, "dur": 2, - "args": { - "queued": 0, "device": 0, "context": 1, - "stream": 7, "correlation": 492, "external id": 116, - "registers per thread": 64, - "shared memory": 12288, - "blocks per SM": 0.05, - "warps per SM": 0.4, - "grid": [1, 4, 1], - "block": [256, 1, 1], - "theoretical occupancy %": 1 - } - }, - { - "ph": "f", "id": 492, "pid": 0, "tid": "stream 7", "ts": 1621401187231692, - "cat": "async", "name": "launch", "bp": "e" - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 24572, "tid": "24610", - "ts": 1621401187231665, "dur": 27, - "args": { - "cbid": 211, "correlation": 492, - "external id": 116, "external ts": 1621401187231491 - } - }, - { - "ph": "s", "id": 492, "pid": 24572, "tid": 24610, "ts": 1621401187231665, - "cat": "async", "name": "launch" - }, - { - "ph": "X", "cat": "Kernel", - "name": "void at::native::vectorized_elementwise_kernel<4, at::native::AddFunctor, at::detail::Array >(int, at::native::AddFunctor, at::detail::Array)", "pid": 0, "tid": "stream 7", - "ts": 1621401187232603, "dur": 1, - "args": { - "queued": 0, "device": 0, "context": 1, - "stream": 7, "correlation": 503, "external id": 126, - "registers per thread": 20, - "shared memory": 0, - "blocks per SM": 0.0125, - "warps per SM": 0.025, - "grid": [1, 1, 1], - "block": [64, 1, 1], - "theoretical occupancy %": 0 - } - }, - { - "ph": "f", "id": 503, "pid": 0, "tid": "stream 7", "ts": 1621401187232603, - "cat": "async", "name": "launch", "bp": "e" - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 24572, "tid": "24610", - "ts": 1621401187232583, "dur": 19, - "args": { - "cbid": 211, "correlation": 503, - "external id": 126, "external ts": 1621401187232535 - } - }, - { - "ph": "s", "id": 503, "pid": 24572, "tid": 24610, "ts": 1621401187232583, - "cat": "async", "name": "launch" - }, - { - "ph": "X", "cat": "Kernel", - "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", "pid": 0, "tid": "stream 7", - "ts": 1621401187232921, "dur": 1, - "args": { - "queued": 0, "device": 0, "context": 1, - "stream": 7, "correlation": 513, "external id": 130, - "registers per thread": 16, - "shared memory": 0, - "blocks per SM": 0.0125, - "warps per SM": 0.025, - "grid": [1, 1, 1], - "block": [64, 1, 1], - "theoretical occupancy %": 0 - } - }, - { - "ph": "f", "id": 513, "pid": 0, "tid": "stream 7", "ts": 1621401187232921, - "cat": "async", "name": "launch", "bp": "e" - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 24572, "tid": "24610", - "ts": 1621401187232901, "dur": 19, - "args": { - "cbid": 211, "correlation": 513, - "external id": 130, "external ts": 1621401187232866 - } - }, - { - "ph": "s", "id": 513, "pid": 24572, "tid": 24610, "ts": 1621401187232901, - "cat": "async", "name": "launch" - }, - { - "ph": "X", "cat": "Kernel", - "name": "void at::native::vectorized_elementwise_kernel<4, at::native::BUnaryFunctor >, at::detail::Array >(int, at::native::BUnaryFunctor >, at::detail::Array)", "pid": 0, "tid": "stream 7", - "ts": 1621401187233342, "dur": 1, - "args": { - "queued": 0, "device": 0, "context": 1, - "stream": 7, "correlation": 526, "external id": 133, - "registers per thread": 16, - "shared memory": 0, - "blocks per SM": 0.0125, - "warps per SM": 0.025, - "grid": [1, 1, 1], - "block": [64, 1, 1], - "theoretical occupancy %": 0 - } - }, - { - "ph": "f", "id": 526, "pid": 0, "tid": "stream 7", "ts": 1621401187233342, - "cat": "async", "name": "launch", "bp": "e" - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 24572, "tid": "24610", - "ts": 1621401187233323, "dur": 18, - "args": { - "cbid": 211, "correlation": 526, - "external id": 133, "external ts": 1621401187233168 - } - }, - { - "ph": "s", "id": 526, "pid": 24572, "tid": 24610, "ts": 1621401187233323, - "cat": "async", "name": "launch" - }, - { - "ph": "X", "cat": "Kernel", - "name": "void at::native::unrolled_elementwise_kernel, OffsetCalculator<3, unsigned int>, at::detail::Array<1, unsigned int>, at::native::memory::LoadWithoutCast, OffsetCalculator::StoreWithoutCast>(int, at::native::(anonymous namespace)::where_kernel_impl(at::TensorIterator&, c10::ScalarType)::{lambda()#1}::operator()() const::{lambda()#8}::operator()() const::{lambda(bool, float, float)#1}, at::detail::Array, OffsetCalculator<3, unsigned int>, at::detail::Array<1, unsigned int>, at::native::memory::LoadWithoutCast, OffsetCalculator::StoreWithoutCast)", "pid": 0, "tid": "stream 7", - 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int, float, 4> >(at::native::ReduceOp::operator()(at::TensorIterator&)::{lambda(float, float)#1}>, unsigned int, float, 4>)", "pid": 0, "tid": "stream 7", - "ts": 1621401187234156, "dur": 3, - "args": { - "queued": 0, "device": 0, "context": 1, - "stream": 7, "correlation": 548, "external id": 147, - "registers per thread": 32, - "shared memory": 16, - "blocks per SM": 0.0125, - "warps per SM": 0.025, - "grid": [1, 1, 1], - "block": [4, 16, 1], - "theoretical occupancy %": 0 - } - }, - { - "ph": "f", "id": 548, "pid": 0, "tid": "stream 7", "ts": 1621401187234156, - "cat": "async", "name": "launch", "bp": "e" - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 24572, "tid": "24610", - "ts": 1621401187234135, "dur": 19, - "args": { - "cbid": 211, "correlation": 548, - "external id": 147, "external ts": 1621401187233990 - } - }, - { - "ph": "s", "id": 548, "pid": 24572, "tid": 24610, "ts": 1621401187234135, - "cat": "async", "name": "launch" - }, - { - "ph": "X", 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"calls": 294, "device_self_duration": 0, "device_total_duration": 27298, "host_self_duration": 2174, "host_total_duration": 11656, "tc_eligible": "No", "tc_self_ratio": 0, "tc_total_ratio": 0.0, "has_call_stack": false}, {"name": "AddBackward0", "calls": 96, "device_self_duration": 0, "device_total_duration": 0, "host_self_duration": 175, "host_total_duration": 175, "tc_eligible": "No", "tc_self_ratio": 0, "tc_total_ratio": 0, "has_call_stack": false}, {"name": "CudnnBatchNormBackward", "calls": 318, "device_self_duration": 0, "device_total_duration": 61939, "host_self_duration": 4148, "host_total_duration": 34608, "tc_eligible": "No", "tc_self_ratio": 0, "tc_total_ratio": 0.0, "has_call_stack": false}, {"name": "aten::cudnn_convolution_backward", "calls": 318, "device_self_duration": 0, "device_total_duration": 273428, "host_self_duration": 6081, "host_total_duration": 87772, "tc_eligible": "Yes", "tc_self_ratio": 0, "tc_total_ratio": 0.0, "has_call_stack": false}, {"name": "CudnnConvolutionBackward", "calls": 318, "device_self_duration": 0, "device_total_duration": 273428, "host_self_duration": 3217, "host_total_duration": 90989, "tc_eligible": "Yes", "tc_self_ratio": 0, "tc_total_ratio": 0.0, "has_call_stack": false}, {"name": "MaxPool2DWithIndicesBackward", "calls": 6, "device_self_duration": 0, "device_total_duration": 4677, "host_self_duration": 73, "host_total_duration": 484, "tc_eligible": "No", "tc_self_ratio": 0, "tc_total_ratio": 0.0, "has_call_stack": false}]} -{"metadata": {"sort": "Total Duration (us)"}, "data": {"columns": [{"type": "string", "name": "Name"}, {"type": "string", "name": "Tensor Cores Used", "tooltip": "Whether this kernel uses Tensor Cores."}, {"type": "number", "name": "Calls"}, {"type": "number", "name": "Total Duration (us)"}, {"type": "number", "name": "Mean Duration (us)"}, {"type": "number", "name": "Max Duration (us)"}, {"type": "number", "name": "Min Duration (us)"}, {"type": "number", "name": "Mean Blocks Per SM", "tooltip": "Blocks Per SM = blocks of this kernel / SM number of this GPU.\nIf this number is less than 1, it indicates the GPU multiprocessors are not fully utilized.\n\"Mean Blocks per SM\" is the weighted average of all calls of this kernel, using each call's execution duration as weight."}, {"type": "number", "name": "Mean Est. Achieved Occupancy (%)", "tooltip": "Est. Achieved Occupancy:\nFor most cases such as memory bandwidth bounded kernels, the higher the better. Occupancy is the ratio of active warps on an SM to the maximum number of active warps supported by the SM. The theoretical occupancy of a kernel is upper limit occupancy of this kernel, limited by multiple factors such as kernel shape, kernel used resource, and the GPU compute capability.\nEst. Achieved Occupancy of a kernel, OCC_K = min(threads of the kernel / SM number / max threads per SM, theoretical occupancy of the kernel). This \"Mean\" number is the weighted average of all calls' OCC_K of the kernel, using each call's execution duration as weight. It shows fine-grained low-level GPU utilization."}], "rows": [["void cudnn::detail::dgrad_engine(int, int, int, float const*, int, float const*, int, float*, kernel_grad_params, unsigned long long, int, unsigned long long, int, float, int, int, int)", "No", 162, 80756, 498, 1017, 323, 42.25, 29.97], ["void cudnn::cnn::wgrad_alg0_engine(int, int, int, float const*, int, float*, float const*, kernel_grad_params, unsigned long long, int, float, int, int, int, int)", "No", 156, 66472, 426, 745, 345, 9.78, 38.0], ["void cudnn::bn_bw_1C11_kernel_new(float, float, float, float, cudnnTensorStruct, float const*, cudnnTensorStruct, float const*, cudnnTensorStruct, float*, float const*, float*, float*, float const*, float const*, float)", "No", 264, 59642, 226, 915, 45, 4.34, 67.98], ["void at::native::vectorized_elementwise_kernel<4, at::native::AddFunctor, at::detail::Array >(int, at::native::AddFunctor, at::detail::Array)", "No", 3090, 39814, 13, 378, 1, 641.54, 92.32], ["void implicit_convolve_sgemm(int, int, int, float const*, int, float*, float const*, kernel_conv_params, unsigned long long, int, float, float, int, float const*, float const*, bool, int, int)", "No", 90, 36957, 411, 748, 347, 12.34, 50.0], ["void at::native::vectorized_elementwise_kernel<4, at::native::threshold_kernel_impl(at::TensorIteratorBase&, float, float)::{lambda(float, float)#1}, at::detail::Array >(int, at::native::threshold_kernel_impl(at::TensorIteratorBase&, float, float)::{lambda(float, float)#1}, at::detail::Array)", "No", 294, 27298, 93, 377, 13, 653.06, 100.0], ["void cudnn::bn_fw_tr_1C11_kernel_NCHW(cudnnTensorStruct, float const*, cudnnTensorStruct, float*, float const*, float const*, float, float, float*, float*, float*, float*, float, float)", "No", 150, 27060, 180, 452, 53, 3.12, 64.06], ["void implicit_convolve_sgemm(int, int, int, float const*, int, float*, float const*, kernel_conv_params, unsigned long long, int, float, float, int, float const*, float const*, bool, int, int)", "No", 60, 25782, 430, 729, 352, 3.9, 42.09], ["volta_sgemm_64x64_nt", "No", 102, 21084, 207, 279, 184, 10.24, 19.38], ["volta_scudnn_128x128_stridedB_splitK_small_nn_v1", "No", 48, 20448, 426, 676, 307, 6.83, 25.0], ["void at::native::vectorized_elementwise_kernel<4, at::native::(anonymous namespace)::clamp_min_scalar_kernel_impl(at::TensorIterator&, c10::Scalar)::{lambda()#1}::operator()() const::{lambda()#8}::operator()() const::{lambda(float)#1}, at::detail::Array >(int, at::native::(anonymous namespace)::clamp_min_scalar_kernel_impl(at::TensorIterator&, c10::Scalar)::{lambda()#1}::operator()() const::{lambda()#8}::operator()() const::{lambda(float)#1}, at::detail::Array)", "No", 294, 17860, 61, 252, 5, 666.65, 100.0], ["volta_scudnn_winograd_128x128_ldg1_ldg4_relu_tile148t_nt_v1", "No", 36, 12704, 353, 362, 344, 22.4, 25.0], ["volta_scudnn_128x64_stridedB_interior_nn_v1", "No", 30, 9597, 320, 510, 252, 12.9, 19.0], ["volta_sgemm_128x32_nt", "No", 24, 8629, 360, 477, 18, 0.97, 11.51], ["volta_sgemm_64x64_nn", "No", 42, 8551, 204, 217, 195, 12.34, 24.14], ["volta_scudnn_128x64_relu_interior_nn_v1", "No", 30, 8022, 267, 316, 94, 37.1, 25.0], ["volta_scudnn_128x64_stridedB_splitK_xregs_large_nn_v1", "No", 12, 7817, 651, 671, 635, 15.96, 19.0], ["void cudnn::bn_fw_tr_1C11_singleread(cudnnTensorStruct, float const*, cudnnTensorStruct, float*, float const*, float const*, float, float, float*, float*, float*, float*, float, float, cudnn::reduced_divisor, int, cudnn::reduced_divisor, cudnn::bnFwPersistentState*, int, float, float, float, int, float, float, cudnnStatus_t*, bool)", "No", 168, 7185, 43, 89, 13, 12.57, 75.0], ["void cudnn::cnn::wgrad_alg0_engine(int, int, int, float const*, int, float*, float const*, kernel_grad_params, unsigned long long, int, float, int, int, int, int)", "No", 12, 7068, 589, 987, 193, 85.34, 37.5], ["void cudnn::winograd_nonfused::winogradForwardOutput4x4(cudnn::winograd_nonfused::WinogradOutputParams)", "No", 120, 5369, 45, 73, 19, 10.0, 50.0], ["void implicit_convolve_sgemm(int, int, int, float const*, int, float*, float const*, kernel_conv_params, unsigned long long, int, float, float, int, float const*, float const*, bool, int, int)", "No", 12, 5219, 435, 437, 432, 9.8, 31.0], ["void explicit_convolve_sgemm(int, int, int, float const*, int, float const*, int, float*, kernel_conv_params, unsigned long long, int, unsigned long long, int, float, float, int, float const*, float const*)", "No", 6, 4759, 793, 796, 790, 9.8, 31.0], ["void cudnn::winograd_nonfused::winogradForwardData4x4(cudnn::winograd_nonfused::WinogradDataParams)", "No", 120, 4710, 39, 66, 17, 10.11, 50.0], ["volta_scudnn_128x128_stridedB_interior_nn_v1", "No", 18, 4693, 261, 281, 252, 9.8, 25.0], ["void cudnn::winograd_nonfused::winogradWgradData4x4(cudnn::winograd_nonfused::WinogradDataParams)", "No", 78, 4692, 60, 126, 20, 15.46, 38.0], ["void cudnn::ops::scalePackedTensor_kernel(cudnnTensor4dStruct, float*, float)", "No", 162, 4631, 29, 143, 5, 496.39, 100.0], ["void cudnn::winograd_nonfused::winogradWgradDelta4x4(cudnn::winograd_nonfused::WinogradDeltaParams)", "No", 78, 4573, 59, 125, 17, 15.69, 50.0], ["void cudnn::cnn::wgrad_alg0_engine(int, int, int, float const*, int, float*, float const*, kernel_grad_params, unsigned long long, int, float, int, int, int, int)", "No", 6, 4065, 678, 692, 652, 6.4, 25.0], ["void implicit_convolve_sgemm(int, int, int, float const*, int, float*, float const*, kernel_conv_params, unsigned long long, int, float, float, int, float const*, float const*, bool, int, int)", "No", 6, 3917, 653, 686, 595, 4.9, 25.0], ["void at::native::(anonymous namespace)::max_pool_backward_nchw(int, float const*, long const*, int, int, int, int, int, int, int, int, int, int, int, int, int, int, float*)", "No", 6, 3822, 637, 638, 636, 1254.4, 100.0], ["volta_scudnn_128x128_stridedB_splitK_medium_nn_v1", "No", 6, 3720, 620, 623, 614, 5.6, 25.0], ["volta_scudnn_128x64_relu_medium_nn_v1", "No", 6, 3627, 604, 606, 603, 39.2, 25.0], ["volta_scudnn_128x128_stridedB_medium_nn_v1", "No", 12, 3501, 292, 296, 286, 19.6, 25.0], ["volta_scudnn_128x32_sliced1x4_ldg4_relu_exp_medium_nhwc_tn_v1", "No", 6, 3270, 545, 627, 526, 4.9, 25.0], ["volta_scudnn_128x64_relu_small_nn_v1", "No", 12, 3265, 272, 279, 254, 9.8, 25.0], ["volta_scudnn_128x64_relu_xregs_large_nn_v1", "No", 6, 3200, 533, 607, 516, 4.9, 19.0], ["volta_sgemm_32x128_nn", "No", 18, 3053, 170, 171, 168, 22.05, 50.0], ["volta_scudnn_128x128_relu_interior_nn_v1", "No", 6, 3010, 502, 508, 495, 9.8, 25.0], ["volta_scudnn_128x128_stridedB_small_nn_v1", "No", 6, 2995, 499, 505, 493, 19.6, 25.0], ["volta_sgemm_32x128_nt", "No", 18, 2843, 158, 159, 156, 22.05, 50.0], ["void cudnn::winograd_nonfused::winogradForwardFilter4x4(cudnn::winograd_nonfused::WinogradFilterParams)", "No", 120, 2662, 22, 67, 5, 8.68, 73.22], ["void at::native::vectorized_elementwise_kernel<4, at::native::MulScalarFunctor, at::detail::Array >(int, at::native::MulScalarFunctor, at::detail::Array)", "No", 966, 2409, 2, 25, 1, 43.72, 58.39], ["void cudnn::bn_bw_1C11_singleread(float, float, float, float, cudnnTensorStruct, float const*, cudnnTensorStruct, float const*, cudnnTensorStruct, float*, float const*, float*, float*, float const*, float const*, float, cudnn::reduced_divisor, int, cudnn::reduced_divisor, cudnn::bnBwPersistentState*, int, float, float, float, int, float, cudnnStatus_t*, bool)", "No", 54, 2297, 43, 73, 18, 20.81, 75.0], ["void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", "No", 978, 1887, 2, 143, 0, 599.07, 86.78], ["void cudnn::winograd_nonfused::winogradWgradOutput4x4(cudnn::winograd_nonfused::WinogradWgradOutputParams)", "No", 78, 1504, 19, 69, 5, 8.06, 41.33], ["void at::native::(anonymous namespace)::max_pool_forward_nchw(int, float const*, int, int, int, int, int, int, int, int, int, int, int, int, int, int, float*, long*)", "No", 6, 1420, 237, 239, 234, 313.6, 100.0], ["void cudnn::cnn::im2col4d_kernel(cudnn::cnn::im2col4d_params, cudnnConvolutionStruct, cudnnTensor4dStruct, float const*, float*)", "No", 6, 614, 102, 103, 101, 0.95, 24.0], ["volta_scudnn_128x64_stridedB_small_nn_v1", "No", 6, 584, 97, 100, 93, 9.8, 19.0], ["void nchwToNhwcKernel(int, int, int, int, float const*, float*, float, float)", "No", 12, 453, 38, 68, 9, 73.28, 100.0], ["cask_cudnn::computeOffsetsKernel(cask_cudnn::ComputeOffsetsParams)", "No", 138, 342, 2, 4, 1, 0.13, 1.73], ["void at::native::vectorized_elementwise_kernel<4, at::native::BUnaryFunctor >, at::detail::Array >(int, at::native::BUnaryFunctor >, at::detail::Array)", "No", 318, 322, 1, 2, 1, 0.01, 0.0], ["void at::native::reduce_kernel<512, 1, at::native::ReduceOp, unsigned int, float, 4> >(at::native::ReduceOp, unsigned int, float, 4>)", "No", 6, 212, 35, 36, 35, 51.2, 100.0], ["volta_sgemm_64x32_sliced1x4_nn", "No", 6, 150, 25, 26, 24, 2.0, 25.0], ["volta_sgemm_64x32_sliced1x4_tn", "No", 6, 149, 25, 26, 24, 1.0, 13.0], ["void at::native::unrolled_elementwise_kernel, at::detail::Array, OffsetCalculator<1, unsigned int>, OffsetCalculator<1, unsigned int>, at::native::memory::LoadWithoutCast, at::native::memory::StoreWithoutCast>(int, at::native::MulScalarFunctor, at::detail::Array, OffsetCalculator<1, unsigned int>, OffsetCalculator<1, unsigned int>, at::native::memory::LoadWithoutCast, at::native::memory::StoreWithoutCast)", "No", 6, 144, 24, 24, 24, 156.8, 100.0], ["void cudnn::winograd::generateWinogradTilesKernel<0, float, float>(cudnn::winograd::GenerateWinogradTilesParams)", "No", 36, 134, 4, 5, 2, 0.4, 3.0], ["void nhwcToNchwKernel(int, int, int, int, float const*, float*, float, float)", "No", 6, 105, 18, 18, 17, 22.4, 100.0], ["cask_cudnn::computeWgradSplitKOffsetsKernel(cask_cudnn::ComputeSplitKOffsetsParams)", "No", 66, 81, 1, 2, 1, 0.15, 1.68], ["cask_cudnn::computeWgradBOffsetsKernel(cask_cudnn::ComputeWgradBOffsetsParams)", "No", 66, 81, 1, 2, 1, 0.02, 0.0], ["cask_cudnn::computeBOffsetsKernel(cask_cudnn::ComputeBOffsetsParams)", "No", 72, 73, 1, 2, 1, 0.02, 0.0], ["void (anonymous namespace)::softmax_warp_backward(float*, float const*, float const*, int, int, int)", "No", 6, 53, 9, 9, 8, 0.1, 1.0], ["void at::native::reduce_kernel<128, 4, at::native::ReduceOp::operator()(at::TensorIterator&)::{lambda(float, float)#1}>, unsigned int, float, 4> >(at::native::ReduceOp::operator()(at::TensorIterator&)::{lambda(float, float)#1}>, unsigned int, float, 4>)", "No", 6, 44, 7, 8, 7, 0.03, 0.0], ["void (anonymous namespace)::softmax_warp_forward(float*, float const*, int, int, int)", "No", 6, 42, 7, 7, 7, 0.1, 1.0], ["void splitKreduce_kernel(cublasSplitKParams, float const*, float const*, float*, float const*, float const*, float const*)", "No", 12, 30, 2, 3, 2, 4.44, 28.0], ["void at::native::unrolled_elementwise_kernel, OffsetCalculator<1, unsigned int>, char*, at::native::memory::LoadWithoutCast, at::detail::Array::StoreWithoutCast>(int, at::native::copy_device_to_device(at::TensorIterator&, bool)::{lambda()#2}::operator()() const::{lambda()#8}::operator()() const::{lambda(float)#1}, at::detail::Array, OffsetCalculator<1, unsigned int>, char*, at::native::memory::LoadWithoutCast, at::detail::Array::StoreWithoutCast)", "No", 6, 30, 5, 5, 5, 1.56, 5.0], ["void cunn_ClassNLLCriterion_updateOutput_kernel(float*, float*, float*, long*, float*, int, int, int, int, long)", "No", 6, 18, 3, 3, 3, 0.01, 0.0], ["void cunn_ClassNLLCriterion_updateGradInput_kernel(float*, float*, long*, float*, float*, int, int, int, int, long)", "No", 6, 12, 2, 2, 2, 0.01, 0.0]]}} -{"total": {"columns": [{"type": "string", "name": "name"}, {"type": "number", "name": "value"}], "rows": [["void cudnn::detail::dgrad_engine(int, int, int, float const*, int, float const*, int, float*, kernel_grad_params, unsigned long long, int, unsigned long long, int, float, int, int, int)", 80756], ["void cudnn::cnn::wgrad_alg0_engine(int, int, int, float const*, int, float*, float const*, kernel_grad_params, unsigned long long, int, float, int, int, int, int)", 66472], ["void cudnn::bn_bw_1C11_kernel_new(float, float, float, float, cudnnTensorStruct, float const*, cudnnTensorStruct, float const*, cudnnTensorStruct, float*, float const*, float*, float*, float const*, float const*, float)", 59642], ["void at::native::vectorized_elementwise_kernel<4, at::native::AddFunctor, at::detail::Array >(int, at::native::AddFunctor, at::detail::Array)", 39814], ["void implicit_convolve_sgemm(int, int, int, float const*, int, float*, float const*, kernel_conv_params, unsigned long long, int, float, float, int, float const*, float const*, bool, int, int)", 36957], ["void at::native::vectorized_elementwise_kernel<4, at::native::threshold_kernel_impl(at::TensorIteratorBase&, float, float)::{lambda(float, float)#1}, at::detail::Array >(int, at::native::threshold_kernel_impl(at::TensorIteratorBase&, float, float)::{lambda(float, float)#1}, at::detail::Array)", 27298], ["void cudnn::bn_fw_tr_1C11_kernel_NCHW(cudnnTensorStruct, float const*, cudnnTensorStruct, float*, float const*, float const*, float, float, float*, float*, float*, float*, float, float)", 27060], ["void implicit_convolve_sgemm(int, int, int, float const*, int, float*, float const*, kernel_conv_params, unsigned long long, int, float, float, int, float const*, float const*, bool, int, int)", 25782], ["volta_sgemm_64x64_nt", 21084], ["volta_scudnn_128x128_stridedB_splitK_small_nn_v1", 20448], ["void at::native::vectorized_elementwise_kernel<4, at::native::(anonymous namespace)::clamp_min_scalar_kernel_impl(at::TensorIterator&, c10::Scalar)::{lambda()#1}::operator()() const::{lambda()#8}::operator()() const::{lambda(float)#1}, at::detail::Array >(int, at::native::(anonymous namespace)::clamp_min_scalar_kernel_impl(at::TensorIterator&, c10::Scalar)::{lambda()#1}::operator()() const::{lambda()#8}::operator()() const::{lambda(float)#1}, at::detail::Array)", 17860], ["volta_scudnn_winograd_128x128_ldg1_ldg4_relu_tile148t_nt_v1", 12704], ["volta_scudnn_128x64_stridedB_interior_nn_v1", 9597], ["volta_sgemm_128x32_nt", 8629], ["volta_sgemm_64x64_nn", 8551], ["volta_scudnn_128x64_relu_interior_nn_v1", 8022], ["volta_scudnn_128x64_stridedB_splitK_xregs_large_nn_v1", 7817], ["void cudnn::bn_fw_tr_1C11_singleread(cudnnTensorStruct, float const*, cudnnTensorStruct, float*, float const*, float const*, float, float, float*, float*, float*, float*, float, float, cudnn::reduced_divisor, int, cudnn::reduced_divisor, cudnn::bnFwPersistentState*, int, float, float, float, int, float, float, cudnnStatus_t*, bool)", 7185], ["void cudnn::cnn::wgrad_alg0_engine(int, int, int, float const*, int, float*, float const*, kernel_grad_params, unsigned long long, int, float, int, int, int, int)", 7068], ["void cudnn::winograd_nonfused::winogradForwardOutput4x4(cudnn::winograd_nonfused::WinogradOutputParams)", 5369], ["void implicit_convolve_sgemm(int, int, int, float const*, int, float*, float const*, kernel_conv_params, unsigned long long, int, float, float, int, float const*, float const*, bool, int, int)", 5219], ["void explicit_convolve_sgemm(int, int, int, float const*, int, float const*, int, float*, kernel_conv_params, unsigned long long, int, unsigned long long, int, float, float, int, float const*, float const*)", 4759], ["void cudnn::winograd_nonfused::winogradForwardData4x4(cudnn::winograd_nonfused::WinogradDataParams)", 4710], ["volta_scudnn_128x128_stridedB_interior_nn_v1", 4693], ["void cudnn::winograd_nonfused::winogradWgradData4x4(cudnn::winograd_nonfused::WinogradDataParams)", 4692], ["void cudnn::ops::scalePackedTensor_kernel(cudnnTensor4dStruct, float*, float)", 4631], ["void cudnn::winograd_nonfused::winogradWgradDelta4x4(cudnn::winograd_nonfused::WinogradDeltaParams)", 4573], ["void cudnn::cnn::wgrad_alg0_engine(int, int, int, float const*, int, float*, float const*, kernel_grad_params, unsigned long long, int, float, int, int, int, int)", 4065], ["void implicit_convolve_sgemm(int, int, int, float const*, int, float*, float const*, kernel_conv_params, unsigned long long, int, float, float, int, float const*, float const*, bool, int, int)", 3917], ["void at::native::(anonymous namespace)::max_pool_backward_nchw(int, float const*, long const*, int, int, int, int, int, int, int, int, int, int, int, int, int, int, float*)", 3822], ["volta_scudnn_128x128_stridedB_splitK_medium_nn_v1", 3720], ["volta_scudnn_128x64_relu_medium_nn_v1", 3627], ["volta_scudnn_128x128_stridedB_medium_nn_v1", 3501], ["volta_scudnn_128x32_sliced1x4_ldg4_relu_exp_medium_nhwc_tn_v1", 3270], ["volta_scudnn_128x64_relu_small_nn_v1", 3265], ["volta_scudnn_128x64_relu_xregs_large_nn_v1", 3200], ["volta_sgemm_32x128_nn", 3053], ["volta_scudnn_128x128_relu_interior_nn_v1", 3010], ["volta_scudnn_128x128_stridedB_small_nn_v1", 2995], ["volta_sgemm_32x128_nt", 2843], ["void cudnn::winograd_nonfused::winogradForwardFilter4x4(cudnn::winograd_nonfused::WinogradFilterParams)", 2662], ["void at::native::vectorized_elementwise_kernel<4, at::native::MulScalarFunctor, at::detail::Array >(int, at::native::MulScalarFunctor, at::detail::Array)", 2409], ["void cudnn::bn_bw_1C11_singleread(float, float, float, float, cudnnTensorStruct, float const*, cudnnTensorStruct, float const*, cudnnTensorStruct, float*, float const*, float*, float*, float const*, float const*, float, cudnn::reduced_divisor, int, cudnn::reduced_divisor, cudnn::bnBwPersistentState*, int, float, float, float, int, float, cudnnStatus_t*, bool)", 2297], ["void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", 1887], ["void cudnn::winograd_nonfused::winogradWgradOutput4x4(cudnn::winograd_nonfused::WinogradWgradOutputParams)", 1504], ["void at::native::(anonymous namespace)::max_pool_forward_nchw(int, float const*, int, int, int, int, int, int, int, int, int, int, int, int, int, int, float*, long*)", 1420], ["void cudnn::cnn::im2col4d_kernel(cudnn::cnn::im2col4d_params, cudnnConvolutionStruct, cudnnTensor4dStruct, float const*, float*)", 614], ["volta_scudnn_128x64_stridedB_small_nn_v1", 584], ["void nchwToNhwcKernel(int, int, int, int, float const*, float*, float, float)", 453], ["cask_cudnn::computeOffsetsKernel(cask_cudnn::ComputeOffsetsParams)", 342], ["void at::native::vectorized_elementwise_kernel<4, at::native::BUnaryFunctor >, at::detail::Array >(int, at::native::BUnaryFunctor >, at::detail::Array)", 322], ["void at::native::reduce_kernel<512, 1, at::native::ReduceOp, unsigned int, float, 4> >(at::native::ReduceOp, unsigned int, float, 4>)", 212], ["volta_sgemm_64x32_sliced1x4_nn", 150], 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const*, float const*, int, int, int)", 53], ["void at::native::reduce_kernel<128, 4, at::native::ReduceOp::operator()(at::TensorIterator&)::{lambda(float, float)#1}>, unsigned int, float, 4> >(at::native::ReduceOp::operator()(at::TensorIterator&)::{lambda(float, float)#1}>, unsigned int, float, 4>)", 44], ["void (anonymous namespace)::softmax_warp_forward(float*, float const*, int, int, int)", 42], ["void splitKreduce_kernel(cublasSplitKParams, float const*, float const*, float*, float const*, float const*, float const*)", 30], ["void at::native::unrolled_elementwise_kernel, OffsetCalculator<1, unsigned int>, char*, at::native::memory::LoadWithoutCast, at::detail::Array::StoreWithoutCast>(int, at::native::copy_device_to_device(at::TensorIterator&, bool)::{lambda()#2}::operator()() const::{lambda()#8}::operator()() const::{lambda(float)#1}, at::detail::Array, OffsetCalculator<1, unsigned int>, char*, at::native::memory::LoadWithoutCast, at::detail::Array::StoreWithoutCast)", 30], ["void cunn_ClassNLLCriterion_updateOutput_kernel(float*, float*, float*, long*, float*, int, int, int, int, long)", 18], ["void cunn_ClassNLLCriterion_updateGradInput_kernel(float*, float*, long*, float*, float*, int, int, int, int, long)", 12]]}} -{"steps": {"columns": [{"type": "string", "name": "Step"}, {"type": "number", "name": "Kernel"}, {"type": "string", "role": "tooltip", "p": {"html": "true"}}, {"type": "number", "name": "Memcpy"}, {"type": "string", "role": "tooltip", "p": {"html": "true"}}, {"type": "number", "name": "Memset"}, {"type": "string", "role": "tooltip", "p": {"html": "true"}}, {"type": "number", "name": "Runtime"}, {"type": "string", "role": "tooltip", "p": {"html": "true"}}, {"type": "number", "name": "DataLoader"}, {"type": "string", "role": "tooltip", "p": {"html": "true"}}, {"type": "number", "name": "CPU Exec"}, {"type": "string", "role": "tooltip", "p": {"html": "true"}}, {"type": "number", "name": "Other"}, {"type": "string", "role": "tooltip", "p": {"html": 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    Step 5
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    Step 5
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    Step 7
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    Step 7
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    Step 10
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    ", 98, "
    Step 10
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    Percentage: 2.99%
    ", 1400, "
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    • Kernels with 68% time are launched by Tensor Cores eligible operators. You could enable Automatic Mixed Precision to speedup by using FP16.
    ", "environments": [{"title": "Number of Worker(s)", "value": "1"}, {"title": "Device Type", "value": "GPU"}], "gpu_metrics": {"title": "GPU Summary", "data": [{"title": "GPU 0:", "value": ""}, {"title": "Name", "value": "Tesla V100-DGXS-32GB"}, {"title": "Memory", "value": "31.74 GB"}, {"title": "Compute Capability", "value": "7.0"}, {"title": "GPU Utilization", "value": "70.27 %"}, {"title": "Est. SM Efficiency", "value": "69.22 %"}, {"title": "Est. Achieved Occupancy", "value": "48.91 %"}, {"title": "Kernel Time using Tensor Cores", "value": "0.0 %"}], "tooltip": "The GPU usage metrics:\n\nGPU Utilization:\nGPU busy time / All steps time. The higher, the better. GPU busy time is the time during which there is at least one GPU kernel running on it. All steps time is the total time of all profiler steps(or called as iterations).\n\nEst. SM Efficiency:\nEstimated Stream Multiprocessor Efficiency. The higher, the better. 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implicit_convolve_sgemm(int, int, int, float const*, int, float*, float const*, kernel_conv_params, unsigned long long, int, float, float, int, float const*, float const*, bool, int, int)", "No", 90, 37016, 411, 735, 346, 12.39, 50.0], ["void implicit_convolve_sgemm(int, int, int, float const*, int, float*, float const*, kernel_conv_params, unsigned long long, int, float, float, int, float const*, float const*, bool, int, int)", "No", 72, 35106, 488, 822, 350, 3.83, 41.64], ["void at::native::vectorized_elementwise_kernel<4, at::native::threshold_kernel_impl(at::TensorIteratorBase&, float, float)::{lambda(float, float)#1}, at::detail::Array >(int, at::native::threshold_kernel_impl(at::TensorIteratorBase&, float, float)::{lambda(float, float)#1}, at::detail::Array)", "No", 294, 27280, 93, 377, 13, 653.26, 100.0], ["void cudnn::bn_fw_tr_1C11_kernel_NCHW(cudnnTensorStruct, float const*, cudnnTensorStruct, float*, float const*, float const*, float, float, float*, float*, float*, float*, float, float)", "No", 150, 27084, 181, 454, 53, 3.12, 64.02], ["volta_scudnn_128x128_stridedB_splitK_medium_nn_v1", "No", 72, 25342, 352, 629, 323, 3.21, 25.0], ["volta_sgemm_64x64_nt", "No", 102, 21125, 207, 281, 184, 10.28, 19.38], ["volta_scudnn_128x128_stridedB_splitK_small_nn_v1", "No", 48, 20473, 427, 681, 309, 6.82, 25.0], ["void at::native::vectorized_elementwise_kernel<4, at::native::(anonymous namespace)::clamp_min_scalar_kernel_impl(at::TensorIterator&, c10::Scalar)::{lambda()#1}::operator()() const::{lambda()#8}::operator()() const::{lambda(float)#1}, at::detail::Array >(int, at::native::(anonymous namespace)::clamp_min_scalar_kernel_impl(at::TensorIterator&, c10::Scalar)::{lambda()#1}::operator()() const::{lambda()#8}::operator()() const::{lambda(float)#1}, at::detail::Array)", "No", 294, 17862, 61, 252, 5, 666.77, 100.0], ["volta_scudnn_winograd_128x128_ldg1_ldg4_relu_tile148t_nt_v1", "No", 36, 12761, 354, 365, 344, 22.4, 25.0], ["volta_scudnn_128x64_stridedB_interior_nn_v1", "No", 30, 9559, 319, 508, 255, 12.91, 19.0], ["volta_sgemm_128x32_nt", "No", 24, 8658, 361, 479, 18, 0.97, 11.51], ["volta_sgemm_64x64_nn", "No", 42, 8544, 203, 210, 197, 12.35, 24.14], ["volta_scudnn_128x64_relu_interior_nn_v1", "No", 30, 7976, 266, 316, 92, 37.08, 25.0], ["void implicit_convolve_sgemm(int, int, int, float const*, int, float*, float const*, kernel_conv_params, unsigned long long, int, float, float, int, float const*, float const*, bool, int, int)", "No", 12, 7939, 662, 733, 584, 7.54, 25.0], ["volta_scudnn_128x64_stridedB_splitK_xregs_large_nn_v1", "No", 12, 7819, 652, 670, 634, 15.96, 19.0], ["void cudnn::bn_fw_tr_1C11_singleread(cudnnTensorStruct, float const*, cudnnTensorStruct, float*, float const*, float const*, float, float, float*, float*, float*, float*, float, float, cudnn::reduced_divisor, int, cudnn::reduced_divisor, cudnn::bnFwPersistentState*, int, float, float, float, int, float, float, cudnnStatus_t*, bool)", "No", 168, 7231, 43, 89, 11, 12.63, 75.0], ["void cudnn::cnn::wgrad_alg0_engine(int, int, int, float const*, int, float*, float const*, kernel_grad_params, unsigned long long, int, float, int, int, int, int)", "No", 12, 7068, 589, 990, 192, 85.38, 37.51], ["void cudnn::ops::scalePackedTensor_kernel(cudnnTensor4dStruct, float*, float)", "No", 180, 5901, 33, 142, 5, 525.02, 100.0], ["void cudnn::winograd_nonfused::winogradForwardOutput4x4(cudnn::winograd_nonfused::WinogradOutputParams)", "No", 120, 5314, 44, 72, 20, 10.02, 50.0], ["void implicit_convolve_sgemm(int, int, int, float const*, int, float*, float const*, kernel_conv_params, unsigned long long, int, float, float, int, float const*, float const*, bool, int, int)", "No", 12, 5221, 435, 440, 431, 9.8, 31.0], ["void cudnn::winograd_nonfused::winogradWgradData4x4(cudnn::winograd_nonfused::WinogradDataParams)", "No", 78, 4681, 60, 126, 20, 15.46, 38.0], ["void cudnn::winograd_nonfused::winogradForwardData4x4(cudnn::winograd_nonfused::WinogradDataParams)", "No", 120, 4648, 39, 67, 17, 10.15, 50.0], ["void cudnn::winograd_nonfused::winogradWgradDelta4x4(cudnn::winograd_nonfused::WinogradDeltaParams)", "No", 78, 4559, 58, 126, 17, 15.71, 50.0], ["void cudnn::cnn::wgrad_alg0_engine(int, int, int, float const*, int, float*, float const*, kernel_grad_params, unsigned long long, int, float, int, int, int, int)", "No", 6, 4038, 673, 691, 649, 6.4, 25.0], ["void at::native::(anonymous namespace)::max_pool_backward_nchw(int, float const*, long const*, int, int, int, int, int, int, int, int, int, int, int, int, int, int, float*)", "No", 6, 3838, 640, 643, 637, 1254.4, 100.0], ["volta_scudnn_128x32_sliced1x4_ldg4_relu_exp_small_nhwc_tn_v1", "No", 6, 3697, 616, 621, 614, 2.6, 25.0], ["volta_scudnn_128x64_relu_medium_nn_v1", "No", 6, 3647, 608, 620, 602, 39.2, 25.0], ["volta_scudnn_128x128_stridedB_medium_nn_v1", "No", 12, 3550, 296, 309, 286, 19.6, 25.0], ["volta_scudnn_128x64_relu_small_nn_v1", "No", 12, 3273, 273, 286, 258, 9.8, 25.0], ["volta_sgemm_32x128_nn", "No", 18, 3059, 170, 173, 167, 22.05, 50.0], ["volta_scudnn_128x128_stridedB_small_nn_v1", "No", 6, 3034, 506, 520, 491, 19.6, 25.0], ["volta_sgemm_32x128_nt", "No", 18, 2837, 158, 159, 156, 22.05, 50.0], ["void cudnn::winograd_nonfused::winogradForwardFilter4x4(cudnn::winograd_nonfused::WinogradFilterParams)", "No", 120, 2632, 22, 67, 4, 8.75, 73.78], ["void at::native::vectorized_elementwise_kernel<4, at::native::MulScalarFunctor, at::detail::Array >(int, at::native::MulScalarFunctor, at::detail::Array)", "No", 966, 2395, 2, 25, 1, 44.01, 58.56], ["void cudnn::bn_bw_1C11_singleread(float, float, float, float, cudnnTensorStruct, float const*, cudnnTensorStruct, float const*, cudnnTensorStruct, float*, float const*, float*, float*, float const*, float const*, float, cudnn::reduced_divisor, int, cudnn::reduced_divisor, cudnn::bnBwPersistentState*, int, float, float, float, int, float, cudnnStatus_t*, bool)", "No", 54, 2331, 43, 75, 19, 20.83, 75.0], ["void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", "No", 978, 1888, 2, 143, 0, 600.2, 86.95], ["void cudnn::winograd_nonfused::winogradWgradOutput4x4(cudnn::winograd_nonfused::WinogradWgradOutputParams)", "No", 78, 1484, 19, 69, 3, 8.13, 41.71], ["void at::native::(anonymous namespace)::max_pool_forward_nchw(int, float const*, int, int, int, int, int, int, int, int, int, int, int, int, int, int, float*, long*)", "No", 6, 1422, 237, 243, 234, 313.6, 100.0], ["volta_scudnn_128x64_stridedB_small_nn_v1", "No", 6, 582, 97, 99, 94, 9.8, 19.0], ["void nchwToNhwcKernel(int, int, int, int, float const*, float*, float, float)", "No", 12, 383, 32, 34, 29, 71.72, 100.0], ["void at::native::vectorized_elementwise_kernel<4, at::native::BUnaryFunctor >, at::detail::Array >(int, at::native::BUnaryFunctor >, at::detail::Array)", "No", 318, 325, 1, 2, 1, 0.01, 0.0], ["cask_cudnn::computeOffsetsKernel(cask_cudnn::ComputeOffsetsParams)", "No", 108, 216, 2, 5, 1, 0.16, 2.0], ["void at::native::reduce_kernel<512, 1, at::native::ReduceOp, unsigned int, float, 4> >(at::native::ReduceOp, unsigned int, float, 4>)", "No", 6, 210, 35, 35, 35, 51.2, 100.0], ["cask_cudnn::computeWgradSplitKOffsetsKernel(cask_cudnn::ComputeSplitKOffsetsParams)", "No", 132, 155, 1, 2, 1, 0.16, 1.83], ["cask_cudnn::computeWgradBOffsetsKernel(cask_cudnn::ComputeWgradBOffsetsParams)", "No", 132, 150, 1, 2, 1, 0.02, 0.0], ["volta_sgemm_64x32_sliced1x4_nn", "No", 6, 149, 25, 25, 24, 2.0, 25.0], ["volta_sgemm_64x32_sliced1x4_tn", "No", 6, 148, 25, 25, 24, 1.0, 13.0], ["void at::native::unrolled_elementwise_kernel, at::detail::Array, OffsetCalculator<1, unsigned int>, OffsetCalculator<1, unsigned int>, at::native::memory::LoadWithoutCast, at::native::memory::StoreWithoutCast>(int, at::native::MulScalarFunctor, at::detail::Array, OffsetCalculator<1, unsigned int>, OffsetCalculator<1, unsigned int>, at::native::memory::LoadWithoutCast, at::native::memory::StoreWithoutCast)", "No", 6, 145, 24, 25, 24, 156.8, 100.0], ["void cudnn::winograd::generateWinogradTilesKernel<0, float, float>(cudnn::winograd::GenerateWinogradTilesParams)", "No", 36, 126, 4, 5, 2, 0.4, 3.0], ["cask_cudnn::computeBOffsetsKernel(cask_cudnn::ComputeBOffsetsParams)", "No", 54, 57, 1, 2, 1, 0.02, 0.0], ["void nhwcToNchwKernel(int, int, int, int, float const*, float*, float, float)", "No", 6, 54, 9, 10, 8, 12.8, 100.0], ["void (anonymous namespace)::softmax_warp_backward(float*, float const*, float const*, int, int, int)", "No", 6, 51, 8, 9, 8, 0.1, 1.0], ["void at::native::reduce_kernel<128, 4, at::native::ReduceOp::operator()(at::TensorIterator&)::{lambda(float, float)#1}>, unsigned int, float, 4> >(at::native::ReduceOp::operator()(at::TensorIterator&)::{lambda(float, float)#1}>, unsigned int, float, 4>)", "No", 6, 45, 8, 8, 7, 0.03, 0.0], ["void (anonymous namespace)::softmax_warp_forward(float*, float const*, int, int, int)", "No", 6, 42, 7, 7, 7, 0.1, 1.0], ["void splitKreduce_kernel(cublasSplitKParams, float const*, float const*, float*, float const*, float const*, float const*)", "No", 12, 31, 3, 4, 2, 4.39, 27.74], ["void at::native::unrolled_elementwise_kernel, OffsetCalculator<1, unsigned int>, char*, at::native::memory::LoadWithoutCast, at::detail::Array::StoreWithoutCast>(int, at::native::copy_device_to_device(at::TensorIterator&, bool)::{lambda()#2}::operator()() const::{lambda()#8}::operator()() const::{lambda(float)#1}, at::detail::Array, OffsetCalculator<1, unsigned int>, char*, at::native::memory::LoadWithoutCast, at::detail::Array::StoreWithoutCast)", "No", 6, 30, 5, 5, 5, 1.56, 5.0], ["void cunn_ClassNLLCriterion_updateOutput_kernel(float*, float*, float*, long*, float*, int, int, int, int, long)", "No", 6, 18, 3, 3, 3, 0.01, 0.0], ["void cunn_ClassNLLCriterion_updateGradInput_kernel(float*, float*, long*, float*, float*, int, int, int, int, long)", "No", 6, 12, 2, 2, 2, 0.01, 0.0]]}} -{"total": {"columns": [{"type": "string", "name": "name"}, {"type": "number", "name": "value"}], "rows": [["void cudnn::detail::dgrad_engine(int, int, int, float const*, int, float const*, int, float*, kernel_grad_params, unsigned long long, int, unsigned long long, int, float, int, int, int)", 86855], ["void cudnn::bn_bw_1C11_kernel_new(float, float, float, float, cudnnTensorStruct, float const*, cudnnTensorStruct, float const*, cudnnTensorStruct, float*, float const*, float*, float*, float const*, float const*, float)", 59568], ["void cudnn::cnn::wgrad_alg0_engine(int, int, int, float const*, int, float*, float const*, kernel_grad_params, unsigned long long, int, float, int, int, int, int)", 43471], ["void at::native::vectorized_elementwise_kernel<4, at::native::AddFunctor, at::detail::Array >(int, at::native::AddFunctor, at::detail::Array)", 39753], ["void implicit_convolve_sgemm(int, int, int, float const*, int, float*, float const*, kernel_conv_params, unsigned long long, int, float, float, int, float const*, float const*, bool, int, int)", 37016], ["void implicit_convolve_sgemm(int, int, int, float const*, int, float*, float const*, kernel_conv_params, unsigned long long, int, float, float, int, float const*, float const*, bool, int, int)", 35106], ["void at::native::vectorized_elementwise_kernel<4, at::native::threshold_kernel_impl(at::TensorIteratorBase&, float, float)::{lambda(float, float)#1}, at::detail::Array >(int, at::native::threshold_kernel_impl(at::TensorIteratorBase&, float, float)::{lambda(float, float)#1}, at::detail::Array)", 27280], ["void cudnn::bn_fw_tr_1C11_kernel_NCHW(cudnnTensorStruct, float const*, cudnnTensorStruct, float*, float const*, float const*, float, float, float*, float*, float*, float*, float, float)", 27084], ["volta_scudnn_128x128_stridedB_splitK_medium_nn_v1", 25342], ["volta_sgemm_64x64_nt", 21125], ["volta_scudnn_128x128_stridedB_splitK_small_nn_v1", 20473], ["void at::native::vectorized_elementwise_kernel<4, at::native::(anonymous namespace)::clamp_min_scalar_kernel_impl(at::TensorIterator&, c10::Scalar)::{lambda()#1}::operator()() const::{lambda()#8}::operator()() const::{lambda(float)#1}, at::detail::Array >(int, at::native::(anonymous namespace)::clamp_min_scalar_kernel_impl(at::TensorIterator&, c10::Scalar)::{lambda()#1}::operator()() const::{lambda()#8}::operator()() const::{lambda(float)#1}, at::detail::Array)", 17862], ["volta_scudnn_winograd_128x128_ldg1_ldg4_relu_tile148t_nt_v1", 12761], ["volta_scudnn_128x64_stridedB_interior_nn_v1", 9559], ["volta_sgemm_128x32_nt", 8658], ["volta_sgemm_64x64_nn", 8544], ["volta_scudnn_128x64_relu_interior_nn_v1", 7976], ["void implicit_convolve_sgemm(int, int, int, float const*, int, float*, float const*, kernel_conv_params, unsigned long long, int, float, float, int, float const*, float const*, bool, int, int)", 7939], ["volta_scudnn_128x64_stridedB_splitK_xregs_large_nn_v1", 7819], ["void cudnn::bn_fw_tr_1C11_singleread(cudnnTensorStruct, float const*, cudnnTensorStruct, float*, float const*, float const*, float, float, float*, float*, float*, float*, float, float, cudnn::reduced_divisor, int, cudnn::reduced_divisor, cudnn::bnFwPersistentState*, int, float, float, float, int, float, float, cudnnStatus_t*, bool)", 7231], ["void cudnn::cnn::wgrad_alg0_engine(int, int, int, float const*, int, float*, float const*, kernel_grad_params, unsigned long long, int, float, int, int, int, int)", 7068], ["void cudnn::ops::scalePackedTensor_kernel(cudnnTensor4dStruct, float*, float)", 5901], ["void cudnn::winograd_nonfused::winogradForwardOutput4x4(cudnn::winograd_nonfused::WinogradOutputParams)", 5314], ["void implicit_convolve_sgemm(int, int, int, float const*, int, float*, float const*, kernel_conv_params, unsigned long long, int, float, float, int, float const*, float const*, bool, int, int)", 5221], ["void cudnn::winograd_nonfused::winogradWgradData4x4(cudnn::winograd_nonfused::WinogradDataParams)", 4681], ["void cudnn::winograd_nonfused::winogradForwardData4x4(cudnn::winograd_nonfused::WinogradDataParams)", 4648], ["void cudnn::winograd_nonfused::winogradWgradDelta4x4(cudnn::winograd_nonfused::WinogradDeltaParams)", 4559], ["void cudnn::cnn::wgrad_alg0_engine(int, int, int, float const*, int, float*, float const*, kernel_grad_params, unsigned long long, int, float, int, int, int, int)", 4038], ["void at::native::(anonymous namespace)::max_pool_backward_nchw(int, float const*, long const*, int, int, int, int, int, int, int, int, int, int, int, int, int, int, float*)", 3838], ["volta_scudnn_128x32_sliced1x4_ldg4_relu_exp_small_nhwc_tn_v1", 3697], ["volta_scudnn_128x64_relu_medium_nn_v1", 3647], ["volta_scudnn_128x128_stridedB_medium_nn_v1", 3550], ["volta_scudnn_128x64_relu_small_nn_v1", 3273], ["volta_sgemm_32x128_nn", 3059], ["volta_scudnn_128x128_stridedB_small_nn_v1", 3034], ["volta_sgemm_32x128_nt", 2837], ["void cudnn::winograd_nonfused::winogradForwardFilter4x4(cudnn::winograd_nonfused::WinogradFilterParams)", 2632], ["void at::native::vectorized_elementwise_kernel<4, at::native::MulScalarFunctor, at::detail::Array >(int, at::native::MulScalarFunctor, at::detail::Array)", 2395], ["void cudnn::bn_bw_1C11_singleread(float, float, float, float, cudnnTensorStruct, float const*, cudnnTensorStruct, float const*, cudnnTensorStruct, float*, float const*, float*, float*, float const*, float const*, float, cudnn::reduced_divisor, int, cudnn::reduced_divisor, cudnn::bnBwPersistentState*, int, float, float, float, int, float, cudnnStatus_t*, bool)", 2331], ["void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", 1888], ["void cudnn::winograd_nonfused::winogradWgradOutput4x4(cudnn::winograd_nonfused::WinogradWgradOutputParams)", 1484], ["void at::native::(anonymous namespace)::max_pool_forward_nchw(int, float const*, int, int, int, int, int, int, int, int, int, int, int, int, int, int, float*, long*)", 1422], ["volta_scudnn_128x64_stridedB_small_nn_v1", 582], ["void nchwToNhwcKernel(int, int, int, int, float const*, float*, float, float)", 383], ["void at::native::vectorized_elementwise_kernel<4, at::native::BUnaryFunctor >, at::detail::Array >(int, at::native::BUnaryFunctor >, at::detail::Array)", 325], ["cask_cudnn::computeOffsetsKernel(cask_cudnn::ComputeOffsetsParams)", 216], ["void at::native::reduce_kernel<512, 1, at::native::ReduceOp, unsigned int, float, 4> >(at::native::ReduceOp, unsigned int, float, 4>)", 210], ["cask_cudnn::computeWgradSplitKOffsetsKernel(cask_cudnn::ComputeSplitKOffsetsParams)", 155], ["cask_cudnn::computeWgradBOffsetsKernel(cask_cudnn::ComputeWgradBOffsetsParams)", 150], ["volta_sgemm_64x32_sliced1x4_nn", 149], ["volta_sgemm_64x32_sliced1x4_tn", 148], ["void at::native::unrolled_elementwise_kernel, at::detail::Array, OffsetCalculator<1, unsigned int>, OffsetCalculator<1, unsigned int>, at::native::memory::LoadWithoutCast, at::native::memory::StoreWithoutCast>(int, at::native::MulScalarFunctor, at::detail::Array, OffsetCalculator<1, unsigned int>, OffsetCalculator<1, unsigned int>, at::native::memory::LoadWithoutCast, at::native::memory::StoreWithoutCast)", 145], ["void cudnn::winograd::generateWinogradTilesKernel<0, float, float>(cudnn::winograd::GenerateWinogradTilesParams)", 126], ["cask_cudnn::computeBOffsetsKernel(cask_cudnn::ComputeBOffsetsParams)", 57], ["void nhwcToNchwKernel(int, int, int, int, float const*, float*, float, float)", 54], ["void (anonymous namespace)::softmax_warp_backward(float*, float const*, float const*, int, int, int)", 51], ["void at::native::reduce_kernel<128, 4, at::native::ReduceOp::operator()(at::TensorIterator&)::{lambda(float, float)#1}>, unsigned int, float, 4> >(at::native::ReduceOp::operator()(at::TensorIterator&)::{lambda(float, float)#1}>, unsigned int, float, 4>)", 45], ["void (anonymous namespace)::softmax_warp_forward(float*, float const*, int, int, int)", 42], ["void splitKreduce_kernel(cublasSplitKParams, float const*, float const*, float*, float const*, float const*, float const*)", 31], ["void at::native::unrolled_elementwise_kernel, OffsetCalculator<1, unsigned int>, char*, at::native::memory::LoadWithoutCast, at::detail::Array::StoreWithoutCast>(int, at::native::copy_device_to_device(at::TensorIterator&, bool)::{lambda()#2}::operator()() const::{lambda()#8}::operator()() const::{lambda(float)#1}, at::detail::Array, OffsetCalculator<1, unsigned int>, char*, at::native::memory::LoadWithoutCast, at::detail::Array::StoreWithoutCast)", 30], ["void cunn_ClassNLLCriterion_updateOutput_kernel(float*, float*, float*, long*, float*, int, int, int, int, long)", 18], ["void cunn_ClassNLLCriterion_updateGradInput_kernel(float*, float*, long*, float*, float*, int, int, int, int, long)", 12]]}} diff --git a/plugins/tensorboard-plugins/tb_plugin/test/test_compare_with_autograd.py b/plugins/tensorboard-plugins/tb_plugin/test/test_compare_with_autograd.py deleted file mode 100644 index d097fbd3c..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/test/test_compare_with_autograd.py +++ /dev/null @@ -1,301 +0,0 @@ -import os -import time -import unittest -import pytest -import torch -import torch.nn as nn -import torch.backends.cudnn as cudnn -import torch.optim -import torch.utils.data -import torchvision -import torchvision.transforms as T -import torchvision.models as models -import torch_tb_profiler.io as io -from torch_tb_profiler.profiler import RunLoader - - -def create_log_dir(): - log_dir_name = './log{}'.format(str(int(time.time()*1000))) - try: - os.makedirs(log_dir_name) - except Exception: - raise RuntimeError("Can't create directory: " + log_dir_name) - return log_dir_name - - -def get_autograd_result(p, worker_name, record_shapes=False, with_stack=False): - avgs = p.key_averages() - sort_by = 'self_cuda_time_total' - avgs = sorted( - avgs, key=lambda evt: getattr(evt, sort_by), reverse=True - ) - is_gpu = False - if avgs[0].self_cuda_time_total > 0: - is_gpu = True - others_prefix = {'enumerate(DataLoader)#', 'Optimizer.zero_grad#', 'Optimizer.step#', - 'ProfilerStep*', - 'Memcpy', 'Memset', - 'cuda'} - postfix_to_type = {'CPU': 'operator', 'CUDA': 'kernel'} - - def get_type(evt): - s = str(evt.device_type) - postfix = s[s.index('.') + 1:] - evt_type = postfix_to_type[postfix] - for prefix in others_prefix: - if evt.key.startswith(prefix): - evt_type = 'Other' - break - return evt_type - - result_dict = dict() - result_dict[worker_name + '#operator'] = list() - if is_gpu: - result_dict[worker_name + '#kernel'] = list() - for avg in avgs: - evt_type = get_type(avg) - if evt_type == 'operator': - line = [avg.key, int(avg.count)] - if is_gpu: - line.extend([int(avg.self_cuda_time_total), int(avg.cuda_time_total)]) - line.extend([int(avg.self_cpu_time_total), int(avg.cpu_time_total)]) - result_dict[worker_name + '#operator'].append(line) - elif is_gpu and evt_type == 'kernel': - line = [avg.key, int(avg.count), int(avg.self_cuda_time_total)] - result_dict[worker_name + '#kernel'].append(line) - if record_shapes: - result_dict[worker_name + '#operator#input_shape'] = list() - avgs = p.key_averages(True) - sort_by = 'self_cuda_time_total' - avgs = sorted( - avgs, key=lambda evt: getattr(evt, sort_by), reverse=True - ) - for avg in avgs: - evt_type = get_type(avg) - if evt_type == 'operator': - line = [avg.key, str(avg.input_shapes) if avg.input_shapes else '[]', int(avg.count)] - if is_gpu: - line.extend([int(avg.self_cuda_time_total), int(avg.cuda_time_total)]) - line.extend([int(avg.self_cpu_time_total), int(avg.cpu_time_total)]) - result_dict[worker_name + '#operator#input_shape'].append(line) - # The call stack for legacy and kineto profiler is different for now, - # The legacy profiler has stack for backward while kineto not - # So, just disable call stack compare for the moment - if False and with_stack: - result_dict[worker_name + '#operator#stack'] = list() - avgs = p.key_averages(False, 100) - sort_by = 'self_cuda_time_total' - avgs = sorted( - avgs, key=lambda evt: getattr(evt, sort_by), reverse=True - ) - for avg in avgs: - evt_type = get_type(avg) - if evt_type == 'operator' and avg.stack: - line = [avg.key, int(avg.count)] - if is_gpu: - line.extend([int(avg.self_cuda_time_total), int(avg.cuda_time_total)]) - line.extend([int(avg.self_cpu_time_total), int(avg.cpu_time_total), ''.join(avg.stack)]) - result_dict[worker_name + '#operator#stack'].append(line) - - result_dict[worker_name + '#operator#stack#input_shape'] = list() - avgs = p.key_averages(True, 100) - sort_by = 'self_cuda_time_total' - avgs = sorted( - avgs, key=lambda evt: getattr(evt, sort_by), reverse=True - ) - for avg in avgs: - evt_type = get_type(avg) - if evt_type == 'operator' and avg.stack: - line = [avg.key, str(avg.input_shapes), int(avg.count)] - if is_gpu: - line.extend([int(avg.self_cuda_time_total), int(avg.cuda_time_total)]) - line.extend([int(avg.self_cpu_time_total), int(avg.cpu_time_total), ''.join(avg.stack)]) - result_dict[worker_name + '#operator#stack#input_shape'].append(line) - - return result_dict - - -def generate_plugin_result_row(data): - row = list() - row.append(data['name']) - if 'input_shape' in data: - row.append(data['input_shape']) - row.append(data['calls']) - if 'device_self_duration' in data: - row.append(data['device_self_duration']) - row.append(data['device_total_duration']) - row.extend([data['host_self_duration'], data['host_total_duration']]) - if 'call_stack' in data: - row.append(data['call_stack']) - return row - - -def get_plugin_result(run, record_shapes=False, with_stack=False): - result_dict = dict() - for (worker_name, span), profile in run.profiles.items(): - worker_name = worker_name.split('.')[0] - assert profile.operation_table_by_name is not None - result_dict[worker_name + '#operator'] = list() - for data in profile.operation_table_by_name['data']: - row = generate_plugin_result_row(data) - result_dict[worker_name + '#operator'].append(row) - if profile.kernel_table is not None: - rows = profile.kernel_table['data']['rows'] - result_dict[worker_name + '#kernel'] = list() - for row in rows: - result_dict[worker_name + '#kernel'].append([row[0], row[2], row[3]]) # row[1] is 'Tensor Cores Used'. - if record_shapes: - assert profile.operation_table_by_name_input is not None - result_dict[worker_name + '#operator#input_shape'] = list() - for data in profile.operation_table_by_name_input['data']: - row = generate_plugin_result_row(data) - result_dict[worker_name + '#operator#input_shape'].append(row) - # The call stack for legacy and kineto profiler is different for now, - # The legacy profiler has stack for backward while kineto not - # So, just disable call stack compare for the moment - if False and with_stack: - assert profile.operation_stack_by_name is not None - assert profile.operation_stack_by_name_input is not None - result_dict[worker_name + '#operator#stack'] = list() - op_stack_dict = profile.operation_stack_by_name - for k, datalist in op_stack_dict.items(): - for data in datalist: - row = generate_plugin_result_row(data) - result_dict[worker_name + '#operator#stack'].append(row) - if record_shapes: - result_dict[worker_name + '#operator#stack#input_shape'] = list() - op_stack_dict = profile.operation_stack_by_name_input - for k, datalist in op_stack_dict.items(): - for data in datalist: - row = generate_plugin_result_row(data) - result_dict[worker_name + '#operator#stack#input_shape'].append(row) - - return result_dict - - -def get_train_func(use_gpu=True): - model = models.resnet50(pretrained=True) - if use_gpu: - model.cuda() - cudnn.benchmark = True - - transform = T.Compose([T.Resize(256), T.CenterCrop(224), T.ToTensor()]) - trainset = torchvision.datasets.CIFAR10(root='./data', train=True, - download=True, transform=transform) - trainloader = torch.utils.data.DataLoader(trainset, batch_size=2, - shuffle=True, num_workers=0) - - if use_gpu: - criterion = nn.CrossEntropyLoss().cuda() - else: - criterion = nn.CrossEntropyLoss() - optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9) - if use_gpu: - device = torch.device('cuda:0') - else: - device = torch.device('cpu') - model.train() - - def train(train_step, prof=None): - for step, data in enumerate(trainloader, 0): - print('step:{}'.format(step)) - inputs, labels = data[0].to(device=device), data[1].to(device=device) - - outputs = model(inputs) - loss = criterion(outputs, labels) - - optimizer.zero_grad() - loss.backward() - optimizer.step() - if prof is not None: - prof.step() - if step >= train_step: - break - return train - - -def get_output_fn(dir_name, profilers_dict): - def output_fn(p): - # In current torch.profiler.profile, at beginning of each span, a new p.profiler will be created. - # So the same p.profiler will not be shared among different spans - worker_name = 'worker{}'.format(p.step_num) - profilers_dict[worker_name] = p.profiler - tb_trace_handler = torch.profiler.tensorboard_trace_handler(dir_name, worker_name) - tb_trace_handler(p) - return output_fn - - -class TestCompareWithAutogradResult(unittest.TestCase): - - def compare_results(self, log_dir, profilers_dict, use_gpu=True, record_shapes=False, with_stack=False): - cache = io.Cache() - loader = RunLoader(os.path.split(log_dir)[-1], log_dir, cache) - run = loader.load() - plugin_result = get_plugin_result(run, record_shapes, with_stack) - count = 0 - for worker_name, p in profilers_dict.items(): - autograd_result = get_autograd_result(p, worker_name, record_shapes, with_stack) - for key in autograd_result.keys(): - count += 1 - self.assertTrue(key in plugin_result.keys()) - self.assertEqual(len(plugin_result[key]), len(autograd_result[key])) - for line in plugin_result[key]: - self.assertTrue(line in autograd_result[key]) - self.assertEqual(count, len(plugin_result.keys())) - - @pytest.mark.skipif(not torch.cuda.is_available(), reason='') - def test_autograd_api(self): - with torch.autograd.profiler.profile(use_cuda=True, use_kineto=True, record_shapes=True) as p: - get_train_func()(5) - log_dir = create_log_dir() - p.export_chrome_trace(os.path.join(log_dir, 'worker0.{}.pt.trace.json'.format(int(time.time() * 1000)))) - self.compare_results(log_dir, {'worker0': p}) - - def base_profiler_api(self, use_gpu, record_shapes, profile_memory, with_stack): - log_dir = create_log_dir() - profilers_dict = dict() - if use_gpu: - activities = [ - torch.profiler.ProfilerActivity.CPU, - torch.profiler.ProfilerActivity.CUDA] - else: - activities = [torch.profiler.ProfilerActivity.CPU] - - with torch.profiler.profile( - activities=activities, - schedule=torch.profiler.schedule( - wait=2, - warmup=2, - active=3), - on_trace_ready=get_output_fn(log_dir, profilers_dict), - record_shapes=record_shapes, - profile_memory=profile_memory, - with_stack=with_stack - ) as p: - get_train_func(use_gpu)(13, p) - self.compare_results(log_dir, profilers_dict, use_gpu, record_shapes, with_stack) - - def test_profiler_api_without_gpu(self): - self.base_profiler_api(False, True, True, False) - - @pytest.mark.skipif(not torch.cuda.is_available(), reason='') - def test_profiler_api_with_record_shapes_memory_stack(self): - self.base_profiler_api(True, True, True, True) - - @pytest.mark.skipif(not torch.cuda.is_available(), reason='') - def test_profiler_api_without_record_shapes_memory_stack(self): - self.base_profiler_api(True, False, False, False) - - @pytest.mark.skipif(not torch.cuda.is_available(), reason='') - def test_profiler_api_without_step(self): - log_dir = create_log_dir() - profilers_dict = dict() - with torch.profiler.profile( - activities=[ - torch.profiler.ProfilerActivity.CPU, - torch.profiler.ProfilerActivity.CUDA], - on_trace_ready=get_output_fn(log_dir, profilers_dict), - record_shapes=True - ): - get_train_func()(7) - self.compare_results(log_dir, profilers_dict) diff --git a/plugins/tensorboard-plugins/tb_plugin/test/test_diffrun.py b/plugins/tensorboard-plugins/tb_plugin/test/test_diffrun.py deleted file mode 100644 index 06fbf398a..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/test/test_diffrun.py +++ /dev/null @@ -1,51 +0,0 @@ -import os -import unittest - -import pytest -from torch_tb_profiler.profiler.data import RunProfileData -from torch_tb_profiler.profiler.diffrun import (compare_op_tree, diff_summary, - print_node, print_ops) -from torch_tb_profiler.utils import timing - - -def load_profile(worker, span, path): - return RunProfileData.parse_gpu(worker, span, path, '.') - - -class TestDiffRun(unittest.TestCase): - - @pytest.mark.skipif(not (os.path.isfile(os.path.expanduser('~/profile_result/worker0.pt.trace.json')) and - os.path.isfile(os.path.expanduser('~/profile_result/worker1.pt.trace.json'))), - reason="file doesn't exist") - def test_happy_path(self): - # path1 = os.path.expanduser('~/profile_result/worker0.pt.trace.json') - path1 = '/home/mike/git/kineto/tb_plugin/examples/result/datapipe0.1638760942588.pt.trace.json' - profile1 = load_profile('worker0', 1, path1) - roots = list(profile1.tid2tree.values()) - root = roots[0] - - # path2 = os.path.expanduser('~/profile_result/worker1.pt.trace.json') - path2 = '/home/mike/git/kineto/tb_plugin/examples/result/datapipe0.1638835897553.pt.trace.json' - profile2 = load_profile('worker0', 1, path2) - roots1 = list(profile2.tid2tree.values()) - root1 = roots1[0] - - with timing('Compare operator tree', True): - node = compare_op_tree(root, root1) - - print_ops(node.children[4].left, prefix=' ') - print('========================================================') - print_ops(node.children[4].right) - - print('*********************** summary *************************') - with timing('Diff summary', True): - stats = diff_summary(node) - - # result = stats.flatten_diff_tree() - # path = '0-1-1' - # json_data = result[path].get_diff_node_summary(path) - print_node(stats, 0, 0) - - -if __name__ == '__main__': - unittest.main() diff --git a/plugins/tensorboard-plugins/tb_plugin/test/test_profiler.py b/plugins/tensorboard-plugins/tb_plugin/test/test_profiler.py deleted file mode 100644 index fda2208aa..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/test/test_profiler.py +++ /dev/null @@ -1,2752 +0,0 @@ -import gzip -import json -import os -import unittest - -from torch_tb_profiler.profiler.data import (DistributedRunProfileData, - RunProfileData) -from torch_tb_profiler.profiler.loader import RunLoader -from torch_tb_profiler.profiler.overall_parser import ProfileRole -from torch_tb_profiler.profiler.gpu_metrics_parser import GPUMetricsParser -from torch_tb_profiler.run import RunProfile - -SCHEMA_VERSION = 1 -WORKER_NAME = 'worker0' - - -def parse_json_trace(json_content, worker_name=WORKER_NAME) -> RunProfileData: - trace_json = json.loads(json_content) - trace_json = {'schemaVersion': 1, 'traceEvents': trace_json} - return RunProfileData.from_json(worker_name, 0, trace_json) - - -''' -All the events in json string are only simulation, not actual generated events. -We removed the data fields that not used by current version of our profiler, -for easy to check correctness and shorter in length. -We even renamed the data values such as kernel name or 'ts', to simplify the string. -''' - - -class TestProfiler(unittest.TestCase): - # A test case including all 7 event categories. - def test_all_categories(self): - json_content = """ - [{ - "ph": "X", "cat": "Operator", - "name": "enumerate(DataLoader)#_SingleProcessDataLoaderIter.__next__", "pid": 13721, "tid": "123", - "ts": 100, "dur": 180, - "args": {"Input Dims": [], "External id": 2} - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::to", "pid": 13721, "tid": "123", - "ts": 200, "dur": 60, - "args": {"Input Dims": [[2, 8, 5], [], [], [], [], [], [], []], "External id": 3} - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::nll_loss_backward", "pid": 13721, "tid": "456", - "ts": 340, "dur": 70, - "args": {"Input Dims": [[], [32, 1000], [32], [], [], [], []], "External id": 4} - }, - { - "ph": "X", "cat": "Operator", - "name": "ProfilerStep#1", "pid": 13721, "tid": "123", - "ts": 50, "dur": 400, - "args": {"Input Dims": [], "External id": 1} - }, - { - "ph": "X", "cat": "Memcpy", - "name": "Memcpy HtoD (Pageable -> Device)", "pid": 0, "tid": "stream 7", - "ts": 405, "dur": 10, - "args": {"stream": 7, "correlation": 334, "external id": 4} - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaMemcpyAsync", "pid": 13721, "tid": "456", - "ts": 360, "dur": 20, - "args": {"correlation": 334, "external id": 4} - }, - { - "ph": "X", "cat": "Memset", - "name": "Memset (Device)", "pid": 0, "tid": "stream 7", - "ts": 420, "dur": 5, - "args": {"stream": 7, "correlation": 40344, "external id": 4} - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaMemsetAsync", "pid": 13721, "tid": "456", - "ts": 390, "dur": 10, - "args": {"correlation": 40344, "external id": 4} - }, - { - "ph": "X", "cat": "Kernel", - "name": "void cunn_ClassNLLCriterion_updateGradInput_kernel", "pid": 0, "tid": "stream 7", - "ts": 430, "dur": 15, - "args": {"correlation": 40348, "external id": 4, "device": 0} - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 13721, "tid": "456", - "ts": 405, "dur": 5, - "args": {"correlation": 40348, "external id": 4} - }] - """ - profile = parse_json_trace(json_content) - profile.process() - - self.assertTrue(profile.has_runtime) - self.assertTrue(profile.has_kernel) - self.assertTrue(profile.has_memcpy_or_memset) - step = profile.steps_costs[0] - self.assertEqual(step.costs[ProfileRole.Kernel], 15) - self.assertEqual(step.costs[ProfileRole.Memcpy], 10) - self.assertEqual(step.costs[ProfileRole.Memset], 5) - self.assertEqual(step.costs[ProfileRole.Runtime], 30) - self.assertEqual(step.costs[ProfileRole.DataLoader], 180) - self.assertEqual(step.costs[ProfileRole.CpuOp], 35) - self.assertEqual(step.costs[ProfileRole.Other], 125) - - self.assertEqual(len(profile.op_list_groupby_name), 2) - self.assertEqual(len(profile.op_list_groupby_name_input), 2) - - def test_op_list(op_list): - op_count = 0 - for op_agg in op_list: - if op_agg.name == 'aten::to': - op_count += 1 - self.assertEqual(op_agg.input_shape, - '[[2, 8, 5], [], [], [], [], [], [], []]') - self.assertEqual(op_agg.calls, 1) - self.assertEqual(op_agg.host_duration, 60) - self.assertEqual(op_agg.device_duration, 0) - self.assertEqual(op_agg.self_host_duration, 60) - self.assertEqual(op_agg.self_device_duration, 0) - if op_agg.name == 'aten::nll_loss_backward': - op_count += 1 - self.assertEqual(op_agg.input_shape, - '[[], [32, 1000], [32], [], [], [], []]') - self.assertEqual(op_agg.calls, 1) - self.assertEqual(op_agg.host_duration, 70) - self.assertEqual(op_agg.device_duration, 30) - self.assertEqual( - op_agg.self_host_duration, 70 - 20 - 10 - 5) - self.assertEqual(op_agg.self_device_duration, 30) - self.assertEqual(op_count, 2) - - test_op_list(profile.op_list_groupby_name) - test_op_list(profile.op_list_groupby_name_input) - - self.assertEqual(len(profile.kernel_list_groupby_name_op), 1) - self.assertEqual(profile.kernel_stat.shape[0], 1) - self.assertEqual(profile.kernel_list_groupby_name_op[0].name, - 'void cunn_ClassNLLCriterion_updateGradInput_kernel') - self.assertEqual( - profile.kernel_list_groupby_name_op[0].op_name, 'aten::nll_loss_backward') - self.assertEqual(profile.kernel_list_groupby_name_op[0].calls, 1) - self.assertEqual( - profile.kernel_list_groupby_name_op[0].total_duration, 15) - self.assertEqual( - profile.kernel_list_groupby_name_op[0].min_duration, 15) - self.assertEqual( - profile.kernel_list_groupby_name_op[0].max_duration, 15) - self.assertEqual(profile.kernel_stat.iloc[0]['count'], 1) - self.assertEqual(profile.kernel_stat.iloc[0]['sum'], 15) - self.assertEqual(profile.kernel_stat.iloc[0]['mean'], 15) - self.assertEqual(profile.kernel_stat.iloc[0]['min'], 15) - self.assertEqual(profile.kernel_stat.iloc[0]['max'], 15) - - # Test using external_id to build relationship between Operator and Runtime. - # Use external_id to build correlation with its father OperatorNode or ProfilerStepNode. - # Because in the case when RuntimeNode has duration 0 and starts at same time as a OperatorNode, - # just use interval containing relationship can't tell it is child or brother of the OperatorNode. - def test_external_id(self): - json_content = """ - [{ - "ph": "X", "cat": "Operator", - "name": "aten::mat_mul", "pid": 13721, "tid": "456", - "ts": 100, "dur": 100, - "args": {"Input Dims": [], "External id": 2} - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::mm", "pid": 13721, "tid": "456", - "ts": 120, "dur": 70, - "args": {"Input Dims": [], "External id": 4} - }, - { - "ph": "X", "cat": "Kernel", - "name": "void cunn_ClassNLLCriterion_updateGradInput_kernel", "pid": 0, "tid": "stream 7", - "ts": 130, "dur": 5, - "args": {"correlation": 334, "external id": 4, "device": 0} - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 13721, "tid": "456", - "ts": 120, "dur": 0, - "args": {"correlation": 334, "external id": 4} - }, - { - "ph": "X", "cat": "Kernel", - "name": "void cunn_ClassNLLCriterion_updateGradInput_kernel", "pid": 0, "tid": "stream 7", - "ts": 130, "dur": 6, - "args": {"correlation": 335, "external id": 2, "device": 0} - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 13721, "tid": "456", - "ts": 120, "dur": 0, - "args": {"correlation": 335, "external id": 2} - }, - { - "ph": "X", "cat": "Kernel", - "name": "void cunn_ClassNLLCriterion_updateGradInput_kernel", "pid": 0, "tid": "stream 7", - "ts": 130, "dur": 7, - "args": {"correlation": 336, "external id": 4, "device": 0} - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 13721, "tid": "456", - "ts": 190, "dur": 0, - "args": {"correlation": 336, "external id": 4} - }, - { - "ph": "X", "cat": "Kernel", - "name": "void cunn_ClassNLLCriterion_updateGradInput_kernel", "pid": 0, "tid": "stream 7", - "ts": 130, "dur": 8, - "args": {"correlation": 337, "external id": 2, "device": 0} - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 13721, "tid": "456", - "ts": 190, "dur": 0, - "args": {"correlation": 337, "external id": 2} - }] - """ - profile = parse_json_trace(json_content) - profile.process() - - op_count = 0 - for op_agg in profile.op_list_groupby_name: - if op_agg.name == 'aten::mat_mul': - op_count += 1 - self.assertEqual(op_agg.device_duration, 5 + 6 + 7 + 8) - self.assertEqual(op_agg.self_device_duration, 6 + 8) - if op_agg.name == 'aten::mm': - op_count += 1 - self.assertEqual(op_agg.device_duration, 5 + 7) - self.assertEqual(op_agg.self_device_duration, 5 + 7) - self.assertEqual(op_count, 2) - - # Test operator's father-child relationship when they have same start time or end time. - def test_operator_relation(self): - # 2 events with same start time. - json_content = """ - [{ - "ph": "X", "cat": "Operator", - "name": "aten::mat_mul", "pid": 13721, "tid": "456", - "ts": 100, "dur": 100, - "args": {"Input Dims": [], "External id": 2} - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::mm", "pid": 13721, "tid": "456", - "ts": 100, "dur": 70, - "args": {"Input Dims": [], "External id": 4} - }] - """ - profile = parse_json_trace(json_content) - profile.process() - op_count = 0 - for op_agg in profile.op_list_groupby_name: - if op_agg.name == 'aten::mat_mul': - op_count += 1 - self.assertEqual(op_agg.self_host_duration, 100 - 70) - if op_agg.name == 'aten::mm': - op_count += 1 - self.assertEqual(op_agg.self_host_duration, 70) - self.assertEqual(op_count, 2) - - # 2 events with same end time. - json_content = """ - [{ - "ph": "X", "cat": "Operator", - "name": "aten::mat_mul", "pid": 13721, "tid": "456", - "ts": 100, "dur": 100, - "args": {"Input Dims": [], "External id": 2} - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::mm", "pid": 13721, "tid": "456", - "ts": 130, "dur": 70, - "args": {"Input Dims": [], "External id": 4} - }] - """ - profile = parse_json_trace(json_content) - profile.process() - op_count = 0 - for op_agg in profile.op_list_groupby_name: - if op_agg.name == 'aten::mat_mul': - op_count += 1 - self.assertEqual(op_agg.self_host_duration, 100 - 70) - if op_agg.name == 'aten::mm': - op_count += 1 - self.assertEqual(op_agg.self_host_duration, 70) - self.assertEqual(op_count, 2) - - # Test multiple father-child operators with same name. - # In this case, all the operators except the top operator should be removed, - # and all runtime/kernels belong to the children operators should be attached to the only kept one. - # This behavior is to keep consistent with _remove_dup_nodes in torch/autograd/profiler.py. - def test_remove_dup_nodes(self): - json_content = """[ - { - "ph": "X", "cat": "Operator", - "name": "aten::mm", "pid": 13721, "tid": "456", - "ts": 100, "dur": 100, - "args": {"Input Dims": [], "External id": 2} - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::mm", "pid": 13721, "tid": "456", - "ts": 110, "dur": 80, - "args": {"Input Dims": [], "External id": 3} - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::mm", "pid": 13721, "tid": "456", - "ts": 120, "dur": 60, - "args": {"Input Dims": [], "External id": 4} - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 13721, "tid": "456", - "ts": 130, "dur": 20, - "args": {"correlation": 335, "external id": 4} - }, - { - "ph": "X", "cat": "Kernel", - "name": "void gemmSN_TN_kernel_64addr", "pid": 0, "tid": "stream 7", - "ts": 220, "dur": 8, - "args": {"correlation": 335, "external id": 4, "device": 0} - } - ] - """ - profile = parse_json_trace(json_content) - profile.process() - self.assertEqual(len(profile.op_list_groupby_name), 1) - self.assertEqual( - profile.op_list_groupby_name[0].self_device_duration, 8) - - # Test Runtime with 'external id' 0. - # This kind of Runtime should not be attached to any operator, - # and should be included in accumulating device time. - def test_top_level_runtime(self): - # This operator is different thread with the runtime. - json_content = """[ - { - "ph": "X", "cat": "Operator", - "name": "aten::mm", "pid": 13721, "tid": "123", - "ts": 100, "dur": 100, - "args": {"Input Dims": [], "External id": 2} - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 13721, "tid": "456", - "ts": 130, "dur": 20, - "args": {"correlation": 335, "external id": 0} - }, - { - "ph": "X", "cat": "Kernel", - "name": "void gemmSN_TN_kernel_64addr", "pid": 0, "tid": "stream 7", - "ts": 220, "dur": 8, - "args": {"correlation": 335, "external id": 0, "device": 0} - } - ] - """ - profile = parse_json_trace(json_content) - profile.process() - self.assertEqual(profile.op_list_groupby_name[0].device_duration, 0) - self.assertEqual( - profile.op_list_groupby_name[0].self_device_duration, 0) - self.assertEqual(profile.kernel_stat.iloc[0]['count'], 1) - - # Test Runtime directly called in ProfilerStep, not inside any operator. - def test_runtime_called_by_profilerstep(self): - json_content = """[ - { - "ph": "X", "cat": "Operator", - "name": "ProfilerStep#1", "pid": 13721, "tid": "456", - "ts": 100, "dur": 300, - "args": {"Input Dims": [], "External id": 2} - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 13721, "tid": "456", - "ts": 130, "dur": 20, - "args": {"correlation": 335, "external id": 2} - }, - { - "ph": "X", "cat": "Kernel", - "name": "void gemmSN_TN_kernel_64addr", "pid": 0, "tid": "stream 7", - "ts": 220, "dur": 8, - "args": {"correlation": 335, "external id": 2, "device": 0} - } - ] - """ - profile = parse_json_trace(json_content) - profile.process() - step = profile.steps_costs[0] - self.assertEqual(step.costs[ProfileRole.Kernel], 8) - self.assertEqual(step.costs[ProfileRole.Runtime], 20) - self.assertEqual(step.costs[ProfileRole.CpuOp], 0) - self.assertEqual(step.costs[ProfileRole.Other], 300 - 8 - 20) - # ProfilerStep is not regarded as an operator. - self.assertEqual(len(profile.op_list_groupby_name), 0) - self.assertEqual(len(profile.op_list_groupby_name_input), 0) - self.assertEqual(profile.kernel_stat.iloc[0]['count'], 1) - self.assertEqual(len(profile.kernel_list_groupby_name_op), 1) - - # Test one Runtime lauch more than one Kernels. - # Sometimes such as running Bert using DataParallel mode(1 process, 2GPUs), - # one runtime such as cudaLaunchCooperativeKernelMultiDevice could trigger more than one kernel, - # each Kernel runs at a seperate GPU card. - def test_runtime_launch_multipe_kernels(self): - json_content = """[ - { - "ph": "X", "cat": "Operator", - "name": "Broadcast", "pid": 13721, "tid": "456", - "ts": 100, "dur": 300, - "args": {"Input Dims": [], "External id": 2} - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchCooperativeKernelMultiDevice", "pid": 13721, "tid": "456", - "ts": 130, "dur": 20, - "args": {"correlation": 335, "external id": 2} - }, - { - "ph": "X", "cat": "Kernel", - "name": "ncclBroadcastRingLLKernel_copy_i8(ncclColl)", "pid": 0, "tid": "stream 13", - "ts": 160, "dur": 120318, - "args": {"device": 0, "context": 1, "stream": 13, - "correlation": 335, "external id": 2, "device": 0} - }, - { - "ph": "X", "cat": "Kernel", - "name": "ncclBroadcastRingLLKernel_copy_i8(ncclColl)", "pid": 0, "tid": "stream 22", - "ts": 170, "dur": 132800, - "args": {"device": 0, "context": 2, "stream": 22, - "correlation": 335, "external id": 2} - } - ] - """ - profile = parse_json_trace(json_content) - profile.process() - self.assertEqual( - profile.op_list_groupby_name[0].device_duration, 120318 + 132800) - self.assertEqual(profile.kernel_stat.iloc[0]['count'], 2) - self.assertEqual(len(profile.kernel_list_groupby_name_op), 1) - - # Test when there is no ProfilerStep#. - def test_no_profilerstep(self): - json_content = """[ - { - "ph": "X", "cat": "Operator", - "name": "aten::to", "pid": 13721, "tid": "123", - "ts": 100, "dur": 60, - "args": {"Input Dims": [[2, 8, 5], [], [], [], [], [], [], []], "External id": 3} - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::nll_loss_backward", "pid": 13721, "tid": "456", - "ts": 300, "dur": 70, - "args": {"Input Dims": [[], [32, 1000], [32], [], [], [], []], "External id": 4} - }, - { - "ph": "X", "cat": "Kernel", - "name": "void cunn_ClassNLLCriterion_updateGradInput_kernel", "pid": 0, "tid": "stream 7", - "ts": 320, "dur": 100, - "args": {"correlation": 40348, "external id": 4, "device": 0} - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 13721, "tid": "456", - "ts": 310, "dur": 20, - "args": {"correlation": 40348, "external id": 4} - } - ] - """ - profile = parse_json_trace(json_content) - profile.process() - - self.assertTrue(profile.has_runtime) - self.assertTrue(profile.has_kernel) - self.assertTrue(not profile.has_memcpy_or_memset) - self.assertEqual(len(profile.steps_costs), 1) - step = profile.steps_costs[0] - - self.assertEqual(step.costs[ProfileRole.Kernel], 100) - self.assertEqual(step.costs[ProfileRole.Memcpy], 0) - self.assertEqual(step.costs[ProfileRole.Memset], 0) - self.assertEqual(step.costs[ProfileRole.Runtime], 320 - 310) - self.assertEqual(step.costs[ProfileRole.DataLoader], 0) - self.assertEqual(step.costs[ProfileRole.CpuOp], 60 + (310 - 300)) - # If no ProfilerStep, all events will be regarded as a step. - self.assertEqual(step.costs[ProfileRole.Other], 300 - (100 + 60)) - self.assertEqual(step.costs[ProfileRole.Total], (320 + 100) - 100) - self.assertEqual(len(profile.op_list_groupby_name), 2) - self.assertEqual(len(profile.op_list_groupby_name_input), 2) - self.assertEqual(profile.kernel_stat.iloc[0]['count'], 1) - self.assertEqual(len(profile.kernel_list_groupby_name_op), 1) - - def test_op_list(op_list): - op_count = 0 - for op_agg in op_list: - if op_agg.name == 'aten::to': - op_count += 1 - self.assertEqual(op_agg.input_shape, - '[[2, 8, 5], [], [], [], [], [], [], []]') - self.assertEqual(op_agg.calls, 1) - self.assertEqual(op_agg.host_duration, 60) - self.assertEqual(op_agg.device_duration, 0) - self.assertEqual(op_agg.self_host_duration, 60) - self.assertEqual(op_agg.self_device_duration, 0) - if op_agg.name == 'aten::nll_loss_backward': - op_count += 1 - self.assertEqual(op_agg.input_shape, - '[[], [32, 1000], [32], [], [], [], []]') - self.assertEqual(op_agg.calls, 1) - self.assertEqual(op_agg.host_duration, 70) - self.assertEqual(op_agg.device_duration, 100) - self.assertEqual(op_agg.self_host_duration, 70 - 20) - self.assertEqual(op_agg.self_device_duration, 100) - self.assertEqual(op_count, 2) - - test_op_list(profile.op_list_groupby_name) - test_op_list(profile.op_list_groupby_name_input) - - self.assertEqual(profile.kernel_list_groupby_name_op[0].name, - 'void cunn_ClassNLLCriterion_updateGradInput_kernel') - self.assertEqual( - profile.kernel_list_groupby_name_op[0].op_name, 'aten::nll_loss_backward') - self.assertEqual(profile.kernel_list_groupby_name_op[0].calls, 1) - self.assertEqual( - profile.kernel_list_groupby_name_op[0].total_duration, 100) - self.assertEqual( - profile.kernel_list_groupby_name_op[0].min_duration, 100) - self.assertEqual( - profile.kernel_list_groupby_name_op[0].max_duration, 100) - self.assertEqual(profile.kernel_stat.iloc[0]['count'], 1) - self.assertEqual(profile.kernel_stat.iloc[0]['sum'], 100) - self.assertEqual(profile.kernel_stat.iloc[0]['mean'], 100) - self.assertEqual(profile.kernel_stat.iloc[0]['min'], 100) - self.assertEqual(profile.kernel_stat.iloc[0]['max'], 100) - - # 2 steps without overlap with each other. - def test_multiple_profilersteps_no_overlap(self): - json_content = """ - [{ - "ph": "X", "cat": "Operator", - "name": "ProfilerStep#1", "pid": 13721, "tid": "123", - "ts": 100, "dur": 200, - "args": {"Input Dims": [], "External id": 1} - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::to", "pid": 13721, "tid": "123", - "ts": 200, "dur": 60, - "args": {"Input Dims": [[2, 8, 5], [], [], [], [], [], [], []], "External id": 2} - }, - { - "ph": "X", "cat": "Operator", - "name": "ProfilerStep#2", "pid": 13721, "tid": "123", - "ts": 350, "dur": 150, - "args": {"Input Dims": [], "External id": 3} - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::mm", "pid": 13721, "tid": "123", - "ts": 360, "dur": 50, - "args": {"Input Dims": [], "External id": 4} - }, - { - "ph": "X", "cat": "Memcpy", - "name": "Memcpy HtoD (Pageable -> Device)", "pid": 0, "tid": "stream 7", - "ts": 280, "dur": 40, - "args": {"stream": 7, "correlation": 334, "external id": 2} - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaMemcpyAsync", "pid": 13721, "tid": "123", - "ts": 250, "dur": 5, - "args": {"correlation": 334, "external id": 2} - }, - { - "ph": "X", "cat": "Kernel", - "name": "void cunn_ClassNLLCriterion_updateGradInput_kernel", "pid": 0, "tid": "stream 7", - "ts": 410, "dur": 200, - "args": {"correlation": 40348, "external id": 4, "device": 0} - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 13721, "tid": "123", - "ts": 400, "dur": 5, - "args": {"correlation": 40348, "external id": 4} - }] - """ - profile = parse_json_trace(json_content) - profile.process() - - self.assertTrue(profile.has_runtime) - self.assertTrue(profile.has_kernel) - self.assertTrue(profile.has_memcpy_or_memset) - self.assertEqual(len(profile.steps_costs), 2) - step = profile.steps_costs[0] - self.assertEqual(step.costs[ProfileRole.Kernel], 0) - self.assertEqual(step.costs[ProfileRole.Memcpy], 40) - self.assertEqual(step.costs[ProfileRole.Memset], 0) - self.assertEqual(step.costs[ProfileRole.Runtime], 5) - self.assertEqual(step.costs[ProfileRole.DataLoader], 0) - self.assertEqual(step.costs[ProfileRole.CpuOp], 60 - 5) - self.assertEqual(step.costs[ProfileRole.Other], 200 - 60 - 20) - # Device side takes effect. - self.assertEqual(step.costs[ProfileRole.Total], 320 - 100) - step = profile.steps_costs[1] - self.assertEqual(step.costs[ProfileRole.Kernel], 200) - self.assertEqual(step.costs[ProfileRole.Memcpy], 0) - self.assertEqual(step.costs[ProfileRole.Memset], 0) - self.assertEqual(step.costs[ProfileRole.Runtime], 5) - self.assertEqual(step.costs[ProfileRole.DataLoader], 0) - self.assertEqual(step.costs[ProfileRole.CpuOp], 50 - 5) - self.assertEqual(step.costs[ProfileRole.Other], 360 - 350) - # Device side takes effect. - self.assertEqual(step.costs[ProfileRole.Total], 610 - 350) - self.assertEqual( - profile.avg_costs.costs[ProfileRole.Total], ((320 - 100) + (610 - 350)) / 2) - - self.assertEqual(len(profile.op_list_groupby_name), 2) - self.assertEqual(len(profile.op_list_groupby_name_input), 2) - - def test_op_list(op_list): - op_count = 0 - for op_agg in op_list: - if op_agg.name == 'aten::to': - op_count += 1 - self.assertEqual(op_agg.input_shape, - '[[2, 8, 5], [], [], [], [], [], [], []]') - self.assertEqual(op_agg.calls, 1) - self.assertEqual(op_agg.host_duration, 60) - self.assertEqual(op_agg.device_duration, 40) - self.assertEqual(op_agg.self_host_duration, 60 - 5) - self.assertEqual(op_agg.self_device_duration, 40) - if op_agg.name == 'aten::mm': - op_count += 1 - self.assertEqual(op_agg.input_shape, '[]') - self.assertEqual(op_agg.calls, 1) - self.assertEqual(op_agg.host_duration, 50) - self.assertEqual(op_agg.device_duration, 200) - self.assertEqual(op_agg.self_host_duration, 50 - 5) - self.assertEqual(op_agg.self_device_duration, 200) - self.assertEqual(op_count, 2) - - test_op_list(profile.op_list_groupby_name) - test_op_list(profile.op_list_groupby_name_input) - - self.assertEqual(len(profile.kernel_list_groupby_name_op), 1) - self.assertEqual(profile.kernel_stat.shape[0], 1) - self.assertEqual(profile.kernel_list_groupby_name_op[0].name, - 'void cunn_ClassNLLCriterion_updateGradInput_kernel') - self.assertEqual( - profile.kernel_list_groupby_name_op[0].op_name, 'aten::mm') - self.assertEqual(profile.kernel_list_groupby_name_op[0].calls, 1) - self.assertEqual( - profile.kernel_list_groupby_name_op[0].total_duration, 200) - self.assertEqual( - profile.kernel_list_groupby_name_op[0].min_duration, 200) - self.assertEqual( - profile.kernel_list_groupby_name_op[0].max_duration, 200) - self.assertEqual(profile.kernel_stat.iloc[0]['count'], 1) - self.assertEqual(profile.kernel_stat.iloc[0]['sum'], 200) - self.assertEqual(profile.kernel_stat.iloc[0]['mean'], 200) - self.assertEqual(profile.kernel_stat.iloc[0]['min'], 200) - self.assertEqual(profile.kernel_stat.iloc[0]['max'], 200) - - # Test self time and total time on operator with nested operator. - def test_self_time(self): - json_content = """ - [{ - "ph": "X", "cat": "Operator", - "name": "aten::mat_mul", "pid": 13721, "tid": "456", - "ts": 100, "dur": 100, - "args": {"Input Dims": [], "External id": 2} - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::mm", "pid": 13721, "tid": "456", - "ts": 120, "dur": 40, - "args": {"Input Dims": [], "External id": 4} - }, - { - "ph": "X", "cat": "Kernel", - "name": "void cunn_ClassNLLCriterion_updateGradInput_kernel", "pid": 0, "tid": "stream 7", - "ts": 155, "dur": 20, - "args": {"correlation": 334, "external id": 4, "device": 0} - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 13721, "tid": "456", - "ts": 150, "dur": 10, - "args": {"correlation": 334, "external id": 4} - }, - { - "ph": "X", "cat": "Kernel", - "name": "void cunn_ClassNLLCriterion_updateGradInput_kernel", "pid": 0, "tid": "stream 7", - "ts": 210, "dur": 16, - "args": {"correlation": 335, "external id": 2, "device": 0} - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 13721, "tid": "456", - "ts": 170, "dur": 25, - "args": {"correlation": 335, "external id": 2} - }] - """ - profile = parse_json_trace(json_content) - - op_count = 0 - for op_agg in profile.op_list_groupby_name: - if op_agg.name == 'aten::mat_mul': - op_count += 1 - self.assertEqual(op_agg.host_duration, 100) - self.assertEqual(op_agg.device_duration, 20 + 16) - self.assertEqual(op_agg.self_host_duration, 100 - 40 - 25) - self.assertEqual(op_agg.self_device_duration, 16) - if op_agg.name == 'aten::mm': - op_count += 1 - self.assertEqual(op_agg.host_duration, 40) - self.assertEqual(op_agg.device_duration, 20) - self.assertEqual(op_agg.self_host_duration, 30) - self.assertEqual(op_agg.self_device_duration, 20) - self.assertEqual(op_count, 2) - - # 2 steps with overlap with each other. - def test_multiple_profilersteps_with_overlap(self): - # The kernel with 'correlation' as 123 is launched by previous step, - # its end time is bigger than 'ProfilerStep#1''s start time, - # so it is regarded as beginning of 'ProfilerStep#1'. - # The memcpy with 'correlation' as 334 is launched by 'ProfilerStep#1', - # its end time is bigger than 'ProfilerStep#2''s start time, - # so it is regarded as beginning of 'ProfilerStep#2'. - json_content = """ - [{ - "ph": "X", "cat": "Operator", - "name": "ProfilerStep#1", "pid": 13721, "tid": "123", - "ts": 100, "dur": 200, - "args": {"Input Dims": [], "External id": 1} - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::to", "pid": 13721, "tid": "123", - "ts": 200, "dur": 60, - "args": {"Input Dims": [[2, 8, 5], [], [], [], [], [], [], []], "External id": 2} - }, - { - "ph": "X", "cat": "Operator", - "name": "ProfilerStep#2", "pid": 13721, "tid": "123", - "ts": 350, "dur": 150, - "args": {"Input Dims": [], "External id": 3} - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::mm", "pid": 13721, "tid": "123", - "ts": 360, "dur": 50, - "args": {"Input Dims": [], "External id": 4} - }, - { - "ph": "X", "cat": "Kernel", - "name": "void cunn_ClassNLLCriterion_updateGradInput_kernel", "pid": 0, "tid": "stream 7", - "ts": 150, "dur": 90, - "args": {"correlation": 123, "external id": 0, "device": 0} - }, - { - "ph": "X", "cat": "Memcpy", - "name": "Memcpy HtoD (Pageable -> Device)", "pid": 0, "tid": "stream 7", - "ts": 280, "dur": 100, - "args": {"stream": 7, "correlation": 334, "external id": 2} - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaMemcpyAsync", "pid": 13721, "tid": "123", - "ts": 250, "dur": 5, - "args": {"correlation": 334, "external id": 2} - }, - { - "ph": "X", "cat": "Kernel", - "name": "void cunn_ClassNLLCriterion_updateGradInput_kernel", "pid": 0, "tid": "stream 7", - "ts": 410, "dur": 200, - "args": {"correlation": 40348, "external id": 4, "device": 0} - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 13721, "tid": "123", - "ts": 400, "dur": 5, - "args": {"correlation": 40348, "external id": 4} - }] - """ - profile = parse_json_trace(json_content) - profile.process() - - self.assertTrue(profile.has_runtime) - self.assertTrue(profile.has_kernel) - self.assertTrue(profile.has_memcpy_or_memset) - self.assertEqual(len(profile.steps_costs), 2) - step = profile.steps_costs[0] - self.assertEqual(step.costs[ProfileRole.Kernel], 0) - self.assertEqual(step.costs[ProfileRole.Memcpy], 100) - self.assertEqual(step.costs[ProfileRole.Memset], 0) - self.assertEqual(step.costs[ProfileRole.Runtime], 5) - self.assertEqual(step.costs[ProfileRole.DataLoader], 0) - self.assertEqual(step.costs[ProfileRole.CpuOp], - (200 + 60) - (150 + 90) - 5) - self.assertEqual(step.costs[ProfileRole.Other], 280 - (200 + 60)) - # Device side takes effect. - self.assertEqual(step.costs[ProfileRole.Total], - (280 + 100) - (150 + 90)) - step = profile.steps_costs[1] - self.assertEqual(step.costs[ProfileRole.Kernel], 200) - self.assertEqual(step.costs[ProfileRole.Memcpy], 0) - self.assertEqual(step.costs[ProfileRole.Memset], 0) - self.assertEqual(step.costs[ProfileRole.Runtime], 5) - self.assertEqual(step.costs[ProfileRole.DataLoader], 0) - self.assertEqual(step.costs[ProfileRole.CpuOp], - (280 + 100) - 360 + (410 - 405)) - self.assertEqual(step.costs[ProfileRole.Other], 0) - # Device side takes effect. - self.assertEqual(step.costs[ProfileRole.Total], 610 - (280 + 100)) - - # Test whether step time is calculated correctly when the last 2 steps have no kernels launched. - def test_last_steps_no_kernel(self): - json_content = """ - [{ - "ph": "X", "cat": "Operator", - "name": "ProfilerStep#1", "pid": 13721, "tid": "123", - "ts": 100, "dur": 200, - "args": {"Input Dims": [], "External id": 1} - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::to", "pid": 13721, "tid": "123", - "ts": 120, "dur": 10, - "args": {"Input Dims": [[2, 8, 5], [], [], [], [], [], [], []], "External id": 2} - }, - { - "ph": "X", "cat": "Operator", - "name": "ProfilerStep#2", "pid": 13721, "tid": "123", - "ts": 300, "dur": 100, - "args": {"Input Dims": [], "External id": 3} - }, - { - "ph": "X", "cat": "Operator", - "name": "ProfilerStep#3", "pid": 13721, "tid": "123", - "ts": 400, "dur": 50, - "args": {"Input Dims": [], "External id": 4} - }, - { - "ph": "X", "cat": "Kernel", - "name": "void cunn_ClassNLLCriterion_updateGradInput_kernel", "pid": 0, "tid": "stream 7", - "ts": 90, "dur": 20, - "args": {"correlation": 123, "external id": 0, "device": 0} - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaMemcpyAsync", "pid": 13721, "tid": "123", - "ts": 125, "dur": 5, - "args": {"correlation": 334, "external id": 2} - }, - { - "ph": "X", "cat": "Kernel", - "name": "void cunn_ClassNLLCriterion_updateGradInput_kernel", "pid": 0, "tid": "stream 7", - "ts": 150, "dur": 180, - "args": {"correlation": 334, "external id": 2, "device": 0} - }] - """ - profile = parse_json_trace(json_content) - profile.process() - - # The last 2 steps without kernels are removed from overall view. - self.assertEqual(len(profile.steps_costs), 1) - step = profile.steps_costs[0] - self.assertEqual( - step.costs[ProfileRole.Total], (150 + 180) - (90 + 20)) - - def test_pure_cpu(self): - json_content = """ - [{ - "ph": "X", "cat": "Operator", - "name": "ProfilerStep#1", "pid": 13721, "tid": "123", - "ts": 100, "dur": 200, - "args": {"Input Dims": [], "External id": 1} - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::to", "pid": 13721, "tid": "123", - "ts": 120, "dur": 10, - "args": {"Input Dims": [[2, 8, 5], [], [], [], [], [], [], []], "External id": 2} - }, - { - "ph": "X", "cat": "Operator", - "name": "ProfilerStep#2", "pid": 13721, "tid": "123", - "ts": 300, "dur": 100, - "args": {"Input Dims": [], "External id": 3} - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::mm", "pid": 13721, "tid": "123", - "ts": 350, "dur": 40, - "args": {"Input Dims": [], "External id": 4} - }] - """ - profile = parse_json_trace(json_content) - profile.process() - - self.assertEqual(len(profile.steps_costs), 2) - step = profile.steps_costs[0] - self.assertEqual(step.costs[ProfileRole.Kernel], 0) - self.assertEqual(step.costs[ProfileRole.Memcpy], 0) - self.assertEqual(step.costs[ProfileRole.Memset], 0) - self.assertEqual(step.costs[ProfileRole.Runtime], 0) - self.assertEqual(step.costs[ProfileRole.DataLoader], 0) - self.assertEqual(step.costs[ProfileRole.CpuOp], 10) - self.assertEqual(step.costs[ProfileRole.Other], 200 - 10) - self.assertEqual(step.costs[ProfileRole.Total], 200) - step = profile.steps_costs[1] - self.assertEqual(step.costs[ProfileRole.Kernel], 0) - self.assertEqual(step.costs[ProfileRole.Memcpy], 0) - self.assertEqual(step.costs[ProfileRole.Memset], 0) - self.assertEqual(step.costs[ProfileRole.Runtime], 0) - self.assertEqual(step.costs[ProfileRole.DataLoader], 0) - self.assertEqual(step.costs[ProfileRole.CpuOp], 40) - self.assertEqual(step.costs[ProfileRole.Other], 100 - 40) - self.assertEqual(step.costs[ProfileRole.Total], 100) - - # Test GPU utilization, est. SM efficiency, and occupancy. - def test_gpu_utilization(self): - json_content = """ - [{ - "ph": "X", "cat": "Operator", - "name": "aten::mat_mul", "pid": 13721, "tid": "456", - "ts": 100, "dur": 100, - "args": {"Input Dims": [], "External id": 2} - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::mm", "pid": 13721, "tid": "456", - "ts": 120, "dur": 70, - "args": {"Input Dims": [], "External id": 4} - }, - { - "ph": "X", "cat": "Kernel", - "name": "void cunn_ClassNLLCriterion_updateGradInput_kernel", "pid": 1, "tid": "stream 7", - "ts": 130, "dur": 10, - "args": {"correlation": 334, "external id": 4, "device": 1, - "blocks per SM": 0.5, "est. achieved occupancy %": 0.6} - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 13721, "tid": "456", - "ts": 120, "dur": 0, - "args": {"correlation": 334, "external id": 4} - }, - { - "ph": "X", "cat": "Kernel", - "name": "void gemmSN_TN_kernel_64addr", "pid": 1, "tid": "stream 8", - "ts": 135, "dur": 15, - "args": {"correlation": 335, "external id": 2, "device": 1, - "blocks per SM": 0.6, "est. achieved occupancy %": 0.1} - }, - { - "ph": "X", "cat": "Kernel", - "name": "void gemmSN_TN_kernel_64addr", "pid": 1, "tid": "stream 8", - "ts": 150, "dur": 0, - "args": {"correlation": 335, "external id": 2, "device": 1, - "blocks per SM": 0.3, "est. achieved occupancy %": 0.2} - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 13721, "tid": "456", - "ts": 120, "dur": 0, - "args": {"correlation": 335, "external id": 2} - }, - { - "ph": "X", "cat": "Kernel", - "name": "void cunn_ClassNLLCriterion_updateGradInput_kernel", "pid": 1, "tid": "stream 7", - "ts": 145, "dur": 25, - "args": {"correlation": 336, "external id": 4, "device": 1, - "blocks per SM": 0.3, "est. achieved occupancy %": 1.0} - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 13721, "tid": "456", - "ts": 125, "dur": 3, - "args": {"correlation": 336, "external id": 4} - }, - { - "ph": "X", "cat": "Kernel", - "name": "void cunn_ClassNLLCriterion_updateGradInput_kernel", "pid": 1, "tid": "stream 7", - "ts": 200, "dur": 20, - "args": {"correlation": 337, "external id": 2, "device": 1, - "blocks per SM": 10.5, "est. achieved occupancy %": 0.3} - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 13721, "tid": "456", - "ts": 195, "dur": 1, - "args": {"correlation": 337, "external id": 2} - }] - """ - profile = parse_json_trace(json_content) - profile.process() - - self.assertEqual(len(profile.gpu_metrics_parser.gpu_ids), 1) - self.assertAlmostEqual(profile.gpu_metrics_parser.gpu_utilization[1], (40 + 20) / 120) - self.assertAlmostEqual(profile.gpu_metrics_parser.avg_approximated_sm_efficiency_per_device[1], - (0.5 * (135 - 130) - + 1.0 * (140 - 135) - + 0.6 * (145 - 140) - + 0.9 * (150 - 145) - + 0.3 * (170 - 150) - + 1.0 * (220 - 200)) / (220 - 100)) - self.assertAlmostEqual(profile.gpu_metrics_parser.avg_occupancy_per_device[1], - (0.6 * 10 + 0.1 * 15 + 1.0 * 25 + 0.3 * 20) / (10 + 15 + 25 + 20)) - - gpu_util_expected = [(100, 0), (110, 0), (120, 0), (130, 1.0), (140, 1.0), (150, 1.0), (160, 1.0), - (170, 0), (180, 0), (190, 0), (200, 1.0), (210, 1.0), (220, 0)] - for gpu_id in profile.gpu_metrics_parser.gpu_ids: - buckets = profile.gpu_metrics_parser.gpu_util_buckets[gpu_id] - gpu_util_id = 0 - for b in buckets: - self.assertEqual(b[0], gpu_util_expected[gpu_util_id][0]) - self.assertAlmostEqual(b[1], gpu_util_expected[gpu_util_id][1]) - gpu_util_id += 1 - self.assertEqual(gpu_util_id, len(gpu_util_expected)) - - sm_efficiency_expected = [(130, 0.5), (135, 0), (135, 1.0), (140, 0), (140, 0.6), (145, 0), (145, 0.9), - (150, 0), (150, 0.3), (170, 0), (170, 0), (200, 0), (200, 1.0), (220, 0)] - for gpu_id in profile.gpu_metrics_parser.gpu_ids: - ranges = profile.gpu_metrics_parser.approximated_sm_efficiency_ranges[gpu_id] - sm_efficiency_id = 0 - for r in ranges: - self.assertEqual( - r[0], sm_efficiency_expected[sm_efficiency_id][0]) - self.assertAlmostEqual( - r[2], sm_efficiency_expected[sm_efficiency_id][1]) - sm_efficiency_id += 1 - self.assertEqual( - r[1], sm_efficiency_expected[sm_efficiency_id][0]) - self.assertAlmostEqual( - 0, sm_efficiency_expected[sm_efficiency_id][1]) - sm_efficiency_id += 1 - self.assertEqual(sm_efficiency_id, len(sm_efficiency_expected)) - - count = 0 - for agg_by_op in profile.kernel_list_groupby_name_op: - if agg_by_op.name == 'void gemmSN_TN_kernel_64addr' and agg_by_op.op_name == 'aten::mat_mul': - self.assertAlmostEqual(agg_by_op.avg_blocks_per_sm, 0.6) - self.assertAlmostEqual(agg_by_op.avg_occupancy, 0.1) - count += 1 - if agg_by_op.name == 'void cunn_ClassNLLCriterion_updateGradInput_kernel' and \ - agg_by_op.op_name == 'aten::mm': - self.assertAlmostEqual( - agg_by_op.avg_blocks_per_sm, (0.5 * 10 + 0.3 * 25) / (10 + 25)) - self.assertAlmostEqual( - agg_by_op.avg_occupancy, (0.6 * 10 + 1.0 * 25) / (10 + 25)) - count += 1 - if agg_by_op.name == 'void cunn_ClassNLLCriterion_updateGradInput_kernel' and \ - agg_by_op.op_name == 'aten::mat_mul': - self.assertAlmostEqual(agg_by_op.avg_blocks_per_sm, 10.5) - self.assertAlmostEqual(agg_by_op.avg_occupancy, 0.3) - count += 1 - self.assertEqual(count, 3) - - count = 0 - for _id, (name, row) in enumerate(profile.kernel_stat.iterrows()): - # The kernel with zero 'dur' should be ignored. - if name == 'void gemmSN_TN_kernel_64addr': - self.assertAlmostEqual(row['blocks_per_sm'], 0.6) - self.assertAlmostEqual(row['occupancy'], 0.1) - count += 1 - if name == 'void cunn_ClassNLLCriterion_updateGradInput_kernel': - self.assertAlmostEqual( - row['blocks_per_sm'], (0.5 * 10 + 0.3 * 25 + 10.5 * 20) / (10 + 25 + 20)) - self.assertAlmostEqual( - row['occupancy'], (0.6 * 10 + 1.0 * 25 + 0.3 * 20) / (10 + 25 + 20)) - count += 1 - self.assertEqual(count, 2) - - # Test GPU utilization 3 metrics works fine if kernel out of ProfilerStep. - def test_gpu_utilization_kernel_out_of_step(self): - json_content = """ - [{ - "ph": "X", "cat": "Operator", - "name": "aten::mat_mul", "pid": 13721, "tid": "456", - "ts": 10, "dur": 10, - "args": {"Input Dims": [], "External id": 1} - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::mm", "pid": 13721, "tid": "456", - "ts": 120, "dur": 70, - "args": {"Input Dims": [], "External id": 3} - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::mm", "pid": 13721, "tid": "456", - "ts": 220, "dur": 20, - "args": {"Input Dims": [], "External id": 4} - }, - { - "ph": "X", "cat": "Operator", - "name": "ProfilerStep#2", "pid": 13721, "tid": "456", - "ts": 100, "dur": 100, - "args": {"Input Dims": [], "External id": 2} - }, - { - "ph": "X", "cat": "Kernel", - "name": "void cunn_ClassNLLCriterion_updateGradInput_kernel", "pid": 1, "tid": "stream 7", - "ts": 60, "dur": 20, - "args": {"correlation": 334, "external id": 1, "device": 1, - "blocks per SM": 0.5, "est. achieved occupancy %": 0.6} - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 13721, "tid": "456", - "ts": 15, "dur": 5, - "args": {"correlation": 334, "external id": 1} - }, - { - "ph": "X", "cat": "Kernel", - "name": "void cunn_ClassNLLCriterion_updateGradInput_kernel", "pid": 1, "tid": "stream 7", - "ts": 240, "dur": 25, - "args": {"correlation": 337, "external id": 4, "device": 1, - "blocks per SM": 10.5, "est. achieved occupancy %": 0.3} - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 13721, "tid": "456", - "ts": 230, "dur": 10, - "args": {"correlation": 337, "external id": 4} - }] - """ - profile = parse_json_trace(json_content) - profile.process() - - self.assertEqual(len(profile.gpu_metrics_parser.gpu_ids), 1) - self.assertAlmostEqual(profile.gpu_metrics_parser.gpu_utilization[1], 0.0) - self.assertTrue(profile.gpu_metrics_parser.avg_approximated_sm_efficiency_per_device[1] is None) - self.assertTrue(profile.gpu_metrics_parser.avg_occupancy_per_device[1] is None) - self.assertTrue(profile.gpu_metrics_parser.blocks_per_sm_count[1] > 0) - self.assertTrue(profile.gpu_metrics_parser.occupancy_count[1] > 0) - - count = 0 - for agg_by_op in profile.kernel_list_groupby_name_op: - if agg_by_op.name == 'void cunn_ClassNLLCriterion_updateGradInput_kernel' \ - and agg_by_op.op_name == 'aten::mat_mul': - self.assertAlmostEqual(agg_by_op.avg_blocks_per_sm, 0.5) - self.assertAlmostEqual(agg_by_op.avg_occupancy, 0.6) - count += 1 - if agg_by_op.name == 'void cunn_ClassNLLCriterion_updateGradInput_kernel' and \ - agg_by_op.op_name == 'aten::mm': - self.assertAlmostEqual( - agg_by_op.avg_blocks_per_sm, 10.5) - self.assertAlmostEqual( - agg_by_op.avg_occupancy, 0.3) - count += 1 - self.assertEqual(count, 2) - - count = 0 - for _id, (name, row) in enumerate(profile.kernel_stat.iterrows()): - # The kernel with zero 'dur' should be ignored. - if name == 'void cunn_ClassNLLCriterion_updateGradInput_kernel': - self.assertAlmostEqual(row['blocks_per_sm'], (20 * 0.5 + 25 * 10.5) / (20 + 25)) - self.assertAlmostEqual(row['occupancy'], (20 * 0.6 + 25 * 0.3) / (20 + 25)) - count += 1 - self.assertEqual(count, 1) - - def test_dump_gpu_metrics(self): - profile = RunProfile('test_dump_gpu_metrics', None) - # Faked data for easy to see in UI. Real data values are 1/100 of these. - gpu_util_buckets = [[(1621401187223005, 0.0), (1621401187224005, 0.0), - (1621401187225005, 0.6), (1621401187226005, 0.5), - (1621401187227005, 0.6), (1621401187228005, 0.2), - (1621401187229005, 0.6), (1621401187230005, 0.1), - (1621401187231005, 0.5), (1621401187232005, 0.2), - (1621401187233005, 0.3), (1621401187234005, 0.4), - (1621401187235005, 0.4219409282700422), - (1621401187236901, 0)]] - # Faked data for easy to see in UI. Real data values are 1/10 of these. - approximated_sm_efficiency_ranges = \ - [[(1621401187225275, 1621401187225278, 0.25), (1621401187225530, 1621401187225532, 0.125), - (1621401187225820, 1621401187225821, 0.125), (1621401187226325, 1621401187226327, 0.25), - (1621401187226575, 1621401187226577, 0.125), (1621401187226912, 1621401187226913, 0.125), - (1621401187227092, 1621401187227094, 0.125), (1621401187227619, 1621401187227620, 0.125), - (1621401187227745, 1621401187227746, 0.125), (1621401187227859, 1621401187227860, 0.125), - (1621401187227973, 1621401187227974, 0.125), (1621401187228279, 1621401187228280, 0.125), - (1621401187228962, 1621401187228963, 0.125), (1621401187229153, 1621401187229155, 0.125), - (1621401187229711, 1621401187229715, 0.125), (1621401187230162, 1621401187230163, 0.125), - (1621401187231100, 1621401187231103, 0.125), (1621401187231692, 1621401187231694, 0.5), - (1621401187232603, 1621401187232604, 0.125), (1621401187232921, 1621401187232922, 0.125), - (1621401187233342, 1621401187233343, 0.125), (1621401187233770, 1621401187233772, 0.125), - (1621401187234156, 1621401187234159, 0.125), (1621401187234445, 1621401187234446, 0.125), - (1621401187235025, 1621401187235028, 0.125), (1621401187235555, 1621401187235556, 0.125), - (1621401187236158, 1621401187236159, 0.125), (1621401187236278, 1621401187236279, 0.125), - (1621401187236390, 1621401187236391, 0.125), (1621401187236501, 1621401187236502, 0.125)]] - - basedir = os.path.dirname(os.path.realpath(__file__)) - trace_json_flat_path = os.path.join(basedir, 'gpu_metrics_input.json') - gpu_metrics_parser = GPUMetricsParser() - gpu_metrics_parser.gpu_util_buckets = gpu_util_buckets - gpu_metrics_parser.approximated_sm_efficiency_ranges = approximated_sm_efficiency_ranges - profile.gpu_metrics = gpu_metrics_parser.get_gpu_metrics() - with open(trace_json_flat_path, 'rb') as file: - raw_data = file.read() - data_with_gpu_metrics_compressed = profile.append_gpu_metrics(raw_data) - data_with_gpu_metrics_flat = gzip.decompress( - data_with_gpu_metrics_compressed) - - trace_json_expected_path = os.path.join(basedir, 'gpu_metrics_expected.json') - with open(trace_json_expected_path, 'rb') as file: - data_expected = file.read() - - # Parse to json in order to ignore text format difference. - data_with_gpu_metrics_json = json.loads( - data_with_gpu_metrics_flat.decode('utf8')) - data_expected_json = json.loads(data_expected.decode('utf8')) - data_with_gpu_metrics_str = json.dumps( - data_with_gpu_metrics_json, sort_keys=True) - data_expected_str = json.dumps(data_expected_json, sort_keys=True) - - self.assertEqual(data_with_gpu_metrics_str, data_expected_str) - - try: - _ = json.loads(data_with_gpu_metrics_flat.decode('utf8')) - except Exception: - self.assertTrue( - False, 'The string fails to be parsed by json after appending gpu metrics.') - - def test_memory_view(self): - json_content = """[ - { - "ph": "X", "cat": "Operator", - "name": "aten::to", "pid": 13721, "tid": "123", - "ts": 10, "dur": 10, - "args": {"Input Dims": [], "External id": 2} - }, - { - "ph": "X", "cat": "Operator", - "name": "enumerate(DataLoader)#_SingleProcessDataLoaderIter.__next__", "pid": 13721, "tid": "123", - "ts": 100, "dur": 180, - "args": {"Input Dims": [], "External id": 2} - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::to", "pid": 13721, "tid": "123", - "ts": 200, "dur": 60, - "args": {"Input Dims": [[2, 8, 5], [], [], [], [], [], [], []], "External id": 3} - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::nll_loss_backward", "pid": 13721, "tid": "123", - "ts": 340, "dur": 70, - "args": {"Input Dims": [[], [32, 1000], [32], [], [], [], []], "External id": 4} - }, - { - "ph": "X", "cat": "Operator", - "name": "ProfilerStep#1", "pid": 13721, "tid": "123", - "ts": 50, "dur": 400, - "args": {"Input Dims": [], "External id": 1} - }, - { - "ph": "X", "cat": "Operator", - "name": "ProfilerStep#2", "pid": 13721, "tid": "123", - "ts": 500, "dur": 500, - "args": {"Input Dims": [], "External id": 1} - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::to", "pid": 13721, "tid": "123", - "ts": 510, "dur": 150, - "args": {"Input Dims": [[2, 8, 5], [], [], [], [], [], [], []], "External id": 3} - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::copy_", "pid": 13721, "tid": "123", - "ts": 520, "dur": 100, - "args": {"Input Dims": [[2, 8, 5], [], [], [], [], [], [], []], "External id": 3} - }, - - { - "ph": "X", "cat": "Operator", - "name": "aten::liner", "pid": 13721, "tid": "123", - "ts": 700, "dur": 100, - "args": {"Input Dims": [[2, 8, 5], [], [], [], [], [], [], []], "External id": 3} - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::t", "pid": 13721, "tid": "123", - "ts": 705, "dur": 40, - "args": {"Input Dims": [[2, 8, 5], [], [], [], [], [], [], []], "External id": 3} - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::transpose", "pid": 13721, "tid": "123", - "ts": 710, "dur": 30, - "args": {"Input Dims": [[2, 8, 5], [], [], [], [], [], [], []], "External id": 3} - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::tranas_stride", "pid": 13721, "tid": "123", - "ts": 720, "dur": 10, - "args": {"Input Dims": [[2, 8, 5], [], [], [], [], [], [], []], "External id": 3} - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::addmm", "pid": 13721, "tid": "123", - "ts": 750, "dur": 40, - "args": {"Input Dims": [[2, 8, 5], [], [], [], [], [], [], []], "External id": 3} - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::to", "pid": 13721, "tid": "123", - "ts": 900, "dur": 100, - "args": {"Input Dims": [[2, 8, 5], [], [], [], [], [], [], []], "External id": 3} - }, - { - "ph": "X", "cat": "Memcpy", - "name": "Memcpy HtoD (Pageable -> Device)", "pid": 0, "tid": "stream 7", - "ts": 405, "dur": 10, - "args": {"stream": 7, "correlation": 334, "external id": 4} - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaMemcpyAsync", "pid": 13721, "tid": "456", - "ts": 360, "dur": 20, - "args": {"correlation": 334, "external id": 4} - }, - { - "ph": "X", "cat": "Memset", - "name": "Memset (Device)", "pid": 0, "tid": "stream 7", - "ts": 420, "dur": 5, - "args": {"stream": 7, "correlation": 40344, "external id": 4} - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaMemsetAsync", "pid": 13721, "tid": "456", - "ts": 390, "dur": 10, - "args": {"correlation": 40344, "external id": 4} - }, - { - "ph": "X", "cat": "Kernel", - "name": "void cunn_ClassNLLCriterion_updateGradInput_kernel", "pid": 0, "tid": "stream 7", - "ts": 430, "dur": 15, - "args": {"correlation": 40348, "external id": 4, "device": 0} - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 13721, "tid": "456", - "ts": 405, "dur": 5, - "args": {"correlation": 40348, "external id": 4} - }, - - - { - "ph": "i", "s": "t", "name": "[memory]", - "pid": 13721, "tid": 123, - "ts": 90, - "args": { - "Device Type": 0, "Device Id": -1, "Addr": 90, "Bytes": 4 - } - }, - { - "ph": "i", "s": "t", "name": "[memory]", - "pid": 13721, "tid": 123, - "ts": 150, - "args": { - "Device Type": 0, "Device Id": -1, "Addr": 150, "Bytes": 4 - } - }, - { - "ph": "i", "s": "t", "name": "[memory]", - "pid": 13721, "tid": 123, - "ts": 200, - "args": { - "Device Type": 0, "Device Id": -1, "Addr": 200, "Bytes": 4 - } - }, - { - "ph": "i", "s": "t", "name": "[memory]", - "pid": 13721, "tid": 123, - "ts": 210, - "args": { - "Device Type": 1, "Device Id": 0, "Addr": 210, "Bytes": 4 - } - }, - { - "ph": "i", "s": "t", "name": "[memory]", - "pid": 13721, "tid": 123, - "ts": 265, - "args": { - "Device Type": 1, "Device Id": 0, "Addr": 265, "Bytes": 4 - } - }, - { - "ph": "i", "s": "t", "name": "[memory]", - "pid": 13721, "tid": 123, - "ts": 300, - "args": { - "Device Type": 1, "Device Id": 0, "Addr": 300, "Bytes": 4 - } - }, - { - "ph": "i", "s": "t", "name": "[memory]", - "pid": 13721, "tid": 123, - "ts": 350, - "args": { - "Device Type": 1, "Device Id": 0, "Addr": 350, "Bytes": 10 - } - }, - { - "ph": "i", "s": "t", "name": "[memory]", - "pid": 13721, "tid": 123, - "ts": 360, - "args": { - "Device Type": 1, "Device Id": 0, "Addr": 350, "Bytes": -10 - } - }, - { - "ph": "i", "s": "t", "name": "[memory]", - "pid": 13721, "tid": 123, - "ts": 450, - "args": { - "Device Type": 0, "Device Id": -1, "Addr": 450, "Bytes": 1000000 - } - }, - - { - "ph": "i", "s": "t", "name": "[memory]", - "pid": 13721, "tid": 123, - "ts": 515, - "args": { - "Device Type": 1, "Device Id": 0, "Addr": 515, "Bytes": 100 - } - }, - { - "ph": "i", "s": "t", "name": "[memory]", - "pid": 13721, "tid": 123, - "ts": 520, - "args": { - "Device Type": 1, "Device Id": 0, "Addr": 520, "Bytes": 100 - } - }, - { - "ph": "i", "s": "t", "name": "[memory]", - "pid": 13721, "tid": 123, - "ts": 600, - "args": { - "Device Type": 1, "Device Id": 0, "Addr": 520, "Bytes": -100 - } - }, - { - "ph": "i", "s": "t", "name": "[memory]", - "pid": 13721, "tid": 123, - "ts": 690, - "args": { - "Device Type": 1, "Device Id": 0, "Addr": 690, "Bytes": 100 - } - }, - { - "ph": "i", "s": "t", "name": "[memory]", - "pid": 13721, "tid": 123, - "ts": 701, - "args": { - "Device Type": 1, "Device Id": 0, "Addr": 701, "Bytes": 100 - } - }, - { - "ph": "i", "s": "t", "name": "[memory]", - "pid": 13721, "tid": 123, - "ts": 796, - "args": { - "Device Type": 1, "Device Id": 0, "Addr": 515, "Bytes": -100 - } - }, - - { - "ph": "i", "s": "t", "name": "[memory]", - "pid": 13721, "tid": 123, - "ts": 708, - "args": { - "Device Type": 1, "Device Id": 0, "Addr": 708, "Bytes": 100 - } - }, - { - "ph": "i", "s": "t", "name": "[memory]", - "pid": 13721, "tid": 123, - "ts": 742, - "args": { - "Device Type": 1, "Device Id": 0, "Addr": 708, "Bytes": -100 - } - }, - { - "ph": "i", "s": "t", "name": "[memory]", - "pid": 13721, "tid": 123, - "ts": 715, - "args": { - "Device Type": 1, "Device Id": 0, "Addr": 715, "Bytes": 50 - } - }, - { - "ph": "i", "s": "t", "name": "[memory]", - "pid": 13721, "tid": 123, - "ts": 735, - "args": { - "Device Type": 1, "Device Id": 0, "Addr": 715, "Bytes": -50 - } - }, - { - "ph": "i", "s": "t", "name": "[memory]", - "pid": 13721, "tid": 123, - "ts": 725, - "args": { - "Device Type": 1, "Device Id": 0, "Addr": 725, "Bytes": 50 - } - }, - { - "ph": "i", "s": "t", "name": "[memory]", - "pid": 13721, "tid": 123, - "ts": 728, - "args": { - "Device Type": 1, "Device Id": 0, "Addr": 725, "Bytes": -50 - } - }, - { - "ph": "i", "s": "t", "name": "[memory]", - "pid": 13721, "tid": 123, - "ts": 729, - "args": { - "Device Type": 0, "Device Id": -1, "Addr": 729, "Bytes": 50 - } - }, - { - "ph": "i", "s": "t", "name": "[memory]", - "pid": 13721, "tid": 123, - "ts": 746, - "args": { - "Device Type": 0, "Device Id": -1, "Addr": 746, "Bytes": 100 - } - }, - { - "ph": "i", "s": "t", "name": "[memory]", - "pid": 13721, "tid": 123, - "ts": 747, - "args": { - "Device Type": 1, "Device Id": 0, "Addr": 747, "Bytes": 20 - } - }, - { - "ph": "i", "s": "t", "name": "[memory]", - "pid": 13721, "tid": 123, - "ts": 749, - "args": { - "Device Type": 0, "Device Id": -1, "Addr": 690, "Bytes": -100 - } - }, - { - "ph": "i", "s": "t", "name": "[memory]", - "pid": 13721, "tid": 123, - "ts": 760, - "args": { - "Device Type": 1, "Device Id": 0, "Addr": 760, "Bytes": 30 - } - }, - { - "ph": "i", "s": "t", "name": "[memory]", - "pid": 13721, "tid": 123, - "ts": 780, - "args": { - "Device Type": 1, "Device Id": 0, "Addr": 760, "Bytes": -30 - } - }, - { - "ph": "i", "s": "t", "name": "[memory]", - "pid": 13721, "tid": 123, - "ts": 795, - "args": { - "Device Type": 1, "Device Id": 0, "Addr": 795, "Bytes": 10 - } - }, - { - "ph": "i", "s": "t", "name": "[memory]", - "pid": 13721, "tid": 123, - "ts": 799, - "args": { - "Device Type": 1, "Device Id": 0, "Addr": 795, "Bytes": -10 - } - } - ] - """ - import logging - - from torch_tb_profiler.utils import get_logger - logger = get_logger() - logger.addHandler(logging.StreamHandler()) - - profile = parse_json_trace(json_content) - profile.process() - memory_stats = profile.memory_snapshot.get_memory_statistics(profile.tid2tree) - - self.assertEqual(len(memory_stats), 2) - self.assertIn('GPU0', memory_stats) - - # validation - gpu_expected_data = { - # self increase size, self allocation size, self allocation count, increase size, allocation size, allocation count, call # noqa: E501 - 'aten::to': [104, 104, 2, 104, 204, 3, 4], - 'aten::nll_loss_backward': [0, 10, 1, 0, 10, 1, 1], - 'aten::copy_': [0, 100, 1, 0, 100, 1, 1], - 'aten::addmm': [0, 30, 1, 0, 30, 1, 1], - 'aten::tranas_stride': [0, 50, 1, 0, 50, 1, 1], - 'aten::transpose': [0, 50, 1, 0, 100, 2, 1], - 'aten::t': [0, 100, 1, 0, 200, 3, 1], - 'aten::liner': [20, 130, 3, 20, 360, 7, 1] - } - - cpu_expected_data = { - 'aten::to': [4, 4, 1, 4, 4, 1, 4], - 'aten::liner': [0, 100, 1, 50, 150, 2, 1], - 'aten::tranas_stride': [50, 50, 1, 50, 50, 1, 1], - 'aten::transpose': [0, 0, 0, 50, 50, 1, 1], - 'aten::t': [0, 0, 0, 50, 50, 1, 1] - } - - validate_data = [ - (memory_stats['CPU'], cpu_expected_data), - (memory_stats['GPU0'], gpu_expected_data) - ] - for (mem_stat, expected_data) in validate_data: - for name, values in expected_data.items(): - self.assertEqual(mem_stat[name], values) - - # Test group by 'kernel detail + op name'. - def test_group_by_kernel_columns(self): - json_content = """[ - { - "ph": "X", "cat": "Operator", - "name": "op1", "pid": 13721, "tid": "123", - "ts": 200, "dur": 60, - "args": {"Input Dims": [[2, 8, 5], [], [], [], [], [], [], []], "External id": 3} - }, - { - "ph": "X", "cat": "Operator", - "name": "op2", "pid": 13721, "tid": "456", - "ts": 340, "dur": 70, - "args": {"Input Dims": [[], [32, 1000], [32], [], [], [], []], "External id": 4} - }, - { - "ph": "X", "cat": "Kernel", - "name": "kernel1", "pid": 0, "tid": "stream 7", - "ts": 230, "dur": 15, - "args": {"correlation": 1000, "external id": 3, "device": 0, - "grid": [16, 1, 1], "block": [16, 16, 16], "registers per thread": 18, "shared memory": 0} - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 13721, "tid": "456", - "ts": 210, "dur": 5, - "args": {"correlation": 1000, "external id": 3} - }, - { - "ph": "X", "cat": "Kernel", - "name": "kernel1", "pid": 0, "tid": "stream 7", - "ts": 250, "dur": 10, - "args": {"correlation": 1001, "external id": 3, "device": 0, - "grid": [16, 1, 1], "block": [16, 16, 16], "registers per thread": 18, "shared memory": 0} - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 13721, "tid": "456", - "ts": 215, "dur": 5, - "args": {"correlation": 1001, "external id": 3} - }, - { - "ph": "X", "cat": "Kernel", - "name": "kernel1", "pid": 0, "tid": "stream 7", - "ts": 250, "dur": 13, - "args": {"correlation": 1002, "external id": 3, "device": 0, - "grid": [16, 1, 1], "block": [16, 16, 64], "registers per thread": 18, "shared memory": 0} - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 13721, "tid": "456", - "ts": 220, "dur": 5, - "args": {"correlation": 1002, "external id": 3} - }, - { - "ph": "X", "cat": "Kernel", - "name": "kernel1", "pid": 0, "tid": "stream 7", - "ts": 250, "dur": 17, - "args": {"correlation": 1003, "external id": 4, "device": 0, - "grid": [16, 1, 1], "block": [16, 16, 64], "registers per thread": 18, "shared memory": 0} - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 13721, "tid": "456", - "ts": 350, "dur": 5, - "args": {"correlation": 1003, "external id": 4} - } - ] - """ - profile = parse_json_trace(json_content) - profile.process() - expected_agg_kernels = [ - { - 'name': 'kernel1', - 'op_name': 'op1', - 'grid': '[16, 1, 1]', - 'block': '[16, 16, 16]', - 'registers per thread': 18, - 'shared memory': 0, - 'calls': 2, - 'total_duration': 15 + 10, - 'avg_duration': (15 + 10) / 2, - 'min_duration': min(15, 10), - 'max_duration': max(15, 10) - }, - { - 'name': 'kernel1', - 'op_name': 'op1', - 'grid': '[16, 1, 1]', - 'block': '[16, 16, 64]', # Only changed this. - 'registers per thread': 18, - 'shared memory': 0, - 'calls': 1, - 'total_duration': 13, - 'avg_duration': 13, - 'min_duration': 13, - 'max_duration': 13 - }, - { - 'name': 'kernel1', - 'op_name': 'op2', # Only changed this. - 'grid': '[16, 1, 1]', - 'block': '[16, 16, 64]', - 'registers per thread': 18, - 'shared memory': 0, - 'calls': 1, - 'total_duration': 17, - 'avg_duration': 17, - 'min_duration': 17, - 'max_duration': 17 - } - ] - index = 0 - self.assertEqual(len(profile.kernel_list_groupby_name_op), len(expected_agg_kernels)) - for agg_kernel in profile.kernel_list_groupby_name_op: - expected_agg_kernel = expected_agg_kernels[index] - self.assertEqual(agg_kernel.name, expected_agg_kernel['name']) - self.assertEqual(agg_kernel.op_name, expected_agg_kernel['op_name']) - self.assertEqual(str(agg_kernel.grid), expected_agg_kernel['grid']) - self.assertEqual(str(agg_kernel.block), expected_agg_kernel['block']) - self.assertEqual(agg_kernel.regs_per_thread, expected_agg_kernel['registers per thread']) - self.assertEqual(agg_kernel.shared_memory, expected_agg_kernel['shared memory']) - self.assertEqual(agg_kernel.calls, expected_agg_kernel['calls']) - self.assertEqual(agg_kernel.total_duration, expected_agg_kernel['total_duration']) - self.assertAlmostEqual(agg_kernel.avg_duration, expected_agg_kernel['avg_duration']) - self.assertEqual(agg_kernel.min_duration, expected_agg_kernel['min_duration']) - self.assertEqual(agg_kernel.max_duration, expected_agg_kernel['max_duration']) - index += 1 - - # Test group by 'kernel detail + op name' with invalid input lack of some kernel field - def test_group_by_kernel_columns_invalid_input(self): - json_content = """[ - { - "ph": "X", "cat": "Operator", - "name": "op1", "pid": 13721, "tid": "123", - "ts": 200, "dur": 60, - "args": {"Input Dims": [[2, 8, 5], [], [], [], [], [], [], []], "External id": 3} - }, - { - "ph": "X", "cat": "Kernel", - "name": "kernel1", "pid": 0, "tid": "stream 7", - "ts": 220, "dur": 1, - "args": {"correlation": 1000, "external id": 3, "device": 0, - "block": [16, 16, 16], "registers per thread": 18, "shared memory": 0} - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 13721, "tid": "456", - "ts": 210, "dur": 5, - "args": {"correlation": 1000, "external id": 3} - }, - { - "ph": "X", "cat": "Kernel", - "name": "kernel1", "pid": 0, "tid": "stream 7", - "ts": 230, "dur": 2, - "args": {"correlation": 1001, "external id": 3, "device": 0, - "grid": [16, 1, 1], "registers per thread": 18, "shared memory": 0} - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 13721, "tid": "456", - "ts": 220, "dur": 5, - "args": {"correlation": 1001, "external id": 3} - }, - { - "ph": "X", "cat": "Kernel", - "name": "kernel1", "pid": 0, "tid": "stream 7", - "ts": 240, "dur": 3, - "args": {"correlation": 1002, "external id": 3, "device": 0, - "grid": [16, 1, 1], "block": [16, 16, 16], "shared memory": 0} - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 13721, "tid": "456", - "ts": 230, "dur": 5, - "args": {"correlation": 1002, "external id": 3} - }, - { - "ph": "X", "cat": "Kernel", - "name": "kernel1", "pid": 0, "tid": "stream 7", - "ts": 250, "dur": 4, - "args": {"correlation": 1003, "external id": 3, "device": 0, - "grid": [16, 1, 1], "block": [16, 16, 16], "registers per thread": 18} - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 13721, "tid": "456", - "ts": 240, "dur": 5, - "args": {"correlation": 1003, "external id": 3} - }, - { - "ph": "X", "cat": "Kernel", - "name": "kernel1", "pid": 0, "tid": "stream 7", - "ts": 260, "dur": 5, - "args": {"correlation": 1004, "external id": 3, "device": 0} - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 13721, "tid": "456", - "ts": 250, "dur": 5, - "args": {"correlation": 1004, "external id": 3} - } - ] - """ - profile = parse_json_trace(json_content) - profile.process() - expected_agg_kernels = [ - { - 'name': 'kernel1', - 'op_name': 'op1', - 'grid': None, - 'block': [16, 16, 16], - 'registers per thread': 18, - 'shared memory': 0, - 'calls': 1, - 'total_duration': 1, - 'avg_duration': 1, - 'min_duration': 1, - 'max_duration': 1 - }, - { - 'name': 'kernel1', - 'op_name': 'op1', - 'grid': [16, 1, 1], - 'block': None, - 'registers per thread': 18, - 'shared memory': 0, - 'calls': 1, - 'total_duration': 2, - 'avg_duration': 2, - 'min_duration': 2, - 'max_duration': 2 - }, - { - 'name': 'kernel1', - 'op_name': 'op1', - 'grid': [16, 1, 1], - 'block': [16, 16, 16], - 'registers per thread': None, - 'shared memory': 0, - 'calls': 1, - 'total_duration': 3, - 'avg_duration': 3, - 'min_duration': 3, - 'max_duration': 3 - }, - { - 'name': 'kernel1', - 'op_name': 'op1', - 'grid': [16, 1, 1], - 'block': [16, 16, 16], - 'registers per thread': 18, - 'shared memory': None, - 'calls': 1, - 'total_duration': 4, - 'avg_duration': 4, - 'min_duration': 4, - 'max_duration': 4 - }, - { - 'name': 'kernel1', - 'op_name': 'op1', - 'grid': None, - 'block': None, - 'registers per thread': None, - 'shared memory': None, - 'calls': 1, - 'total_duration': 5, - 'avg_duration': 5, - 'min_duration': 5, - 'max_duration': 5 - } - ] - index = 0 - self.assertEqual(len(profile.kernel_list_groupby_name_op), len(expected_agg_kernels)) - for agg_kernel in profile.kernel_list_groupby_name_op: - expected_agg_kernel = expected_agg_kernels[index] - self.assertEqual(agg_kernel.name, expected_agg_kernel['name']) - self.assertEqual(agg_kernel.op_name, expected_agg_kernel['op_name']) - self.assertEqual(agg_kernel.grid, expected_agg_kernel['grid']) - self.assertEqual(agg_kernel.block, expected_agg_kernel['block']) - self.assertEqual(agg_kernel.regs_per_thread, expected_agg_kernel['registers per thread']) - print(agg_kernel.name, agg_kernel.grid, agg_kernel.block, agg_kernel.shared_memory) - self.assertEqual(agg_kernel.shared_memory, expected_agg_kernel['shared memory']) - self.assertEqual(agg_kernel.calls, expected_agg_kernel['calls']) - self.assertEqual(agg_kernel.total_duration, expected_agg_kernel['total_duration']) - self.assertAlmostEqual(agg_kernel.avg_duration, expected_agg_kernel['avg_duration']) - self.assertEqual(agg_kernel.min_duration, expected_agg_kernel['min_duration']) - self.assertEqual(agg_kernel.max_duration, expected_agg_kernel['max_duration']) - index += 1 - - # Test tensor core related feature. - def test_tensor_core(self): - json_content = """[ - { - "ph": "X", "cat": "Operator", - "name": "aten::conv2d", "pid": 13721, "tid": "123", - "ts": 200, "dur": 100, - "args": {"Input Dims": [[]], "External id": 3} - }, - { - "ph": "X", "cat": "Operator", - "name": "op_no_tc", "pid": 13721, "tid": "123", - "ts": 205, "dur": 10, - "args": {"Input Dims": [[]], "External id": 4} - }, - { - "ph": "X", "cat": "Operator", - "name": "aten::cudnn_convolution", "pid": 13721, "tid": "123", - "ts": 215, "dur": 10, - "args": {"Input Dims": [[]], "External id": 5} - }, - { - "ph": "X", "cat": "Kernel", - "name": "kernel_no_tc", "pid": 0, "tid": "stream 7", - "ts": 210, "dur": 10, - "args": {"correlation": 1000, "external id": 4, "device": 0} - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 13721, "tid": "123", - "ts": 205, "dur": 5, - "args": {"correlation": 1000, "external id": 4} - }, - { - "ph": "X", "cat": "Kernel", - "name": "volta_fp16_s884cudnn_fp16_128x128_ldg8_splitK_relu_f2f_exp_small_nhwc_tn_v1", - "pid": 0, "tid": "stream 7", - "ts": 220, "dur": 15, - "args": {"correlation": 1001, "external id": 5, "device": 0} - }, - { - "ph": "X", "cat": "Runtime", - "name": "cudaLaunchKernel", "pid": 13721, "tid": "123", - "ts": 215, "dur": 5, - "args": {"correlation": 1001, "external id": 5} - } - ] - """ - profile = parse_json_trace(json_content) - profile.process() - - expected_agg_ops = { - 'aten::conv2d': { - 'tc_eligible': True, - 'tc_self_ratio': 0, - 'tc_total_ratio': 15 / (15 + 10) - }, - 'op_no_tc': { - 'tc_eligible': False, - 'tc_self_ratio': 0, - 'tc_total_ratio': 0 - }, - 'aten::cudnn_convolution': { - 'tc_eligible': True, - 'tc_self_ratio': 1.0, - 'tc_total_ratio': 1.0 - } - } - self.assertEqual(len(profile.op_list_groupby_name), len(expected_agg_ops)) - for agg_op in profile.op_list_groupby_name: - expected_agg_op = expected_agg_ops[agg_op.name] - self.assertEqual(agg_op.tc_eligible, expected_agg_op['tc_eligible']) - self.assertAlmostEqual(agg_op.tc_self_ratio, expected_agg_op['tc_self_ratio']) - self.assertAlmostEqual(agg_op.tc_total_ratio, expected_agg_op['tc_total_ratio']) - - expected_kernels_groupby_op = { - 'kernel_no_tc': { - 'op_name': 'op_no_tc', - 'tc_used': False, - 'op_tc_eligible': False - }, - 'volta_fp16_s884cudnn_fp16_128x128_ldg8_splitK_relu_f2f_exp_small_nhwc_tn_v1': { - 'op_name': 'aten::cudnn_convolution', - 'tc_used': True, - 'op_tc_eligible': True - } - } - self.assertEqual(len(profile.kernel_list_groupby_name_op), len(expected_kernels_groupby_op)) - for agg_kernel in profile.kernel_list_groupby_name_op: - expected_agg_kernel = expected_kernels_groupby_op[agg_kernel.name] - self.assertEqual(agg_kernel.op_name, expected_agg_kernel['op_name']) - self.assertEqual(agg_kernel.tc_used, expected_agg_kernel['tc_used']) - self.assertEqual(agg_kernel.op_tc_eligible, expected_agg_kernel['op_tc_eligible']) - - self.assertAlmostEqual(profile.tc_ratio[0], 15 / (15 + 10)) - self.assertAlmostEqual(profile.tc_eligible_ops_kernel_ratio, 15 / (15 + 10)) - - -class TestDistributed(unittest.TestCase): - - def test_distributed_nccl(self): - json_content0 = """[ - { - "ph": "X", "cat": "cpu_op", - "name": "nccl:broadcast", "pid": 23803, "tid": "23803", - "ts": 0, "dur": 75, - "args": {"External id": 146, "Input Dims": [[53120]], "Input type": ["float"]} - }, - { - "ph": "X", "cat": "Kernel", - "name": "ncclKernel_Broadcast_RING_LL_Sum_int8_t(ncclWorkElem)", "pid": 0, "tid": "stream 16", - "ts": 16, "dur": 16, - "args": {"device": 0, "correlation": 28506, "external id": 146} - }, - { - "ph": "X", "cat": "cpu_op", - "name": "aten::add_", "pid": 23803, "tid": "23803", - "ts": 100, "dur": 20, - "args": {"External id": 24504, "Input Dims": [[1000], [1000], []], "Input type": ["float", "float", "Int"]} - }, - { - "ph": "X", "cat": "Kernel", - "name": "void at::native::vectorized_elementwise_kernel", "pid": 0, "tid": "stream 7", - "ts": 130, "dur": 161, - "args": {"device": 0, "correlation": 99765, "external id": 24504} - }, - { - "ph": "X", "cat": "cpu_op", - "name": "nccl:all_reduce", "pid": 23803, "tid": "25166", - "ts": 160, "dur": 75, - "args": {"External id": 2513, "Input Dims": [[2049000]], "Input type": ["float"]} - }, - { - "ph": "X", "cat": "Kernel", - "name": "ncclKernel_AllReduce_RING_LL_Sum_float(ncclWorkElem)", "pid": 0, "tid": "stream 16", - "ts": 162, "dur": 1556, - "args": {"device": 0, "correlation": 33218, "external id": 2513} - } - ] - """ - json_content1 = """[ - { - "ph": "X", "cat": "cpu_op", - "name": "nccl:broadcast", "pid": 23803, "tid": "23803", - "ts": 0, "dur": 20, - "args": {"External id": 146, "Input Dims": [[53120]], "Input type": ["float"]} - }, - { - "ph": "X", "cat": "Kernel", - "name": "ncclKernel_Broadcast_RING_LL_Sum_int8_t(ncclWorkElem)", "pid": 0, "tid": "stream 16", - "ts": 8, "dur": 31, - "args": {"device": 0, "correlation": 28506, "external id": 146} - }, - { - "ph": "X", "cat": "cpu_op", - "name": "aten::add_", "pid": 23803, "tid": "23803", - "ts": 25, "dur": 20, - "args": {"External id": 24504, "Input Dims": [[1000], [1000], []], "Input type": ["float", "float", "Int"]} - }, - { - "ph": "X", "cat": "Kernel", - "name": "void at::native::vectorized_elementwise_kernel", "pid": 0, "tid": "stream 7", - "ts": 30, "dur": 161, - "args": {"device": 0, "correlation": 99765, "external id": 24504} - }, - { - "ph": "X", "cat": "cpu_op", - "name": "nccl:all_reduce", "pid": 23803, "tid": "25166", - "ts": 160, "dur": 75, - "args": {"External id": 2513, "Input Dims": [[2049000]], "Input type": ["float"]} - }, - { - "ph": "X", "cat": "Kernel", - "name": "ncclKernel_AllReduce_RING_LL_Sum_float(ncclWorkElem)", "pid": 0, "tid": "stream 16", - "ts": 562, "dur": 1058, - "args": {"device": 0, "correlation": 33218, "external id": 2513} - } - ] - """ - - profile0 = parse_json_trace(json_content0, 'worker0') - dist_data0 = DistributedRunProfileData(profile0) - self.assertTrue(profile0.has_communication) - self.assertEqual(len(profile0.comm_node_list), 2) - self.assertEqual(profile0.steps_costs[0].costs, [105, 0, 0, 16, 0, 0, 79, 35, 235]) - - profile1 = parse_json_trace(json_content1, 'worker1') - dist_data1 = DistributedRunProfileData(profile1) - self.assertTrue(profile1.has_communication) - self.assertEqual(len(profile1.comm_node_list), 2) - self.assertEqual(profile1.steps_costs[0].costs[3], 22) - - loader = RunLoader('test_nccl', '', None) - dist_profile = loader._process_distributed_profiles([dist_data0, dist_data1], 0) - self.assertEqual(dist_profile.steps_to_overlap['data']['0']['worker0'], [32, 73, 16, 114]) - self.assertEqual(dist_profile.steps_to_overlap['data']['0']['worker1'], [152, 9, 22, 52]) - self.assertEqual(dist_profile.steps_to_wait['data']['0']['worker0'], [1074, 498]) - self.assertEqual(dist_profile.steps_to_wait['data']['0']['worker1'], [1074, 15]) - self.assertEqual(dist_profile.comm_ops['data']['worker0']['rows'], - [['nccl:broadcast', 1, 212480, 212480, 16, 16, 16, 16], - ['nccl:all_reduce', 1, 8196000, 8196000, 1556, 1556, 1058, 1058]]) - self.assertEqual(dist_profile.comm_ops['data']['worker1']['rows'], - [['nccl:broadcast', 1, 212480, 212480, 31, 31, 16, 16], - ['nccl:all_reduce', 1, 8196000, 8196000, 1058, 1058, 1058, 1058]]) - - def test_distributed_gloo_gpu(self): - json_content0 = """[ - { - "ph": "X", "cat": "cpu_op", - "name": "gloo:broadcast", "pid": 23803, "tid": "23803", - "ts": 16, "dur": 38, - "args": {"External id": 165, "Input Dims": [[53120]], "Input type": ["float"]} - }, - { - "ph": "X", "cat": "cpu_op", - "name": "gloo:broadcast", "pid": 23803, "tid": "23805", - "ts": 25, "dur": 36, - "args": {"External id": 166, "Input Dims": [[53120]], "Input type": ["float"]} - }, - { - "ph": "X", "cat": "cpu_op", - "name": "gloo:broadcast", "pid": 23803, "tid": "23803", - "ts": 66, "dur": 18, - "args": {"External id": 167, "Input Dims": [[53120]], "Input type": ["float"]} - }, - { - "ph": "X", "cat": "cpu_op", - "name": "aten::add_", "pid": 23803, "tid": "23800", - "ts": 0, "dur": 20, - "args": {"External id": 24504, "Input Dims": [[1000], [1000], []], "Input type": ["float", "float", "Int"]} - }, - { - "ph": "X", "cat": "Kernel", - "name": "void at::native::vectorized_elementwise_kernel", "pid": 0, "tid": "stream 7", - "ts": 30, "dur": 101, - "args": {"device": 0, "correlation": 99765, "external id": 24504} - }, - { - "ph": "X", "cat": "cpu_op", - "name": "gloo:all_reduce", "pid": 23803, "tid": "23805", - "ts": 110, "dur": 18, - "args": {"External id": 2513, "Input Dims": [[2049000]], "Input type": ["float"]} - }, - { - "ph": "X", "cat": "cpu_op", - "name": "gloo:all_reduce", "pid": 23803, "tid": "23803", - "ts": 120, "dur": 36, - "args": {"External id": 2516, "Input Dims": [[2049000]], "Input type": ["float"]} - } - ] - """ - json_content1 = """[ - { - "ph": "X", "cat": "cpu_op", - "name": "gloo:broadcast", "pid": 23803, "tid": "23803", - "ts": 20, "dur": 28, - "args": {"External id": 256, "Input Dims": [[53120]], "Input type": ["float"]} - }, - { - "ph": "X", "cat": "cpu_op", - "name": "gloo:broadcast", "pid": 23803, "tid": "23805", - "ts": 28, "dur": 30, - "args": {"External id": 257, "Input Dims": [[53120]], "Input type": ["float"]} - }, - { - "ph": "X", "cat": "cpu_op", - "name": "gloo:broadcast", "pid": 23803, "tid": "23803", - "ts": 77, "dur": 6, - "args": {"External id": 258, "Input Dims": [[53120]], "Input type": ["float"]} - }, - { - "ph": "X", "cat": "cpu_op", - "name": "aten::add_", "pid": 23803, "tid": "23800", - "ts": 0, "dur": 30, - "args": {"External id": 24504, "Input Dims": [[1000], [1000], []], "Input type": ["float", "float", "Int"]} - }, - { - "ph": "X", "cat": "Kernel", - "name": "void at::native::vectorized_elementwise_kernel", "pid": 0, "tid": "stream 7", - "ts": 70, "dur": 70, - "args": {"device": 0, "correlation": 99765, "external id": 24504} - }, - { - "ph": "X", "cat": "cpu_op", - "name": "gloo:all_reduce", "pid": 23803, "tid": "23805", - "ts": 88, "dur": 38, - "args": {"External id": 2513, "Input Dims": [[2049000]], "Input type": ["float"]} - }, - { - "ph": "X", "cat": "cpu_op", - "name": "gloo:all_reduce", "pid": 23803, "tid": "23803", - "ts": 130, "dur": 16, - "args": {"External id": 2516, "Input Dims": [[2049000]], "Input type": ["float"]} - } - ] - """ - - profile0 = parse_json_trace(json_content0, 'worker0') - dist_data0 = DistributedRunProfileData(profile0) - self.assertTrue(profile0.has_communication) - self.assertEqual(len(profile0.comm_node_list), 5) - self.assertEqual(profile0.steps_costs[0].costs, [101, 0, 0, 39, 0, 0, 16, 0, 156]) - - profile1 = parse_json_trace(json_content1, 'worker1') - dist_data1 = DistributedRunProfileData(profile1) - self.assertTrue(profile1.has_communication) - self.assertEqual(len(profile1.comm_node_list), 5) - self.assertEqual(profile1.steps_costs[0].costs, [70, 0, 0, 44, 0, 0, 20, 12, 146]) - - loader = RunLoader('test_gloo_gpu', '', None) - dist_profile = loader._process_distributed_profiles([dist_data0, dist_data1], 0) - self.assertEqual(dist_profile.steps_to_overlap['data']['0']['worker0'], [31, 70, 39, 16]) - self.assertEqual(dist_profile.steps_to_overlap['data']['0']['worker1'], [16, 54, 44, 32]) - self.assertEqual(dist_profile.steps_to_wait['data']['0']['worker0'], [75, 34]) - self.assertEqual(dist_profile.steps_to_wait['data']['0']['worker1'], [78, 20]) - self.assertEqual(dist_profile.comm_ops['data']['worker0']['rows'], - [['gloo:broadcast', 3, 637440, 212480, 63, 21, 41, 14], - ['gloo:all_reduce', 2, 16392000, 8196000, 46, 23, 34, 17]]) - self.assertEqual(dist_profile.comm_ops['data']['worker1']['rows'], - [['gloo:broadcast', 3, 637440, 212480, 44, 15, 44, 15], - ['gloo:all_reduce', 2, 16392000, 8196000, 54, 27, 34, 17]]) - - def test_distributed_gloo_cpu(self): - json_content0 = """[ - { - "ph": "X", "cat": "cpu_op", - "name": "gloo:broadcast", "pid": 23803, "tid": "23803", - "ts": 16, "dur": 38, - "args": {"External id": 165, "Input Dims": [[53120]], "Input type": ["float"]} - }, - { - "ph": "X", "cat": "cpu_op", - "name": "gloo:broadcast", "pid": 23803, "tid": "23805", - "ts": 25, "dur": 36, - "args": {"External id": 166, "Input Dims": [[53120]], "Input type": ["float"]} - }, - { - "ph": "X", "cat": "cpu_op", - "name": "gloo:broadcast", "pid": 23803, "tid": "23803", - "ts": 66, "dur": 18, - "args": {"External id": 167, "Input Dims": [[53120]], "Input type": ["float"]} - }, - { - "ph": "X", "cat": "cpu_op", - "name": "aten::add_", "pid": 23803, "tid": "23800", - "ts": 0, "dur": 20, - "args": {"External id": 24504, "Input Dims": [[1000], [1000], []], "Input type": ["float", "float", "Int"]} - }, - { - "ph": "X", "cat": "cpu_op", - "name": "aten::mul", "pid": 23803, "tid": "23800", - "ts": 30, "dur": 101, - "args": {"External id": 24505} - }, - { - "ph": "X", "cat": "cpu_op", - "name": "gloo:all_reduce", "pid": 23803, "tid": "23805", - "ts": 110, "dur": 18, - "args": {"External id": 2513, "Input Dims": [[2049000]], "Input type": ["float"]} - }, - { - "ph": "X", "cat": "cpu_op", - "name": "gloo:all_reduce", "pid": 23803, "tid": "23803", - "ts": 120, "dur": 36, - "args": {"External id": 2516, "Input Dims": [[2049000]], "Input type": ["float"]} - } - ] - """ - json_content1 = """[ - { - "ph": "X", "cat": "cpu_op", - "name": "gloo:broadcast", "pid": 23803, "tid": "23803", - "ts": 20, "dur": 28, - "args": {"External id": 256, "Input Dims": [[53120]], "Input type": ["float"]} - }, - { - "ph": "X", "cat": "cpu_op", - "name": "gloo:broadcast", "pid": 23803, "tid": "23805", - "ts": 28, "dur": 30, - "args": {"External id": 257, "Input Dims": [[53120]], "Input type": ["float"]} - }, - { - "ph": "X", "cat": "cpu_op", - "name": "gloo:broadcast", "pid": 23803, "tid": "23803", - "ts": 77, "dur": 6, - "args": {"External id": 258, "Input Dims": [[53120]], "Input type": ["float"]} - }, - { - "ph": "X", "cat": "cpu_op", - "name": "aten::add_", "pid": 23803, "tid": "23800", - "ts": 0, "dur": 30, - "args": {"External id": 24504, "Input Dims": [[1000], [1000], []], "Input type": ["float", "float", "Int"]} - }, - { - "ph": "X", "cat": "cpu_op", - "name": "aten::mul", "pid": 23803, "tid": "23800", - "ts": 70, "dur": 70, - "args": {"External id": 24505} - }, - { - "ph": "X", "cat": "cpu_op", - "name": "gloo:all_reduce", "pid": 23803, "tid": "23805", - "ts": 88, "dur": 38, - "args": {"External id": 2513, "Input Dims": [[2049000]], "Input type": ["float"]} - }, - { - "ph": "X", "cat": "cpu_op", - "name": "gloo:all_reduce", "pid": 23803, "tid": "23803", - "ts": 130, "dur": 16, - "args": {"External id": 2516, "Input Dims": [[2049000]], "Input type": ["float"]} - } - ] - """ - - profile0 = parse_json_trace(json_content0, 'worker0') - dist_data0 = DistributedRunProfileData(profile0) - self.assertTrue(profile0.has_communication) - self.assertEqual(len(profile0.comm_node_list), 5) - self.assertEqual(profile0.steps_costs[0].costs, [0, 0, 0, 109, 0, 0, 47, 0, 156]) - - profile1 = parse_json_trace(json_content1, 'worker1') - dist_data1 = DistributedRunProfileData(profile1) - self.assertTrue(profile1.has_communication) - self.assertEqual(len(profile1.comm_node_list), 5) - self.assertEqual(profile1.steps_costs[0].costs, [0, 0, 0, 98, 0, 0, 36, 12, 146]) - - loader = RunLoader('test_gloo_cpu', '', None) - dist_profile = loader._process_distributed_profiles([dist_data0, dist_data1], 0) - self.assertEqual(dist_profile.steps_to_overlap['data']['0']['worker0'], [47, 74, 35, 0]) - self.assertEqual(dist_profile.steps_to_overlap['data']['0']['worker1'], [36, 64, 34, 12]) - self.assertEqual(dist_profile.steps_to_wait['data']['0']['worker0'], [75, 34]) - self.assertEqual(dist_profile.steps_to_wait['data']['0']['worker1'], [78, 20]) - self.assertEqual(dist_profile.comm_ops['data']['worker0']['rows'], - [['gloo:broadcast', 3, 637440, 212480, 63, 21, 41, 14], - ['gloo:all_reduce', 2, 16392000, 8196000, 46, 23, 34, 17]]) - self.assertEqual(dist_profile.comm_ops['data']['worker1']['rows'], - [['gloo:broadcast', 3, 637440, 212480, 44, 15, 44, 15], - ['gloo:all_reduce', 2, 16392000, 8196000, 54, 27, 34, 17]]) - - -class TestMemoryCurve(unittest.TestCase): - - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - - self.event_data_cpu = [ - [1, 0, 0, 1, 4, 4, 0], # alloc 1 - [20, 0, 0, 1, -4, 0, 0], # free 1 - [100, 0, 0, 2, 8000, 8000, 0], # alloc 2 - [200, 0, 0, 2, -8000, 0, 0], # free 2 - [300, 0, 0, 3, 4, 4, 0], # alloc 3 - [400, 0, 0, 4, 16, 20, 0], # alloc 4 - [500, 0, 0, 5, 4000, 4020, 0], # alloc 5 - [600, 0, 0, 4, -16, 4004, 0], # free 4 - [700, 0, 0, 7, 80, 4084, 0], # alloc 7 - [800, 0, 0, 3, -4, 4080, 0], # free 3 - [900, 0, 0, 7, -80, 4000, 0], # free 7 - [905, 0, 0, 4, -4000, 0, 0], # free 5 - ] - - self.event_data_gpu = [ - [2, 1, 0, 11, 400, 400, 512], # alloc 11 - [22, 1, 0, 11, -400, 0, 512], # free 11 - [105, 1, 0, 12, 5000, 5000, 10240], # alloc 12 - [106, 1, 0, 13, 3000, 8000, 10240], # alloc 13 - [205, 1, 0, 12, -5000, 3000, 10240], # free 12 - [401, 1, 0, 14, 1024, 4024, 10240], # alloc 14 - [499, 1, 0, 15, 4, 4028, 10240], # alloc 15 - [501, 1, 0, 13, -3000, 1028, 10240], # free 13 - [502, 1, 0, 15, -4, 1024, 10240], # free 15 - [906, 1, 0, 14, -1024, 0, 10240], # free 14 - ] - - self.all_events = sorted(self.event_data_cpu + self.event_data_gpu, key=lambda e: e[0]) - - def entry(self, ts, dev, dev_id, addr, alloc_size, total_allocated, total_reserved): - return { - 'ph': 'i', 's': 't', 'name': '[memory]', 'pid': 0, 'tid': 0, 'ts': ts, - 'args': { - 'Device Type': dev, - 'Device Id': dev_id, - 'Addr': addr, - 'Bytes': alloc_size, - 'Total Allocated': total_allocated, - 'Total Reserved': total_reserved, - }, - } - - def test_memory_curve_no_step_plot(self): - json_content = json.dumps([self.entry(*data) for data in self.all_events]) - - profile = parse_json_trace(json_content) - profile.process() - result = RunProfile.get_memory_curve(profile, time_metric='us', memory_metric='B', patch_for_step_plot=False) - - start_ts = profile.profiler_start_ts - self.assertEqual(1, start_ts) - - curves = result['rows'] - - self.assertIn('CPU', curves) - self.assertIn('GPU0', curves) - - self.assertEqual(len(self.event_data_cpu), len(curves['CPU'])) - for i in range(len(self.event_data_cpu)): - # adjusted timestamp - self.assertEqual(self.event_data_cpu[i][0] - start_ts, curves['CPU'][i][0]) - # total allocated - self.assertEqual(self.event_data_cpu[i][-2], curves['CPU'][i][1]) - # total reserved - self.assertEqual(self.event_data_cpu[i][-1], curves['CPU'][i][2]) - - self.assertEqual(len(self.event_data_gpu), len(curves['GPU0'])) - for i in range(len(self.event_data_gpu)): - self.assertEqual(self.event_data_gpu[i][0] - start_ts, curves['GPU0'][i][0]) - self.assertEqual(self.event_data_gpu[i][-2], curves['GPU0'][i][1]) - self.assertEqual(self.event_data_gpu[i][-1], curves['GPU0'][i][2]) - - def test_memory_curve_step_plot(self): - json_content = json.dumps([self.entry(*data) for data in self.all_events]) - - profile = parse_json_trace(json_content) - profile.process() - result = RunProfile.get_memory_curve(profile, time_metric='us', memory_metric='B', patch_for_step_plot=True) - - start_ts = profile.profiler_start_ts - self.assertEqual(1, start_ts) - - curves = result['rows'] - - self.assertIn('CPU', curves) - self.assertIn('GPU0', curves) - - self.assertEqual(2 * len(self.event_data_cpu) - 1, len(curves['CPU'])) - for i in range(len(curves['CPU'])): - if i % 2 == 0: # original values - # adjusted timestamp - self.assertEqual(self.event_data_cpu[i//2][0] - start_ts, curves['CPU'][i][0]) - # total allocated - self.assertEqual(self.event_data_cpu[i//2][-2], curves['CPU'][i][1]) - # total reserved - self.assertEqual(self.event_data_cpu[i//2][-1], curves['CPU'][i][2]) - else: # interpolated values - self.assertEqual(self.event_data_cpu[i//2+1][0] - start_ts, curves['CPU'][i][0]) - self.assertEqual(self.event_data_cpu[i//2][-2], curves['CPU'][i][1]) - self.assertEqual(self.event_data_cpu[i//2][-1], curves['CPU'][i][2]) - - self.assertEqual(2 * len(self.event_data_gpu) - 1, len(curves['GPU0'])) - for i in range(len(self.event_data_gpu)): - if i % 2 == 0: # original values - self.assertEqual(self.event_data_gpu[i//2][0] - start_ts, curves['GPU0'][i][0]) - self.assertEqual(self.event_data_gpu[i//2][-2], curves['GPU0'][i][1]) - self.assertEqual(self.event_data_gpu[i//2][-1], curves['GPU0'][i][2]) - else: # interpolated values - self.assertEqual(self.event_data_gpu[i//2+1][0] - start_ts, curves['GPU0'][i][0]) - self.assertEqual(self.event_data_gpu[i//2][-2], curves['GPU0'][i][1]) - self.assertEqual(self.event_data_gpu[i//2][-1], curves['GPU0'][i][2]) - - -class TestModuleView(unittest.TestCase): - - def test_build_module_hierarchy(self): - from torch_tb_profiler.profiler import trace - from torch_tb_profiler.profiler.module_op import ( - _build_module_hierarchy, aggegate_module_view) - - json_content = """[ - { - "ph": "X", - "cat": "python_function", - "name": "test_root", - "pid": 1908, - "tid": 1908, - "ts": 1, - "dur": 19367, - "args": { - "External id": 0, - "Trace name": "PyTorch Profiler", - "Trace iteration": 0, - "Python id": 1, - "Python thread": 0 - } - }, - { - "ph": "X", - "cat": "python_function", - "name": "nn.Module: MyModule", - "pid": 1908, - "tid": 1908, - "ts": 2, - "dur": 211, - "args": { - "External id": 0, - "Trace name": "PyTorch Profiler", - "Trace iteration": 0, - "Python id": 2, - "Python parent id": 1, - "Python module id": 0 - } - }, - { - "ph": "X", - "cat": "python_function", - "name": "nn.Module: Linear", - "pid": 1908, - "tid": 1908, - "ts": 5, - "dur": 62, - "args": { - "External id": 0, - "Trace name": "PyTorch Profiler", - "Trace iteration": 0, - "Python id": 3, - "Python parent id": 2, - "Python thread": 0, - "Python module id": 1 - } - }, - { - "ph": "X", - "cat": "cpu_op", - "name": "aten::addmm", - "pid": 1908, - "tid": 1908, - "ts": 10, - "dur": 31, - "args": { - "External id": 12182, - "Trace name": "PyTorch Profiler", - "Trace iteration": 0, - "Fwd thread id": 0, - "Sequence number": 4006, - "python_caller_id": 3 - } - }, - { - "ph": "X", - "cat": "python_function", - "name": "nn.Module: MyModule", - "pid": 1908, - "tid": 1908, - "ts": 1000, - "dur": 211, - "args": { - "External id": 0, - "Trace name": "PyTorch Profiler", - "Trace iteration": 0, - "Python id": 4, - "Python parent id": 1, - "Python module id": 0 - } - }, - { - "ph": "X", - "cat": "python_function", - "name": "nn.Module: Linear", - "pid": 1908, - "tid": 1908, - "ts": 1001, - "dur": 62, - "args": { - "External id": 0, - "Trace name": "PyTorch Profiler", - "Trace iteration": 0, - "Python id": 5, - "Python parent id": 4, - "Python thread": 0, - "Python module id": 1 - } - }, - { - "ph": "X", - "cat": "cpu_op", - "name": "aten::addmm", - "pid": 1908, - "tid": 1908, - "ts": 1002, - "dur": 32, - "args": { - "External id": 12182, - "Trace name": "PyTorch Profiler", - "Trace iteration": 0, - "Fwd thread id": 0, - "Sequence number": 4006, - "python_caller_id": 5 - } - }, - { - "ph": "X", - "cat": "python_function", - "name": "nn.Module: MyModule", - "pid": 1908, - "tid": 1908, - "ts": 2000, - "dur": 211, - "args": { - "External id": 0, - "Trace name": "PyTorch Profiler", - "Trace iteration": 0, - "Python id": 6, - "Python parent id": 1, - "Python module id": 0 - } - }, - { - "ph": "X", - "cat": "python_function", - "name": "nn.Module: Linear", - "pid": 1908, - "tid": 1908, - "ts": 2001, - "dur": 62, - "args": { - "External id": 0, - "Trace name": "PyTorch Profiler", - "Trace iteration": 0, - "Python id": 7, - "Python parent id": 6, - "Python thread": 0, - "Python module id": 1 - } - }, - { - "ph": "X", - "cat": "cpu_op", - "name": "aten::addmm", - "pid": 1908, - "tid": 1908, - "ts": 2002, - "dur": 33, - "args": { - "External id": 12182, - "Trace name": "PyTorch Profiler", - "Trace iteration": 0, - "Fwd thread id": 0, - "Sequence number": 4006, - "python_caller_id": 7 - } - }, - { - "ph": "X", - "cat": "python_function", - "name": "nn.Module: Conv2", - "pid": 1908, - "tid": 1908, - "ts": 3000, - "dur": 211, - "args": { - "External id": 0, - "Trace name": "PyTorch Profiler", - "Trace iteration": 0, - "Python id": 8, - "Python parent id": 1, - "Python module id": 100 - } - } - ] - """ - data = parse_json_trace(json_content) - stats = aggegate_module_view(data.tid2tree, data.events) - stats.sort(key=lambda x: x.name) - self.assertEqual(2, len(stats)) - self.assertEqual('Conv2', stats[0].name) - self.assertEqual('MyModule', stats[1].name) - self.assertEqual(1, len(stats[1].children)) - self.assertEqual('Linear', stats[1].children[0].name) - - content = json.loads(json_content) - - events = [] - for data in content: - event = trace.create_event(data, False) - events.append(event) - - roots = _build_module_hierarchy(events) - roots.sort(key=lambda x: x.name) - self.assertEqual(2, len(roots)) - self.assertEqual('nn.Module: Conv2', roots[0].name) - self.assertEqual('nn.Module: MyModule', roots[1].name) - self.assertEqual(1, len(roots[1].children)) - self.assertEqual('nn.Module: Linear', roots[1].children[0].name) - - -class TestDataPipe(unittest.TestCase): - - def test_datapipe(self): - json_content = """[ - { - "ph": "X", "cat": "cpu_op", - "name": "enumerate(DataPipe)#ShufflerIterDataPipe", "pid": 7557, "tid": 7557, - "ts": 100, "dur": 23, - "args": { - "External id": 34, - "Trace name": "PyTorch Profiler", "Trace iteration": 0 - } - } - ]""" - profile = parse_json_trace(json_content) - profile.process() - - dataloader_ranges = profile.role_ranges[ProfileRole.DataLoader] - datapipe_range = None - for range in dataloader_ranges: - if range[0] == 100 and range[1] == 123: - datapipe_range = range - break - self.assertTrue(datapipe_range is not None) - - root = next(iter(profile.tid2tree.values())) - ops, _ = root.get_operator_and_kernels() - datapipe_op = None - for op in ops: - if op.name.startswith('enumerate(DataPipe)'): - datapipe_op = op - break - - self.assertTrue(datapipe_op is None) - - -if __name__ == '__main__': - unittest.main() diff --git a/plugins/tensorboard-plugins/tb_plugin/test/test_ranges.py b/plugins/tensorboard-plugins/tb_plugin/test/test_ranges.py deleted file mode 100644 index d40fc3e02..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/test/test_ranges.py +++ /dev/null @@ -1,50 +0,0 @@ -import unittest -import math - -from torch_tb_profiler.profiler.overall_parser import ( - merge_ranges, subtract_ranges_lists, intersection_ranges_lists, get_ranges_sum -) - - -def check_ranges_equal(ranges1, ranges2): - if len(ranges1) != len(ranges2): - return False - for i in range(len(ranges1)): - if ranges1[i][0] != ranges2[i][0] or ranges1[i][1] != ranges2[i][1]: - return False - return True - - -class TestOverallParser(unittest.TestCase): - def test_merge_ranges(self): - src_ranges = [(1.1, 2.2), (1.5, 2.3), (3.3, 3.9), (3.5, 3.6), (3.7, 3.8), (4.1, 4.2)] - expected_ranges = [(1.1, 2.3), (3.3, 3.9), (4.1, 4.2)] - dst_ranges = merge_ranges(src_ranges, True) - is_equal = check_ranges_equal(dst_ranges, expected_ranges) - self.assertTrue(is_equal) - - def test_subtract_ranges_lists(self): - ranges1 = [(1.1, 2.2), (3.3, 4.4), (5.5, 6.6)] - ranges2 = [(0, 0.1), (1.0, 1.4), (1.5, 1.6), (1.9, 3.4), (4.3, 4.6)] - expected_ranges = [(1.4, 1.5), (1.6, 1.9), (3.4, 4.3), (5.5, 6.6)] - dst_ranges = subtract_ranges_lists(ranges1, ranges2) - is_equal = check_ranges_equal(dst_ranges, expected_ranges) - self.assertTrue(is_equal) - - def test_intersection_ranges_lists(self): - ranges1 = [(1.1, 2.2), (3.3, 4.4), (5.5, 6.6)] - ranges2 = [(0, 0.1), (1.0, 1.4), (1.5, 1.6), (1.9, 3.4), (4.3, 4.6)] - expected_ranges = [(1.1, 1.4), (1.5, 1.6), (1.9, 2.2), (3.3, 3.4), (4.3, 4.4)] - dst_ranges = intersection_ranges_lists(ranges1, ranges2) - is_equal = check_ranges_equal(dst_ranges, expected_ranges) - self.assertTrue(is_equal) - - def test_get_ranges_sum(self): - ranges = [(1.1, 2.2), (3.3, 4.4), (5.5, 6.6)] - expected_sum = 3.3 - dst_sum = get_ranges_sum(ranges) - self.assertTrue(math.isclose(dst_sum, expected_sum)) - - -if __name__ == '__main__': - unittest.main() diff --git a/plugins/tensorboard-plugins/tb_plugin/test/test_tensorboard_end2end.py b/plugins/tensorboard-plugins/tb_plugin/test/test_tensorboard_end2end.py deleted file mode 100644 index 46636d118..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/test/test_tensorboard_end2end.py +++ /dev/null @@ -1,170 +0,0 @@ -import json -import os -import random -import shutil -import socket -import tempfile -import time -import unittest -import urllib -import urllib.request -from subprocess import Popen -from urllib.error import HTTPError - - -def get_samples_dir(): - return os.path.join(os.path.dirname(os.path.abspath(__file__)), 'resources') - - -class TestEnd2End(unittest.TestCase): - - # def test_tensorboard_gs(self): - # test_folder = 'gs://pe-tests-public/tb_samples/' - # expected_runs = b'["resnet50_profiler_api_num_workers_0", "resnet50_profiler_api_num_workers_4"]' - # self._test_tensorboard_with_arguments(test_folder, expected_runs, {'TORCH_PROFILER_START_METHOD':'spawn'}) - - def test_tensorboard_end2end(self): - test_folder = get_samples_dir() - expected_runs = b'["resnet50_num_workers_0", "resnet50_num_workers_4"]' - - print('starting spawn mode testing...') - self._test_tensorboard_with_arguments(test_folder, expected_runs, {'TORCH_PROFILER_START_METHOD': 'spawn'}) - - @unittest.skip('fork is not use anymore') - def test_tensorboard_fork(self): - test_folder = get_samples_dir() - expected_runs = b'["resnet50_num_workers_0", "resnet50_num_workers_4"]' - - print('starting fork mode testing') - self._test_tensorboard_with_arguments(test_folder, expected_runs) - - def test_tensorboard_with_path_prefix(self): - test_folder = get_samples_dir() - expected_runs = b'["resnet50_num_workers_0", "resnet50_num_workers_4"]' - self._test_tensorboard_with_arguments(test_folder, expected_runs, path_prefix='/tensorboard/viewer/') - - def test_tensorboard_with_symlinks(self): - logdir = tempfile.mkdtemp(prefix='tensorboard_logdir') - - samples_dir = get_samples_dir() - - # Create the following layout, with 1 symlink to a run dir, and 1 regular run dir: - # logdir/ - # run_concrete/ - # run_symlink/ --> path/to/samples/resnet50_num_workers_4/ - shutil.copytree(os.path.join(samples_dir, 'resnet50_num_workers_0'), os.path.join(logdir, 'run_concrete')) - os.symlink(os.path.join(samples_dir, 'resnet50_num_workers_4'), os.path.join(logdir, 'run_symlink')) - - expected_runs = b'["run_concrete", "run_symlink"]' - self._test_tensorboard_with_arguments(logdir, expected_runs) - - shutil.rmtree(logdir) - - def _test_tensorboard_with_arguments(self, test_folder, expected_runs, env=None, path_prefix=None): - host = 'localhost' - port = random.randint(6008, 65535) - - try: - if env: - env_copy = os.environ.copy() - env_copy.update(env) - env = env_copy - if not path_prefix: - tb = Popen(['tensorboard', '--logdir='+test_folder, '--port='+str(port)], env=env) - else: - tb = Popen(['tensorboard', '--logdir='+test_folder, '--port='+str(port), - '--path_prefix='+path_prefix], env=env) - self._test_tensorboard(host, port, expected_runs, path_prefix) - finally: - pid = tb.pid - print('tensorboard process {} is terminating.'.format(pid)) - tb.terminate() - - def _test_tensorboard(self, host, port, expected_runs, path_prefix): - if not path_prefix: - link_prefix = 'http://{}:{}/data/plugin/pytorch_profiler/'.format(host, port) - else: - path_prefix = path_prefix.strip('/') - link_prefix = 'http://{}:{}/{}/data/plugin/pytorch_profiler/'.format(host, port, path_prefix) - run_link = link_prefix + 'runs' - - expected_links_format = [ - link_prefix + 'overview?run={}&worker=worker0&span=1&view=Overview', - link_prefix + 'operation?run={}&worker=worker0&span=1&view=Operator&group_by=Operation', - link_prefix + 'operation/table?run={}&worker=worker0&span=1&view=Operator&group_by=Operation', - link_prefix + 'kernel/table?run={}&worker=worker0&span=1&view=Kernel&group_by=Kernel', - link_prefix + 'kernel?run={}&worker=worker0&span=1&view=Kernel&group_by=Kernel' - ] - - retry_times = 60 - while True: - try: - socket.socket(socket.AF_INET, socket.SOCK_STREAM).connect((host, port)) - print('tensorboard start successfully') - break - except socket.error: - time.sleep(2) - retry_times -= 1 - if retry_times < 0: - self.fail('tensorboard start timeout') - continue - - retry_times = 60 - - while True: - try: - response = urllib.request.urlopen(run_link) - data = response.read() - runs = None - if data: - data = json.loads(data) - runs = data.get('runs') - if runs: - runs = '[{}]'.format(', '.join(['"{}"'.format(i) for i in runs])) - runs = runs.encode('utf-8') - if runs == expected_runs: - break - if retry_times % 10 == 0: - print('receive mismatched data, retrying', data) - time.sleep(2) - retry_times -= 1 - if retry_times < 0: - self.fail('Load run timeout') - except Exception: - if retry_times > 0: - continue - else: - raise - - links = [] - for run in json.loads(expected_runs): - for expected_link in expected_links_format: - links.append(expected_link.format(run)) - - if os.environ.get('TORCH_PROFILER_REGEN_RESULT_CHECK') == '1': - with open('result_check_file.txt', 'w', encoding='utf-8') as f: - # NOTE: result_check_file.txt is manually generated and verified. - # And then checked-in so that we can make sure that frontend - # content change can be detected on code change. - for link in links: - response = urllib.request.urlopen(link) - f.write(response.read().decode('utf-8')) - f.write('\n') - else: - with open('result_check_file.txt', 'r') as f: - lines = f.readlines() - i = 0 - print('starting testing...') - for link in links: - try: - response = urllib.request.urlopen(link) - self.assertEqual(response.read(), lines[i].strip().encode(encoding='utf-8')) - i = i + 1 - except HTTPError as e: - self.fail(e) - self.assertEqual(i, 10) - print('ending testing...') - - -if __name__ == '__main__': - unittest.main() diff --git a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/__init__.py b/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/__init__.py deleted file mode 100644 index f7b951e60..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -# ------------------------------------------------------------------------- -# Copyright (c) Microsoft Corporation. All rights reserved. -# -------------------------------------------------------------------------- - -# Entry point for Pytorch TensorBoard plugin package. - -__version__ = '0.4.0.11' diff --git a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/config/config.ini b/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/config/config.ini deleted file mode 100644 index 500d472d2..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/config/config.ini +++ /dev/null @@ -1,11 +0,0 @@ -[URL] -pytorch_data_loading_url = https://pytorch.org/docs/stable/data.html#single-and-multi-process-data-loading -pytorch_amp_url = https://pytorch.org/docs/stable/amp.html -pytorch_ckp_url = https://pytorch.org/docs/stable/checkpoint.html -cuda_nn_ddp_instead_url = https://pytorch.org/docs/stable/notes/cuda.html#cuda-nn-ddp-instead -compress_url = https://pytorch.org/docs/stable/ddp_comm_hooks.html -grad_acc_url = https://towardsdatascience.com/what-is-gradient-accumulation-in-deep-learning-ec034122cfa -lamb_url = https://nvidia.github.io/apex/optimizers.html#apex.optimizers.FusedLAMB -repository_url = https://gitee.com/ascend/att/tree/master/plugins/tensorboard-plugins/tb_plugin -[EMAIL] -author_email = pmail_mindstudio@huawei.com \ No newline at end of file diff --git a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/consts.py b/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/consts.py deleted file mode 100644 index b3e202af6..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/consts.py +++ /dev/null @@ -1,116 +0,0 @@ -# ------------------------------------------------------------------------- -# Copyright (c) Microsoft Corporation. All rights reserved. -# -# Copyright(c) 2023 Huawei Technologies. -# All rights reserved -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# Modifications: Add visualization of PyTorch Ascend profiling. -# -------------------------------------------------------------------------- -import enum -import re -from collections import namedtuple - -PLUGIN_NAME = 'pytorch_profiler' - -WORKER_PATTERN = re.compile(r"""^(.*?) # worker name - (\.\d+)? # optional timestamp like 1619499959628 used as span name - \.pt\.trace\.json$""", re.X) - -TRACE_PATTERN = re.compile(r"""^trace_view\.json$""") -WORKER_SPAN_PATTERN = re.compile(r"""^(.*?)_(\d+(\.\d+)?)_ascend_pt$""") - -NODE_PROCESS_PATTERN = re.compile(r"""^(.*)_(\d+)""") -MONITOR_RUN_REFRESH_INTERNAL_IN_SECONDS = 10 -MAX_GPU_PER_NODE = 64 -MAX_FILE_SIZE = 500 * 1024 * 1024 -MAX_LINUX_PATH_LENGTH = 4096 -MAX_WINDOWS_PATH_LENGTH = 260 - -View = namedtuple('View', 'id, name, display_name') -OVERALL_VIEW = View(1, 'overall', 'Overview') -OP_VIEW = View(2, 'operator', 'Operator') -KERNEL_VIEW = View(3, 'kernel', 'Kernel') -TRACE_VIEW = View(4, 'trace', 'Trace') -DISTRIBUTED_VIEW = View(5, 'distributed', 'Distributed') -MEMORY_VIEW = View(6, 'memory', 'Memory') -MODULE_VIEW = View(7, 'module', 'Module') -LIGHTNING_VIEW = View(8, 'lightning', 'Lightning') - -TOOLTIP_GPU_UTIL = \ - 'GPU Utilization:\n' \ - 'GPU busy time / All steps time. The higher, the better. ' \ - 'GPU busy time is the time during which there is at least one GPU kernel running on it. ' \ - 'All steps time is the total time of all profiler steps(or called as iterations).\n' -TOOLTIP_SM_EFFICIENCY = \ - 'Est. SM Efficiency:\n' \ - 'Estimated Stream Multiprocessor Efficiency. The higher, the better. ' \ - 'This metric of a kernel, SM_Eff_K = min(blocks of this kernel / SM number of this GPU, 100%). ' \ - "This overall number is the sum of all kernels' SM_Eff_K weighted by kernel's execution duration, " \ - 'divided by all steps time.\n' -TOOLTIP_OCCUPANCY_COMMON = \ - 'Est. Achieved Occupancy:\n' \ - 'For most cases such as memory bandwidth bounded kernels, the higher the better. ' \ - 'Occupancy is the ratio of active warps on an SM ' \ - 'to the maximum number of active warps supported by the SM. ' \ - 'The theoretical occupancy of a kernel is upper limit occupancy of this kernel, ' \ - 'limited by multiple factors such as kernel shape, kernel used resource, ' \ - 'and the GPU compute capability.\n' \ - 'Est. Achieved Occupancy of a kernel, OCC_K = ' \ - 'min(threads of the kernel / SM number / max threads per SM, theoretical occupancy of the kernel). ' -TOOLTIP_OCCUPANCY_OVERVIEW = \ - "This overall number is the weighted average of all kernels' OCC_K " \ - "using kernel's execution duration as weight. " \ - 'It shows fine-grained low-level GPU utilization.\n' -TOOLTIP_TENSOR_CORES = \ - 'Kernel using Tensor Cores:\n' \ - 'Total GPU Time for Tensor Core kernels / Total GPU Time for all kernels.\n' -TOOLTIP_OCCUPANCY_TABLE = \ - "This \"Mean\" number is the weighted average of all calls' OCC_K of the kernel, " \ - "using each call's execution duration as weight. " \ - 'It shows fine-grained low-level GPU utilization.' -TOOLTIP_BLOCKS_PER_SM = \ - 'Blocks Per SM = blocks of this kernel / SM number of this GPU.\n' \ - 'If this number is less than 1, it indicates the GPU multiprocessors are not fully utilized.\n' \ - '\"Mean Blocks per SM\" is the weighted average of all calls of this kernel, ' \ - "using each call's execution duration as weight." -TOOLTIP_OP_TC_ELIGIBLE = \ - 'Whether this operator is eligible to use Tensor Cores.' -TOOLTIP_OP_TC_SELF = \ - 'Time of self-kernels with Tensor Cores / Time of self-kernels.' -TOOLTIP_OP_TC_TOTAL = \ - 'Time of kernels with Tensor Cores / Time of kernels.' -TOOLTIP_KERNEL_USES_TC = \ - 'Whether this kernel uses Tensor Cores.' -TOOLTIP_KERNEL_OP_TC_ELIGIBLE = \ - 'Whether the operator launched this kernel is eligible to use Tensor Cores.' -TOOLTIP_OP_TC_ELIGIBLE_AICORE = \ - 'Whether this operator is eligible to use AICore.' -TOOLTIP_OP_TC_SELF_AICORE = \ - 'Time of Device Self Duration With AICore / Device Self Duration.' -TOOLTIP_OP_TC_TOTAL_AICORE = \ - 'Time of Device Total Duration With AICore / Device Total Duration.' - - -class InputFilesType(enum.Enum): - KERNEL_DETAILS_CSV = 'kernel_details.csv' - MEMORY_RECORD_CSV = 'memory_record.csv' - MEMORY_OPERATOR_CSV = 'operator_memory.csv' - MEMORY_COMPONENT_CSV = 'npu_module_mem.csv' - OPERATOR_DETAILS_CSV = 'operator_details.csv' - DISTRIBUTED_STEP_CSV = 'step_trace_time.csv' - DISTRIBUTED_COMMUNICATION_JSON = 'communication.json' - - -INPUT_FILE_LIST = [e.value for e in InputFilesType] diff --git a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/io/__init__.py b/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/io/__init__.py deleted file mode 100644 index 296f53b7c..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/io/__init__.py +++ /dev/null @@ -1,23 +0,0 @@ -# ------------------------------------------------------------------------- -# Copyright (c) Microsoft Corporation. -# Copyright(c) 2023 Huawei Technologies. -# All rights reserved -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# Modifications: Add visualization of PyTorch Ascend profiling. -# -------------------------------------------------------------------------- -from .cache import Cache -from .file import (BaseFileSystem, StatData, abspath, basename, download_file, - exists, get_filesystem, glob, isdir, join, listdir, - makedirs, read, register_filesystem, relpath, walk, stat, check_file_valid) diff --git a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/io/azureblob.py b/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/io/azureblob.py deleted file mode 100644 index 2fcd69fee..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/io/azureblob.py +++ /dev/null @@ -1,187 +0,0 @@ -# ------------------------------------------------------------------------- -# Copyright (c) Microsoft Corporation. All rights reserved. -# ------------------------------------------------------------------------- -import os - -from azure.storage.blob import ContainerClient - -from .. import utils -from .base import BaseFileSystem, RemotePath, StatData -from .utils import as_bytes, as_text, parse_blob_url - -logger = utils.get_logger() - - -class AzureBlobSystem(RemotePath, BaseFileSystem): - """Provides filesystem access to S3.""" - - def __init__(self): - if not ContainerClient: - raise ImportError('azure-storage-blob must be installed for Azure Blob support.') - self.connection_string = os.environ.get('AZURE_STORAGE_CONNECTION_STRING', None) - - def exists(self, filename): - """Returns whether the path is a directory or not.""" - basename, parts = self.split_blob_path(filename) - if basename is None or parts is None: - return False - if basename == '': - # root container case - return True - else: - return basename == parts[0] - - def read(self, file, binary_mode=False, size=None, continue_from=None): - """Reads contents of a file to a string.""" - logger.info('azure blob: starting reading file %s' % file) - account, container, path = self.container_and_path(file) - client = self.create_container_client(account, container) - blob_client = client.get_blob_client(path) - if not blob_client.exists(): - raise FileNotFoundError("file %s doesn't exist!" % path) - - downloader = blob_client.download_blob(offset=continue_from, length=size) - if continue_from is not None: - continuation_token = continue_from + downloader.size - else: - continuation_token = downloader.size - - data = downloader.readall() - logger.info('azure blob: file %s download is done, size is %d' % (file, len(data))) - if binary_mode: - return as_bytes(data), continuation_token - else: - return as_text(data), continuation_token - - def write(self, filename, file_content, binary_mode=False): - """Writes string file contents to a file.""" - account, container, path = self.container_and_path(filename) - client = self.create_container_client(account, container) - - if binary_mode: - if not isinstance(file_content, bytes): - raise TypeError('File content type must be bytes') - else: - file_content = as_bytes(file_content) - client.upload_blob(path, file_content) - - def download_file(self, file_to_download, file_to_save): - logger.info('azure blob: starting downloading file %s as %s' % (file_to_download, file_to_save)) - account, container, path = self.container_and_path(file_to_download) - client = self.create_container_client(account, container) - blob_client = client.get_blob_client(path) - if not blob_client.exists(): - raise FileNotFoundError("file %s doesn't exist!" % path) - - downloader = blob_client.download_blob() - with open(file_to_save, 'wb') as downloaded_file: - data = downloader.readall() - downloaded_file.write(data) - logger.info('azure blob: file %s is downloaded as %s, size is %d' % - (file_to_download, file_to_save, len(data))) - - def glob(self, filename): - """Returns a list of files that match the given pattern(s).""" - # Only support prefix with * at the end and no ? in the string - star_i = filename.find('*') - quest_i = filename.find('?') - if quest_i >= 0: - raise NotImplementedError( - '{} not supported by compat glob'.format(filename) - ) - if star_i != len(filename) - 1: - return [] - - filename = filename[:-1] - - account, container, path = self.container_and_path(filename) - client = self.create_container_client(account, container) - blobs = client.list_blobs(name_starts_with=path) - return [blob.name for blob in blobs] - - def isdir(self, dirname): - """Returns whether the path is a directory or not.""" - basename, parts = self.split_blob_path(dirname) - if basename is None or parts is None: - return False - if basename == '': - # root container case - return True - else: - return basename == parts[0] and len(parts) > 1 - - def listdir(self, dirname): - """Returns a list of entries contained within a directory.""" - account, container, path = self.container_and_path(dirname) - client = self.create_container_client(account, container) - blob_iter = client.list_blobs(name_starts_with=path) - items = [] - for blob in blob_iter: - item = self.relpath(blob.name, path) - if items not in items: - items.append(item) - return items - - def makedirs(self, path): - """No need create directory since the upload blob will automatically create""" - pass - - def stat(self, filename): - """Returns file statistics for a given path.""" - account, container, path = self.container_and_path(filename) - client = self.create_container_client(account, container) - blob_client = client.get_blob_client(path) - props = blob_client.get_blob_properties() - return StatData(props.size) - - def walk(self, top, topdown=True, onerror=None): - account, container, path = self.container_and_path(top) - client = self.create_container_client(account, container) - blobs = client.list_blobs(name_starts_with=path) - results = {} - for blob in blobs: - dirname, basename = self.split(blob.name) - dirname = 'https://{}/{}/{}'.format(account, container, dirname) - results.setdefault(dirname, []).append(basename) - for key, value in results.items(): - yield key, None, value - - def split_blob_path(self, blob_path): - """ Find the first blob start with blob_path, then get the relative path starting from dirname(blob_path). - Finally, split the relative path. - return (basename(blob_path), [relative splitted paths]) - If blob_path doesn't exist, return (None, None) - For example, - For blob https://trainingdaemon.blob.core.windows.net/tests/test1/test2/test.txt - * If the blob_path is '', return ('', [test1, test2, test.txt]) - * If the blob_path is test1, return (test1, [test2, test.txt]) - * If the blob_path is test1/test2, return (test2, [test2, test.txt]) - * If the blob_path is test1/test2/test.txt, return (test.txt, [test.txt]) - """ - account, container, path = self.container_and_path(blob_path) - client = self.create_container_client(account, container) - blobs = client.list_blobs(name_starts_with=path, maxresults=1) - - for blob in blobs: - dir_path, basename = self.split(path) - if dir_path: - rel_path = blob.name[len(dir_path):] - parts = rel_path.lstrip('/').split('/') - else: - parts = blob.name.split('/') - return (basename, parts) - return (None, None) - - def container_and_path(self, url): - """Split an Azure blob -prefixed URL into container and blob path.""" - root, parts = parse_blob_url(url) - if len(parts) != 2: - raise ValueError('Invalid azure blob url %s' % url) - return root, parts[0], parts[1] - - def create_container_client(self, account, container): - if self.connection_string: - client = ContainerClient.from_connection_string(self.connection_string, container) - else: - client = ContainerClient.from_container_url('https://{}/{}'.format(account, container)) - return client diff --git a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/io/base.py b/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/io/base.py deleted file mode 100644 index ab0a40f04..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/io/base.py +++ /dev/null @@ -1,114 +0,0 @@ -# ------------------------------------------------------------------------- -# Copyright (c) Microsoft Corporation. All rights reserved. -# ------------------------------------------------------------------------- -import os -from abc import ABC, abstractmethod -from collections import namedtuple - -# Data returned from the Stat call. -StatData = namedtuple('StatData', ['length']) - - -class BaseFileSystem(ABC): - def support_append(self): - return False - - def append(self, filename, file_content, binary_mode=False): - pass - - def download_file(self, file_to_download, file_to_save): - pass - - @abstractmethod - def exists(self, filename): - raise NotImplementedError - - @abstractmethod - def read(self, file, binary_mode=False, size=None, continue_from=None): - raise NotImplementedError - - @abstractmethod - def write(self, filename, file_content, binary_mode=False): - raise NotImplementedError - - @abstractmethod - def glob(self, filename): - raise NotImplementedError - - @abstractmethod - def isdir(self, dirname): - raise NotImplementedError - - @abstractmethod - def listdir(self, dirname): - raise NotImplementedError - - @abstractmethod - def makedirs(self, path): - raise NotImplementedError - - @abstractmethod - def stat(self, filename): - raise NotImplementedError - - -class BasePath(ABC): - @abstractmethod - def join(self, path, *paths): - pass - - @abstractmethod - def abspath(self, path): - pass - - @abstractmethod - def basename(self, path): - pass - - @abstractmethod - def relpath(self, path, start): - pass - - -class LocalPath(BasePath): - def abspath(self, path): - return os.path.abspath(os.path.expanduser(os.path.expandvars(path))) - - def basename(self, path): - return os.path.basename(path) - - def relpath(self, path, start): - return os.path.relpath(path, start) - - def join(self, path, *paths): - return os.path.join(path, *paths) - - -class RemotePath(BasePath): - def split(self, path): - """ - Split a pathname. Returns tuple '(head, tail)' where 'tail' is - everything after the final slash. Either part may be empty. - """ - sep = '/' - i = path.rfind(sep) + 1 - head, tail = path[:i], path[i:] - head = head.rstrip(sep) - return (head, tail) - - def join(self, path, *paths): - """Join paths with a slash.""" - return '/'.join((path,) + paths) - - def abspath(self, path): - return path - - def basename(self, path): - return path.split('/')[-1] - - def relpath(self, path, start): - if not path.startswith(start): - return path - start = start.rstrip('/') - begin = len(start) + 1 # include the ending slash '/' - return path[begin:] diff --git a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/io/cache.py b/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/io/cache.py deleted file mode 100644 index ea9afab66..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/io/cache.py +++ /dev/null @@ -1,81 +0,0 @@ -# ------------------------------------------------------------------------- -# Copyright (c) Microsoft Corporation. All rights reserved. -# ------------------------------------------------------------------------- -import tempfile - -from .. import utils -from .. import multiprocessing as mp -from . import file -from .file import basename, is_local, download_file, read - -logger = utils.get_logger() - - -class Cache: - def __init__(self, cache_dir=None): - self._lock = mp.Lock() - self._manager = mp.Manager() - self._cache_dict = self._manager.dict() - self._cache_dir = cache_dir - - def __getstate__(self): - """The multiprocessing module can start one of three ways: spawn, fork, or forkserver. - The default mode is fork in Unix and spawn on Windows and macOS. - Therefore, the __getstate__ and __setstate__ are used to pickle/unpickle the state in spawn mode. - """ - data = self.__dict__.copy() - # remove the _manager to bypass the following pickle error - # TypeError: cannot pickle 'weakref' object - if hasattr(self, '_manager'): - del data['_manager'] - logger.debug('Cache.__getstate__: %s ' % data) - return data, file._REGISTERED_FILESYSTEMS - - def __setstate__(self, state): - """The default logging level in new process is warning. Only warning and error log can be written to - streams. - So, we need call use_absl_handler in the new process. - """ - from absl import logging - logging.use_absl_handler() - logger.debug('Cache.__setstate__ %s ' % (state,)) - data, file._REGISTERED_FILESYSTEMS = state - self.__dict__.update(data) - - def read(self, filename): - local_file = self.get_remote_cache(filename) - return read(local_file) - - @property - def cache_dir(self): - return self._cache_dir - - def get_remote_cache(self, filename): - """Try to get the local file in the cache. download it to local if it cannot be found in cache.""" - local_file = self.get_file(filename) - if local_file is None: - if is_local(filename): - return filename - else: - local_file = tempfile.NamedTemporaryFile( - 'w+t', suffix='.%s' % basename(filename), dir=self._cache_dir, delete=False) - local_file.close() - download_file(filename, local_file.name) - self.add_file(filename, local_file.name) - return local_file.name - - return local_file - - def get_file(self, filename): - return self._cache_dict.get(filename) - - def add_file(self, source_file, local_file): - with self._lock: - logger.debug('add local cache %s for file %s' % (local_file, source_file)) - self._cache_dict[source_file] = local_file - - def __enter__(self): - return self - - def __exit__(self, exc_type, exc_value, traceback): - self._manager.__exit__(exc_type, exc_value, traceback) diff --git a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/io/file.py b/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/io/file.py deleted file mode 100644 index 9ef5d8485..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/io/file.py +++ /dev/null @@ -1,658 +0,0 @@ -""" -This file is forked from -https://github.com/tensorflow/tensorboard/blob/master/tensorboard/compat/tensorflow_stub/io/gfile.py. -The following functionalities are added after forking: -* Check Azure Blob & Google Cloud available or not -* get_filesystem changes to support Azure Blobs -* add BaseFileSystem and PathBase abstracted class for the filesystem. -* add download_file for each file system to cache the remote file to local temporary folder. -* add AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY for S3 file system which is not supported by tensorboard. -* add Azure blob file system -* add Google Cloud file system -* add specialized walk for Local file system, Azure Blob and Google Cloud to improve the walk performance. -* add global wrapper for abspath, basename, join, download_file. -* change the global walk wrapper to support specialized walk. -""" -import glob as py_glob -import os -import platform -import sys -import tempfile - -from .. import utils -from .base import BaseFileSystem, LocalPath, RemotePath, StatData -from .utils import as_bytes, as_text, parse_blob_url -from ..consts import MAX_FILE_SIZE, MAX_WINDOWS_PATH_LENGTH, MAX_LINUX_PATH_LENGTH - -logger = utils.get_logger() - -S3_ENABLED = True -try: - import boto3 - import botocore.exceptions -except ImportError: - S3_ENABLED = False - -BLOB_ENABLED = True -try: - from azure.storage.blob import ContainerClient -except ImportError: - BLOB_ENABLED = False - -GS_ENABLED = True -try: - # Imports the Google Cloud client library - from google.cloud import storage -except ImportError: - GS_ENABLED = False - - -_DEFAULT_BLOCK_SIZE = 16 * 1024 * 1024 - -# Registry of filesystems by prefix. -# -# Currently supports: -# * "s3://" URLs for S3 based on boto3 -# * "https://.blob.core.windows.net" for Azure Blob based on azure-storage-blob -# * "gs://" URLs for Google Cloud based on google-cloud-storage -# * Local filesystem when not match any prefix. -_REGISTERED_FILESYSTEMS = {} - - -def register_filesystem(prefix, filesystem): - if ":" in prefix: - raise ValueError("Filesystem prefix cannot contain a :") - _REGISTERED_FILESYSTEMS[prefix] = filesystem - - -def get_filesystem(filename): - """Return the registered filesystem for the given file.""" - prefix = "" - index = filename.find("://") - if index >= 0: - prefix = filename[:index] - if prefix.upper() in ('HTTP', 'HTTPS'): - root, _ = parse_blob_url(filename) - if root.lower().endswith('.blob.core.windows.net'): - fs = _REGISTERED_FILESYSTEMS.get('blob', None) - else: - raise ValueError("Not supported file system for prefix %s" % root) - else: - fs = _REGISTERED_FILESYSTEMS.get(prefix, None) - if fs is None: - raise ValueError("No recognized filesystem for prefix %s" % prefix) - return fs - - -class LocalFileSystem(LocalPath, BaseFileSystem): - def __init__(self): - pass - - @staticmethod - def islink(path): - return os.path.islink(path) - - def exists(self, filename): - return os.path.exists(filename) - - def read(self, file, binary_mode=False, size=None, continue_from=None): - mode = "rb" if binary_mode else "r" - encoding = None if binary_mode else "utf8" - if not self.exists(file): - raise FileNotFoundError(file) - - offset = None - if continue_from is not None: - offset = continue_from.get("opaque_offset", None) - with open(file, mode, encoding=encoding) as f: - if offset is not None: - f.seek(offset) - data = f.read(size) - # The new offset may not be `offset + len(data)`, due to decoding - # and newline translation. - # So, just measure it in whatever terms the underlying stream uses. - continuation_token = {"opaque_offset": f.tell()} - return (data, continuation_token) - - def write(self, filename, file_content, binary_mode=False): - """Writes string file contents to a file, overwriting any existing contents. - """ - self._write(filename, file_content, "wb" if binary_mode else "w") - - def support_append(self): - return True - - def append(self, filename, file_content, binary_mode=False): - """Append string file contents to a file. - """ - self._write(filename, file_content, "ab" if binary_mode else "a") - - def _write(self, filename, file_content, mode): - encoding = None if "b" in mode else "utf8" - with open(filename, mode, encoding=encoding) as f: - compatify = as_bytes if "b" in mode else as_text - f.write(compatify(file_content)) - - def glob(self, filename): - """Returns a list of files that match the given pattern(s).""" - if isinstance(filename, str): - return [ - matching_filename - for matching_filename in py_glob.glob(filename) - ] - else: - return [ - matching_filename - for single_filename in filename - for matching_filename in py_glob.glob(single_filename) - ] - - def isdir(self, dirname): - return os.path.isdir(dirname) - - def listdir(self, dirname): - entries = os.listdir(dirname) - entries = [item for item in entries] - return entries - - def makedirs(self, path): - os.makedirs(path, exist_ok=True) - - def stat(self, filename): - """Returns file statistics for a given path.""" - # NOTE: Size of the file is given by .st_size as returned from - # os.stat(), but we convert to .length - file_length = os.stat(filename).st_size - return StatData(file_length) - - def walk(self, top, topdown=True, onerror=None): - yield from os.walk(top, topdown, onerror, followlinks=True) - - -class S3FileSystem(RemotePath, BaseFileSystem): - """Provides filesystem access to S3.""" - - def __init__(self): - if not boto3: - raise ImportError("boto3 must be installed for S3 support.") - self._s3_endpoint = os.environ.get("S3_ENDPOINT", None) - access_key = os.environ.get("AWS_ACCESS_KEY_ID") - secret_key = os.environ.get("AWS_SECRET_ACCESS_KEY") - if access_key and secret_key: - boto3.setup_default_session( - aws_access_key_id=access_key, aws_secret_access_key=secret_key) - - def bucket_and_path(self, url): - """Split an S3-prefixed URL into bucket and path.""" - if url.startswith("s3://"): - url = url[len("s3://"):] - idx = url.index("/") - bucket = url[:idx] - path = url[(idx + 1):] - return bucket, path - - def exists(self, filename): - """Determines whether a path exists or not.""" - client = boto3.client("s3", endpoint_url=self._s3_endpoint) - bucket, path = self.bucket_and_path(filename) - r = client.list_objects(Bucket=bucket, Prefix=path, Delimiter="/") - if r.get("Contents") or r.get("CommonPrefixes"): - return True - return False - - def read(self, file, binary_mode=False, size=None, continue_from=None): - """Reads contents of a file to a string.""" - s3 = boto3.resource("s3", endpoint_url=self._s3_endpoint) - bucket, path = self.bucket_and_path(file) - args = {} - - # S3 use continuation tokens of the form: {byte_offset: number} - offset = 0 - if continue_from is not None: - offset = continue_from.get("byte_offset", 0) - - endpoint = "" - if size is not None: - endpoint = offset + size - - if offset != 0 or endpoint != "": - args["Range"] = "bytes={}-{}".format(offset, endpoint) - - logger.info("s3: starting reading file %s" % file) - try: - stream = s3.Object(bucket, path).get(**args)["Body"].read() - except botocore.exceptions.ClientError as exc: - if exc.response["Error"]["Code"] in ["416", "InvalidRange"]: - if size is not None: - # Asked for too much, so request just to the end. Do this - # in a second request so we don't check length in all cases. - client = boto3.client("s3", endpoint_url=self._s3_endpoint) - obj = client.head_object(Bucket=bucket, Key=path) - content_length = obj["ContentLength"] - endpoint = min(content_length, offset + size) - if offset == endpoint: - # Asked for no bytes, so just return empty - stream = b"" - else: - args["Range"] = "bytes={}-{}".format(offset, endpoint) - stream = s3.Object(bucket, path).get(**args)["Body"].read() - else: - raise - - logger.info("s3: file %s download is done, size is %d" % - (file, len(stream))) - # `stream` should contain raw bytes here (i.e., there has been neither decoding nor newline translation), - # so the byte offset increases by the expected amount. - continuation_token = {"byte_offset": (offset + len(stream))} - if binary_mode: - return (bytes(stream), continuation_token) - else: - return (stream.decode("utf-8"), continuation_token) - - def write(self, filename, file_content, binary_mode=False): - """Writes string file contents to a file.""" - client = boto3.client("s3", endpoint_url=self._s3_endpoint) - bucket, path = self.bucket_and_path(filename) - if binary_mode: - if not isinstance(file_content, bytes): - raise TypeError("File content type must be bytes") - else: - file_content = as_bytes(file_content) - client.put_object(Body=file_content, Bucket=bucket, Key=path) - - def download_file(self, file_to_download, file_to_save): - logger.info("s3: starting downloading file %s as %s" % - (file_to_download, file_to_save)) - s3 = boto3.resource("s3", endpoint_url=self._s3_endpoint) - bucket, path = self.bucket_and_path(file_to_download) - s3.Bucket(bucket).download_file(path, file_to_save) - logger.info("s3: file %s is downloaded as %s" % (file_to_download, file_to_save)) - return - - def glob(self, filename): - """Returns a list of files that match the given pattern(s).""" - # Only support prefix with * at the end and no ? in the string - star_i = filename.find("*") - quest_i = filename.find("?") - if quest_i >= 0: - raise NotImplementedError("{} not supported".format(filename)) - if star_i != len(filename) - 1: - return [] - - filename = filename[:-1] - client = boto3.client("s3", endpoint_url=self._s3_endpoint) - bucket, path = self.bucket_and_path(filename) - p = client.get_paginator("list_objects") - keys = [] - for r in p.paginate(Bucket=bucket, Prefix=path): - for content in r.get("Contents", []): - key = content["Key"][len(path):] - if key: - keys.append(filename + key) - return keys - - def isdir(self, dirname): - """Returns whether the path is a directory or not.""" - client = boto3.client("s3", endpoint_url=self._s3_endpoint) - bucket, path = self.bucket_and_path(dirname) - if not path.endswith("/"): - path += "/" - r = client.list_objects(Bucket=bucket, Prefix=path, Delimiter="/") - if r.get("Contents") or r.get("CommonPrefixes"): - return True - return False - - def listdir(self, dirname): - """Returns a list of entries contained within a directory.""" - client = boto3.client("s3", endpoint_url=self._s3_endpoint) - bucket, path = self.bucket_and_path(dirname) - p = client.get_paginator("list_objects") - if not path.endswith("/"): - path += "/" - keys = [] - for r in p.paginate(Bucket=bucket, Prefix=path, Delimiter="/"): - keys.extend( - prefixes["Prefix"][len(path): -1] for prefixes in r.get("CommonPrefixes", []) - ) - for content in r.get("Contents", []): - key = content["Key"][len(path):] - if key: - keys.append(key) - return keys - - def makedirs(self, path): - """Creates a directory and all parent/intermediate directories.""" - if not self.exists(path): - client = boto3.client("s3", endpoint_url=self._s3_endpoint) - bucket, dir_path = self.bucket_and_path(path) - if not dir_path.endswith("/"): - dir_path += "/" - client.put_object(Body="", Bucket=bucket, Key=dir_path) - - def stat(self, filename): - """Returns file statistics for a given path.""" - # Size of the file is given by ContentLength from S3 - client = boto3.client("s3", endpoint_url=self._s3_endpoint) - bucket, path = self.bucket_and_path(filename) - - obj = client.head_object(Bucket=bucket, Key=path) - return StatData(obj["ContentLength"]) - - -register_filesystem("", LocalFileSystem()) -if S3_ENABLED: - register_filesystem("s3", S3FileSystem()) - -if BLOB_ENABLED: - from .azureblob import AzureBlobSystem - register_filesystem("blob", AzureBlobSystem()) - -if GS_ENABLED: - from .gs import GoogleBlobSystem - register_filesystem("gs", GoogleBlobSystem()) - - -class File(object): - def __init__(self, filename, mode): - if mode not in ("r", "rb", "br", "w", "wb", "bw"): - raise ValueError("mode {} not supported by File".format(mode)) - self.filename = filename - self.fs = get_filesystem(self.filename) - self.fs_supports_append = self.fs.support_append() - self.buff = None - self.buff_chunk_size = _DEFAULT_BLOCK_SIZE - self.buff_offset = 0 - self.continuation_token = None - self.write_temp = None - self.write_started = False - self.binary_mode = "b" in mode - self.write_mode = "w" in mode - self.closed = False - - def __enter__(self): - return self - - def __exit__(self, *args): - self.close() - self.buff = None - self.buff_offset = 0 - self.continuation_token = None - - def __iter__(self): - return self - - def _read_buffer_to_offset(self, new_buff_offset): - old_buff_offset = self.buff_offset - read_size = min(len(self.buff), new_buff_offset) - old_buff_offset - self.buff_offset += read_size - return self.buff[old_buff_offset: old_buff_offset + read_size] - - def read(self, n=None): - """Reads contents of file to a string. - - Args: - n: int, number of bytes or characters to read, otherwise - read all the contents of the file - - Returns: - Subset of the contents of the file as a string or bytes. - """ - if self.write_mode: - raise OSError("File not opened in read mode") - - result = None - if self.buff and len(self.buff) > self.buff_offset: - # read from local buffer - if n is not None: - chunk = self._read_buffer_to_offset(self.buff_offset + n) - if len(chunk) == n: - return chunk - result = chunk - n -= len(chunk) - else: - # add all local buffer and update offsets - result = self._read_buffer_to_offset(len(self.buff)) - - # read from filesystem - read_size = max(self.buff_chunk_size, n) if n is not None else None - (self.buff, self.continuation_token) = self.fs.read( - self.filename, self.binary_mode, read_size, self.continuation_token) - self.buff_offset = 0 - - # add from filesystem - if n is not None: - chunk = self._read_buffer_to_offset(n) - else: - # add all local buffer and update offsets - chunk = self._read_buffer_to_offset(len(self.buff)) - result = result + chunk if result else chunk - - return result - - def write(self, file_content): - """Writes string file contents to file, clearing contents of the file - on first write and then appending on subsequent calls. - """ - if not self.write_mode: - raise OSError("File not opened in write mode") - - if self.closed: - raise OSError("File already closed") - - if self.fs_supports_append: - if not self.write_started: - # write the first chunk to truncate file if it already exists - self.fs.write(self.filename, file_content, self.binary_mode) - self.write_started = True - else: - # append the later chunks - self.fs.append(self.filename, file_content, self.binary_mode) - else: - # add to temp file, but wait for flush to write to final filesystem - if self.write_temp is None: - mode = "w+b" if self.binary_mode else "w+" - self.write_temp = tempfile.TemporaryFile(mode) - - compatify = as_bytes if self.binary_mode else as_text - self.write_temp.write(compatify(file_content)) - - def __next__(self): - line = None - while True: - if not self.buff: - # read one unit into the buffer - line = self.read(1) - if line and (line[-1] == "\n" or not self.buff): - return line - if not self.buff: - return None - else: - index = self.buff.find("\n", self.buff_offset) - if index != -1: - # include line until now plus newline - chunk = self.read(index + 1 - self.buff_offset) - line = line + chunk if line else chunk - return line - - # read one unit past end of buffer - chunk = self.read(len(self.buff) + 1 - self.buff_offset) - line = line + chunk if line else chunk - if line and (line[-1] == "\n" or not self.buff): - return line - if not self.buff: - return None - - def next(self): - return self.__next__() - - def flush(self): - if self.closed: - raise OSError("File already closed") - - if not self.fs_supports_append: - if self.write_temp is not None: - # read temp file from the beginning - self.write_temp.flush() - self.write_temp.seek(0) - chunk = self.write_temp.read() - if chunk is not None: - # write full contents and keep in temp file - self.fs.write(self.filename, chunk, self.binary_mode) - self.write_temp.seek(len(chunk)) - - def close(self): - self.flush() - if self.write_temp is not None: - self.write_temp.close() - self.write_temp = None - self.write_started = False - self.closed = True - - -def exists(filename): - """Determines whether a path exists or not.""" - return get_filesystem(filename).exists(filename) - - -def abspath(path): - return get_filesystem(path).abspath(path) - - -def basename(path): - return get_filesystem(path).basename(path) - - -def relpath(path, start): - return get_filesystem(path).relpath(path, start) - - -def join(path, *paths): - return get_filesystem(path).join(path, *paths) - - -def download_file(file_to_download, file_to_save): - """Downloads the file, returning a temporary path to the file after finishing.""" - get_filesystem(file_to_download).download_file(file_to_download, file_to_save) - - -def glob(filename): - """Returns a list of files that match the given pattern(s).""" - return get_filesystem(filename).glob(filename) - - -def is_local(path): - """Returns whether the path is a local path""" - return isinstance(get_filesystem(path), LocalFileSystem) - - -def isdir(dirname): - """Returns whether the path is a directory or not.""" - return get_filesystem(dirname).isdir(dirname) - - -def listdir(dirname): - """Returns a list of entries contained within a directory. - - The list is in arbitrary order. It does not contain the special entries "." - and "..". - """ - return get_filesystem(dirname).listdir(dirname) - - -def makedirs(path): - """Creates a directory and all parent/intermediate directories.""" - return get_filesystem(path).makedirs(path) - - -def walk(top, topdown=True, onerror=None): - """Recursive directory tree generator for directories. - - Args: - top: string, a Directory name - topdown: bool, Traverse pre order if True, post order if False. - onerror: optional handler for errors. Should be a function, it will be - called with the error as argument. Rethrowing the error aborts the walk. - - Errors that happen while listing directories are ignored. - - Yields: - Each yield is a 3-tuple: the pathname of a directory, followed by lists - of all its subdirectories and leaf files. - (dirname, [subdirname, subdirname, ...], [filename, filename, ...]) - as strings - """ - fs = get_filesystem(top) - if hasattr(fs, "walk"): - yield from fs.walk(top, topdown, onerror) - else: - top = fs.abspath(top) - listing = fs.listdir(top) - - files = [] - subdirs = [] - for item in listing: - full_path = fs.join(top, item) - if fs.isdir(full_path): - subdirs.append(item) - else: - files.append(item) - - here = (top, subdirs, files) - - if topdown: - yield here - - for subdir in subdirs: - joined_subdir = fs.join(top, subdir) - for subitem in walk(joined_subdir, topdown, onerror=onerror): - yield subitem - - if not topdown: - yield here - - -def stat(filename): - """Returns file statistics for a given path.""" - return get_filesystem(filename).stat(filename) - - -def read(file): - with File(file, 'rb') as f: - return f.read() - - -def is_link(path): - return LocalFileSystem.islink(path) - - -def is_too_big_file(filepath): - return stat(filepath).length > MAX_FILE_SIZE - - -def has_too_long_path(filepath): - if platform.system() == 'Windows' and len(filepath) > MAX_WINDOWS_PATH_LENGTH: - logger.warning( - f'The path length of the file "{filepath}" exceeds the maximum limit of {MAX_WINDOWS_PATH_LENGTH} ' - f'and will be skipped.') - return True - elif len(filepath) > MAX_WINDOWS_PATH_LENGTH: - logger.warning( - f'The path length of the file "{filepath}" exceeds the maximum limit of {MAX_LINUX_PATH_LENGTH} ' - f'and will be skipped.') - return True - else: - return False - - -def check_file_valid(filepath): - if is_link(filepath): - logger.warning(f'File "{filepath}" is a soft link and will be skipped.') - return False - if is_too_big_file(filepath): - logger.warning( - f'File "{filepath}" exceeds the maximum limit size of 500MB and will be skipped.') - return False - if has_too_long_path(filepath): - return False - return True - diff --git a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/io/gs.py b/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/io/gs.py deleted file mode 100644 index 8596bce2b..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/io/gs.py +++ /dev/null @@ -1,126 +0,0 @@ -# ------------------------------------------------------------------------- -# Copyright (c) Microsoft Corporation. All rights reserved. -# ------------------------------------------------------------------------- -from google.cloud import storage - -from .. import utils -from .base import BaseFileSystem, RemotePath, StatData - -logger = utils.get_logger() - - -class GoogleBlobSystem(RemotePath, BaseFileSystem): - """Provides filesystem access to S3.""" - - def __init__(self): - if not storage: - raise ImportError('google-cloud-storage must be installed for Google Cloud Blob support.') - - def exists(self, filename): - """Returns whether the path is a directory or not.""" - bucket_name, path = self.bucket_and_path(filename) - client = self.create_google_cloud_client() - bucket = client.bucket(bucket_name) - return bucket.blob(path).exists() - - def read(self, file, binary_mode=False, size=None, continue_from=None): - raise NotImplementedError - - def write(self, filename, file_content, binary_mode=False): - raise NotImplementedError - - def glob(self, filename): - raise NotImplementedError - - def download_file(self, file_to_download, file_to_save): - bucket_name, path = self.bucket_and_path(file_to_download) - client = self.create_google_cloud_client() - bucket = client.bucket(bucket_name) - blob = bucket.blob(path) - blob.download_to_filename(file_to_save) - - def isdir(self, dirname): - """Returns whether the path is a directory or not.""" - basename, parts = self.split_blob_path(dirname) - if basename is None or parts is None: - return False - if basename == '': - # root container case - return True - else: - return basename == parts[0] and len(parts) > 1 - - def listdir(self, dirname): - """Returns a list of entries contained within a directory.""" - bucket_name, path = self.bucket_and_path(dirname) - client = self.create_google_cloud_client() - blobs = client.list_blobs(bucket_name, prefix=path) - items = [] - for blob in blobs: - item = self.relpath(blob.name, path) - if items not in items: - items.append(item) - return items - - def makedirs(self, path): - """No need create directory since the upload blob will automatically create""" - pass - - def stat(self, filename): - """Returns file statistics for a given path.""" - bucket_name, path = self.bucket_and_path(filename) - client = self.create_google_cloud_client() - bucket = client.bucket(bucket_name) - blob = bucket.get_blob(path) - return StatData(blob.size) - - def walk(self, top, topdown=True, onerror=None): - bucket_name, path = self.bucket_and_path(top) - client = self.create_google_cloud_client() - blobs = client.list_blobs(bucket_name, prefix=path) - results = {} - for blob in blobs: - dirname, basename = self.split(blob.name) - dirname = 'gs://{}/{}'.format(bucket_name, dirname) - results.setdefault(dirname, []).append(basename) - for key, value in results.items(): - yield key, None, value - - def split_blob_path(self, blob_path): - """ Find the first blob start with blob_path, then get the relative path starting from dirname(blob_path). - Finally, split the relative path. - return (basename(blob_path), [relative splitted paths]) - If blob_path doesn't exist, return (None, None) - For example, - For blob gs://tests/test1/test2/test.txt - * If the blob_path is '', return ('', [test1, test2, test.txt]) - * If the blob_path is test1, return (test1, [test2, test.txt]) - * If the blob_path is test1/test2, return (test2, [test2, test.txt]) - * If the blob_path is test1/test2/test.txt, return (test.txt, [test.txt]) - """ - bucket_name, path = self.bucket_and_path(blob_path) - client = self.create_google_cloud_client() - blobs = client.list_blobs(bucket_name, prefix=path, delimiter=None, max_results=1) - - for blob in blobs: - dir_path, basename = self.split(path) - if dir_path: - rel_path = blob.name[len(dir_path):] - parts = rel_path.lstrip('/').split('/') - else: - parts = blob.name.split('/') - return (basename, parts) - return (None, None) - - def bucket_and_path(self, url): - """Split an S3-prefixed URL into bucket and path.""" - if url.startswith('gs://'): - url = url[len('gs://'):] - idx = url.index('/') - bucket = url[:idx] - path = url[(idx + 1):] - return bucket, path - - def create_google_cloud_client(self): - client = storage.Client.create_anonymous_client() - return client diff --git a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/io/utils.py b/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/io/utils.py deleted file mode 100644 index 79e9afc39..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/io/utils.py +++ /dev/null @@ -1,72 +0,0 @@ -def as_str_any(value): - """Converts to `str` as `str(value)`, but use `as_str` for `bytes`. - - Args: - value: A object that can be converted to `str`. - - Returns: - A `str` object. - """ - if isinstance(value, bytes): - return as_str(value) - else: - return str(value) - - -def as_text(bytes_or_text, encoding="utf-8"): - """Returns the given argument as a unicode string. - - Args: - bytes_or_text: A `bytes`, `str`, or `unicode` object. - encoding: A string indicating the charset for decoding unicode. - - Returns: - A `str` (Python 3) object. - - Raises: - TypeError: If `bytes_or_text` is not a binary or unicode string. - """ - if isinstance(bytes_or_text, str): - return bytes_or_text - elif isinstance(bytes_or_text, bytes): - return bytes_or_text.decode(encoding) - else: - raise TypeError( - "Expected binary or unicode string, got %r" % bytes_or_text - ) - - -# Convert an object to a `str` in both Python 2 and 3. -as_str = as_text - - -def as_bytes(bytes_or_text, encoding="utf-8"): - """Converts either bytes or unicode to `bytes`, using utf-8 encoding for - text. - - Args: - bytes_or_text: A `bytes`, `str`, or `unicode` object. - encoding: A string indicating the charset for encoding unicode. - - Returns: - A `bytes` object. - - Raises: - TypeError: If `bytes_or_text` is not a binary or unicode string. - """ - if isinstance(bytes_or_text, str): - return bytes_or_text.encode(encoding) - elif isinstance(bytes_or_text, bytes): - return bytes_or_text - else: - raise TypeError( - "Expected binary or unicode string, got %r" % (bytes_or_text,) - ) - - -def parse_blob_url(url): - from urllib import parse - url_path = parse.urlparse(url) - - parts = url_path.path.lstrip('/').split('/', 1) - return url_path.netloc, tuple(parts) diff --git a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/multiprocessing.py b/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/multiprocessing.py deleted file mode 100644 index b71773505..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/multiprocessing.py +++ /dev/null @@ -1,13 +0,0 @@ -# ------------------------------------------------------------------------- -# Copyright (c) Microsoft Corporation. All rights reserved. -# ------------------------------------------------------------------------- -import multiprocessing as mp -import os - - -def get_start_method(): - return os.getenv('TORCH_PROFILER_START_METHOD', 'spawn') - - -__all__ = [x for x in dir(mp.get_context(get_start_method())) if not x.startswith('_')] -globals().update((name, getattr(mp.get_context(get_start_method()), name)) for name in __all__) diff --git a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/plugin.py b/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/plugin.py deleted file mode 100644 index 2651f87c0..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/plugin.py +++ /dev/null @@ -1,651 +0,0 @@ -# ------------------------------------------------------------------------- -# Copyright (c) Microsoft Corporation. -# Copyright(c) 2023 Huawei Technologies. -# All rights reserved -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# Modifications: Add visualization of PyTorch Ascend profiling. -# -------------------------------------------------------------------------- -import atexit -import copy -import gzip -import json -import os -import shutil -import sys -import tempfile -import threading -import time -from collections import OrderedDict -from queue import Queue - -import werkzeug -from tensorboard.plugins import base_plugin -from werkzeug import exceptions, wrappers - -from . import consts, io, utils -from .profiler import RunLoader -from .run import DistributedRunProfile, Run, RunProfile - -logger = utils.get_logger() - - -def decorate_headers(func): - def wrapper(*args, **kwargs): - headers = func(*args, **kwargs) - headers.extend(TorchProfilerPlugin.headers) - return headers - - return wrapper - - -exceptions.HTTPException.get_headers = decorate_headers(exceptions.HTTPException.get_headers) - - -class TorchProfilerPlugin(base_plugin.TBPlugin): - """TensorBoard plugin for Torch Profiler.""" - - plugin_name = consts.PLUGIN_NAME - headers = [('X-Content-Type-Options', 'nosniff')] - CONTENT_TYPE = 'application/json' - - def __init__(self, context: base_plugin.TBContext): - """Instantiates TorchProfilerPlugin. - Args: - context: A base_plugin.TBContext instance. - """ - super(TorchProfilerPlugin, self).__init__(context) - self.logdir = io.abspath(context.logdir.rstrip('/')) - - self._load_lock = threading.Lock() - self._load_threads = [] - - self._runs = OrderedDict() - self._runs_lock = threading.Lock() - - self._temp_dir = tempfile.mkdtemp() - self._cache = io.Cache(self._temp_dir) - self._queue = Queue() - self._gpu_metrics_file_dict = {} - monitor_runs = threading.Thread(target=self._monitor_runs, name='monitor_runs', daemon=True) - monitor_runs.start() - - receive_runs = threading.Thread(target=self._receive_runs, name='receive_runs', daemon=True) - receive_runs.start() - - self.diff_run_cache = {} - self.diff_run_flatten_cache = {} - - def clean(): - logger.debug('starting cleanup...') - self._cache.__exit__(*sys.exc_info()) - logger.debug('remove temporary cache directory %s' % self._temp_dir) - shutil.rmtree(self._temp_dir) - - atexit.register(clean) - - def is_active(self): - """Returns whether there is relevant data for the plugin to process. - If there is no any pending run, hide the plugin - """ - if self.is_loading: - return True - else: - with self._runs_lock: - return bool(self._runs) - - def get_plugin_apps(self): - return { - '/index.js': self.static_file_route, - '/index.html': self.static_file_route, - '/trace_viewer_full.html': self.static_file_route, - '/trace_embedding.html': self.static_file_route, - '/trace_script.js': self.static_file_route, - '/runs': self.runs_route, - '/views': self.views_route, - '/workers': self.workers_route, - '/spans': self.spans_route, - '/overview': self.overview_route, - '/operation': self.operation_pie_route, - '/operation/table': self.operation_table_route, - '/operation/stack': self.operation_stack_route, - '/kernel': self.kernel_pie_route, - '/kernel/table': self.kernel_table_route, - '/kernel/tc_pie': self.kernel_tc_route, - '/trace': self.trace_route, - '/distributed/gpuinfo': self.dist_gpu_info_route, - '/distributed/overlap': self.comm_overlap_route, - '/distributed/waittime': self.comm_wait_route, - '/distributed/commops': self.comm_ops_route, - '/memory': self.memory_route, - '/memory_curve': self.memory_curve_route, - '/memory_events': self.memory_events_route, - '/module': self.module_route, - '/tree': self.op_tree_route, - '/diff': self.diff_run_route, - '/diffnode': self.diff_run_node_route, - } - - def frontend_metadata(self): - return base_plugin.FrontendMetadata(es_module_path='/index.js', disable_reload=True) - - @wrappers.Request.application - def runs_route(self, request: werkzeug.Request): - with self._runs_lock: - names = list(self._runs.keys()) - - data = { - 'runs': names, - 'loading': self.is_loading - } - return self.respond_as_json(data) - - @wrappers.Request.application - def views_route(self, request: werkzeug.Request): - name = request.args.get('run') - self._validate(run=name) - run = self._get_run(name) - views_list = [view.display_name for view in run.views] - data = { - 'device_target': run.device_target, - 'views': views_list - } - return self.respond_as_json(data) - - @wrappers.Request.application - def workers_route(self, request: werkzeug.Request): - name = request.args.get('run') - view = request.args.get('view') - self._validate(run=name, view=view) - run = self._get_run(name) - # Required datas are in file 'trace_view.json' in Ascend. - if run.device_target == 'Ascend' and view == 'Overview': - view = 'Trace' - return self.respond_as_json(run.get_workers(view)) - - @wrappers.Request.application - def spans_route(self, request: werkzeug.Request): - name = request.args.get('run') - worker = request.args.get('worker') - self._validate(run=name, worker=worker) - run = self._get_run(name) - return self.respond_as_json(run.get_spans(worker)) - - @wrappers.Request.application - def overview_route(self, request: werkzeug.Request): - profile = self._get_profile_for_request(request) - name = request.args.get('run') - run = self._get_run(name) - data = profile.overview - is_gpu_used = profile.has_runtime or profile.has_kernel or profile.has_memcpy_or_memset - normal_workers = [worker for worker in run.workers if worker != 'All'] - data['environments'] = [{'title': 'Number of Worker(s)', 'value': str(len(normal_workers))}, - {'title': 'Device Type', 'value': 'GPU' if is_gpu_used else 'CPU'}] - if profile.gpu_summary and profile.gpu_tooltip: - data['gpu_metrics'] = {'title': 'GPU Summary', - 'data': profile.gpu_summary, - 'tooltip': profile.gpu_tooltip} - - return self.respond_as_json(data) - - @wrappers.Request.application - def operation_pie_route(self, request: werkzeug.Request): - profile = self._get_profile_for_request(request) - - group_by = request.args.get('group_by') - if group_by == 'OperationAndInputShape': - return self.respond_as_json(profile.operation_pie_by_name_input) - else: - return self.respond_as_json(profile.operation_pie_by_name) - - @wrappers.Request.application - def operation_table_route(self, request: werkzeug.Request): - profile = self._get_profile_for_request(request) - - group_by = request.args.get('group_by') - if group_by == 'OperationAndInputShape': - return self.respond_as_json(profile.operation_table_by_name_input) - else: - return self.respond_as_json(profile.operation_table_by_name) - - @wrappers.Request.application - def operation_stack_route(self, request: werkzeug.Request): - profile = self._get_profile_for_request(request) - - op_name = request.args.get('op_name') - self._validate(op_name=op_name) - group_by = request.args.get('group_by') - input_shape = request.args.get('input_shape') - if group_by == 'OperationAndInputShape': - return self.respond_as_json(profile.operation_stack_by_name_input[str(op_name) + '###' + str(input_shape)]) - else: - return self.respond_as_json(profile.operation_stack_by_name[str(op_name)]) - - @wrappers.Request.application - def kernel_pie_route(self, request: werkzeug.Request): - profile = self._get_profile_for_request(request) - - return self.respond_as_json(profile.kernel_pie) - - @wrappers.Request.application - def kernel_table_route(self, request: werkzeug.Request): - profile = self._get_profile_for_request(request) - - group_by = request.args.get('group_by') - if group_by == 'Kernel': - return self.respond_as_json(profile.kernel_table) - else: - return self.respond_as_json(profile.kernel_op_table) - - @wrappers.Request.application - def kernel_tc_route(self, request: werkzeug.Request): - profile = self._get_profile_for_request(request) - - return self.respond_as_json(profile.tc_pie) - - @wrappers.Request.application - def trace_route(self, request: werkzeug.Request): - profile = self._get_profile_for_request(request) - - if not profile.has_kernel: # Pure CPU. - raw_data = self._cache.read(profile.trace_file_path) - if not profile.trace_file_path.endswith('.gz'): - raw_data = gzip.compress(raw_data, 1) - else: - file_with_gpu_metrics = self._gpu_metrics_file_dict.get(profile.trace_file_path) - if file_with_gpu_metrics: - raw_data = io.read(file_with_gpu_metrics) - else: - raw_data = self._cache.read(profile.trace_file_path) - if not profile.trace_file_path.endswith('.gz'): - raw_data = gzip.compress(raw_data, 1) - - # write the data to temp file - fp = tempfile.NamedTemporaryFile('w+b', suffix='.json.gz', dir=self._temp_dir, delete=False) - fp.close() - # Already compressed, no need to gzip.open - with open(fp.name, mode='wb') as file: - file.write(raw_data) - self._gpu_metrics_file_dict[profile.trace_file_path] = fp.name - - headers = [('Content-Encoding', 'gzip')] - headers.extend(TorchProfilerPlugin.headers) - return werkzeug.Response(raw_data, content_type=TorchProfilerPlugin.CONTENT_TYPE, headers=headers) - - @wrappers.Request.application - def dist_gpu_info_route(self, request: werkzeug.Request): - profile = self._get_distributed_profile_for_request(request) - return self.respond_as_json(profile.gpu_info) - - @wrappers.Request.application - def comm_overlap_route(self, request: werkzeug.Request): - profile = self._get_distributed_profile_for_request(request) - return self.respond_as_json(profile.steps_to_overlap) - - @wrappers.Request.application - def comm_wait_route(self, request: werkzeug.Request): - profile = self._get_distributed_profile_for_request(request) - return self.respond_as_json(profile.steps_to_wait) - - @wrappers.Request.application - def comm_ops_route(self, request: werkzeug.Request): - profile = self._get_distributed_profile_for_request(request) - return self.respond_as_json(profile.comm_ops) - - @wrappers.Request.application - def memory_route(self, request: werkzeug.Request): - profile = self._get_profile_for_request(request) - start_ts = request.args.get('start_ts', None) - end_ts = request.args.get('end_ts', None) - memory_metric = request.args.get('memory_metric', 'KB') - if start_ts is not None: - start_ts = int(start_ts) - if end_ts is not None: - end_ts = int(end_ts) - - return self.respond_as_json( - profile.get_memory_stats(start_ts=start_ts, end_ts=end_ts, memory_metric=memory_metric), True) - - @wrappers.Request.application - def memory_curve_route(self, request: werkzeug.Request): - profile = self._get_profile_for_request(request) - if profile.device_target == 'Ascend': - return self.respond_as_json(profile.memory_all_curve, True) - else: - time_metric = request.args.get('time_metric', 'ms') - memory_metric = request.args.get('memory_metric', 'MB') - return self.respond_as_json( - profile.get_memory_curve(time_metric=time_metric, memory_metric=memory_metric), True) - - @wrappers.Request.application - def memory_events_route(self, request: werkzeug.Request): - profile = self._get_profile_for_request(request) - start_ts = request.args.get('start_ts', None) - end_ts = request.args.get('end_ts', None) - time_metric = request.args.get('time_metric', 'ms') - memory_metric = request.args.get('memory_metric', 'KB') - if profile.device_target == 'Ascend': - temp_memory_events = copy.deepcopy(profile.memory_events) - operator_memory_events = temp_memory_events['operator']['rows'] - if start_ts is not None: - start_ts = float(start_ts) - if end_ts is not None: - end_ts = float(end_ts) - for key in operator_memory_events: - if start_ts is not None and end_ts is not None: - operator_memory_events[key] = [ - i - for i in operator_memory_events[key] - if i[2] and start_ts <= i[2] <= end_ts - ] - elif start_ts is not None: - operator_memory_events[key] = [ - i - for i in operator_memory_events[key] - if i[2] and start_ts <= i[2] - ] - elif end_ts is not None: - operator_memory_events[key] = [ - i - for i in operator_memory_events[key] - if i[2] and end_ts >= i[2] - ] - return self.respond_as_json(temp_memory_events, True) - else: - if start_ts is not None: - start_ts = int(start_ts) - if end_ts is not None: - end_ts = int(end_ts) - return self.respond_as_json( - profile.get_memory_events(start_ts, end_ts, time_metric=time_metric, - memory_metric=memory_metric), True) - - @wrappers.Request.application - def module_route(self, request: werkzeug.Request): - profile = self._get_profile_for_request(request) - content = profile.get_module_view() - if content: - return self.respond_as_json(content, True) - else: - name = request.args.get('run') - worker = request.args.get('worker') - span = request.args.get('span') - raise exceptions.NotFound('could not find the run for %s/%s/%s' % (name, worker, span)) - - @wrappers.Request.application - def op_tree_route(self, request: werkzeug.Request): - profile = self._get_profile_for_request(request) - content = profile.get_operator_tree() - return self.respond_as_json(content, True) - - @wrappers.Request.application - def diff_run_route(self, request: werkzeug.Request): - base, exp = self.get_diff_runs(request) - diff_stats = self.get_diff_status(base, exp) - content = diff_stats.get_diff_tree_summary() - return self.respond_as_json(content, True) - - @wrappers.Request.application - def diff_run_node_route(self, request: werkzeug.Request): - base, exp = self.get_diff_runs(request) - path = request.args.get('path', '0') - stats_dict = self.get_diff_stats_dict(base, exp) - diff_stat = stats_dict.get(path) - if diff_stat is None: - raise exceptions.NotFound('could not find diff run for %s' % (path)) - content = diff_stat.get_diff_node_summary(path) - return self.respond_as_json(content, True) - - @wrappers.Request.application - def static_file_route(self, request: werkzeug.Request): - filename = os.path.basename(request.path) - extension = os.path.splitext(filename)[1] - if extension == '.html': - mimetype = 'text/html' - elif extension == '.css': - mimetype = 'text/css' - elif extension == '.js': - mimetype = 'application/javascript' - else: - mimetype = 'application/octet-stream' - filepath = os.path.join(os.path.dirname(__file__), 'static', filename) - try: - with open(filepath, 'rb') as infile: - contents = infile.read() - except IOError as e: - raise exceptions.NotFound('404 Not Found') from e - return werkzeug.Response( - contents, content_type=mimetype, headers=TorchProfilerPlugin.headers - ) - - @staticmethod - def respond_as_json(obj, compress: bool = False): - content = json.dumps(obj) - headers = [] - headers.extend(TorchProfilerPlugin.headers) - if compress: - content_bytes = content.encode('utf-8') - raw_data = gzip.compress(content_bytes, 1) - headers.append(('Content-Encoding', 'gzip')) - return werkzeug.Response(raw_data, content_type=TorchProfilerPlugin.CONTENT_TYPE, headers=headers) - else: - return werkzeug.Response(content, content_type=TorchProfilerPlugin.CONTENT_TYPE, headers=headers) - - @property - def is_loading(self): - with self._load_lock: - return bool(self._load_threads) - - def get_diff_runs(self, request: werkzeug.Request): - name = request.args.get('run') - span = request.args.get('span') - worker = request.args.get('worker') - self._validate(run=name, worker=worker, span=span) - base = self._get_profile(name, worker, span) - - exp_name = request.args.get('exp_run') - exp_span = request.args.get('exp_span') - exp_worker = request.args.get('exp_worker') - self._validate(exp_run=exp_name, exp_worker=exp_worker, exp_span=exp_span) - exp = self._get_profile(exp_name, exp_worker, exp_span) - - return base, exp - - def get_diff_status(self, base: RunProfile, exp: RunProfile): - key = (base, exp) - diff_stats = self.diff_run_cache.get(key) - if diff_stats is None: - diff_stats = base.compare_run(exp) - self.diff_run_cache[key] = diff_stats - - return diff_stats - - def get_diff_stats_dict(self, base: RunProfile, exp: RunProfile): - key = (base, exp) - stats_dict = self.diff_run_flatten_cache.get(key) - if stats_dict is None: - diff_stats = self.get_diff_status(base, exp) - stats_dict = diff_stats.flatten_diff_tree() - self.diff_run_flatten_cache[key] = stats_dict - return stats_dict - - def _monitor_runs(self): - logger.info('Monitor runs begin') - touched = set() - try: - while True: - try: - logger.debug('Scan run dir') - run_dirs = self._get_run_dirs() - - has_dir = False - # Assume no deletion on run directories, trigger async load if find a new run - for run_dir in run_dirs: - has_dir = True - run_device = f'{run_dir["name"]}_{run_dir["device_target"]}' - if run_device not in touched: - touched.add(run_device) - logger.info('Find run directory %s', run_dir['name']) - # Use threading to avoid UI stall and reduce data parsing time - t = threading.Thread(target=self._load_run, args=(run_dir,)) - t.start() - with self._load_lock: - self._load_threads.append(t) - - if not has_dir: - # handle directory removed case. - self._runs.clear() - except Exception as ex: - logger.warning('Failed to scan runs. Exception=%s', ex, exc_info=True) - - time.sleep(consts.MONITOR_RUN_REFRESH_INTERNAL_IN_SECONDS) - except Exception: - logger.exception('Failed to start monitor_runs') - - def _receive_runs(self): - while True: - run: Run = self._queue.get() - if run is None: - continue - - logger.info('Add run %s', run.name) - with self._runs_lock: - is_new = run.name not in self._runs - self._runs[run.name] = run - if is_new: - self._runs = OrderedDict(sorted(self._runs.items())) - - def _get_run_dirs(self): - """Scan logdir, find PyTorch Profiler run directories. - A directory is considered to be a gpu run if it contains 1 or more *.pt.trace.json[.gz]. - E.g. there are 2 runs: run1, run2 - /run1 - /[worker1].pt.trace.json.gz - /[worker2].pt.trace.json.gz - /run2 - /[worker1].pt.trace.json - A directory is considered to be an ascend run if it satisfies the following two conditions: - 1.At least one subdirectory with the name in this format: {worker}_{span}_ascend_pt. - 2.The subdirectory in condition 1 has a 'ASCEND_PROFILER_OUTPUT' subdirectory which - contains at least one of these 4 kind of files: - [ - 'trace_view.json(.gz)', - 'kernel_details.csv', - 'operator_details.csv', - 'operator_memory.csv' & 'memory_record.csv' - ] - E.g. there are 2 runs: run1, run2 - /run1 - /[worker1]_[span1]_ascend_pt - /ASCEND_PROFILER_OUTPUT - /trace_view.json - /kernel_details.csv - /operator_details.csv - /operator_memory.csv - /memory_record.csv - /[worker2]_[span1]_ascend_pt - /ASCEND_PROFILER_OUTPUT - /trace_view.json - /operator_details.csv - /run2 - /[worker1]_[span1]_ascend_pt - /ASCEND_PROFILER_OUTPUT - /memory_record.csv - /operator_memory.csv - """ - for root, subdirs, files in io.walk(self.logdir): - for subdir in subdirs: - if str(subdir) == 'ASCEND_PROFILER_OUTPUT': - match = consts.WORKER_SPAN_PATTERN.match(io.basename(root)) - if match is not None: - run_name = io.abspath(io.join(root, '..')) - yield {'name': run_name, 'device_target': 'Ascend'} - break - - for file in files: - if utils.is_gpu_chrome_trace_file(file): - yield {'name': root, 'device_target': 'GPU'} - break - - def _load_run(self, run_dir): - try: - name = self._get_run_name(run_dir['name']) - logger.info('Load run %s', name) - # Currently, assume run data is immutable, so just load once - loader = RunLoader(name, run_dir['name'], self._cache, run_dir['device_target']) - run = loader.load() - if run.profiles: - self._queue.put(run) - logger.info('Run %s loaded', name) - else: - logger.warning(f'Run {name} skipped') - except Exception as ex: - logger.warning('Failed to load run %s. Exception=%s', ex, name, exc_info=True) - - t = threading.current_thread() - with self._load_lock: - try: - self._load_threads.remove(t) - except ValueError: - logger.warning('could not find the thread {}'.format(run_dir)) - - def _get_run(self, name) -> Run: - with self._runs_lock: - run = self._runs.get(name, None) - - if run is None: - raise exceptions.NotFound('could not find the run for %s' % (name)) - - return run - - def _get_run_name(self, run_dir): - logdir = io.abspath(self.logdir) - if run_dir == logdir: - name = io.basename(run_dir) - else: - name = io.relpath(run_dir, logdir) - return name - - def _get_profile_for_request(self, request: werkzeug.Request) -> RunProfile: - name = request.args.get('run') - span = request.args.get('span') - worker = request.args.get('worker') - self._validate(run=name, worker=worker) - profile = self._get_profile(name, worker, span) - if not isinstance(profile, RunProfile): - raise exceptions.BadRequest('Get an unexpected profile type %s for %s/%s' % (type(profile), name, worker)) - - return profile - - def _get_distributed_profile_for_request(self, request: werkzeug.Request) -> DistributedRunProfile: - name = request.args.get('run') - span = request.args.get('span') - self._validate(run=name) - profile = self._get_profile(name, 'All', span) - if not isinstance(profile, DistributedRunProfile): - raise exceptions.BadRequest('Get an unexpected distributed profile type %s for %s' % (type(profile), name)) - - return profile - - def _get_profile(self, name, worker, span): - run = self._get_run(name) - profile = run.get_profile(worker, span) - if profile is None: - raise exceptions.NotFound('could not find the profile for %s/%s/%s ' % (name, worker, span)) - return profile - - def _validate(self, **kwargs): - for name, v in kwargs.items(): - if v is None: - raise exceptions.BadRequest('Must specify %s in request url' % (name)) diff --git a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/__init__.py b/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/__init__.py deleted file mode 100644 index 59a0e6415..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -# ------------------------------------------------------------------------- -# Copyright (c) Microsoft Corporation. All rights reserved. -# -------------------------------------------------------------------------- -__all__ = ['RunLoader'] - -from .loader import RunLoader diff --git a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/communication.py b/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/communication.py deleted file mode 100644 index 0afcdb11a..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/communication.py +++ /dev/null @@ -1,95 +0,0 @@ -# ------------------------------------------------------------------------- -# Copyright (c) Microsoft Corporation. All rights reserved. -# ------------------------------------------------------------------------- -from typing import Dict, List, Tuple - -from .. import utils -from .node import CommunicationNode -from .range_utils import get_ranges_sum, merge_ranges - -logger = utils.get_logger() - - -def generate_communication_nodes( - communication_data: Dict[int, CommunicationNode], - steps: List[Tuple[int, int]], - steps_names: List[str]): - comm_node_list: List[CommunicationNode] = [] - - # Sort the communication node according the start time, this is for correlating communication node between workers - for comm_node in communication_data.values(): - comm_node.kernel_ranges.sort(key=lambda x: (x[0], -x[1])) - comm_node_list.append(comm_node) - comm_node_list.sort(key=lambda x: (x.start_time, -x.end_time)) - - # Find each communication node belong to which step - index = 0 - valid_steps = len(steps) - for comm_node in comm_node_list: - while index < valid_steps: - if comm_node.start_time >= steps[index][0] and comm_node.end_time <= steps[index][1]: - comm_node.step_name = steps_names[index] - break - elif comm_node.start_time >= steps[index][1]: - index += 1 - else: - logger.error('Found a communication op not belong to any step.') - break - if index >= valid_steps: - logger.error('Found communication ops not belong to any step. ') - break - - return comm_node_list - - -def analyze_communication_nodes(comm_node_list: List[CommunicationNode])\ - -> Tuple[Dict[str, Tuple[int, int]], Dict[str, List[int]]]: - step_comm_stats: Dict[str, Tuple[int, int]] = {} - total_comm_stats: Dict[str, Tuple[int, int, List, List]] = {} - - step_to_comm_ranges: Dict[str, Tuple[List, List]] = {} - for comm_node in comm_node_list: - if comm_node.step_name not in step_to_comm_ranges: - step_to_comm_ranges[comm_node.step_name] = [[], []] - step_to_comm_ranges[comm_node.step_name][0].extend(comm_node.kernel_ranges) - step_to_comm_ranges[comm_node.step_name][1].extend(comm_node.real_time_ranges) - - if comm_node.name not in total_comm_stats: - total_comm_stats[comm_node.name] = [0, 0, [], []] - total_comm_stats[comm_node.name][0] += 1 - bytes_one_value = 0 - if comm_node.input_shape: - for i, shape in enumerate(comm_node.input_shape): - if comm_node.input_type[i] == 'long int': - bytes_one_value = 8 - elif comm_node.input_type[i] == 'float': - bytes_one_value = 4 - elif comm_node.input_type[i] == 'int': - bytes_one_value = 4 - elif comm_node.input_type[i] == 'c10::Half': - bytes_one_value = 2 - elif comm_node.input_type[i] == 'c10:BFloat16': - bytes_one_value = 2 - elif comm_node.input_type[i] == 'unsigned char': - bytes_one_value = 1 - else: - logger.warning('Found an unknown tensor type: {}'.format(comm_node.input_type[i])) - bytes_one_value = 0 - total_size = 1 - for size in shape: - total_size *= size - total_comm_stats[comm_node.name][1] += total_size * bytes_one_value - total_comm_stats[comm_node.name][2].extend(comm_node.kernel_ranges) - total_comm_stats[comm_node.name][3].extend(comm_node.real_time_ranges) - - for step, comm_ranges in step_to_comm_ranges.items(): - step_comm_stats[step] = [ - get_ranges_sum(merge_ranges(comm_ranges[0])), - get_ranges_sum(merge_ranges(comm_ranges[1])) - ] - - for _, stats in total_comm_stats.items(): - stats[2] = get_ranges_sum(merge_ranges(stats[2])) - stats[3] = get_ranges_sum(merge_ranges(stats[3])) - - return step_comm_stats, total_comm_stats diff --git a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/data.py b/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/data.py deleted file mode 100644 index 00544e635..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/data.py +++ /dev/null @@ -1,488 +0,0 @@ -# ------------------------------------------------------------------------- -# Copyright (c) Microsoft Corporation. All rights reserved. -# -# Copyright(c) 2023 Huawei Technologies. -# All rights reserved -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# Modifications: Add visualization of PyTorch Ascend profiling. -# -------------------------------------------------------------------------- -import gzip -import io as sysio -import json -import math -import os.path -import re -import tempfile -from json.decoder import JSONDecodeError -from typing import Dict, List, Optional -from configparser import ConfigParser - -from .op_tree import OpTreeBuilder -from .. import io, utils -from ..consts import InputFilesType, INPUT_FILE_LIST -from ..utils import href -from . import trace -from .communication import analyze_communication_nodes -from .event_parser import CommLibTypes, EventParser, ProfileRole -from .gpu_metrics_parser import GPUMetricsParser -from .kernel_parser import KernelParser -from .memory_parser import MemoryParser, MemorySnapshot -from .node import OperatorNode -from .op_agg import ModuleAggregator -from .overall_parser import OverallParser -from .tensor_cores_parser import TensorCoresParser -from .trace import BaseEvent, EventTypes, MemoryEvent - -logger = utils.get_logger() -config = ConfigParser() -config_path = os.path.join(os.getcwd(), 'torch_tb_profiler', 'config', '../config/config.ini') -config.read(config_path) - - -class RunProfileData(object): - def __init__(self, worker: str, span: str, trace_json: Dict): - self.worker = worker - self.span = span - - # metadatas - self.is_pytorch_lightning = trace_json.get('Framework', None) == 'pytorch-lightning' - self.data_schema_version = trace_json.get('schemaVersion', None) - self.distributed_info = trace_json.get('distributedInfo', None) - self.device_props = trace_json.get('deviceProperties', None) - - self.profiler_start_ts = float('inf') - self.events: List[BaseEvent] = [] - - trace_body = trace_json.get('traceEvents', None) - fwd_bwd_events = [] - if trace_body is not None: - for data in trace_body: - if data.get('ts') is not None: - try: - self.profiler_start_ts = min(self.profiler_start_ts, float(data.get('ts'))) - except ValueError: - logger.warning(f'The operator {data.get("name")} has wrong "ts" format, expected a number.') - if data.get('cat') == 'forward_backward': - fwd_bwd_events.append(data) - else: - event = trace.create_event(data, self.is_pytorch_lightning) - if event is not None: - event.ts = float(event.ts) - self.events.append(event) - - self.events.sort(key=lambda e: e.ts) - self.forward_backward_events = trace.create_association_events(fwd_bwd_events) - - self.trace_file_path: str = None - self.kernel_file_path: str = None - self.kernel_statistic_path: str = None - - # Event Parser results - self.tid2tree: Dict[int, OperatorNode] = None - self.pl_tid2tree: Dict[int, OperatorNode] = None - self.used_devices = [] - self.use_dp: bool = False - self.use_ddp: bool = False - self.comm_lib = None - self.has_runtime: bool = False - self.has_kernel: bool = True - self.has_trace: bool = True - self.has_communication: bool = False - self.has_memcpy_or_memset: bool = False - self.role_ranges = None - self.steps_costs = None - self.steps_names = None - self.avg_costs = None - self.has_memory: bool = False - self.has_operator_view: bool = False - - # GPU parser - self.gpu_metrics_parser: GPUMetricsParser = None - - # Operator aggregator - self.op_list_groupby_name = None - self.op_list_groupby_name_input = None - self.stack_lists_group_by_name = None - self.stack_lists_group_by_name_input = None - self.kernel_list_groupby_name_op = None - - # Kernel and Tensor Core - self.kernel_stat = None - self.tc_ratio = None - self.tc_eligible_ops_kernel_ratio = None - self.tc_used_ratio = None # If it's a pure CPU run, then this keeps as None. - - # Communicator - self.comm_node_list = None - self.comm_overlap_costs = None - self.memory_snapshot: Optional[MemorySnapshot] = None - - # recommendation based on analysis result. - self.recommendations = [] - - # npu memory data - self.memory_operator_path: str = None - self.memory_curve_path: str = None - self.memory_component_path: str = None - - # npu operator data - self.operator_path: str = None - - # npu communication data - self.distributed_csv_path: str = None - self.communication_json_path: str = None - - self.step_to_overlap = None - self.step_to_wait = None - self.comm_op = None - - @staticmethod - def parse_gpu(worker, span, path, cache_dir): - trace_path, trace_json = RunProfileData._preprocess_file(path, cache_dir, 'GPU') - if not trace_json: - return None - - profile = RunProfileData.from_json(worker, span, trace_json) - profile.trace_file_path = trace_path - return profile - - @staticmethod - def parse_npu(worker, span, path, cache_dir): - trace_json = {} - trace_path = path - has_trace = False - has_kernel = False - has_memory_record = False - has_memory_operator = False - has_communication_overlap = False - has_communication_wait_ops = False - - for file in io.listdir(path): - if utils.is_npu_trace_path(file) and io.check_file_valid(io.join(path, file)): - has_trace = True - trace_file = io.join(path, file) - trace_path, trace_json = RunProfileData._preprocess_file(trace_file, cache_dir, 'Ascend') - break - - profile = RunProfileData(worker, span, trace_json) - with utils.timing('EventParser.parse'): - parser = EventParser() - with utils.timing('EventParser: parse nodes'): - tid2list, tid2zero_rt_list, staled_device_nodes, _ = parser.parse_nodes(profile.events) - - with utils.timing('EventParser: build operator tree'): - builder = OpTreeBuilder() - profile.tid2tree = builder.build_tree(tid2list, tid2zero_rt_list, staled_device_nodes, - fwd_bwd_map=profile.forward_backward_events, is_ascend=True) - profile.trace_file_path = trace_path - profile.has_trace = has_trace - if math.isinf(profile.profiler_start_ts): - profile.profiler_start_ts = 0 - - for file in io.listdir(path): - if str(file) in INPUT_FILE_LIST and io.check_file_valid(io.join(path, file)): - if InputFilesType(file) == InputFilesType.KERNEL_DETAILS_CSV: - has_kernel = True - profile.kernel_file_path = io.join(path, file) - if InputFilesType(file) == InputFilesType.MEMORY_RECORD_CSV: - has_memory_record = True - profile.memory_curve_path = io.join(path, file) - if InputFilesType(file) == InputFilesType.MEMORY_OPERATOR_CSV: - has_memory_operator = True - profile.memory_operator_path = io.join(path, file) - if InputFilesType(file) == InputFilesType.MEMORY_COMPONENT_CSV: - profile.memory_component_path = io.join(path, file) - if InputFilesType(file) == InputFilesType.OPERATOR_DETAILS_CSV: - profile.has_operator_view = True - profile.operator_path = io.join(path, file) - if InputFilesType(file) == InputFilesType.DISTRIBUTED_STEP_CSV: - has_communication_overlap = True - profile.distributed_csv_path = io.join(path, file) - if InputFilesType(file) == InputFilesType.DISTRIBUTED_COMMUNICATION_JSON: - has_communication_wait_ops = True - profile.communication_json_path = io.join(path, file) - - profile.has_kernel = has_kernel - profile.has_memory = has_memory_operator and has_memory_record - profile.has_communication = has_communication_wait_ops and has_communication_overlap - if profile.has_communication: - with utils.timing('EventParser.parse'): - parser = EventParser() - with utils.timing('EventParser: parse steps times'): - # Process steps - parser.parse_steps(profile.events, parser.communication_data) - - profile.steps_names = parser.steps_names - return profile - - @staticmethod - def from_json(worker, span, trace_json: Dict): - profile = RunProfileData(worker, span, trace_json) - with utils.timing('Data processing'): - profile.process() - profile.analyze() - return profile - - @staticmethod - def _preprocess_file(trace_path, cache_dir, device_target): - if not io.exists(trace_path): - raise FileNotFoundError(trace_path) - - data = io.read(trace_path) - if trace_path.endswith('.gz'): - data = gzip.decompress(data) - - json_reencode = False - try: - trace_json = json.loads(data) - except JSONDecodeError as e: - # Kineto may export json file with non-ascii code. before this is fixed, use a workaround - # to handle JSONDecodeError, re-encode it and save to a temp file - try: - trace_json = json.loads(data, strict=False) - except JSONDecodeError: - with sysio.StringIO() as fout: - str_data = data.decode('utf-8') - # only replace the N/A without surrounding double quote - fout.write(re.sub(r'(? 24 * 3600 * 1000: - del trace_json['traceEvents'][end_index] - json_reencode = True - - if json_reencode: - fp = tempfile.NamedTemporaryFile('w+t', suffix='.json.gz', dir=cache_dir, delete=False) - fp.close() - with gzip.open(fp.name, mode='wt') as fzip: - fzip.write(json.dumps(trace_json)) - trace_path = fp.name - - return trace_path, trace_json - - def process(self): - with utils.timing('EventParser.parse'): - parser = EventParser() - self.tid2tree, self.pl_tid2tree = parser.parse(self.events, self.forward_backward_events) - - self.has_runtime = parser.has_runtime - self.has_kernel = parser.has_kernel - self.has_memcpy_or_memset = parser.has_memcpy_or_memset - self.steps_names = parser.steps_names - self.used_devices = sorted(list(parser.used_devices)) - self.use_dp = parser.use_dp - self.use_ddp = parser.use_ddp - self.role_ranges = parser.role_ranges - - self.comm_lib = parser.comm_lib - self.has_communication = parser.has_communication - self.comm_node_list = parser.comm_node_list - - # Starting aggregate - logger.debug('ModuleAggregator') - with utils.timing('ModuleAggregator aggegation'): - module_aggregator = ModuleAggregator() - module_aggregator.aggregate(self.tid2tree) - self.op_list_groupby_name = module_aggregator.op_list_groupby_name - self.op_list_groupby_name_input = module_aggregator.op_list_groupby_name_input - self.stack_lists_group_by_name = module_aggregator.stack_lists_group_by_name - self.stack_lists_group_by_name_input = module_aggregator.stack_lists_group_by_name_input - self.kernel_list_groupby_name_op = module_aggregator.kernel_list_groupby_name_op - - logger.debug('OverallParser') - with utils.timing('OverallParser aggegation'): - overall_parser = OverallParser() - overall_parser.aggregate(parser.steps, parser.role_ranges) - self.avg_costs = overall_parser.avg_costs - self.steps_costs = overall_parser.steps_costs - self.comm_overlap_costs = overall_parser.communication_overlap - - logger.debug('GPUMetricsParser') - self.gpu_metrics_parser = GPUMetricsParser.parse_events( - self.events, parser.global_start_ts, parser.global_end_ts, parser.steps[0][0], parser.steps[-1][1]) - - logger.debug('TensorCoresParser') - tensorcores_parser = TensorCoresParser.parse_events( - self.tid2tree, module_aggregator.ops, self.gpu_metrics_parser.gpu_ids) - self.tc_eligible_ops_kernel_ratio = tensorcores_parser.tc_eligible_ops_kernel_ratio - self.tc_ratio = tensorcores_parser.tc_ratio - - if self.has_kernel: - logger.debug('KernelParser') - with utils.timing('parse kernels'): - kernel_parser = KernelParser() - kernel_parser.parse_events(self.events) - self.kernel_stat = kernel_parser.kernel_stat - self.tc_used_ratio = kernel_parser.tc_used_ratio - - memory_events = self._memory_events() - if memory_events: - memory_parser = MemoryParser(memory_events) - self.memory_snapshot = memory_parser.find_memory_nodes(self.tid2tree) - - def analyze(self): - self.recommendations = [] - - dataloader_ratio = self.avg_costs.costs[ProfileRole.DataLoader] / self.avg_costs.costs[ProfileRole.Total] - if dataloader_ratio > 0.05: - percentage = dataloader_ratio * 100 - url = config.get('URL', 'pytorch_data_loading_url') - self.recommendations.append( - f'This run has high time cost on input data loading. {percentage:.1f}% of the step ' + - "time is in DataLoader. You could try to set num_workers on DataLoader's construction " + - f"and {href('enable multi-processes on data loading', url)}." - ) - - self._analyze_distributed_metrics() - self._analyze_gpu_metrics() - - if self.device_props: - # Tensor Cores feature is available on GPU cards with compute capability >= 7.0 - major = self.device_props[0].get('computeMajor') - # If it's a pure CPU run, then self.tc_used_ratio is None, this rule will not be triggered. - if major is not None and major >= 7: - if math.isclose(self.tc_used_ratio, 0.0) and self.tc_eligible_ops_kernel_ratio > 0.0: - url = config.get('URL', 'pytorch_amp_url') - self.recommendations.append( - f'Kernels with {round(self.tc_eligible_ops_kernel_ratio * 100)}%' - ' time are launched by Tensor Cores eligible operators. ' - f"You could enable {href('Automatic Mixed Precision', url)} to speedup by using FP16.") - - # Memory related - if self.memory_snapshot: - for (dev_type, dev_id), peak_mem in self.memory_snapshot.get_peak_memory().items(): - if dev_type == -1: # ignore cpu - continue - total_mem = self.device_props[dev_id].get('totalGlobalMem') - if total_mem is not None and peak_mem > total_mem * 0.9: - percentage = peak_mem / total_mem * 100 if total_mem > 0 else 0 - total_mem_gb = total_mem / 1024 / 1024 / 1024 - ckp_url = config.get('URL', 'pytorch_ckp_url') - amp_url = config.get('URL', 'pytorch_amp_url') - self.recommendations.append( - f'Device memory usage is at the limit of device memory capacity ' - f'({percentage:.1f}% of {total_mem_gb:.1f}GB on GPU{dev_id}). ' - 'To get better value of your GPU or to use larger batch size for training, please refer to ' - f"{href('Gradient Checkpoint', ckp_url)} or {href('Automatic Mixed Precision', amp_url)}.") - break - - def _analyze_distributed_metrics(self): - if self.use_dp and len(self.used_devices) > 1: - url = config.get('URL', 'cuda_nn_ddp_instead_url') - self.recommendations.append( - f"It is recommended to {href('use DistributedDataParallel instead of DataParallel', url)}" - ' to do multi-GPU training.') - - if self.use_ddp and CommLibTypes.Nccl not in self.comm_lib and self.device_props: - for device_prop in self.device_props: - major = device_prop.get('computeMajor') - minor = device_prop.get('computeMinor') - if major is None or minor is None: - continue - compute_capability = '{}.{}'.format(major, minor) - if float(compute_capability) >= 3.5: - text = ( - 'Nccl backend is currently the fastest and highly recommended backend' - ' when using DDP for training.') - self.recommendations.append(text) - break - - communication_ratio = self.avg_costs.costs[ProfileRole.Communication] / self.avg_costs.costs[ProfileRole.Total] - if communication_ratio > 0.1: - percentage = communication_ratio * 100 - compress_url = config.get('URL', 'compress_url') - grad_acc_url = config.get('URL', 'grad_acc_url') - lamb_url = config.get('URL', 'lamb_url') - self.recommendations.append( - f'This run has high time cost on communication. {percentage:.1f}% of the step time is in ' - f"communication. You could try {href('Gradient Compression', compress_url)} or " - f"{href('Gradient Accumulation', grad_acc_url)} or increase the batch size. " - 'Note: Gradient accumulation will increase global effective batch size, which may hurt model ' - f"convergence and accuracy. For such case, you may want to evaluate {href('LAMB optimizer', lamb_url)}." - ) - - def _memory_events(self) -> List[MemoryEvent]: - memory_events = [e for e in self.events if e.type == EventTypes.MEMORY] - memory_events.sort(key=lambda e: e.ts) - return memory_events - - def _analyze_gpu_metrics(self): - def get_gpus_str(gpus): - gpu_list_str = str(gpus[0]) - for i in range(1, len(gpus)): - if i == len(gpus) - 1: - gpu_list_str += 'and {}'.format(gpus[i]) - else: - gpu_list_str += ', {}'.format(gpus[i]) - has_str = 'has' if len(gpu_list_str) == 1 else 'have' - return gpu_list_str, has_str - - low_util_gpus = [] - for gpu_id in self.gpu_metrics_parser.gpu_ids: - if self.gpu_metrics_parser.gpu_utilization[gpu_id] < 0.5: - low_util_gpus.append(gpu_id) - if len(low_util_gpus) > 0: - gpu_list_str, has_str = get_gpus_str(low_util_gpus) - text = 'GPU {} {} low utilization. You could try to ' \ - 'increase batch size to improve. Note: Increasing batch size ' \ - 'may affect the speed and stability of model convergence.'.format(gpu_list_str, has_str) - self.recommendations.append(text) - - -class DistributedRunProfileData: - def __init__(self, run_profile_data: RunProfileData): - self.worker = run_profile_data.worker - self.span = run_profile_data.span - self.steps_names = run_profile_data.steps_names - self.has_communication = run_profile_data.has_communication - self.comm_lib = run_profile_data.comm_lib - self.comm_node_list = run_profile_data.comm_node_list - self.comm_overlap_costs = run_profile_data.comm_overlap_costs - self.used_devices = run_profile_data.used_devices - self.device_props = run_profile_data.device_props - self.distributed_info = run_profile_data.distributed_info - - self.step_to_overlap = run_profile_data.step_to_overlap - self.step_to_wait = run_profile_data.step_to_wait - self.comm_op = run_profile_data.comm_op - - self.total_comm_stats = None - self.step_comm_stats = None - - def communication_parse(self): - self.step_comm_stats, self.total_comm_stats = analyze_communication_nodes(self.comm_node_list) diff --git a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/diffrun/__init__.py b/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/diffrun/__init__.py deleted file mode 100644 index 3dae52e35..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/diffrun/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .contract import DiffStats, OpAgg -from .tree import (DiffNode, compare_op_tree, diff_summary, print_node, - print_ops) diff --git a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/diffrun/contract.py b/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/diffrun/contract.py deleted file mode 100644 index ce0cba35d..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/diffrun/contract.py +++ /dev/null @@ -1,99 +0,0 @@ -# ------------------------------------------------------------------------- -# Copyright (c) Microsoft Corporation. All rights reserved. -# ------------------------------------------------------------------------- -from collections import namedtuple -from typing import Dict, List - -OpAgg = namedtuple('OpAgg', [ - 'name', - 'calls', - 'host_duration', - 'device_duration', - 'self_host_duration', - 'self_device_duration']) - - -class OpStats: - def __init__(self, - name, - duration, - device_duration, - total_duration, - aggs: List[OpAgg]): - self.name = name - self.duration = duration - self.device_duration = device_duration - self.total_duration = total_duration - self.op_aggs = aggs - - def __str__(self) -> str: - return f'{self.name}: {self.duration}/{self.device_duration}/{len(self.op_aggs)}' - - -class DiffStats: - def __init__(self, left: OpStats, right: OpStats): - self.left = left - self.right = right - self.children: List[DiffStats] = [] - - def flatten_diff_tree(self) -> Dict[str, 'DiffStats']: - result: Dict[str, DiffStats] = {} - - def traverse(node: DiffStats, path: str): - result[path] = node - for i, child in enumerate(node.children): - traverse(child, f'{path}-{i}') - - traverse(self, '0') - return result - - def to_dict(self): - d = { - 'left': { - 'name': self.left.name, - 'duration': self.left.duration, - 'device_duration': self.left.device_duration, - 'total_duration': self.left.total_duration, - 'aggs': [] - }, - 'right': { - 'name': self.right.name, - 'duration': self.right.duration, - 'device_duration': self.right.device_duration, - 'total_duration': self.right.total_duration, - 'aggs': [] - } - } - - for agg in self.left.op_aggs: - d['left']['aggs'].append(agg._asdict()) - - for agg in self.right.op_aggs: - d['right']['aggs'].append(agg._asdict()) - - return d - - def get_diff_tree_summary(self): - def traverse_node_recursive(node: DiffStats): - d = node.to_dict() - - d['children'] = [] - for c in node.children: - d['children'].append(traverse_node_recursive(c)) - - return d - - return traverse_node_recursive(self) - - def get_diff_node_summary(self, path: str): - def traverse_node(node: DiffStats, path: str): - d = node.to_dict() - d['path'] = path - return d - - d = traverse_node(self, path) - d['children'] = [] - for i, c in enumerate(self.children): - d['children'].append(traverse_node(c, f'{path}-{i}')) - - return d diff --git a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/diffrun/operator.py b/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/diffrun/operator.py deleted file mode 100644 index 4434c65ad..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/diffrun/operator.py +++ /dev/null @@ -1,124 +0,0 @@ -# ------------------------------------------------------------------------- -# Copyright (c) Microsoft Corporation. All rights reserved. -# ------------------------------------------------------------------------- -from abc import ABCMeta -from typing import List, Tuple, Union - -from ..node import DeviceNode, OperatorNode -from ..op_agg import aggregate_ops -from .contract import OpAgg - - -class Operator(metaclass=ABCMeta): - def __init__(self, name) -> None: - self.name: str = name - - def __str__(self) -> str: - return f'{self.name}: {self.duration}' - - @property - def duration(self) -> int: - return 0 - - @property - def device_duration(self) -> int: - return 0 - - @property - def total_duration(self): - return self.device_duration or self.duration - - def aggregate_ops(self): - ops, _ = self.get_operators_and_kernels() - agg_result = aggregate_ops(ops, [lambda x: x.name])[0] - for agg in agg_result.values(): - yield OpAgg( - agg.name, - agg.calls, - round(agg.host_duration, 3), - round(agg.device_duration, 3), - round(agg.self_host_duration, 3), - round(agg.self_device_duration, 3)) - - def get_operators_and_kernels(self) -> Tuple[List[OperatorNode], List[DeviceNode]]: - return [], [] - - -class BlankOp(Operator): - def __init__(self) -> None: - super().__init__('Blank') - - -class UnknownOp(Operator): - def __init__(self, device_duration: int, duration: int) -> None: - super().__init__('Unknown') - self.device_duration = device_duration - self.duration = duration - - @property - def duration(self) -> int: - return self.duration - - @property - def device_duration(self) -> int: - return self.device_duration - - -class Operators(Operator): - def __init__(self, nodes: Union[OperatorNode, List[OperatorNode]]): - if not nodes: - raise ValueError('the operator node is None or empty') - if isinstance(nodes, OperatorNode): - super().__init__(nodes.name) - elif isinstance(nodes, list): - super().__init__('CompositeNodes') - - self.op_nodes: Union[OperatorNode, List[OperatorNode]] = nodes - - @property - def duration(self): - if isinstance(self.op_nodes, list): - return sum(n.duration for n in self.op_nodes) - else: - return self.op_nodes.duration - - @property - def device_duration(self): - if isinstance(self.op_nodes, list): - return sum(n.device_duration for n in self.op_nodes) - else: - return self.op_nodes.device_duration - - @property - def total_duration(self): - if isinstance(self.op_nodes, list): - return sum(n.device_duration or n.duration for n in self.op_nodes) - else: - return self.op_nodes.device_duration or self.op_nodes.duration - - def __str__(self) -> str: - if isinstance(self.op_nodes, list): - return f'{self.name}: {len(self.op_nodes)}: {self.op_nodes[0].name}: {self.total_duration}' - else: - return f'{self.name}: {self.op_nodes.__class__.__name__}: {self.total_duration}' - - def get_operators_and_kernels(self) -> Tuple[List[OperatorNode], List[DeviceNode]]: - if isinstance(self.op_nodes, list): - nodes = self.op_nodes - else: - nodes = [self.op_nodes] - - ops: List[OperatorNode] = [] - kernels: List[DeviceNode] = [] - for n in nodes: - op, k = n.get_operator_and_kernels() - ops.extend(op) - kernels.extend(k) - return ops, kernels - - -def create_operator(op_nodes: Union[OperatorNode, List[OperatorNode]]) -> Operator: - if op_nodes: - return Operators(op_nodes) - else: - return BlankOp() diff --git a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/diffrun/tree.py b/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/diffrun/tree.py deleted file mode 100644 index c5cf5fad4..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/diffrun/tree.py +++ /dev/null @@ -1,163 +0,0 @@ -# ------------------------------------------------------------------------- -# Copyright (c) Microsoft Corporation. All rights reserved. -# ------------------------------------------------------------------------- -import sys -from typing import Generator, List, Union - -from ..node import (BackwardNode, DataLoaderNode, ModuleNode, OperatorNode, - OptimizerNode, ProfilerStepNode) -from .contract import DiffStats, OpStats -from .operator import Operator, Operators, create_operator - -INDENT = ' ' -RUN_NODE_TYPES = (BackwardNode, DataLoaderNode, ModuleNode, OptimizerNode, ProfilerStepNode) - - -class DiffNode: - def __init__(self, left: Operator, right: Operator): - self.left: Operator = left - self.right: Operator = right - self.children: List[DiffNode] = [] - - def build_tree(self): - """build the children from the left_node and right_node""" - if not isinstance(self.left, Operators) or not isinstance(self.right, Operators): - return - - if isinstance(self.left.op_nodes, OperatorNode) and isinstance(self.right.op_nodes, OperatorNode): - # simple node match. - diff_nodes = list(DiffNode.compare_operator_nodes( - self.left.op_nodes.children, self.right.op_nodes.children)) - if diff_nodes: - self.children.extend(diff_nodes) - elif isinstance(self.left.op_nodes, list) and isinstance(self.right.op_nodes, list): - # compare two list - diff_nodes = list(DiffNode.compare_operator_nodes(self.left.op_nodes, self.right.op_nodes)) - if diff_nodes: - self.children.extend(diff_nodes) - else: - # one single item and one list - pass - - @staticmethod - def create_node( - left: Union[OperatorNode, List[OperatorNode]], - right: Union[OperatorNode, List[OperatorNode]]) -> 'DiffNode': - if isinstance(left, list) and len(left) == 1: - left = left[0] - if isinstance(right, list) and len(right) == 1: - right = right[0] - - node = DiffNode(create_operator(left), create_operator(right)) - node.build_tree() - return node - - @staticmethod - def compare_operator_nodes( - left_nodes: List[OperatorNode], - right_nodes: List[OperatorNode]) -> Generator['DiffNode', None, None]: - """Given two OperatorNode lists, find the DataLoader/Module/Backward/Optimizer node and - create the child list DiffNode - """ - right_keys = [(type(r), r.name) for r in right_nodes] - - # find matching points in the two list - matched_paris = [] - key_index = 0 - for i, left_node in enumerate(left_nodes): - if not isinstance(left_node, RUN_NODE_TYPES): - # only handle DataLoader/Module/Backward/Optimizer nodes - continue - - for j in range(key_index, len(right_keys)): - if right_keys[j] == (type(left_node), left_node.name): - matched_paris.append((i, j)) - key_index = j + 1 - break - - if not matched_paris: - # there is not any matching points. - return - - # split the two list by the matching points - l_iter = 0 - r_iter = 0 - - for (left, r) in matched_paris: - left_child = left_nodes[l_iter:left] - right_child = right_nodes[r_iter:r] - if left_child or right_child: - yield DiffNode.create_node(left_child, right_child) - - yield DiffNode.create_node(left_nodes[left], right_nodes[r]) - l_iter = left + 1 - r_iter = r + 1 - # end time is bigger than threshold. - # Or do we need move the logic into frondend for visualization? - - # process the remaining nodes - left_remaining = left_nodes[l_iter:] - right_remaining = right_nodes[r_iter:] - if left_remaining or right_remaining: - yield DiffNode.create_node(left_remaining, right_remaining) - - -def compare_op_tree(left: OperatorNode, right: OperatorNode) -> DiffNode: - '''Create the diff tree from two root node - TODO: need handle the different threads case - need add runtimes besides of children? - ''' - left_children = list(get_tree_operators(left)) - right_children = list(get_tree_operators(right)) - return DiffNode.create_node(left_children, right_children) - - -def get_tree_operators(root: OperatorNode) -> Generator[OperatorNode, None, None]: - '''Get the operators by the root operators by excluding the ProfilerStepNode - ''' - profiler_nodes = [c for c in root.children if isinstance(c, ProfilerStepNode)] - if not profiler_nodes: - # there is no ProfilerStepNode at all - yield from root.children - else: - yield from (child for p in profiler_nodes for child in p.children) - - -def diff_summary(node: DiffNode) -> DiffStats: - if not node: - return None - - left = OpStats( - node.left.name, - node.left.duration, - node.left.device_duration, - node.left.total_duration, - list(node.left.aggregate_ops())) - right = OpStats( - node.right.name, - node.right.duration, - node.right.device_duration, - node.right.total_duration, - list(node.right.aggregate_ops())) - - stats = DiffStats(left, right) - for child in node.children: - stats.children.append(diff_summary(child)) - - return stats - - -def print_node(node: Union[DiffNode, DiffStats], level: int, index: int, file=sys.stdout): - file.write(f'{INDENT * level}level {level}, index {index}:\n') - file.write(f'{INDENT * (level + 1)}left : {node.left}\n') - file.write(f'{INDENT * (level + 1)}right: {node.right}\n') - for i, child in enumerate(node.children): - print_node(child, level + 1, i, file=file) - - -def print_ops(op: Operators, prefix: str = INDENT, file=sys.stdout): - if isinstance(op.op_nodes, list): - for n in op.op_nodes: - file.write(f'{prefix}{n.name}\n') - else: - file.write(f'{prefix}{op.op_nodes.name}\n') diff --git a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/event_parser.py b/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/event_parser.py deleted file mode 100644 index 9b364e0db..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/event_parser.py +++ /dev/null @@ -1,467 +0,0 @@ -# ------------------------------------------------------------------------- -# Copyright (c) Microsoft Corporation. All rights reserved. -# ------------------------------------------------------------------------- -import sys -from collections import defaultdict -from dataclasses import dataclass -from enum import IntEnum -from typing import Dict, Iterable, List, Optional, Tuple - -from .. import utils -from .communication import generate_communication_nodes -from .node import (CommunicationNode, DeviceNode, ModuleNode, OperatorNode, PLModuleNode, PLProfileNode, - ProfilerStepNode, RuntimeNode, create_operator_node) -from .op_tree import OpTreeBuilder -from .range_utils import merge_ranges -from .trace import BaseEvent, DurationEvent, EventTypes, KernelEvent, NcclOpNameSet, GlooOpNameSet - -logger = utils.get_logger() - -CommLibTypes = IntEnum('CommLibTypes', ['Nccl', 'Gloo'], start=0) - - -class ProfileRole(IntEnum): - Kernel = 0 - Memcpy = 1 - Memset = 2 - Communication = 3 - Runtime = 4 - DataLoader = 5 - CpuOp = 6 - Other = 7 - Total = 8 - - -@dataclass -class NodeInfoParams: - event: DurationEvent - corrid_to_device: Dict[int, List[DeviceNode]] - corrid_to_runtime: Dict[int, RuntimeNode] - externalid_to_runtime: Dict[int, List[RuntimeNode]] - tid2list: Dict[int, List[OperatorNode]] - pl_tid2list: Dict[int, List[PLProfileNode]] - tid2zero_rt_list: Dict[int, List[RuntimeNode]] - - -class NodeParserMixin: - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - - self.communication_data: Dict[int, CommunicationNode] = {} - self.device_node_list: List[DeviceNode] = [] - self.runtime_node_list: List[RuntimeNode] = [] - self.used_devices = set() - self.use_dp = False - self.use_ddp = False - self.comm_lib = set() - - def parse_nodes(self, events: Iterable[BaseEvent]): - # For OperatorNode and ProfilerStepNode: - # Use time interval containing relationship to build father-child correlation, - # which is consistent with autograd profiler. - # For RuntimeNode: - # Use external_id to build correlation with its father OperatorNode or ProfilerStepNode. - # Because in the case when RuntimeNode has duration 0 and starts at same time as a OperatorNode, - # just use interval containing relationship can't tell it is child or brother of the OperatorNode. - # value is a list of OperatorNode and ProfilerStepNode. Do not include RuntimeNode - tid2list: Dict[int, List[OperatorNode]] = defaultdict(list) - # value is a list of PLProfileNode. Do not include RuntimeNode - pl_tid2list: Dict[int, List[PLProfileNode]] = defaultdict(list) - # value is a list of RuntimeNode with external_id=0. They will be attached to root nodes. - tid2zero_rt_list: Dict[int, List[RuntimeNode]] = defaultdict(list) - corrid_to_device: Dict[int, List[DeviceNode]] = defaultdict(list) # value is a list of DeviceNode - - corrid_to_runtime: Dict[int, RuntimeNode] = {} # value is a RuntimeNode - externalid_to_runtime: Dict[int, List[RuntimeNode]] = defaultdict(list) # value is a list of RuntimeNode - - for event in events: - if event.type == EventTypes.MEMORY: - continue - params = NodeInfoParams(event, corrid_to_device, corrid_to_runtime, externalid_to_runtime, tid2list, - pl_tid2list, tid2zero_rt_list) - self._parse_node(params) - - if CommLibTypes.Nccl in self.comm_lib: - for event in events: - if event.type == EventTypes.KERNEL: - self._update_communication_node(event) - - # associate CUDA Runtimes with CPU events - for op_list in tid2list.values(): - for op in op_list: - runtime_nodes = externalid_to_runtime.pop(op.external_id, []) - if runtime_nodes: - op.runtimes.extend(runtime_nodes) - - if len(corrid_to_device) > 0: - node_count_dict = defaultdict(int) - for nodes in corrid_to_device.values(): - for n in nodes: - node_count_dict[n.type] += 1 - - logger.debug(("Some events doesn't belongs to any operators: " - f"{', '.join([':'.join((k, str(v))) for k, v in node_count_dict.items()])}")) - - staled_device_nodes = [] - for device_nodes in corrid_to_device.values(): - staled_device_nodes.extend([n for n in device_nodes if n.type == EventTypes.KERNEL]) - - return tid2list, tid2zero_rt_list, staled_device_nodes, pl_tid2list - - def _update_communication_node(self, event: KernelEvent): - """Update the communication node by using the TraceEvent instance""" - external_id = event.external_id - comm_node = self.communication_data.get(external_id) - if comm_node: - ts = event.ts - dur = event.duration - comm_node.kernel_ranges.append((ts, ts + dur)) - comm_node.total_time += dur - - return comm_node is not None - - def _parse_node(self, params: NodeInfoParams): - event = params.event - corrid_to_device = params.corrid_to_device - corrid_to_runtime = params.corrid_to_runtime - externalid_to_runtime = params.externalid_to_runtime - tid2list = params.tid2list - pl_tid2list = params.pl_tid2list - tid2zero_rt_list = params.tid2zero_rt_list - corrid = event.correlation_id - tid = event.tid - if event.type in [EventTypes.KERNEL, EventTypes.MEMCPY, EventTypes.MEMSET]: - self.used_devices.add(event.pid) - device_node = DeviceNode.create(event) - if corrid in corrid_to_runtime: - rt_node = corrid_to_runtime[corrid] # Don't pop it because it may be used by next kernel. - if rt_node.device_nodes is None: - rt_node.device_nodes = [] - rt_node.device_nodes.append(device_node) - else: - corrid_to_device[corrid].append(device_node) - self.device_node_list.append(device_node) - elif event.type == EventTypes.RUNTIME: - device_nodes = corrid_to_device.pop(corrid, None) - rt_node = RuntimeNode.create(event, device_nodes) - corrid_to_runtime[corrid] = rt_node - externalid_to_runtime[rt_node.external_id].append(rt_node) - # Some runtimes has external_id 0, which will not be correlated to any operator. - # So get them and attach them to root node. - if rt_node.external_id == 0: - tid2zero_rt_list[tid].append(rt_node) - self.runtime_node_list.append(rt_node) - - # check the external_id - if device_nodes: - for device_node in device_nodes: - if rt_node.external_id != device_node.external_id: - logger.warning( - 'Runtime and Device-op have same correlation id %s but with different external id!' - ' (rt external_id, device external_id): (%s, %s)' % - (corrid, rt_node.external_id, device_node.external_id)) - elif event.type in [EventTypes.PYTHON, - EventTypes.OPERATOR, - EventTypes.PL_MODULE, - EventTypes.PROFILER_STEP, - EventTypes.MODULE, - EventTypes.USER_ANNOTATION]: - if event.type == EventTypes.PROFILER_STEP: - op_node = ProfilerStepNode.create(event) - elif event.type == EventTypes.MODULE: - op_node = ModuleNode.create(event) - elif event.type == EventTypes.PL_MODULE: - op_node = PLModuleNode.create(event) - else: - op_node = create_operator_node(event) - if event.name in NcclOpNameSet or event.name in GlooOpNameSet: - comm_node = CommunicationNode.create(event) - if event.name in NcclOpNameSet: - self.comm_lib.add(CommLibTypes.Nccl) - if event.name in GlooOpNameSet: - self.comm_lib.add(CommLibTypes.Gloo) - ts = event.ts - dur = event.duration - comm_node.kernel_ranges.append((ts, ts + dur)) - comm_node.total_time = dur - self.communication_data[op_node.external_id] = comm_node - if event.name == 'DataParallel.forward': - self.use_dp = True - if event.name == 'DistributedDataParallel.forward': - self.use_ddp = True - if op_node: - tid2list[int(tid)].append(op_node) - elif event.type == EventTypes.PL_PROFILE: - op_node = PLProfileNode.create(event) - pl_tid2list[int(tid)].append(op_node) - - -class StepParser: - def __init__(self): - # we could not use [[]] * len here since they all point to same memory - self.role_ranges: List[List[Tuple[int, int]]] = [[] for _ in range(ProfileRole.Total - 1)] - self.steps: List[Tuple[int, int]] = [] - self.steps_names: List[str] = [] - self.cpu_min_ts = sys.maxsize # Min time of CPU side events. - self.cpu_max_ts = -sys.maxsize - 1 # Max time of CPU side events. - self.global_min_ts = sys.maxsize # Min time of all events. - self.global_max_ts = -sys.maxsize - 1 # Max time of all events. - # The below two form time range for adding gpu utilization to trace view. - # Use 'PyTorch Profiler (0)' as them. - # If not exists, assign global_min_ts and global_max_ts to them. - self.global_start_ts = sys.maxsize - self.global_end_ts = -sys.maxsize - 1 - - def parse_steps(self, events: Iterable[DurationEvent], comm_nodes: Dict[int, CommunicationNode]): - for event in events: - if event.type == EventTypes.MEMORY: - continue - - self._parse_step(event, comm_nodes) - if event.type == EventTypes.TRACE and event.name == 'PyTorch Profiler (0)': - self.global_start_ts = event.ts - self.global_end_ts = event.ts + event.duration - if self.global_start_ts == sys.maxsize: - self.global_start_ts = self.global_min_ts - if self.global_end_ts == -sys.maxsize - 1: - self.global_end_ts = self.global_max_ts - - if len(self.steps) == 0: - self.steps.append((self.cpu_min_ts, self.cpu_max_ts)) - self.steps_names.append('0') - - for i, role_range in enumerate(self.role_ranges): - self.role_ranges[i] = merge_ranges(role_range) - - def update_device_steps(self, runtime_node_list: List[RuntimeNode]): - self._update_steps_duration(*self._find_device_steps(runtime_node_list)) - - @property - def has_runtime(self): - return bool(self.role_ranges[ProfileRole.Runtime]) - - @property - def has_kernel(self): - return bool(self.role_ranges[ProfileRole.Kernel]) - - @property - def has_communication(self): - return bool(self.role_ranges[ProfileRole.Communication]) - - @property - def has_memcpy_or_memset(self): - return bool(self.role_ranges[ProfileRole.Memcpy] or self.role_ranges[ProfileRole.Memset]) - - def _parse_step(self, event: DurationEvent, comm_nodes: Dict[int, CommunicationNode]): - def check_name(name: str): - return (name.startswith('enumerate(DataLoader)#') and name.endswith('.__next__')) or name.startswith( - 'enumerate(DataPipe)#') - - ts = event.ts - dur = event.duration - evt_type = event.type - if evt_type == EventTypes.KERNEL: - if event.external_id in comm_nodes: - self.role_ranges[ProfileRole.Communication].append((ts, ts + dur)) - else: - self.role_ranges[ProfileRole.Kernel].append((ts, ts + dur)) - elif evt_type == EventTypes.MEMCPY: - self.role_ranges[ProfileRole.Memcpy].append((ts, ts + dur)) - elif evt_type == EventTypes.MEMSET: - self.role_ranges[ProfileRole.Memset].append((ts, ts + dur)) - elif evt_type == EventTypes.RUNTIME: - self.role_ranges[ProfileRole.Runtime].append((ts, ts + dur)) - elif evt_type in [EventTypes.OPERATOR, EventTypes.USER_ANNOTATION] and check_name(event.name): - self.role_ranges[ProfileRole.DataLoader].append((ts, ts + dur)) - elif event.type == EventTypes.PROFILER_STEP: - self.steps.append((ts, ts + dur)) - self.steps_names.append(str(event.step)) - elif evt_type in [EventTypes.PYTHON, EventTypes.OPERATOR, EventTypes.USER_ANNOTATION]: - if event.name in GlooOpNameSet or event.name in NcclOpNameSet: - self.role_ranges[ProfileRole.Communication].append((ts, ts + dur)) - else: - self.role_ranges[ProfileRole.CpuOp].append((ts, ts + dur)) - - # Record host side min and max time. - if evt_type in [EventTypes.PYTHON, EventTypes.OPERATOR, EventTypes.PROFILER_STEP]: - self.cpu_min_ts = min(self.cpu_min_ts, ts) - self.cpu_max_ts = max(self.cpu_max_ts, ts + dur) - # Record global wise min and max time. - self.global_min_ts = min(self.global_min_ts, ts) - self.global_max_ts = max(self.global_max_ts, ts + dur) - - def _find_device_steps(self, runtime_node_list: List[RuntimeNode]): - """return steps associated with device nodes. - """ - runtime_node_list = sorted(runtime_node_list, key=lambda x: x.start_time) - - # Use similar code with two-way merge to get all runtimes inside each host-side step span, - # then record each step's min kernel start time and max kernel end time: - steps_device: List[Tuple[int, int]] = [(sys.maxsize, -sys.maxsize - 1)] * len(self.steps) - # where the steps associated with devcie node, if yes, the related array item is larger than 0. - steps_matched_device_nodes: List[int] = [0] * len(self.steps) - - i_step = 0 - i_runtime = 0 - step_device_min_ts = sys.maxsize - step_device_max_ts = -sys.maxsize - 1 - matched_device_nodes = set() - - while i_step < len(self.steps) and i_runtime < len(runtime_node_list): - step_host_start_time = self.steps[i_step][0] - step_host_end_time = self.steps[i_step][1] - if runtime_node_list[i_runtime].start_time < step_host_start_time: - # This runtime is ahead of or intersects with this step span. Skip this runtime. - i_runtime += 1 - elif runtime_node_list[i_runtime].end_time <= step_host_end_time: - # and runtime_node_list[i_runtime].start_time >= step_host_start_time - # This runtime is inside this step span. Scan its device_nodes. - rt = runtime_node_list[i_runtime] - if rt.device_nodes is not None: - for device_node in rt.device_nodes: - step_device_min_ts = min(device_node.start_time, step_device_min_ts) - step_device_max_ts = max(device_node.end_time, step_device_max_ts) - matched_device_nodes.add(device_node) - steps_matched_device_nodes[i_step] += 1 - i_runtime += 1 - elif runtime_node_list[i_runtime].start_time < step_host_end_time: - # and runtime_node_list[i_runtime].end_time > step_host_end_time - # This runtime intersects with this step span. Skip this runtime. - i_runtime += 1 - else: - # runtime_node_list[i_runtime].start_time >= step_host_end_time - # This runtime starts after this step's end. Record and move forward this step. - steps_device[i_step] = (step_device_min_ts, step_device_max_ts) - i_step += 1 - step_device_min_ts = sys.maxsize - step_device_max_ts = -sys.maxsize - 1 - - while i_step < len(self.steps): - # This step doesn't launch any device side event, just assign it as empty. - steps_device[i_step] = (step_device_min_ts, step_device_max_ts) - step_device_min_ts = sys.maxsize - step_device_max_ts = -sys.maxsize - 1 - i_step += 1 - - # If there are matched device, find the first step end time before steps_device[0][0] - prev_step_end_time: Optional[int] = None - if len(matched_device_nodes) > 0: - prev_step_end_time = self.steps[0][0] - if steps_device[0][0] != sys.maxsize: # When step 0 has device event. - for device_node in self.device_node_list: - if device_node not in matched_device_nodes: - # Now this device_node is not launched inside any step span. - if device_node.end_time < steps_device[0][0]: - prev_step_end_time = max(prev_step_end_time, device_node.end_time) - - return prev_step_end_time, steps_device, steps_matched_device_nodes - - def _update_steps_duration(self, - prev_step_end_time: Optional[int], - steps_device: List[Tuple[int, int]], - steps_matched_device_nodes: List[int]): - """Update self.steps considering device side events launched by each host side step. - Update self.steps_names if some tail steps are removed.""" - - # Change step time to device side on the condition that any step have device time. - is_use_gpu = prev_step_end_time is not None - if is_use_gpu: - for i_step, step in enumerate(self.steps): - step_start_time = max(prev_step_end_time, step[0]) - step_end_time = step[1] - if steps_device[i_step][0] == sys.maxsize: # When step i_step has no device event. - # Assign to step_start_time when kernel is behind host step end. - step_end_time = max(step_end_time, step_start_time) - else: - step_end_time = max(step_end_time, steps_device[i_step][1]) - if step_end_time < step_start_time: - logger.warning( - 'Abnormal step_end_time of step {}: [{}, {}]'.format( - i_step, step_start_time, step_end_time)) - step_end_time = step_start_time - self.steps[i_step] = (step_start_time, step_end_time) # Update step time considering device side. - prev_step_end_time = step_end_time - - is_remove_tail_steps = True - if is_use_gpu and len(self.steps) > 1 and is_remove_tail_steps: - i_step = len(self.steps) - 1 - while i_step >= 0: - if steps_matched_device_nodes[i_step] > 0: - break - i_step -= 1 - if i_step >= 0: - keep_steps = i_step + 1 - if i_step > 0 and steps_matched_device_nodes[i_step - 1] * 0.8 > steps_matched_device_nodes[i_step]: - keep_steps = i_step - if keep_steps < len(self.steps): - logger.warning( - 'Remove the last {} steps from overview. ' - 'Because the profiler may fail to capture all the kernels launched by these steps.'.format( - len(self.steps) - keep_steps - )) - self.steps = self.steps[:keep_steps] - self.steps_names = self.steps_names[:keep_steps] - - -class EventParser(NodeParserMixin, StepParser): - def __init__(self): - super().__init__() - self.comm_node_list: List[CommunicationNode] = None - - def parse(self, events: Iterable[BaseEvent], fwd_bwd_map: Dict[int, int]) -> Dict[int, List[OperatorNode]]: - with utils.timing('EventParser: parse nodes'): - tid2list, tid2zero_rt_list, staled_device_nodes, pl_tid2list = self.parse_nodes(events) - - with utils.timing('EventParser: build operator tree'): - builder = OpTreeBuilder() - tid2tree = builder.build_tree(tid2list, tid2zero_rt_list, staled_device_nodes, fwd_bwd_map=fwd_bwd_map) - pl_tid2tree = builder.build_tree(pl_tid2list, {}, [], {}) - - with utils.timing('EventParser: parse steps times'): - # Process steps - self.parse_steps(events, self.communication_data) - if len(self.comm_lib) > 1: - logger.warning( - 'Multiple communication libs are found. To avoid confusing, we disable the distributed view.') - self.communication_data.clear() - - # Move the interleaved logic out of each NodeParser and StepParser - self.update_device_steps(self.runtime_node_list) - - self.comm_node_list = generate_communication_nodes(self.communication_data, self.steps, self.steps_names) - return tid2tree, pl_tid2tree - - @staticmethod - def print_tree(root): - class Ctx: - tid: int = -1 - name_stack: list = [] - - ctx = Ctx() - - def print_node_set_prefix(node: OperatorNode): - header = f'[{ctx.tid}]' + '.'.join(ctx.name_stack[1:]) # omit the CallTreeRoot - prefix_len = len(ctx.name_stack) * 4 - 4 - 1 - if len(ctx.name_stack) > 1: - logger.info(header) - prefix = ' ' * prefix_len - logger.info(prefix, node.name) - logger.info(prefix, 'time:', node.start_time, '-->', node.end_time) - - def push(node: OperatorNode): - ctx.name_stack.append(node.name) - - def pop(): - ctx.name_stack.pop() - - def traverse_opeartor_node(node: OperatorNode): - print_node_set_prefix(node) - - push(node) - for n in node.children: - traverse_opeartor_node(n) - pop() - - ctx.tid = root.tid - traverse_opeartor_node(root) - ctx.tid = -1 diff --git a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/gpu_metrics_parser.py b/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/gpu_metrics_parser.py deleted file mode 100644 index 1321d5f6d..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/gpu_metrics_parser.py +++ /dev/null @@ -1,314 +0,0 @@ -# ------------------------------------------------------------------------- -# Copyright (c) Microsoft Corporation. All rights reserved. -# -------------------------------------------------------------------------- -from typing import Iterable, List - -from .. import consts, utils -from .range_utils import (get_ranges_sum, intersection_ranges_lists, - intersection_ranges_lists_with_value, merge_ranges, - merge_ranges_with_value) -from .trace import BaseEvent, EventTypes, KernelEvent - -logger = utils.get_logger() - - -# For calculating GPU utilization, and approximated SM efficiency. -class GPUMetricsParser(object): - def __init__(self): - # All gpu ids that used by any kernel. - self.gpu_ids = set() - # For calculating GPU utilization. - self.kernel_ranges_per_device = [[] for _ in range(consts.MAX_GPU_PER_NODE)] - self.gpu_utilization = [None] * consts.MAX_GPU_PER_NODE - self.gpu_util_timeline_unit_size = 0 - self.gpu_util_timeline_unit_name = '' - self.gpu_util_buckets = [[] for _ in range(consts.MAX_GPU_PER_NODE)] - # For calculating approximated SM efficiency. - self.blocks_per_sm_per_device = [[] for _ in range(consts.MAX_GPU_PER_NODE)] - self.avg_approximated_sm_efficiency_per_device = [None] * consts.MAX_GPU_PER_NODE - self.approximated_sm_efficiency_ranges = [[] for _ in range(consts.MAX_GPU_PER_NODE)] - self.gpu_sm_efficiency_json = None - self.blocks_per_sm_count = [0] * consts.MAX_GPU_PER_NODE - # For calculating averaged occupancy. - self.occupancy_per_device = [[] for _ in range(consts.MAX_GPU_PER_NODE)] - self.avg_occupancy_per_device = [None] * consts.MAX_GPU_PER_NODE - self.occupancy_count = [0] * consts.MAX_GPU_PER_NODE - - def calculate_gpu_utilization(self, global_start_time, global_end_time, steps_start_time, steps_end_time): - # Make bucket_size to 10-power's of us, and number of buckets to (10, 100]. - # 10-power's of us, in order to straight forward for user to understand. - # If number of buckets are too many, the value of gpu utilization will be either 0 or 1. - def get_bucket_info(range_micro_seconds): - max_buckets = 100 - bucket_size = 1 - while range_micro_seconds / bucket_size > max_buckets: - bucket_size *= 10 - buckets = int(range_micro_seconds / bucket_size) - unit = bucket_size - unit_str = 'us' - if unit >= 1000: - unit /= 1000 - unit_str = 'ms' - if unit >= 1000: - unit /= 1000 - unit_str = 's' - return int(bucket_size), int(buckets), int(unit), unit_str - - gpu_utilization_timeline = [[] for _ in range(consts.MAX_GPU_PER_NODE)] - for gpu_id in self.gpu_ids: - self.kernel_ranges_per_device[gpu_id] = merge_ranges(self.kernel_ranges_per_device[gpu_id]) - - # Top-level number still consider steps, to be consistent with overview's breakdown. - kernel_ranges_all_steps = intersection_ranges_lists( - self.kernel_ranges_per_device[gpu_id], [(steps_start_time, steps_end_time)]) - ranges_sum = get_ranges_sum(kernel_ranges_all_steps) - self.gpu_utilization[gpu_id] = ranges_sum / (steps_end_time - steps_start_time) - - # The timeline will use 'PyTorch Profiler (0)' as start, - # in order to draw previous step's kernels' gpu utilization. - bucket_size, buckets, self.gpu_util_timeline_unit_size, self.gpu_util_timeline_unit_name = \ - get_bucket_info(global_end_time - global_start_time) - buckets_ranges = [] - for i in range(buckets): - buckets_ranges.append((global_start_time + i * bucket_size, - global_start_time + (i + 1) * bucket_size if i < buckets - 1 - else global_end_time)) # The last bucket may be longer. - gpu_utilization_timeline[gpu_id] = [0] * buckets - if len(self.kernel_ranges_per_device[gpu_id]) > 0: - current_range_index = 0 - current_range = self.kernel_ranges_per_device[gpu_id][current_range_index] - current_bucket_index = 0 - current_bucket = buckets_ranges[0] - while (current_range_index < len(self.kernel_ranges_per_device[gpu_id]) - and current_bucket_index < buckets): - if current_bucket[1] <= current_range[0]: - current_bucket_index += 1 - current_bucket = buckets_ranges[current_bucket_index] if current_bucket_index < buckets \ - else None - elif current_bucket[0] >= current_range[1]: - current_range_index += 1 - if current_range_index < len(self.kernel_ranges_per_device[gpu_id]): - current_range = self.kernel_ranges_per_device[gpu_id][current_range_index] - else: - left_bound = max(current_range[0], current_bucket[0]) - right_bound = min(current_range[1], current_bucket[1]) - gpu_utilization_timeline[gpu_id][current_bucket_index] += (right_bound - left_bound) - if current_bucket[1] < current_range[1]: - current_bucket_index += 1 - current_bucket = buckets_ranges[current_bucket_index] if current_bucket_index < buckets \ - else None - else: - current_range_index += 1 - if current_range_index < len(self.kernel_ranges_per_device[gpu_id]): - current_range = self.kernel_ranges_per_device[gpu_id][current_range_index] - for i_bucket in range(buckets): - bucket_size = buckets_ranges[i_bucket][1] - buckets_ranges[i_bucket][0] - gpu_utilization_timeline[gpu_id][i_bucket] /= bucket_size - start_time = buckets_ranges[i_bucket][0] - self.gpu_util_buckets[gpu_id].append((start_time, gpu_utilization_timeline[gpu_id][i_bucket])) - start_time = buckets_ranges[-1][1] - self.gpu_util_buckets[gpu_id].append((start_time, 0)) - - self.kernel_ranges_per_device = None # Release memory. - - def calculate_approximated_sm_efficiency(self, steps_start_time, steps_end_time): - def calculate_avg(approximated_sm_efficiency_ranges, total_dur): - total_weighted_sm_efficiency = 0.0 - for r in approximated_sm_efficiency_ranges: - dur = r[1] - r[0] - total_weighted_sm_efficiency += r[2] * dur - avg_approximated_sm_efficiency = total_weighted_sm_efficiency / total_dur - return avg_approximated_sm_efficiency - - total_dur = steps_end_time - steps_start_time - for gpu_id in self.gpu_ids: - blocks_per_sm_ranges = self.blocks_per_sm_per_device[gpu_id] - approximated_sm_efficiency_ranges = merge_ranges_with_value(blocks_per_sm_ranges) - # To be consistent with GPU utilization, here it must also intersect with all steps, - # in order to remove the kernels out of steps range. - approximated_sm_efficiency_ranges_all_steps = intersection_ranges_lists_with_value( - approximated_sm_efficiency_ranges, [(steps_start_time, steps_end_time)]) - if len(approximated_sm_efficiency_ranges_all_steps) > 0: - avg_approximated_sm_efficiency = calculate_avg(approximated_sm_efficiency_ranges_all_steps, total_dur) - self.avg_approximated_sm_efficiency_per_device[gpu_id] = avg_approximated_sm_efficiency - - # The timeline still uses all kernels including out of steps scope's. - if len(approximated_sm_efficiency_ranges) > 0: - self.approximated_sm_efficiency_ranges[gpu_id] = approximated_sm_efficiency_ranges - - self.blocks_per_sm_per_device = None # Release memory. - - # Weighted average. Weighted by kernel's time duration. - def calculate_occupancy(self, steps_start_time, steps_end_time): - for gpu_id in self.gpu_ids: - occupancys_on_a_device = self.occupancy_per_device[gpu_id] - total_time = 0 - total_occupancy = 0.0 - for r in occupancys_on_a_device: - min_time = max(r[0], steps_start_time) - max_time = min(r[1], steps_end_time) - if min_time < max_time: - dur = max_time - min_time - total_occupancy += r[2] * dur - total_time += dur - if total_time > 0: - self.avg_occupancy_per_device[gpu_id] = total_occupancy / total_time - - @classmethod - def parse_events(cls, - events: Iterable[BaseEvent], - global_start_time: int, - global_end_time: int, - steps_start_time: int, - steps_end_time: int): - parser = GPUMetricsParser() - logger.debug('GPU Metrics, parse events') - for event in events: - if event.type == EventTypes.KERNEL: - parser.parse_event(event) - - parser.calculate_gpu_utilization(global_start_time, global_end_time, steps_start_time, steps_end_time) - parser.calculate_approximated_sm_efficiency(steps_start_time, steps_end_time) - parser.calculate_occupancy(steps_start_time, steps_end_time) - return parser - - def parse_event(self, event: KernelEvent): - ts = event.ts - dur = event.duration - gpu_id = event.device_id - if gpu_id != event.pid: - logger.warning("pid '{}' is not equal to args.device '{}' on event with ts '{}'".format( - event.pid, gpu_id, event.ts)) - if gpu_id is not None: - if gpu_id not in self.gpu_ids: - self.gpu_ids.add(gpu_id) - self.kernel_ranges_per_device[gpu_id].append((ts, ts + dur)) - if event.blocks_per_sm is not None: - if event.blocks_per_sm > 0.0: - self.blocks_per_sm_per_device[gpu_id].append((ts, ts + dur, event.blocks_per_sm)) - self.blocks_per_sm_count[gpu_id] += 1 - else: - # Workaround for negative value input. - logger.warning('blocks per SM {} with ts {} is not positive!'.format(event.blocks_per_sm, ts)) - if event.occupancy is not None: - if event.occupancy >= 0.0: - self.occupancy_per_device[gpu_id].append((ts, ts + dur, event.occupancy)) - self.occupancy_count[gpu_id] += 1 - else: - # Workaround for negative value input. - logger.warning('est. achieved occupancy % {} with ts {} is negative!'.format(event.occupancy, ts)) - - def get_gpu_metrics_columns(self): - columns = [] - if self.has_blocks_per_sm: - columns.append({'type': 'number', 'name': 'Mean Blocks Per SM', - 'tooltip': consts.TOOLTIP_BLOCKS_PER_SM}) - if self.has_occupancy: - columns.append({'type': 'number', 'name': 'Mean Est. Achieved Occupancy (%)', - 'tooltip': consts.TOOLTIP_OCCUPANCY_COMMON + consts.TOOLTIP_OCCUPANCY_TABLE}) - return columns - - @property - def has_blocks_per_sm(self): - return sum(self.blocks_per_sm_count) > 0 - - @property - def has_occupancy(self): - return sum(self.occupancy_count) > 0 - - def get_gpu_metrics(self): - def build_trace_counter_gpu_util(gpu_id, start_time, counter_value): - util_json = ("{{\"ph\":\"C\", \"name\":\"GPU {} Utilization\", \"pid\":{}, \"ts\":{}, " - "\"args\":{{\"GPU Utilization\":{}}}}}").format(gpu_id, gpu_id, start_time, counter_value) - return util_json - - def build_trace_counter_sm_efficiency(gpu_id, start_time, counter_value): - util_json = ("{{\"ph\":\"C\", \"name\":\"GPU {} Est. SM Efficiency\", \"pid\":{}, \"ts\":{}, " - "\"args\":{{\"Est. SM Efficiency\":{}}}}}").format(gpu_id, gpu_id, start_time, counter_value) - return util_json - - def add_trace_counter_gpu_util(gpu_id, start_time, counter_value, counter_json_list: List): - json_str = build_trace_counter_gpu_util(gpu_id, start_time, counter_value) - counter_json_list.append(json_str) - - def add_trace_counter_sm_efficiency(gpu_id, start_time, end_time, value, counter_json_list: List): - efficiency_json_start = build_trace_counter_sm_efficiency(gpu_id, start_time, value) - efficiency_json_finish = build_trace_counter_sm_efficiency(gpu_id, end_time, 0) - counter_json_list.append(efficiency_json_start) - counter_json_list.append(efficiency_json_finish) - - counter_json_list = [] - for gpu_id, buckets in enumerate(self.gpu_util_buckets): - if len(buckets) > 0: - # Adding 1 as baseline. To avoid misleading virtualization when the max value is less than 1. - add_trace_counter_gpu_util(gpu_id, buckets[0][0], 1, counter_json_list) - add_trace_counter_gpu_util(gpu_id, buckets[0][0], 0, counter_json_list) - for b in buckets: - add_trace_counter_gpu_util(gpu_id, b[0], b[1], counter_json_list) - for gpu_id, ranges in enumerate(self.approximated_sm_efficiency_ranges): - buckets = self.gpu_util_buckets[gpu_id] - if len(ranges) > 0 and len(buckets) > 0: - # Adding 1 as baseline. To avoid misleading virtualization when the max value is less than 1. - add_trace_counter_sm_efficiency(gpu_id, buckets[0][0], buckets[0][0], 1, counter_json_list) - for r in ranges: - add_trace_counter_sm_efficiency(gpu_id, r[0], r[1], r[2], counter_json_list) - - return counter_json_list - - def get_gpu_metrics_data_tooltip( - self, - gpu_infos, - tc_ratio): - if not self.gpu_ids: - return None, None - - has_sm_efficiency = False - has_occupancy = False - has_tc = False - - gpu_metrics_data = [] - gpu_info_columns = ['Name', 'Memory', 'Compute Capability'] - - def process_gpu(gpu_id: int): - nonlocal has_sm_efficiency, has_occupancy, has_tc - gpu_metrics_data.append({'title': 'GPU {}:'.format(gpu_id), 'value': ''}) - gpu_info = gpu_infos.get(gpu_id, None) - if gpu_info is not None: - for key in gpu_info_columns: - if key in gpu_info: - gpu_metrics_data.append({'title': key, 'value': gpu_info[key]}) - else: - # the legacy chrome tracing file would not have gpu info. - pass - gpu_metrics_data.append({'title': 'GPU Utilization', 'value': '{} %'.format( - round(self.gpu_utilization[gpu_id] * 100, 2))}) - if self.avg_approximated_sm_efficiency_per_device[gpu_id] is not None: - gpu_metrics_data.append({'title': 'Est. SM Efficiency', 'value': '{} %'.format( - round(self.avg_approximated_sm_efficiency_per_device[gpu_id] * 100, 2))}) - has_sm_efficiency = True - if self.avg_occupancy_per_device[gpu_id] is not None: - gpu_metrics_data.append({'title': 'Est. Achieved Occupancy', 'value': '{} %'.format( - round(self.avg_occupancy_per_device[gpu_id], 2))}) - has_occupancy = True - if tc_ratio[gpu_id] is not None: - gpu_metrics_data.append({'title': 'Kernel Time using Tensor Cores', 'value': '{} %'.format( - round(tc_ratio[gpu_id] * 100, 2))}) - has_tc = True - - gpu_ids = list(self.gpu_ids) - process_gpu(gpu_ids[0]) - for idx in range(1, len(gpu_ids)): - # Append separator line for beautiful to see. - gpu_metrics_data.append({'title': '
    ', 'value': ''}) - process_gpu(gpu_ids[idx]) - - tooltip_summary = 'The GPU usage metrics:\n' - tooltip = '{}\n{}'.format(tooltip_summary, consts.TOOLTIP_GPU_UTIL) - if has_sm_efficiency: - tooltip += '\n' + consts.TOOLTIP_SM_EFFICIENCY - if has_occupancy: - tooltip += '\n' + consts.TOOLTIP_OCCUPANCY_COMMON + consts.TOOLTIP_OCCUPANCY_OVERVIEW - if has_tc: - tooltip += '\n' + consts.TOOLTIP_TENSOR_CORES - - return gpu_metrics_data, tooltip diff --git a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/kernel_parser.py b/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/kernel_parser.py deleted file mode 100644 index 229251e60..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/kernel_parser.py +++ /dev/null @@ -1,45 +0,0 @@ -# ------------------------------------------------------------------------- -# Copyright (c) Microsoft Corporation. All rights reserved. -# -------------------------------------------------------------------------- -from typing import Optional - -import numpy as np -import pandas as pd - -from .tensor_core import TcAllowlist -from .trace import EventTypes - - -class KernelParser: - def __init__(self): - self.kernel_stat: Optional[pd.DataFrame] = None - self.tc_used_ratio = 0.0 - - def parse_events(self, events): - events = [vars(event) for event in events if event.type == EventTypes.KERNEL] - events = pd.DataFrame(events) - events = events.astype({'type': 'category', 'name': 'string'}, copy=False) - events['tc_used'] = events['name'].map(lambda name: name in TcAllowlist) - - def weighted_avg(x: pd.Series): - try: - # fill these None as zero - x = x.fillna(0) - return np.average(x, weights=events.loc[x.index, 'duration']) - except ZeroDivisionError: - return 0 - - self.kernel_stat = events.groupby('name').agg( - tc_used=('tc_used', 'first'), - count=('duration', 'count'), - sum=('duration', 'sum'), - mean=('duration', 'mean'), - max=('duration', 'max'), - min=('duration', 'min'), - blocks_per_sm=('blocks_per_sm', weighted_avg), - occupancy=('occupancy', weighted_avg)).sort_values('sum', ascending=False) - - tc_total = self.kernel_stat['sum'].sum() - tc_self = self.kernel_stat[self.kernel_stat['tc_used']]['sum'].sum() - if tc_total > 0: - self.tc_used_ratio = tc_self / tc_total diff --git a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/loader.py b/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/loader.py deleted file mode 100644 index b31374ea4..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/loader.py +++ /dev/null @@ -1,230 +0,0 @@ -# ------------------------------------------------------------------------- -# Copyright (c) Microsoft Corporation. All rights reserved. -# -# Copyright(c) 2023 Huawei Technologies. -# All rights reserved -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# Modifications: Add visualization of PyTorch Ascend profiling. -# -------------------------------------------------------------------------- -import bisect -import os -import sys -from collections import defaultdict -from typing import List, Tuple - -from .. import consts, io, utils -from ..multiprocessing import Process, Queue -from ..run import Run, RunProfile -from .data import DistributedRunProfileData, RunProfileData -from .node import CommunicationNode -from .run_generator import DistributedRunGenerator, RunGenerator - -logger = utils.get_logger() - - -class RunLoader(object): - def __init__(self, name, run_dir, caches: io.Cache, device_target="GPU"): - self.run_name = name - self.run_dir = run_dir - self.caches = caches - self.queue = Queue() - self.device_target = device_target - - def load(self): - workers = [] - spans_by_workers = defaultdict(list) - if self.device_target == 'Ascend': - for path in io.listdir(self.run_dir): - if io.isdir(io.join(self.run_dir, path)) and utils.is_worker_span_dir(path) and io.isdir( - io.join(self.run_dir, path, 'ASCEND_PROFILER_OUTPUT')): - data_path = io.join(self.run_dir, path, 'ASCEND_PROFILER_OUTPUT') - for file in io.listdir(data_path): - if utils.is_npu_trace_path(file) or str(file) in consts.INPUT_FILE_LIST: - match = consts.WORKER_SPAN_PATTERN.match(path) - worker = match.group(1) - span = match.group(2) - if span is not None: - bisect.insort(spans_by_workers[worker], span) - workers.append((worker, span, io.join(path, 'ASCEND_PROFILER_OUTPUT'))) - break - else: - for path in io.listdir(self.run_dir): - if io.isdir(io.join(self.run_dir, path)): - continue - match = consts.WORKER_PATTERN.match(path) - if not match: - continue - absolute_path = io.join(self.run_dir, path) - if io.stat(absolute_path).length > consts.MAX_FILE_SIZE: - logger.warning( - f'File "{absolute_path}" exceeds the maximum limit size of 500MB and will be skipped.') - continue - - worker = match.group(1) - span = match.group(2) - if span is not None: - # remove the starting dot (.) - span = span[1:] - bisect.insort(spans_by_workers[worker], span) - - workers.append((worker, span, path)) - - span_index_map = {} - for worker, span_array in spans_by_workers.items(): - for i, span in enumerate(span_array, 1): - span_index_map[(worker, span)] = i - - for worker, span, path in workers: - # convert the span timestamp to the index. - span_index = None if span is None else span_index_map[(worker, span)] - p = Process(target=self._process_data, args=(worker, span, span_index, path)) - p.start() - logger.info('started all processing') - - distributed_run = Run(self.run_name, self.run_dir, self.device_target) - run = Run(self.run_name, self.run_dir, self.device_target) - num_items = len(workers) - while num_items > 0: - item: Tuple[RunProfile, DistributedRunProfileData] = self.queue.get() - num_items -= 1 - r, d = item - if r or d: - logger.debug('Loaded profile via mp.Queue') - if r is not None: - run.add_profile(r) - if d is not None: - distributed_run.add_profile(d) - - distributed_profiles = self._process_spans(distributed_run) - for d in distributed_profiles: - if d is not None: - run.add_profile(d) - - # for no daemon process, no need to join them since it will automatically join - return run - - def _process_data(self, worker, span_name, span, path): - import absl.logging - absl.logging.use_absl_handler() - - try: - logger.debug('Parse trace, run_dir=%s, data_dir=%s', self.run_dir, path) - local_file = self.caches.get_remote_cache(io.join(self.run_dir, path)) - if self.device_target == 'Ascend': - data = RunProfileData.parse_npu(worker, span, local_file, self.caches.cache_dir) - else: - data = RunProfileData.parse_gpu(worker, span, local_file, self.caches.cache_dir) - if not data: - self.queue.put((None, None)) - logger.debug('finishing process data') - return - if data.trace_file_path != local_file: - self.caches.add_file(local_file, data.trace_file_path) - - generator = RunGenerator(worker, span, data, self.device_target) - profile = generator.generate_run_profile() - if self.device_target == 'Ascend': - data.step_to_overlap = profile.step_to_overlap - data.step_to_wait = profile.step_to_wait - data.comm_op = profile.comm_op - dist_data = DistributedRunProfileData(data) - - logger.debug('Sending back profile via mp.Queue') - self.queue.put((profile, dist_data)) - except KeyboardInterrupt: - logger.warning('tb_plugin receive keyboard interrupt signal, process %d will exit' % (os.getpid())) - sys.exit(1) - except Exception as ex: - if self.device_target == 'Ascend': - worker_name = f'{worker}_{span_name}_ascend_pt' - else: - worker_name = worker - logger.warning('Failed to parse profile data for Run %s on %s. Exception=%s', - self.run_name, worker_name, ex, exc_info=True) - self.queue.put((None, None)) - logger.debug('finishing process data') - - def _process_spans(self, distributed_run: Run): - spans = distributed_run.get_spans() - if spans is None: - return [self._process_distributed_profiles(distributed_run.get_profiles(), None)] - else: - span_profiles = [] - for span in spans: - profiles = distributed_run.get_profiles(span=span) - p = self._process_distributed_profiles(profiles, span) - if p is not None: - span_profiles.append(p) - return span_profiles - - def _process_distributed_profiles(self, profiles: List[DistributedRunProfileData], span): - if self.device_target != 'Ascend': - return self._gpu_distributed(profiles, span) - else: - for data in profiles: - if not data.has_communication: - logger.debug('There is no communication profile in this NPU run.') - return None - generator = DistributedRunGenerator(profiles, span, self.device_target) - profile = generator.generate_run_profile() - return profile - - def _gpu_distributed(self, profiles, span): - has_communication = True - comm_node_lists: List[List[CommunicationNode]] = [] - for data in profiles: - logger.debug('Processing profile data') - # Set has_communication to False and disable distributed view if any one worker has no communication - if data.has_communication and data.comm_node_list: - comm_node_lists.append(data.comm_node_list) - if len(comm_node_lists[-1]) != len(comm_node_lists[0]): - logger.error("Number of communication operation nodes don't match between workers in run: %s" - % self.run_name) - has_communication = False - else: - has_communication = False - logger.debug('Processing profile data finish') - - if not has_communication: - logger.debug('There is no communication profile in this GPU run.') - return None - - worker_num = len(comm_node_lists) - for i, node in enumerate(comm_node_lists[0]): - kernel_range_size = len(node.kernel_ranges) - # loop for all communication kernel ranges in order - for j in range(kernel_range_size): - min_range = sys.maxsize - # For each kernel_range, find the minist between workers as the real communication time - for k in range(worker_num): - kernel_ranges = comm_node_lists[k][i].kernel_ranges - if len(kernel_ranges) != kernel_range_size: - logger.error("Number of communication kernels don't match between workers in run: %s" - % self.run_name) - has_communication = False - return None - if kernel_ranges: - if kernel_ranges[j][1] - kernel_ranges[j][0] < min_range: - min_range = kernel_ranges[j][1] - kernel_ranges[j][0] - for k in range(worker_num): - kernel_range = comm_node_lists[k][i].kernel_ranges[j] - comm_node_lists[k][i].real_time_ranges.append((kernel_range[1] - min_range, kernel_range[1])) - - for data in profiles: - data.communication_parse() - - generator = DistributedRunGenerator(profiles, span, self.device_target) - profile = generator.generate_run_profile() - return profile diff --git a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/memory_parser.py b/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/memory_parser.py deleted file mode 100644 index 64b78127a..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/memory_parser.py +++ /dev/null @@ -1,331 +0,0 @@ -# ------------------------------------------------------------------------- -# Copyright (c) Microsoft Corporation. All rights reserved. -# -------------------------------------------------------------------------- -from collections import defaultdict -from enum import IntEnum -from typing import Dict, Iterable, List, Optional, Tuple - -from .. import utils -from .node import OperatorNode, is_operator_node -from .op_agg import aggregate_ops -from .trace import DeviceType, MemoryEvent - -logger = utils.get_logger() - - -class MemoryMetrics(IntEnum): - SelfIncreaseSize = 0 - SelfAllocationSize = 1 - SelfAllocationCount = 2 - IncreaseSize = 3 - AllocationSize = 4 - AllocationCount = 5 - - -class MemoryRecord: - def __init__(self, scope: str, pid: int, tid: int, ts: int, - device_type: DeviceType, device_id: int, - address: int, record_bytes: int, total_allocated: float, total_reserved: float): - self.scope = scope - self.tid = tid - self.pid = pid - self.ts = ts - self.device_type = device_type - self.device_id = device_id - self.addr = address - self.bytes = record_bytes - self.total_allocated = total_allocated - self.total_reserved = total_reserved - self.op_name: Optional[str] = None - self.parent_op_name: Optional[str] = None - - @property - def device_name(self): - if self.device_type == DeviceType.CPU: - return 'CPU' - elif self.device_type == DeviceType.CUDA: - return 'GPU{}'.format(self.device_id) - else: - return None - - @property - def is_allocation(self): - return self.bytes > 0 - - @property - def op_name_or_unknown(self): - return self.op_name if self.op_name else '' - - @classmethod - def from_event(cls, event: MemoryEvent): - return cls(event.scope, event.pid, event.tid, event.ts, event.device_type, event.device_id, - event.addr, event.bytes, event.total_allocated, event.total_reserved) - - def __repr__(self) -> str: - return f"<{'+' if self.bytes>0 else ''}{self.bytes}B, addr: {self.addr}, ts: {self.ts}>" - - -class MemorySnapshot: - def __init__(self, memory_records: Iterable[MemoryRecord], - op_memory_table: Dict[OperatorNode, List[MemoryRecord]], - processed_nodes: Dict[OperatorNode, int]) -> None: - self.memory_records = memory_records - self.op_memory_table = op_memory_table - # the visited node times from parent to child - # troubleshooting issue purpose. - self.processed_node = processed_nodes - self.unreached_node = defaultdict(list) - - def get_peak_memory(self) -> Dict[Tuple[DeviceType, int], int]: - peaks = defaultdict(int) - for r in self.memory_records: - if r.total_allocated == r.total_allocated: # !isnan - peaks[(r.device_type, r.device_id)] = max(peaks[(r.device_type, r.device_id)], r.total_allocated) - return peaks - - def get_memory_statistics(self, - tid2tree: Dict[int, OperatorNode], - start_ts=None, end_ts=None) -> Dict[str, Dict[str, List[int]]]: - metric_length = len(MemoryMetrics) - self_metric_length = metric_length // 2 - - def dict_factory(): - return defaultdict(lambda: [0] * metric_length) - - # traverse outputs - op_list: List[OperatorNode] = [] - # two level keys dictionary - # first keyed by node, then keyed by device (CPU/GPU0/GPU1/etc.) - memory_metrics_keyed_by_node: Dict[OperatorNode, Dict[str, List[int]]] = defaultdict(dict_factory) - - def traverse_node_memory(node: OperatorNode): - if start_ts is not None and node.end_time < start_ts: - return - if end_ts is not None and node.start_time > end_ts: - return - - is_op = is_operator_node(node) - if is_op: - op_list.append(node) - - if node not in self.processed_node: - self.unreached_node[tid].append(node) - # since the node has not been visited for insert memory records, just ignore all childrens - return - elif is_op: - node_memory_metrics = self.get_memory_metrics(node, start_ts, end_ts) - for device, metrics in node_memory_metrics.items(): - # device is name of device like: CPU/GPU0 - # metrics is an arrary [SelfIncreaseSize, SelfAllocationSize, SelfAllocationCount] - for i, value in enumerate(metrics): - memory_metrics_keyed_by_node[node][device][i] = value - memory_metrics_keyed_by_node[node][device][i + self_metric_length] += value - else: - logger.debug('node {}:{} is not operator node, will skip its self metrics processing'.format( - node.name, node.start_time)) - - # recursive the children nodes - for child in node.children: - traverse_node_memory(child) - # sum up the child metrics - for device, metrics in memory_metrics_keyed_by_node[child].items(): - for i in range(self_metric_length, metric_length): - memory_metrics_keyed_by_node[node][device][i] += metrics[i] - - for _, root in tid2tree.items(): - for child in root.children: - traverse_node_memory(child) - - # keyed first by device name like CPU/GPU0 etc, then keyed by operator name. - # the value is array [items indexed by MemoryMetrics] - memory_metrics_keyed_by_nodename: Dict[str, Dict[str, List[int]]] = defaultdict(dict_factory) - # node: the instance, device_keyed_metrics: dictionary keyed by device name like CPU/GPU0 - for node, device_keyed_metrics in memory_metrics_keyed_by_node.items(): - if not is_operator_node(node): - # skip the node like Optimizer.step, DataLoader, ProfilerStep#1 etc. - continue - - for device, metrics in device_keyed_metrics.items(): - for i, metric in enumerate(metrics): - memory_metrics_keyed_by_nodename[device][node.name][i] += metric - - # get the op_calls dictionary from module parser result. - op_calls: Dict[str, int] = defaultdict(int) - agg_result = aggregate_ops(op_list, [lambda op: op.name]) - for op_name, op_agg in agg_result[0].items(): - op_calls[op_name] += op_agg.calls - - result: Dict[str, Dict[str, List[int]]] = defaultdict(defaultdict) - for device, node_metrics in memory_metrics_keyed_by_nodename.items(): - for node, values in node_metrics.items(): - if any(values): - result[device][node] = values + [op_calls[node]] - - return result - - def get_memory_metrics(self, op: OperatorNode, start_ts, end_ts): - metrics_count = len([e.name for e in MemoryMetrics if e.name.startswith('Self')]) - memory_metrics: Dict[str, List[int]] = defaultdict(lambda: [0] * metrics_count) - for record in self.op_memory_table[op]: - if start_ts is not None and record.ts < start_ts: - continue - if end_ts is not None and record.ts > end_ts: - continue - name = record.device_name - if name is None: - continue - - memory_metrics[name][MemoryMetrics.SelfIncreaseSize] += record.bytes - if record.bytes > 0: - memory_metrics[name][MemoryMetrics.SelfAllocationSize] += record.bytes - memory_metrics[name][MemoryMetrics.SelfAllocationCount] += 1 - - return memory_metrics - - -class MemoryParser: - def __init__(self, memory_events: Iterable[MemoryEvent]): - # statistics purpose - self.staled_records: List[MemoryRecord] = [] - self.processed_records: List[MemoryRecord] = [] - self.memory_records: List[MemoryRecord] = [MemoryRecord.from_event(e) for e in memory_events] - - def find_memory_nodes(self, tid2tree: Dict[int, OperatorNode]) -> MemorySnapshot: - records_by_tid: Dict[int, List[MemoryRecord]] = defaultdict(list) - for r in self.memory_records: - records_by_tid[r.tid].append(r) - - op_memory_table: Dict[OperatorNode, List[MemoryRecord]] = defaultdict(list) - processed_node = defaultdict(int) - - tree_height = 0 - for tid, records in records_by_tid.items(): - if not records: - continue - - # each item is (parent_node, child_index) that it is visiting. - node_stack: List[Tuple[OperatorNode, int]] = [] - - record_index = 0 - current_node: OperatorNode = tid2tree.get(tid) - child_index = 0 - - if current_node: - processed_node[current_node] += 1 - - while record_index < len(records): - """In the loop, one pass will process one record. The basic logic is: - It will search from the node that last visited since both the records and tree is ordered already - 1. it current node contains the records, then find the exactly child which just embrace it. - 2. otherwise, find the parent node and set the child_index, so that the parent node could continue from - previous visited node. # noqa: E501 - 3. if there is not any node contains the records, then all remaining records will be ignored. - """ - record = records[record_index] - - if len(node_stack) > tree_height: - tree_height = len(node_stack) - - if current_node is None or current_node.start_time is None or current_node.end_time is None: - # 3. Ignore all remaining records. - logger.debug( - 'could not find the node for tid %d, timestamp: %d, record index: %d, total records: %d' % ( - record.tid, record.ts, record_index, len(records))) - self.staled_records.append(records[record_index]) - record_index += 1 - continue - - if record.ts < current_node.start_time: - # this should only happens for root node. - logger.debug('record timestamp %d is less that the start time of %s' % - (record.ts, current_node.name)) - # This record has no chance to be appended to following tree node. - self.staled_records.append(record) - record_index += 1 - continue - elif record.ts >= current_node.end_time: - # 2. pop parent node and update the child_index accordingly. - if len(node_stack) > 0: - current_node, child_index = node_stack.pop() - child_index += 1 - else: - # if there is not item in stack, set it to None - current_node = None - continue - - # 1. find the real node embrace the record. - # Find the node which contains the records from top to downmost. - while child_index < len(current_node.children): - if record.ts < current_node.children[child_index].start_time: - # if current record timestamp is less than the current child's startime, - # we will break the search and keep the child_index not change. So that next time - # we can continue from here. - # there is no any child contains the record.timestamp - # child_find is False at this case. - break - elif record.ts >= current_node.children[child_index].end_time: - # if the record timestamp is greater than the children end time, increment to next child - # until find one contains the record - child_index += 1 - else: - # current children contains the record - processed_node[current_node.children[child_index]] += 1 - - # push child index which will be visited, then continue the loop - node_stack.append((current_node, child_index)) - current_node = current_node.children[child_index] - child_index = 0 - - # the current_node is the one contains the record at this moment. - if is_operator_node(current_node): - op_memory_table[current_node].append(record) - # NOTE: only allocation record can be associated with op. Because deallocation happens at the end - # of a tensor's lifetime which is not deterministic. - if record.is_allocation: - record.op_name = current_node.name - if len(node_stack) > 0: - record.parent_op_name = node_stack[-1][0].name - self.processed_records.append(record) - else: - self.staled_records.append(record) - - # the record is processed - record_index += 1 - - # show summary information - if len(self.staled_records) > 0 and len(self.memory_records) > 0: - logger.debug('{} memory records are skipped in total {} memory records and only {} get processed'.format( - len(self.staled_records), len(self.memory_records), len(self.processed_records))) - if tree_height > 0: - logger.debug('max tree height is {}'.format(tree_height)) - - all_records = self.get_preprocessed_records() - return MemorySnapshot(all_records, op_memory_table, processed_node) - - def get_preprocessed_records(self): - memory_records = sorted(self.memory_records, key=lambda r: r.ts) - - alloc = {} # allocation events may or may not have paired free event - prev_ts = float('-inf') # ensure ordered memory records is ordered - for i, r in enumerate(memory_records): - if r.addr is None: - # profile json data prior to pytorch 1.10 do not have addr - # we should ignore them - continue - if prev_ts > r.ts: - logger.error(f'Invalid value, prev_ts {prev_ts} is greater than end_ts {r.ts}') - return [] - prev_ts = r.ts - addr = r.addr - size = r.bytes - if size > 0: - # Allocation event, to be matched with a Release event - alloc[addr] = i - else: - # Processing a Release event - if addr in alloc: - alloc_r = memory_records[alloc[addr]] - r.op_name = alloc_r.op_name - r.parent_op_name = alloc_r.parent_op_name - del alloc[addr] - return memory_records diff --git a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/module_op.py b/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/module_op.py deleted file mode 100644 index 15f1e4ef9..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/module_op.py +++ /dev/null @@ -1,262 +0,0 @@ -# ------------------------------------------------------------------------- -# Copyright (c) Microsoft Corporation. All rights reserved. -# ------------------------------------------------------------------------- -from collections import namedtuple -from typing import Dict, Generator, Iterable, List, Optional, Set, Tuple, Union - -from .node import (DataLoaderNode, ModuleNode, OperatorNode, OptimizerNode, - PLModuleNode, ProfilerStepNode, is_operator_node) -from .trace import BaseEvent, EventTypes, PLModuleEvent, PythonFunctionEvent - - -class Module: - def __init__(self, name: str, module_id: int, shape: str = ''): - self.name = name - self.module_id = module_id - self.children: List[Module] = [] - - def __hash__(self): - return hash((self.name, self.module_id, tuple(self.children))) - - def __eq__(self, obj) -> bool: - if not isinstance(obj, Module): - return False - - return (self.name == obj.name and - self.module_id == obj.module_id and - self.children == obj.children) - - -class ModuleStats: - def __init__(self, name: str, module_id: int): - self.name = name - self.module_id = module_id - self.occurences: int = 0 - self.operators: int = 0 - self.host_duration: int = 0 - self.device_duration: int = 0 - self.self_host_duration: int = 0 - self.self_device_duration: int = 0 - - @property - def avg_host_duration(self): - return self.host_duration / self.occurences if self.occurences != 0 else 0 - - @property - def avg_device_duration(self): - return self.device_duration / self.occurences if self.occurences != 0 else 0 - - -Stats = namedtuple('Stats', [ - 'name', - 'id', - 'occurences', - 'operators', - 'host_duration', - 'self_host_duration', - 'device_duration', - 'self_device_duration', - 'avg_duration', - 'children']) - - -def aggegate_module_view(tid2tree: Dict[int, OperatorNode], events: List[BaseEvent]) -> Optional[List[Stats]]: - roots = _build_module_hierarchy(events) - modules = _get_node_list(tid2tree, ModuleNode) - if modules and roots: - return _process_module_statistics(modules, roots) - else: - return None - - -def aggegate_pl_module_view(tid2tree: Dict[int, OperatorNode], events: List[BaseEvent]) -> Optional[List[Stats]]: - roots = _build_module_hierarchy_from_name(events) - modules = _get_node_list(tid2tree, PLModuleNode) - if modules and roots: - return _process_module_statistics(modules, roots) - else: - return None - - -def _build_module_hierarchy_from_name(events: List[PLModuleEvent]) -> List[Module]: - pl_module_events = [e for e in events if e.type == EventTypes.PL_MODULE] - name2module: Dict[str, Module] = {} - no_root: Set[str] = set() - - for event in pl_module_events: - if event.name not in name2module: - name2module[event.name] = Module(event.name, 0) - - for name, module in name2module.items(): - if name.find('.') == -1: - continue - parent_name = name[:name.rfind('.')] - if parent_name in name2module: - name2module[parent_name].children.append(module) - no_root.add(module.name) - - return [module for name, module in name2module.items() if name not in no_root] - - -def _build_module_hierarchy(events: List[PythonFunctionEvent]) -> List[Module]: - """Get the module hierarchy from the chome trace events - """ - python_events = [e for e in events if e.type in (EventTypes.PYTHON_FUNCTION, EventTypes.MODULE)] - id_to_event = {e.python_id: e for e in python_events} - - # Extract Python function topology. - children: Dict[int, List[int]] = {} - for e in python_events: - e_id = e.python_id - children.setdefault(e_id, []) - e_parent_id = e.python_parent_id - children.setdefault(e_parent_id, []) - children.get(e_parent_id).append(e_id) - function_leaves = [k for k, v in children.items() if not v] - - # Convert Python function topology to Module topology. - # This is a simple O(n) tree walking algorithm where we start from the leaves - # and walk up, discarding any nodes which are not Module nodes. - module_parent_map = {} - seen = set() - for i in function_leaves: - e = id_to_event[i] - current_module = None - while e is not None: - e_id = e.python_id - if e.type == EventTypes.MODULE: - if current_module is not None: - module_parent_map[current_module.python_id] = e_id - current_module = e - module_parent_map.setdefault(e_id, None) - - seen_key = (e_id, id(current_module)) - if seen_key in seen: - break - seen.add(seen_key) - - e = id_to_event.get(e.python_parent_id, None) - - module_roots = [k for k, v in module_parent_map.items() if v is None] - module_child_map: Dict[int, List[int]] = {} - for child_id, parent_id in module_parent_map.items(): - module_child_map.setdefault(child_id, []) - module_child_map.setdefault(parent_id, []) - module_child_map.get(parent_id).append(child_id) - - # The traverse order is well defined which guarantees that a given topology - # will produce a unique and unambiguous hierarchy. - def append_hierarchy(e_id) -> Module: - e = id_to_event[e_id] - module = Module(e.name, e.module_id) - for idx in module_child_map.get(e_id): - child = append_hierarchy(idx) - module.children.append(child) - return module - - unique_modules: Set[Module] = set() - for e_id in module_roots: - root = append_hierarchy(e_id) - unique_modules.add(root) - - return list(unique_modules) - - -def _aggregate_modules(modules: Iterable[Union[ModuleNode, PLModuleNode]]) -> Dict[Tuple[str, int], ModuleStats]: - """Aggregate the modules based on the name and module_id""" - module_aggs: Dict[Tuple(str, int), ModuleStats] = {} - for m in modules: - key = (m.name, m.module_id) - if key not in module_aggs: - module_aggs[key] = ModuleStats(m.name, m.module_id) - agg = module_aggs[key] - agg.occurences += 1 - - agg.operators += sum(is_operator_node(child) for child in m.children) - - agg.self_host_duration += m.self_host_duration - agg.host_duration += m.end_time - m.start_time - - agg.self_device_duration += m.self_device_duration - agg.device_duration += m.device_duration - - return module_aggs - - -def _get_node_list(tid2tree: Dict[int, OperatorNode], node_class) -> Generator[OperatorNode, None, None]: - """Get all node with node_class from the operator tree""" - def traverse_node(node): - # Check OptimizerNode here because in PytorchLightning PLModuleNode is under OptimizerNoder. - if type(node) not in (ProfilerStepNode, ModuleNode, OperatorNode, OptimizerNode, PLModuleNode, DataLoaderNode): - return - - if isinstance(node, node_class): - yield node - - for child in node.children: - yield from traverse_node(child) - - for _, root in tid2tree.items(): - for child in root.children: - yield from traverse_node(child) - - -def _process_module_statistics( - modules_nodes: Iterable[Union[ModuleNode, PLModuleNode]], - hierarchy: Iterable[Module]) -> List[Stats]: - """Get the module statistics from the ModuleNode(s) and the hierarchy - """ - module_aggs = _aggregate_modules(modules_nodes) - - def process_modules(h_modules: Iterable[Module]): - for m in h_modules: - name = m.name.replace('nn.Module: ', '') - stats = module_aggs.get((m.name, m.module_id)) - - child_stats = list(process_modules(m.children)) - yield Stats( - name, - m.module_id, - stats.occurences, - stats.operators, - stats.host_duration, - stats.self_host_duration, - stats.device_duration, - stats.self_device_duration, - stats.avg_device_duration if stats.avg_device_duration > 0 else stats.avg_host_duration, - child_stats) - - data = sorted(process_modules(hierarchy), key=lambda x: x.name) - return data - - -def get_module_tree(tid2tree: Dict[int, OperatorNode]): - """Get the module tree in timeline""" - from copy import copy - - modules = [] - - def traverse_node(node, parent: Optional[ModuleNode]): - if type(node) not in (ProfilerStepNode, ModuleNode): - return - - if isinstance(node, ModuleNode): - module = copy(node) - # remove the children after copy to keep the module only - module.children = [] - - if parent is None: - modules.append(module) - else: - parent.children.append(module) - parent = module - - for child in node.children: - traverse_node(child, parent) - - for _, root in tid2tree.items(): - for child in root.children: - # since the root node is CallTreeRoot, there is no parent ModuleNode - traverse_node(child, None) - - return modules diff --git a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/node.py b/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/node.py deleted file mode 100644 index 0528491c2..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/node.py +++ /dev/null @@ -1,321 +0,0 @@ -# ------------------------------------------------------------------------- -# Copyright (c) Microsoft Corporation. All rights reserved. -# ------------------------------------------------------------------------- -import sys -from abc import ABC -from typing import List, Optional, Tuple - -from .. import utils -from .tensor_core import TcAllowlist, TcOpAllowlist -from .trace import (DurationEvent, EventTypes, KernelEvent, ModuleEvent, - OperatorEvent, PLProfileEvent, NcclOpNameSet, GlooOpNameSet) - -logger = utils.get_logger() - -ExcludeOpName = ['DataParallel.forward', 'DistributedDataParallel.forward'] - - -class BaseNode(ABC): - def __init__(self, name: str, start_time: int, end_time: int, node_type: str, tid: int, - external_id: Optional[int] = None): - self.name = name - self.start_time = start_time - self.end_time = end_time - self.type = node_type - self.tid = tid - self.external_id = external_id # For consistency check. - - @staticmethod - def get_node_argument(event: DurationEvent): - kwargs = {} - kwargs['name'] = event.name - kwargs['start_time'] = event.ts - kwargs['end_time'] = event.ts + event.duration - kwargs['node_type'] = event.type - kwargs['tid'] = event.tid - - external_id = getattr(event, 'external_id', None) - if external_id is not None: - kwargs['external_id'] = external_id - - return kwargs - - @property - def duration(self) -> int: - if self.start_time is not None and self.end_time is not None: - return self.end_time - self.start_time - else: - return 0 - - -class CommunicationNode(BaseNode): - def __init__(self, input_shape: List[List[int]], input_type: List[str], **kwargs): - super().__init__(**kwargs) - self.input_shape = input_shape - self.input_type = input_type - self.kernel_ranges: List[Tuple[int, int]] = [] - self.real_time_ranges: List[Tuple[int, int]] = [] - self.total_time: int = 0 - self.real_time: int = 0 - self.step_name: str = None - - @classmethod - def create(cls, event: OperatorEvent): - kwargs = BaseNode.get_node_argument(event) - return cls(input_shape=event.input_shape, input_type=event.input_type, **kwargs) - - -class HostNode(BaseNode): - def __init__(self, device_duration: int = 0, **kwargs): - super().__init__(**kwargs) - self.device_duration = device_duration # Total time of Kernel, GPU Memcpy, GPU Memset. - - -class OperatorNode(HostNode): - # Don't use [] as default parameters - def __init__(self, children=None, runtimes=None, input_shape: Optional[List[List[int]]] = None, - input_type: Optional[List[str]] = None, callstack: Optional[str] = None, - self_host_duration: int = 0, self_device_duration: int = 0, **kwargs): - super().__init__(**kwargs) - self.children: List[OperatorNode] = [] if children is None else children # OperatorNode and ProfilerStepNode. - self.runtimes: List[RuntimeNode] = [] if runtimes is None else runtimes # RuntimeNode - self.input_shape = input_shape - self.input_type = input_type - self.callstack = callstack - self.self_host_duration = self_host_duration - self.self_device_duration = self_device_duration - self.tc_eligible = self.name in TcOpAllowlist - self.tc_self_duration = 0 # Time of TC kernels launched by this op excluding its children operators. - self.tc_total_duration = 0 # Time of TC kernels launched by this op including its children operators. - - def fill_stats(self): - def sort_key(x): - if x.start_time and x.end_time: - return x.start_time, -x.end_time - else: - return sys.maxsize, -sys.maxsize - 1 - self.children.sort(key=lambda x: (x.start_time, -x.end_time)) - self.runtimes.sort(key=sort_key) - - for child in self.children: - child.fill_stats() - for rt in self.runtimes: - rt.fill_stats(self) - - self.self_host_duration = self.end_time - self.start_time - for child in self.children: - self.device_duration += child.device_duration - self.self_host_duration -= (child.end_time - child.start_time) - self.tc_total_duration += child.tc_total_duration - # Mark TC eligible as True if any child operator is TC eligible. - if self.type == EventTypes.OPERATOR and not self.tc_eligible and child.tc_eligible: - self.tc_eligible = True - for rt in self.runtimes: - # From PyTorch 1.8 RC1, cpu_self_time does not include runtime's time. - # So here we keep consistent with it. - if rt.end_time is not None and rt.start_time is not None: - self.self_host_duration -= (rt.end_time - rt.start_time) - self.device_duration += rt.device_duration - self.self_device_duration += rt.device_duration - self.tc_self_duration += rt.tc_duration - self.tc_total_duration += rt.tc_duration - if self.type == EventTypes.OPERATOR and not self.tc_eligible and rt.tc_duration > 0: - logger.warning("New Tensor Cores eligible operator found: '{}'!".format(self.name)) - self.tc_eligible = True - - def get_operator_and_kernels(self): - ops: List[OperatorNode] = [] - kernels: List[DeviceNode] = [] - for child in self.children: - child_ops, child_kernels = child.get_operator_and_kernels() - ops.extend(child_ops) - kernels.extend(child_kernels) - for rt in self.runtimes: - kernels.extend(list(rt.get_kernels())) - - if is_operator_node(self): - ops.append(self) - - return ops, kernels - - @classmethod - def create(cls, event: OperatorEvent): - kwargs = BaseNode.get_node_argument(event) - return cls(input_shape=event.input_shape, input_type=event.input_type, callstack=event.callstack, **kwargs) - - -class ProfilerStepNode(OperatorNode): - def __init__(self, **kwargs): - super().__init__(**kwargs) - - -class ModuleNode(OperatorNode): - def __init__(self, module_id: int, python_id: int, python_parent_id: int, **kwargs): - super().__init__(**kwargs) - self.module_id = module_id - self.python_id = python_id - self.python_parent_id = python_parent_id - - def fill_stats(self): - super().fill_stats() - self.self_device_duration += get_chilren_self_device_time(self) - - @classmethod - def create(cls, event: ModuleEvent): - kwargs = BaseNode.get_node_argument(event) - kwargs['module_id'] = event.module_id - kwargs['python_id'] = event.python_id - kwargs['python_parent_id'] = event.python_parent_id - # From the time being, the ModuleNode always have external_id to 0. - # As the result, we need reset the external_id to None to ignore adding the runtime nodes for ModuleNode - kwargs.pop('external_id', None) - return cls(**kwargs) - - -class BackwardNode(OperatorNode): - def __init__(self, **kwargs): - super().__init__(**kwargs) - - def fill_stats(self): - """Override the timestamps and duration for BackwardNode only - """ - self.children.sort(key=lambda x: (x.start_time, -x.end_time)) - self.start_time = self.children[0].start_time - self.end_time = self.children[-1].end_time - - self.self_host_duration = self.end_time - self.start_time - for child in self.children: - self.device_duration += child.device_duration - self.self_host_duration -= (child.end_time - child.start_time) - self.tc_total_duration += child.tc_total_duration - # Mark TC eligible as True if any child operator is TC eligible. - if not self.tc_eligible and child.tc_eligible: - self.tc_eligible = True - - -class PLProfileNode(OperatorNode): - def __init__(self, **kwargs): - super().__init__(**kwargs) - - @classmethod - def create(cls, event: PLProfileEvent): - kwargs = BaseNode.get_node_argument(event) - return cls(**kwargs) - - -class PLModuleNode(OperatorNode): - def __init__(self, module_id: int, **kwargs): - super().__init__(**kwargs) - self.module_id = module_id - - def fill_stats(self): - super().fill_stats() - self.self_device_duration += get_chilren_self_device_time(self) - - @classmethod - def create(cls, event: PLProfileEvent): - kwargs = BaseNode.get_node_argument(event) - kwargs['module_id'] = event.module_id - return cls(**kwargs) - - -class DataLoaderNode(OperatorNode): - def __init__(self, **kwargs): - super().__init__(**kwargs) - - -class OptimizerNode(OperatorNode): - def __init__(self, **kwargs): - super().__init__(**kwargs) - - -class RuntimeNode(HostNode): - def __init__(self, device_nodes: Optional[List['DeviceNode']] = None, **kwargs): - super().__init__(**kwargs) - # One runtime could trigger more than one kernel, such as cudaLaunchCooperativeKernelMultiDevice. - self.device_nodes = sorted(device_nodes, key=lambda x: (x.start_time, -x.end_time)) if device_nodes else None - self.tc_duration: int = 0 # Time summarization of all its launched kernels. - - def fill_stats(self, op_node: OperatorNode = None): - if self.device_nodes: - for device_node in self.device_nodes: - if op_node: - device_node.op_name = op_node.name - device_node.op_tc_eligible = op_node.tc_eligible - device_duration = device_node.end_time - device_node.start_time - self.device_duration += device_duration - self.tc_duration += device_duration if device_node.tc_used else 0 - - def get_kernels(self): - if self.device_nodes: - for d in self.device_nodes: - if d.type == EventTypes.KERNEL: - yield d - - @classmethod - def create(cls, event, device_nodes: Optional[List['DeviceNode']]): - kwargs = BaseNode.get_node_argument(event) - return cls(device_nodes=device_nodes, **kwargs) - - -class DeviceNode(BaseNode): - def __init__(self, - blocks_per_sm: Optional[float] = None, - occupancy: int = None, - grid: Optional[List[int]] = None, - block: Optional[List[int]] = None, - regs_per_thread: int = None, - shared_memory: int = None, - device_id: int = None, **kwargs): - super().__init__(**kwargs) - self.op_tc_eligible = False - self.op_name = None - self.blocks_per_sm = blocks_per_sm - self.occupancy = occupancy - self.grid = grid - self.block = block - self.regs_per_thread = regs_per_thread - self.shared_memory = shared_memory - self.tc_used = self.name in TcAllowlist - self.device_id = device_id - - @classmethod - def create(cls, event: KernelEvent): - kwargs = BaseNode.get_node_argument(event) - if event.type == EventTypes.KERNEL: - kwargs['blocks_per_sm'] = event.blocks_per_sm - kwargs['occupancy'] = event.occupancy - kwargs['grid'] = event.grid - kwargs['block'] = event.block - kwargs['regs_per_thread'] = event.regs_per_thread - kwargs['shared_memory'] = event.shared_memory - kwargs['device_id'] = event.device_id - return cls(**kwargs) - - -def create_operator_node(event: OperatorEvent): - if (event.name.startswith('enumerate(DataLoader)#') and event.name.endswith('.__next__') - or event.name.startswith('enumerate(DataPipe)#')): - return DataLoaderNode.create(event) - elif event.name.startswith('Optimizer.step'): - return OptimizerNode.create(event) - elif event.type == EventTypes.USER_ANNOTATION: - if event.name in GlooOpNameSet or event.name in NcclOpNameSet: - return OperatorNode.create(event) - else: - return None - else: - return OperatorNode.create(event) - - -def is_operator_node(node: BaseNode): - return bool(isinstance(node, OperatorNode) and node.type == EventTypes.OPERATOR and node.name not in ExcludeOpName - and not node.name.startswith("Optimizer.")) # exclude Optimizer.zero_grad - - -def get_chilren_self_device_time(node): - self_device_duration = 0 - for child in node.children: - if is_operator_node(child): - self_device_duration += child.device_duration - return self_device_duration diff --git a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/op_agg.py b/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/op_agg.py deleted file mode 100644 index d6fdb5903..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/op_agg.py +++ /dev/null @@ -1,164 +0,0 @@ -# ------------------------------------------------------------------------- -# Copyright (c) Microsoft Corporation. All rights reserved. -# -------------------------------------------------------------------------- -import sys -from collections import defaultdict -from typing import Callable, Dict, List - -from .. import utils -from .node import DeviceNode, OperatorNode - -logger = utils.get_logger() - - -class OperatorAgg: - def __init__(self, op: OperatorNode): - self.name = op.name - self.input_shape = str(op.input_shape) # Optional - - self.callstacks = set() # Optional - self.calls: int = 0 - self.host_duration: int = 0 - self.device_duration: int = 0 - self.self_host_duration: int = 0 - self.self_device_duration: int = 0 - self.tc_eligible = op.tc_eligible - self.tc_self_duration: int = 0 - self.tc_total_duration: int = 0 - - @property - def tc_self_ratio(self) -> float: - return self.tc_self_duration / self.self_device_duration if self.self_device_duration > 0 else 0 - - @property - def tc_total_ratio(self) -> float: - return self.tc_total_duration / self.device_duration if self.device_duration > 0 else 0 - - -def aggregate_ops(op_list: List[OperatorNode], - keys_func: List[Callable[[OperatorNode], str]]) -> List[Dict[str, OperatorAgg]]: - def aggregate(key_to_agg: Dict[str, OperatorAgg], key: str, op: OperatorNode): - if key not in key_to_agg: - key_to_agg[key] = OperatorAgg(op) - agg = key_to_agg[key] - agg.callstacks.add(op.callstack) - agg.calls += 1 - agg.host_duration += op.duration - agg.device_duration += op.device_duration - agg.self_host_duration += op.self_host_duration - agg.self_device_duration += op.self_device_duration - agg.tc_self_duration += op.tc_self_duration - agg.tc_total_duration += op.tc_total_duration - - agg_dicts: List[Dict[str, OperatorAgg]] = [{} for _ in range(len(keys_func))] - for op in op_list: - for i, key_func in enumerate(keys_func): - key = key_func(op) - aggregate(agg_dicts[i], key, op) - - return agg_dicts - - -class KernelAggByNameOp: - def __init__(self, kernel: DeviceNode, op_name: str): - self.name = kernel.name - self.op_name = op_name - self.grid = kernel.grid - self.block = kernel.block - self.regs_per_thread = kernel.regs_per_thread - self.shared_memory = kernel.shared_memory - - self.calls: int = 0 - self.total_duration: int = 0 - self.min_duration: int = sys.maxsize - self.max_duration: int = 0 - self.blocks_per_sm = 0.0 - self.occupancy = 0.0 - self.tc_used = kernel.tc_used - self.op_tc_eligible = kernel.op_tc_eligible - - @property - def avg_duration(self): - return self.total_duration / self.calls if self.calls > 0 else 0 - - @property - def avg_blocks_per_sm(self) -> float: - return self.blocks_per_sm / self.total_duration if self.total_duration > 0 else 0 - - @property - def avg_occupancy(self) -> float: - return self.occupancy / self.total_duration if self.total_duration > 0 else 0 - - -def aggregate_kernels(kernel_list: List[DeviceNode]) -> List[KernelAggByNameOp]: - name_op_to_agg: Dict[str, KernelAggByNameOp] = {} - for kernel in kernel_list: - dur = kernel.end_time - kernel.start_time - op_name = 'N/A' if kernel.op_name is None else kernel.op_name - key = '###'.join((kernel.name, op_name, - str(kernel.grid), str(kernel.block), - str(kernel.regs_per_thread or '0'), str(kernel.shared_memory or '0'))) - if key not in name_op_to_agg: - name_op_to_agg[key] = KernelAggByNameOp(kernel, op_name) - agg = name_op_to_agg[key] - agg.calls += 1 - agg.total_duration += dur - agg.min_duration = min(agg.min_duration, dur) - agg.max_duration = max(agg.max_duration, dur) - agg.blocks_per_sm += float(kernel.blocks_per_sm or 0) * dur - agg.occupancy += float(kernel.occupancy or 0) * dur - - kernel_list_groupby_name_op = list(name_op_to_agg.values()) - return kernel_list_groupby_name_op - - -class ModuleAggregator: - - def __init__(self): - self.op_list_groupby_name: List[OperatorAgg] = None # For Operator-view. - self.op_list_groupby_name_input: List[OperatorAgg] = None # For Operator-view. - self.kernel_list_groupby_name_op: List[KernelAggByNameOp] = None # For Kernel-view. - self.stack_lists_group_by_name: Dict[str, List[OperatorAgg]] = None - self.stack_lists_group_by_name_input: Dict[str, List[OperatorAgg]] = None - self.ops: List[OperatorNode] = None - - def aggregate(self, tid2tree: Dict[int, OperatorNode]): - # get the operators and kernels recursively by traverse the node tree root. - ops: List[OperatorNode] = [] - kernels: List[DeviceNode] = [] - for root in tid2tree.values(): - root_ops, root_kernels = root.get_operator_and_kernels() - ops.extend(root_ops) - kernels.extend(root_kernels) - - # aggregate both kernels and operators - self.kernel_list_groupby_name_op = aggregate_kernels(kernels) - - keys: List[Callable[[OperatorNode], str]] = [ - lambda x: x.name, - lambda x: '###'.join((x.name, str(x.input_shape))), - lambda x: '###'.join((x.name, str(x.callstack))), - lambda x: '###'.join((x.name, str(x.input_shape), str(x.callstack))) - ] - agg_result = aggregate_ops(ops, keys) - stack_lists_group_by_name: Dict[str, List[OperatorAgg]] = defaultdict(list) - stack_lists_group_by_name_input: Dict[str, List[OperatorAgg]] = defaultdict(list) - for agg in agg_result[2].values(): - if len(agg.callstacks) != 1: - logger.error(f'Incorrect length of callstacks, expected 1 but got {len(agg.callstacks)}') - break - if list(agg.callstacks)[0]: - stack_lists_group_by_name[agg.name].append(agg) - for agg in agg_result[3].values(): - if len(agg.callstacks) != 1: - logger.error(f'Incorrect length of callstacks, expected 1 but got {len(agg.callstacks)}') - break - if list(agg.callstacks)[0]: - key = agg.name + '###' + str(agg.input_shape) - stack_lists_group_by_name_input[key].append(agg) - - self.op_list_groupby_name = list(agg_result[0].values()) - self.op_list_groupby_name_input = list(agg_result[1].values()) - self.stack_lists_group_by_name = stack_lists_group_by_name - self.stack_lists_group_by_name_input = stack_lists_group_by_name_input - self.ops = ops diff --git a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/op_tree.py b/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/op_tree.py deleted file mode 100644 index fe919b29c..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/op_tree.py +++ /dev/null @@ -1,355 +0,0 @@ -# ------------------------------------------------------------------------- -# Copyright (c) Microsoft Corporation. All rights reserved. -# ------------------------------------------------------------------------- -import math -import sys -from collections import defaultdict -from typing import Dict, Iterable, List, Optional, Tuple - -from .. import utils -from .node import (BackwardNode, DeviceNode, ModuleNode, OperatorNode, - ProfilerStepNode, RuntimeNode, is_operator_node) -from .trace import EventTypes - -logger = utils.get_logger() - - -class OpTreeBuilder: - BACKWARD_ROOT_PREFIX = 'autograd::engine::evaluate_function:' - BACKWARD_ACCUMULATE_GRAD = 'autograd::engine::evaluate_function: torch::autograd::AccumulateGrad' - - def __init__(self): - self.main_tid: int = None - self.tid2tree: Dict[int, OperatorNode] = None - - def build_tree(self, - tid2list: Dict[int, List[OperatorNode]], - tid2zero_rt_list: Dict[int, List[RuntimeNode]], - staled_device_nodes: List[DeviceNode], - fwd_bwd_map: Dict[int, int], - is_ascend=False): - """Construct the BackwardNode and replace the original backward nodes - """ - self.tid2tree = self._build_tree(tid2list, tid2zero_rt_list, staled_device_nodes, is_ascend) - - # if could not find any forward/backward association, skip the processing - if not fwd_bwd_map: - logger.debug('there is no any forwarwd backward association, skip processing backward correlation.') - return self.tid2tree - - self._set_main_tid() - - modules, backward_nodes = self._get_modules() - if not modules or not backward_nodes: - return self.tid2tree - - _, ts2parent = OpTreeBuilder._get_node_parents(backward_nodes) - agg_nodes = OpTreeBuilder._group_backward_nodes(backward_nodes) - fwd_bwd_root = self._get_backward_roots(fwd_bwd_map, ts2parent, agg_nodes) - if len(agg_nodes) > 0: - logger.warning('some nodes cannot find forward nodes') - - backward_modules: List[BackwardNode] = [] - for module in modules: - OpTreeBuilder._build_backward_module(module, None, fwd_bwd_root, backward_modules) - OpTreeBuilder._insert_backward_modules(self.tid2tree.get(self.main_tid), backward_modules) - self.tid2tree = {tid: root for tid, root in self.tid2tree.items() if len(root.children) > 0} - - return self.tid2tree - - def _build_tree(self, tid2list: Dict[int, List[OperatorNode]], tid2zero_rt_list, staled_device_nodes, is_ascend): - tid2tree = {} - - for tid, op_list in tid2list.items(): - zero_rt_list = tid2zero_rt_list[tid] if tid in tid2zero_rt_list else [] - # Note that when 2 start_time are equal, the one with bigger end_time should be ahead of the other. - op_list.sort(key=lambda x: (x.start_time, -x.end_time)) - main_tid = any([op.name.startswith('ProfilerStep#') for op in op_list]) - if main_tid: - # only append the staled device nodes into main thread - self.main_tid = op_list[0].tid - root_node = OpTreeBuilder._build_tree_internal(op_list, zero_rt_list, tid, staled_device_nodes, - is_ascend) - else: - root_node = OpTreeBuilder._build_tree_internal(op_list, zero_rt_list, tid, [], is_ascend) - tid2tree[int(tid)] = root_node - - return tid2tree - - def _set_main_tid(self): - if self.main_tid is None and self.tid2tree: - if len(self.tid2tree) == 1: - self.main_tid = next(iter(self.tid2tree)) - else: - # there are multiple tids - backward_tid = self._find_backward_tid() - tid2len = { - tid: root.end_time - root.start_time - for tid, root in self.tid2tree.items() - if tid != backward_tid or backward_tid is None - } - # get the maximum length as the main thread - self.main_tid = max(tid2len, key=tid2len.get) - - def _find_backward_tid(self): - for root in self.tid2tree.values(): - for child in root.children: - if child.name.startswith(OpTreeBuilder.BACKWARD_ROOT_PREFIX): - return child.tid - - return None - - @staticmethod - def _build_tree_internal(host_node_list, zero_rt_list, tid, staled_device_nodes, is_ascend): - """host_node_list: list of OperatorNode and ProfilerStepNode. - zero_rt_list: list of RuntimeNode with external_id=0.""" - - def build_tree_relationship(host_node_list: Iterable[OperatorNode], zero_rt_list, staled_device_nodes): - dummpy_rt: List[RuntimeNode] = [] - if staled_device_nodes: - # Note: Although kernels of this dummy runtime is put under main thread's tree, - # we don't know which thread launches them. - dummpy_rt.append(RuntimeNode( - name='dummy', - start_time=None, - end_time=None, - node_type=EventTypes.RUNTIME, - tid=0, - device_nodes=staled_device_nodes)) - dummpy_rt[0].fill_stats() - node_stack: List[OperatorNode] = [] - root_node = OperatorNode( - name='CallTreeRoot', - start_time=-sys.maxsize - 1, - end_time=sys.maxsize, - node_type=EventTypes.PYTHON, - tid=tid, - runtimes=zero_rt_list + dummpy_rt) # Give the list of RuntimeNode with external_id=0 to root node. - node_stack.append(root_node) - for node in host_node_list: - while True: # break loop when the node is inserted. - tail_node = node_stack[-1] - if node.start_time < tail_node.end_time: - if node.end_time <= tail_node.end_time or ( - is_ascend and math.isclose(node.end_time, tail_node.end_time, rel_tol=1)): - tail_node.children.append(node) - node_stack.append(node) - else: - logger.error('Error in input data: ranges on the same thread should not intersect!' - 'Father:({},{},{}) Child:({},{},{})' - .format(tail_node.name, tail_node.start_time, tail_node.end_time, - node.name, node.start_time, node.end_time)) - break - else: - node_stack.pop() - return root_node - - # Merge the consecutive calls to same function into one. - # Just follow the same pattern in torch/autograd/profiler.py, - # EventList._remove_dup_nodes - def remove_dup_nodes(node: OperatorNode): - if node.type == EventTypes.RUNTIME: - return - if len(node.children) == 1: - child = node.children[0] - if node.name == child.name and node.type == EventTypes.OPERATOR and child.type == EventTypes.OPERATOR: - node.children = child.children - node.runtimes = child.runtimes # Keep consistent with autograd profiler. - remove_dup_nodes(node) # This node may have to merge with child's child. - return - - for child in node.children: - remove_dup_nodes(child) - - root_node = build_tree_relationship(host_node_list, zero_rt_list, staled_device_nodes) - remove_dup_nodes(root_node) - root_node.fill_stats() - - # replace the root_node start_time/end_time - root_node.start_time = next((child.start_time for child in root_node.children - if child.start_time is not None), None) - root_node.end_time = next((child.end_time for child in reversed(root_node.children) - if child.end_time is not None), None) - return root_node - - def _get_modules(self) -> Tuple[List[ModuleNode], List[OperatorNode]]: - """Get the ModuleNodes and backward root nodes - If there are any ModuleNodes, the backward roots will be removed from the tree - so that later a new BackwardNode will be replaced. - """ - modules: List[ModuleNode] = [] - backward_nodes: Dict[OperatorNode, List[OperatorNode]] = defaultdict(list) - - def traverse_node(parent, node: OperatorNode): - if isinstance(node, ModuleNode): - modules.append(node) - elif isinstance(node, ProfilerStepNode): - for child in node.children: - traverse_node(node, child) - else: - if node.name.startswith(OpTreeBuilder.BACKWARD_ROOT_PREFIX): - backward_nodes[parent].append(node) - else: - pass - - for root in self.tid2tree.values(): - for child in root.children: - traverse_node(root, child) - - if modules: - backward_nodes_flatten: List[OperatorNode] = [] - # only remove the backward nodes when the module information exist - for p, nodes in backward_nodes.items(): - p.children = [child for child in p.children if child not in nodes] - backward_nodes_flatten.extend(nodes) - - return modules, backward_nodes_flatten - else: - return None, None - - @staticmethod - def _get_node_parents(nodes: Iterable[OperatorNode]): - """Get the child->parent relationship for these nodes""" - ts_to_node: Dict[int, OperatorNode] = {} - ts_to_parent: Dict[int, OperatorNode] = {} - - def traverse_node(node: OperatorNode): - if node.start_time not in ts_to_node: - ts_to_node[node.start_time] = node - for child in node.children: - if child.start_time not in ts_to_parent: - ts_to_parent[child.start_time] = node - traverse_node(child) - - for node in nodes: - traverse_node(node) - return ts_to_node, ts_to_parent - - @staticmethod - def _group_backward_nodes(nodes: Iterable[OperatorNode]) -> Dict[OperatorNode, List[OperatorNode]]: - """All nodes are backward nodes startswith autograd::engine::evaluate_function. - If one node's name is autograd::engine::evaluate_function: torch::autograd::AccumulateGrad, - it should be grouped with previous normal backward node. Otherwise, a new backward node should be started - """ - grouped_bwd_nodes: List[List[OperatorNode]] = [] - for node in nodes: - if node.name == OpTreeBuilder.BACKWARD_ACCUMULATE_GRAD: - grouped_bwd_nodes[-1].append(node) - else: - grouped_bwd_nodes.append([node]) - - # return the root backward node -> aggregated backward nodes array - # if there is no any AccumulateGrad accompanied with it, then the key:value is itself. - return {nodes[0]: nodes for nodes in grouped_bwd_nodes} - - @staticmethod - def _get_backward_roots(fwd_bwd_map: Dict[int, int], - ts2parent: Dict[int, OperatorNode], - backward_nodes: Dict[OperatorNode, List[OperatorNode]]) -> Dict[int, List[OperatorNode]]: - if not fwd_bwd_map: - return None - - fwd_to_bwdroot: Dict[int, List[OperatorNode]] = {} - for fwd, bwd in fwd_bwd_map.items(): - parent = ts2parent.get(bwd) - while parent is not None and not parent.name.startswith(OpTreeBuilder.BACKWARD_ROOT_PREFIX): - parent = ts2parent.get(parent.start_time) - - if parent: - fwd_to_bwdroot[fwd] = backward_nodes.pop(parent) - else: - logger.warning('parent is None for', bwd) - - return fwd_to_bwdroot - - @staticmethod - def _build_backward_module(node: ModuleNode, - parent: Optional[BackwardNode], - fwd_bwd_map: Dict[int, List[OperatorNode]], - result: List[BackwardNode]): - """Construct the backward module from root (node argument) and - insert it into result array if there is no any parent associated with it. - """ - if not fwd_bwd_map: - logger.warning('The forward backward map is empty. The backward construction is skipped.') - return - - if isinstance(node, ModuleNode): - backward_node = BackwardNode(name=node.name + '.backward', start_time=None, end_time=None, - node_type='backward', tid=0) - if parent is None: - result.append(backward_node) - else: - parent.children.append(backward_node) - parent = backward_node - - for child in node.children: - if parent: - if is_operator_node(child): - bwd_ops = fwd_bwd_map.get(child.start_time) - if bwd_ops: - parent.children.extend(bwd_ops) - - OpTreeBuilder._build_backward_module(child, parent, fwd_bwd_map, result) - - if isinstance(node, ModuleNode) and parent and parent.children: - parent.fill_stats() - parent.tid = parent.children[0].tid - - @staticmethod - def _insert_backward_modules(root: OperatorNode, backward_modules: List[BackwardNode]): - backward_modules.sort(key=lambda x: (x.start_time, -x.end_time)) - - # each item is (parent_node, child_index) that it is visiting. - node_stack = [] - module_index = 0 - child_index = 0 - current_node = root - - staled_modules = [] - - while module_index < len(backward_modules): - module = backward_modules[module_index] - if current_node is None: - # ignore all remaining modules - staled_modules.append(module) - module_index += 1 - continue - - if module.end_time < current_node.start_time: - staled_modules.append(module) - module_index += 1 - continue - elif module.start_time > current_node.end_time: - if node_stack: - # pop parent node and update the child_index accordingly. - current_node, child_index = node_stack.pop() - child_index += 1 - else: - # if there is not item in stack, set it to None - current_node = None - continue - - while child_index < len(current_node.children): - if module.end_time < current_node.children[child_index].start_time: - # if current module is before next child, - # we will break the search and keep the child_index not change. - # As the result, the module will be treated as child of 'current_node' - # So that next time we can continue from here. - # there is no any child contains the record.timestamp - # child_find is False at this case. - break - elif module.start_time >= current_node.children[child_index].end_time: - child_index += 1 - else: - # current children contains the record - node_stack.append((current_node, child_index)) - current_node = current_node.children[child_index] - child_index = 0 - - # when code execute here, it means the current_node will be the parent of backward module - # Add the module into current_node - current_node.children.insert(child_index, module) - # since the children number is increased by 1, we need increment the child_index. - child_index += 1 - module_index += 1 diff --git a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/overall_parser.py b/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/overall_parser.py deleted file mode 100644 index c646a33b8..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/overall_parser.py +++ /dev/null @@ -1,114 +0,0 @@ -# ------------------------------------------------------------------------- -# Copyright (c) Microsoft Corporation. All rights reserved. -# -------------------------------------------------------------------------- -from typing import List, Tuple - -from .. import utils -from .event_parser import ProfileRole -from .range_utils import (get_ranges_sum, intersection_ranges_lists, - merge_ranges, subtract_ranges_lists) - -logger = utils.get_logger() - - -class OverallParser(object): - class Costs: - def __init__(self, costs: List[float] = None): - # the cost length is len(ProfileRole) - if costs is None: - self.costs = [0.] * len(ProfileRole) - else: - self.costs = costs - - @classmethod - def create_from_statistics(cls, statistics: 'OverallParser.Statistics', total_duration: int): - costs = [0.] * len(ProfileRole) - for i, cost_range in enumerate(statistics.cost_ranges): - costs[i] = get_ranges_sum(cost_range) - costs[ProfileRole.Total] = total_duration - return cls(costs) - - class Statistics: - def __init__(self, cost_ranges: List[List[Tuple[int, int]]]): - if not cost_ranges: - raise ValueError('the cost ranges is None') - - self.cost_ranges = cost_ranges - - @classmethod - def create_from_range(cls, steps: List[Tuple[int, int]], role_ranges: List[List[Tuple[int, int]]]): - if len(role_ranges) != ProfileRole.Total - 1: - return cls([]) - - cost_ranges: List[List[Tuple[int, int]]] = [] - slots: List[Tuple[int, int]] = [] - for role in role_ranges: - if slots: - inter_range = intersection_ranges_lists(slots, role) - else: - inter_range = role - slots = merge_ranges(list(steps)) - cost_ranges.append(inter_range) - slots = subtract_ranges_lists(slots, inter_range) - # The last one is ProfileRole.Other - cost_ranges.append(slots) - - return cls(cost_ranges) - - def intersection_with_step(self, step: Tuple[int, int]): - cost_ranges: List[List[Tuple[int, int]]] = [] - step = [step] - for cost_range in self.cost_ranges: - cost_ranges.append(intersection_ranges_lists(step, cost_range)) - - return OverallParser.Statistics(cost_ranges) - - class StepCommunicationCosts: - def __init__(self): - self.computation: int = 0 - self.communication: int = 0 - self.overlap: int = 0 - self.other: int = 0 - - def __init__(self): - self.steps_costs: List[OverallParser.Costs] = [] - self.avg_costs = OverallParser.Costs() - self.communication_overlap: List[OverallParser.StepCommunicationCosts] = [] - - def aggregate(self, steps: List[Tuple[int, int]], role_ranges: List[List[Tuple[int, int]]]): - logger.debug('Overall, statistics') - if len(steps) <= 0: - logger.error('Invalid steps number of 0') - return - global_stats = OverallParser.Statistics.create_from_range(steps, role_ranges) - if role_ranges[ProfileRole.Kernel]: - comm_comp_overlap = intersection_ranges_lists( - role_ranges[ProfileRole.Kernel], role_ranges[ProfileRole.Communication]) - else: - comm_comp_overlap = intersection_ranges_lists( - role_ranges[ProfileRole.CpuOp], role_ranges[ProfileRole.Communication]) - - logger.debug('Overall, aggregation') - for i, step in enumerate(steps): - steps_stat = global_stats.intersection_with_step(step) - self.steps_costs.append(OverallParser.Costs.create_from_statistics(steps_stat, step[1] - step[0])) - for cost_index, _ in enumerate(self.avg_costs.costs): - self.avg_costs.costs[cost_index] += self.steps_costs[i].costs[cost_index] - - comm_costs = OverallParser.StepCommunicationCosts() - comm_costs.overlap = get_ranges_sum(intersection_ranges_lists([step], comm_comp_overlap)) - if role_ranges[ProfileRole.Kernel]: - comm_costs.computation = get_ranges_sum( - intersection_ranges_lists([step], role_ranges[ProfileRole.Kernel])) - else: - comm_costs.computation = get_ranges_sum( - intersection_ranges_lists([step], role_ranges[ProfileRole.CpuOp])) - comm_costs.communication = get_ranges_sum( - intersection_ranges_lists([step], role_ranges[ProfileRole.Communication])) - comm_costs.other = self.steps_costs[i].costs[ProfileRole.Total] +\ - comm_costs.overlap - comm_costs.computation - comm_costs.communication - self.communication_overlap.append(comm_costs) - - valid_steps = len(steps) - for i, _ in enumerate(self.avg_costs.costs): - self.avg_costs.costs[i] /= valid_steps diff --git a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/range_utils.py b/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/range_utils.py deleted file mode 100644 index 7344762a6..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/range_utils.py +++ /dev/null @@ -1,190 +0,0 @@ -# ------------------------------------------------------------------------- -# Copyright (c) Microsoft Corporation. All rights reserved. -# ------------------------------------------------------------------------- -from typing import List, Tuple - - -# src_ranges: item of (start_time, end_time, value) -def merge_ranges_with_value(src_ranges): - from collections import namedtuple - from enum import IntEnum - - class EndpointTypes(IntEnum): - START = 0 - END = 1 - - EndPoint = namedtuple('EndPoint', ['time', 'pt_type', 'value']) - - merged_ranges = [] - if len(src_ranges) > 0: - # Build tuple of (time, type, value) - endpoints: List[EndPoint] = [] - for r in src_ranges: - endpoints.append(EndPoint(r[0], EndpointTypes.START, r[2])) - endpoints.append(EndPoint(r[1], EndpointTypes.END, r[2])) - endpoints.sort(key=lambda x: [x.time, int(x.pt_type)]) # Make START in front of END if equal on time. - - last_endpoint_time = endpoints[0].time - last_value = endpoints[0].value - for i in range(1, len(endpoints)): - ep = endpoints[i] - if ep.time > last_endpoint_time and last_value > 0.0: - approximated_sm_efficiency = min(last_value, 1.0) - merged_ranges.append((last_endpoint_time, ep.time, approximated_sm_efficiency)) - last_endpoint_time = ep.time - if ep.pt_type == EndpointTypes.START: - last_value += ep.value - else: - last_value -= ep.value - - return merged_ranges - - -# range_list1 item is length 3. range_list2 item is length 2. -# Reture value's item is length 3. -def intersection_ranges_lists_with_value(range_list1, range_list2) -> List[Tuple[int, int, int]]: - range_list_dst = [] - if len(range_list1) == 0 or len(range_list2) == 0: - return range_list_dst - r1 = range_list1[0] - r2 = range_list2[0] - i1 = i2 = 0 - while i1 < len(range_list1): - if i2 == len(range_list2): - break - elif r2[1] <= r1[0]: - r2, i2 = pop_list(range_list2, i2) - elif r2[0] <= r1[0] and r2[1] < r1[1]: - if r2[1] > r1[0]: - range_list_dst.append((r1[0], r2[1], r1[2])) - r1 = (r2[1], r1[1], r1[2]) - r2, i2 = pop_list(range_list2, i2) - elif r2[0] <= r1[0]: - if r2[1] >= r1[1]: - range_list_dst.append(r1) - r2 = (r1[1], r2[1]) - r1, i1 = pop_list(range_list1, i1) - elif r2[1] < r1[1]: - if r2[0] > r1[0]: - range_list_dst.append((r2[0], r2[1], r1[2])) - r1 = (r2[1], r1[1], r1[2]) - r2, i2 = pop_list(range_list2, i2) - elif r2[0] < r1[1]: - if r2[1] >= r1[1]: - range_list_dst.append((r2[0], r1[1], r1[2])) - r2 = (r1[1], r2[1]) - r1, i1 = pop_list(range_list1, i1) - else: - if r2[0] >= r1[1]: - r1, i1 = pop_list(range_list1, i1) - return range_list_dst - - -def subtract_ranges_lists(range_list1: List[Tuple[int, int]], - range_list2: List[Tuple[int, int]]) -> List[Tuple[int, int]]: - range_list_dst = [] - if len(range_list1) == 0: - return range_list_dst - if len(range_list2) == 0: - range_list_dst = list(range_list1) - return range_list_dst - r1 = range_list1[0] - r2 = range_list2[0] - i1 = i2 = 0 - while i1 < len(range_list1): - if i2 == len(range_list2): - range_list_dst.append(r1) - r1, i1 = pop_list(range_list1, i1) - elif r2[1] <= r1[0]: - r2, i2 = pop_list(range_list2, i2) - elif r2[0] <= r1[0] and r2[1] < r1[1]: - r1 = (r2[1], r1[1]) - r2, i2 = pop_list(range_list2, i2) - elif r2[0] <= r1[0]: - if r2[1] >= r1[1]: - r2 = (r1[1], r2[1]) - r1, i1 = pop_list(range_list1, i1) - elif r2[0] < r1[1]: - if r2[0] > r1[0]: - range_list_dst.append((r1[0], r2[0])) - r1 = (r2[0], r1[1]) - else: - if r2[0] >= r1[1]: - range_list_dst.append(r1) - r1, i1 = pop_list(range_list1, i1) - return range_list_dst - - -def intersection_ranges_lists(range_list1: List[Tuple[int, int]], - range_list2: List[Tuple[int, int]]) -> List[Tuple[int, int]]: - range_list_dst = [] - if len(range_list1) == 0 or len(range_list2) == 0: - return range_list_dst - r1 = range_list1[0] - r2 = range_list2[0] - i1 = i2 = 0 - while i1 < len(range_list1): - if i2 == len(range_list2): - break - elif r2[1] <= r1[0]: - r2, i2 = pop_list(range_list2, i2) - elif r2[0] <= r1[0] and r2[1] < r1[1]: - if r2[1] > r1[0]: - range_list_dst.append((r1[0], r2[1])) - r1 = (r2[1], r1[1]) - r2, i2 = pop_list(range_list2, i2) - elif r2[0] <= r1[0]: - if r2[1] >= r1[1]: - range_list_dst.append(r1) - r2 = (r1[1], r2[1]) - r1, i1 = pop_list(range_list1, i1) - elif r2[1] < r1[1]: - if r2[0] > r1[0]: - range_list_dst.append(r2) - r1 = (r2[1], r1[1]) - r2, i2 = pop_list(range_list2, i2) - elif r2[0] < r1[1]: - if r2[1] >= r1[1]: - range_list_dst.append((r2[0], r1[1])) - r2 = (r1[1], r2[1]) - r1, i1 = pop_list(range_list1, i1) - else: - if r2[0] >= r1[1]: - r1, i1 = pop_list(range_list1, i1) - return range_list_dst - - -def get_ranges_sum(ranges: List[Tuple[int, int]]) -> int: - total: int = 0 - for item in ranges: - total += (item[1] - item[0]) - return total - - -def pop_list(range_list, index): - next_index = index + 1 - if next_index >= len(range_list): - return None, len(range_list) - next_item = range_list[next_index] - return next_item, next_index - - -def merge_ranges(src_ranges, is_sorted=False) -> List[Tuple[int, int]]: - if not src_ranges: - # return empty list if src_ranges is None or its length is zero. - return [] - - if not is_sorted: - src_ranges.sort(key=lambda x: x[0]) - - merged_ranges = [] - merged_ranges.append(src_ranges[0]) - for src_id in range(1, len(src_ranges)): - src_range = src_ranges[src_id] - if src_range[1] > merged_ranges[-1][1]: - if src_range[0] <= merged_ranges[-1][1]: - merged_ranges[-1] = (merged_ranges[-1][0], src_range[1]) - else: - merged_ranges.append((src_range[0], src_range[1])) - - return merged_ranges diff --git a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/run_generator.py b/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/run_generator.py deleted file mode 100644 index 111dc34e8..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/run_generator.py +++ /dev/null @@ -1,1402 +0,0 @@ -# ------------------------------------------------------------------------- -# Copyright (c) Microsoft Corporation. All rights reserved. -# -# Copyright(c) 2023 Huawei Technologies. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# Modifications: Add visualization of PyTorch Ascend profiling. -# -------------------------------------------------------------------------- -import csv -import json -import re -import io as sysio -from collections import OrderedDict, defaultdict -from json import JSONDecodeError -from typing import Dict, Iterable, List -import numpy as np - -from .. import consts, utils, io -from ..run import DistributedRunProfile, RunProfile -from .data import DistributedRunProfileData, RunProfileData -from .module_op import aggegate_module_view, aggegate_pl_module_view -from .op_agg import KernelAggByNameOp, OperatorAgg -from .overall_parser import ProfileRole -from ..utils import Canonicalizer - -logger = utils.get_logger() - - -class RunGenerator(object): - def __init__(self, worker, span, profile_data: RunProfileData, device_target="GPU"): - self.worker = worker - self.span = span - self.profile_data = profile_data - self.statistic_data = {} - self.accelerator_data = {} - self.device_target = device_target - self.component_curve_data = {} - self.process_data = {} - - @staticmethod - def check_overlap_data(title): - # csv: step / compute time / communication_not_overlap / overlap / communication / free time - length = len(title) - if length < 5: - return [] - key = ["computing", "overlapped", "communication(not overlapped)", "free"] - get_key = list() - for j in key: - for i in range(length): - if j == title[i]: - get_key.append(i) - if len(get_key) < 4: - return [] - return get_key - - @staticmethod - def get_table_head(name: str, input_shape: str, call_stack: str, value: list): - if name is None: - return {} - temp = { - 'name': name, 'calls': 0, 'host_self_duration': 0, - 'host_total_duration': 0, 'device_self_duration': 0, 'device_total_duration': 0, - 'tc_self_ratio': 0, 'tc_total_ratio': 0, 'tc_eligible': 'Yes' - } - if input_shape is not None: - temp['input_shape'] = input_shape - if call_stack is not None: - temp['call_stack'] = call_stack - else: - temp['has_call_stack'] = False - else: - if call_stack is not None: - temp['call_stack'] = call_stack - else: - temp['has_call_stack'] = False - for vl in iter(value): - if 'has_call_stack' in temp and vl[2]: - temp['has_call_stack'] = True - temp['calls'] += 1 - temp['host_self_duration'] = round(temp['host_self_duration'] + vl[3], 2) - temp['host_total_duration'] = round(temp['host_total_duration'] + vl[4], 2) - temp['device_self_duration'] = round(temp['device_self_duration'] + vl[5], 2) - temp['device_total_duration'] = round(temp['device_total_duration'] + vl[6], 2) - temp['tc_self_ratio'] = round(temp['tc_self_ratio'] + vl[7], 2) - temp['tc_total_ratio'] = round(temp['tc_total_ratio'] + vl[8], 2) - temp['tc_eligible'] = 'Yes' if temp['tc_self_ratio'] > 0 or temp['tc_total_ratio'] > 0 else 'No' - temp['tc_self_ratio'] = 0 if temp['device_self_duration'] == 0 \ - else round(temp['tc_self_ratio'] / temp['device_self_duration'] * 100, 2) - temp['tc_total_ratio'] = 0 if temp['device_total_duration'] == 0 \ - else round(temp['tc_total_ratio'] / temp['device_total_duration'] * 100, 2) - return temp - - @staticmethod - def get_wait_table_by_ops(op, ops): - total_trans = 0 - total_synchronize = 0 - for key, data in op.items(): - if str(key) == "Total Op Info" and data.get("Communication Time Info"): - total_trans += float(data.get("Communication Time Info").get("Transit Time(ms)")) - total_synchronize += float(data.get("Communication Time Info").get("Synchronization Time(ms)")) - continue - k = re.sub(r'[0-9]+', ' ', key).split(" ")[0] - if k not in ops: - ops[k] = [0, 0, 0, 0] - ops[k][0] += 1 - for _, band in data.get("Communication Bandwidth Info").items(): - ops[k][1] += float(band.get("Transit Size(MB)")) - if data.get("Communication Time Info") is not None: - ops[k][2] += data.get("Communication Time Info").get("Elapse Time(ms)") - ops[k][3] += data.get("Communication Time Info").get("Transit Time(ms)") - return total_trans, total_synchronize - - @staticmethod - def trans_shape(shape: str): - result = list() - if ';' not in shape: - result.append('[' + shape.strip() + ']') - return '[' + ', '.join(result) + ']' - if len(shape.strip()) <= 1: - result.append('[]') - return '[' + ', '.join(result) + ']' - shape_spl = shape.split("\n") - for shape_div in iter(shape_spl): - result.append('[' + str(shape_div.replace(';', '')) + ']') - return '[' + ', '.join(result) + ']' - - @staticmethod - def get_process_peaks_and_devices_type(process_data: dict, memory_metric: str): - devices_type = [] - peaks = {} - for device in process_data: - devices_type.append(device) - reserved_list = process_data.get(device).get('Allocated') - if reserved_list is not None: - max_reserved = 0 - for array_value in reserved_list: - max_reserved = max(array_value[1], max_reserved) - peaks[device] = f'Peak Memory Usage: {max_reserved:.1f}{memory_metric}' - return devices_type, peaks - - @staticmethod - def get_pta_ge_peaks_and_devices_type(process_data: dict, memory_metric: str): - devices_type = [] - peaks = {} - for device in process_data: - devices_type.append(device) - peaks[device] = 'Reserved Peak Memory Usage:' - for component in process_data.get(device): - max_reserved = 0 - for array_value in process_data.get(device).get(component): - max_reserved = max(array_value[2], max_reserved) - peaks[device] += f' {component}-{max_reserved:.1f}{memory_metric} |' - return devices_type, peaks - - @staticmethod - def check_csv_columns(columns: list, column_idxs: dict): - column_exist_count = 0 - for idx, column in enumerate(columns): - if column in column_idxs: - column_idxs[column] = idx - column_exist_count += 1 - return column_idxs.values(), column_exist_count - - @staticmethod - def get_csv_data(path: str): - if path is None: - return [] - datas = [] - with open(path, encoding='utf-8-sig') as f: - for row in csv.reader(f, skipinitialspace=True): - datas.append(row) - return datas - - def generate_run_profile(self): - profile_run = RunProfile(self.worker, self.span) - profile_run.is_pytorch_lightning = self.profile_data.is_pytorch_lightning - profile_run.has_runtime = self.profile_data.has_runtime - profile_run.has_kernel = self.profile_data.has_kernel - profile_run.has_communication = self.profile_data.has_communication - profile_run.has_memcpy_or_memset = self.profile_data.has_memcpy_or_memset - profile_run.profiler_start_ts = self.profile_data.profiler_start_ts - profile_run.device_target = self.device_target - - if self.device_target != 'Ascend': - profile_run.views.append(consts.OVERALL_VIEW) - profile_run.overview = self._generate_overview() - - profile_run.views.append(consts.OP_VIEW) - profile_run.operation_pie_by_name = self._generate_op_pie() - profile_run.operation_table_by_name = self._generate_op_table(self.profile_data.op_list_groupby_name) - profile_run.operation_stack_by_name = self._generate_op_table_for_stack(False) - profile_run.operation_pie_by_name_input = self._generate_op_pie(True) - profile_run.operation_table_by_name_input = self._generate_op_table( - self.profile_data.op_list_groupby_name_input, True) - profile_run.operation_stack_by_name_input = self._generate_op_table_for_stack(True) - - if self.profile_data.has_kernel: - profile_run.views.append(consts.KERNEL_VIEW) - profile_run.kernel_table = self._generate_kernel_table_gpu() - profile_run.kernel_op_table = self._generate_kernel_op_table_gpu() - profile_run.kernel_pie = self._generate_kernel_pie_gpu() - profile_run.tc_pie = self._generate_tc_pie_gpu() - - if self.profile_data.memory_snapshot: - profile_run.views.append(consts.MEMORY_VIEW) - profile_run.memory_snapshot = self.profile_data.memory_snapshot - - profile_run.gpu_metrics = self.profile_data.gpu_metrics_parser.get_gpu_metrics() - - gpu_infos = {gpu_id: RunGenerator.get_gpu_info(self.profile_data.device_props, gpu_id) - for gpu_id in self.profile_data.gpu_metrics_parser.gpu_ids} - gpu_infos = {gpu_id: gpu_info for gpu_id, gpu_info in gpu_infos.items() if gpu_info is not None} - - profile_run.gpu_summary, profile_run.gpu_tooltip = \ - self.profile_data.gpu_metrics_parser.get_gpu_metrics_data_tooltip( - gpu_infos, self.profile_data.tc_ratio) - - profile_run.pl_tid2tree = self.profile_data.pl_tid2tree - - profile_run.module_stats = aggegate_module_view(self.profile_data.tid2tree, self.profile_data.events) - profile_run.pl_module_stats = aggegate_pl_module_view(self.profile_data.tid2tree, self.profile_data.events) - if profile_run.is_pytorch_lightning and profile_run.pl_module_stats: - profile_run.views.append(consts.LIGHTNING_VIEW) - elif profile_run.module_stats: - profile_run.views.append(consts.MODULE_VIEW) - else: - if self.profile_data.has_operator_view: - profile_run.views.append(consts.OP_VIEW) - profile_run.operation_pie_by_name = self._get_operator_pie() - profile_run.operation_table_by_name = self._get_operator_table_by_name() - profile_run.operation_stack_by_name = self._get_call_stack_by_name() - profile_run.operation_pie_by_name_input = self._get_operator_pie(True) - profile_run.operation_table_by_name_input = self._get_operator_table_by_name(True) - profile_run.operation_stack_by_name_input = self._get_call_stack_by_name_shapes(True) - - if self.profile_data.has_kernel: - profile_run.views.append(consts.KERNEL_VIEW) - profile_run.kernel_table = self._generate_kernel_table_npu() - profile_run.kernel_op_table = self._generate_kernel_op_table_npu() - profile_run.kernel_pie = self._generate_kernel_pie_npu() - profile_run.tc_pie = self._generate_tc_pie_npu() - - if self.profile_data.has_memory: - profile_run.views.append(consts.MEMORY_VIEW) - profile_run.memory_div_curve = None - self.process_data, self.component_curve_data = self._handle_memory_data() - profile_run.memory_all_curve = self._get_memory_all_curve() - peak_memory_events = self._handle_memory_component() - profile_run.memory_events = self._get_memory_event(peak_memory_events) - - if self.profile_data.has_communication: - profile_run.step_to_overlap = self._npu_get_overlap() - profile_run.step_to_wait, profile_run.comm_op = self._npu_get_wait_table() - - profile_run.tid2tree = self.profile_data.tid2tree - if self.profile_data.has_trace: - profile_run.views.append(consts.TRACE_VIEW) - profile_run.trace_file_path = self.profile_data.trace_file_path - - return profile_run - - def _npu_get_overlap(self): - path = self.profile_data.distributed_csv_path - overlap_by_steps: Dict[str, List[float]] = OrderedDict() - data = RunGenerator.get_csv_data(path) - if len(data) <= 1: - return overlap_by_steps - title = [x.lower() for x in data[0]] - title_name = RunGenerator.check_overlap_data(title) - if not title_name: - logger.error(f"Incomplete content of CSV file {path}.") - return overlap_by_steps - - for idx, step in enumerate(data[1:]): - try: - key = step[0] - if key == '': - key = 'all' - overlap = [ - float(step[int(title_name[0])]), float(step[int(title_name[1])]), - float(step[int(title_name[2])]), float(step[int(title_name[3])]) - ] - if key in overlap_by_steps: - overlap_by_steps[key] = list(np.add(overlap, overlap_by_steps[key])) - else: - overlap_by_steps[key] = list(overlap) - except (ValueError, IndexError): - logger.error(f'File "{path}" has wrong data format in row {idx + 2} and will skip it.') - return overlap_by_steps - - def _npu_get_wait_table(self): - path = self.profile_data.communication_json_path - if not io.exists(path): - raise FileNotFoundError(path) - data = io.read(path) - wait_by_step: Dict[str, Dict[str, float]] = OrderedDict() - table_ops: Dict[str, List[float]] = OrderedDict() - try: - communication_json = json.loads(data, strict=False) - except JSONDecodeError as e: - try: - communication_json = json.loads(data, strict=False) - except JSONDecodeError: - with sysio.StringIO() as fout: - str_data = data.decode('utf-8') - # only replace the N/A without surrounding double quote - fout.write(re.sub(r'(?', abs(float(ls[size_idx])), - round((float(ls[allocation_idx]) - self.profile_data.profiler_start_ts) / 1000, 3) if ls[ - allocation_idx] else None, - round((float(ls[release_idx]) - self.profile_data.profiler_start_ts) / 1000, 3) if ls[ - release_idx] else None, - round(float(ls[duration_idx]) / 1000, 3) if ls[duration_idx] else None - ] - display_datas[device_type].append(nums) - except ValueError: - logger.error(f'File "{path}" has wrong data format in row {idx + 2} and will skip it.') - table['rows'] = display_datas - for name in display_datas: - devices_type.append(name) - table['metadata'].update({'default_device': devices_type[0]}) - return { - 'operator': table, - 'component': peak_memory_events - } - - def _get_memory_all_curve(self): - time_metric: str = 'ms' - memory_metric: str = 'MB' - cano = Canonicalizer(time_metric, memory_metric) - process_devices_type, process_peaks = RunGenerator.get_process_peaks_and_devices_type(self.process_data, - memory_metric) - total_result = { - 'metadata': { - 'devices': process_devices_type, - 'default_device': process_devices_type[0] if len(process_devices_type) > 0 else '', - 'peaks': process_peaks, - 'totals': {}, - 'first_ts': 0, - 'time_metric': cano.time_metric, - 'memory_metric': cano.memory_metric, - 'time_factor': cano.time_factor, - 'memory_factor': cano.memory_factor, - }, - 'columns': defaultdict(list), - 'rows': self.process_data - } - for device in process_devices_type: - if self.process_data.get(device).get('Allocated') is not None and self.process_data.get(device).get( - 'Reserved') is not None: - total_result['columns'][device].append( - {'name': f'Allocated ({cano.memory_metric})', 'type': 'number', 'tooltip': 'PTA+GE memory in use.'}) - total_result['columns'][device].append( - {'name': f'Reserved ({cano.memory_metric})', 'type': 'number', - 'tooltip': 'PTA+GE reserved memory by allocator, both used and unused.'}) - if len(total_result['columns'][device]) > 0: - total_result['columns'][device].insert(0, {'name': f'Time ({cano.time_metric})', 'type': 'number', - 'tooltip': 'Time since profiler starts.'}) - pta_ge_devices_type, pta_ge_peaks = RunGenerator.get_pta_ge_peaks_and_devices_type(self.component_curve_data, - memory_metric) - component_curve_result = { - 'metadata': { - 'devices': pta_ge_devices_type, - 'default_device': pta_ge_devices_type[0] if len(pta_ge_devices_type) > 0 else '', - 'peaks': pta_ge_peaks, - 'totals': {}, - 'first_ts': 0, - 'time_metric': cano.time_metric, - 'memory_metric': cano.memory_metric, - 'time_factor': cano.time_factor, - 'memory_factor': cano.memory_factor, - }, - 'columns': defaultdict(list), - 'rows': self.component_curve_data - } - for device in pta_ge_devices_type: - if self.component_curve_data.get(device).get('PTA') is not None: - component_curve_result['columns'][device] += [ - {'name': f'PTA Allocated ({cano.memory_metric})', 'type': 'number', - 'tooltip': 'PTA memory in use.'}, - {'name': f'PTA Reserved ({cano.memory_metric})', 'type': 'number', - 'tooltip': 'PTA reserved memory by allocator, both used and unused.'}] - if self.component_curve_data.get(device).get('GE') is not None: - component_curve_result['columns'][device] += [ - {'name': f'GE Allocated ({cano.memory_metric})', 'type': 'number', 'tooltip': 'GE memory in use.'}, - {'name': f'GE Reserved ({cano.memory_metric})', 'type': 'number', - 'tooltip': 'GE reserved memory by allocator, both used and unused.'}] - if self.component_curve_data.get(device).get('APP') is not None: - component_curve_result['columns'][device] += [ - {'name': f'APP Reserved ({cano.memory_metric})', 'type': 'number', - 'tooltip': 'APP reserved memory by allocator, both used and unused.'}] - if len(component_curve_result['columns'][device]) > 0: - component_curve_result['columns'][device].insert(0, {'name': f'Time ({cano.time_metric})', - 'type': 'number', - 'tooltip': 'Time since profiler starts.'}) - device_types = list(set(process_devices_type + pta_ge_devices_type)) - return { - 'devices': device_types, - 'default_device': device_types[0], - 'total': total_result, - 'ptaGe': component_curve_result - } - - def _handle_memory_data(self): - process_data = defaultdict() - pta_or_ge_data = defaultdict() - path = self.profile_data.memory_curve_path - datas = RunGenerator.get_csv_data(path) - required_column_idxs = { - 'Component': -1, - 'Device Type': -1, - 'Timestamp(us)': -1, - 'Total Reserved(MB)': -1, - 'Total Allocated(MB)': -1 - } - (tag_type_idx, device_type_idx, time_idx, reserved_idx, allocated_idx), column_exist_count = \ - RunGenerator.check_csv_columns(datas[0], required_column_idxs) - if column_exist_count < len(required_column_idxs): - logger.error('Required column is missing in file "memory_record.csv"') - else: - for idx, ls in enumerate(datas[1:]): - try: - time_column = round((float(ls[time_idx]) - self.profile_data.profiler_start_ts) / 1000, 3) - device_type = ls[device_type_idx] - if ls[tag_type_idx] == 'PTA+GE': - process_data.setdefault(device_type, {}).setdefault('Allocated', []).append( - [time_column, round(float(ls[allocated_idx]), 3)]) - process_data.setdefault(device_type, {}).setdefault('Reserved', []).append( - [time_column, round(float(ls[reserved_idx]), 3)]) - elif ls[tag_type_idx] == 'APP': - line_chart_data = [time_column, None, round(float(ls[reserved_idx]), 3)] - pta_or_ge_data.setdefault(device_type, {}).setdefault(ls[tag_type_idx], []).append( - line_chart_data) - elif ls[tag_type_idx] in ('PTA', 'GE'): - line_chart_data = [ - time_column, round(float(ls[allocated_idx]), 3), - round(float(ls[reserved_idx]), 3) - ] - pta_or_ge_data.setdefault(device_type, {}).setdefault(ls[tag_type_idx], []).append( - line_chart_data) - except ValueError: - logger.error(f'File "{path}" has wrong data format in row {idx + 2} and will skip it.') - - return process_data, pta_or_ge_data - - def _handle_memory_component(self): - peak_memory_events = { - 'metadata': { - 'title': 'Component Peak Memory', - 'default_device': '', - }, - 'columns': [{'name': 'Component', 'type': 'string'}, - {'name': 'Peak Memory Reserved(MB)', 'type': 'number'}, - {'name': 'Time(ms)', 'type': 'number'}] - } - peak_memory_rows = defaultdict(list) - path = self.profile_data.memory_component_path - component_datas = RunGenerator.get_csv_data(path) - if component_datas: - required_column_idxs = { - 'Component': -1, - 'Timestamp(us)': -1, - 'Total Reserved(MB)': -1, - 'Device': -1 - } - (tag_type_idx, time_idx, reserved_idx, device_type_idx), column_exist_count = \ - RunGenerator.check_csv_columns(component_datas[0], required_column_idxs) - if column_exist_count < len(required_column_idxs): - logger.error(f'Required column is missing in file "{path}"') - else: - for idx, ls in enumerate(component_datas[1:]): - memory_curve_id_dict = { - 'device_type_idx': device_type_idx, - 'reserved_idx': reserved_idx, - 'tag_type_idx': tag_type_idx, - 'time_idx': time_idx - } - try: - self._handle_peak_memory_rows(memory_curve_id_dict, ls, peak_memory_rows) - except (ValueError, TypeError): - logger.error(f'File "{path}" has wrong data format in row {idx + 2} and will skip it.') - peak_memory_events['rows'] = peak_memory_rows - return peak_memory_events - - def _handle_peak_memory_rows(self, memory_curve_id_dict, ls, peak_memory_rows): - # Record the peak memory usage of other components. - has_flag = False - device_type_idx = memory_curve_id_dict.get('device_type_idx') - reserved_idx = memory_curve_id_dict.get('reserved_idx') - tag_type_idx = memory_curve_id_dict.get('tag_type_idx') - time_idx = memory_curve_id_dict.get('time_idx') - time_column = round((float(ls[time_idx]) - self.profile_data.profiler_start_ts) / 1000, 3) - for item in peak_memory_rows[ls[device_type_idx]]: - if item[0] == ls[tag_type_idx]: - if item[1] < ls[reserved_idx]: - item[1] = ls[reserved_idx] - item[2] = time_column - elif item[1] == ls[reserved_idx]: - item[2] = min(item[2], time_column) - has_flag = True - break - if not has_flag: - peak_memory_rows[ls[device_type_idx]].append([ls[tag_type_idx], ls[reserved_idx], time_column]) - - def _generate_overview(self): - def build_part_time_str(part_cost: float, part_name: str): - format_str = ('
    ' - 'Step {}
    ' - 'Total: {}us
    ' - '{}: {}us
    ' - 'Percentage: {}%' - '
    ') - percentage = 0.0 if costs.costs[ProfileRole.Total] == 0 else round( - 100 * part_cost / costs.costs[ProfileRole.Total], 2) - return format_str.format(step_name, costs.costs[ProfileRole.Total], part_name, part_cost, percentage) - - def build_avg_cost_dict(part_name: str, part_cost: float): - profiler_total_cost = self.profile_data.avg_costs.costs[ProfileRole.Total] - cost_dict = {'name': part_name, - 'description': '', - 'value': round(part_cost), - 'extra': 0.0 if profiler_total_cost == 0 else round(100 * part_cost / profiler_total_cost, 2)} - return cost_dict - - show_gpu = (self.profile_data.has_runtime - or self.profile_data.has_kernel or self.profile_data.has_memcpy_or_memset) - - column_tootip = {'type': 'string', 'role': 'tooltip', 'p': {'html': 'true'}} - data = {} - data['steps'] = {} - data['steps']['columns'] = ['Step'] - if show_gpu: - data['steps']['columns'].extend(['Kernel', 'Memcpy', 'Memset']) - if self.profile_data.has_communication: - data['steps']['columns'].append('Communication') - if show_gpu: - data['steps']['columns'].append('Runtime') - data['steps']['columns'].extend(['DataLoader', 'CPU Exec', 'Other']) - - data['steps']['rows'] = [] - for i, costs in enumerate(self.profile_data.steps_costs): - step_name = self.profile_data.steps_names[i] - row = [{'value': step_name}] - if show_gpu: - row.extend([{'value': costs.costs[ProfileRole.Kernel], - 'tooltip': build_part_time_str(costs.costs[ProfileRole.Kernel], 'Kernel')}, - {'value': costs.costs[ProfileRole.Memcpy], - 'tooltip': build_part_time_str(costs.costs[ProfileRole.Memcpy], 'Memcpy')}, - {'value': costs.costs[ProfileRole.Memset], - 'tooltip': build_part_time_str(costs.costs[ProfileRole.Memset], 'Memset')}]) - if self.profile_data.has_communication: - row.append({'value': costs.costs[ProfileRole.Communication], - 'tooltip': build_part_time_str(costs.costs[ProfileRole.Communication], 'Communication')}) - if show_gpu: - row.append({'value': costs.costs[ProfileRole.Runtime], - 'tooltip': build_part_time_str(costs.costs[ProfileRole.Runtime], 'Runtime')}) - row.extend([{'value': costs.costs[ProfileRole.DataLoader], - 'tooltip': build_part_time_str(costs.costs[ProfileRole.DataLoader], 'DataLoader')}, - {'value': costs.costs[ProfileRole.CpuOp], - 'tooltip': build_part_time_str(costs.costs[ProfileRole.CpuOp], 'CPU Exec')}, - {'value': costs.costs[ProfileRole.Other], - 'tooltip': build_part_time_str(costs.costs[ProfileRole.Other], 'Other')}]) - data['steps']['rows'].append(row) - - avg_costs = [] - if show_gpu: - avg_costs.extend([ - build_avg_cost_dict('Kernel', self.profile_data.avg_costs.costs[ProfileRole.Kernel]), - build_avg_cost_dict('Memcpy', self.profile_data.avg_costs.costs[ProfileRole.Memcpy]), - build_avg_cost_dict('Memset', self.profile_data.avg_costs.costs[ProfileRole.Memset]) - ]) - if self.profile_data.has_communication: - avg_costs.extend([ - build_avg_cost_dict('Communication', self.profile_data.avg_costs.costs[ProfileRole.Communication]) - ]) - if show_gpu: - avg_costs.extend([ - build_avg_cost_dict('Runtime', self.profile_data.avg_costs.costs[ProfileRole.Runtime]) - ]) - avg_costs.extend([ - build_avg_cost_dict('DataLoader', self.profile_data.avg_costs.costs[ProfileRole.DataLoader]), - build_avg_cost_dict('CPU Exec', self.profile_data.avg_costs.costs[ProfileRole.CpuOp]), - build_avg_cost_dict('Other', self.profile_data.avg_costs.costs[ProfileRole.Other]) - ]) - - data['performance'] = [ - {'name': 'Average Step Time', 'description': '', - 'value': round(self.profile_data.avg_costs.costs[ProfileRole.Total]), - 'extra': 100, 'children': avg_costs} - ] - - if len(self.profile_data.recommendations) == 0: - html = '
  1. N/A
    2. ' - else: - html = '' - for recommendation in self.profile_data.recommendations: - html += '
  2. {}
    3. '.format(recommendation) - data['recommendations'] = '
      {}
    '.format(html) - - return data - - def _generate_op_pie(self, group_by_input_shape: bool = False): - op_device_total_time = [] - op_device_self_time = [] - op_host_total_time = [] - op_host_self_time = [] - - if group_by_input_shape: - op_list = self.profile_data.op_list_groupby_name_input - else: - op_list = self.profile_data.op_list_groupby_name - - for op_agg in op_list: - # Whether device_duration & self_device_duration are accurate or not depends on the input tracing data. - if op_agg.device_duration > 0: - op_device_total_time.append([op_agg.name, op_agg.device_duration]) - if op_agg.self_device_duration > 0: - op_device_self_time.append([op_agg.name, op_agg.self_device_duration]) - if op_agg.host_duration > 0: - op_host_total_time.append([op_agg.name, op_agg.host_duration]) - if op_agg.self_host_duration > 0: - op_host_self_time.append([op_agg.name, op_agg.self_host_duration]) - - op_device_total_time.sort(key=lambda x: x[1], reverse=True) - op_device_self_time.sort(key=lambda x: x[1], reverse=True) - op_host_total_time.sort(key=lambda x: x[1], reverse=True) - op_host_self_time.sort(key=lambda x: x[1], reverse=True) - - data = {} - device_total_time = {} - device_self_time = {} - host_total_time = {} - host_self_time = {} - - if len(op_device_total_time) > 0: - device_total_time['title'] = 'Device Total Time (us)' - device_total_time['columns'] = [{'type': 'string', 'name': 'name'}, {'type': 'number', 'name': 'value'}] - device_total_time['rows'] = op_device_total_time - else: - device_total_time = None - - if len(op_device_self_time) > 0: - device_self_time['title'] = 'Device Self Time (us)' - device_self_time['columns'] = [{'type': 'string', 'name': 'name'}, {'type': 'number', 'name': 'value'}] - device_self_time['rows'] = op_device_self_time - else: - device_self_time = None - - if len(op_host_total_time) > 0: - host_total_time['title'] = 'Host Total Time (us)' - host_total_time['columns'] = [{'type': 'string', 'name': 'name'}, {'type': 'number', 'name': 'value'}] - host_total_time['rows'] = op_host_total_time - else: - host_total_time = None - - if len(op_host_self_time) > 0: - host_self_time['title'] = 'Host Self Time (us)' - host_self_time['columns'] = [{'type': 'string', 'name': 'name'}, {'type': 'number', 'name': 'value'}] - host_self_time['rows'] = op_host_self_time - else: - host_self_time = None - - data['device_total_time'] = device_total_time - data['device_self_time'] = device_self_time - data['host_total_time'] = host_total_time - data['host_self_time'] = host_self_time - - return data - - def _generate_op_table(self, op_list: Iterable[OperatorAgg], group_by_input_shape=False, call_stack=False): - show_gpu = self.profile_data.has_kernel or self.profile_data.has_memcpy_or_memset - - if group_by_input_shape: - stack_list_dict = self.profile_data.stack_lists_group_by_name_input - else: - stack_list_dict = self.profile_data.stack_lists_group_by_name - - op_list = sorted(op_list, - key=lambda x: x.self_device_duration if show_gpu else x.self_host_duration, - reverse=True) - - data = list() - result = { - 'metadata': { - 'sort': 'device_self_duration' if show_gpu else 'host_self_duration', - 'tooltips': { - 'tc_eligible': consts.TOOLTIP_OP_TC_ELIGIBLE, - 'tc_self_ratio': consts.TOOLTIP_OP_TC_SELF, - 'tc_total_ratio': consts.TOOLTIP_OP_TC_TOTAL - } - }, - 'data': data - } - for op in op_list: - # Whether device_duration & self_device_duration are accurate or not depends on the input tracing data. - row = dict() - row['name'] = op.name - if group_by_input_shape: - row['input_shape'] = op.input_shape - row['calls'] = op.calls - if show_gpu: - row['device_self_duration'] = round(op.self_device_duration) - row['device_total_duration'] = round(op.device_duration) - row['host_self_duration'] = round(op.self_host_duration) - row['host_total_duration'] = round(op.host_duration) - row['tc_eligible'] = 'Yes' if op.tc_eligible else 'No' - row['tc_self_ratio'] = round(100 * op.tc_self_ratio, 2) - row['tc_total_ratio'] = round(100 * op.tc_total_ratio, 2) - if call_stack: - row['call_stack'] = op.callstacks.pop() - else: - if group_by_input_shape: - key = op.name + '###' + str(op.input_shape) - else: - key = op.name - row['has_call_stack'] = key in stack_list_dict - data.append(row) - - return result - - def _generate_op_table_for_stack(self, group_by_input_shape: bool): - if group_by_input_shape: - stack_list_dict = self.profile_data.stack_lists_group_by_name_input - else: - stack_list_dict = self.profile_data.stack_lists_group_by_name - - result = dict() - for k, v in stack_list_dict.items(): - result[k] = self._generate_op_table(v, group_by_input_shape, True) - return result - - def _generate_kernel_op_table_gpu(self): - table = {} - result = { - 'metadata': { - 'sort': 'Total Duration (us)' - }, - 'data': table - } - table['columns'] = [ - {'type': 'string', 'name': 'Name'}, - {'type': 'string', 'name': 'Operator'}, - {'type': 'string', 'name': 'Grid'}, - {'type': 'string', 'name': 'Block'}, - {'type': 'number', 'name': 'Register Per Thread'}, - {'type': 'number', 'name': 'Shared Memory'}, - {'type': 'string', 'name': 'Kernel Uses Tensor Cores', - 'tooltip': consts.TOOLTIP_KERNEL_USES_TC}, - {'type': 'string', 'name': 'Op is Tensor Cores eligible', - 'tooltip': consts.TOOLTIP_KERNEL_OP_TC_ELIGIBLE} - ] - col_names = ['Calls', 'Total Duration (us)', 'Mean Duration (us)', 'Max Duration (us)', 'Min Duration (us)'] - for column in col_names: - table['columns'].append({'type': 'number', 'name': column}) - gpu_metrics_columns = self.profile_data.gpu_metrics_parser.get_gpu_metrics_columns() - table['columns'].extend(gpu_metrics_columns) - - table['rows'] = [] - kernel_list: List[KernelAggByNameOp] = sorted( - self.profile_data.kernel_list_groupby_name_op, key=lambda x: x.total_duration, reverse=True) - for agg_by_name_op in kernel_list: - kernel_op_row = [ - agg_by_name_op.name, agg_by_name_op.op_name, - str(agg_by_name_op.grid), str(agg_by_name_op.block), - str(agg_by_name_op.regs_per_thread or '0'), str(agg_by_name_op.shared_memory or '0'), - 'Yes' if agg_by_name_op.tc_used else 'No', - 'Yes' if agg_by_name_op.op_tc_eligible else 'No', - agg_by_name_op.calls, - agg_by_name_op.total_duration, round(agg_by_name_op.avg_duration), - agg_by_name_op.max_duration, agg_by_name_op.min_duration - ] - if self.profile_data.gpu_metrics_parser.has_blocks_per_sm: - kernel_op_row.append(round(agg_by_name_op.avg_blocks_per_sm, 2)) - if self.profile_data.gpu_metrics_parser.has_occupancy: - kernel_op_row.append(round(agg_by_name_op.avg_occupancy, 2)) - table['rows'].append(kernel_op_row) - return result - - def _generate_kernel_pie_gpu(self): - pie = {'columns': [{'type': 'string', 'name': 'name'}, {'type': 'number', 'name': 'value'}], 'rows': []} - for _id, (name, row) in enumerate(self.profile_data.kernel_stat.iterrows()): - pie['rows'].append([name, row['sum']]) - data = {'total': pie, 'device_target': self.device_target} - return data - - def _generate_kernel_table_gpu(self): - table = {} - result = { - 'metadata': { - 'sort': 'Total Duration (us)' - }, - 'data': table - } - table['columns'] = [ - {'type': 'string', 'name': 'Name'}, - {'type': 'string', 'name': 'Tensor Cores Used', - 'tooltip': consts.TOOLTIP_KERNEL_USES_TC} - ] - columns = ['count', 'sum', 'mean', 'max', 'min'] - round_digits = [0, 0, 0, 0, 0] - if self.profile_data.gpu_metrics_parser.has_blocks_per_sm: - columns.append('blocks_per_sm') - round_digits.append(2) - if self.profile_data.gpu_metrics_parser.has_occupancy: - columns.append('occupancy') - round_digits.append(2) - col_names = ['Calls', 'Total Duration (us)', 'Mean Duration (us)', 'Max Duration (us)', 'Min Duration (us)'] - for column in col_names: - table['columns'].append({'type': 'number', 'name': column}) - gpu_metrics_columns = self.profile_data.gpu_metrics_parser.get_gpu_metrics_columns() - table['columns'].extend(gpu_metrics_columns) - - table['rows'] = [] - for _id, (name, row) in enumerate(self.profile_data.kernel_stat.iterrows()): - kernel_row = [name, 'Yes' if row['tc_used'] else 'No'] - for i, column in enumerate(columns): - kernel_row.append(round(row[column]) if round_digits[i] == 0 - else round(row[column], round_digits[i])) - table['rows'].append(kernel_row) - return result - - def _generate_tc_pie_gpu(self): - pie = {'columns': [{'type': 'string', 'name': 'name'}, {'type': 'number', 'name': 'value'}], 'rows': []} - pie['rows'].append(['Using Tensor Cores', self.profile_data.tc_used_ratio]) - pie['rows'].append(['Not Using Tensor Cores', 1.0 - self.profile_data.tc_used_ratio]) - data = {'total': pie} - return data - - def _generate_kernel_op_table_npu(self): - table = {} - result = { - 'metadata': { - 'sort': 'Calls' - }, - 'data': table - } - table['columns'] = [{'type': 'string', 'name': 'Name'}, - {'type': 'number', 'name': 'Calls'}, - {'type': 'number', 'name': 'Total Durations(us)'}, - {'type': 'number', 'name': 'Min Durations(us)'}, - {'type': 'number', 'name': 'Avg Durations(us)'}, - {'type': 'number', 'name': 'Max Durations(us)'} - ] - table['rows'] = [] - for key, value in self.statistic_data.items(): - temp = [key] - for val in value.values(): - temp.append(val) - table['rows'].append(temp) - return result - - def _generate_kernel_pie_npu(self): - pie = {'columns': [{'type': 'string', 'name': 'name'}, {'type': 'number', 'name': 'value'}], 'rows': []} - for key, val in self.statistic_data.items(): - data = [key, float(val['Total'])] - pie['rows'].append(data) - datas = {'total': pie, 'device_target': self.device_target} - return datas - - def _generate_kernel_table_npu(self): - table = {'columns': [], 'rows': []} - result = { - 'metadata': { - 'sort': 'Duration (us)' - }, - 'data': table - } - path = self.profile_data.kernel_file_path - datas = RunGenerator.get_csv_data(path) - required_column_idxs = { - 'Name': -1, - 'Duration(us)': -1, - 'Accelerator Core': -1 - } - (name_idx, duration_idx, core_type_idx), column_exist_count = \ - RunGenerator.check_csv_columns(datas[0], required_column_idxs) - if column_exist_count < 3: - logger.error('Required column is missing in file "kernel_details.csv"') - else: - for column in datas[0]: - if column in ('Duration(us)', 'Start Time(us)', 'Wait Time(us)', 'Block Dim'): - table['columns'].append({'type': 'number', 'name': column}) - else: - table['columns'].append({'type': 'string', 'name': column}) - - self._handle_kernel_table_rows(name_idx, duration_idx, core_type_idx, datas[1:], path) - table['rows'] = datas[1:] - return result - - def _generate_tc_pie_npu(self): - pie = {'columns': [{'type': 'string', 'name': 'name'}, {'type': 'number', 'name': 'value'}], 'rows': []} - for key, val in self.accelerator_data.items(): - pie['rows'].append(['Using ' + key.replace('_', ' '), val]) - data = {'total': pie} - return data - - @staticmethod - def get_gpu_info(device_props, gpu_id): - if (device_props is None) or (gpu_id >= len(device_props)) or (gpu_id < 0): - return None - - device_prop: Dict = device_props[gpu_id] - gpu_info = {} - name = device_prop.get('name') - if name is not None: - gpu_info['Name'] = name - - mem = device_prop.get('totalGlobalMem') - if mem is not None: - try: - gpu_info['Memory'] = '{} GB'.format(round(float(mem) / 1024 / 1024 / 1024, 2)) - gpu_info['Memory Raw'] = mem - except ValueError: - logger.warning('The value of "totalGlobalMem" must be number in the JSON, please check it.') - - major = device_prop.get('computeMajor') - minor = device_prop.get('computeMinor') - if major is not None and minor is not None: - gpu_info['Compute Capability'] = '{}.{}'.format(major, minor) - - return gpu_info - - def _handle_kernel_table_rows(self, name_idx, duration_idx, core_type_idx, rows, path): - for idx, row in enumerate(rows): - call_name = row[name_idx] - try: - call_duration = float(row[duration_idx]) - except ValueError: - logger.error( - f'File "{path}" has wrong data format in row {idx + 2} in Duration column and will skip the row.') - continue - call_type = row[core_type_idx] - if self.accelerator_data.get(call_type) is not None: - self.accelerator_data[call_type] += call_duration - else: - self.accelerator_data[call_type] = call_duration - - if self.statistic_data.get(call_name) is not None: - temp = self.statistic_data.get(call_name, {}) - temp['Max'] = max(temp.get('Max', 0), call_duration) - temp['Min'] = min(temp.get('Min', 0), call_duration) - temp['Total'] = round(temp.get('Total', 0) + call_duration, 2) - temp['Calls'] = temp.get('Calls', 0) + 1 - if temp['Calls'] == 0: - logger.error( - f'temp["Calls"] is zero which can not be divisor.') - temp['Average'] = 0 - else: - temp['Average'] = round(temp['Total'] / temp['Calls'], 2) - else: - self.statistic_data[call_name] = { - 'Calls': 1, - 'Total': call_duration, - 'Min': call_duration, - 'Average': call_duration, - 'Max': call_duration - } - - -class DistributedRunGenerator(object): - def __init__(self, all_profile_data: Iterable[DistributedRunProfileData], span, device_target): - self.all_profile_data = all_profile_data - self.span = span - self.device_target = device_target - - def generate_run_profile(self): - profile_run = DistributedRunProfile(self.span) - profile_run.views.append(consts.DISTRIBUTED_VIEW) - profile_run.gpu_info = self._generate_gpu_info() - profile_run.steps_to_overlap = self._generate_overlap_graph() - profile_run.steps_to_wait = self._generate_wait_graph() - profile_run.comm_ops = self._generate_ops_table() - return profile_run - - def _generate_gpu_info(self): - # first key is node name, the second key is process id, the third key is GPU0/, - # the value is the gpu info json - result: Dict[str, Dict[str, Dict[str, Dict]]] = OrderedDict() - index = 0 - for data in sorted(self.all_profile_data, key=lambda x: x.worker): - if not data.device_props: - continue - - match = consts.NODE_PROCESS_PATTERN.match(data.worker) - if match: - node = match.group(1) - process_id = match.group(2) - else: - logger.warning('cannot parse node name from worker name {}'.format(data.worker)) - node = data.worker - process_id = index - index += 1 - if node not in result: - result[node] = OrderedDict() - - process_id = 'Process ' + str(process_id) - result[node][process_id] = OrderedDict() - for used_device in data.used_devices: - gpu_info = RunGenerator.get_gpu_info(data.device_props, used_device) - if gpu_info is not None: - result[node][process_id]['GPU' + str(used_device)] = gpu_info - - if result: - for k, v in result.items(): - result[k] = OrderedDict(sorted(v.items())) - return { - 'metadata': {'title': 'Device Information'}, - 'data': result - } - else: - return None - - def _generate_overlap_graph(self): - result = dict() - result['metadata'] = { - 'title': 'Computation/Communication Overview', - 'legends': ['Computation', 'Overlapping', 'Communication', 'Other'], - 'units': 'us' - } - steps_to_overlap: Dict[str, Dict[str, List[int]]] = OrderedDict() - steps_to_overlap['all'] = OrderedDict() - for data in self.all_profile_data: - steps_to_overlap['all'][data.worker] = [0, 0, 0, 0] - step_number = len(data.steps_names) - if step_number <= 0: - return None - if self.device_target != 'Ascend': - DistributedRunGenerator._get_gpu_overlap_data(data, steps_to_overlap) - else: - DistributedRunGenerator._get_npu_overlap_data(data, steps_to_overlap) - - steps_to_overlap['all'][data.worker] = [int(x / step_number) for x in steps_to_overlap['all'][data.worker]] - for k, v in steps_to_overlap.items(): - steps_to_overlap[k] = OrderedDict(sorted(v.items())) - result['data'] = steps_to_overlap - return result - - @staticmethod - def _get_gpu_overlap_data(data, steps_to_overlap): - for i, step_name in enumerate(data.steps_names): - steps_to_overlap.setdefault(step_name, OrderedDict()) - costs = data.comm_overlap_costs[i] - steps_to_overlap[step_name][data.worker] = [ - round(costs.computation - costs.overlap, 3), - round(costs.overlap, 3), - round(costs.communication - costs.overlap, 3), - round(costs.other, 3) - ] - steps_to_overlap['all'][data.worker] = [ - sum(x) - for x in zip(steps_to_overlap['all'][data.worker], steps_to_overlap[step_name][data.worker]) - ] - - @staticmethod - def _get_npu_overlap_data(data, steps_to_overlap): - steps = data.step_to_overlap - for k, v in steps.items(): - steps_to_overlap.setdefault(k, OrderedDict()) - # v: computation / overlap / communication_not_overlap / free time - # steps_to_overlap: computation_not_overlap / overlap / communication_not_overlap / other - steps_to_overlap[k][data.worker] = list( - [round(v[0] - v[1], 3), round(v[1], 3), round(v[2], 3), round(v[3], 3)]) - steps_to_overlap['all'][data.worker] = [ - sum(x) - for x in zip(steps_to_overlap['all'][data.worker], steps_to_overlap[k][data.worker]) - ] - - @staticmethod - def _get_npu_wait_data(data, steps_to_wait): - step_number = len(data.step_to_wait) - if step_number <= 0: - return - steps = data.step_to_wait - for k, v in steps.items(): - steps_to_wait.setdefault(k, OrderedDict()) - - trans = round(v.get('trans') * 1000, 3) # 1ms = 1000us - wait = round(v.get('Synchronize') * 1000, 3) # 1ms = 1000us - steps_to_wait[k][data.worker] = list([trans, wait]) - steps_to_wait['all'][data.worker] = [ - sum(x) - for x in zip(steps_to_wait['all'][data.worker], steps_to_wait[k][data.worker]) - ] - steps_to_wait['all'][data.worker] = [x / step_number for x in steps_to_wait['all'][data.worker]] - - @staticmethod - def _get_gpu_wait_data(data, steps_to_wait): - step_number = len(data.step_comm_stats.values()) - if step_number <= 0: - return - for step, comm_stats in data.step_comm_stats.items(): - steps_to_wait.setdefault(step, OrderedDict())[data.worker] = [ - round(comm_stats[1], 3), - round(comm_stats[0] - comm_stats[1], 3) - ] - steps_to_wait['all'][data.worker] = [ - sum(x) - for x in zip(steps_to_wait['all'][data.worker], steps_to_wait[step][data.worker]) - ] - steps_to_wait['all'][data.worker] = [int(x / step_number) for x in steps_to_wait['all'][data.worker]] - - def _generate_wait_graph(self): - result = dict() - result['metadata'] = { - 'title': 'Synchronizing/Communication Overview', - 'legends': ['Data Transfer Time', 'Synchronizing Time'], - 'units': 'us' - } - steps_to_wait: Dict[str, Dict[str, List[int]]] = OrderedDict() - - steps_to_wait['all'] = OrderedDict() - for data in self.all_profile_data: - steps_to_wait['all'][data.worker] = [0, 0] - if self.device_target != 'Ascend': - DistributedRunGenerator._get_gpu_wait_data(data, steps_to_wait) - else: - DistributedRunGenerator._get_npu_wait_data(data, steps_to_wait) - for k, v in steps_to_wait.items(): - steps_to_wait[k] = OrderedDict(sorted(v.items())) - result['data'] = steps_to_wait - return result - - def _generate_ops_table(self): - result = dict() - result['metadata'] = {'title': 'Communication Operations Stats'} - workers_to_comm_ops = OrderedDict() - # Ignore the span for distributed view - for data in self.all_profile_data: - table = {} - if self.device_target != 'Ascend': - DistributedRunGenerator._get_gpu_table(data, table) - else: - DistributedRunGenerator._get_npu_table(data, table) - workers_to_comm_ops[data.worker] = table - result['data'] = OrderedDict(sorted(workers_to_comm_ops.items())) - return result - - @staticmethod - def _get_npu_table(data, table): - table['columns'] = [{'type': 'string', 'name': 'Name'}] - col_names = [ - 'Calls', - 'Total Transit Size (bytes)', - 'Avg Transit Size (bytes)', - 'Elapse Time (us)', - 'Avg Elapse Time (us)', - 'Transit Time (us)', - 'Avg Transit Time (us)' - ] - for column in col_names: - table['columns'].append({'type': 'number', 'name': column}) - table['rows'] = [] - ops = data.comm_op - for op, stats in ops.items(): - row = [ - op, - stats[0], - round(stats[1] * 1024 * 1024, 3), - round(stats[1] * 1024 * 1024 / stats[0] if stats != 0 else 0), # 1MB = 1024 * 1024 bytes - round(stats[2] * 1000, 3), - round(stats[2] * 1000 / stats[0] if stats != 0 else 0), # 1ms = 1000us - round(stats[3] * 1000, 3), - round(stats[3] * 1000 / stats[0] if stats != 0 else 0) # 1ms = 1000us - ] - table['rows'].append(row) - - @staticmethod - def _get_gpu_table(data, table): - table['columns'] = [{'type': 'string', 'name': 'Name'}] - col_names = [ - 'Calls', - 'Total Size (bytes)', - 'Avg Size (bytes)', - 'Total Latency (us)', - 'Avg Latency (us)', - 'Data Transfer Time (us)', - 'Avg Data Transfer Time (us)' - ] - for column in col_names: - table['columns'].append({'type': 'number', 'name': column}) - table['rows'] = [] - for op, stats in data.total_comm_stats.items(): - row = [ - op, - stats[0], - round(stats[1], 3), - - round(stats[1] / stats[0] if stats[0] != 0 else 0), - round(stats[2], 3), - round(stats[2] / stats[0] if stats[0] != 0 else 0), - round(stats[3], 3), - round(stats[3] / stats[0] if stats[0] != 0 else 0) - ] - table['rows'].append(row) diff --git a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/tensor_core.py b/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/tensor_core.py deleted file mode 100644 index cc53ab217..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/tensor_core.py +++ /dev/null @@ -1,50 +0,0 @@ -# ------------------------------------------------------------------------- -# Copyright (c) Microsoft Corporation. All rights reserved. -# ------------------------------------------------------------------------- -class TcAllowlistMeta(type): - # Enable grammar sugar as 'v in TcAllowlist'. - def __contains__(cls, item): - return cls.__contains__(item) - - -class TcAllowlist(metaclass=TcAllowlistMeta): - allowlist = ['h884', 's884', 'h1688', 's1688', 'hmma', 'i8816', '16816', - 'dgrad_1x1_stride_2x2', 'first_layer_wgrad_kernel', 'conv1x1', - 'conv2d_c1_k1', 'direct_group', 'xmma_implicit_gemm', - 'xmma_sparse_conv', 'xmma_warp_specialized_implicit_gemm', - 'xmma_gemm', 'xmma_sparse_gemm', 'c1688'] - - @classmethod - def __contains__(cls, item): - # If kernel name contains substring equal to any one in allowlist, then it uses tensor core. - for pattern in cls.allowlist: - if pattern in item: - return True - return False - - -class TcOpAllowlist(metaclass=TcAllowlistMeta): - allowlist = ['aten::_convolution', 'aten::conv1d', 'aten::conv2d', 'aten::conv3d', 'aten::conv_tbc', - 'aten::conv_transpose1d', 'aten::conv_transpose2d', 'aten::conv_transpose3d', - 'aten::convolution', 'aten::cudnn_convolution', 'aten::cudnn_convolution_transpose', - 'aten::prelu', 'aten::addmm', 'aten::addmv', 'aten::addr', - 'aten::matmul', 'aten::mm', 'aten::mv', - 'aten::linear', 'aten::addbmm', 'aten::baddbmm', 'aten::bmm', - 'aten::chain_matmul', 'aten::linalg_multi_dot', - 'aten::_thnn_fused_lstm_cell', 'aten::_thnn_fused_gru_cell', 'aten::lstm_cell', - 'aten::gru_cell', 'aten::rnn_tanh_cell', 'aten::rnn_relu_cell', - # The backward ops are got by running above ops' backward - # and recording whether it launched kernels. - 'CudnnConvolutionBackward', 'BmmBackward0', - 'aten::cudnn_convolution_transpose_backward', 'CudnnConvolutionTransposeBackward', - 'MmBackward', 'aten::cudnn_convolution_backward_weight', 'aten::addmm_', - 'AddmvBackward', 'MvBackward', - 'aten::cudnn_convolution_transpose_backward_weight', - 'aten::cudnn_convolution_transpose_backward_input', - 'AddmmBackward', 'aten::cudnn_convolution_backward_input', - 'AddbmmBackward', 'aten::cudnn_convolution_backward'] - - @classmethod - def __contains__(cls, item): - # If operator name equals to any one in allowlist, then it is tensor core eligible. - return item in cls.allowlist diff --git a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/tensor_cores_parser.py b/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/tensor_cores_parser.py deleted file mode 100644 index e2372d9ad..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/tensor_cores_parser.py +++ /dev/null @@ -1,77 +0,0 @@ -# ------------------------------------------------------------------------- -# Copyright (c) Microsoft Corporation. All rights reserved. -# ------------------------------------------------------------------------- -from typing import Dict, Iterable, List - -from .. import consts -from .node import OperatorNode - - -class TensorCoresParser: - def __init__(self, tc_ratio: List[float], tc_eligible_ops_kernel_ratio: float): - # For calculating Tensor Cores time ratio per GPU. - self.tc_ratio = tc_ratio - self.tc_eligible_ops_kernel_ratio = tc_eligible_ops_kernel_ratio - - @classmethod - def parse_events(cls, tid2tree: Dict[str, OperatorNode], ops: Iterable[OperatorNode], gpu_ids: Iterable[int]): - tc_ratio = cls._calculate_tc_ratio(ops, gpu_ids) - tc_eligible_ops_kernel_ratio = cls._get_tc_eligible_ops_kernel_ratio(tid2tree, ops) - return cls(tc_ratio, tc_eligible_ops_kernel_ratio) - - @staticmethod - def _calculate_tc_ratio(ops: Iterable[OperatorNode], gpu_ids: Iterable[int]): - tc_ratio: List[float] = [None] * consts.MAX_GPU_PER_NODE - tc_time = [0] * consts.MAX_GPU_PER_NODE - total_time = [0] * consts.MAX_GPU_PER_NODE - has_kernel = False - for op in ops: - for rt in op.runtimes: - # 'CallTreeRoot' & 'dummy' kernels are launched out of profiler step, so don't count them. - if not (op.name == 'CallTreeRoot' and rt.name == 'dummy'): - for k in rt.get_kernels(): - has_kernel = True - dur = k.end_time - k.start_time - is_tc_used = k.tc_used - if is_tc_used: - tc_time[k.device_id] += dur - total_time[k.device_id] += dur - if has_kernel: # If no kernel, then keep all self.tc_ratio as None. - for gpu_id in gpu_ids: - if total_time[gpu_id] > 0: - tc_ratio[gpu_id] = tc_time[gpu_id] / total_time[gpu_id] - else: - tc_ratio[gpu_id] = 0.0 - return tc_ratio - - @staticmethod - def _get_bottom_tc_eligible_operators(op_tree_node: OperatorNode): - ops: List[OperatorNode] = [] - for child in op_tree_node.children: - child_ops = TensorCoresParser._get_bottom_tc_eligible_operators(child) - ops.extend(child_ops) - # TC-eligible ops which have children TC-eligible ops will not be regarded as 'bottom'. - if op_tree_node.tc_eligible and len(ops) == 0: - ops.append(op_tree_node) - return ops - - @staticmethod - def _get_tc_eligible_ops_kernel_ratio(tid2tree: Dict[int, OperatorNode], ops: Iterable[OperatorNode]): - def sum_self_kernel_time(ops: Iterable[OperatorNode]): - sum_time = 0 - for op in ops: - for rt in op.runtimes: - # 'CallTreeRoot' & 'dummy' kernels are launched out of profiler step, so don't count them. - if not (op.name == 'CallTreeRoot' and rt.name == 'dummy'): - for k in rt.get_kernels(): - sum_time += k.end_time - k.start_time - return sum_time - - ops_bottom_tc_eligible = [] - for root in tid2tree.values(): - ops_bottom_tc_eligible.extend(TensorCoresParser._get_bottom_tc_eligible_operators(root)) - ops_bottom_tc_eligible_kernel_sum = sum_self_kernel_time(ops_bottom_tc_eligible) - ops_kernel_sum = sum_self_kernel_time(ops) - tc_eligible_ops_kernel_ratio = ops_bottom_tc_eligible_kernel_sum / ops_kernel_sum \ - if ops_kernel_sum > 0 else 0.0 - return tc_eligible_ops_kernel_ratio diff --git a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/trace.py b/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/trace.py deleted file mode 100644 index ea09f7966..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/profiler/trace.py +++ /dev/null @@ -1,241 +0,0 @@ -# ------------------------------------------------------------------------- -# Copyright (c) Microsoft Corporation. All rights reserved. -# -------------------------------------------------------------------------- -__all__ = ['EventTypes', 'create_event'] - -from enum import IntEnum -from typing import Dict, Optional - -from .. import utils - -logger = utils.get_logger() - -NcclOpNameSet = ['nccl:broadcast', 'nccl:reduce', 'nccl:all_reduce', 'nccl:all_gather', 'nccl:reduce_scatter'] -GlooOpNameSet = ['gloo:broadcast', 'gloo:reduce', 'gloo:all_reduce', 'gloo:all_gather', 'gloo:reduce_scatter'] - - -class DeviceType(IntEnum): - CPU = 0 - CUDA = 1 - - -class EventTypes(object): - TRACE = 'Trace' - OPERATOR = 'Operator' - PROFILER_STEP = 'ProfilerStep' - RUNTIME = 'Runtime' - KERNEL = 'Kernel' - MEMCPY = 'Memcpy' - MEMSET = 'Memset' - PYTHON = 'Python' - MEMORY = 'Memory' - PYTHON_FUNCTION = 'python_function' - MODULE = 'Module' - PL_PROFILE = 'pl_profile' - PL_MODULE = 'pl_module' - USER_ANNOTATION = 'user_annotation' - - -EventTypeMap = { - 'trace': EventTypes.TRACE, - 'cpu_op': EventTypes.OPERATOR, - 'operator': EventTypes.OPERATOR, - 'runtime': EventTypes.RUNTIME, - 'cuda_runtime': EventTypes.RUNTIME, - 'kernel': EventTypes.KERNEL, - 'memcpy': EventTypes.MEMCPY, - 'gpu_memcpy': EventTypes.MEMCPY, - 'memset': EventTypes.MEMSET, - 'gpu_memset': EventTypes.MEMSET, - 'python': EventTypes.PYTHON, - 'memory': EventTypes.MEMORY, - 'python_function': EventTypes.PYTHON_FUNCTION, - 'user_annotation': EventTypes.USER_ANNOTATION, - 'gpu_user_annotation': EventTypes.USER_ANNOTATION -} - - -class BaseEvent(object): - def __init__(self, event_type, data): - self.type: str = event_type - self.name: str = data.get('name') - self.ts: int = data.get('ts') - self.pid: int = data.get('pid') - self.tid: int = data.get('tid') - self.args: Dict = data.get('args', {}) - - -class DurationEvent(BaseEvent): - def __init__(self, event_type, data): - super().__init__(event_type, data) - self.category: str = data.get('cat', '') - self.duration: int = data.get('dur') - - extern_id: Optional[int] = self.args.get('external id') - if extern_id is None: - extern_id = self.args.get('External id') - self.external_id = extern_id - self.correlation_id: Optional[int] = self.args.get('correlation') - - -class KernelEvent(DurationEvent): - def __init__(self, event_type, data): - super().__init__(event_type, data) - self.occupancy = self.args.get('est. achieved occupancy %') - self.blocks_per_sm = self.args.get('blocks per SM') - self.grid = self.args.get('grid') - self.block = self.args.get('block') - self.regs_per_thread = self.args.get('registers per thread') - self.shared_memory = self.args.get('shared memory') - self.device_id = self.args.get('device') - - -class OperatorEvent(DurationEvent): - def __init__(self, event_type, data): - super().__init__(event_type, data) - self.callstack = self.args.get('Call stack') - self.input_type = self.args.get('Input type') - - shape = self.args.get('Input Dims') - if shape is None: - # Setting shape to '[]' other None is to align with autograd result - shape = self.args.get('Input dims', []) - self.input_shape = shape - - -class ProfilerStepEvent(OperatorEvent): - def __init__(self, data): - super().__init__(EventTypes.PROFILER_STEP, data) - # torch.profiler.profile.step will invoke record_function with name like 'ProfilerStep#5' - self.step: int = int(self.name.split('#')[1]) - - -class MemoryEvent(BaseEvent): - def __init__(self, event_type, data): - super().__init__(event_type, data) - self.scope: str = data.get('s', '') - self.device_id: int = self.args.get('Device Id') - dtype = self.args.get('Device Type') - if dtype is not None: - try: - dtype = DeviceType(dtype) - except ValueError: - dtype = None - - self.device_type: DeviceType = dtype - - @property - def addr(self): - return self.args.get('Addr') - - @property - def bytes(self): - return self.args.get('Bytes', 0) - - @property - def total_allocated(self): - return self.args.get('Total Allocated', float('nan')) - - @property - def total_reserved(self): - return self.args.get('Total Reserved', float('nan')) - - -class PythonFunctionEvent(DurationEvent): - def __init__(self, event_type, data): - super().__init__(event_type, data) - self.python_id: int = self.args.get('Python id') - self.python_parent_id: int = self.args.get('Python parent id') - - -class ModuleEvent(PythonFunctionEvent): - def __init__(self, data): - super().__init__(EventTypes.MODULE, data) - self.module_id: int = self.args.get('Python module id') - - -class PLProfileEvent(DurationEvent): - def __init__(self, data): - super().__init__(EventTypes.PL_PROFILE, data) - self.name = self.name.replace('[pl][profile]', '') - - -class PLModuleEvent(DurationEvent): - def __init__(self, data): - super().__init__(EventTypes.PL_MODULE, data) - self.module_id = 0 # just to be compatible with ModuleEvent processing - self.name = self.name.replace('[pl][module]', '') - self.module_type = self.name[:self.name.find(': ')] - self.name = self.name[self.name.find(': ') + 2:] - - -def create_event(event, is_pytorch_lightning) -> Optional[BaseEvent]: - try: - event_type = event.get('ph') - if event_type == 'X': - return create_trace_event(event, is_pytorch_lightning) - elif event_type == 'i' and event.get('name') == '[memory]': - return MemoryEvent(EventTypes.MEMORY, event) - else: - return None - except Exception as ex: - logger.warning('Failed to parse profile event. Exception=%s. Event=%s', ex, event, exc_info=True) - raise - - -def create_trace_event(event, is_pytorch_lightning) -> Optional[BaseEvent]: - category = event.get('cat') - event_type = EventTypeMap.get(category.lower()) if category else None - if event_type == EventTypes.USER_ANNOTATION: - name = event.get('name') - if name and name.startswith('ProfilerStep#'): - return ProfilerStepEvent(event) - if name in GlooOpNameSet or name in NcclOpNameSet: - return OperatorEvent(event_type, event) - elif event_type == EventTypes.OPERATOR: - name = event.get('name') - if name and name.startswith('ProfilerStep#'): - return ProfilerStepEvent(event) - if is_pytorch_lightning: - if name and name.startswith('[pl][profile]'): - return PLProfileEvent(event) - elif name and name.startswith('[pl][module]'): - return PLModuleEvent(event) - return OperatorEvent(event_type, event) - elif event_type == EventTypes.PYTHON: - return OperatorEvent(event_type, event) - elif event_type == EventTypes.KERNEL: - return KernelEvent(event_type, event) - elif event_type == EventTypes.PYTHON_FUNCTION: - if is_pytorch_lightning: - return None - args = event.get('args') - if args and args.get('Python module id') is not None: - return ModuleEvent(event) - else: - return PythonFunctionEvent(event_type, event) - elif event_type is not None: - return DurationEvent(event_type, event) - return None - - -def create_association_events(events) -> Dict[int, int]: - forward_map = {} - backward_map = {} - - result = {} - for e in events: - ph = e.get('ph') - e_id = e['id'] - ts = e['ts'] - if ph == 's': - forward_map[e_id] = ts - elif ph == 'f': - backward_map[e_id] = ts - - for e_id, ts in forward_map.items(): - backward_ts = backward_map.get(e_id) - if backward_ts is not None: - result[ts] = backward_ts - - return result diff --git a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/run.py b/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/run.py deleted file mode 100644 index 9e30f2252..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/run.py +++ /dev/null @@ -1,525 +0,0 @@ -# ------------------------------------------------------------------------- -# Copyright (c) Microsoft Corporation. All rights reserved. -# -# Copyright(c) 2023 Huawei Technologies. -# All rights reserved -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# Modifications: Add visualization of PyTorch Ascend profiling. -# -------------------------------------------------------------------------- -from collections import defaultdict -from typing import Any, Dict, Iterable, List, Optional, Tuple, Union - -from . import consts, utils -from .profiler.diffrun import compare_op_tree, diff_summary -from .profiler.memory_parser import MemoryMetrics, MemoryRecord, MemorySnapshot -from .profiler.module_op import Stats -from .profiler.node import OperatorNode -from .utils import Canonicalizer, DisplayRounder - -logger = utils.get_logger() - - -class Run(object): - """ A profiler run. For visualization purpose only. - May contain profiling results from multiple workers. E.g. distributed scenario. - """ - - def __init__(self, name, run_dir, device_target='GPU'): - self.name = name - self.run_dir = run_dir - self.profiles: Dict[Tuple[str, str], RunProfile] = {} - self.span_view = {} - self.device_target = device_target - - @property - def workers(self): - # get full worker list and remove the duplicated - worker_list, _ = zip(*self.profiles.keys()) - worker_list = sorted(list(dict.fromkeys(worker_list))) - return worker_list - - @property - def views(self) -> List[consts.View]: - view_set = set() - for profile in self.profiles.values(): - view_set.update(profile.views) - return sorted(list(view_set), key=lambda x: x.id) - - def get_workers(self, view): - worker_set = set() - temp_span_view = {} - for profile in self.profiles.values(): - for v in profile.views: - if v.display_name == view: - worker_set.add(profile.worker) - if not temp_span_view.get(profile.worker): - temp_span_view[profile.worker] = [str(profile.span) if profile.span is not None else 'default'] - else: - temp_span_view[profile.worker].append( - str(profile.span) if profile.span is not None else 'default') - break - self.span_view = temp_span_view - return sorted(list(worker_set)) - - def get_spans(self, worker=None): - if worker is not None: - if self.span_view.get(worker) is None: - return None - spans = self.span_view.get(worker, []) - else: - spans = [s for _, s in self.profiles.keys()] - - spans = list(set(spans)) - if len(spans) == 1 and spans[0] is None: - return None - else: - return sorted(spans) - - def add_profile(self, profile: Union['DistributedRunProfile', 'RunProfile']): - span = profile.span - if span is None: - span = 'default' - else: - span = str(span) - self.profiles[(profile.worker, span)] = profile - - def get_profile(self, worker, span) -> Union['DistributedRunProfile', 'RunProfile']: - if worker is None: - raise ValueError('the worker parameter is mandatory') - - if len(self.profiles) == 0: - return None - - return self.profiles.get((worker, span), None) - - def get_profiles(self, *, worker=None, span=None) \ - -> Optional[Union[List['RunProfile'], List['DistributedRunProfile']]]: - # Note: we could not use if span to check it is None or not - # since the span 0 will be skipped at this case. - if worker is not None and span is not None: - return self.profiles.get((worker, span), None) - elif worker is not None: - return [p for (w, s), p in self.profiles.items() if worker == w] - elif span is not None: - return [p for (w, s), p in self.profiles.items() if span == s] - else: - return self.profiles.values() - - -class RunProfile(object): - """ Cooked profiling result for a worker. For visualization purpose only. - """ - - def __init__(self, worker, span): - self.worker = worker - self.span = span - self.views: List[consts.View] = [] - self.is_pytorch_lightning = False - self.has_runtime = False - self.has_kernel = False - self.has_communication = False - self.has_memcpy_or_memset = False - self.profiler_start_ts = float('inf') - self.overview = None - self.operation_pie_by_name = None - self.operation_table_by_name = None - self.operation_stack_by_name: Dict = None - self.operation_pie_by_name_input = None - self.operation_table_by_name_input = None - self.operation_stack_by_name_input: Dict = None - self.kernel_op_table = None - self.kernel_pie = None - self.kernel_table = None - self.tc_pie = None - self.trace_file_path: str = None - - self.gpu_metrics = None - - self.gpu_summary = None - self.gpu_tooltip = None - - # for memory stats and curve - self.memory_snapshot: Optional[MemorySnapshot] = None - self.tid2tree: Dict[int, OperatorNode] = None - self.pl_tid2tree: Dict[int, OperatorNode] = None - - self.module_stats: Optional[List(Stats)] = None - self.pl_module_stats: Optional[List(Stats)] = None - self.device_target = None - - self.memory_all_curve = None - self.memory_div_curve = None - self.memory_events = None - - self.step_to_overlap = None - self.step_to_wait = None - self.comm_op = None - - def append_gpu_metrics(self, raw_data: bytes): - counter_json_str = ', {}'.format(', '.join(self.gpu_metrics)) - counter_json_bytes = bytes(counter_json_str, 'utf-8') - - raw_data_without_tail = raw_data[: raw_data.rfind(b']')] - raw_data = b''.join([raw_data_without_tail, counter_json_bytes, b']}']) - - import gzip - raw_data = gzip.compress(raw_data, 1) - return raw_data - - @staticmethod - def _filtered_by_ts(events: Iterable[MemoryRecord], start_ts, end_ts): - """Returns time-ordered events of memory allocation and free""" - if start_ts is not None and end_ts is not None: - events = [e for e in events if start_ts <= e.ts and e.ts <= end_ts] - elif start_ts is not None: - events = [e for e in events if start_ts <= e.ts] - elif end_ts is not None: - events = [e for e in events if e.ts <= end_ts] - - return events - - def get_memory_stats(self, start_ts=None, end_ts=None, memory_metric='K'): - cano = Canonicalizer(memory_metric=memory_metric) - rounder = DisplayRounder(ndigits=3) - - stats = self.memory_snapshot.get_memory_statistics(self.tid2tree, start_ts=start_ts, end_ts=end_ts) - - result = { - 'metadata': { - 'title': 'Memory View', - 'default_device': 'CPU', - 'search': 'Operator Name', - 'sort': f'Self Size Increase ({cano.memory_metric})' - }, - 'columns': [ - {'name': 'Operator Name', 'type': 'string'}, - {'name': 'Calls', 'type': 'number', 'tooltip': '# of calls of the operator.'}, - {'name': f'Size Increase ({cano.memory_metric})', 'type': 'number', - 'tooltip': 'The memory increase size include all children operators.'}, - {'name': f'Self Size Increase ({cano.memory_metric})', 'type': 'number', - 'tooltip': 'The memory increase size associated with the operator itself.'}, - {'name': 'Allocation Count', 'type': 'number', - 'tooltip': 'The allocation count including all chidren operators.'}, - {'name': 'Self Allocation Count', 'type': 'number', - 'tooltip': 'The allocation count belonging to the operator itself.'}, - {'name': f'Allocation Size ({cano.memory_metric})', 'type': 'number', - 'tooltip': 'The allocation size including all children operators.'}, - {'name': f'Self Allocation Size ({cano.memory_metric})', 'type': 'number', - 'tooltip': ('The allocation size belonging to the operator itself.\n' - 'It will sum up all allocation bytes without considering the memory free.')}, - ], - 'rows': {} - } - - for name in stats: - these_rows = [] - result['rows'][name] = these_rows - - memory = stats[name] - for op_name, stat in sorted(memory.items()): - these_rows.append([ - op_name, - stat[6], - rounder(cano.convert_memory(stat[MemoryMetrics.IncreaseSize])), - rounder(cano.convert_memory(stat[MemoryMetrics.SelfIncreaseSize])), - stat[MemoryMetrics.AllocationCount], - stat[MemoryMetrics.SelfAllocationCount], - rounder(cano.convert_memory(stat[MemoryMetrics.AllocationSize])), - rounder(cano.convert_memory(stat[MemoryMetrics.SelfAllocationSize])), - ]) - - for dev_name in sorted(stats.keys()): - if dev_name.startswith('GPU'): - result['metadata']['default_device'] = dev_name - break - - return result - - def get_memory_curve( - self, - time_metric: str = 'ms', - memory_metric: str = 'K', - patch_for_step_plot=True): - def get_curves_and_peaks(records: List[MemoryRecord], cano: Canonicalizer): - """Inputs: - records: Sorted list of MemoryRecord - - For example: - ```py - { - 'CPU': [# Timestamp, Total Allocated, Total Reserved, Device Total Memory, operator - [1, 4, 4, 1000000, 'aten::add'], - [2, 16, 16, 1000000, "aten::empty], - [4, 4, 16, 1000000, '...'], - ], - 'GPU0': ... - } - ```""" - curves = defaultdict(list) - peaks = defaultdict(float) - for r in records: - if r.addr is None: - continue - dev = r.device_name - ts = r.ts - ta = r.total_allocated - tr = r.total_reserved - - if ta != ta or tr != tr: # isnan - continue - - curves[dev].append([ - round(cano.convert_time(ts - self.profiler_start_ts), 3), - round(cano.convert_memory(ta), 3), - round(cano.convert_memory(tr), 3), - ]) - peaks[dev] = max(peaks[dev], ta) - - for dev in curves: - if len(curves[dev]) == 0: - del curves[dev] - del peaks[dev] - - return curves, peaks - - # NOTE: this should have been occured in frontend - def patch_curves_for_step_plot(curves: Dict[str, List]): - # For example, if a curve is [(0, 0), (1, 1), (2,2)], the line plot - # is a stright line. Interpolating it as [(0, 0), (1, 0), (1, 1), - # (2,1) (2,2)], then the line plot will work as step plot. - new_curves = defaultdict(list) - for dev, curve in curves.items(): - new_curve = [] - for i, p in enumerate(curve): - if i != 0: - new_curve.append(p[:1] + new_curve[-1][1:]) - new_curve.append(p) - new_curves[dev] = new_curve - return new_curves - - cano = Canonicalizer(time_metric, memory_metric) - - curves, peaks = get_curves_and_peaks(self.memory_snapshot.memory_records, cano) - if patch_for_step_plot: - curves = patch_curves_for_step_plot(curves) - peaks_formatted = {} - totals = {} - for dev, value in peaks.items(): - peaks_formatted[dev] = 'Peak Memory Usage: {:.1f}{}'.format(cano.convert_memory(value), cano.memory_metric) - if dev != 'CPU': - try: - totals[dev] = cano.convert_memory(self.gpu_infos[int(dev[3:])]['Memory Raw']) - except BaseException: - pass - - devices: List[str] = sorted(list(curves.keys())) - default_device = 'CPU' - for dev in devices: - if dev.startswith('GPU'): - default_device = dev - break - - return { - 'metadata': { - 'default_device': default_device, - 'devices': devices, - 'peaks': peaks_formatted, - 'totals': totals, - 'first_ts': self.profiler_start_ts, - 'time_metric': cano.time_metric, - 'memory_metric': cano.memory_metric, - 'time_factor': cano.time_factor, - 'memory_factor': cano.memory_factor, - }, - 'columns': [ - {'name': f'Time ({cano.time_metric})', 'type': 'number', 'tooltip': 'Time since profiler starts.'}, - {'name': f'Allocated ({cano.memory_metric})', 'type': 'number', 'tooltip': 'Total memory in use.'}, - {'name': f'Reserved ({cano.memory_metric})', 'type': 'number', - 'tooltip': 'Total reserved memory by allocator, both used and unused.'}, - ], - 'rows': curves, - } - - def get_memory_events( - self, - start_ts=None, - end_ts=None, - time_metric: str = 'ms', - memory_metric: str = 'K'): - def get_op_name_or_ctx(record: MemoryRecord): - name = record.op_name_or_unknown - if name.startswith('aten::empty') and record.parent_op_name: - # aten::empty can be treated as the 'malloc' in pytorch - name = f'{record.parent_op_name} ({name})' - return name - - cano = Canonicalizer(time_metric=time_metric, memory_metric=memory_metric) - rounder = DisplayRounder(ndigits=3) - - profiler_start_ts = self.profiler_start_ts - memory_records = RunProfile._filtered_by_ts(self.memory_snapshot.memory_records, start_ts, end_ts) - - events = defaultdict(list) - alloc = {} # allocation events may or may not have paired free event - free = {} # free events that does not have paired alloc event - prev_ts = float('-inf') # ensure ordered memory records is ordered - for i, r in enumerate(memory_records): - if r.addr is None: - # profile json data prior to pytorch 1.10 do not have addr - # we should ignore them - continue - if prev_ts > r.ts: - logger.error(f'Invalid value, prev_ts {prev_ts} is greater than end_ts {r.ts}') - return {} - prev_ts = r.ts - addr = r.addr - size = r.bytes - if r.is_allocation: - # to be matched with a release event - alloc[addr] = i - else: - if addr in alloc: - alloc_r = memory_records[alloc[addr]] - alloc_ts = alloc_r.ts - free_ts = r.ts - events[alloc_r.device_name].append([ - get_op_name_or_ctx(alloc_r), - rounder(cano.convert_memory(-size)), - rounder(cano.convert_time(alloc_ts - profiler_start_ts)), - rounder(cano.convert_time(free_ts - profiler_start_ts)), - rounder(cano.convert_time(free_ts - alloc_ts)), - ]) - del alloc[addr] - else: - if addr in free: - logger.warning(f'Address {addr} is freed multiple times') - free[addr] = i - - for i in alloc.values(): - r = memory_records[i] - events[r.device_name].append([ - get_op_name_or_ctx(r), - rounder(cano.convert_memory(r.bytes)), - rounder(cano.convert_time(r.ts - profiler_start_ts)), - None, - None, - ]) - - for i in free.values(): - r = memory_records[i] - events[r.device_name].append([ - get_op_name_or_ctx(r), - rounder(cano.convert_memory(-r.bytes)), - None, - rounder(cano.convert_time(r.ts - profiler_start_ts)), - None, - ]) - - default_device = 'CPU' - for dev_name in sorted(events.keys()): - if dev_name.startswith('GPU'): - default_device = dev_name - break - - return { - 'metadata': { - 'title': 'Memory Events', - 'default_device': default_device, - }, - 'columns': [ - {'name': 'Operator', 'type': 'string', 'tooltip': ''}, - {'name': f'Size ({cano.memory_metric})', 'type': 'number', 'tooltip': ''}, - {'name': f'Allocation Time ({cano.time_metric})', 'type': 'number', 'tooltip': ''}, - {'name': f'Release Time ({cano.time_metric})', 'type': 'number', 'tooltip': ''}, - {'name': f'Duration ({cano.time_metric})', 'type': 'number', 'tooltip': ''}, - ], - 'rows': events, # in the form of { 'CPU': [...], 'GPU0': [...], ... } - } - - def get_module_view(self): - if self.is_pytorch_lightning and self.pl_module_stats: - module_stats = self.pl_module_stats - elif self.module_stats: - module_stats = self.module_stats - else: - return None - - result = { - 'columns': [ - {'name': 'Module Name', 'type': 'string', 'key': 'name'}, - {'name': 'Occurences', 'type': 'number', 'key': 'occurences'}, - {'name': 'Operators', 'type': 'number', 'key': 'operators'}, - {'name': 'Host Total Time', 'type': 'number', 'key': 'host_duration'}, - {'name': 'Host Self Time', 'type': 'number', 'key': 'self_host_duration'}, - {'name': 'Device Total Time', 'type': 'number', 'key': 'device_duration'}, - {'name': 'Device Self Time', 'type': 'number', 'key': 'self_device_duration'} - ], - 'data': [] - } - - def process_modules_stats(parent: List[Any], modules_stats: List[Stats]): - for stats in modules_stats: - d = stats._asdict() - d['children'] = [] - parent.append(d) - process_modules_stats(d['children'], stats.children) - - process_modules_stats(result['data'], module_stats) - return result - - def get_operator_tree(self): - if self.is_pytorch_lightning: - root = next(iter(self.pl_tid2tree.values())) - else: - root = next(iter(self.tid2tree.values())) - - result = [] - - def traverse_node(parent: List, node: OperatorNode): - d = { - 'name': node.name, - 'start_time': node.start_time, - 'end_time': node.end_time, - 'type': node.type, - 'tid': node.tid, - 'children': [] - } - parent.append(d) - for child in node.children: - traverse_node(d['children'], child) - - traverse_node(result, root) - return result[0] - - def compare_run(self, exp: 'RunProfile'): - base_root = next(iter(self.tid2tree.values())) - exp_root = next(iter(exp.tid2tree.values())) - diff_root = compare_op_tree(base_root, exp_root) - diff_stats = diff_summary(diff_root) - return diff_stats - - -class DistributedRunProfile(object): - """ Profiling all workers in a view. - """ - - def __init__(self, span: str): - self.worker = 'All' - self.span = span - self.views = [] - self.gpu_info = None - self.steps_to_overlap = None - self.steps_to_wait = None - self.comm_ops = None diff --git a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/static/index.html b/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/static/index.html deleted file mode 100644 index 66f6aca5f..000000000 --- a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/static/index.html +++ /dev/null @@ -1,2 +0,0 @@ -
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n.prototype = { get target() { return d(u(this).target) } }; var w = { configurable: !0, enumerable: !0, get: null };["clientX", "clientY", "screenX", "screenY", "pageX", "pageY", "identifier", "webkitRadiusX", "webkitRadiusY", "webkitRotationAngle", "webkitForce"].forEach(function (e) { w.get = function () { return u(this)[e] }, Object.defineProperty(n.prototype, e, w) }), r.prototype = { item: function (e) { return this[e] } }, i.prototype = Object.create(a.prototype), s(i.prototype, { get touches() { return o(u(this).touches) }, get targetTouches() { return o(u(this).targetTouches) }, get changedTouches() { return o(u(this).changedTouches) }, initTouchEvent: function () { throw new Error("Not implemented") } }), c(p, i, h), e.wrappers.Touch = n, e.wrappers.TouchEvent = i, e.wrappers.TouchList = r } }(window.ShadowDOMPolyfill), function (e) { "use strict"; function t(e, t) { Object.defineProperty(e, t, s) } function n() { this.length = 0, t(this, "length") } function r(e) { if (null == e) return e; for (var t = new n, r = 0, o = e.length; r < o; r++)t[r] = a(e[r]); return t.length = o, t } function o(e, t) { e.prototype[t] = function () { return r(i(this)[t].apply(i(this), arguments)) } } var i = e.unsafeUnwrap, a = e.wrap, s = { enumerable: !1 }; n.prototype = { item: function (e) { return this[e] } }, t(n.prototype, "item"), e.wrappers.NodeList = n, e.addWrapNodeListMethod = o, e.wrapNodeList = r }(window.ShadowDOMPolyfill), function (e) { "use strict"; e.wrapHTMLCollection = e.wrapNodeList, e.wrappers.HTMLCollection = e.wrappers.NodeList }(window.ShadowDOMPolyfill), function (e) { "use strict"; function t(e) { O(e instanceof _) } function n(e) { var t = new T; return t[0] = e, t.length = 1, t } function r(e, t, n) { N(t, "childList", { removedNodes: n, previousSibling: e.previousSibling, nextSibling: e.nextSibling }) } function o(e, t) { N(e, "childList", { removedNodes: t }) } function i(e, t, r, o) { if (e instanceof DocumentFragment) { var i = s(e); U = !0; for (var a = i.length - 1; 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n !== t.ownerDocument && n.adoptNode(t) } function f(t, n) { if (n.length) { var r = t.ownerDocument; if (r !== n[0].ownerDocument) for (var o = 0; o < n.length; o++)e.adoptNodeNoRemove(n[o], r) } } function m(e, t) { f(e, t); var n = t.length; if (1 === n) return P(t[0]); for (var r = P(e.ownerDocument.createDocumentFragment()), o = 0; o < n; o++)r.appendChild(P(t[o])); return r } function w(e) { if (void 0 !== e.firstChild_) for (var t = e.firstChild_; t;) { var n = t; t = t.nextSibling_, n.parentNode_ = n.previousSibling_ = n.nextSibling_ = void 0 } e.firstChild_ = e.lastChild_ = void 0 } function v(e) { if (e.invalidateShadowRenderer()) { for (var t = e.firstChild; t;) { O(t.parentNode === e); var n = t.nextSibling, r = P(t), o = r.parentNode; o && X.call(o, r), t.previousSibling_ = t.nextSibling_ = t.parentNode_ = null, t = n } e.firstChild_ = e.lastChild_ = null } else for (var n, i = P(e), a = i.firstChild; a;)n = a.nextSibling, X.call(i, a), a = n } function g(e) { var t = e.parentNode; 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(o += t.data, r.push(t)) : e = t : this.removeChild(t) : (e && r.length && (e.data += o, b(r)), r = [], o = "", e = null, t.childNodes.length && t.normalize()); e && r.length && (e.data += o, b(r)) } }), L(_, "ownerDocument"), x(V, _, document.createDocumentFragment()), delete _.prototype.querySelector, delete _.prototype.querySelectorAll, _.prototype = D(Object.create(S.prototype), _.prototype), e.cloneNode = y, e.nodeWasAdded = l, e.nodeWasRemoved = d, e.nodesWereAdded = u, e.nodesWereRemoved = p, e.originalInsertBefore = $, e.originalRemoveChild = X, e.snapshotNodeList = c, e.wrappers.Node = _ }(window.ShadowDOMPolyfill), function (e) { - "use strict"; function t(t, n, r, o) { for (var i = null, a = null, s = 0, c = t.length; s < c; s++)i = b(t[s]), !o && (a = v(i).root) && a instanceof e.wrappers.ShadowRoot || (r[n++] = i); return n } function n(e) { return String(e).replace(/\/deep\/|::shadow|>>>/g, " ") } function r(e) { return String(e).replace(/:host\(([^\s]+)\)/g, "$1").replace(/([^\s]):host/g, "$1").replace(":host", "*").replace(/\^|\/shadow\/|\/shadow-deep\/|::shadow|\/deep\/|::content|>>>/g, " ") } function o(e, t) { for (var n, r = e.firstElementChild; r;) { if (r.matches(t)) return r; if (n = o(r, t)) return n; r = r.nextElementSibling } return null } function i(e, t) { return e.matches(t) } function a(e, t, n) { var r = e.localName; return r === t || r === n && e.namespaceURI === j } function s() { return !0 } function c(e, t, n) { return e.localName === n } function l(e, t) { return e.namespaceURI === t } function u(e, t, n) { return e.namespaceURI === t && e.localName === n } function d(e, t, n, r, o, i) { for (var a = e.firstElementChild; a;)r(a, o, i) && (n[t++] = a), t = d(a, t, n, r, o, i), a = a.nextElementSibling; return t } function p(n, r, o, i, a) { var s, c = g(this), l = v(this).root; if (l instanceof e.wrappers.ShadowRoot) return d(this, r, o, n, i, null); if (c instanceof N) s = S.call(c, i); else { if (!(c instanceof C)) return d(this, r, o, n, i, null); s = _.call(c, i) } return t(s, r, o, a) } function h(n, r, o, i, a) { var s, c = g(this), l = v(this).root; if (l instanceof e.wrappers.ShadowRoot) return d(this, r, o, n, i, a); if (c instanceof N) s = M.call(c, i, a); else { if (!(c instanceof C)) return d(this, r, o, n, i, a); s = T.call(c, i, a) } return t(s, r, o, !1) } function f(n, r, o, i, a) { var s, c = g(this), l = v(this).root; if (l instanceof e.wrappers.ShadowRoot) return d(this, r, o, n, i, a); if (c instanceof N) s = L.call(c, i, a); else { if (!(c instanceof C)) return d(this, r, o, n, i, a); s = O.call(c, i, a) } return t(s, r, o, !1) } var m = e.wrappers.HTMLCollection, w = e.wrappers.NodeList, v = e.getTreeScope, g = e.unsafeUnwrap, b = e.wrap, y = document.querySelector, E = document.documentElement.querySelector, _ = document.querySelectorAll, S = document.documentElement.querySelectorAll, T = document.getElementsByTagName, M = document.documentElement.getElementsByTagName, O = document.getElementsByTagNameNS, L = document.documentElement.getElementsByTagNameNS, N = window.Element, C = window.HTMLDocument || window.Document, j = "http://www.w3.org/1999/xhtml", D = { - querySelector: function (t) { var r = n(t), i = r !== t; t = r; var a, s = g(this), c = v(this).root; if (c instanceof e.wrappers.ShadowRoot) return o(this, t); if (s instanceof N) a = b(E.call(s, t)); else { if (!(s instanceof C)) return o(this, t); a = b(y.call(s, t)) } return a && !i && (c = v(a).root) && c instanceof e.wrappers.ShadowRoot ? o(this, t) : a }, querySelectorAll: function (e) { var t = n(e), r = t !== e; e = t; var o = new w; return o.length = p.call(this, i, 0, o, e, r), o } - }, H = { matches: function (t) { return t = r(t), e.originalMatches.call(g(this), t) } }, x = { getElementsByTagName: function (e) { var t = new m, n = "*" === e ? s : a; return t.length = h.call(this, n, 0, t, e, e.toLowerCase()), t }, getElementsByClassName: function (e) { return this.querySelectorAll("." + e) }, getElementsByTagNameNS: function (e, t) { var n = new m, r = null; return r = "*" === e ? "*" === t ? s : c : "*" === t ? l : u, n.length = f.call(this, r, 0, n, e || null, t), n } }; e.GetElementsByInterface = x, e.SelectorsInterface = D, e.MatchesInterface = H -}(window.ShadowDOMPolyfill), function (e) { "use strict"; function t(e) { for (; e && e.nodeType !== Node.ELEMENT_NODE;)e = e.nextSibling; return e } function n(e) { for (; e && e.nodeType !== Node.ELEMENT_NODE;)e = e.previousSibling; return e } var r = e.wrappers.NodeList, o = { get firstElementChild() { return t(this.firstChild) }, get lastElementChild() { return n(this.lastChild) }, get childElementCount() { for (var e = 0, t = this.firstElementChild; t; t = t.nextElementSibling)e++; return e }, get children() { for (var e = new r, t = 0, n = this.firstElementChild; n; n = n.nextElementSibling)e[t++] = n; return e.length = t, e }, remove: function () { var e = this.parentNode; e && e.removeChild(this) } }, i = { get nextElementSibling() { return t(this.nextSibling) }, get previousElementSibling() { return n(this.previousSibling) } }, a = { getElementById: function (e) { return /[ \t\n\r\f]/.test(e) ? null : this.querySelector('[id="' + e + '"]') } }; e.ChildNodeInterface = i, e.NonElementParentNodeInterface = a, e.ParentNodeInterface = o }(window.ShadowDOMPolyfill), function (e) { "use strict"; function t(e) { r.call(this, e) } var n = e.ChildNodeInterface, r = e.wrappers.Node, o = e.enqueueMutation, i = e.mixin, a = e.registerWrapper, s = e.unsafeUnwrap, c = window.CharacterData; t.prototype = Object.create(r.prototype), i(t.prototype, { get nodeValue() { return this.data }, set nodeValue(e) { this.data = e }, get textContent() { return this.data }, set textContent(e) { this.data = e }, get data() { return s(this).data }, set data(e) { var t = s(this).data; o(this, "characterData", { oldValue: t }), s(this).data = e } }), i(t.prototype, n), a(c, t, document.createTextNode("")), e.wrappers.CharacterData = t }(window.ShadowDOMPolyfill), function (e) { "use strict"; function t(e) { return e >>> 0 } function n(e) { r.call(this, e) } var r = e.wrappers.CharacterData, o = (e.enqueueMutation, e.mixin), i = e.registerWrapper, a = window.Text; n.prototype = Object.create(r.prototype), o(n.prototype, { splitText: function (e) { e = t(e); var n = this.data; if (e > n.length) throw new Error("IndexSizeError"); var r = n.slice(0, e), o = n.slice(e); this.data = r; var i = this.ownerDocument.createTextNode(o); return this.parentNode && this.parentNode.insertBefore(i, this.nextSibling), i } }), i(a, n, document.createTextNode("")), e.wrappers.Text = n }(window.ShadowDOMPolyfill), function (e) { "use strict"; function t(e) { return i(e).getAttribute("class") } function n(e, t) { a(e, "attributes", { name: "class", namespace: null, oldValue: t }) } function r(t) { e.invalidateRendererBasedOnAttribute(t, "class") } function o(e, o, i) { var a = e.ownerElement_; if (null == a) return o.apply(e, i); var s = t(a), c = o.apply(e, i); return t(a) !== s && (n(a, s), r(a)), c } if (!window.DOMTokenList) return void console.warn("Missing DOMTokenList prototype, please include a compatible classList polyfill such as http://goo.gl/uTcepH."); var i = e.unsafeUnwrap, a = e.enqueueMutation, s = DOMTokenList.prototype.add; DOMTokenList.prototype.add = function () { o(this, s, arguments) }; var c = DOMTokenList.prototype.remove; DOMTokenList.prototype.remove = function () { o(this, c, arguments) }; var l = DOMTokenList.prototype.toggle; DOMTokenList.prototype.toggle = function () { return o(this, l, arguments) } }(window.ShadowDOMPolyfill), function (e) { "use strict"; function t(t, n) { var r = t.parentNode; if (r && r.shadowRoot) { var o = e.getRendererForHost(r); o.dependsOnAttribute(n) && o.invalidate() } } function n(e, t, n) { u(e, "attributes", { name: t, namespace: null, oldValue: n }) } function r(e) { a.call(this, e) } var o = e.ChildNodeInterface, i = e.GetElementsByInterface, a = e.wrappers.Node, s = e.ParentNodeInterface, c = e.SelectorsInterface, l = e.MatchesInterface, u = (e.addWrapNodeListMethod, e.enqueueMutation), d = e.mixin, p = (e.oneOf, e.registerWrapper), h = e.unsafeUnwrap, f = e.wrappers, m = window.Element, w = ["matches", "mozMatchesSelector", "msMatchesSelector", "webkitMatchesSelector"].filter(function (e) { return m.prototype[e] }), v = w[0], g = m.prototype[v], b = new WeakMap; r.prototype = Object.create(a.prototype), d(r.prototype, { createShadowRoot: function () { var t = new f.ShadowRoot(this); h(this).polymerShadowRoot_ = t; var n = e.getRendererForHost(this); return n.invalidate(), t }, get shadowRoot() { return h(this).polymerShadowRoot_ || null }, setAttribute: function (e, r) { var o = h(this).getAttribute(e); h(this).setAttribute(e, r), n(this, e, o), t(this, e) }, removeAttribute: function (e) { var r = h(this).getAttribute(e); h(this).removeAttribute(e), n(this, e, r), t(this, e) }, get classList() { var e = b.get(this); if (!e) { if (e = h(this).classList, !e) return; e.ownerElement_ = this, b.set(this, e) } return e }, get className() { return h(this).className }, set className(e) { this.setAttribute("class", e) }, get id() { return h(this).id }, set id(e) { this.setAttribute("id", e) } }), w.forEach(function (e) { "matches" !== e && (r.prototype[e] = function (e) { return this.matches(e) }) }), m.prototype.webkitCreateShadowRoot && (r.prototype.webkitCreateShadowRoot = r.prototype.createShadowRoot), d(r.prototype, o), d(r.prototype, i), d(r.prototype, s), d(r.prototype, c), d(r.prototype, l), p(m, r, document.createElementNS(null, "x")), e.invalidateRendererBasedOnAttribute = t, e.matchesNames = w, e.originalMatches = g, e.wrappers.Element = r }(window.ShadowDOMPolyfill), function (e) { "use strict"; function t(e) { switch (e) { case "&": return "&"; case "<": return "<"; case ">": return ">"; case '"': return """; case " ": return " " } } function n(e) { return e.replace(L, t) } function r(e) { return e.replace(N, t) } function o(e) { for (var t = {}, n = 0; n < e.length; n++)t[e[n]] = !0; return t } function i(e) { if (e.namespaceURI !== D) return !0; var t = e.ownerDocument.doctype; return t && t.publicId && t.systemId } function a(e, t) { switch (e.nodeType) { case Node.ELEMENT_NODE: for (var o, a = e.tagName.toLowerCase(), c = "<" + a, l = e.attributes, u = 0; o = l[u]; u++)c += " " + o.name + '="' + n(o.value) + '"'; return C[a] ? (i(e) && (c += "/"), c + ">") : c + ">" + s(e) + ""; case Node.TEXT_NODE: var d = e.data; return t && j[t.localName] ? d : r(d); case Node.COMMENT_NODE: return ""; default: throw console.error(e), new Error("not implemented") } } function s(e) { e instanceof O.HTMLTemplateElement && (e = e.content); for (var t = "", n = e.firstChild; n; n = n.nextSibling)t += a(n, e); return t } function c(e, t, n) { var r = n || "div"; e.textContent = ""; var o = T(e.ownerDocument.createElement(r)); o.innerHTML = t; for (var i; i = o.firstChild;)e.appendChild(M(i)) } function l(e) { m.call(this, e) } function u(e, t) { var n = T(e.cloneNode(!1)); n.innerHTML = t; for (var r, o = T(document.createDocumentFragment()); r = n.firstChild;)o.appendChild(r); return M(o) } function d(t) { return function () { return e.renderAllPending(), S(this)[t] } } function p(e) { w(l, e, d(e)) } function h(t) { Object.defineProperty(l.prototype, t, { get: d(t), set: function (n) { e.renderAllPending(), S(this)[t] = n }, configurable: !0, enumerable: !0 }) } function f(t) { Object.defineProperty(l.prototype, t, { value: function () { return e.renderAllPending(), S(this)[t].apply(S(this), arguments) }, configurable: !0, enumerable: !0 }) } var m = e.wrappers.Element, w = e.defineGetter, v = e.enqueueMutation, g = e.mixin, b = e.nodesWereAdded, y = e.nodesWereRemoved, E = e.registerWrapper, _ = e.snapshotNodeList, S = e.unsafeUnwrap, T = e.unwrap, M = e.wrap, O = e.wrappers, L = /[&\u00A0"]/g, N = /[&\u00A0<>]/g, C = o(["area", "base", "br", "col", "command", "embed", "hr", "img", "input", "keygen", "link", "meta", "param", "source", "track", "wbr"]), j = o(["style", "script", "xmp", "iframe", "noembed", "noframes", "plaintext", "noscript"]), D = "http://www.w3.org/1999/xhtml", H = /MSIE/.test(navigator.userAgent), x = window.HTMLElement, R = window.HTMLTemplateElement; l.prototype = Object.create(m.prototype), g(l.prototype, { get innerHTML() { return s(this) }, set innerHTML(e) { if (H && j[this.localName]) return void (this.textContent = e); var t = _(this.childNodes); this.invalidateShadowRenderer() ? this instanceof O.HTMLTemplateElement ? c(this.content, e) : c(this, e, this.tagName) : !R && this instanceof O.HTMLTemplateElement ? c(this.content, e) : S(this).innerHTML = e; var n = _(this.childNodes); v(this, "childList", { addedNodes: n, removedNodes: t }), y(t), b(n, this) }, get outerHTML() { return a(this, this.parentNode) }, set outerHTML(e) { var t = this.parentNode; if (t) { t.invalidateShadowRenderer(); var n = u(t, e); t.replaceChild(n, this) } }, insertAdjacentHTML: function (e, t) { var n, r; switch (String(e).toLowerCase()) { case "beforebegin": n = this.parentNode, r = this; break; case "afterend": n = this.parentNode, r = this.nextSibling; break; case "afterbegin": n = this, r = this.firstChild; break; case "beforeend": n = this, r = null; break; default: return }var o = u(n, t); n.insertBefore(o, r) }, get hidden() { return this.hasAttribute("hidden") }, set hidden(e) { e ? this.setAttribute("hidden", "") : this.removeAttribute("hidden") } }), ["clientHeight", "clientLeft", "clientTop", "clientWidth", "offsetHeight", "offsetLeft", "offsetTop", "offsetWidth", "scrollHeight", "scrollWidth"].forEach(p), ["scrollLeft", "scrollTop"].forEach(h), ["focus", "getBoundingClientRect", "getClientRects", "scrollIntoView"].forEach(f), E(x, l, document.createElement("b")), e.wrappers.HTMLElement = l, e.getInnerHTML = s, e.setInnerHTML = c }(window.ShadowDOMPolyfill), function (e) { "use strict"; function t(e) { n.call(this, e) } var n = e.wrappers.HTMLElement, r = e.mixin, o = e.registerWrapper, i = e.unsafeUnwrap, a = e.wrap, s = window.HTMLCanvasElement; t.prototype = Object.create(n.prototype), r(t.prototype, { getContext: function () { var e = i(this).getContext.apply(i(this), arguments); return e && a(e) } }), o(s, t, document.createElement("canvas")), e.wrappers.HTMLCanvasElement = t }(window.ShadowDOMPolyfill), function (e) { "use strict"; function t(e) { n.call(this, e) } var n = e.wrappers.HTMLElement, r = e.mixin, o = e.registerWrapper, i = window.HTMLContentElement; t.prototype = Object.create(n.prototype), r(t.prototype, { constructor: t, get select() { return this.getAttribute("select") }, set select(e) { this.setAttribute("select", e) }, setAttribute: function (e, t) { n.prototype.setAttribute.call(this, e, t), "select" === String(e).toLowerCase() && this.invalidateShadowRenderer(!0) } }), i && o(i, t), e.wrappers.HTMLContentElement = t }(window.ShadowDOMPolyfill), function (e) { "use strict"; function t(e) { n.call(this, e) } var n = e.wrappers.HTMLElement, r = e.mixin, o = e.registerWrapper, i = e.wrapHTMLCollection, a = e.unwrap, s = window.HTMLFormElement; t.prototype = Object.create(n.prototype), r(t.prototype, { get elements() { return i(a(this).elements) } }), o(s, t, document.createElement("form")), e.wrappers.HTMLFormElement = t }(window.ShadowDOMPolyfill), function (e) { "use strict"; function t(e) { r.call(this, e) } function n(e, t) { if (!(this instanceof n)) throw new TypeError("DOM object constructor cannot be called as a function."); var o = i(document.createElement("img")); r.call(this, o), a(o, this), void 0 !== e && (o.width = e), void 0 !== t && (o.height = t) } var r = e.wrappers.HTMLElement, o = e.registerWrapper, i = e.unwrap, a = e.rewrap, s = window.HTMLImageElement; t.prototype = Object.create(r.prototype), o(s, t, document.createElement("img")), n.prototype = t.prototype, e.wrappers.HTMLImageElement = t, e.wrappers.Image = n }(window.ShadowDOMPolyfill), function (e) { "use strict"; function t(e) { n.call(this, e) } var n = e.wrappers.HTMLElement, r = (e.mixin, e.wrappers.NodeList, e.registerWrapper), o = window.HTMLShadowElement; t.prototype = Object.create(n.prototype), t.prototype.constructor = t, o && r(o, t), e.wrappers.HTMLShadowElement = t }(window.ShadowDOMPolyfill), function (e) { "use strict"; function t(e) { if (!e.defaultView) return e; var t = d.get(e); if (!t) { for (t = e.implementation.createHTMLDocument(""); t.lastChild;)t.removeChild(t.lastChild); d.set(e, t) } return t } function n(e) { for (var n, r = t(e.ownerDocument), o = c(r.createDocumentFragment()); n = e.firstChild;)o.appendChild(n); return o } function r(e) { if (o.call(this, e), !p) { var t = n(e); u.set(this, l(t)) } } var o = e.wrappers.HTMLElement, i = e.mixin, a = e.registerWrapper, s = e.unsafeUnwrap, c = e.unwrap, l = e.wrap, u = new WeakMap, d = new WeakMap, p = window.HTMLTemplateElement; r.prototype = Object.create(o.prototype), i(r.prototype, { constructor: r, get content() { return p ? l(s(this).content) : u.get(this) } }), p && a(p, r), e.wrappers.HTMLTemplateElement = r }(window.ShadowDOMPolyfill), function (e) { "use strict"; function t(e) { n.call(this, e) } var n = e.wrappers.HTMLElement, r = e.registerWrapper, o = window.HTMLMediaElement; o && (t.prototype = Object.create(n.prototype), r(o, t, document.createElement("audio")), e.wrappers.HTMLMediaElement = t) }(window.ShadowDOMPolyfill), function (e) { "use strict"; function t(e) { r.call(this, e) } function n(e) { if (!(this instanceof n)) throw new TypeError("DOM object constructor cannot be called as a function."); var t = i(document.createElement("audio")); r.call(this, t), a(t, this), t.setAttribute("preload", "auto"), void 0 !== e && t.setAttribute("src", e) } var r = e.wrappers.HTMLMediaElement, o = e.registerWrapper, i = e.unwrap, a = e.rewrap, s = window.HTMLAudioElement; s && (t.prototype = Object.create(r.prototype), o(s, t, document.createElement("audio")), n.prototype = t.prototype, e.wrappers.HTMLAudioElement = t, e.wrappers.Audio = n) }(window.ShadowDOMPolyfill), function (e) { "use strict"; function t(e) { return e.replace(/\s+/g, " ").trim() } function n(e) { o.call(this, e) } function r(e, t, n, i) { if (!(this instanceof r)) throw new TypeError("DOM object constructor cannot be called as a function."); var a = c(document.createElement("option")); o.call(this, a), s(a, this), void 0 !== e && (a.text = e), void 0 !== t && a.setAttribute("value", t), n === !0 && a.setAttribute("selected", ""), a.selected = i === !0 } var o = e.wrappers.HTMLElement, i = e.mixin, a = e.registerWrapper, s = e.rewrap, c = e.unwrap, l = e.wrap, u = window.HTMLOptionElement; n.prototype = Object.create(o.prototype), i(n.prototype, { get text() { return t(this.textContent) }, set text(e) { this.textContent = t(String(e)) }, get form() { return l(c(this).form) } }), a(u, n, document.createElement("option")), r.prototype = n.prototype, e.wrappers.HTMLOptionElement = n, e.wrappers.Option = r }(window.ShadowDOMPolyfill), function (e) { "use strict"; function t(e) { n.call(this, e) } var n = e.wrappers.HTMLElement, r = e.mixin, o = e.registerWrapper, i = e.unwrap, a = e.wrap, s = window.HTMLSelectElement; t.prototype = Object.create(n.prototype), r(t.prototype, { add: function (e, t) { "object" == typeof t && (t = i(t)), i(this).add(i(e), t) }, remove: function (e) { return void 0 === e ? void n.prototype.remove.call(this) : ("object" == typeof e && (e = i(e)), void i(this).remove(e)) }, get form() { return a(i(this).form) } }), o(s, t, document.createElement("select")), e.wrappers.HTMLSelectElement = t }(window.ShadowDOMPolyfill), function (e) { "use strict"; function t(e) { n.call(this, e) } var n = e.wrappers.HTMLElement, r = e.mixin, o = e.registerWrapper, i = e.unwrap, a = e.wrap, s = e.wrapHTMLCollection, c = window.HTMLTableElement; t.prototype = Object.create(n.prototype), r(t.prototype, { get caption() { return a(i(this).caption) }, createCaption: function () { return a(i(this).createCaption()) }, get tHead() { return a(i(this).tHead) }, createTHead: function () { return a(i(this).createTHead()) }, createTFoot: function () { return a(i(this).createTFoot()) }, get tFoot() { return a(i(this).tFoot) }, get tBodies() { return s(i(this).tBodies) }, createTBody: function () { return a(i(this).createTBody()) }, get rows() { return s(i(this).rows) }, insertRow: function (e) { return a(i(this).insertRow(e)) } }), o(c, t, document.createElement("table")), e.wrappers.HTMLTableElement = t }(window.ShadowDOMPolyfill), function (e) { "use strict"; function t(e) { n.call(this, e) } var n = e.wrappers.HTMLElement, r = e.mixin, o = e.registerWrapper, i = e.wrapHTMLCollection, a = e.unwrap, s = e.wrap, c = window.HTMLTableSectionElement; t.prototype = Object.create(n.prototype), r(t.prototype, { constructor: t, get rows() { return i(a(this).rows) }, insertRow: function (e) { return s(a(this).insertRow(e)) } }), o(c, t, document.createElement("thead")), e.wrappers.HTMLTableSectionElement = t }(window.ShadowDOMPolyfill), function (e) { "use strict"; function t(e) { n.call(this, e) } var n = e.wrappers.HTMLElement, r = e.mixin, o = e.registerWrapper, i = e.wrapHTMLCollection, a = e.unwrap, s = e.wrap, c = window.HTMLTableRowElement; t.prototype = Object.create(n.prototype), r(t.prototype, { get cells() { return i(a(this).cells) }, insertCell: function (e) { return s(a(this).insertCell(e)) } }), o(c, t, document.createElement("tr")), e.wrappers.HTMLTableRowElement = t }(window.ShadowDOMPolyfill), function (e) { "use strict"; function t(e) { switch (e.localName) { case "content": return new n(e); case "shadow": return new o(e); case "template": return new i(e) }r.call(this, e) } var n = e.wrappers.HTMLContentElement, r = e.wrappers.HTMLElement, o = e.wrappers.HTMLShadowElement, i = e.wrappers.HTMLTemplateElement, a = (e.mixin, e.registerWrapper), s = window.HTMLUnknownElement; t.prototype = Object.create(r.prototype), a(s, t), e.wrappers.HTMLUnknownElement = t }(window.ShadowDOMPolyfill), function (e) { "use strict"; function t(e) { n.call(this, e) } var n = e.wrappers.Element, r = e.wrappers.HTMLElement, o = e.registerWrapper, i = (e.defineWrapGetter, e.unsafeUnwrap), a = e.wrap, s = e.mixin, c = "http://www.w3.org/2000/svg", l = window.SVGElement, u = document.createElementNS(c, "title"); if (!("classList" in u)) { var d = Object.getOwnPropertyDescriptor(n.prototype, "classList"); Object.defineProperty(r.prototype, "classList", d), delete n.prototype.classList } t.prototype = Object.create(n.prototype), s(t.prototype, { get ownerSVGElement() { return a(i(this).ownerSVGElement) } }), o(l, t, document.createElementNS(c, "title")), e.wrappers.SVGElement = t }(window.ShadowDOMPolyfill), function (e) { "use strict"; function t(e) { p.call(this, e) } var n = e.mixin, r = e.registerWrapper, o = e.unwrap, i = e.wrap, a = window.SVGUseElement, s = "http://www.w3.org/2000/svg", c = i(document.createElementNS(s, "g")), l = document.createElementNS(s, "use"), u = c.constructor, d = Object.getPrototypeOf(u.prototype), p = d.constructor; t.prototype = Object.create(d), "instanceRoot" in l && n(t.prototype, { get instanceRoot() { return i(o(this).instanceRoot) }, get animatedInstanceRoot() { return i(o(this).animatedInstanceRoot) } }), r(a, t, l), e.wrappers.SVGUseElement = t }(window.ShadowDOMPolyfill), function (e) { "use strict"; function t(e) { n.call(this, e) } var n = e.wrappers.EventTarget, r = e.mixin, o = e.registerWrapper, i = e.unsafeUnwrap, a = e.wrap, s = window.SVGElementInstance; s && (t.prototype = Object.create(n.prototype), r(t.prototype, { get correspondingElement() { return a(i(this).correspondingElement) }, get correspondingUseElement() { return a(i(this).correspondingUseElement) }, get parentNode() { return a(i(this).parentNode) }, get childNodes() { throw new Error("Not implemented") }, get firstChild() { return a(i(this).firstChild) }, get lastChild() { return a(i(this).lastChild) }, get previousSibling() { return a(i(this).previousSibling) }, get nextSibling() { return a(i(this).nextSibling) } }), o(s, t), e.wrappers.SVGElementInstance = t) }(window.ShadowDOMPolyfill), function (e) { "use strict"; function t(e) { o(e, this) } var n = e.mixin, r = e.registerWrapper, o = e.setWrapper, i = e.unsafeUnwrap, a = e.unwrap, s = e.unwrapIfNeeded, c = e.wrap, l = window.CanvasRenderingContext2D; n(t.prototype, { get canvas() { return c(i(this).canvas) }, drawImage: function () { arguments[0] = s(arguments[0]), i(this).drawImage.apply(i(this), arguments) }, createPattern: function () { return arguments[0] = a(arguments[0]), i(this).createPattern.apply(i(this), arguments) } }), r(l, t, document.createElement("canvas").getContext("2d")), e.wrappers.CanvasRenderingContext2D = t }(window.ShadowDOMPolyfill), function (e) { "use strict"; function t(e) { i(e, this) } var n = e.addForwardingProperties, r = e.mixin, o = e.registerWrapper, i = e.setWrapper, a = e.unsafeUnwrap, s = e.unwrapIfNeeded, c = e.wrap, l = window.WebGLRenderingContext; if (l) { r(t.prototype, { get canvas() { return c(a(this).canvas) }, texImage2D: function () { arguments[5] = s(arguments[5]), a(this).texImage2D.apply(a(this), arguments) }, texSubImage2D: function () { arguments[6] = s(arguments[6]), a(this).texSubImage2D.apply(a(this), arguments) } }); var u = Object.getPrototypeOf(l.prototype); u !== Object.prototype && n(u, t.prototype); var d = /WebKit/.test(navigator.userAgent) ? { drawingBufferHeight: null, drawingBufferWidth: null } : {}; o(l, t, d), e.wrappers.WebGLRenderingContext = t } }(window.ShadowDOMPolyfill), function (e) { "use strict"; function t(e) { n.call(this, e) } var n = e.wrappers.Node, r = e.GetElementsByInterface, o = e.NonElementParentNodeInterface, i = e.ParentNodeInterface, a = e.SelectorsInterface, s = e.mixin, c = e.registerObject, l = e.registerWrapper, u = window.DocumentFragment; t.prototype = Object.create(n.prototype), s(t.prototype, i), s(t.prototype, a), s(t.prototype, r), s(t.prototype, o), l(u, t, document.createDocumentFragment()), e.wrappers.DocumentFragment = t; var d = c(document.createComment("")); e.wrappers.Comment = d }(window.ShadowDOMPolyfill), function (e) { "use strict"; function t(e) { var t = d(u(e).ownerDocument.createDocumentFragment()); n.call(this, t), c(t, this); var o = e.shadowRoot; f.set(this, o), this.treeScope_ = new r(this, a(o || e)), h.set(this, e) } var n = e.wrappers.DocumentFragment, r = e.TreeScope, o = e.elementFromPoint, i = e.getInnerHTML, a = e.getTreeScope, s = e.mixin, c = e.rewrap, l = e.setInnerHTML, u = e.unsafeUnwrap, d = e.unwrap, p = e.wrap, h = new WeakMap, f = new WeakMap; t.prototype = Object.create(n.prototype), s(t.prototype, { constructor: t, get innerHTML() { return i(this) }, set innerHTML(e) { l(this, e), this.invalidateShadowRenderer() }, get olderShadowRoot() { return f.get(this) || null }, get host() { return h.get(this) || null }, invalidateShadowRenderer: function () { return h.get(this).invalidateShadowRenderer() }, elementFromPoint: function (e, t) { return o(this, this.ownerDocument, e, t) }, getSelection: function () { return document.getSelection() }, get activeElement() { var e = d(this).ownerDocument.activeElement; if (!e || !e.nodeType) return null; for (var t = p(e); !this.contains(t);) { for (; t.parentNode;)t = t.parentNode; if (!t.host) return null; t = t.host } return t } }), e.wrappers.ShadowRoot = t }(window.ShadowDOMPolyfill), function (e) { "use strict"; function t(e) { var t = d(e).root; return t instanceof h ? t.host : null } function n(t, n) { if (t.shadowRoot) { n = Math.min(t.childNodes.length - 1, n); var r = t.childNodes[n]; if (r) { var o = e.getDestinationInsertionPoints(r); if (o.length > 0) { var i = o[0].parentNode; i.nodeType == Node.ELEMENT_NODE && (t = i) } } } return t } function r(e) { return e = u(e), t(e) || e } function o(e) { a(e, this) } var i = e.registerWrapper, a = e.setWrapper, s = e.unsafeUnwrap, c = e.unwrap, l = e.unwrapIfNeeded, u = e.wrap, d = e.getTreeScope, p = window.Range, h = e.wrappers.ShadowRoot; o.prototype = { get startContainer() { return r(s(this).startContainer) }, get endContainer() { return r(s(this).endContainer) }, get commonAncestorContainer() { return r(s(this).commonAncestorContainer) }, setStart: function (e, t) { e = n(e, t), s(this).setStart(l(e), t) }, setEnd: function (e, t) { e = n(e, t), s(this).setEnd(l(e), t) }, setStartBefore: function (e) { s(this).setStartBefore(l(e)) }, setStartAfter: function (e) { s(this).setStartAfter(l(e)) }, setEndBefore: function (e) { s(this).setEndBefore(l(e)) }, setEndAfter: function (e) { s(this).setEndAfter(l(e)) }, selectNode: function (e) { s(this).selectNode(l(e)) }, selectNodeContents: function (e) { s(this).selectNodeContents(l(e)) }, compareBoundaryPoints: function (e, t) { return s(this).compareBoundaryPoints(e, c(t)) }, extractContents: function () { return u(s(this).extractContents()) }, cloneContents: function () { return u(s(this).cloneContents()) }, insertNode: function (e) { s(this).insertNode(l(e)) }, surroundContents: function (e) { s(this).surroundContents(l(e)) }, cloneRange: function () { return u(s(this).cloneRange()) }, isPointInRange: function (e, t) { return s(this).isPointInRange(l(e), t) }, comparePoint: function (e, t) { return s(this).comparePoint(l(e), t) }, intersectsNode: function (e) { return s(this).intersectsNode(l(e)) }, toString: function () { return s(this).toString() } }, p.prototype.createContextualFragment && (o.prototype.createContextualFragment = function (e) { return u(s(this).createContextualFragment(e)) }), i(window.Range, o, document.createRange()), e.wrappers.Range = o }(window.ShadowDOMPolyfill), function (e) { "use strict"; function t(e) { e.previousSibling_ = e.previousSibling, e.nextSibling_ = e.nextSibling, e.parentNode_ = e.parentNode } function n(n, o, i) { var a = x(n), s = x(o), c = i ? x(i) : null; if (r(o), t(o), i) n.firstChild === i && (n.firstChild_ = i), i.previousSibling_ = i.previousSibling; else { n.lastChild_ = n.lastChild, n.lastChild === n.firstChild && (n.firstChild_ = n.firstChild); var l = R(a.lastChild); l && (l.nextSibling_ = l.nextSibling) } e.originalInsertBefore.call(a, s, c) } function r(n) { var r = x(n), o = r.parentNode; if (o) { var i = R(o); t(n), n.previousSibling && (n.previousSibling.nextSibling_ = n), n.nextSibling && (n.nextSibling.previousSibling_ = n), i.lastChild === n && (i.lastChild_ = n), i.firstChild === n && (i.firstChild_ = n), e.originalRemoveChild.call(o, r) } } function o(e) { P.set(e, []) } function i(e) { var t = P.get(e); return t || P.set(e, t = []), t } function a(e) { for (var t = [], n = 0, r = e.firstChild; r; r = r.nextSibling)t[n++] = r; return t } function s() { for (var e = 0; e < F.length; e++) { var t = F[e], n = t.parentRenderer; n && n.dirty || t.render() } F = [] } function c() { T = null, s() } function l(e) { var t = A.get(e); return t || (t = new h(e), A.set(e, t)), t } function u(e) { var t = j(e).root; return t instanceof C ? t : null } function d(e) { return l(e.host) } function p(e) { this.skip = !1, this.node = e, this.childNodes = [] } function h(e) { this.host = e, this.dirty = !1, this.invalidateAttributes(), this.associateNode(e) } function f(e) { for (var t = [], n = e.firstChild; n; n = n.nextSibling)E(n) ? t.push.apply(t, i(n)) : t.push(n); return t } function m(e) { if (e instanceof L) return e; if (e instanceof O) return null; for (var t = e.firstChild; t; t = t.nextSibling) { var n = m(t); if (n) return n } return null } function w(e, t) { i(t).push(e); var n = k.get(e); n ? n.push(t) : k.set(e, [t]) } function v(e) { return k.get(e) } function g(e) { k.set(e, void 0) } function b(e, t) { var n = t.getAttribute("select"); if (!n) return !0; if (n = n.trim(), !n) return !0; if (!(e instanceof M)) return !1; if (!q.test(n)) return !1; try { return e.matches(n) } catch (r) { return !1 } } function y(e, t) { var n = v(t); return n && n[n.length - 1] === e } function E(e) { return e instanceof O || e instanceof L } function _(e) { return e.shadowRoot } function S(e) { for (var t = [], n = e.shadowRoot; n; n = n.olderShadowRoot)t.push(n); return t } var T, M = e.wrappers.Element, O = e.wrappers.HTMLContentElement, L = e.wrappers.HTMLShadowElement, N = e.wrappers.Node, C = e.wrappers.ShadowRoot, j = (e.assert, e.getTreeScope), D = (e.mixin, e.oneOf), H = e.unsafeUnwrap, x = e.unwrap, R = e.wrap, I = e.ArraySplice, P = new WeakMap, k = new WeakMap, A = new WeakMap, W = D(window, ["requestAnimationFrame", "mozRequestAnimationFrame", "webkitRequestAnimationFrame", "setTimeout"]), F = [], U = new I; U.equals = function (e, t) { return x(e.node) === t }, p.prototype = { append: function (e) { var t = new p(e); return this.childNodes.push(t), t }, sync: function (e) { if (!this.skip) { for (var t = this.node, o = this.childNodes, i = a(x(t)), s = e || new WeakMap, c = U.calculateSplices(o, i), l = 0, u = 0, d = 0, p = 0; p < c.length; p++) { for (var h = c[p]; d < h.index; d++)u++, o[l++].sync(s); for (var f = h.removed.length, m = 0; m < f; m++) { var w = R(i[u++]); s.get(w) || r(w) } for (var v = h.addedCount, g = i[u] && R(i[u]), m = 0; m < v; m++) { var b = o[l++], y = b.node; n(t, y, g), s.set(y, !0), b.sync(s) } d += v } for (var p = d; p < o.length; p++)o[p].sync(s) } } }, h.prototype = { render: function (e) { if (this.dirty) { this.invalidateAttributes(); var t = this.host; this.distribution(t); var n = e || new p(t); this.buildRenderTree(n, t); var r = !e; r && n.sync(), this.dirty = !1 } }, get parentRenderer() { return j(this.host).renderer }, invalidate: function () { if (!this.dirty) { this.dirty = !0; var e = this.parentRenderer; if (e && e.invalidate(), F.push(this), T) return; T = window[W](c, 0) } }, distribution: function (e) { this.resetAllSubtrees(e), this.distributionResolution(e) }, resetAll: function (e) { E(e) ? o(e) : g(e), this.resetAllSubtrees(e) }, resetAllSubtrees: function (e) { for (var t = e.firstChild; t; t = t.nextSibling)this.resetAll(t); e.shadowRoot && this.resetAll(e.shadowRoot), e.olderShadowRoot && this.resetAll(e.olderShadowRoot) }, distributionResolution: function (e) { if (_(e)) { for (var t = e, n = f(t), r = S(t), o = 0; o < r.length; o++)this.poolDistribution(r[o], n); for (var o = r.length - 1; o >= 0; o--) { var i = r[o], a = m(i); if (a) { var s = i.olderShadowRoot; s && (n = f(s)); for (var c = 0; c < n.length; c++)w(n[c], a) } this.distributionResolution(i) } } for (var l = e.firstChild; l; l = l.nextSibling)this.distributionResolution(l) }, poolDistribution: function (e, t) { if (!(e instanceof L)) if (e instanceof O) { var n = e; this.updateDependentAttributes(n.getAttribute("select")); for (var r = !1, o = 0; o < t.length; o++) { var e = t[o]; e && b(e, n) && (w(e, n), t[o] = void 0, r = !0) } if (!r) for (var i = n.firstChild; i; i = i.nextSibling)w(i, n) } else for (var i = e.firstChild; i; i = i.nextSibling)this.poolDistribution(i, t) }, buildRenderTree: function (e, t) { for (var n = this.compose(t), r = 0; r < n.length; r++) { var o = n[r], i = e.append(o); this.buildRenderTree(i, o) } if (_(t)) { var a = l(t); a.dirty = !1 } }, compose: function (e) { for (var t = [], n = e.shadowRoot || e, r = n.firstChild; r; r = r.nextSibling)if (E(r)) { this.associateNode(n); for (var o = i(r), a = 0; a < o.length; a++) { var s = o[a]; y(r, s) && t.push(s) } } else t.push(r); return t }, invalidateAttributes: function () { this.attributes = Object.create(null) }, updateDependentAttributes: function (e) { if (e) { var t = this.attributes; /\.\w+/.test(e) && (t["class"] = !0), /#\w+/.test(e) && (t.id = !0), e.replace(/\[\s*([^\s=\|~\]]+)/g, function (e, n) { t[n] = !0 }) } }, dependsOnAttribute: function (e) { return this.attributes[e] }, associateNode: function (e) { H(e).polymerShadowRenderer_ = this } }; var q = /^(:not\()?[*.#[a-zA-Z_|]/; N.prototype.invalidateShadowRenderer = function (e) { var t = H(this).polymerShadowRenderer_; return !!t && (t.invalidate(), !0) }, O.prototype.getDistributedNodes = L.prototype.getDistributedNodes = function () { return s(), i(this) }, M.prototype.getDestinationInsertionPoints = function () { return s(), v(this) || [] }, O.prototype.nodeIsInserted_ = L.prototype.nodeIsInserted_ = function () { this.invalidateShadowRenderer(); var e, t = u(this); t && (e = d(t)), H(this).polymerShadowRenderer_ = e, e && e.invalidate() }, e.getRendererForHost = l, e.getShadowTrees = S, e.renderAllPending = s, e.getDestinationInsertionPoints = v, e.visual = { insertBefore: n, remove: r } }(window.ShadowDOMPolyfill), function (e) { "use strict"; function t(t) { if (window[t]) { r(!e.wrappers[t]); var c = function (e) { n.call(this, e) }; c.prototype = Object.create(n.prototype), o(c.prototype, { get form() { return s(a(this).form) } }), i(window[t], c, document.createElement(t.slice(4, -7))), e.wrappers[t] = c } } var n = e.wrappers.HTMLElement, r = e.assert, o = e.mixin, i = e.registerWrapper, a = e.unwrap, s = e.wrap, c = ["HTMLButtonElement", "HTMLFieldSetElement", "HTMLInputElement", "HTMLKeygenElement", "HTMLLabelElement", "HTMLLegendElement", "HTMLObjectElement", "HTMLOutputElement", "HTMLTextAreaElement"]; c.forEach(t) }(window.ShadowDOMPolyfill), function (e) { "use strict"; function t(e) { r(e, this) } var n = e.registerWrapper, r = e.setWrapper, o = e.unsafeUnwrap, i = e.unwrap, a = e.unwrapIfNeeded, s = e.wrap, c = window.Selection; t.prototype = { get anchorNode() { return s(o(this).anchorNode) }, get focusNode() { return s(o(this).focusNode) }, addRange: function (e) { o(this).addRange(a(e)) }, collapse: function (e, t) { o(this).collapse(a(e), t) }, containsNode: function (e, t) { return o(this).containsNode(a(e), t) }, getRangeAt: function (e) { return s(o(this).getRangeAt(e)) }, removeRange: function (e) { o(this).removeRange(i(e)) }, selectAllChildren: function (e) { o(this).selectAllChildren(e instanceof ShadowRoot ? o(e.host) : a(e)) }, toString: function () { return o(this).toString() } }, c.prototype.extend && (t.prototype.extend = function (e, t) { o(this).extend(a(e), t) }), n(window.Selection, t, window.getSelection()), e.wrappers.Selection = t }(window.ShadowDOMPolyfill), function (e) { "use strict"; function t(e) { r(e, this) } var n = e.registerWrapper, r = e.setWrapper, o = e.unsafeUnwrap, i = e.unwrapIfNeeded, a = e.wrap, s = window.TreeWalker; t.prototype = { get root() { return a(o(this).root) }, get currentNode() { return a(o(this).currentNode) }, set currentNode(e) { o(this).currentNode = i(e) }, get filter() { return o(this).filter }, parentNode: function () { return a(o(this).parentNode()) }, firstChild: function () { return a(o(this).firstChild()) }, lastChild: function () { return a(o(this).lastChild()) }, previousSibling: function () { return a(o(this).previousSibling()) }, previousNode: function () { return a(o(this).previousNode()) }, nextNode: function () { return a(o(this).nextNode()) } }, n(s, t), e.wrappers.TreeWalker = t }(window.ShadowDOMPolyfill), function (e) { - "use strict"; function t(e) { u.call(this, e), this.treeScope_ = new w(this, null) } function n(e) { var n = document[e]; t.prototype[e] = function () { return j(n.apply(N(this), arguments)) } } function r(e, t) { x.call(N(t), C(e)), o(e, t) } function o(e, t) { e.shadowRoot && t.adoptNode(e.shadowRoot), e instanceof m && i(e, t); for (var n = e.firstChild; n; n = n.nextSibling)o(n, t) } function i(e, t) { var n = e.olderShadowRoot; n && t.adoptNode(n) } function a(e) { L(e, this) } function s(e, t) { - var n = document.implementation[t]; e.prototype[t] = function () { - return j(n.apply(N(this), arguments)) - } - } function c(e, t) { var n = document.implementation[t]; e.prototype[t] = function () { return n.apply(N(this), arguments) } } var l = e.GetElementsByInterface, u = e.wrappers.Node, d = e.ParentNodeInterface, p = e.NonElementParentNodeInterface, h = e.wrappers.Selection, f = e.SelectorsInterface, m = e.wrappers.ShadowRoot, w = e.TreeScope, v = e.cloneNode, g = e.defineGetter, b = e.defineWrapGetter, y = e.elementFromPoint, E = e.forwardMethodsToWrapper, _ = e.matchesNames, S = e.mixin, T = e.registerWrapper, M = e.renderAllPending, O = e.rewrap, L = e.setWrapper, N = e.unsafeUnwrap, C = e.unwrap, j = e.wrap, D = e.wrapEventTargetMethods, H = (e.wrapNodeList, new WeakMap); t.prototype = Object.create(u.prototype), b(t, "documentElement"), b(t, "body"), b(t, "head"), g(t, "activeElement", function () { var e = C(this).activeElement; if (!e || !e.nodeType) return null; for (var t = j(e); !this.contains(t);) { for (; t.parentNode;)t = t.parentNode; if (!t.host) return null; t = t.host } return t }), ["createComment", "createDocumentFragment", "createElement", "createElementNS", "createEvent", "createEventNS", "createRange", "createTextNode"].forEach(n); var x = document.adoptNode, R = document.getSelection; S(t.prototype, { adoptNode: function (e) { return e.parentNode && e.parentNode.removeChild(e), r(e, this), e }, elementFromPoint: function (e, t) { return y(this, this, e, t) }, importNode: function (e, t) { return v(e, t, N(this)) }, getSelection: function () { return M(), new h(R.call(C(this))) }, getElementsByName: function (e) { return f.querySelectorAll.call(this, "[name=" + JSON.stringify(String(e)) + "]") } }); var I = document.createTreeWalker, P = e.wrappers.TreeWalker; if (t.prototype.createTreeWalker = function (e, t, n, r) { var o = null; return n && (n.acceptNode && "function" == typeof n.acceptNode ? o = { acceptNode: function (e) { return n.acceptNode(j(e)) } } : "function" == typeof n && (o = function (e) { return n(j(e)) })), new P(I.call(C(this), C(e), t, o, r)) }, document.registerElement) { var k = document.registerElement; t.prototype.registerElement = function (t, n) { function r(e) { return e ? void L(e, this) : i ? document.createElement(i, t) : document.createElement(t) } var o, i; if (void 0 !== n && (o = n.prototype, i = n["extends"]), o || (o = Object.create(HTMLElement.prototype)), e.nativePrototypeTable.get(o)) throw new Error("NotSupportedError"); for (var a, s = Object.getPrototypeOf(o), c = []; s && !(a = e.nativePrototypeTable.get(s));)c.push(s), s = Object.getPrototypeOf(s); if (!a) throw new Error("NotSupportedError"); for (var l = Object.create(a), u = c.length - 1; u >= 0; u--)l = Object.create(l);["createdCallback", "attachedCallback", "detachedCallback", "attributeChangedCallback"].forEach(function (e) { var t = o[e]; t && (l[e] = function () { j(this) instanceof r || O(this), t.apply(j(this), arguments) }) }); var d = { prototype: l }; i && (d["extends"] = i), r.prototype = o, r.prototype.constructor = r, e.constructorTable.set(l, r), e.nativePrototypeTable.set(o, l); k.call(C(this), t, d); return r }, E([window.HTMLDocument || window.Document], ["registerElement"]) } E([window.HTMLBodyElement, window.HTMLDocument || window.Document, window.HTMLHeadElement, window.HTMLHtmlElement], ["appendChild", "compareDocumentPosition", "contains", "getElementsByClassName", "getElementsByTagName", "getElementsByTagNameNS", "insertBefore", "querySelector", "querySelectorAll", "removeChild", "replaceChild"]), E([window.HTMLBodyElement, window.HTMLHeadElement, window.HTMLHtmlElement], _), E([window.HTMLDocument || window.Document], ["adoptNode", "importNode", "contains", "createComment", "createDocumentFragment", "createElement", "createElementNS", "createEvent", "createEventNS", "createRange", "createTextNode", "createTreeWalker", "elementFromPoint", "getElementById", "getElementsByName", "getSelection"]), S(t.prototype, l), S(t.prototype, d), S(t.prototype, f), S(t.prototype, p), S(t.prototype, { get implementation() { var e = H.get(this); return e ? e : (e = new a(C(this).implementation), H.set(this, e), e) }, get defaultView() { return j(C(this).defaultView) } }), T(window.Document, t, document.implementation.createHTMLDocument("")), window.HTMLDocument && T(window.HTMLDocument, t), D([window.HTMLBodyElement, window.HTMLDocument || window.Document, window.HTMLHeadElement]); var A = document.implementation.createDocument; a.prototype.createDocument = function () { return arguments[2] = C(arguments[2]), j(A.apply(N(this), arguments)) }, s(a, "createDocumentType"), s(a, "createHTMLDocument"), c(a, "hasFeature"), T(window.DOMImplementation, a), E([window.DOMImplementation], ["createDocument", "createDocumentType", "createHTMLDocument", "hasFeature"]), e.adoptNodeNoRemove = r, e.wrappers.DOMImplementation = a, e.wrappers.Document = t -}(window.ShadowDOMPolyfill), function (e) { "use strict"; function t(e) { n.call(this, e) } var n = e.wrappers.EventTarget, r = e.wrappers.Selection, o = e.mixin, i = e.registerWrapper, a = e.renderAllPending, s = e.unwrap, c = e.unwrapIfNeeded, l = e.wrap, u = window.Window, d = window.getComputedStyle, p = window.getDefaultComputedStyle, h = window.getSelection; 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All rights reserved. -# -# Copyright(c) 2023 Huawei Technologies. -# All rights reserved -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# Modifications: Add visualization of PyTorch Ascend profiling. -# -------------------------------------------------------------------------- -import logging -import math -import os -import time -from contextlib import contextmanager - -from . import consts - -predefined_logging_level = ('CRITICAL', 'ERROR', 'WARNING', 'INFO', 'DEBUG', 'NOTSET') - - -def get_logging_level(): - log_level = os.environ.get('TORCH_PROFILER_LOG_LEVEL', 'INFO').upper() - if log_level not in predefined_logging_level: - log_level = logging.getLevelName(logging.INFO) - return log_level - - -logger = None - - -def get_logger(): - global logger - if logger is None: - logger = logging.getLogger(consts.PLUGIN_NAME) - logger.setLevel(get_logging_level()) - return logger - - -def is_gpu_chrome_trace_file(path): - return consts.WORKER_PATTERN.match(path) - - -def is_worker_span_dir(path): - return consts.WORKER_SPAN_PATTERN.match(path) - - -def is_npu_trace_path(path): - return consts.TRACE_PATTERN.match(path) - - -def href(text, url): - """"return html formatted hyperlink string - - Note: - target="_blank" causes this link to be opened in new tab if clicked. - """ - return f'{text}' - - -class Canonicalizer: - def __init__( - self, - time_metric='us', - memory_metric='B', - *, - input_time_metric='us', - input_memory_metric='B'): - # raw timestamp is in microsecond - time_metric_to_factor = { - 'us': 1, - 'ms': 1e3, - 's': 1e6, - } - # raw memory is in bytes - memory_metric_to_factor = { - 'B': math.pow(1024, 0), - 'KB': math.pow(1024, 1), - 'MB': math.pow(1024, 2), - 'GB': math.pow(1024, 3), - } - - # canonicalize the memory metric to a string - self.canonical_time_metrics = { - 'micro': 'us', 'microsecond': 'us', 'us': 'us', - 'milli': 'ms', 'millisecond': 'ms', 'ms': 'ms', - '': 's', 'second': 's', 's': 's', - } - # canonicalize the memory metric to a string - self.canonical_memory_metrics = { - '': 'B', 'B': 'B', - 'K': 'KB', 'KB': 'KB', - 'M': 'MB', 'MB': 'MB', - 'G': 'GB', 'GB': 'GB', - } - - self.time_metric = self.canonical_time_metrics.get(time_metric) - self.memory_metric = self.canonical_memory_metrics.get(memory_metric) - - # scale factor scale input to output - self.time_factor = time_metric_to_factor.get(self.canonical_time_metrics.get(input_time_metric)) /\ - time_metric_to_factor.get(self.time_metric) - self.memory_factor = memory_metric_to_factor.get(self.canonical_memory_metrics.get(input_memory_metric)) /\ - memory_metric_to_factor.get(self.memory_metric) - - def convert_time(self, t): - return self.time_factor * t - - def convert_memory(self, m): - return self.memory_factor * m - - -class DisplayRounder: - """Round a value for display purpose.""" - - def __init__(self, ndigits): - self.ndigits = ndigits - self.precision = math.pow(10, -ndigits) - - def __call__(self, v: float): - _v = abs(v) - if _v >= self.precision or v == 0: - return round(v, 3) - else: - ndigit = abs(math.floor(math.log10(_v))) - return round(v, ndigit) - - -@contextmanager -def timing(description: str, force: bool = False) -> None: - if force or os.environ.get('TORCH_PROFILER_BENCHMARK', '0') == '1': - start = time.time() - yield - elapsed_time = time.time() - start - logger.info(f'{description}: {elapsed_time}') - else: - yield -- Gitee From 0c1047f6f787a1d960ee879be16a38bcd629d372 Mon Sep 17 00:00:00 2001 From: curry3 <485078529@qq.com> Date: Mon, 23 Jun 2025 21:21:06 +0800 Subject: [PATCH 15/79] =?UTF-8?q?=E3=80=90bugfix=E3=80=91dump=E8=A7=A3?= =?UTF-8?q?=E5=86=B3=E5=A4=9A=E7=BA=BF=E7=A8=8B=E5=9C=BA=E6=99=AF=E6=95=B0?= =?UTF-8?q?=E6=8D=AE=E9=87=87=E9=9B=86=E9=94=99=E8=AF=AF=E9=97=AE=E9=A2=98?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../msprobe/core/common/utils.py | 97 +++++++++-- .../msprobe/core/data_dump/data_collector.py | 5 +- .../core/debugger/precision_debugger.py | 21 ++- .../msprobe/core/hook_manager.py | 157 ++++++++++-------- .../msprobe/mindspore/cell_processor.py | 49 ++++-- .../mindspore/debugger/precision_debugger.py | 45 ++--- .../mindspore/dump/hook_cell/hook_cell.py | 23 ++- .../dump/hook_cell/ms_hook_manager.py | 19 ++- .../dump/hook_cell/primitive_hooks.py | 40 +++-- .../msprobe/mindspore/dump/jit_dump.py | 9 +- .../msprobe/mindspore/mindspore_service.py | 4 +- .../pytorch/debugger/precision_debugger.py | 9 +- .../dump/module_dump/module_processer.py | 48 ++++-- .../pytorch/hook_module/hook_module.py | 14 +- .../pytorch/hook_module/pt_hook_manager.py | 14 +- .../core_ut/data_dump/test_data_collector.py | 16 +- .../msprobe/test/core_ut/test_hook_manager.py | 92 +++++----- .../hook_module/test_ms_hook_manager.py | 49 +++--- .../test/mindspore_ut/test_cell_processor.py | 70 ++++---- .../debugger/test_pt_precision_debugger.py | 2 + .../pytorch_ut/dump/test_module_processer.py | 34 ++-- .../hook_module/test_pt_hook_manager.py | 4 +- .../hook_module/test_pt_hook_module.py | 10 +- 23 files changed, 509 insertions(+), 322 deletions(-) diff --git a/debug/accuracy_tools/msprobe/core/common/utils.py b/debug/accuracy_tools/msprobe/core/common/utils.py index 668e9a0ad..0ac134a55 100644 --- a/debug/accuracy_tools/msprobe/core/common/utils.py +++ b/debug/accuracy_tools/msprobe/core/common/utils.py @@ -14,21 +14,22 @@ # limitations under the License. import collections +import functools +import inspect import os import re -import subprocess +import threading import time -import inspect +from collections import OrderedDict from datetime import datetime, timezone import numpy as np -from msprobe.core.common.file_utils import (FileOpen, check_file_or_directory_path, load_json) from msprobe.core.common.const import Const, CompareConst -from msprobe.core.common.log import logger -from msprobe.core.common.exceptions import MsprobeException from msprobe.core.common.decorator import recursion_depth_decorator - +from msprobe.core.common.exceptions import MsprobeException +from msprobe.core.common.file_utils import (FileOpen, check_file_or_directory_path, load_json) +from msprobe.core.common.log import logger device = collections.namedtuple('device', ['type', 'index']) prefixes = ['api_stack', 'list', 'range', 'acl'] @@ -112,6 +113,82 @@ class DumpException(MsprobeBaseException): return f"Dump Error Code {self.code}: {self.error_info}" +class ThreadSafe: + """ + 线程安全控制工具类，提供三种使用方式： + 1.上下文管理器：with ThreadSafe() + 2.主动加锁与释放锁：ThreadSafe.acquire()/ThreadSafe.release() + 3.方法装饰器：@ThreadSafe.synchronized + """ + _lock = threading.RLock() + + def __enter__(self): + self.__class__._lock.acquire() + + def __exit__(self, exc_type, exc_val, exc_tb): + self.__class__._lock.release() + + @classmethod + def acquire(cls): + cls._lock.acquire() + + @classmethod + def release(cls): + cls._lock.release() + + @classmethod + def synchronized(cls, func): + @functools.wraps(func) + def wrapper(*args, **kwargs): + with cls._lock: + return func(*args, **kwargs) + + return wrapper + + +class ModuleQueue: + def __init__(self): + self.queue = OrderedDict() + + def add_name(self, name): + self.queue[name] = True + + def remove_name(self, name): + if name in self.queue: + del self.queue[name] + + def find_last(self, name): + """ + 在队列中找到当前 Module/Cell 的父节点名称并返回，若找不到则返回None + + Args: + name: 需要寻找父节点的 Module/Cell 的名称 + + Returns: + 返回父节点名称，找不到则返回None + + Examples: + 父节点名称格式: Module.module1.module1.forward.0 + 子节点名称格式: Module.module1.module2.Module2.forward.0 + 匹配关系: Module/Cell 的名称总能被点(.)分割符分成5个部分及以上，子节点截断后4个点和父节点截断后3个点的前缀名称是匹配的 + """ + child_parts = name.split('.') + if len(child_parts) < 5: + return None + child_name_prefix = '.'.join(child_parts[:-4]) + if child_name_prefix in Const.MODULE_PREFIX: + return None + + for parent_name in reversed(self.queue): + parent_parts = parent_name.split('.') + if len(parent_parts) < 5: + return None + parent_name_prefix = '.'.join(parent_parts[:-3]) + if parent_name_prefix == child_name_prefix: + return parent_name + return None + + def is_json_file(file_path): if isinstance(file_path, str) and file_path.lower().endswith('.json'): return True @@ -282,9 +359,9 @@ def set_dump_path(input_param): npu_path = input_param.get("npu_json_path", None) bench_path = input_param.get("bench_json_path", None) dump_json_path_valid = npu_path is not None and npu_path.endswith("dump.json") and \ - bench_path is not None and bench_path.endswith("dump.json") + bench_path is not None and bench_path.endswith("dump.json") debug_json_path_valid = npu_path is not None and npu_path.endswith("debug.json") and \ - bench_path is not None and bench_path.endswith("debug.json") + bench_path is not None and bench_path.endswith("debug.json") if not dump_json_path_valid and not debug_json_path_valid: logger.error(f"Please check the json path is valid and ensure that neither npu_path nor bench_path is None.") raise CompareException(CompareException.INVALID_PATH_ERROR) @@ -457,10 +534,10 @@ def get_real_step_or_rank(step_or_rank_input, obj): def check_init_step(step): if not is_int(step): raise MsprobeException(MsprobeException.INVALID_PARAM_ERROR, - f"{step} must be an integer") + f"{step} must be an integer") if not step >= 0: raise MsprobeException(MsprobeException.INVALID_PARAM_ERROR, - f"{step} must be greater than or equal to 0") + f"{step} must be greater than or equal to 0") def check_token_range(token_range): diff --git a/debug/accuracy_tools/msprobe/core/data_dump/data_collector.py b/debug/accuracy_tools/msprobe/core/data_dump/data_collector.py index 4bc404551..5a79ef34c 100644 --- a/debug/accuracy_tools/msprobe/core/data_dump/data_collector.py +++ b/debug/accuracy_tools/msprobe/core/data_dump/data_collector.py @@ -15,6 +15,7 @@ import atexit import os +import threading import traceback from msprobe.core.data_dump.scope import ScopeFactory @@ -255,7 +256,9 @@ class DataCollector: else: if self.config.level == Const.LEVEL_MIX and \ not (name.startswith(Const.MODULE) or name.startswith(Const.CELL)): - self.data_writer.update_construct({name: self.module_processor.api_parent_node}) + self.data_writer.update_construct( + {name: self.module_processor.api_parent_node.get(threading.get_ident())} + ) self.data_writer.update_construct(self.module_processor.module_node) diff --git a/debug/accuracy_tools/msprobe/core/debugger/precision_debugger.py b/debug/accuracy_tools/msprobe/core/debugger/precision_debugger.py index 99388de0d..9cfbd76c1 100644 --- a/debug/accuracy_tools/msprobe/core/debugger/precision_debugger.py +++ b/debug/accuracy_tools/msprobe/core/debugger/precision_debugger.py @@ -18,7 +18,7 @@ import os from msprobe.core.common.const import Const, FileCheckConst, MsgConst from msprobe.core.common.exceptions import MsprobeException from msprobe.core.common.file_utils import FileChecker, load_json -from msprobe.core.common.utils import get_real_step_or_rank, check_init_step +from msprobe.core.common.utils import get_real_step_or_rank, check_init_step, ThreadSafe from msprobe.core.common_config import CommonConfig @@ -27,13 +27,14 @@ class BasePrecisionDebugger: tasks_not_need_debugger = [Const.GRAD_PROBE] def __new__(cls, *args, **kwargs): - if cls._instance is None: - cls._instance = super(BasePrecisionDebugger, cls).__new__(cls) - cls._instance.config = None - cls._instance.enable_dataloader = False - cls._instance.initialized = False - cls.service = None - cls.first_start = False + if not cls._instance: + with ThreadSafe(): + if not cls._instance: + cls._instance = super(BasePrecisionDebugger, cls).__new__(cls) + cls._instance.config = None + cls._instance.initialized = False + cls.service = None + cls.first_start = False return cls._instance def __init__( @@ -83,11 +84,13 @@ class BasePrecisionDebugger: raise NotImplementedError("Subclass must implement _get_task_config") @classmethod + @ThreadSafe.synchronized def forward_backward_dump_end(cls): instance = cls._instance instance.stop() @classmethod + @ThreadSafe.synchronized def set_init_step(cls, step): instance = cls._instance if not instance: @@ -97,6 +100,7 @@ class BasePrecisionDebugger: instance.service.loop = 0 @classmethod + @ThreadSafe.synchronized def register_custom_api(cls, module, api, api_prefix=None): if not api_prefix: api_prefix = getattr(module, "__name__", "Custom") @@ -112,6 +116,7 @@ class BasePrecisionDebugger: instance.service.register_custom_api(module, api, api_prefix) @classmethod + @ThreadSafe.synchronized def restore_custom_api(cls, module, api): if not hasattr(module, api): raise MsprobeException( diff --git a/debug/accuracy_tools/msprobe/core/hook_manager.py b/debug/accuracy_tools/msprobe/core/hook_manager.py index 17d9ee0b5..db989df0b 100644 --- a/debug/accuracy_tools/msprobe/core/hook_manager.py +++ b/debug/accuracy_tools/msprobe/core/hook_manager.py @@ -13,12 +13,13 @@ # See the License for the specific language governing permissions and # limitations under the License. - -from abc import ABC, abstractmethod import os +import threading +from abc import ABC, abstractmethod +from collections import defaultdict from msprobe.core.common.runtime import Runtime -from msprobe.core.common.utils import Const +from msprobe.core.common.utils import Const, ThreadSafe from msprobe.core.data_dump.data_processor.base import (ModuleBackwardInputsOutputs, ModuleForwardInputsOutputs) @@ -31,7 +32,7 @@ class HookSet: class BaseHookManager(ABC): - inner_switch = False + inner_switch = defaultdict(bool) hook_handle_dict = {} params_grad_info = {} @@ -86,7 +87,7 @@ class BaseHookManager(ABC): grad_name = ori_name + Const.SEP + Const.PARAMS_GRAD # 首次执行前向hook时，添加params_grad_name属性，并注册参数hook setattr(module, 'params_grad_name', grad_name) - # data_mode为forward时，不注册参数hook + # data_mode为forward时，不注册参数hook if not (Const.FORWARD in self.config.data_mode and Const.BACKWARD not in self.config.data_mode): for param_name, param in params_dict.items(): if param.requires_grad: @@ -116,7 +117,7 @@ class BaseHookManager(ABC): # 记录当前模块的参数梯度信息已占位 BaseHookManager.params_grad_info[grad_name] = True - def _should_execute_hook(self, hook_type, module, is_forward): + def _should_execute_hook(self, hook_type, module, is_forward, tid): is_module_hook = hook_type == Const.MODULE if is_module_hook and not Runtime.is_running: return False @@ -124,7 +125,7 @@ class BaseHookManager(ABC): return False elif not is_module_hook and not is_forward and not module.forward_data_collected: return False - if BaseHookManager.inner_switch: + if BaseHookManager.inner_switch[tid]: return False if not self.data_collector or self.data_collector.data_processor.is_terminated: return False @@ -132,30 +133,38 @@ class BaseHookManager(ABC): def _build_grad_hook(self, module, ori_name, param_name): def hook_fn(grad): - if not self._should_execute_hook(Const.MODULE, module, False): + tid = threading.get_ident() + if not self._should_execute_hook(Const.MODULE, module, False, tid): return - BaseHookManager.inner_switch = True - self.data_collector.params_data_collect(ori_name, param_name, self._pid, grad) - BaseHookManager.inner_switch = False + with ThreadSafe(): + BaseHookManager.inner_switch[tid] = True + self.data_collector.params_data_collect(ori_name, param_name, self._pid, grad) + BaseHookManager.inner_switch[tid] = False return + return hook_fn def _build_forward_pre_hook(self, hook_type, full_name, api_name): def forward_pre_hook(module, args, kwargs=None): if hook_type == Const.MODULE: return - if not self._should_execute_hook(hook_type, module, True): + + tid = threading.get_ident() + if not self._should_execute_hook(hook_type, module, True, tid): + ThreadSafe.release() return + if kwargs is None: kwargs = module.msprobe_input_kwargs if hasattr(module, 'msprobe_input_kwargs') else {} with self._no_grad_context(): - BaseHookManager.inner_switch = False + BaseHookManager.inner_switch[tid] = False module.forward_data_collected = True self._add_count(api_name) module_input_output = ModuleForwardInputsOutputs(args=args, kwargs=kwargs, output=None) self.data_collector.update_api_or_module_name(full_name) if getattr(self.config, "online_run_ut", False): - BaseHookManager.inner_switch = False + BaseHookManager.inner_switch[tid] = False + ThreadSafe.release() return self.data_collector.forward_input_data_collect( full_name, @@ -164,79 +173,89 @@ class BaseHookManager(ABC): module_input_output, self._is_recompute ) - BaseHookManager.inner_switch = False + BaseHookManager.inner_switch[tid] = False + ThreadSafe.release() + return forward_pre_hook def _build_forward_hook(self, hook_type, full_name): def forward_hook(module, args, kwargs_or_output, output_or_kwargs=None): - if not self._should_execute_hook(hook_type, module, True): + tid = threading.get_ident() + if not self._should_execute_hook(hook_type, module, True, tid): self._clear_input_kwargs(module) return None - kwargs, output = self._process_kwargs_and_output(module, hook_type, kwargs_or_output, output_or_kwargs) - BaseHookManager.inner_switch = True - self.data_collector.update_api_or_module_name(full_name) - module_input_output = ModuleForwardInputsOutputs(args=args, kwargs=kwargs, output=output) - with self._no_grad_context(): - if getattr(self.config, "online_run_ut", False): - if self.data_collector.scope and not self.data_collector.scope.check(full_name): + + with ThreadSafe(): + kwargs, output = self._process_kwargs_and_output(module, hook_type, kwargs_or_output, output_or_kwargs) + BaseHookManager.inner_switch[tid] = True + self.data_collector.update_api_or_module_name(full_name) + module_input_output = ModuleForwardInputsOutputs(args=args, kwargs=kwargs, output=output) + with self._no_grad_context(): + if getattr(self.config, "online_run_ut", False): + if self.data_collector.scope and not self.data_collector.scope.check(full_name): + return None + if self.attl_manager: + self.attl_manager.attl_send(full_name, args, kwargs, output) + BaseHookManager.inner_switch[tid] = False return None - if self.attl_manager: - self.attl_manager.attl_send(full_name, args, kwargs, output) - BaseHookManager.inner_switch = False - return None - if hook_type == Const.MODULE: - params_dict = self._get_params_dict(module) - setattr(module_input_output, Const.PARAMS, params_dict) - if params_dict: - self._register_param_hook(full_name, module, params_dict) - self.data_collector.update_api_or_module_name(full_name) - self.data_collector.forward_data_collect( - full_name, - module, - self._pid, - module_input_output, - self._is_recompute - ) - self._init_params_grad_info(module, params_dict) - else: - self.data_collector.forward_output_data_collect( - full_name, - module, - self._pid, - module_input_output, - self._is_recompute - ) - self._clear_input_kwargs(module) + if hook_type == Const.MODULE: + params_dict = self._get_params_dict(module) + setattr(module_input_output, Const.PARAMS, params_dict) + if params_dict: + self._register_param_hook(full_name, module, params_dict) + self.data_collector.update_api_or_module_name(full_name) + self.data_collector.forward_data_collect( + full_name, + module, + self._pid, + module_input_output, + self._is_recompute + ) + self._init_params_grad_info(module, params_dict) + else: + self.data_collector.forward_output_data_collect( + full_name, + module, + self._pid, + module_input_output, + self._is_recompute + ) + self._clear_input_kwargs(module) + + if self.data_collector.if_return_forward_new_output(): + forward_new_output = self.data_collector.get_forward_new_output() + BaseHookManager.inner_switch[tid] = False + return forward_new_output - if self.data_collector.if_return_forward_new_output(): - forward_new_output = self.data_collector.get_forward_new_output() - BaseHookManager.inner_switch = False - return forward_new_output + BaseHookManager.inner_switch[tid] = False + return output - BaseHookManager.inner_switch = False - return output return forward_hook def _build_backward_hook(self, hook_type, full_name): def backward_hook(module, grad_input, grad_output): - if not self._should_execute_hook(hook_type, module, False): + tid = threading.get_ident() + if not self._should_execute_hook(hook_type, module, False, tid): return - BaseHookManager.inner_switch = True - self.data_collector.update_api_or_module_name(full_name) - if getattr(self.config, "online_run_ut", False): - BaseHookManager.inner_switch = False - return - need_exchange = self._need_exchange(module) if hook_type == Const.MODULE else True - if need_exchange: - module_input_output = ModuleBackwardInputsOutputs(grad_input=grad_output, grad_output=grad_input) - else: - module_input_output = ModuleBackwardInputsOutputs(grad_input=grad_input, grad_output=grad_output) - self.data_collector.backward_data_collect( + + with ThreadSafe(): + BaseHookManager.inner_switch[tid] = True + self.data_collector.update_api_or_module_name(full_name) + if getattr(self.config, "online_run_ut", False): + BaseHookManager.inner_switch[tid] = False + return + need_exchange = self._need_exchange(module) if hook_type == Const.MODULE else True + if need_exchange: + module_input_output = ModuleBackwardInputsOutputs(grad_input=grad_output, grad_output=grad_input) + else: + module_input_output = ModuleBackwardInputsOutputs(grad_input=grad_input, grad_output=grad_output) + self.data_collector.backward_data_collect( full_name, module, self._pid, module_input_output, self._is_recompute ) - BaseHookManager.inner_switch = False + BaseHookManager.inner_switch[tid] = False + return backward_hook diff --git a/debug/accuracy_tools/msprobe/mindspore/cell_processor.py b/debug/accuracy_tools/msprobe/mindspore/cell_processor.py index e46e07697..ef72ea050 100644 --- a/debug/accuracy_tools/msprobe/mindspore/cell_processor.py +++ b/debug/accuracy_tools/msprobe/mindspore/cell_processor.py @@ -13,6 +13,7 @@ # See the License for the specific language governing permissions and # limitations under the License. +import threading from collections import OrderedDict from mindspore import Tensor @@ -21,6 +22,8 @@ from mindspore.ops.operations import _inner_ops as inner from msprobe.core.common.const import Const from msprobe.core.common.exceptions import MsprobeException +from msprobe.core.common.runtime import Runtime +from msprobe.core.common.utils import ModuleQueue, ThreadSafe from msprobe.core.data_dump.scope import ModuleRangeScope, MixRangeScope, BaseScope from msprobe.mindspore.common.const import Const as MsConst from msprobe.mindspore.common.log import logger @@ -32,7 +35,6 @@ from msprobe.mindspore.common.utils import ( ) from msprobe.mindspore.debugger.debugger_config import DebuggerConfig from msprobe.mindspore.dump.graph_mode_cell_dump import GraphModeCellDump -from msprobe.core.common.runtime import Runtime def get_cell_construct(construct): @@ -40,13 +42,15 @@ def get_cell_construct(construct): if hasattr(self, 'msprobe_hook'): setattr(self, 'msprobe_input_kwargs', kwargs) return construct(self, *args, **kwargs) + return _construct class CellProcessor: + cell_queue = ModuleQueue() cell_count = {} - cell_stack = [] - api_parent_node = None + cell_stack = {} + api_parent_node = {} module_node = {} cell_bw_hook_kernels = {} cell_backward_pre_hook = [] @@ -65,9 +69,10 @@ class CellProcessor: @classmethod def reset_cell_stats(cls): + cls.cell_queue = ModuleQueue() cls.cell_count = {} - cls.cell_stack = [] - cls.api_parent_node = None + cls.cell_stack = {} + cls.api_parent_node = {} cls.module_node = {} cls.cell_bw_hook_kernels = {} cls.cell_backward_pre_hook = [] @@ -122,6 +127,7 @@ class CellProcessor: GraphModeCellDump(config, cells_and_names_in_graph_mode, strict=False).handle() def build_cell_hook(self, cell_name, build_data_hook): + @ThreadSafe.synchronized def forward_pre_hook(cell, args): index = CellProcessor.set_and_get_calls_number(cell_name) full_forward_name = f'{cell_name}{Const.FORWARD}{Const.SEP}{index}' @@ -146,11 +152,13 @@ class CellProcessor: setattr(cell, 'msprobe_forward_hook', True) def get_backward_hook(backward_data_hook, full_backward_name): + @ThreadSafe.synchronized def backward_hook_fn(cell, grad_input, grad_output): new_output = backward_data_hook(cell, grad_input, grad_output) self.set_construct_info_in_hook(full_backward_name) cell.has_pre_hook_called = False return new_output + return backward_hook_fn enable_hooked = sum( @@ -170,13 +178,14 @@ class CellProcessor: return args + @ThreadSafe.synchronized def forward_hook(cell, args, kwargs_or_output, output_or_kwargs=None): index = CellProcessor.cell_count.get(cell_name, 0) full_forward_name = f'{cell_name}{Const.FORWARD}{Const.SEP}{index}' full_backward_name = f'{cell_name}{Const.BACKWARD}{Const.SEP}{index}' self.set_construct_info_in_hook(full_forward_name) - + hook_set = build_data_hook(BaseScope.Module_Type_Module, full_forward_name) hook_result = hook_set.forward_hook(cell, args, kwargs_or_output, output_or_kwargs) if hook_result is not None: @@ -199,6 +208,7 @@ class CellProcessor: outputs = new_outputs def get_backward_pre_hook(full_backward_name, backward_data_hook): + @ThreadSafe.synchronized def backward_pre_hook_fn(cell, grad_output): cell.has_pre_hook_called = True self.set_construct_info_in_pre_hook(full_backward_name) @@ -206,6 +216,7 @@ class CellProcessor: backward_data_hook(cell, (), grad_output) self.set_construct_info_in_hook(full_backward_name) cell.has_pre_hook_called = False + return backward_pre_hook_fn backward_pre_hook = OrderedDict() @@ -233,18 +244,28 @@ class CellProcessor: return forward_pre_hook def set_construct_info_in_pre_hook(self, full_name): - if self.cell_stack: - CellProcessor.module_node[full_name] = self.cell_stack[-1] + tid = threading.get_ident() + if tid not in self.cell_stack: + CellProcessor.cell_stack[tid] = [] + + if self.cell_stack[tid]: + CellProcessor.module_node[full_name] = self.cell_stack[tid][-1] else: - CellProcessor.module_node[full_name] = None - CellProcessor.cell_stack.append(full_name) - CellProcessor.api_parent_node = full_name + parent_name = CellProcessor.cell_queue.find_last(full_name) + CellProcessor.module_node[full_name] = parent_name + + CellProcessor.cell_queue.add_name(full_name) + CellProcessor.cell_stack[tid].append(full_name) + CellProcessor.api_parent_node[tid] = full_name if self.scope: self.scope.begin_module(full_name) def set_construct_info_in_hook(self, full_name): - if self.cell_stack: - CellProcessor.cell_stack.pop() - CellProcessor.api_parent_node = CellProcessor.cell_stack[-1] if self.cell_stack else None + tid = threading.get_ident() + CellProcessor.api_parent_node[tid] = None + if self.cell_stack.get(tid): + CellProcessor.cell_stack[tid].pop() + if self.cell_stack.get(tid): + CellProcessor.api_parent_node[tid] = CellProcessor.cell_stack[tid][-1] if self.scope: self.scope.end_module(full_name) diff --git a/debug/accuracy_tools/msprobe/mindspore/debugger/precision_debugger.py b/debug/accuracy_tools/msprobe/mindspore/debugger/precision_debugger.py index 05c0bf8e0..bc5bfd284 100644 --- a/debug/accuracy_tools/msprobe/mindspore/debugger/precision_debugger.py +++ b/debug/accuracy_tools/msprobe/mindspore/debugger/precision_debugger.py @@ -20,7 +20,7 @@ import mindspore as ms from mindspore._c_expression import MSContext from msprobe.core.common.const import Const, MsgConst -from msprobe.core.common.utils import check_token_range +from msprobe.core.common.utils import check_token_range, ThreadSafe from msprobe.core.common.runtime import Runtime from msprobe.core.debugger.precision_debugger import BasePrecisionDebugger from msprobe.mindspore.cell_processor import CellProcessor @@ -57,18 +57,14 @@ ConfigParameters = namedtuple("ConfigParameters", ["config_path", "task", "dump_ class PrecisionDebugger(BasePrecisionDebugger): - def __new__(cls, config_path=None, task=None, dump_path=None, - level=None, step=None, opt=None): - if not cls._instance: - cls._instance = super().__new__(cls) - cls._instance.initialized = False - cls._instance.config = None - cls.service = None - cls.first_start = False - return cls._instance - - def __init__(self, config_path=None, task=None, dump_path=None, - level=None, step=None): + def __init__( + self, + config_path=None, + task=None, + dump_path=None, + level=None, + step=None + ): if self.initialized: return set_register_backward_hook_functions() @@ -135,10 +131,11 @@ class PrecisionDebugger(BasePrecisionDebugger): check_token_range(token_range) instance.config.execution_mode = cls._get_execution_mode() if cls._need_service(): - if not instance.service: - instance.service = MindsporeService(instance.config) - instance.config.check_model(model, token_range) - instance.service.start(model, token_range) + with ThreadSafe(): + if not instance.service: + instance.service = MindsporeService(instance.config) + instance.config.check_model(model, token_range) + instance.service.start(model, token_range) else: if not instance.first_start: get_api_register().restore_all_api() @@ -156,14 +153,15 @@ class PrecisionDebugger(BasePrecisionDebugger): if instance.task == Const.GRAD_PROBE: instance.gm.stop() if instance.service: - instance.service.stop() + with ThreadSafe(): + instance.service.stop() else: Runtime.is_running = False if enable_dynamic_kbyk_dump: _dump_stop() if cls._is_kernel_dump() and _msprobe_c: _msprobe_c._PrecisionDebugger().stop() - + @classmethod def step(cls): instance = cls._get_instance() @@ -171,7 +169,8 @@ class PrecisionDebugger(BasePrecisionDebugger): return if instance.service: - instance.service.step() + with ThreadSafe(): + instance.service.step() if is_graph_mode_cell_dump_allowed(instance.config): GraphModeCellDump.step() if enable_dynamic_kbyk_dump: @@ -184,6 +183,7 @@ class PrecisionDebugger(BasePrecisionDebugger): Runtime.step_count += 1 @classmethod + @ThreadSafe.synchronized def monitor(cls, opt): instance = cls._instance if not instance: @@ -193,6 +193,7 @@ class PrecisionDebugger(BasePrecisionDebugger): instance.gm.monitor(opt) @classmethod + @ThreadSafe.synchronized def save(cls, variable, name, save_backward=True): instance = cls._instance if not instance: @@ -221,14 +222,14 @@ class PrecisionDebugger(BasePrecisionDebugger): return False else: return instance.config.task != Const.FREE_BENCHMARK - + @classmethod def _is_kernel_dump(cls): instance = cls._instance if not instance: raise Exception(MsgConst.NOT_CREATED_INSTANCE) return instance.config.level_ori == Const.LEVEL_L2 - + @classmethod def _start_kernel_dump(cls): instance = cls.get_instance() diff --git a/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/hook_cell.py b/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/hook_cell.py index 62e14e9f2..dcb17855c 100644 --- a/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/hook_cell.py +++ b/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/hook_cell.py @@ -13,15 +13,16 @@ # See the License for the specific language governing permissions and # limitations under the License. +import threading from collections import defaultdict import mindspore as ms from mindspore import nn from msprobe.core.common.runtime import Runtime +from msprobe.core.common.utils import ThreadSafe from msprobe.mindspore.common.utils import is_mindtorch, register_backward_hook_functions - ms_version = ms.__version__ @@ -35,16 +36,17 @@ def get_cell_count(name): def __init__(self, hook_build_func) -> None: super(HOOKCell, self).__init__() - self.changed_status = False self.msprobe_input_kwargs = {} - if not HOOKCell.g_stop_hook: - HOOKCell.g_stop_hook = True - self.changed_status = True + + self.tid = threading.get_ident() + self.stop_hook = HOOKCell.inner_stop_hook.get(self.tid, False) + if not self.stop_hook: self.forward_data_collected = False if not Runtime.is_running: return prefix = self.prefix_api_name if hasattr(self, "prefix_api_name") else "" + ThreadSafe.acquire() if callable(hook_build_func): hook_set = hook_build_func(prefix) if ms_version < "2.6.0" and not is_mindtorch(): @@ -59,21 +61,24 @@ def __init__(self, hook_build_func) -> None: # 重载call，加全局标志。 def __call__(self, *args, **kwargs): + changed = False + if not self.stop_hook: + HOOKCell.inner_stop_hook[self.tid] = True + changed = True try: self.msprobe_input_kwargs = kwargs out = super(HOOKCell, self).__call__(*args, **kwargs) except Exception as e: raise e finally: - if self.changed_status: - self.changed_status = False - HOOKCell.g_stop_hook = False + if changed: + HOOKCell.inner_stop_hook[self.tid] = False return out hook_cell_dict = { "cell_count": defaultdict(int), - "g_stop_hook": False, + "inner_stop_hook": defaultdict(bool), "add_cell_count": staticmethod(add_cell_count), "get_cell_count": staticmethod(get_cell_count), "__init__": __init__, diff --git a/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/ms_hook_manager.py b/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/ms_hook_manager.py index 5581a44ca..0a4a39329 100644 --- a/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/ms_hook_manager.py +++ b/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/ms_hook_manager.py @@ -13,9 +13,11 @@ # See the License for the specific language governing permissions and # limitations under the License. +import threading + from mindspore.common.api import _no_grad from msprobe.core.common.const import Const -from msprobe.core.common.utils import replace_last_occurrence +from msprobe.core.common.utils import replace_last_occurrence, ThreadSafe from msprobe.core.data_dump.data_processor.base import ModuleBackwardInputs from msprobe.core.hook_manager import BaseHookManager, HookSet from msprobe.mindspore.common.utils import has_kwargs_in_forward_hook @@ -78,11 +80,14 @@ class MindsproeHookManager(BaseHookManager): def backward_pre_hook(module, grad_input): if self.config.level != Const.LEVEL_L2: return - if not self._should_execute_hook(hook_type, module, False): + tid = threading.get_ident() + if not self._should_execute_hook(hook_type, module, False, tid): return - BaseHookManager.inner_switch = True - module_input = ModuleBackwardInputs(grad_input=grad_input) - self.data_collector.update_api_or_module_name(name) - self.data_collector.backward_input_data_collect(name, module, self._pid, module_input) - BaseHookManager.inner_switch = False + + with ThreadSafe(): + BaseHookManager.inner_switch[tid] = True + module_input = ModuleBackwardInputs(grad_input=grad_input) + self.data_collector.update_api_or_module_name(name) + self.data_collector.backward_input_data_collect(name, module, self._pid, module_input) + BaseHookManager.inner_switch[tid] = False return backward_pre_hook diff --git a/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/primitive_hooks.py b/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/primitive_hooks.py index 8bf4a94ac..71f218bd5 100644 --- a/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/primitive_hooks.py +++ b/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/primitive_hooks.py @@ -14,13 +14,16 @@ # limitations under the License. import os +import threading from mindspore import ops from mindspore.common.tensor import Tensor - -from msprobe.core.common.utils import Const, DumpException -from msprobe.core.data_dump.data_processor.base import (ModuleBackwardInputs, ModuleBackwardOutputs, - ModuleForwardInputsOutputs) +from msprobe.core.common.utils import Const, DumpException, ThreadSafe +from msprobe.core.data_dump.data_processor.base import ( + ModuleBackwardInputs, + ModuleBackwardOutputs, + ModuleForwardInputsOutputs +) from msprobe.core.hook_manager import BaseHookManager from msprobe.mindspore.common.log import logger @@ -56,10 +59,13 @@ class PrimitiveHookService: callable: 反向 hook 函数。 """ + @ThreadSafe.synchronized def backward_hook(grad): + tid = threading.get_ident() + BaseHookManager.inner_switch[tid] = True + captured_grads.extend(grad) backward_primitive_name = f"{updated_primitive_name}{Const.SEP}{Const.BACKWARD}" - self.service_instance.inner_switch = True try: if hook_type == Const.INPUT: self.service_instance.data_collector.update_api_or_module_name(backward_primitive_name) @@ -78,7 +84,7 @@ class PrimitiveHookService: logger.error(f"This is a primitive op {hook_type}_backward dump error: {exception}, " f"updated_primitive_name: {updated_primitive_name}") raise DumpException(DumpException.BACKWARD_DATA_COLLECTION_ERROR) from exception - self.service_instance.inner_switch = False + BaseHookManager.inner_switch[tid] = False return backward_hook @@ -137,9 +143,13 @@ class PrimitiveHookService: return tuple(hooked_outputs) return out + @ThreadSafe.synchronized def pre_forward_hook(primitive_name, primitive_instance, args, kwargs): + tid = threading.get_ident() + BaseHookManager.inner_switch[tid] = True + + self.service_instance.data_collector.update_api_or_module_name(primitive_name) module_input_output = ModuleForwardInputsOutputs(args=args, kwargs=kwargs, output=None) - self.service_instance.inner_switch = True try: self.service_instance.data_collector.forward_input_data_collect( primitive_name, @@ -151,11 +161,15 @@ class PrimitiveHookService: logger.error(f"This is a primitive op dump error during forward input data collection: {exception}, " f"primitive_name: {primitive_name}") raise DumpException(DumpException.FORWARD_DATA_COLLECTION_ERROR) from exception - self.service_instance.inner_switch = False + BaseHookManager.inner_switch[tid] = False + @ThreadSafe.synchronized def post_forward_hook(primitive_name, primitive_instance, args, kwargs, output): + tid = threading.get_ident() + BaseHookManager.inner_switch[tid] = True + + self.service_instance.data_collector.update_api_or_module_name(primitive_name) module_input_output = ModuleForwardInputsOutputs(args=args, kwargs=kwargs, output=output) - self.service_instance.inner_switch = True try: self.service_instance.data_collector.forward_output_data_collect( primitive_name, @@ -167,7 +181,7 @@ class PrimitiveHookService: logger.error(f"This is a primitive op dump error during forward output data collection: {exception}, " f"primitive_name: {primitive_name}") raise DumpException(DumpException.FORWARD_DATA_COLLECTION_ERROR) from exception - self.service_instance.inner_switch = False + BaseHookManager.inner_switch[tid] = False def wrapped_primitive_call(instance_self, *args, **kwargs): """ @@ -185,7 +199,8 @@ class PrimitiveHookService: current_count = self.primitive_counters.get(primitive_name, 0) updated_primitive_name = f"{Const.PRIMITIVE_PREFIX}{Const.SEP}{primitive_name}{Const.SEP}{current_count}" - if not self.service_instance.primitive_switch or BaseHookManager.inner_switch: + tid = threading.get_ident() + if not self.service_instance.primitive_switch or BaseHookManager.inner_switch[tid]: return origin_func(*args, **kwargs) captured_grads_input, captured_grads_output = [], [] @@ -198,8 +213,6 @@ class PrimitiveHookService: raise DumpException(DumpException.INPUT_HOOK_ERROR) from exception forward_primitive_name = f"{updated_primitive_name}{Const.SEP}{Const.FORWARD}" - self.service_instance.data_collector.update_api_or_module_name(forward_primitive_name) - pre_forward_hook(forward_primitive_name, instance_self, hooked_inputs, kwargs) try: out = origin_func(*hooked_inputs, **kwargs) @@ -220,6 +233,7 @@ class PrimitiveHookService: return wrapped_primitive_call + @ThreadSafe.synchronized def update_primitive_counters(self, primitive_name): if primitive_name not in self.primitive_counters: self.primitive_counters[primitive_name] = 0 diff --git a/debug/accuracy_tools/msprobe/mindspore/dump/jit_dump.py b/debug/accuracy_tools/msprobe/mindspore/dump/jit_dump.py index 90565ac6d..5f4564394 100644 --- a/debug/accuracy_tools/msprobe/mindspore/dump/jit_dump.py +++ b/debug/accuracy_tools/msprobe/mindspore/dump/jit_dump.py @@ -13,13 +13,14 @@ # See the License for the specific language governing permissions and # limitations under the License. -from collections import defaultdict import os import types +from collections import defaultdict import mindspore from mindspore import nn from mindspore._c_expression import PyNativeExecutor_ + try: from mindspore.common.api import _MindsporeFunctionExecutor except ImportError: @@ -27,19 +28,21 @@ except ImportError: from msprobe.core.common.log import logger from msprobe.core.common.const import Const +from msprobe.core.common.utils import ThreadSafe from msprobe.core.common.runtime import Runtime from msprobe.core.data_dump.data_processor.base import ModuleForwardInputsOutputs, ModuleBackwardInputsOutputs from msprobe.mindspore.common.const import Const as MsConst from msprobe.mindspore.dump.hook_cell.api_register import get_api_register - _api_register = get_api_register() def dump_jit(name, in_feat, out_feat, is_forward): pid = os.getpid() name = name if name else "JitFunction" - if JitDump.need_dump(): + if not JitDump.need_dump(): + return + with ThreadSafe(): if is_forward: if name in JitDump.jit_count: JitDump.jit_count[name] += 1 diff --git a/debug/accuracy_tools/msprobe/mindspore/mindspore_service.py b/debug/accuracy_tools/msprobe/mindspore/mindspore_service.py index 48c6a41aa..b5ffce6a6 100644 --- a/debug/accuracy_tools/msprobe/mindspore/mindspore_service.py +++ b/debug/accuracy_tools/msprobe/mindspore/mindspore_service.py @@ -39,11 +39,11 @@ else: pijit_label = True -class MindsporeService(BaseService): +class MindsporeService(BaseService): @property def _get_framework_type(self): return Const.MT_FRAMEWORK if is_mindtorch() else Const.MS_FRAMEWORK - + @staticmethod def _get_current_rank(): return get_rank_if_initialized() diff --git a/debug/accuracy_tools/msprobe/pytorch/debugger/precision_debugger.py b/debug/accuracy_tools/msprobe/pytorch/debugger/precision_debugger.py index 8569d1474..0e784dafb 100644 --- a/debug/accuracy_tools/msprobe/pytorch/debugger/precision_debugger.py +++ b/debug/accuracy_tools/msprobe/pytorch/debugger/precision_debugger.py @@ -17,7 +17,7 @@ from torch.utils.data import dataloader from msprobe.core.common.const import Const, MsgConst from msprobe.core.common.exceptions import MsprobeException -from msprobe.core.common.utils import check_token_range +from msprobe.core.common.utils import check_token_range, ThreadSafe from msprobe.core.debugger.precision_debugger import BasePrecisionDebugger from msprobe.pytorch.common.log import logger from msprobe.pytorch.common.utils import check_save_param, is_torch_nn_module @@ -81,6 +81,7 @@ class PrecisionDebugger(BasePrecisionDebugger): return func_wrapper @classmethod + @ThreadSafe.synchronized def start(cls, model=None, token_range=None): instance = cls._get_instance() if instance is None: @@ -95,6 +96,7 @@ class PrecisionDebugger(BasePrecisionDebugger): instance.service.start(instance.model, token_range) @classmethod + @ThreadSafe.synchronized def stop(cls): instance = cls._get_instance() if instance is None: @@ -105,6 +107,7 @@ class PrecisionDebugger(BasePrecisionDebugger): instance.service.stop() @classmethod + @ThreadSafe.synchronized def step(cls): instance = cls._get_instance() if instance is None: @@ -112,6 +115,7 @@ class PrecisionDebugger(BasePrecisionDebugger): cls._instance.service.step() @classmethod + @ThreadSafe.synchronized def monitor(cls, model): if not cls._instance: raise Exception(MsgConst.NOT_CREATED_INSTANCE) @@ -120,6 +124,7 @@ class PrecisionDebugger(BasePrecisionDebugger): cls._instance.gm.monitor(model) @classmethod + @ThreadSafe.synchronized def save(cls, variable, name, save_backward=True): instance = cls._instance if not instance: @@ -143,6 +148,7 @@ class PrecisionDebugger(BasePrecisionDebugger): dataloader._BaseDataLoaderIter.__next__ = self._iter_tracer(dataloader._BaseDataLoaderIter.__next__) +@ThreadSafe.synchronized def module_dump(module, dump_name): if not is_torch_nn_module(module): raise MsprobeException( @@ -164,6 +170,7 @@ def module_dump(module, dump_name): instance.module_dumper.start_module_dump(module, dump_name) +@ThreadSafe.synchronized def module_dump_end(): instance = PrecisionDebugger._instance if not instance: diff --git a/debug/accuracy_tools/msprobe/pytorch/dump/module_dump/module_processer.py b/debug/accuracy_tools/msprobe/pytorch/dump/module_dump/module_processer.py index 5b273c197..6130ee50f 100644 --- a/debug/accuracy_tools/msprobe/pytorch/dump/module_dump/module_processer.py +++ b/debug/accuracy_tools/msprobe/pytorch/dump/module_dump/module_processer.py @@ -13,12 +13,14 @@ # See the License for the specific language governing permissions and # limitations under the License. +import threading from collections import OrderedDict import torch from torch.utils.hooks import BackwardHook, RemovableHandle from msprobe.core.common.const import Const +from msprobe.core.common.utils import ModuleQueue, ThreadSafe from msprobe.core.data_dump.scope import BaseScope, ModuleRangeScope, MixRangeScope from msprobe.pytorch.common.log import logger from msprobe.pytorch.common.utils import is_torch_nn_module, register_forward_pre_hook @@ -46,13 +48,15 @@ def wrap_megatron_deallocate(func): out.data = torch.empty((1,), device=out.device, dtype=out.dtype, ) return func(out_clone, deallocate_pipeline_outputs) return func(out, deallocate_pipeline_outputs) + return wrapper_func class ModuleProcesser: + module_queue = ModuleQueue() module_count = {} - module_stack = [] - api_parent_node = "" + module_stack = {} + api_parent_node = {} module_node = {} module_bw_hook_kernels = {} module_with_backward_hook = {} @@ -103,9 +107,10 @@ class ModuleProcesser: @classmethod def reset_module_stats(cls): + cls.module_queue = ModuleQueue() cls.module_count = {} - cls.module_stack = [] - cls.api_parent_node = "" + cls.module_stack = {} + cls.api_parent_node = {} cls.module_node = {} cls.module_bw_hook_kernels = {} cls.enable_module_dump = False @@ -144,6 +149,7 @@ class ModuleProcesser: register_forward_pre_hook(module, forward_pre_hook) def build_module_hook(self, module_name, build_data_hook): + @ThreadSafe.synchronized def forward_pre_hook(module, args, kwargs=None): if kwargs is None: kwargs = {} @@ -171,15 +177,19 @@ class ModuleProcesser: hook_set = build_data_hook(BaseScope.Module_Type_Module, full_forward_name) def get_backward_pre_hook(full_backward_name): + @ThreadSafe.synchronized def backward_pre_hook_fn(module, grad_output): self.set_construct_info_in_pre_hook(full_backward_name) + return backward_pre_hook_fn def get_backward_hook(backward_data_hook, full_backward_name): + @ThreadSafe.synchronized def backward_hook_fn(module, grad_input, grad_output): new_output = backward_data_hook(module, grad_input, grad_output) self.set_construct_info_in_hook(full_backward_name, is_forward=False) return new_output + return backward_hook_fn if not ModuleProcesser.module_with_backward_hook.get(module_name): @@ -193,6 +203,7 @@ class ModuleProcesser: args = bw_hook.setup_input_hook(args) return (args, kwargs) if torch_version_above_or_equal_2 else args + @ThreadSafe.synchronized def forward_hook(module, args, kwargs_or_output, output_or_kwargs=None): if hasattr(module, 'msprobe_module_dump') and not self.enable_module_dump: return output_or_kwargs if torch_version_above_or_equal_2 else kwargs_or_output @@ -218,23 +229,34 @@ class ModuleProcesser: return forward_pre_hook def set_construct_info_in_pre_hook(self, full_name): - if self.module_stack: - ModuleProcesser.module_node[full_name] = self.module_stack[-1] + tid = threading.get_ident() + if tid not in self.module_stack: + ModuleProcesser.module_stack[tid] = [] + + if self.module_stack[tid]: + ModuleProcesser.module_node[full_name] = self.module_stack[tid][-1] else: - ModuleProcesser.module_node[full_name] = None - ModuleProcesser.module_stack.append(full_name) - ModuleProcesser.api_parent_node = full_name + parent_name = ModuleProcesser.module_queue.find_last(full_name) + ModuleProcesser.module_node[full_name] = parent_name + + ModuleProcesser.module_queue.add_name(full_name) + ModuleProcesser.module_stack[tid].append(full_name) + ModuleProcesser.api_parent_node[tid] = full_name if self.scope: self.scope.begin_module(full_name) def set_construct_info_in_hook(self, full_name, is_forward=True): + tid = threading.get_ident() if torch_version_above_or_equal_2 or is_forward: - if self.module_stack: - ModuleProcesser.module_stack.pop() - ModuleProcesser.api_parent_node = ModuleProcesser.module_stack[-1] if self.module_stack else None + ModuleProcesser.module_queue.remove_name(full_name) + ModuleProcesser.api_parent_node[tid] = None + if self.module_stack.get(tid): + ModuleProcesser.module_stack[tid].pop() + if self.module_stack.get(tid): + ModuleProcesser.api_parent_node[tid] = ModuleProcesser.module_stack[tid][-1] if self.scope: self.scope.end_module(full_name) else: if self.scope: self.scope.begin_module(full_name) - ModuleProcesser.api_parent_node = full_name + ModuleProcesser.api_parent_node[tid] = full_name diff --git a/debug/accuracy_tools/msprobe/pytorch/hook_module/hook_module.py b/debug/accuracy_tools/msprobe/pytorch/hook_module/hook_module.py index 0a55f6a9d..5a1450a0d 100644 --- a/debug/accuracy_tools/msprobe/pytorch/hook_module/hook_module.py +++ b/debug/accuracy_tools/msprobe/pytorch/hook_module/hook_module.py @@ -22,20 +22,19 @@ import torch.nn as nn import torch.utils.hooks as full_hooks from msprobe.core.common.runtime import Runtime +from msprobe.core.common.utils import ThreadSafe from msprobe.pytorch.common.utils import is_float8_tensor, register_forward_pre_hook, register_forward_hook class HOOKModule(nn.Module): module_count = defaultdict(int) - inner_stop_hook = {} + inner_stop_hook = defaultdict(bool) def __init__(self, hook_build_func) -> None: super(HOOKModule, self).__init__() self.has_overflow = False - self.current_thread = threading.current_thread().ident - if self.current_thread not in HOOKModule.inner_stop_hook: - HOOKModule.inner_stop_hook[self.current_thread] = False - self.stop_hook = HOOKModule.inner_stop_hook.get(self.current_thread, False) + self.tid = threading.get_ident() + self.stop_hook = HOOKModule.inner_stop_hook.get(self.tid, False) if not self.stop_hook: self.forward_data_collected = False @@ -43,6 +42,7 @@ class HOOKModule(nn.Module): if not Runtime.is_running: return prefix = self.prefix_api_name if hasattr(self, "prefix_api_name") else "" + ThreadSafe.acquire() if callable(hook_build_func): hook_set = hook_build_func(prefix) register_forward_pre_hook(self, hook_set.forward_pre_hook) @@ -52,11 +52,11 @@ class HOOKModule(nn.Module): def __call__(self, *args, **kwargs): changed = False if not self.stop_hook: - HOOKModule.inner_stop_hook[self.current_thread] = True + HOOKModule.inner_stop_hook[self.tid] = True changed = True result = self._call_func(*args, **kwargs) if changed: - HOOKModule.inner_stop_hook[self.current_thread] = False + HOOKModule.inner_stop_hook[self.tid] = False return result @staticmethod diff --git a/debug/accuracy_tools/msprobe/pytorch/hook_module/pt_hook_manager.py b/debug/accuracy_tools/msprobe/pytorch/hook_module/pt_hook_manager.py index 413ad3da0..3c2a13215 100644 --- a/debug/accuracy_tools/msprobe/pytorch/hook_module/pt_hook_manager.py +++ b/debug/accuracy_tools/msprobe/pytorch/hook_module/pt_hook_manager.py @@ -23,7 +23,7 @@ from msprobe.pytorch.common.utils import is_recomputation, torch_version_above_o from msprobe.pytorch.hook_module.hook_module import HOOKModule -class PytorchHookManager(BaseHookManager): +class PytorchHookManager(BaseHookManager): @property def _is_recompute(self): return is_recomputation() @@ -41,7 +41,7 @@ class PytorchHookManager(BaseHookManager): kwargs = kwargs_or_output if torch_version_above_or_equal_2 else {} output = output_or_kwargs if torch_version_above_or_equal_2 else kwargs_or_output return kwargs, output - + def build_hook(self, hook_type, name): if hook_type == Const.API: full_forward_name = name + str(HOOKModule.get_module_count(name)) + Const.SEP + Const.FORWARD @@ -51,10 +51,10 @@ class PytorchHookManager(BaseHookManager): hookset = HookSet( forward_hook=self._build_forward_hook(hook_type, full_forward_name), forward_pre_hook=self._build_forward_pre_hook(hook_type, full_forward_name, name), - backward_hook=self._build_backward_hook(hook_type, full_backward_name) + backward_hook=self._build_backward_hook(hook_type, full_backward_name) ) return hookset - + def _need_exchange(self, module): return True @@ -62,7 +62,7 @@ class PytorchHookManager(BaseHookManager): params_dict = {} if self.config.task != Const.STRUCTURE: params_dict = { - key.split(Const.SEP)[-1]: value - for key, value in module.named_parameters(recurse=False) - } + key.split(Const.SEP)[-1]: value + for key, value in module.named_parameters(recurse=False) + } return params_dict diff --git a/debug/accuracy_tools/msprobe/test/core_ut/data_dump/test_data_collector.py b/debug/accuracy_tools/msprobe/test/core_ut/data_dump/test_data_collector.py index 6be254530..b93a122ac 100644 --- a/debug/accuracy_tools/msprobe/test/core_ut/data_dump/test_data_collector.py +++ b/debug/accuracy_tools/msprobe/test/core_ut/data_dump/test_data_collector.py @@ -14,6 +14,7 @@ # limitations under the License. import os + import unittest from unittest.mock import patch, mock_open, MagicMock @@ -253,21 +254,6 @@ class TestDataCollector(unittest.TestCase): self.data_collector.module_processor.module_node ) - def test_update_construct_without_module_prefix(self): - self.data_collector.module_processor = MagicMock() - self.data_collector.data_writer = MagicMock() - self.data_collector.config.level = "mix" - self.data_collector.optimizer_status = "other_status" - self.data_collector.module_processor.api_parent_node = "parent_node" - test_name = "api_name" - - self.data_collector.update_construct(test_name) - calls = [ - unittest.mock.call({"api_name": "parent_node"}), - unittest.mock.call(self.data_collector.module_processor.module_node) - ] - self.data_collector.data_writer.update_construct.assert_has_calls(calls) - def test_handle_data(self): with patch.object(DataCollector, "update_data") as mock_update_data, \ patch.object(DataCollector, "write_json") as mock_write_json, \ diff --git a/debug/accuracy_tools/msprobe/test/core_ut/test_hook_manager.py b/debug/accuracy_tools/msprobe/test/core_ut/test_hook_manager.py index 68239bc7f..f03a7d889 100644 --- a/debug/accuracy_tools/msprobe/test/core_ut/test_hook_manager.py +++ b/debug/accuracy_tools/msprobe/test/core_ut/test_hook_manager.py @@ -13,9 +13,13 @@ # See the License for the specific language governing permissions and # limitations under the License. +import threading + import unittest from unittest.mock import MagicMock, patch + from msprobe.core.common.const import Const +from msprobe.core.common.utils import ThreadSafe from msprobe.core.common.runtime import Runtime from msprobe.core.hook_manager import BaseHookManager @@ -25,25 +29,25 @@ class TestBaseHookManager(unittest.TestCase): @property def _is_recompute(self): return False - + @staticmethod def _no_grad_context(): return MagicMock() - + @staticmethod def _add_count(name): pass - + @staticmethod def _process_kwargs_and_output(module, hook_type, kwargs_or_output, output_or_kwargs): return {"kwargs": kwargs_or_output}, output_or_kwargs - + def build_hook(self): pass - + def _get_params_dict(self, module): return {} - + def _need_exchange(self, module): return False @@ -53,91 +57,93 @@ class TestBaseHookManager(unittest.TestCase): self.mock_config.data_mode = ["all"] self.mock_attl_manager = MagicMock() self.manager = self.MockBaseHookManager( - self.mock_data_collector, - self.mock_config, + self.mock_data_collector, + self.mock_config, self.mock_attl_manager ) - BaseHookManager.inner_switch = False + BaseHookManager.inner_switch[threading.get_ident()] = False BaseHookManager.hook_handle_dict = {} BaseHookManager.params_grad_info = {} - + def test_init(self): self.assertEqual(self.manager.data_collector, self.mock_data_collector) self.assertEqual(self.manager.config, self.mock_config) self.assertEqual(self.manager.attl_manager, self.mock_attl_manager) - + def test_should_execute_hook_conditions(self): module = MagicMock() module.forward_data_collected = True Runtime.is_running = True self.mock_data_collector.data_processor.is_terminated = False - self.assertTrue(self.manager._should_execute_hook(Const.MODULE, module, True)) - self.assertTrue(self.manager._should_execute_hook(Const.API, module, False)) - + self.assertTrue(self.manager._should_execute_hook(Const.MODULE, module, True, threading.get_ident())) + self.assertTrue(self.manager._should_execute_hook(Const.API, module, False, threading.get_ident())) + Runtime.is_running = False - self.assertFalse(self.manager._should_execute_hook(Const.MODULE, module, True)) - + self.assertFalse(self.manager._should_execute_hook(Const.MODULE, module, True, threading.get_ident())) + Runtime.is_running = True module.forward_data_collected = False - self.assertFalse(self.manager._should_execute_hook(Const.API, module, False)) - - BaseHookManager.inner_switch = True - self.assertFalse(self.manager._should_execute_hook(Const.MODULE, module, True)) - + self.assertFalse(self.manager._should_execute_hook(Const.API, module, False, threading.get_ident())) + + BaseHookManager.inner_switch[threading.get_ident()] = True + self.assertFalse(self.manager._should_execute_hook(Const.MODULE, module, True, threading.get_ident())) + self.mock_data_collector.data_processor.is_terminated = True - BaseHookManager.inner_switch = False - self.assertFalse(self.manager._should_execute_hook(Const.MODULE, module, True)) - self.assertFalse(self.manager._should_execute_hook(Const.API, module, True)) - + BaseHookManager.inner_switch[threading.get_ident()] = False + self.assertFalse(self.manager._should_execute_hook(Const.MODULE, module, True, threading.get_ident())) + self.assertFalse(self.manager._should_execute_hook(Const.API, module, True, threading.get_ident())) + def test_clear_input_kwargs(self): module = MagicMock() module.msprobe_input_kwargs = {"key": "value"} self.manager._clear_input_kwargs(module) self.assertFalse(hasattr(module, 'msprobe_input_kwargs')) - + def test_register_param_hook(self): module = MagicMock() params = {"param1": MagicMock(requires_grad=True)} full_name = "module.forward" - + with patch.object(self.manager, '_build_grad_hook') as mock_build: self.manager._register_param_hook(full_name, module, params) - + self.assertEqual(len(BaseHookManager.hook_handle_dict), 1) self.assertTrue("module.param1" in BaseHookManager.hook_handle_dict) - + self.assertEqual(module.params_grad_name, "module.parameters_grad") - + def test_init_params_grad_info(self): module = MagicMock() module.params_grad_name = "grad_name" params = {"param1": MagicMock(requires_grad=True)} - + self.manager._init_params_grad_info(module, params) self.mock_data_collector.handle_data.assert_called() self.assertTrue(BaseHookManager.params_grad_info.get("grad_name")) - + self.manager._init_params_grad_info(module, params) self.mock_data_collector.handle_data.assert_called_once() - @patch.object(BaseHookManager, "_should_execute_hook") - def test_forward_pre_hook_behavior(self, mock_should_execute_hook): + @patch.object(ThreadSafe, "release") + @patch.object(BaseHookManager, "_should_execute_hook") + def test_forward_pre_hook_behavior(self, mock_should_execute_hook, mock_release): mock_should_execute_hook.return_value = True + mock_release.return_value = None self.manager.config.online_run_ut = None hook = self.manager._build_forward_pre_hook(Const.API, "api_name", "func_name") module = MagicMock() module.msprobe_input_kwargs = {"kwarg": "value"} args = (1, 2) - + Runtime.is_running = True module.forward_data_collected = True self.mock_data_collector.data_processor.is_terminated = False - + with patch.object(self.manager, '_no_grad_context') as mock_ctx: hook(module, args) self.mock_data_collector.forward_input_data_collect.assert_called_once() self.assertEqual(module.forward_data_collected, True) - + @patch.object(BaseHookManager, "_should_execute_hook") def test_forward_hook_behavior(self, mock_should_execute_hook): mock_should_execute_hook.return_value = True @@ -146,11 +152,11 @@ class TestBaseHookManager(unittest.TestCase): args = (1, 2) kwargs = {"kwargs": []} output = MagicMock() - + self.manager.config.online_run_ut = True hook(module, args, output) self.mock_attl_manager.attl_send.assert_called_once() - + self.manager.config.online_run_ut = None self.mock_data_collector.if_return_forward_new_output.return_value = False with patch.object(self.manager, '_get_params_dict', return_value={}): @@ -158,14 +164,14 @@ class TestBaseHookManager(unittest.TestCase): self.assertEqual(result, output) self.mock_data_collector.forward_data_collect.assert_called_once() self.mock_data_collector.get_forward_new_output.assert_not_called() - + self.mock_data_collector.if_return_forward_new_output.return_value = True self.mock_data_collector.get_forward_new_output.return_value = "new_output" with patch.object(self.manager, '_get_params_dict', return_value={}): result = hook(module, args, output) self.assertEqual(result, "new_output") - @patch.object(BaseHookManager, "_should_execute_hook") + @patch.object(BaseHookManager, "_should_execute_hook") def test_backward_hook_behavior(self, mock_should_execute_hook): mock_should_execute_hook.return_value = True self.manager.config.online_run_ut = None @@ -173,11 +179,11 @@ class TestBaseHookManager(unittest.TestCase): module = MagicMock() grad_input = (MagicMock(),) grad_output = (MagicMock(),) - + module.forward_data_collected = True Runtime.is_running = True hook(module, grad_input, grad_output) - + self.mock_data_collector.backward_data_collect.assert_called_once() with patch.object(self.manager, '_need_exchange', return_value=True): diff --git a/debug/accuracy_tools/msprobe/test/mindspore_ut/hook_module/test_ms_hook_manager.py b/debug/accuracy_tools/msprobe/test/mindspore_ut/hook_module/test_ms_hook_manager.py index 2b1eaa1b8..49247535f 100644 --- a/debug/accuracy_tools/msprobe/test/mindspore_ut/hook_module/test_ms_hook_manager.py +++ b/debug/accuracy_tools/msprobe/test/mindspore_ut/hook_module/test_ms_hook_manager.py @@ -13,11 +13,14 @@ # See the License for the specific language governing permissions and # limitations under the License. +import threading + import unittest from unittest.mock import MagicMock, patch -from msprobe.mindspore.dump.hook_cell.ms_hook_manager import MindsproeHookManager + from msprobe.core.common.const import Const from msprobe.core.hook_manager import HookSet, BaseHookManager +from msprobe.mindspore.dump.hook_cell.ms_hook_manager import MindsproeHookManager class TestMindsproeHookManager(unittest.TestCase): @@ -28,14 +31,14 @@ class TestMindsproeHookManager(unittest.TestCase): self.mock_config.task = "statistics" self.mock_config.level = Const.LEVEL_L1 self.manager = MindsproeHookManager( - self.mock_data_collector, + self.mock_data_collector, self.mock_config ) - BaseHookManager.inner_switch = False + BaseHookManager.inner_switch[threading.get_ident()] = False def test_properties(self): self.assertIsNone(self.manager._is_recompute) - + with patch('msprobe.mindspore.dump.hook_cell.ms_hook_manager._no_grad') as mock_no_grad: ctx = self.manager._no_grad_context() mock_no_grad.assert_called_once() @@ -48,13 +51,13 @@ class TestMindsproeHookManager(unittest.TestCase): def test_process_kwargs_and_output(self): mock_module = MagicMock() mock_module.msprobe_input_kwargs = {"kw1": "v1"} - + kwargs, output = self.manager._process_kwargs_and_output( mock_module, Const.API, "output_value", "ignored" ) self.assertEqual(kwargs, {"kw1": "v1"}) self.assertEqual(output, "output_value") - + with patch('msprobe.mindspore.dump.hook_cell.ms_hook_manager.has_kwargs_in_forward_hook', return_value=True): kwargs, output = self.manager._process_kwargs_and_output( mock_module, Const.MODULE, "kwargs_value", "output_value" @@ -63,14 +66,14 @@ class TestMindsproeHookManager(unittest.TestCase): self.assertEqual(output, "output_value") def test_build_hook(self): - hookset = self.manager.build_hook(Const.API, "test_api") - self.assertIsInstance(hookset, HookSet) - self.assertEqual(hookset.forward_hook.__name__, "forward_hook") - self.assertEqual(hookset.forward_pre_hook.__name__, "forward_pre_hook") - self.assertEqual(hookset.backward_hook.__name__, "backward_hook") - self.assertEqual(hookset.backward_pre_hook.__name__, "backward_pre_hook") - hookset = self.manager.build_hook(Const.MODULE, "test_module") - self.assertEqual(hookset.forward_pre_hook.__name__, "forward_pre_hook") + hookset = self.manager.build_hook(Const.API, "test_api") + self.assertIsInstance(hookset, HookSet) + self.assertEqual(hookset.forward_hook.__name__, "forward_hook") + self.assertEqual(hookset.forward_pre_hook.__name__, "forward_pre_hook") + self.assertEqual(hookset.backward_hook.__name__, "backward_hook") + self.assertEqual(hookset.backward_pre_hook.__name__, "backward_pre_hook") + hookset = self.manager.build_hook(Const.MODULE, "test_module") + self.assertEqual(hookset.forward_pre_hook.__name__, "forward_pre_hook") def test_need_exchange(self): mock_module = MagicMock() @@ -89,7 +92,7 @@ class TestMindsproeHookManager(unittest.TestCase): self.mock_config.task = Const.STRUCTURE params_dict = self.manager._get_params_dict(mock_module) self.assertEqual(params_dict, {}) - + self.mock_config.task = "statistics" mock_params = { "test_module.weight": "w1", @@ -102,28 +105,28 @@ class TestMindsproeHookManager(unittest.TestCase): def test_build_backward_pre_hook(self): hook_fn = self.manager._build_backward_pre_hook(Const.MODULE, "test_module_backward") - + mock_module = MagicMock() mock_grad_input = ("grad1", "grad2") - + with patch.object(self.manager, '_should_execute_hook', return_value=False): hook_fn(mock_module, mock_grad_input) self.mock_data_collector.backward_input_data_collect.assert_not_called() - + self.mock_config.level = Const.LEVEL_L2 with patch.object(self.manager, '_should_execute_hook', return_value=True): hook_fn(mock_module, mock_grad_input) - + self.mock_data_collector.update_api_or_module_name.assert_called_with("test_module_backward") self.mock_data_collector.backward_input_data_collect.assert_called_once() call_args = self.mock_data_collector.backward_input_data_collect.call_args[0] module_input = call_args[3] self.assertEqual(module_input.grad_input, mock_grad_input) - - self.assertFalse(BaseHookManager.inner_switch) - + + self.assertFalse(BaseHookManager.inner_switch[threading.get_ident()]) + self.mock_config.level = Const.LEVEL_L1 with patch.object(self.manager, '_should_execute_hook', return_value=True): hook_fn(mock_module, mock_grad_input) - self.mock_data_collector.backward_input_data_collect.assert_called_once() + self.mock_data_collector.backward_input_data_collect.assert_called_once() diff --git a/debug/accuracy_tools/msprobe/test/mindspore_ut/test_cell_processor.py b/debug/accuracy_tools/msprobe/test/mindspore_ut/test_cell_processor.py index e9848e7e4..73f829f54 100644 --- a/debug/accuracy_tools/msprobe/test/mindspore_ut/test_cell_processor.py +++ b/debug/accuracy_tools/msprobe/test/mindspore_ut/test_cell_processor.py @@ -13,6 +13,8 @@ # See the License for the specific language governing permissions and # limitations under the License. +import threading + import unittest from unittest.mock import MagicMock, patch @@ -85,8 +87,8 @@ class TestCellProcessor(unittest.TestCase): def test_reset_cell_stats(self): CellProcessor.cell_count['cell'] = 0 - CellProcessor.cell_stack.append('cell') - CellProcessor.api_parent_node = 'cell' + CellProcessor.cell_stack['tid'] = 'cell' + CellProcessor.api_parent_node['tid'] = 'cell' CellProcessor.module_node['cell'] = 'null' CellProcessor.cell_bw_hook_kernels['cell'] = 'bw' CellProcessor.cell_backward_pre_hook.append('backward_pre_hook') @@ -94,8 +96,8 @@ class TestCellProcessor(unittest.TestCase): CellProcessor.reset_cell_stats() self.assertEqual(CellProcessor.cell_count, {}) - self.assertEqual(CellProcessor.cell_stack, []) - self.assertIsNone(CellProcessor.api_parent_node) + self.assertEqual(CellProcessor.cell_stack, {}) + self.assertEqual(CellProcessor.api_parent_node, {}) self.assertEqual(CellProcessor.module_node, {}) self.assertEqual(CellProcessor.cell_bw_hook_kernels, {}) self.assertEqual(CellProcessor.cell_backward_pre_hook, []) @@ -182,7 +184,7 @@ class TestCellProcessor(unittest.TestCase): with patch.object(_inner_ops, 'CellBackwardHook') as mock_CellBackwardHook: forward_pre_hook = self.processor.build_cell_hook(cell_name, mock_build_data_hook) - forward_hook = forward_pre_hook.__closure__[2].cell_contents + forward_hook = forward_pre_hook.__closure__[1].cell_contents.__closure__[2].cell_contents mock_bw = mock_CellBackwardHook.return_value mock_bw.return_value = (Tensor([0.0]),) @@ -193,8 +195,8 @@ class TestCellProcessor(unittest.TestCase): # call testing function - forward_pre_hook ret = forward_pre_hook(mock_cell, args) self.assertIsNone(CellProcessor.module_node[full_forward_name]) - self.assertEqual(CellProcessor.cell_stack, [full_forward_name]) - self.assertEqual(CellProcessor.api_parent_node, full_forward_name) + self.assertEqual(CellProcessor.cell_stack[threading.get_ident()], [full_forward_name]) + self.assertEqual(CellProcessor.api_parent_node[threading.get_ident()], full_forward_name) self.scope.begin_module.assert_called_with(full_forward_name) mock_build_data_hook.assert_called_with('Module', full_forward_name) self.assertEqual(len(CellProcessor.cell_backward_hook), 1) @@ -211,8 +213,8 @@ class TestCellProcessor(unittest.TestCase): ret = backward_hook(mock_cell, grad_input, grad_output) mock_backward_data_hook.assert_called_with(mock_cell, grad_input, grad_output) self.assertFalse(mock_cell.has_pre_hook_called) - self.assertEqual(CellProcessor.cell_stack, []) - self.assertIsNone(CellProcessor.api_parent_node) + self.assertEqual(CellProcessor.cell_stack[threading.get_ident()], []) + self.assertIsNone(CellProcessor.api_parent_node[threading.get_ident()]) self.scope.end_module.assert_called_with(full_backward_name) self.assertTrue((ret[0] == target_grad_output[0]).all()) @@ -222,16 +224,16 @@ class TestCellProcessor(unittest.TestCase): # call testing function - forward_pre_hook ret = forward_pre_hook(mock_cell, args) self.assertIsNone(CellProcessor.module_node[full_forward_name]) - self.assertEqual(CellProcessor.cell_stack, [full_forward_name]) - self.assertEqual(CellProcessor.api_parent_node, full_forward_name) + self.assertEqual(CellProcessor.cell_stack[threading.get_ident()], [full_forward_name]) + self.assertEqual(CellProcessor.api_parent_node[threading.get_ident()], full_forward_name) self.scope.begin_module.assert_called_with(full_forward_name) self.assertEqual(len(CellProcessor.cell_backward_hook), 1) mock_build_data_hook.assert_not_called() full_forward_name = f'{cell_name}{Const.FORWARD}.0' CellProcessor.cell_count = {cell_name: 0} - CellProcessor.cell_stack = [full_forward_name] - CellProcessor.api_parent_node = full_forward_name + CellProcessor.cell_stack[threading.get_ident()] = [full_forward_name] + CellProcessor.api_parent_node[threading.get_ident()] = full_forward_name CellProcessor.module_node = {full_forward_name: None} self.scope.reset_mock() mock_CellBackwardHook.reset_mock() @@ -249,8 +251,8 @@ class TestCellProcessor(unittest.TestCase): # call testing function - forward_hook ret = forward_hook(mock_cell, args, output) self.assertEqual(CellProcessor.cell_count.get(cell_name), 0) - self.assertEqual(CellProcessor.cell_stack, []) - self.assertIsNone(CellProcessor.api_parent_node) + self.assertEqual(CellProcessor.cell_stack[threading.get_ident()], []) + self.assertIsNone(CellProcessor.api_parent_node[threading.get_ident()]) self.scope.end_module.assert_called_with(full_forward_name) self.assertEqual(mock_bw.call_count, 2) self.assertEqual(mock_bw.call_args_list[0][0][0], output) @@ -266,16 +268,16 @@ class TestCellProcessor(unittest.TestCase): ret = backward_pre_hook(mock_cell, grad_output) self.assertTrue(mock_cell.has_pre_hook_called) self.scope.begin_module.assert_called_with(full_backward_name) - self.assertEqual(CellProcessor.cell_stack, [full_backward_name]) - self.assertEqual(CellProcessor.api_parent_node, full_backward_name) + self.assertEqual(CellProcessor.cell_stack[threading.get_ident()], [full_backward_name]) + self.assertEqual(CellProcessor.api_parent_node[threading.get_ident()], full_backward_name) self.assertEqual(CellProcessor.module_node, {full_forward_name: None, full_backward_name: None}) self.scope.begin_module.assert_called_with(full_backward_name) mock_backward_data_hook.assert_not_called() self.assertIsNone(ret) CellProcessor.cell_count = {cell_name: 0} - CellProcessor.cell_stack = [full_forward_name] - CellProcessor.api_parent_node = full_forward_name + CellProcessor.cell_stack[threading.get_ident()] = [full_forward_name] + CellProcessor.api_parent_node[threading.get_ident()] = full_forward_name CellProcessor.module_node = {full_forward_name: None} mock_bw.reset_mock() args = (Tensor([1.0]),) @@ -290,8 +292,8 @@ class TestCellProcessor(unittest.TestCase): self.assertTrue((ret[0] == target_output[0]).all()) CellProcessor.cell_count = {cell_name: 0} - CellProcessor.cell_stack = [full_forward_name] - CellProcessor.api_parent_node = full_forward_name + CellProcessor.cell_stack[threading.get_ident()] = [full_forward_name] + CellProcessor.api_parent_node[threading.get_ident()] = full_forward_name CellProcessor.module_node = {full_forward_name: None} CellProcessor.cell_bw_hook_kernels.clear() CellProcessor.cell_backward_pre_hook.clear() @@ -313,8 +315,8 @@ class TestCellProcessor(unittest.TestCase): self.assertFalse(mock_cell.has_pre_hook_called) self.scope.begin_module.assert_called_with(full_backward_name) mock_backward_data_hook.assert_called_with(mock_cell, (), grad_output) - self.assertEqual(CellProcessor.cell_stack, []) - self.assertIsNone(CellProcessor.api_parent_node) + self.assertEqual(CellProcessor.cell_stack[threading.get_ident()], []) + self.assertIsNone(CellProcessor.api_parent_node[threading.get_ident()]) self.assertEqual(CellProcessor.module_node, {full_forward_name: None, full_backward_name: None}) self.scope.end_module.assert_called_with(full_backward_name) self.assertIsNone(ret) @@ -325,15 +327,15 @@ class TestCellProcessor(unittest.TestCase): CellProcessor.reset_cell_stats() self.processor.set_construct_info_in_pre_hook('full_name') self.assertEqual(CellProcessor.module_node['full_name'], None) - self.assertEqual(CellProcessor.cell_stack, ['full_name']) - self.assertEqual(CellProcessor.api_parent_node, 'full_name') + self.assertEqual(CellProcessor.cell_stack[threading.get_ident()], ['full_name']) + self.assertEqual(CellProcessor.api_parent_node[threading.get_ident()], 'full_name') self.scope.begin_module.assert_called_with('full_name') self.scope.begin_module.reset_mock() self.processor.set_construct_info_in_pre_hook('sub_cell_name') self.assertEqual(CellProcessor.module_node, {'full_name': None, 'sub_cell_name': 'full_name'}) - self.assertEqual(CellProcessor.cell_stack, ['full_name', 'sub_cell_name']) - self.assertEqual(CellProcessor.api_parent_node, 'sub_cell_name') + self.assertEqual(CellProcessor.cell_stack[threading.get_ident()], ['full_name', 'sub_cell_name']) + self.assertEqual(CellProcessor.api_parent_node[threading.get_ident()], 'sub_cell_name') self.scope.begin_module.assert_called_with('sub_cell_name') CellProcessor.reset_cell_stats() @@ -341,21 +343,21 @@ class TestCellProcessor(unittest.TestCase): def test_set_construct_info_in_hook(self): CellProcessor.reset_cell_stats() self.processor.set_construct_info_in_hook('full_name') - self.assertIsNone(CellProcessor.api_parent_node) + self.assertIsNone(CellProcessor.api_parent_node[threading.get_ident()]) self.scope.end_module.assert_called_with('full_name') self.scope.end_module.reset_mock() - CellProcessor.cell_stack = ['full_name'] + CellProcessor.cell_stack[threading.get_ident()] = ['full_name'] self.processor.set_construct_info_in_hook('full_name') - self.assertEqual(CellProcessor.cell_stack, []) - self.assertIsNone(CellProcessor.api_parent_node) + self.assertEqual(CellProcessor.cell_stack, {threading.get_ident(): []}) + self.assertIsNone(CellProcessor.api_parent_node[threading.get_ident()]) self.scope.end_module.assert_called_with('full_name') self.scope.end_module.reset_mock() - CellProcessor.cell_stack = ['Cell.0', 'Cell.1'] + CellProcessor.cell_stack[threading.get_ident()] = ['Cell.0', 'Cell.1'] self.processor.set_construct_info_in_hook('full_name') - self.assertEqual(CellProcessor.cell_stack, ['Cell.0']) - self.assertEqual(CellProcessor.api_parent_node, 'Cell.0') + self.assertEqual(CellProcessor.cell_stack, {threading.get_ident():['Cell.0']}) + self.assertEqual(CellProcessor.api_parent_node[threading.get_ident()], 'Cell.0') self.scope.end_module.assert_called_with('full_name') CellProcessor.reset_cell_stats() diff --git a/debug/accuracy_tools/msprobe/test/pytorch_ut/debugger/test_pt_precision_debugger.py b/debug/accuracy_tools/msprobe/test/pytorch_ut/debugger/test_pt_precision_debugger.py index 249432717..69df2c892 100644 --- a/debug/accuracy_tools/msprobe/test/pytorch_ut/debugger/test_pt_precision_debugger.py +++ b/debug/accuracy_tools/msprobe/test/pytorch_ut/debugger/test_pt_precision_debugger.py @@ -108,6 +108,7 @@ class TestPrecisionDebugger(unittest.TestCase): self.assertIsNone(checked_start) def test_start_statistics(self): + PrecisionDebugger._instance = None with patch.object(BasePrecisionDebugger, "_parse_config_path", return_value=(self.statistics_common_config, self.statistics_task_config)): debugger = PrecisionDebugger(dump_path="./dump_path") @@ -143,6 +144,7 @@ class TestPrecisionDebugger(unittest.TestCase): self.assertIsNone(checked_stop) def test_stop_statistics(self): + PrecisionDebugger._instance = None debugger = PrecisionDebugger(dump_path="./dump_path") debugger.service = MagicMock() debugger.task = '' diff --git a/debug/accuracy_tools/msprobe/test/pytorch_ut/dump/test_module_processer.py b/debug/accuracy_tools/msprobe/test/pytorch_ut/dump/test_module_processer.py index 7a1524d0c..ec89ff6f1 100644 --- a/debug/accuracy_tools/msprobe/test/pytorch_ut/dump/test_module_processer.py +++ b/debug/accuracy_tools/msprobe/test/pytorch_ut/dump/test_module_processer.py @@ -13,9 +13,10 @@ # See the License for the specific language governing permissions and # limitations under the License. +import threading + import unittest from unittest.mock import patch, MagicMock - import torch import msprobe.pytorch.dump.module_dump.module_processer as mp @@ -78,9 +79,9 @@ class TestWrapper(unittest.TestCase): class TestModuleProcesser(unittest.TestCase): def setUp(self): ModuleProcesser.module_count = {} - ModuleProcesser.module_stack = [] + ModuleProcesser.module_stack = {} ModuleProcesser.module_node = {} - ModuleProcesser.api_parent_node = "" + ModuleProcesser.api_parent_node = {} self.scope = ModuleRangeScope([], []) self.mock_scope = MagicMock() @@ -199,22 +200,25 @@ class TestModuleProcesser(unittest.TestCase): ModuleProcesser.reset_module_stats() self.assertEqual(ModuleProcesser.module_count, {}) - self.assertEqual(ModuleProcesser.module_stack, []) - self.assertEqual(ModuleProcesser.api_parent_node, "") + self.assertEqual(ModuleProcesser.module_stack, {}) + self.assertEqual(ModuleProcesser.api_parent_node, {}) self.assertEqual(ModuleProcesser.module_node, {}) self.assertEqual(ModuleProcesser.module_bw_hook_kernels, {}) self.assertFalse(ModuleProcesser.enable_module_dump) def test_set_construct_info_in_pre_hook_with_stack(self): processor = ModuleProcesser(self.mock_scope) - ModuleProcesser.module_stack = ["parent_module"] + ModuleProcesser.module_stack[threading.get_ident()] = ["parent_module"] processor.scope = self.mock_scope processor.set_construct_info_in_pre_hook("current_module") self.assertEqual(ModuleProcesser.module_node["current_module"], "parent_module") - self.assertEqual(ModuleProcesser.module_stack, ["parent_module", "current_module"]) - self.assertEqual(ModuleProcesser.api_parent_node, "current_module") + self.assertEqual( + ModuleProcesser.module_stack[threading.get_ident()], + ["parent_module", "current_module"] + ) + self.assertEqual(ModuleProcesser.api_parent_node[threading.get_ident()], "current_module") self.mock_scope.begin_module.assert_called_once_with("current_module") def test_set_construct_info_in_pre_hook_empty_stack(self): @@ -223,19 +227,19 @@ class TestModuleProcesser(unittest.TestCase): processor.set_construct_info_in_pre_hook("root_module") self.assertIsNone(ModuleProcesser.module_node["root_module"]) - self.assertEqual(ModuleProcesser.module_stack, ["root_module"]) - self.assertEqual(ModuleProcesser.api_parent_node, "root_module") + self.assertEqual(ModuleProcesser.module_stack[threading.get_ident()], ["root_module"]) + self.assertEqual(ModuleProcesser.api_parent_node[threading.get_ident()], "root_module") def test_set_construct_info_in_hook_with_forward(self): mp.torch_version_above_or_equal_2 = True processor = ModuleProcesser(self.mock_scope) - ModuleProcesser.module_stack = ["parent", "current"] + ModuleProcesser.module_stack = {threading.get_ident(): ["parent", "current"]} processor.scope = self.mock_scope processor.set_construct_info_in_hook("current") - self.assertEqual(ModuleProcesser.module_stack, ["parent"]) - self.assertEqual(ModuleProcesser.api_parent_node, "parent") + self.assertEqual(ModuleProcesser.module_stack[threading.get_ident()], ["parent"]) + self.assertEqual(ModuleProcesser.api_parent_node[threading.get_ident()], "parent") self.mock_scope.end_module.assert_called_once_with("current") def test_set_construct_info_in_hook_with_backward(self): @@ -245,7 +249,7 @@ class TestModuleProcesser(unittest.TestCase): processor.set_construct_info_in_hook("backward_module", is_forward=False) - self.assertEqual(ModuleProcesser.api_parent_node, "backward_module") + self.assertEqual(ModuleProcesser.api_parent_node[threading.get_ident()], "backward_module") self.mock_scope.begin_module.assert_called_once_with("backward_module") def test_set_construct_info_in_hook_empty_stack(self): @@ -254,7 +258,7 @@ class TestModuleProcesser(unittest.TestCase): processor.set_construct_info_in_hook("module") - self.assertIsNone(ModuleProcesser.api_parent_node) + self.assertEqual(ModuleProcesser.api_parent_node, {threading.get_ident(): None}) if __name__ == "__main__": diff --git a/debug/accuracy_tools/msprobe/test/pytorch_ut/hook_module/test_pt_hook_manager.py b/debug/accuracy_tools/msprobe/test/pytorch_ut/hook_module/test_pt_hook_manager.py index aa21afae7..8d99bc926 100644 --- a/debug/accuracy_tools/msprobe/test/pytorch_ut/hook_module/test_pt_hook_manager.py +++ b/debug/accuracy_tools/msprobe/test/pytorch_ut/hook_module/test_pt_hook_manager.py @@ -13,6 +13,8 @@ # See the License for the specific language governing permissions and # limitations under the License. +import threading + import unittest from unittest.mock import MagicMock, patch from contextlib import nullcontext @@ -31,7 +33,7 @@ class TestPytorchHookManager(unittest.TestCase): self.mock_data_collector, self.mock_config ) - BaseHookManager.inner_switch = False + BaseHookManager.inner_switch[threading.get_ident()] = False def test_properties(self): with patch('msprobe.pytorch.hook_module.pt_hook_manager.is_recomputation', return_value=True): diff --git a/debug/accuracy_tools/msprobe/test/pytorch_ut/hook_module/test_pt_hook_module.py b/debug/accuracy_tools/msprobe/test/pytorch_ut/hook_module/test_pt_hook_module.py index 5b7294a2a..a2ba7365d 100644 --- a/debug/accuracy_tools/msprobe/test/pytorch_ut/hook_module/test_pt_hook_module.py +++ b/debug/accuracy_tools/msprobe/test/pytorch_ut/hook_module/test_pt_hook_module.py @@ -26,14 +26,14 @@ class TestHOOKModule(unittest.TestCase): def setUp(self): self.mock_build_hook = MagicMock(return_value=HookSet(MagicMock(), MagicMock(), MagicMock())) HOOKModule.module_count = defaultdict(int) - HOOKModule.inner_stop_hook = {} + HOOKModule.inner_stop_hook = defaultdict(bool) def test_init_with_stop_hook(self): - expected_thread = threading.current_thread().ident + expected_thread = threading.get_ident() HOOKModule.inner_stop_hook[expected_thread] = True module1 = HOOKModule(self.mock_build_hook) - current_thread = module1.current_thread + current_thread = module1.tid self.assertEqual(current_thread, expected_thread) self.assertTrue(module1.inner_stop_hook[current_thread]) @@ -42,8 +42,8 @@ class TestHOOKModule(unittest.TestCase): def test_init_with_start_hook(self): module1 = HOOKModule(self.mock_build_hook) - current_thread = module1.current_thread - expected_thread = threading.current_thread().ident + current_thread = module1.tid + expected_thread = threading.get_ident() self.assertEqual(current_thread, expected_thread) self.assertFalse(module1.inner_stop_hook[current_thread]) -- Gitee From 5ae94bb1aac323793ec8247aea08e71891b42294 Mon Sep 17 00:00:00 2001 From: Linwei-Ying Date: Tue, 24 Jun 2025 15:39:12 +0800 Subject: [PATCH 16/79] save_excel add malicious_check --- .../msprobe/core/common/file_utils.py | 26 +++++++++++++++++++ 1 file changed, 26 insertions(+) diff --git a/debug/accuracy_tools/msprobe/core/common/file_utils.py b/debug/accuracy_tools/msprobe/core/common/file_utils.py index 807f9327d..e51438147 100644 --- a/debug/accuracy_tools/msprobe/core/common/file_utils.py +++ b/debug/accuracy_tools/msprobe/core/common/file_utils.py @@ -460,7 +460,33 @@ def save_excel(path, data): return "list" raise ValueError("Data must be a DataFrame or a list of (DataFrame, sheet_name) pairs.") + def check_value_is_valid(value: str) -> bool: + if not isinstance(value, str): + return True + try: + # -1.00 or +1.00 should be considered as digit numbers + float(value) + except ValueError: + # otherwise, they will be considered as formular injections + return not bool(re.compile(FileCheckConst.CSV_BLACK_LIST).search(value)) + return True + + def malicious_check(df): + for row_name in df.index: + if not check_value_is_valid(row_name): + raise RuntimeError(f"Malicious value [{row_name}] not allowed to be written into the excel: {path}.") + + for col_name in df.columns: + if not check_value_is_valid(col_name): + raise RuntimeError(f"Malicious value [{col_name}] not allowed to be written into the excel: {path}.") + + for _, row in df.iterrows(): + for _, value in row.items(): + if not check_value_is_valid(value): + raise RuntimeError(f"Malicious value [{value}] not allowed to be written into the excel: {path}.") + def save_in_slice(df, base_name): + malicious_check(df) df_length = len(df) if df_length < CompareConst.MAX_EXCEL_LENGTH: df.to_excel(writer, sheet_name=base_name if base_name else 'Sheet1', index=False) -- Gitee From 3a4f9d9e743807cb5d24c27cf4464b579e171df4 Mon Sep 17 00:00:00 2001 From: wugengjun <451676383@qq.com> Date: Tue, 24 Jun 2025 15:59:11 +0800 Subject: [PATCH 17/79] =?UTF-8?q?cleancode=E4=BF=AE=E6=94=B9?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- debug/accuracy_tools/msprobe/core/compare/utils.py | 4 ++-- .../tensor_transport_layer/device_dispatch.py | 2 +- .../api_accuracy_checker/tensor_transport_layer/utils.py | 6 ++---- debug/accuracy_tools/msprobe/pytorch/monitor/module_hook.py | 2 +- 4 files changed, 6 insertions(+), 8 deletions(-) diff --git a/debug/accuracy_tools/msprobe/core/compare/utils.py b/debug/accuracy_tools/msprobe/core/compare/utils.py index c07acf04a..691025c22 100644 --- a/debug/accuracy_tools/msprobe/core/compare/utils.py +++ b/debug/accuracy_tools/msprobe/core/compare/utils.py @@ -509,8 +509,8 @@ def get_accuracy(result, n_dict, b_dict, dump_mode): result.append(result_item) - n_num, n_num_input, n_num_output, n_num_params, n_num_params_grad = count_struct(n_dict) - b_num, b_num_input, b_num_output, b_num_params, b_num_params_grad = count_struct(b_dict) + _, n_num_input, n_num_output, n_num_params, n_num_params_grad = count_struct(n_dict) + _, b_num_input, b_num_output, b_num_params, b_num_params_grad = count_struct(b_dict) get_accuracy_core(0, n_num_input, 0, b_num_input, CompareConst.INPUT_STRUCT) get_accuracy_core(n_num_input + n_num_output, n_num_params, b_num_input + b_num_output, b_num_params, diff --git a/debug/accuracy_tools/msprobe/pytorch/api_accuracy_checker/tensor_transport_layer/device_dispatch.py b/debug/accuracy_tools/msprobe/pytorch/api_accuracy_checker/tensor_transport_layer/device_dispatch.py index 3edc852e6..6fc36bcde 100644 --- a/debug/accuracy_tools/msprobe/pytorch/api_accuracy_checker/tensor_transport_layer/device_dispatch.py +++ b/debug/accuracy_tools/msprobe/pytorch/api_accuracy_checker/tensor_transport_layer/device_dispatch.py @@ -43,9 +43,9 @@ CommonCompareConfig = namedtuple('CommonCompareConfig', ['compare', 'handle_func def get_gpu_device(): + is_gpu = False try: import torch_npu - is_gpu = False except ImportError: is_gpu = True return is_gpu diff --git a/debug/accuracy_tools/msprobe/pytorch/api_accuracy_checker/tensor_transport_layer/utils.py b/debug/accuracy_tools/msprobe/pytorch/api_accuracy_checker/tensor_transport_layer/utils.py index d48b1b537..05dd50a3f 100644 --- a/debug/accuracy_tools/msprobe/pytorch/api_accuracy_checker/tensor_transport_layer/utils.py +++ b/debug/accuracy_tools/msprobe/pytorch/api_accuracy_checker/tensor_transport_layer/utils.py @@ -111,10 +111,8 @@ def load_ssl_pem(key_file, cert_file, ca_file, crl_file): try: # your_private_key_password - passphrase = "" - if not passphrase: - import pwinput - passphrase = pwinput.pwinput("Enter your password: ") + import pwinput + passphrase = pwinput.pwinput("Enter your password: ") with FileOpen(key_file, "rb") as f: key = crypto.load_privatekey(crypto.FILETYPE_PEM, f.read(), passphrase.encode()) del passphrase diff --git a/debug/accuracy_tools/msprobe/pytorch/monitor/module_hook.py b/debug/accuracy_tools/msprobe/pytorch/monitor/module_hook.py index f3750d1c9..042ccf651 100644 --- a/debug/accuracy_tools/msprobe/pytorch/monitor/module_hook.py +++ b/debug/accuracy_tools/msprobe/pytorch/monitor/module_hook.py @@ -592,7 +592,7 @@ class TrainerMon: context.param_adam_update = mv_result.update context.param_adam_ratio = mv_result.ratio - self.generate_wgrad_metrics(grad_dict) + _, _ = self.generate_wgrad_metrics(grad_dict) self.generate_mv_metrics(context) self.generate_param_metrics(context, MonitorConst.PRE_PARAM) -- Gitee From 1819b450edddd1ad9752593329a46919b2007fd0 Mon Sep 17 00:00:00 2001 From: zhouxianqi <13165993773@163.com> Date: Tue, 24 Jun 2025 10:25:21 +0800 Subject: [PATCH 18/79] add_compare_db_st --- ...compare_tools_cmd_pytorch_db_npu_vs_npu.py | 77 +++++++++++++++++++ 1 file changed, 77 insertions(+) create mode 100644 profiler/msprof_analyze/test/st/compare_tools/test_compare_tools_cmd_pytorch_db_npu_vs_npu.py diff --git a/profiler/msprof_analyze/test/st/compare_tools/test_compare_tools_cmd_pytorch_db_npu_vs_npu.py b/profiler/msprof_analyze/test/st/compare_tools/test_compare_tools_cmd_pytorch_db_npu_vs_npu.py new file mode 100644 index 000000000..5485f99da --- /dev/null +++ b/profiler/msprof_analyze/test/st/compare_tools/test_compare_tools_cmd_pytorch_db_npu_vs_npu.py @@ -0,0 +1,77 @@ +# Copyright (c) 2025, Huawei Technologies Co., Ltd. +# All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import os +from unittest import TestCase + +import pandas as pd + +from msprof_analyze.prof_common.path_manager import PathManager +from msprof_analyze.test.st.utils import execute_cmd, check_result_file +from msprof_analyze.test.st.utils import ST_DATA_PATH + + +class TestCompareToolsCmdPytorchDbNpuVsNpu(TestCase): + BASE_PROFILING_PATH = os.path.join(ST_DATA_PATH, "cluster_data_2_db", + "n122-120-121_12321_20240911113658382_ascend_pt") + COMPARISON_PROFILING_PATH = os.path.join(ST_DATA_PATH, "cluster_data_2_db", + "n122-120-121_12322_20240911113658370_ascend_pt") + OUTPUT_PATH = os.path.join(os.path.abspath(os.path.dirname(__file__)), "CompareToolsCmdPytorchDbNpuVsNpu") + COMMAND_SUCCESS = 0 + result_excel = "" + + def setup_class(self): + PathManager.make_dir_safety(self.OUTPUT_PATH) + cmd = ["msprof-analyze", "compare", "-d", self.COMPARISON_PROFILING_PATH, "-bp", self.BASE_PROFILING_PATH, "-o", + self.OUTPUT_PATH, "--force"] + if execute_cmd(cmd) != self.COMMAND_SUCCESS or not os.path.exists(self.OUTPUT_PATH): + self.assertTrue(False, msg="comparison task failed.") + if not check_result_file(self.OUTPUT_PATH): + self.assertTrue(False, msg="comparison result excel is not find.") + self.result_excel = os.path.join(self.OUTPUT_PATH, check_result_file(self.OUTPUT_PATH)) + + def teardown_class(self): + PathManager.remove_path_safety(self.OUTPUT_PATH) + + def test_overall_metrics(self): + duration_exp = [ + 14302.47, 1128.78, 1128.78, 10320.26, 10320.26, 2837.1, 445.59, 2391.52, 16.33, 50636.60, 2595.59, 11.69, + 2583.90, 117.70, 116.68, 1.03, 82.64, 0.16, 82.47, 99.71, 0.00, 99.71, 13013.70, 2032.30, 10981.41, + 17478.85, 17308.09, 170.76, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 5045.58, + 5045.58, 0.00, 682.94, 682.94, 65622.00 + ] + + diff_exp = [6.48, 4.84, 4.84, -9.23, -9.23, 10.78, -0.83, 11.61, 0.09, 33.92, 94.13, 100.38, -6.26, -117.70, + -116.68, -1.03, -82.64, -0.16, -82.47, -82.30, 0.19, -82.49, -13013.70, -2032.30, + -10981.41, -17478.85, -17308.09, -170.76, 170.83, 169.81, 1.02, 81.11, 0.42, 80.69, 13836.68, + 1954.07, 11882.61, 17478.49, 17307.83, 170.67, 223.37, -5045.58, 5268.95, -48.46, -48.46, -8.05] + + df = pd.read_excel(self.result_excel, sheet_name="OverallMetrics", header=2) + for index, row in df.iterrows(): + self.assertEqual(duration_exp[index], round(row["Duration(ms)"], 2), + msg="pytorch npu vs npu compare results 'Duration(ms)" + "' column is wrong" + ) + self.assertEqual(diff_exp[index], round(row["Diff Duration(ms)"], 2), + msg="pytorch npu vs npu compare results 'Diff Duration(ms)" + "' column is wrong" + ) + + def test_kernel_compare(self): + headers = ["Order Id", "Kernel", "Input Shape", "Total Duration(us)", "Avg Duration(us)", "Max Duration(us)", + "Min Duration(us)", "Calls", "Total Duration(us).1", "Avg Duration(us).1", "Max Duration(us).1", + "Min Duration(us).1", "Calls.1", "Diff Total Ratio", "Diff Avg Ratio"] + df = pd.read_excel(self.result_excel, sheet_name="KernelCompare", header=2) + self.assertEqual(len(df), 706, msg="pytorch npu vs npu compare results quantity is wrong") + self.assertEqual(headers, df.columns.tolist(), msg="pytorch npu vs npu compare results headers is wrong") -- Gitee From 44376a767b3471f386f0a8940670dfac1f8f7220 Mon Sep 17 00:00:00 2001 From: liangchaoming Date: Tue, 24 Jun 2025 16:54:30 +0800 Subject: [PATCH 19/79] update mstx2comm rules convert mstx comm info to commop table --- .../recipes/mstx2commop/__init__.py | 14 ++ .../recipes/mstx2commop/mstx2commop.py | 178 ++++++++++++++++++ .../prof_exports/mstx2commop_export.py | 45 +++++ 3 files changed, 237 insertions(+) create mode 100644 profiler/msprof_analyze/cluster_analyse/recipes/mstx2commop/__init__.py create mode 100644 profiler/msprof_analyze/cluster_analyse/recipes/mstx2commop/mstx2commop.py create mode 100644 profiler/msprof_analyze/prof_exports/mstx2commop_export.py diff --git a/profiler/msprof_analyze/cluster_analyse/recipes/mstx2commop/__init__.py b/profiler/msprof_analyze/cluster_analyse/recipes/mstx2commop/__init__.py new file mode 100644 index 000000000..7101187a2 --- /dev/null +++ b/profiler/msprof_analyze/cluster_analyse/recipes/mstx2commop/__init__.py @@ -0,0 +1,14 @@ +# Copyright (c) 2024, Huawei Technologies Co., Ltd. +# All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/profiler/msprof_analyze/cluster_analyse/recipes/mstx2commop/mstx2commop.py b/profiler/msprof_analyze/cluster_analyse/recipes/mstx2commop/mstx2commop.py new file mode 100644 index 000000000..77a7095ab --- /dev/null +++ b/profiler/msprof_analyze/cluster_analyse/recipes/mstx2commop/mstx2commop.py @@ -0,0 +1,178 @@ +# Copyright (c) 2024, Huawei Technologies Co., Ltd. +# All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import json +import os +import pandas as pd + +from msprof_analyze.cluster_analyse.recipes.base_recipe_analysis import BaseRecipeAnalysis +from msprof_analyze.prof_common.db_manager import DBManager +from msprof_analyze.prof_common.constant import Constant +from msprof_analyze.prof_common.logger import get_logger +from msprof_analyze.prof_exports.mstx2commop_export import Mstx2CommopExport +from msprof_analyze.prof_common.database_service import DatabaseService + +logger = get_logger() + +TABLE_COMMUNICATION_OP = "COMMUNICATION_OP" +TABLE_STRING_IDS = "STRING_IDS" + + +def double_hash(data): + uint32_bits = 32 + uint32_max = 0xFFFFFFFF # 32 位无符号整数的最大值 + prime = [29, 131] + hash_values = [0, 0] + + for d in data: + hash_values[0] = (hash_values[0] * prime[0] + ord(d)) & uint32_max + hash_values[1] = (hash_values[1] * prime[1] + ord(d)) & uint32_max + + return ((hash_values[0] << uint32_bits) | hash_values[1]) + + +class Mstx2Commop(BaseRecipeAnalysis): + + def __init__(self, params): + super().__init__(params) + logger.info("Mstx2Commop init.") + self.communication_op = None + self.string_ids_insert = None + + @property + def base_dir(self): + return os.path.basename(os.path.dirname(__file__)) + + def run(self, context): + self.mapper_func(context) + + def _mapper_func(self, data_map, analysis_class): + profiler_db_path = data_map.get(Constant.PROFILER_DB_PATH) + data_service = DatabaseService(profiler_db_path) + data_service.add_table_for_query("ENUM_HCCL_DATA_TYPE", ["id", "name"]) + data_service.add_table_for_query("STRING_IDS", ["id", "value"]) + df_dict = data_service.query_data() + + df = Mstx2CommopExport(profiler_db_path, analysis_class).read_export_db() + + if df is None or df.empty: + logger.warning(f"There is no stats data in {profiler_db_path}.") + return None + + df_hccl_dt = df_dict.get("ENUM_HCCL_DATA_TYPE") + + if df_hccl_dt is None or df_hccl_dt.empty: + logger.warning(f"There is no stats data in {profiler_db_path}.") + return None + + df_string_ids = df_dict.get("STRING_IDS") + + if df_string_ids is None or df_string_ids.empty: + logger.warning(f"There is no stats data in {profiler_db_path}.") + return None + + value_len = 4 + optype_index, op_start_index = 0, 9 + groupname_index, datatype_index, count_index = 1, 2, 3 + + # json格式数据转化 + if df.loc[0, 'value'][0] == '{': + df['value'] = df['value'].apply(lambda x: json.loads(x)) + df['opType_primal'] = df['value'].apply(lambda x: x['opName'] + '_') + df['groupName_primal'] = df['value'].apply(lambda x: x['groupName']) + df['dataType'] = df['value'].apply(lambda x: x['dataType']) + df['count'] = df['value'].apply(lambda x: x['count']) + # 非json格式数据转化 + else: + df['value_list'] = df['value'].apply(lambda x: x.split(',')) + df['value_list_len'] = df['value_list'].apply(len) + df = df[df['value_list_len'] == value_len] + df['opType_primal'] = df['value_list'].apply(lambda x: 'hcom_' + x[optype_index][op_start_index:] + '_') + df['groupName_primal'] = df['value_list'].apply(lambda x: x[groupname_index]) + df['dataType'] = df['value_list'].apply(lambda x: x[datatype_index]) + df['count'] = df['value_list'].apply(lambda x: x[count_index]) + + df['groupName_hash'] = df['groupName_primal'].apply(double_hash).apply(str) + + df['gN_oT'] = df['groupName_primal'] + df['opType_primal'] + + gnot_set = set(list(df['gN_oT'])) + + df_concat = pd.DataFrame() + for g_o in gnot_set: + df_split = df[df['gN_oT'] == g_o] + df_split = df_split.copy() + df_split['queue'] = list(range(len(df_split))) + df_concat = pd.concat([df_concat, df_split], axis=0) + + df_concat['queue'] = df_concat['queue'].apply(str) + + df_concat['groupId'] = df_concat['groupName_hash'].apply(lambda x: "_" + x[-3:]) + + df_concat['opName_primal'] = df_concat['opType_primal'] + df_concat['groupId'] + '_' + df_concat['queue'] + '_1' + + df_concat['opId'] = list(range(len(df_concat))) + df_concat['relay'] = None + df_concat['retry'] = None + df_concat['algType'] = None + + df_hccl_dt['name'] = df_hccl_dt['name'].apply(lambda x: x.lower()) + hccl_data_type_dict = dict(zip(df_hccl_dt['name'], df_hccl_dt['id'])) + + string_ids_dict = dict(zip(df_string_ids['value'], df_string_ids['id'])) + + string_ids_max = df_string_ids['id'].max() + + df_concat['dataType'] = df_concat['dataType'].apply(lambda x: hccl_data_type_dict[x]) + + df_concat['string_id_opType_primal'] = df_concat['opType_primal'].apply( + lambda x: 1 if x in string_ids_dict else 0) + df_concat['string_id_opName_primal'] = df_concat['opName_primal'].apply( + lambda x: 1 if x in string_ids_dict else 0) + df_concat['string_id_groupName_primal'] = df_concat['groupName_primal'].apply( + lambda x: 1 if x in string_ids_dict else 0) + optype_primal_list = list(set(df_concat[df_concat['string_id_opType_primal'] == 0]['opType_primal'])) + opname_primal_list = list(set(df_concat[df_concat['string_id_opName_primal'] == 0]['opName_primal'])) + groupname_primal_list = list(set(df_concat[df_concat['string_id_groupName_primal'] == 0]['groupName_primal'])) + + special_primal_list = optype_primal_list + opname_primal_list + groupname_primal_list + special_id_list = list(range(string_ids_max + 1, string_ids_max + len(special_primal_list) + 1)) + + special_id_dict = dict(zip(special_primal_list, special_id_list)) + + df_concat['opType'] = df_concat['opType_primal'].apply( + lambda x: string_ids_dict[x] if x in string_ids_dict else special_id_dict[x] + ) + df_concat['opName'] = df_concat['opName_primal'].apply( + lambda x: string_ids_dict[x] if x in string_ids_dict else special_id_dict[x] + ) + df_concat['groupName'] = df_concat['groupName_primal'].apply( + lambda x: string_ids_dict[x] if x in string_ids_dict else special_id_dict[x] + ) + + communication_op = df_concat[ + ['opName', 'startNs', 'endNs', 'connectionId', 'groupName', 'opId', 'relay', 'retry', 'dataType', 'algType', + 'count', 'opType']] + communication_op = communication_op.copy() + communication_op.sort_values('startNs', ascending=True, inplace=True) + communication_op.set_index('opId', inplace=True) + string_ids_insert = list(map(list, zip(special_id_list, special_primal_list))) + + DBManager.insert_data_into_db(data_map.get(Constant.PROFILER_DB_PATH), TABLE_STRING_IDS, string_ids_insert) + + self.dump_data(data=communication_op, file_name=data_map.get(Constant.PROFILER_DB_PATH), + table_name=TABLE_COMMUNICATION_OP, custom_db_path=data_map.get(Constant.PROFILER_DB_PATH)) + + return data_map.get(Constant.RANK_ID) diff --git a/profiler/msprof_analyze/prof_exports/mstx2commop_export.py b/profiler/msprof_analyze/prof_exports/mstx2commop_export.py new file mode 100644 index 000000000..5ed239603 --- /dev/null +++ b/profiler/msprof_analyze/prof_exports/mstx2commop_export.py @@ -0,0 +1,45 @@ +# Copyright (c) 2024, Huawei Technologies Co., Ltd. +# All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from msprof_analyze.prof_exports.base_stats_export import BaseStatsExport + +QUERY = """ +SELECT + ta.startNs, + ta.endNs, + ta.connectionId, + si.value +FROM + MSTX_EVENTS ms +JOIN + TASK ta + ON ms.connectionId == ta.connectionId +JOIN + STRING_IDS si + ON ms.message == si.id +WHERE + si.value LIKE '%"streamId":%' + AND si.value LIKE '%"count":%' + AND si.value LIKE '%"dataType":%' + AND si.value LIKE '%"groupName":%' + AND si.value LIKE '%"opName":%' + """ + + +class Mstx2CommopExport(BaseStatsExport): + + def __init__(self, db_path, recipe_name): + super().__init__(db_path, recipe_name) + self._query = QUERY -- Gitee From 0bcc023fe04fe372412daf0be589eabd2fe96233 Mon Sep 17 00:00:00 2001 From: liangchaoming Date: Tue, 24 Jun 2025 16:56:47 +0800 Subject: [PATCH 20/79] mstx2commop fit for output param --- .../recipes/mstx2commop/mstx2commop.py | 27 ++++++++++++++++--- 1 file changed, 24 insertions(+), 3 deletions(-) diff --git a/profiler/msprof_analyze/cluster_analyse/recipes/mstx2commop/mstx2commop.py b/profiler/msprof_analyze/cluster_analyse/recipes/mstx2commop/mstx2commop.py index 77a7095ab..f2b9c01b1 100644 --- a/profiler/msprof_analyze/cluster_analyse/recipes/mstx2commop/mstx2commop.py +++ b/profiler/msprof_analyze/cluster_analyse/recipes/mstx2commop/mstx2commop.py @@ -15,7 +15,10 @@ import json import os +import shutil + import pandas as pd +from msprof_analyze.prof_common.path_manager import PathManager from msprof_analyze.cluster_analyse.recipes.base_recipe_analysis import BaseRecipeAnalysis from msprof_analyze.prof_common.db_manager import DBManager @@ -50,6 +53,7 @@ class Mstx2Commop(BaseRecipeAnalysis): logger.info("Mstx2Commop init.") self.communication_op = None self.string_ids_insert = None + self.set_output = Constant.CLUSTER_ANALYSIS_OUTPUT_PATH in params # 是否设置了output_path参数 @property def base_dir(self): @@ -170,9 +174,26 @@ class Mstx2Commop(BaseRecipeAnalysis): communication_op.set_index('opId', inplace=True) string_ids_insert = list(map(list, zip(special_id_list, special_primal_list))) - DBManager.insert_data_into_db(data_map.get(Constant.PROFILER_DB_PATH), TABLE_STRING_IDS, string_ids_insert) + new_profiler_db = self._prepare_output_profiler_db(data_map.get(Constant.PROFILER_DB_PATH)) + + DBManager.insert_data_into_db(new_profiler_db, TABLE_STRING_IDS, string_ids_insert) - self.dump_data(data=communication_op, file_name=data_map.get(Constant.PROFILER_DB_PATH), - table_name=TABLE_COMMUNICATION_OP, custom_db_path=data_map.get(Constant.PROFILER_DB_PATH)) + self.dump_data(data=communication_op, file_name="", table_name=TABLE_COMMUNICATION_OP, + custom_db_path=new_profiler_db) return data_map.get(Constant.RANK_ID) + + def _prepare_output_profiler_db(self, profiler_db_path): + """ + copy profiler_db to output if not exist + """ + output_dir = os.path.join(self._cluster_analysis_output_path, self._recipe_name) + relative_db_path = os.path.relpath(profiler_db_path, start=self._collection_dir) + relative_dir = os.path.dirname(relative_db_path) + + new_path = os.path.join(output_dir, relative_dir) + new_db_path = os.path.join(output_dir, relative_db_path) + PathManager.make_dir_safety(new_path) + shutil.copyfile(profiler_db_path, new_db_path) + return new_db_path + -- Gitee From 6fd1c56c060bfc021ab21e20f2de842d324576d9 Mon Sep 17 00:00:00 2001 From: l30036321 Date: Tue, 24 Jun 2025 10:47:25 +0800 Subject: [PATCH 21/79] fix handler bug --- .../mindspore/overflow_check/overflow_check_tool_factory.py | 2 +- .../test/mindspore_ut/test_overflow_check_tool_factory.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/debug/accuracy_tools/msprobe/mindspore/overflow_check/overflow_check_tool_factory.py b/debug/accuracy_tools/msprobe/mindspore/overflow_check/overflow_check_tool_factory.py index 1a31c6e65..0b3ed6221 100644 --- a/debug/accuracy_tools/msprobe/mindspore/overflow_check/overflow_check_tool_factory.py +++ b/debug/accuracy_tools/msprobe/mindspore/overflow_check/overflow_check_tool_factory.py @@ -48,4 +48,4 @@ class OverflowCheckToolFactory: logger.error(f"Overflow check is not supported in {config.execution_mode} mode " f"when level is {config.level}.") raise ValueError - return tool(config) + return (tool(config),) diff --git a/debug/accuracy_tools/msprobe/test/mindspore_ut/test_overflow_check_tool_factory.py b/debug/accuracy_tools/msprobe/test/mindspore_ut/test_overflow_check_tool_factory.py index 31fe6254a..520a688dc 100644 --- a/debug/accuracy_tools/msprobe/test/mindspore_ut/test_overflow_check_tool_factory.py +++ b/debug/accuracy_tools/msprobe/test/mindspore_ut/test_overflow_check_tool_factory.py @@ -52,5 +52,5 @@ class TestOverflowCheckToolFactory(TestCase): f"when level is {config.level}.") config.level = "kernel" - dumper = OverflowCheckToolFactory.create(config) + dumper = OverflowCheckToolFactory.create(config)[0] self.assertEqual(dumper.dump_json["common_dump_settings"]["file_format"], "npy") -- Gitee From eecc52d80b7cff2ab7c9e7473b6957aff71b7cf5 Mon Sep 17 00:00:00 2001 From: curry3 <485078529@qq.com> Date: Tue, 24 Jun 2025 16:24:26 +0800 Subject: [PATCH 22/79] =?UTF-8?q?=E3=80=90bugfix=E3=80=91=E3=80=90?= =?UTF-8?q?=E5=AE=89=E5=85=A8=E3=80=91=E5=8A=A0=E8=BD=BD=20stack.json=20?= =?UTF-8?q?=E6=97=B6=E5=A2=9E=E5=8A=A0=E7=B1=BB=E5=9E=8B=E5=88=A4=E6=96=AD?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- debug/accuracy_tools/msprobe/core/common/utils.py | 13 ++++++++++++- 1 file changed, 12 insertions(+), 1 deletion(-) diff --git a/debug/accuracy_tools/msprobe/core/common/utils.py b/debug/accuracy_tools/msprobe/core/common/utils.py index 0ac134a55..4635679c4 100644 --- a/debug/accuracy_tools/msprobe/core/common/utils.py +++ b/debug/accuracy_tools/msprobe/core/common/utils.py @@ -645,14 +645,25 @@ def replace_last_occurrence(text, old, new): def load_stack_json(stack_path): stack_dict = load_json(stack_path) + + if not isinstance(stack_dict, dict): + raise MsprobeException( + MsprobeException.INVALID_PARAM_ERROR, + "The format of the stack.json is incorrect, the outermost layer of stack.json should be a dict type." + ) + if not stack_dict.get(Const.NEW_STACK_FLAG): return stack_dict new_stack_dict = {} for stack_info in stack_dict.values(): - if len(stack_info) != 2: + if not isinstance(stack_info, list) or len(stack_info) != 2: continue + api_list, stack_str = stack_info + if not isinstance(api_list, list): + continue + for api_name in api_list: new_stack_dict.update({api_name: stack_str}) return new_stack_dict -- Gitee From 462343c8a6c570bcdfc096f99b540ba3e3f5e059 Mon Sep 17 00:00:00 2001 From: wuyulong17 <2284273586@qq.com> Date: Tue, 24 Jun 2025 20:22:58 +0800 Subject: [PATCH 23/79] =?UTF-8?q?=E8=A1=A5=E5=85=85=E6=A8=A1=E5=9E=8B?= =?UTF-8?q?=E5=88=86=E7=BA=A7=E5=8F=AF=E8=A7=86=E5=8C=96=E9=80=9A=E4=BF=A1?= =?UTF-8?q?=E7=9F=A9=E9=98=B5=E8=AF=B4=E6=98=8E?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- plugins/tensorboard-plugins/tb_graph_ascend/README.md | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/plugins/tensorboard-plugins/tb_graph_ascend/README.md b/plugins/tensorboard-plugins/tb_graph_ascend/README.md index fee496d87..3c92c12c2 100644 --- a/plugins/tensorboard-plugins/tb_graph_ascend/README.md +++ b/plugins/tensorboard-plugins/tb_graph_ascend/README.md @@ -144,7 +144,7 @@ 满足[相关依赖](#1-相关依赖)中要求的 TensorBoard 版本皆可正常使用本插件功能，但为 TensorBoard 本身安全风险考虑，建议使用最新版本 TensorBoard 。 #### 4.1.3 远程查看数据 -如果网络浏览器与启动 TensorBoard 的机器不在同一台机器上， TensorBoard 提供了远程查看数据的指令启动方式，但此种方式会将服务器对应端口在局域网内公开，请用户自行关注安全风险。 +如果网络浏览器与启动 TensorBoard 的机器不在同一台机器上， TensorBoard 提供了远程查看数据的指令启动方式，但此种方式会将服务器对应端口在局域网内公开（全零监听），请用户自行关注安全风险。 * 在启动指令尾部加上`--bind_all`或`--host={服务器IP}`参数启用远程查看方式，如： @@ -156,6 +156,10 @@ * 在打开浏览器访问界面时，需将 URL 内主机名由`localhost`替换为主机的 ip 地址，如`http://xxx.xxx.xxx.xxx:6006` -### 4.2 公网地址说明 +### 4.2 通信矩阵 +| 序号 | 代码仓 | 功能 | 源设备 | 源IP | 源端口 | 目的设备 | 目的IP | 目的端口
    （侦听） | 协议 | 端口说明 | 端口配置| 侦听端口是否可更改 | 所属平面 | 版本 | 特殊场景 | 备注 | +|:----|:---|:--|:--|:---|:---|:---|:----|:--|:--|:---|:---|:---|:---|:-----|:-----|:---| +| 1 | tensorboard-plugins | TensorBoard底座前后端通信 | 访问TensorBoard浏览器所在机器 | 访问TensorBoard浏览器所在机器ip | | TensorBoard服务所在机器 | TensorBoard服务所在服务器的ip | 6006 | HTTP | tensorboard服务通信 | `--port` | 可修改 | 业务面 | 所有版本 | 无 | | +### 4.3 公网地址说明 [公网地址说明](./doc/公网地址说明.csv) -- Gitee From 082be195ee3b993906f4b4e2f4d992f447bbbace Mon Sep 17 00:00:00 2001 From: lcw Date: Thu, 19 Jun 2025 17:29:47 +0800 Subject: [PATCH 24/79] =?UTF-8?q?=E3=80=90bugfix=E3=80=91pytorch=E9=80=9A?= =?UTF-8?q?=E4=BF=A1=E7=AE=97=E5=AD=90wait=E6=96=B9=E5=BC=8F=E4=BF=AE?= =?UTF-8?q?=E6=94=B9?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../msprobe/core/data_dump/data_collector.py | 2 +- .../msprobe/core/hook_manager.py | 2 ++ .../pytorch/hook_module/api_register.py | 32 ++++++++++++++++--- .../msprobe/pytorch/pytorch_service.py | 3 +- .../core_ut/data_dump/test_data_collector.py | 1 - .../msprobe/test/core_ut/test_hook_manager.py | 1 + .../hook_module/test_pt_api_register.py | 12 ------- 7 files changed, 33 insertions(+), 20 deletions(-) diff --git a/debug/accuracy_tools/msprobe/core/data_dump/data_collector.py b/debug/accuracy_tools/msprobe/core/data_dump/data_collector.py index 5a79ef34c..42a8d8e9c 100644 --- a/debug/accuracy_tools/msprobe/core/data_dump/data_collector.py +++ b/debug/accuracy_tools/msprobe/core/data_dump/data_collector.py @@ -118,6 +118,7 @@ class DataCollector: self.set_is_recomputable(data_info, is_recompute) if self.config.level == Const.LEVEL_L2: return + self.call_stack_collect(name) self.handle_data(name, data_info, flush=self.data_processor.is_terminated) except Exception: @@ -139,7 +140,6 @@ class DataCollector: self.set_is_recomputable(data_info, is_recompute) if self.config.level == Const.LEVEL_L2: return - self.call_stack_collect(name) self.handle_data(name, data_info, flush=self.data_processor.is_terminated) except Exception: diff --git a/debug/accuracy_tools/msprobe/core/hook_manager.py b/debug/accuracy_tools/msprobe/core/hook_manager.py index db989df0b..5e5f7a62e 100644 --- a/debug/accuracy_tools/msprobe/core/hook_manager.py +++ b/debug/accuracy_tools/msprobe/core/hook_manager.py @@ -119,6 +119,8 @@ class BaseHookManager(ABC): def _should_execute_hook(self, hook_type, module, is_forward, tid): is_module_hook = hook_type == Const.MODULE + if hasattr(module, 'async_op_dump_flag') and getattr(module, 'async_op_dump_flag'): + return False if is_module_hook and not Runtime.is_running: return False elif not is_module_hook and is_forward and not Runtime.is_running: diff --git a/debug/accuracy_tools/msprobe/pytorch/hook_module/api_register.py b/debug/accuracy_tools/msprobe/pytorch/hook_module/api_register.py index 9bb4c3c8a..552a62a7a 100644 --- a/debug/accuracy_tools/msprobe/pytorch/hook_module/api_register.py +++ b/debug/accuracy_tools/msprobe/pytorch/hook_module/api_register.py @@ -44,6 +44,8 @@ torch_version_above_2 = torch.__version__.split('+')[0] > '2.0' _inner_used_api = {} _supported_api_list_path = (os.path.join(os.path.dirname(os.path.realpath(__file__)), Const.SUPPORT_API_FILE_NAME),) _cuda_func_mapping = {"npu_fusion_attention": "gpu_fusion_attention"} +dist_data_collect_func = {} +origin_wait = getattr(dist.Work, 'wait') _api_types = { Const.PT_FRAMEWORK: { @@ -94,16 +96,35 @@ def dist_module_forward(module, *args, **kwargs): use_async_op_flag = False logger.warning(f"fail to get dist api's func signature because {e}, no wait") - if use_async_op_flag or module.api_name in ["isend", "irecv"]: - if handle and hasattr(handle, 'wait'): - handle.wait() - if module.api_name == "batch_isend_irecv": + def create_async_callback_func(catch_func): + def store_data(): + module.async_op_dump_flag = False + catch_func(module, args, kwargs, handle) + return store_data + + if len(module._forward_hooks.values()) == 0: + return handle + if use_async_op_flag or module.api_name in ['isend', 'irecv']: + module.async_op_dump_flag = True + dist_data_collect_func[handle] = create_async_callback_func(list(module._forward_hooks.values())[0]) + if module.api_name == 'batch_isend_irecv': if isinstance(handle, list): for req in handle: - req.wait() + dist_data_collect_func[req] = create_async_callback_func(list(module._forward_hooks.values())[0]) return handle +def redirect_wait(): + def wrapped_wait(work): + def wrapped_wait(*args, **kwargs): + origin_wait(*args, **kwargs) + if args[0] in dist_data_collect_func: + store_func = dist_data_collect_func.pop(args[0]) + store_func() + return wrapped_wait + dist.Work.wait = wrapped_wait(dist.Work) + + def npu_module_forward(module, *args, **kwargs): if not module.need_hook: if module.api_name not in npu_custom_functions: @@ -130,6 +151,7 @@ class ApiTemplate(HOOKModule): self.prefix_api_name = prefix + Const.SEP + str(api_name.split(Const.SEP)[-1]) + Const.SEP self.need_hook = need_hook self.device = device + self.async_op_dump_flag = False if self.need_hook: super().__init__(hook_build_func) if prefix == Const.DIST_API_TYPE_PREFIX: diff --git a/debug/accuracy_tools/msprobe/pytorch/pytorch_service.py b/debug/accuracy_tools/msprobe/pytorch/pytorch_service.py index aad49a90d..1368e4341 100644 --- a/debug/accuracy_tools/msprobe/pytorch/pytorch_service.py +++ b/debug/accuracy_tools/msprobe/pytorch/pytorch_service.py @@ -19,7 +19,7 @@ from msprobe.pytorch.attl_manager import ATTLManager from msprobe.pytorch.common.log import logger from msprobe.pytorch.common.utils import get_rank_if_initialized, torch_version_above_or_equal_2 from msprobe.pytorch.dump.module_dump.module_processer import ModuleProcesser -from msprobe.pytorch.hook_module.api_register import get_api_register, ApiTemplate +from msprobe.pytorch.hook_module.api_register import get_api_register, ApiTemplate, redirect_wait from msprobe.pytorch.hook_module.hook_module import HOOKModule from msprobe.pytorch.hook_module.jit_script_wrapper import wrap_jit_script_func from msprobe.pytorch.hook_module.pt_hook_manager import PytorchHookManager @@ -57,6 +57,7 @@ class PytorchService(BaseService): def _register_api_hook(self): super()._register_api_hook() wrap_jit_script_func() + redirect_wait() def _register_module_hook(self): ModuleProcesser.enable_module_dump = True diff --git a/debug/accuracy_tools/msprobe/test/core_ut/data_dump/test_data_collector.py b/debug/accuracy_tools/msprobe/test/core_ut/data_dump/test_data_collector.py index b93a122ac..67ea481a9 100644 --- a/debug/accuracy_tools/msprobe/test/core_ut/data_dump/test_data_collector.py +++ b/debug/accuracy_tools/msprobe/test/core_ut/data_dump/test_data_collector.py @@ -384,7 +384,6 @@ class TestForwardDataCollect(unittest.TestCase): mock_data = {"key": "value"} self.data_collector.data_processor.analyze_forward_output.return_value = mock_data self.data_collector.forward_output_data_collect("test", "module", 123, "data", True) - self.data_collector.call_stack_collect.assert_called_once_with("test") self.data_collector.handle_data.assert_called_once_with( "test", mock_data, diff --git a/debug/accuracy_tools/msprobe/test/core_ut/test_hook_manager.py b/debug/accuracy_tools/msprobe/test/core_ut/test_hook_manager.py index f03a7d889..acab05db4 100644 --- a/debug/accuracy_tools/msprobe/test/core_ut/test_hook_manager.py +++ b/debug/accuracy_tools/msprobe/test/core_ut/test_hook_manager.py @@ -73,6 +73,7 @@ class TestBaseHookManager(unittest.TestCase): def test_should_execute_hook_conditions(self): module = MagicMock() module.forward_data_collected = True + module.async_op_dump_flag = False Runtime.is_running = True self.mock_data_collector.data_processor.is_terminated = False self.assertTrue(self.manager._should_execute_hook(Const.MODULE, module, True, threading.get_ident())) diff --git a/debug/accuracy_tools/msprobe/test/pytorch_ut/hook_module/test_pt_api_register.py b/debug/accuracy_tools/msprobe/test/pytorch_ut/hook_module/test_pt_api_register.py index bb2091c34..da6e21049 100644 --- a/debug/accuracy_tools/msprobe/test/pytorch_ut/hook_module/test_pt_api_register.py +++ b/debug/accuracy_tools/msprobe/test/pytorch_ut/hook_module/test_pt_api_register.py @@ -53,18 +53,6 @@ class TestAPIRegister(unittest.TestCase): self.assertEqual(result, "test_handle") mock_logger.assert_not_called() - def test_dist_module_forward_with_batch_op(self): - mock_reqs = [MagicMock(), MagicMock()] - mock_module = MagicMock() - mock_module.api_func.return_value = mock_reqs - mock_module.api_name = "batch_isend_irecv" - - result = dist_module_forward(mock_module) - - for req in mock_reqs: - req.wait.assert_called_once() - self.assertEqual(result, mock_reqs) - @patch('msprobe.pytorch.hook_module.api_register.ApiRegistry') def test_get_api_register_with_new_obj(self, mock_api_registry): get_api_register(return_new=True) -- Gitee From 7d9ed19b510e55e03d724f5eec996ef153fbecb3 Mon Sep 17 00:00:00 2001 From: wangchao426 Date: Wed, 25 Jun 2025 10:12:23 +0800 Subject: [PATCH 25/79] =?UTF-8?q?=E5=BC=BA=E5=88=B6=E6=9B=B4=E6=96=B0?= =?UTF-8?q?=E5=B7=B2=E5=AF=BC=E5=85=A5deallocate=5Foutput=5Ftensor?= =?UTF-8?q?=E7=9A=84=E6=A8=A1=E5=9D=97=E4=B8=AD=E5=87=BD=E6=95=B0=E7=9A=84?= =?UTF-8?q?=E5=BC=95=E7=94=A8?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../msprobe/pytorch/dump/module_dump/module_processer.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/debug/accuracy_tools/msprobe/pytorch/dump/module_dump/module_processer.py b/debug/accuracy_tools/msprobe/pytorch/dump/module_dump/module_processer.py index 6130ee50f..6236d2cee 100644 --- a/debug/accuracy_tools/msprobe/pytorch/dump/module_dump/module_processer.py +++ b/debug/accuracy_tools/msprobe/pytorch/dump/module_dump/module_processer.py @@ -14,6 +14,7 @@ # limitations under the License. import threading +import sys from collections import OrderedDict import torch @@ -69,6 +70,11 @@ class ModuleProcesser: try: from megatron.core.pipeline_parallel import schedules schedules.deallocate_output_tensor = wrap_megatron_deallocate(schedules.deallocate_output_tensor) + for module in list(sys.modules.values()): + if 'deallocate_output_tensor' in module.__dict__: + if module.__name__ == 'schedules': + continue + module.func = schedules.deallocate_output_tensor logger.info_on_rank_0("Patch megatron method success.") except ImportError: logger.info_on_rank_0("No megatron find.") -- Gitee From fec1abd60e413560f4119dc55d162f80405e7d49 Mon Sep 17 00:00:00 2001 From: zhouxianqi <13165993773@163.com> Date: Wed, 25 Jun 2025 10:13:56 +0800 Subject: [PATCH 26/79] update_msprof-analyze_whl_package --- profiler/msprof_analyze/README.md | 1 + profiler/msprof_analyze/version.txt | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/profiler/msprof_analyze/README.md b/profiler/msprof_analyze/README.md index 13e32a14a..290118b83 100644 --- a/profiler/msprof_analyze/README.md +++ b/profiler/msprof_analyze/README.md @@ -117,6 +117,7 @@ Successfully installed msprof-analyze-{version} | profiler版本 | 发布日期 | 下载链接 | 校验码 | |------------|------------|-------------------------------------------------------------------------------------------------------------------------------------------| ------------------------------------------------------------ | +| 2.1.0 | 2025-06-25 | [msprof_analyze-2.0.2-py3-none-any.whl](https://ptdbg.obs.myhuaweicloud.com/profiler/package/2.1.0/msprof_analyze-2.1.0-py3-none-any.whl) | 5959ba8ac24a36c9a349274c773440e99cd7e50963189587ecfa0a1ddb8c8d60 | | 2.0.2 | 2025-03-31 | [msprof_analyze-2.0.2-py3-none-any.whl](https://ptdbg.obs.myhuaweicloud.com/profiler/package/2.0.2/msprof_analyze-2.0.2-py3-none-any.whl) | 4227ff628187297b2f3bc14b9dd3a8765833ed25d527f750bc266a8d29f86935 | | 2.0.1 | 2025-02-28 | [msprof_analyze-2.0.1-py3-none-any.whl](https://ptdbg.obs.myhuaweicloud.com/profiler/package/2.0.1/msprof_analyze-2.0.1-py3-none-any.whl) | 82dfe2c779dbab9015f61d36ea0c32d832b6d182454b3f7db68e6c0ed49c0423 | | 2.0.0 | 2025-02-08 | [msprof_analyze-2.0.0-py3-none-any.whl](https://ptdbg.obs.myhuaweicloud.com/profiler/package/2.0.0/msprof_analyze-2.0.0-py3-none-any.whl) | 8e44e5f3e7681c377bb2657a600ad9841d3bed11061ddd7844c30e8a97242101 | diff --git a/profiler/msprof_analyze/version.txt b/profiler/msprof_analyze/version.txt index f93ea0ca3..50aea0e7a 100644 --- a/profiler/msprof_analyze/version.txt +++ b/profiler/msprof_analyze/version.txt @@ -1 +1 @@ -2.0.2 \ No newline at end of file +2.1.0 \ No newline at end of file -- Gitee From 3bc87b9790e6381f57be3463db17f4a7153caf3f Mon Sep 17 00:00:00 2001 From: wangchao426 Date: Wed, 25 Jun 2025 10:19:06 +0800 Subject: [PATCH 27/79] =?UTF-8?q?=E5=BC=BA=E5=88=B6=E6=9B=B4=E6=96=B0?= =?UTF-8?q?=E5=B7=B2=E5=AF=BC=E5=85=A5deallocate=5Foutput=5Ftensor?= =?UTF-8?q?=E7=9A=84=E6=A8=A1=E5=9D=97=E4=B8=AD=E5=87=BD=E6=95=B0=E7=9A=84?= =?UTF-8?q?=E5=BC=95=E7=94=A8?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../msprobe/pytorch/dump/module_dump/module_processer.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/debug/accuracy_tools/msprobe/pytorch/dump/module_dump/module_processer.py b/debug/accuracy_tools/msprobe/pytorch/dump/module_dump/module_processer.py index 6236d2cee..c0467e349 100644 --- a/debug/accuracy_tools/msprobe/pytorch/dump/module_dump/module_processer.py +++ b/debug/accuracy_tools/msprobe/pytorch/dump/module_dump/module_processer.py @@ -71,10 +71,10 @@ class ModuleProcesser: from megatron.core.pipeline_parallel import schedules schedules.deallocate_output_tensor = wrap_megatron_deallocate(schedules.deallocate_output_tensor) for module in list(sys.modules.values()): - if 'deallocate_output_tensor' in module.__dict__: + if hasattr(module, 'deallocate_output_tensor'): if module.__name__ == 'schedules': continue - module.func = schedules.deallocate_output_tensor + module.deallocate_output_tensor = schedules.deallocate_output_tensor logger.info_on_rank_0("Patch megatron method success.") except ImportError: logger.info_on_rank_0("No megatron find.") -- Gitee From 40ce219d0b483af6bde51c91374688c6361d21bf Mon Sep 17 00:00:00 2001 From: pxp1 <958876660@qq.com> Date: Wed, 25 Jun 2025 10:46:17 +0800 Subject: [PATCH 28/79] =?UTF-8?q?=E4=BF=AE=E5=A4=8D=E4=BB=BB=E6=84=8F?= =?UTF-8?q?=E8=B7=AF=E5=BE=84=E5=88=A0=E9=99=A4=E7=9A=84=E9=97=AE=E9=A2=98?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../msprobe/core/config_check/checkers/base_checker.py | 2 ++ .../msprobe/core/config_check/config_check_cli.py | 2 +- .../accuracy_tools/msprobe/core/config_check/config_checker.py | 3 +-- 3 files changed, 4 insertions(+), 3 deletions(-) diff --git a/debug/accuracy_tools/msprobe/core/config_check/checkers/base_checker.py b/debug/accuracy_tools/msprobe/core/config_check/checkers/base_checker.py index ee598c7f0..1e6e002ed 100644 --- a/debug/accuracy_tools/msprobe/core/config_check/checkers/base_checker.py +++ b/debug/accuracy_tools/msprobe/core/config_check/checkers/base_checker.py @@ -15,6 +15,7 @@ import os +from msprobe.core.common.file_utils import check_path_pattern_valid from msprobe.core.common.framework_adapter import FmkAdp from msprobe.core.common.const import FileCheckConst @@ -32,6 +33,7 @@ class PackInput: raise Exception(f"model is not torch.nn.Module/mindspore.nn.Cell or module list.") if not isinstance(self.output_zip_path, str) or not self.output_zip_path.endswith(FileCheckConst.ZIP_SUFFIX): raise Exception(f"output zip path must be a string and ends with '.zip'") + check_path_pattern_valid(self.output_zip_path) class BaseChecker: diff --git a/debug/accuracy_tools/msprobe/core/config_check/config_check_cli.py b/debug/accuracy_tools/msprobe/core/config_check/config_check_cli.py index cc2db1924..d7ea45ff0 100644 --- a/debug/accuracy_tools/msprobe/core/config_check/config_check_cli.py +++ b/debug/accuracy_tools/msprobe/core/config_check/config_check_cli.py @@ -29,7 +29,7 @@ def compare(bench_zip_path, cmp_zip_path, output_path, framework): def _config_checking_parser(parser): parser.add_argument('-d', '--dump', nargs='*', help='Collect the train config into a zip file') parser.add_argument('-c', '--compare', nargs=2, help='Compare two zip files or checkpoints') - parser.add_argument('-o', '--output', help='output path, default is current directory') + parser.add_argument('-o', '--output', help='output path, default is ./config_check_result') def _run_config_checking_command(args): diff --git a/debug/accuracy_tools/msprobe/core/config_check/config_checker.py b/debug/accuracy_tools/msprobe/core/config_check/config_checker.py index 2dc908398..537bbda1a 100644 --- a/debug/accuracy_tools/msprobe/core/config_check/config_checker.py +++ b/debug/accuracy_tools/msprobe/core/config_check/config_checker.py @@ -43,8 +43,7 @@ class ConfigChecker: @staticmethod def compare(bench_zip_path, cmp_zip_path, output_path, fmk=Const.PT_FRAMEWORK): - if os.path.exists(output_path): - shutil.rmtree(output_path) + create_directory(output_path) bench_dir = os.path.join(output_path, "bench") cmp_dir = os.path.join(output_path, "cmp") extract_zip(bench_zip_path, bench_dir) -- Gitee From d82911635fc19ea75af557dbb5ee36c815b7f48b Mon Sep 17 00:00:00 2001 From: Linwei-Ying Date: Wed, 25 Jun 2025 10:39:53 +0800 Subject: [PATCH 29/79] =?UTF-8?q?msprobe=E5=91=BD=E4=BB=A4=E8=A1=8C?= =?UTF-8?q?=E5=8F=82=E6=95=B0=E8=A1=A5=E5=85=85?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit msprobe命令行参数补充 --- .../docs/07.accuracy_checker_PyTorch.md | 25 ++++--- .../docs/09.accuracy_checker_MindSpore.md | 22 +++--- .../docs/10.accuracy_compare_PyTorch.md | 4 +- .../docs/11.accuracy_compare_MindSpore.md | 6 +- .../msprobe/docs/12.overflow_check_PyTorch.md | 5 +- .../msprobe/docs/14.data_parse_PyTorch.md | 67 ++++++++++--------- .../msprobe/docs/21.visualization_PyTorch.md | 17 ++--- .../docs/22.visualization_MindSpore.md | 1 + .../msprobe/docs/24.code_mapping_Mindspore.md | 11 +-- .../msprobe/docs/31.config_check.md | 8 +-- .../msprobe/docs/35.nan_analyze.md | 7 +- 11 files changed, 95 insertions(+), 78 deletions(-) diff --git a/debug/accuracy_tools/msprobe/docs/07.accuracy_checker_PyTorch.md b/debug/accuracy_tools/msprobe/docs/07.accuracy_checker_PyTorch.md index 0979dfe53..08e982592 100644 --- a/debug/accuracy_tools/msprobe/docs/07.accuracy_checker_PyTorch.md +++ b/debug/accuracy_tools/msprobe/docs/07.accuracy_checker_PyTorch.md @@ -34,16 +34,17 @@ run_ut 预检操作包括以下两种方式： msprobe -f pytorch run_ut -api_info ./dump_path/step{step_number}/rank{rank_number}/dump.json ``` - | 参数名称 | 解释 | 是否必选 | - | ---------------------------- | ------------------------------------------------------------ | ---------------------------------- | - | -api_info 或 --api_info_file | 指定 API 信息文件 dump.json。 | 是 | - | -save_error_data | 保存精度未达标的 API 输入输出数据。 | 否 | - | -o 或 --out_path | 指定 run_ut 执行结果存盘路径，默认“./”。 | 否 | - | -j 或 --jit_compile | 开启 jit 编译。 | 否 | - | -d 或 --device | 指定 Device ID，选择 UT 代码运行所在的卡，默认值为 0。 | 否 | + | 参数名称 | 解释 | 是否必选 | + |-------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| ---------------------------------- | + | -f 或 --framework | 指定训练框架。pytorch。 | 是 | + | -api_info 或 --api_info_file | 指定 API 信息文件 dump.json。 | 是 | + | -save_error_data | 保存精度未达标的 API 输入输出数据。 | 否 | + | -o 或 --out_path | 指定 run_ut 执行结果存盘路径，默认“./”。 | 否 | + | -j 或 --jit_compile | 开启 jit 编译。 | 否 | + | -d 或 --device | 指定 Device ID，选择 UT 代码运行所在的卡，默认值为 0。 | 否 | | -csv_path 或 --result_csv_path | 指定本次运行中断时生成的 `accuracy_checking_result_{timestamp}.csv` 文件路径，执行 run_ut 中断时，若想从中断处继续执行，配置此参数即可。需要指定为上次中断的 `accuracy_checking_result_{timestamp}.csv` 文件。详见 [3.3 断点续检](#33-断点续检)。 | run_ut 操作中断后继续执行场景下必须配置 | - | -f 或 --filter_api | 过滤模型中除最大值和最小值以外其他参数和结构相同的 API。适用于模型较大且重复 API 较多的场景。 | 否 | - | -config 或 --config_path | 指定离线预检操作过程中额外配置（包括黑名单、白名单等）的 [config.json](../config.json) 文件，默认未配置。config.json 文件的配置可参考[配置文件介绍](./02.config_introduction.md)。 | 否 | + | -f 或 --filter_api | 过滤模型中除最大值和最小值以外其他参数和结构相同的 API。适用于模型较大且重复 API 较多的场景。 | 否 | + | -config 或 --config_path | 指定离线预检操作过程中额外配置（包括黑名单、白名单等）的 [config.json](../config.json) 文件，默认未配置。config.json 文件的配置可参考[配置文件介绍](./02.config_introduction.md)。 | 否 | run_ut 执行结果包括 `accuracy_checking_result_{timestamp}.csv` 和 `accuracy_checking_details_{timestamp}.csv` 两个文件。`accuracy_checking_result_{timestamp}.csv` 属于 API 级，标明每个 API 是否通过测试。建议用户先查看 `accuracy_checking_result_{timestamp}.csv` 文件，对于其中没有通过测试的或者特定感兴趣的 API，根据其 API name 字段在 `accuracy_checking_details_{timestamp}.csv` 中查询其各个输出的达标情况以及比较指标。详细介绍请参见[ 4 预检结果](#4-预检结果)。 @@ -103,6 +104,7 @@ msprobe -f pytorch multi_run_ut -api_info ./dump_path/step{step_number}/rank{ran | 参数名称 | 解释 | 是否必选 | | ---------------------------- | ------------------------------------------------------------ | ---------------------------------- | +| -f 或 --framework | 指定训练框架。pytorch。 | 是 | | -api_info 或 --api_info_file | 指定 API 信息文件 dump.json。 | 是 | | -save_error_data | 保存精度未达标的 API 输入输出数据。 | 否 | | -o 或 --out_path | 指定 run_ut 执行结果存盘路径，默认“./”。 | 否 | @@ -212,8 +214,9 @@ Forward Test Success 和 Backward Test Success 是否通过测试是由 `accurac msprobe -f pytorch api_precision_compare -npu /home/xxx/npu/accuracy_checking_details_{timestamp}.csv -gpu /home/xxx/gpu/accuracy_checking_details_{timestamp}.csv -o /home/xxx/ ``` -| 参数名称 | 说明 | 是否必选 | -| -------------------- | ------------- | -------- | +| 参数名称 | 说明 | 是否必选 | +|-----------------------| ------------- | -------- | +| -f 或 --framework | 指定训练框架。pytorch。 | 是 | | -npu 或 --npu_csv_path | NPU 预检结果 `accuracy_checking_details_{timestamp}.csv` 文件路径。默认从当前目录下识别该文件。 | 是 | | -gpu 或 --gpu_csv_path | GPU 预检结果 `accuracy_checking_details_{timestamp}.csv` 文件路径。默认从当前目录下识别该文件。 | 是 | | -o 或 --out_path | 指定 api_precision_compare.py 执行结果存盘路径，默认为当前目录。 | 否 | diff --git a/debug/accuracy_tools/msprobe/docs/09.accuracy_checker_MindSpore.md b/debug/accuracy_tools/msprobe/docs/09.accuracy_checker_MindSpore.md index d2f938459..f4efe1ebd 100644 --- a/debug/accuracy_tools/msprobe/docs/09.accuracy_checker_MindSpore.md +++ b/debug/accuracy_tools/msprobe/docs/09.accuracy_checker_MindSpore.md @@ -29,12 +29,13 @@ c. 在预检时可以由工具构造随机数据或者获取真实dump数据进 msprobe -f mindspore run_ut -api_info ./dump.json -o ./checker_result ``` -| 参数名称 | 说明 |参数类型 | 是否必选 | -| ---------------------------- |---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------- | ---------------------------------- | -| -api_info 或 --api_info_file | 指定 API 信息文件 dump.json。对其中的mint api以及部分Tensor api进行预检，预检支持的Tensor api列表详见 [ 预检支持列表](../mindspore/api_accuracy_checker/checker_support_api.yaml)。 | str | 是 | -| -o 或 --out_path | 指定预检结果存盘路径，默认“./”。 | str | 否 | -| -csv_path 或 --result_csv_path | 指定本次运行中断时生成的 `accuracy_checking_result_{timestamp}.csv` 文件路径，执行 run_ut 中断时，若想从中断处继续执行，配置此参数即可。需要指定为上次中断的 `accuracy_checking_result_{timestamp}.csv` 文件。详见 [3.3 断点续检](#33-断点续检)。 | str | 否 | -| -save_error_data | 保存(随机数据模式)精度未达标的 API 输入输出数据。 | 空 | 否 | +| 参数名称 | 说明 | 参数类型 | 是否必选 | +| ---------------------------- |--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------| ---------------------------------- | +| -f 或 --framework | 指定训练框架。mindspore。 | str | 是 | +| -api_info 或 --api_info_file | 指定 API 信息文件 dump.json。对其中的mint api以及部分Tensor api进行预检，预检支持的Tensor api列表详见 [ 预检支持列表](../mindspore/api_accuracy_checker/checker_support_api.yaml)。 | str | 是 | +| -o 或 --out_path | 指定预检结果存盘路径，默认“./”。 | str | 否 | +| -csv_path 或 --result_csv_path | 指定本次运行中断时生成的 `accuracy_checking_result_{timestamp}.csv` 文件路径，执行 run_ut 中断时，若想从中断处继续执行，配置此参数即可。需要指定为上次中断的 `accuracy_checking_result_{timestamp}.csv` 文件。详见 [3.3 断点续检](#33-断点续检)。 | str | 否 | +| -save_error_data | 保存(随机数据模式)精度未达标的 API 输入输出数据。 | 空 | 否 | 预检执行结果包括 `accuracy_checking_result_{timestamp}.csv` 和 `accuracy_checking_details_{timestamp}.csv` 两个文件。`accuracy_checking_result_{timestamp}.csv` 属于 API 级，标明每个 API 是否通过测试。建议用户先查看 `accuracy_checking_result_{timestamp}.csv` 文件，对于其中没有通过测试的或者特定感兴趣的 API，根据其 API Name 字段在 `accuracy_checking_details_{timestamp}.csv` 中查询其各个输出的达标情况以及比较指标。详细介绍请参见 [4 预检结果](#4-预检结果)。 @@ -54,13 +55,14 @@ multi_run_ut 脚本，可以并行在多个Device执行 run_ut 操作，从而 msprobe -f mindspore multi_run_ut -api_info ./dump.json -d 0 1 2 3 ``` -| 参数名称 | 说明 | 参数类型 | 是否必选 | -| ---------------------------- |---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------| ---------------------------------- | +| 参数名称 | 说明 | 参数类型 | 是否必选 | +|-------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------| ---------------------------------- | +| -f 或 --framework | 指定训练框架。mindspore。 | str | 是 | | -api_info 或 --api_info_file | 指定 API 信息文件 dump.json。对其中的mint api以及部分Tensor api进行预检，预检支持的Tensor api列表详见 [ 预检支持列表](../mindspore/api_accuracy_checker/checker_support_api.yaml)。 | str | 是 | -| -o 或 --out_path | 指定预检结果存盘路径，默认“./”。 | str | 否 | +| -o 或 --out_path | 指定预检结果存盘路径，默认“./”。 | str | 否 | | -csv_path 或 --result_csv_path | 指定本次运行中断时生成的 `accuracy_checking_result_{timestamp}.csv` 文件路径，执行 run_ut 中断时，若想从中断处继续执行，配置此参数即可。需要指定为上次中断的 `accuracy_checking_result_{timestamp}.csv` 文件。详见 [3.3 断点续检](#33-断点续检)。 | str | 否 | | -d 或 --device | 指定 Device ID，选择 UT 代码运行所在的卡，默认值为 0，支持同时指定 0 ~ Device数量 - 1 ，例如 0 1 2 3 4。 | List[int] | 否 | -| -save_error_data | 保存(随机数据模式)精度未达标的 API 输入输出数据。 | 空 | 否 | +| -save_error_data | 保存(随机数据模式)精度未达标的 API 输入输出数据。 | 空 | 否 | 在不同卡数下，使用38B语言大模型的预检耗时基线参考 [multi_run_ut耗时基线](accuracy_checker_MindSpore/accuracy_checker_MindSpore_baseline.md) diff --git a/debug/accuracy_tools/msprobe/docs/10.accuracy_compare_PyTorch.md b/debug/accuracy_tools/msprobe/docs/10.accuracy_compare_PyTorch.md index 2dd206b0e..6727e01fe 100644 --- a/debug/accuracy_tools/msprobe/docs/10.accuracy_compare_PyTorch.md +++ b/debug/accuracy_tools/msprobe/docs/10.accuracy_compare_PyTorch.md @@ -53,6 +53,7 @@ msprobe -f pytorch compare -i ./compare.json -o ./output -s | 参数名 | 说明 | 是否必选 | |-------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -------- | +| -f 或 --framework | 指定训练框架。pytorch。 | 是 | | -i 或 --input_path | 指定[比对文件](#51-比对文件)，str 类型。 | 是 | | -o 或 --output_path | 配置比对结果文件存盘目录，str 类型，默认在当前目录创建output目录。文件名称基于时间戳自动生成，格式为：`compare_result_{timestamp}.xlsx`。
    提示：output目录下与结果件同名文件将被删除覆盖。 | 否 | | -s 或 --stack_mode | 比对结果展示调用栈信息（NPU_Stack_Info）的开关，bool 类型。单卡场景开启时，根据[比对文件](#51-比对文件)的参数说明配置stack_path；多卡场景开启时，自动识别npu_dump目录下stack.json文件，如存在生成详细调用栈信息，否则不生成，此参数不生效。通过直接配置该参数开启，默认未配置，表示关闭。 | 否 | @@ -314,7 +315,8 @@ msprobe -f pytorch merge_result -i ./input_dir -o ./output_dir -config ./config. **完整参数说明** | 参数名 | 说明 | 是否必选 | -| ---------------------- |-------------------------------------------------------------------------------------------------------------------| -------- | +| --------------------- |-------------------------------------------------------------------------------------------------------------------| -------- | +| -f 或 --framework | 指定训练框架。pytorch。 | 是 | | -i 或 --input_dir | 多卡比对结果存盘目录，即使用compare比对的结果输出目录，str类型。所有比对结果应全部为真实数据比对结果或统计数据比对结果，否则可能导致汇总数据不完整。 | 是 | | -o 或 --output_dir | 数据提取汇总结果存盘目录，str类型。文件名称基于时间戳自动生成，格式为：`multi_ranks_compare_merge_{timestamp}.xlsx`。
    提示：output目录下与结果件同名文件将被删除覆盖。 | 是 | | -config或--config-path | 指定需要汇总数据的API和比对指标的yaml文件路径，str类型。
    yaml文件详细介绍见下文“**yaml文件说明**”。 | 是 | diff --git a/debug/accuracy_tools/msprobe/docs/11.accuracy_compare_MindSpore.md b/debug/accuracy_tools/msprobe/docs/11.accuracy_compare_MindSpore.md index a7194f293..55a148058 100644 --- a/debug/accuracy_tools/msprobe/docs/11.accuracy_compare_MindSpore.md +++ b/debug/accuracy_tools/msprobe/docs/11.accuracy_compare_MindSpore.md @@ -37,6 +37,7 @@ msprobe -f mindspore compare -i ./compare.json -o ./output -s | 参数名 | 说明 | 是否必选 | | -------------------- |--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -------- | +| -f 或 --framework | 指定训练框架。mindspore。 | 是 | | -i或--input_path | 指定比对文件。比对文件内容及示例请参见[比对文件](#41-比对文件)或[比对文件（kernel）](#42-比对文件kernel)（比对文件（kernel）仅[不同版本下的全量kernel比对](#23-不同版本下的全量kernel比对)场景支持）。 | 是 | | -o或--output_path | 配置比对结果文件存盘目录，默认会在当前目录创建output目录。文件名称基于时间戳自动生成，格式为：
    `compare_result_{timestamp}.xlsx`
    `compare_result_{rank_id}_{step_id}_{timestamp}.xlsx`（仅[不同版本下的全量kernel比对](#23-不同版本下的全量kernel比对)场景支持）。
    提示：output目录下与结果件同名文件将被删除覆盖。 | 否 | | -s或--stack_mode | 比对结果展示调用栈信息（NPU_Stack_Info）的开关，bool 类型。单卡场景开启时，需要使用[比对文件](#41-比对文件)的单卡场景配置stack_path指定stack.json文件，才能生成详细调用栈信息，否则在比对时会报错；暂不支持多卡场景。通过直接配置该参数开启，默认未配置，表示关闭。 | 否 | @@ -222,8 +223,9 @@ msprobe -f mindspore merge_result -i ./input_dir -o ./output_dir -config ./confi **完整参数说明** -| 参数名 | 说明 | 是否必选 | -| ---------------------- |-------------------------------------------------------------------------------------------------------------------| -------- | +| 参数名 | 说明 | 是否必选 | +|-----------------------|-------------------------------------------------------------------------------------------------------------------| -------- | +| -f 或 --framework | 指定训练框架。mindspore。 | 是 | | -i 或 --input_dir | 多卡比对结果存盘目录，即使用compare比对的结果输出目录，str类型。所有比对结果应全部为真实数据比对结果或统计数据比对结果，否则可能导致汇总数据不完整。 | 是 | | -o 或 --output_dir | 数据提取汇总结果存盘目录，str类型。文件名称基于时间戳自动生成，格式为：`multi_ranks_compare_merge_{timestamp}.xlsx`。
    提示：output目录下与结果件同名文件将被删除覆盖。 | 是 | | -config或--config-path | 指定需要汇总数据的API和比对指标的yaml文件路径，str类型。
    yaml文件详细介绍见下文“**yaml文件说明**”。 | 是 | diff --git a/debug/accuracy_tools/msprobe/docs/12.overflow_check_PyTorch.md b/debug/accuracy_tools/msprobe/docs/12.overflow_check_PyTorch.md index a1cd8db8f..ea41bcdc1 100644 --- a/debug/accuracy_tools/msprobe/docs/12.overflow_check_PyTorch.md +++ b/debug/accuracy_tools/msprobe/docs/12.overflow_check_PyTorch.md @@ -58,8 +58,9 @@ export INF_NAN_MODE_ENABLE=1 msprobe -f pytorch run_overflow_check -api_info ./dump_path/step{step_number}/rank{rank_number}/dump.json ``` -| 参数名称 | 说明 | 是否必选 | -| -------------------------- |------------------------------------| -------- | +| 参数名称 | 说明 | 是否必选 | +|---------------------------|------------------------------------| -------- | +| -f 或 --framework | 指定训练框架。pytorch。 | 是 | | -api_info或--api_info_file | 指定采集下来的 API 信息文件 dump.json。 | 是 | | -j或--jit_compile | 开启 jit 编译。 | 否 | | -d或--device | 指定 Device ID，选择 UT 代码运行所在的卡，默认值为0。 | 否 | diff --git a/debug/accuracy_tools/msprobe/docs/14.data_parse_PyTorch.md b/debug/accuracy_tools/msprobe/docs/14.data_parse_PyTorch.md index 7efc6af74..931e74a6d 100644 --- a/debug/accuracy_tools/msprobe/docs/14.data_parse_PyTorch.md +++ b/debug/accuracy_tools/msprobe/docs/14.data_parse_PyTorch.md @@ -18,6 +18,9 @@ msprobe -f pytorch parse Parse >>> ``` +| 参数名称 | 说明 | 是否必选 | +|---------------------------|------------------------------------| -------- | +| -f 或 --framework | 指定训练框架。pytorch。 | 是 | 可在 parse 的界面中执行 Shell 命令，以及如下场景的相关解析命令（详细介绍请参见以下章节。）： @@ -38,11 +41,11 @@ Ctrl+C 可以退出 parse 交互式界面。 cad -m my_dump_path [-out output_path] [-asc msaccucmp_path] ``` -| 参数名称 | 说明 | 是否必选 | -| -------- | ------------------------------------------------------------ | -------- | -| -m | 待转换 kernel dump 数据目录。需要指定到 kernel dump 数据的 deviceid 级目录。 | 是 | -| -out | 结果输出目录，须指定已存在的目录，默认为 ./parse_data/acl_batch_convert。未指定时保存在默认路径下，比对结束后会打印 log 提示输出结果存放路径。 | 否 | -| -asc | 指定 msaccucmp 路径，默认路径为：/usr/local/Ascend/ascend-toolkit/latest/tools/operator_cmp/compare/msaccucmp.py。 | 否 | +| 参数名称 | 说明 | 是否必选 | +|-------------------------| ------------------------------------------------------------ | -------- | +| -m 或 --my_dump_path | 待转换 kernel dump 数据目录。需要指定到 kernel dump 数据的 deviceid 级目录。 | 是 | +| -out 或 --output_path | 结果输出目录，须指定已存在的目录，默认为 ./parse_data/acl_batch_convert。未指定时保存在默认路径下，比对结束后会打印 log 提示输出结果存放路径。 | 否 | +| -asc 或 --msaccucmp_path | 指定 msaccucmp 路径，默认路径为：/usr/local/Ascend/ascend-toolkit/latest/tools/operator_cmp/compare/msaccucmp.py。 | 否 | **示例代码**： @@ -99,12 +102,12 @@ Parse >>> cad -m /home/xxx/my_dump_path/20000124003856/0 vc -m my_dump_path -g golden_dump_path [-out output_path] [-cmp_path msaccucmp_path] ``` -| 参数名称 | 说明 | 是否必选 | -| --------- | ------------------------------------------------------------ | -------- | -| -m | 待比对 kernel dump 数据目录。如果比对单个算子，需要指定到 kernel dump 数据的 model_id 级目录；如果批量比对，则指定到 cad 转换后的 timestamp 级目录。 | 是 | -| -g | 标杆 kernel dump 数据目录。如果比对单个算子，需要指定到 kernel dump 数据的 model_id 级目录；如果批量比对，则指定到 cad 转换后的 timestamp 级目录。 | 是 | -| -out | 结果输出目录，须指定已存在的目录，默认为 ./parse_data/acl_batch_compare。未指定时保存在默认路径下，比对结束后会打印 log 提示输出结果存放路径。 | 否 | -| -cmp_path | 指定 msaccucmp 路径，默认路径为：/usr/local/Ascend/ascend-toolkit/latest/tools/operator_cmp/compare/msaccucmp.py | 否 | +| 参数名称 | 说明 | 是否必选 | +|------------------------------| ------------------------------------------------------------ | -------- | +| -m 或 --my_dump_path | 待比对 kernel dump 数据目录。如果比对单个算子，需要指定到 kernel dump 数据的 model_id 级目录；如果批量比对，则指定到 cad 转换后的 timestamp 级目录。 | 是 | +| -g 或 --golden_dump_path | 标杆 kernel dump 数据目录。如果比对单个算子，需要指定到 kernel dump 数据的 model_id 级目录；如果批量比对，则指定到 cad 转换后的 timestamp 级目录。 | 是 | +| -out 或 --output_path | 结果输出目录，须指定已存在的目录，默认为 ./parse_data/acl_batch_compare。未指定时保存在默认路径下，比对结束后会打印 log 提示输出结果存放路径。 | 否 | +| -cmp_path 或 --msaccucmp_path | 指定 msaccucmp 路径，默认路径为：/usr/local/Ascend/ascend-toolkit/latest/tools/operator_cmp/compare/msaccucmp.py | 否 | 输出结果：`batch_compare_{timestamp}.csv` 文件。 @@ -124,12 +127,12 @@ Parse >>> vc -m ./my_dump_path -g ./golden_data_path dc -n file_name/file_path [-f format] [-out output_path] ``` -| 参数名称 | 说明 | 是否必选 | -| --------- | ------------------------------------------------------------ | -------- | -| -n | 需转换的 dump 数据文件或 dump 数据文件目录。 | 是 | -| -f | 开启 format 转换，指定该参数时需要配置 format 格式。当前内置的 Format 转换支持如下类型：
    FRACTAL_NZ 转换 NCHW；
    FRACTAL_NZ 转换成 NHWC；
    FRACTAL_NZ 转换 ND；
    HWCN 转换 FRACTAL_Z；
    HWCN 转换成 NCHW；
    HWCN 转换成 NHWC；
    NC1HWC0 转换成 HWCN；
    NC1HWC0 转换成 NCHW；
    NC1HWC0 转换成 NHWC；
    NCHW 转换成 FRACTAL_Z；
    NCHW转换成NHWC；
    NHWC转换成FRACTAL_Z；
    NHWC转换成HWCN；
    NHWC转换成NCHW；
    NDC1HWC0转换成NCDHW。 | 否 | -| -out | 结果输出目录。 | 否 | -| -cmp_path | 指定 msaccucmp 路径，默认路径为：/usr/local/Ascend/ascend-toolkit/latest/tools/operator_cmp/compare/msaccucmp.py | 否 | +| 参数名称 | 说明 | 是否必选 | +|-------------------------| ------------------------------------------------------------ | -------- | +| -n 或 --name | 需转换的 dump 数据文件或 dump 数据文件目录。 | 是 | +| -f 或 --format | 开启 format 转换，指定该参数时需要配置 format 格式。当前内置的 Format 转换支持如下类型：
    FRACTAL_NZ 转换 NCHW；
    FRACTAL_NZ 转换成 NHWC；
    FRACTAL_NZ 转换 ND；
    HWCN 转换 FRACTAL_Z；
    HWCN 转换成 NCHW；
    HWCN 转换成 NHWC；
    NC1HWC0 转换成 HWCN；
    NC1HWC0 转换成 NCHW；
    NC1HWC0 转换成 NHWC；
    NCHW 转换成 FRACTAL_Z；
    NCHW转换成NHWC；
    NHWC转换成FRACTAL_Z；
    NHWC转换成HWCN；
    NHWC转换成NCHW；
    NDC1HWC0转换成NCDHW。 | 否 | +| -out 或 --output_path | 结果输出目录。 | 否 | +| -cmp_path 或 --msaccucmp | 指定 msaccucmp 路径，默认路径为：/usr/local/Ascend/ascend-toolkit/latest/tools/operator_cmp/compare/msaccucmp.py | 否 | - 输出结果：npy 文件。 @@ -143,9 +146,9 @@ dc -n file_name/file_path [-f format] [-out output_path] pt -n file_path ``` - | 参数名称 | 说明 | 是否必选 | - | -------- | ------------- | -------- | - | -n | npy 文件路径。 | 是 | + | 参数名称 | 说明 | 是否必选 | + |-------------| ------------- | -------- | + | -n 或 --name | npy 文件路径。 | 是 | 打印统计信息：shape, dtype, max, min 和 mean。默认在 npy 文件路径下将该数据保存为 txt 文件。 @@ -191,10 +194,10 @@ TextFile:./parse_data/dump_convert/Add.fp32_vars_add_1fp32_vars_Relu_6.24.5.1636 pk -f pkl_path -n api_name ``` -| 参数名称 | 说明 | 是否必选 | -| -------- | ----------------------- | -------- | -| -f | 指定 dump.json 文件路径。 | 是 | -| -n | 指定 API 名称。 | 是 | +| 参数名称 | 说明 | 是否必选 | +|-------------| ----------------------- | -------- | +| -f 或 --file | 指定 dump.json 文件路径。 | 是 | +| -n 或 --name | 指定 API 名称。 | 是 | - 输出结果：打印统计信息（shape, dtype, max和min mean）。 - 若 pkl 文件中存在相应的堆栈信息，则会打印堆栈信息。 @@ -225,14 +228,14 @@ cn -m my_data*.npy -g golden*.npy [-p num] [-al atol] [-rl rtol] - 统计级比对：对 tensor 整体进行余弦值及相对误差的计算。 - 像素级比对：对输入的两个 npy 文件进行逐元素比对。若两个 tensor 对应元素的相对误差或绝对误差大于**误差阈值**（-al 和 -rl 配置）则被标记为错误数据。 -| 参数名称 | 说明 | 是否必选 | -| -------- | ----------------------------------------------- | -------- | -| -m | 待比对数据。 | 是 | -| -g | 标杆数据。 | 是 | -| -p | 设置比对结束后打印错误元素的个数，默认值 20。 | 否 | -| -al | 判定数据存在精度问题的绝对误差阈值，默认 0.001。 | 否 | -| -rl | 判定数据存在精度问题的相对误差阈值，默认 0.001。 | 否 | -| -s | 将 npy 文件保存成 txt 文件，用于查看，默认开启。 | 否 | +| 参数名称 | 说明 | 是否必选 | +|-------------------------| ----------------------------------------------- | -------- | +| -m 或 --my_dump_path | 待比对数据。 | 是 | +| -g 或 --golden_dump_path | 标杆数据。 | 是 | +| -p 或 --print | 设置比对结束后打印错误元素的个数，默认值 20。 | 否 | +| -al 或 --atol | 判定数据存在精度问题的绝对误差阈值，默认 0.001。 | 否 | +| -rl 或 --rtol | 判定数据存在精度问题的相对误差阈值，默认 0.001。 | 否 | +| -s 或 --save | 将 npy 文件保存成 txt 文件，用于查看，默认开启。 | 否 | 输出结果： diff --git a/debug/accuracy_tools/msprobe/docs/21.visualization_PyTorch.md b/debug/accuracy_tools/msprobe/docs/21.visualization_PyTorch.md index daf81c004..945033f40 100644 --- a/debug/accuracy_tools/msprobe/docs/21.visualization_PyTorch.md +++ b/debug/accuracy_tools/msprobe/docs/21.visualization_PyTorch.md @@ -57,14 +57,15 @@ msprobe -f pytorch graph -i ./compare.json -o ./output ``` **命令行参数说明**： -| 参数名 | 说明 | 是否必选 | -|------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------| -| -i 或 --input_path | 指定比对文件，参考[比对文件说明](#313-比对文件说明) | 是 | -| -o 或 --output_path | 配置比对结果文件存盘目录，str 类型。文件名称基于时间戳自动生成，格式为：`compare_{timestamp}.vis或build_{timestamp}.vis`。 | 是 | -| -lm 或 --layer_mapping | 跨套件比对，例如同一个模型分别使用了DeepSpeed和Megatron套件的比对场景。配置该参数时表示开启跨套件Layer层的比对功能，指定模型代码中的Layer层后，可以识别对应dump数据中的模块或API。需要指定自定义映射文件*.yaml。自定义映射文件的格式请参见[自定义映射文件（Layer）](#71-自定义映射文件layer)，如何配置自定义映射文件请参考[模型分级可视化如何配置layer mapping映射文件](./visualization/layer_mapping_example.md)。 配置该参数后，将仅按节点名称进行比对，忽略节点的 type 和 shape。如果调试侧和标杆侧有名称不同的节点，则需要配置自定义映射文件，-lm参数传入自定义映射文件路径；如果调试侧和标杆侧节点名称相同，则仅指定-lm即可。| 否 | -| -oc 或 --overflow_check | 是否开启溢出检测模式，开启后会在输出vis文件中（`compare_{timestamp}.vis或build_{timestamp}.vis`）对每个溢出节点进行标记溢出等级，溢出等级说明参考[溢出等级说明](#312-溢出等级说明) | 否 | -| -f 或 --fuzzy_match | 是否开启模糊匹配，bool类型。模糊匹配说明参考[匹配说明](#311-匹配说明) | 否 | -| -cs 或 --complete_stack | 是否使用完整的堆栈信息，bool类型。默认使用精简的堆栈信息，数据量小有助于增加流畅度。完整堆栈和精简堆栈信息参考[堆栈信息说明](#72-堆栈信息说明) | 否 | +| 参数名 | 说明 | 是否必选 | +|------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------| +| -f 或 --framework | 指定训练框架。pytorch。 | 是 | +| -i 或 --input_path | 指定比对文件，参考[比对文件说明](#313-比对文件说明) | 是 | +| -o 或 --output_path | 配置比对结果文件存盘目录，str 类型。文件名称基于时间戳自动生成，格式为：`compare_{timestamp}.vis或build_{timestamp}.vis`。 | 是 | +| -lm 或 --layer_mapping | 跨套件比对，例如同一个模型分别使用了DeepSpeed和Megatron套件的比对场景。配置该参数时表示开启跨套件Layer层的比对功能，指定模型代码中的Layer层后，可以识别对应dump数据中的模块或API。需要指定自定义映射文件*.yaml。自定义映射文件的格式请参见[自定义映射文件（Layer）](#71-自定义映射文件layer)，如何配置自定义映射文件请参考[模型分级可视化如何配置layer mapping映射文件](./visualization/layer_mapping_example.md)。 配置该参数后，将仅按节点名称进行比对，忽略节点的 type 和 shape。如果调试侧和标杆侧有名称不同的节点，则需要配置自定义映射文件，-lm参数传入自定义映射文件路径；如果调试侧和标杆侧节点名称相同，则仅指定-lm即可。 | 否 | +| -oc 或 --overflow_check | 是否开启溢出检测模式，开启后会在输出vis文件中（`compare_{timestamp}.vis或build_{timestamp}.vis`）对每个溢出节点进行标记溢出等级，溢出等级说明参考[溢出等级说明](#312-溢出等级说明) | 否 | +| -f 或 --fuzzy_match | 是否开启模糊匹配，bool类型。模糊匹配说明参考[匹配说明](#311-匹配说明) | 否 | +| -cs 或 --complete_stack | 是否使用完整的堆栈信息，bool类型。默认使用精简的堆栈信息，数据量小有助于增加流畅度。完整堆栈和精简堆栈信息参考[堆栈信息说明](#72-堆栈信息说明) | 否 | #### 3.1.1 匹配说明 diff --git a/debug/accuracy_tools/msprobe/docs/22.visualization_MindSpore.md b/debug/accuracy_tools/msprobe/docs/22.visualization_MindSpore.md index 57bf3e27e..2e5c17764 100644 --- a/debug/accuracy_tools/msprobe/docs/22.visualization_MindSpore.md +++ b/debug/accuracy_tools/msprobe/docs/22.visualization_MindSpore.md @@ -59,6 +59,7 @@ msprobe -f mindspore graph -i ./compare.json -o ./output | 参数名 | 说明 | 是否必选 | |-------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -------- | +| -f 或 --framework | 指定训练框架。mindspore。 | 是 | | -i 或 --input_path | 指定比对文件，参考[比对文件说明](#313-比对文件说明) | 是 | | -o 或 --output_path | 配置比对结果文件存盘目录，str 类型。文件名称基于时间戳自动生成，格式为：`compare_{timestamp}.vis或build_{timestamp}.vis`。 | 是 | | -lm 或 --layer_mapping| 跨框架比对，MindSpore和PyTorch的比对场景。配置该参数时表示开启跨框架Layer层的比对功能，指定模型代码中的Layer层后，可以识别对应dump数据中的模块或API。需要指定自定义映射文件*.yaml。自定义映射文件的格式请参见[自定义映射文件（Layer）](#71-自定义映射文件layer), 如何配置自定义映射文件请参考[模型分级可视化如何配置layer mapping映射文件](./visualization/layer_mapping_example.md)。配置该参数后，将仅按节点名称进行比对，忽略节点的 type 和 shape。如果调试侧和标杆侧有名称不同的节点，则需要配置自定义映射文件，-lm参数传入自定义映射文件路径；如果调试侧和标杆侧节点名称相同，则仅指定-lm即可。 | 否 | diff --git a/debug/accuracy_tools/msprobe/docs/24.code_mapping_Mindspore.md b/debug/accuracy_tools/msprobe/docs/24.code_mapping_Mindspore.md index 05e3900d2..9e7413053 100644 --- a/debug/accuracy_tools/msprobe/docs/24.code_mapping_Mindspore.md +++ b/debug/accuracy_tools/msprobe/docs/24.code_mapping_Mindspore.md @@ -20,9 +20,10 @@ msprobe -f mindspore code_mapping --ir --dump_data [--outp ``` -| 参数名称 | 说明 |参数类型 | 是否必选 | -| ---------------------------- |-------------------------------------------------------------------------------------------------------------------------------------------|---------------------- | ---------------------------------- | -| --ir | 指定 MindSpore 静态图运行时生成的IR图文件。 | str | 是 | -| --dump_data | 指定dump数据文件（支持tensor或statistic模式的dump数据）。可指定单个dump数据 文件或dump数据文件的父目录，指定父目录表示关联目录下的所有dump数据文件。 | str | 是 | -| --output | 关联结果输出目录，默认为"./"，只在tensor模式时生效，会把数据文件路径和代码调用栈的关联关系存到output路径下的code_mapping_{时间戳}.csv中。如果关联的是statistic模式，则会把statistic.csv中每个条目加上该条目对应的代码栈。 | str | 否 | +| 参数名称 | 说明 | 参数类型 | 是否必选 | +| ---------------------------- |-------------------------------------------------------------------------------------------------------------------------------------------|------| ---------------------------------- | +| -f 或 --framework | 指定训练框架。mindspore。 | str | 是 | +| --ir | 指定 MindSpore 静态图运行时生成的IR图文件。 | str | 是 | +| --dump_data | 指定dump数据文件（支持tensor或statistic模式的dump数据）。可指定单个dump数据 文件或dump数据文件的父目录，指定父目录表示关联目录下的所有dump数据文件。 | str | 是 | +| --output | 关联结果输出目录，默认为"./"，只在tensor模式时生效，会把数据文件路径和代码调用栈的关联关系存到output路径下的code_mapping_{时间戳}.csv中。如果关联的是statistic模式，则会把statistic.csv中每个条目加上该条目对应的代码栈。 | str | 否 | diff --git a/debug/accuracy_tools/msprobe/docs/31.config_check.md b/debug/accuracy_tools/msprobe/docs/31.config_check.md index 673ee81c7..2f531fc17 100644 --- a/debug/accuracy_tools/msprobe/docs/31.config_check.md +++ b/debug/accuracy_tools/msprobe/docs/31.config_check.md @@ -31,11 +31,11 @@ ```shell msprobe -f pytorch/mindspore config_check -d **.sh **.yaml -o output_path ``` --f 代表训练框架，传入pytorch或mindspore，必选。 +-f或--framework 代表训练框架，传入pytorch或mindspore，必选。 --d 代表数据采集模式，可传入启动训练的 shell 脚本路径或 yaml 配置文件路径，可选，不传入代表不采集。 +-d或--dump 代表数据采集模式，可传入启动训练的 shell 脚本路径或 yaml 配置文件路径，可选，不传入代表不采集。 --o 代表输出路径，可选，默认为 config_check_pack.zip。 +-o或--output 代表输出路径，可选，默认为 config_check_pack.zip。 #### 动态数据采集 @@ -79,7 +79,7 @@ ConfigChecker：对模型挂上数据采集所需的hook，会在每次模型前 msprobe -f pytorch config_check -c bench_zip_path cmp_zip_path -o output_path ``` --c 表示compare，数据对比，有两个参数。其中**bench_zip_path** 为标杆侧采集到的数据， **cmp_zip_path** 为待对比侧采集到的数据。 +-c或--compare 表示compare，数据对比，有两个参数。其中**bench_zip_path** 为标杆侧采集到的数据， **cmp_zip_path** 为待对比侧采集到的数据。 **output_path 会被删掉再新建**，不传默认为"./config_check_result"， 在 **output_path** 里会生成2个目录和1个文件： - bench：bench_zip_path里打包的数据。 diff --git a/debug/accuracy_tools/msprobe/docs/35.nan_analyze.md b/debug/accuracy_tools/msprobe/docs/35.nan_analyze.md index 8fed6cca1..37b0be8bb 100644 --- a/debug/accuracy_tools/msprobe/docs/35.nan_analyze.md +++ b/debug/accuracy_tools/msprobe/docs/35.nan_analyze.md @@ -23,10 +23,11 @@ msprobe -f pytorch nan_analyze -i dump_step_path -o output_dir_path ``` -| 参数 | 说明 | -|--------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| 参数 | 说明 | +|--------------------|---------------------------------------------| +| -f 或 --framework | 指定训练框架。pytorch。必选。 | | -i 或 --input_path | dump数据的目录。需指定到step层级，如`-i /xxx/dump/step0/` | -| -o 或 --output_path | 输出文件的目录，可选，不填时默认在当前目录下创建 \"./output/" 目录。 | +| -o 或 --output_path | 输出文件的目录，可选，不填时默认在当前目录下创建 \"./output/" 目录。 | ### 输出文件介绍 -- Gitee From 990c9952ff174731c9f791324f0290615b133411 Mon Sep 17 00:00:00 2001 From: yu-liang-bin Date: Tue, 24 Jun 2025 18:07:55 +0800 Subject: [PATCH 30/79] fix hccl bug --- msmonitor/README.md | 16 ++-- msmonitor/dynolog_npu/cli/src/main.rs | 2 +- .../dynolog/src/tracing/IPCMonitor.cpp | 2 - msmonitor/plugin/ipc_monitor/InputParser.cpp | 3 +- .../ipc_monitor/metric/MetricApiProcess.cpp | 2 +- .../metric/MetricCommunicationProcess.cpp | 95 +++++++++++++++++++ .../metric/MetricCommunicationProcess.h | 52 ++++++++++ .../ipc_monitor/metric/MetricHcclProcess.cpp | 2 +- .../metric/MetricKernelProcess.cpp | 2 +- .../ipc_monitor/metric/MetricManager.cpp | 2 + .../ipc_monitor/metric/MetricMarkProcess.cpp | 2 +- .../metric/MetricMemCpyProcess.cpp | 2 +- .../ipc_monitor/metric/MetricMemProcess.cpp | 2 +- .../metric/MetricMemSetProcess.cpp | 2 +- .../plugin/ipc_monitor/mspti_monitor/mspti.h | 34 +++++++ 15 files changed, 202 insertions(+), 18 deletions(-) create mode 100644 msmonitor/plugin/ipc_monitor/metric/MetricCommunicationProcess.cpp create mode 100644 msmonitor/plugin/ipc_monitor/metric/MetricCommunicationProcess.h diff --git a/msmonitor/README.md b/msmonitor/README.md index b4d6e1530..53ed7c33d 100644 --- a/msmonitor/README.md +++ b/msmonitor/README.md @@ -114,7 +114,9 @@ msmonitor-plugin wheel包提供IPCMonitor，MsptiMonitor等公共能力，使用 ## 使用方式 -- **说明**：**NPU Monitor**功能和**Profiler trace dump** 功能不能**同时开启。 +- **说明**： +- 1. **NPU Monitor**功能和**Profiler trace dump** 功能不能**同时开启。 +- 2. **NPU Monitor**功能暂不支持图模式场景 ### NPU Monitor功能 NPU Monitor基于MSPTI/MSTX能力开发，实现了轻量级在线监控能力，能够用于性能问题的初步定位。 @@ -133,12 +135,12 @@ dyno --certs-dir npu-monitor [SUBCOMMANDS] npu-monitor的SUBCOMMANDS（子命令）选项如下： -| 子命令 | 参数类型 | 说明 | PyTorch支持 | MindSpore支持 | 是否必选 | -|-----------------------|-------|----------------------------------------------------------------------------------------------------------------------------------|:---------:|:-----------:|:-----------:| -| --npu-monitor-start | action | 开启性能监控，设置参数后生效，默认不生效 | Y | Y | N | -| --npu-monitor-stop | action | 停止性能监控，设置参数后生效，默认不生效 | Y | Y | N | -| --report-interval-s | int | 性能监控数据上报周期，单位s，需要在启动时设置。默认值60 | Y | Y | N | -| --mspti-activity-kind | String | 性能监控数据上报数据类型，可以设置单个或多个，多个类型以逗号分隔，每次设置时刷新全局上报类型。可选值范围[`Marker`, `Kernel`, `API`, `Hccl`, `Memory`, `MemSet`, `MemCpy`] , 默认值`Marker` | Y | Y | N | +| 子命令 | 参数类型 | 说明 | PyTorch支持 | MindSpore支持 | 是否必选 | +|-----------------------|-------|------------------------------------------------------------------------------------------------------------------------------------------------------|:---------:|:-----------:|:-----------:| +| --npu-monitor-start | action | 开启性能监控，设置参数后生效，默认不生效 | Y | Y | N | +| --npu-monitor-stop | action | 停止性能监控，设置参数后生效，默认不生效 | Y | Y | N | +| --report-interval-s | int | 性能监控数据上报周期，单位s，需要在启动时设置。默认值60 | Y | Y | N | +| --mspti-activity-kind | String | 性能监控数据上报数据类型，可以设置单个或多个，多个类型以逗号分隔，每次设置时刷新全局上报类型。可选值范围[`Marker`, `Kernel`, `API`, `Hccl`, `Memory`, `MemSet`, `MemCpy`, 'Communication'] , 默认值`Marker` | Y | Y | N | - npu-monitor使用方法 diff --git a/msmonitor/dynolog_npu/cli/src/main.rs b/msmonitor/dynolog_npu/cli/src/main.rs index 6df6dfb19..d1dd1dd49 100644 --- a/msmonitor/dynolog_npu/cli/src/main.rs +++ b/msmonitor/dynolog_npu/cli/src/main.rs @@ -66,7 +66,7 @@ struct Opts { cmd: Command, } -const ALLOWED_VALUES: &[&str] = &["Marker", "Kernel", "API", "Hccl", "Memory", "MemSet", "MemCpy"]; +const ALLOWED_VALUES: &[&str] = &["Marker", "Kernel", "API", "Hccl", "Memory", "MemSet", "MemCpy", "Communication"]; fn parse_mspti_activity_kinds(src: &str) -> Result{ let allowed_values: HashSet<&str> = ALLOWED_VALUES.iter().cloned().collect(); diff --git a/msmonitor/dynolog_npu/dynolog/src/tracing/IPCMonitor.cpp b/msmonitor/dynolog_npu/dynolog/src/tracing/IPCMonitor.cpp index 2f419d379..85a71d957 100644 --- a/msmonitor/dynolog_npu/dynolog/src/tracing/IPCMonitor.cpp +++ b/msmonitor/dynolog_npu/dynolog/src/tracing/IPCMonitor.cpp @@ -147,8 +147,6 @@ void IPCMonitor::getLibkinetoOnDemandRequest( try { ret_config = LibkinetoConfigManager::getInstance()->obtainOnDemandConfig( std::to_string(req->jobid), pids, req->type); - VLOG(0) << "getLibkinetoOnDemandRequest() : job id " << req->jobid - << " pids = " << pids[0]; } catch (const std::runtime_error& ex) { LOG(ERROR) << "Kineto config manager exception : " << ex.what(); } diff --git a/msmonitor/plugin/ipc_monitor/InputParser.cpp b/msmonitor/plugin/ipc_monitor/InputParser.cpp index 9dd9f4bb4..c18ffa72e 100644 --- a/msmonitor/plugin/ipc_monitor/InputParser.cpp +++ b/msmonitor/plugin/ipc_monitor/InputParser.cpp @@ -41,7 +41,8 @@ const std::unordered_map kindStrMap { {"Hccl", MSPTI_ACTIVITY_KIND_HCCL}, {"Memory", MSPTI_ACTIVITY_KIND_MEMORY}, {"MemSet", MSPTI_ACTIVITY_KIND_MEMSET}, - {"MemCpy", MSPTI_ACTIVITY_KIND_MEMCPY} + {"MemCpy", MSPTI_ACTIVITY_KIND_MEMCPY}, + {"Communication", MSPTI_ACTIVITY_KIND_COMMUNICATION}, }; std::set str2Kinds(const std::string& kindStrs) diff --git a/msmonitor/plugin/ipc_monitor/metric/MetricApiProcess.cpp b/msmonitor/plugin/ipc_monitor/metric/MetricApiProcess.cpp index 41850426c..fea6c99e4 100644 --- a/msmonitor/plugin/ipc_monitor/metric/MetricApiProcess.cpp +++ b/msmonitor/plugin/ipc_monitor/metric/MetricApiProcess.cpp @@ -38,7 +38,7 @@ void MetricApiProcess::ConsumeMsptiData(msptiActivity *record) { msptiActivityApi* apiData = ReinterpretConvert(record); msptiActivityApi* tmp = ReinterpretConvert(MsptiMalloc(sizeof(msptiActivityApi), ALIGN_SIZE)); - if (memcpy_s(tmp, sizeof(msptiActivityApi), apiData, sizeof(msptiActivityApi)) != EOK) { + if (tmp == nullptr || memcpy_s(tmp, sizeof(msptiActivityApi), apiData, sizeof(msptiActivityApi)) != EOK) { MsptiFree(ReinterpretConvert(tmp)); LOG(ERROR) << "memcpy_s failed" << IPC_ERROR(ErrCode::MEMORY); return; diff --git a/msmonitor/plugin/ipc_monitor/metric/MetricCommunicationProcess.cpp b/msmonitor/plugin/ipc_monitor/metric/MetricCommunicationProcess.cpp new file mode 100644 index 000000000..f9f806809 --- /dev/null +++ b/msmonitor/plugin/ipc_monitor/metric/MetricCommunicationProcess.cpp @@ -0,0 +1,95 @@ +/* + * Copyright (C) 2025-2025. Huawei Technologies Co., Ltd. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "MetricCommunicationProcess.h" +#include +#include +#include "utils.h" + +namespace dynolog_npu { +namespace ipc_monitor { +namespace metric { + +std::string CommunicationMetric::seriesToJson() +{ + nlohmann::json jsonMsg; + jsonMsg["kind"] = "Communication"; + jsonMsg["deviceId"] = deviceId; + jsonMsg["duration"] = duration; + jsonMsg["timestamp"] = timestamp; + return jsonMsg.dump(); +} + +void MetricCommunicationProcess::ConsumeMsptiData(msptiActivity *record) +{ + msptiActivityCommunication* communicationData = ReinterpretConvert(record); + msptiActivityCommunication* tmp = ReinterpretConvert(MsptiMalloc(sizeof(msptiActivityCommunication), ALIGN_SIZE)); + if (tmp == nullptr || memcpy_s(tmp, sizeof(msptiActivityCommunication), communicationData, sizeof(msptiActivityCommunication)) != EOK) { + MsptiFree(ReinterpretConvert(tmp)); + LOG(ERROR) << "memcpy_s failed" << IPC_ERROR(ErrCode::MEMORY); + return; + } + { + std::unique_lock lock(dataMutex); + records.emplace_back(tmp); + } +} + +std::vector MetricCommunicationProcess::AggregatedData() +{ + std::vector> copyRecords; + { + std::unique_lock lock(dataMutex); + copyRecords = std::move(records); + records.clear(); + } + if (copyRecords.empty()) { + return {}; + } + std::unordered_map>> deviceId2CommunicationData = + groupby(copyRecords, [](const std::shared_ptr& data) -> std::uint32_t { + return data->ds.deviceId; + }); + std::vector ans; + auto curTimestamp = getCurrentTimestamp64(); + for (auto& pair: deviceId2CommunicationData) { + CommunicationMetric communicationMetric{}; + auto& communicationDatas = pair.second; + communicationMetric.duration = std::accumulate(communicationDatas.begin(), communicationDatas.end(), 0ULL, + [](uint64_t acc, std::shared_ptr communication) { + return acc + communication->end - communication->start; + }); + communicationMetric.deviceId = pair.first; + communicationMetric.timestamp = curTimestamp; + ans.emplace_back(communicationMetric); + } + return ans; +} + +void MetricCommunicationProcess::SendProcessMessage() +{ + auto afterAggregated = AggregatedData(); + for (auto& metric: afterAggregated) { + SendMessage(metric.seriesToJson()); + } +} + +void MetricCommunicationProcess::Clear() +{ + records.clear(); +} +} +} +} diff --git a/msmonitor/plugin/ipc_monitor/metric/MetricCommunicationProcess.h b/msmonitor/plugin/ipc_monitor/metric/MetricCommunicationProcess.h new file mode 100644 index 000000000..745652f8e --- /dev/null +++ b/msmonitor/plugin/ipc_monitor/metric/MetricCommunicationProcess.h @@ -0,0 +1,52 @@ +/* + * Copyright (C) 2025-2025. Huawei Technologies Co., Ltd. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef METRIC_COMMUNICATION_PROCESS_H +#define METRIC_COMMUNICATION_PROCESS_H + +#include +#include +#include "MetricProcessBase.h" + + +namespace dynolog_npu { +namespace ipc_monitor { +namespace metric { + +struct CommunicationMetric { + std::string kindName; + uint64_t duration; + uint64_t timestamp; + uint32_t deviceId; +public: + std::string seriesToJson(); +}; + +class MetricCommunicationProcess : public MetricProcessBase { +public: + MetricCommunicationProcess() = default; + void ConsumeMsptiData(msptiActivity *record) override; + std::vector AggregatedData(); + void SendProcessMessage() override; + void Clear() override; +private: + std::mutex dataMutex; + std::vector> records; +}; +} +} +} + +#endif \ No newline at end of file diff --git a/msmonitor/plugin/ipc_monitor/metric/MetricHcclProcess.cpp b/msmonitor/plugin/ipc_monitor/metric/MetricHcclProcess.cpp index b28e4e7d5..9f19c602c 100644 --- a/msmonitor/plugin/ipc_monitor/metric/MetricHcclProcess.cpp +++ b/msmonitor/plugin/ipc_monitor/metric/MetricHcclProcess.cpp @@ -36,7 +36,7 @@ void MetricHcclProcess::ConsumeMsptiData(msptiActivity *record) { msptiActivityHccl* hcclData = ReinterpretConvert(record); msptiActivityHccl* tmp = ReinterpretConvert(MsptiMalloc(sizeof(msptiActivityHccl), ALIGN_SIZE)); - if (memcpy_s(tmp, sizeof(msptiActivityHccl), hcclData, sizeof(msptiActivityHccl)) != EOK) { + if (tmp == nullptr || memcpy_s(tmp, sizeof(msptiActivityHccl), hcclData, sizeof(msptiActivityHccl)) != EOK) { MsptiFree(ReinterpretConvert(tmp)); LOG(ERROR) << "memcpy_s failed" << IPC_ERROR(ErrCode::MEMORY); return; diff --git a/msmonitor/plugin/ipc_monitor/metric/MetricKernelProcess.cpp b/msmonitor/plugin/ipc_monitor/metric/MetricKernelProcess.cpp index f0d31e99f..359931ca9 100644 --- a/msmonitor/plugin/ipc_monitor/metric/MetricKernelProcess.cpp +++ b/msmonitor/plugin/ipc_monitor/metric/MetricKernelProcess.cpp @@ -35,7 +35,7 @@ void MetricKernelProcess::ConsumeMsptiData(msptiActivity *record) { msptiActivityKernel* kernel = ReinterpretConvert(record); msptiActivityKernel* ptr = ReinterpretConvert(MsptiMalloc(sizeof(msptiActivityKernel), ALIGN_SIZE)); - if (memcpy_s(ptr, sizeof(msptiActivityKernel), kernel, sizeof(msptiActivityKernel)) != EOK) { + if (ptr == nullptr || memcpy_s(ptr, sizeof(msptiActivityKernel), kernel, sizeof(msptiActivityKernel)) != EOK) { MsptiFree(ReinterpretConvert(ptr)); LOG(ERROR) << "memcpy_s failed" << IPC_ERROR(ErrCode::MEMORY); return; diff --git a/msmonitor/plugin/ipc_monitor/metric/MetricManager.cpp b/msmonitor/plugin/ipc_monitor/metric/MetricManager.cpp index 36029313c..b5661ffa8 100644 --- a/msmonitor/plugin/ipc_monitor/metric/MetricManager.cpp +++ b/msmonitor/plugin/ipc_monitor/metric/MetricManager.cpp @@ -21,6 +21,7 @@ #include "MetricMarkProcess.h" #include "MetricMemSetProcess.h" #include "MetricMemProcess.h" +#include "MetricCommunicationProcess.h" #include "utils.h" namespace dynolog_npu { @@ -37,6 +38,7 @@ MetricManager::MetricManager(): TimerTask("MetricManager", DEFAULT_FLUSH_INTERVA metrics[MSPTI_ACTIVITY_KIND_MEMSET] = std::make_shared(); metrics[MSPTI_ACTIVITY_KIND_HCCL] = std::make_shared(); metrics[MSPTI_ACTIVITY_KIND_MEMORY] = std::make_shared(); + metrics[MSPTI_ACTIVITY_KIND_COMMUNICATION] = std::make_shared(); } void MetricManager::ReleaseResource() diff --git a/msmonitor/plugin/ipc_monitor/metric/MetricMarkProcess.cpp b/msmonitor/plugin/ipc_monitor/metric/MetricMarkProcess.cpp index 14a414519..ac83a3fd0 100644 --- a/msmonitor/plugin/ipc_monitor/metric/MetricMarkProcess.cpp +++ b/msmonitor/plugin/ipc_monitor/metric/MetricMarkProcess.cpp @@ -79,7 +79,7 @@ void MetricMarkProcess::ConsumeMsptiData(msptiActivity *record) { msptiActivityMarker* markerData = ReinterpretConvert(record); msptiActivityMarker* tmp = ReinterpretConvert(MsptiMalloc(sizeof(msptiActivityMarker), ALIGN_SIZE)); - if (memcpy_s(tmp, sizeof(msptiActivityMarker), markerData, sizeof(msptiActivityMarker)) != EOK) { + if (tmp == nullptr || memcpy_s(tmp, sizeof(msptiActivityMarker), markerData, sizeof(msptiActivityMarker)) != EOK) { MsptiFree(ReinterpretConvert(tmp)); LOG(ERROR) << "memcpy_s failed" << IPC_ERROR(ErrCode::MEMORY); return; diff --git a/msmonitor/plugin/ipc_monitor/metric/MetricMemCpyProcess.cpp b/msmonitor/plugin/ipc_monitor/metric/MetricMemCpyProcess.cpp index c53f17412..ea2e3cff2 100644 --- a/msmonitor/plugin/ipc_monitor/metric/MetricMemCpyProcess.cpp +++ b/msmonitor/plugin/ipc_monitor/metric/MetricMemCpyProcess.cpp @@ -35,7 +35,7 @@ void MetricMemCpyProcess::ConsumeMsptiData(msptiActivity *record) { msptiActivityMemcpy* kernel = ReinterpretConvert(record); msptiActivityMemcpy* ptr = ReinterpretConvert(MsptiMalloc(sizeof(msptiActivityMemcpy), ALIGN_SIZE)); - if (memcpy_s(ptr, sizeof(msptiActivityMemcpy), kernel, sizeof(msptiActivityMemcpy)) != EOK) { + if (ptr == nullptr || memcpy_s(ptr, sizeof(msptiActivityMemcpy), kernel, sizeof(msptiActivityMemcpy)) != EOK) { MsptiFree(ReinterpretConvert(ptr)); LOG(ERROR) << "memcpy_s failed" << IPC_ERROR(ErrCode::MEMORY); return; diff --git a/msmonitor/plugin/ipc_monitor/metric/MetricMemProcess.cpp b/msmonitor/plugin/ipc_monitor/metric/MetricMemProcess.cpp index e7422de52..71f81c451 100644 --- a/msmonitor/plugin/ipc_monitor/metric/MetricMemProcess.cpp +++ b/msmonitor/plugin/ipc_monitor/metric/MetricMemProcess.cpp @@ -35,7 +35,7 @@ void MetricMemProcess::ConsumeMsptiData(msptiActivity *record) { msptiActivityMemory* mem = ReinterpretConvert(record); msptiActivityMemory* ptr = ReinterpretConvert(MsptiMalloc(sizeof(msptiActivityMemory), ALIGN_SIZE)); - if (memcpy_s(ptr, sizeof(msptiActivityMemory), mem, sizeof(msptiActivityMemory)) != EOK) { + if (ptr == nullptr || memcpy_s(ptr, sizeof(msptiActivityMemory), mem, sizeof(msptiActivityMemory)) != EOK) { MsptiFree(ReinterpretConvert(ptr)); LOG(ERROR) << "memcpy_s failed" << IPC_ERROR(ErrCode::MEMORY); return; diff --git a/msmonitor/plugin/ipc_monitor/metric/MetricMemSetProcess.cpp b/msmonitor/plugin/ipc_monitor/metric/MetricMemSetProcess.cpp index 1b5870cd3..34a1e5289 100644 --- a/msmonitor/plugin/ipc_monitor/metric/MetricMemSetProcess.cpp +++ b/msmonitor/plugin/ipc_monitor/metric/MetricMemSetProcess.cpp @@ -35,7 +35,7 @@ void MetricMemSetProcess::ConsumeMsptiData(msptiActivity *record) { msptiActivityMemset* memSet = ReinterpretConvert(record); msptiActivityMemset* ptr = ReinterpretConvert(MsptiMalloc(sizeof(msptiActivityMemset), ALIGN_SIZE)); - if (memcpy_s(ptr, sizeof(msptiActivityMemset), memSet, sizeof(msptiActivityMemset)) != EOK) { + if (ptr == nullptr || memcpy_s(ptr, sizeof(msptiActivityMemset), memSet, sizeof(msptiActivityMemset)) != EOK) { MsptiFree(ReinterpretConvert(ptr)); LOG(ERROR) << "memcpy_s failed" << IPC_ERROR(ErrCode::MEMORY); return; diff --git a/msmonitor/plugin/ipc_monitor/mspti_monitor/mspti.h b/msmonitor/plugin/ipc_monitor/mspti_monitor/mspti.h index 3ff0d2b8d..17831575c 100644 --- a/msmonitor/plugin/ipc_monitor/mspti_monitor/mspti.h +++ b/msmonitor/plugin/ipc_monitor/mspti_monitor/mspti.h @@ -90,6 +90,7 @@ typedef enum { MSPTI_ACTIVITY_KIND_MEMSET = 6, MSPTI_ACTIVITY_KIND_MEMCPY = 7, MSPTI_ACTIVITY_KIND_EXTERNAL_CORRELATION = 8, + MSPTI_ACTIVITY_KIND_COMMUNICATION = 9, MSPTI_ACTIVITY_KIND_COUNT, MSPTI_ACTIVITY_KIND_FORCE_INT = 0x7fffffff } msptiActivityKind; @@ -128,6 +129,23 @@ typedef enum { MSPTI_ACTIVITY_MEMCPY_KIND_DEFAULT = 5 } msptiActivityMemcpyKind; +typedef enum { + MSPTI_ACTIVITY_COMMUNICATION_INT8 = 0, + MSPTI_ACTIVITY_COMMUNICATION_INT16 = 1, + MSPTI_ACTIVITY_COMMUNICATION_INT32 = 2, + MSPTI_ACTIVITY_COMMUNICATION_FP16 = 3, + MSPTI_ACTIVITY_COMMUNICATION_FP32 = 4, + MSPTI_ACTIVITY_COMMUNICATION_INT64 = 5, + MSPTI_ACTIVITY_COMMUNICATION_UINT64 = 6, + MSPTI_ACTIVITY_COMMUNICATION_UINT8 = 7, + MSPTI_ACTIVITY_COMMUNICATION_UINT16 = 8, + MSPTI_ACTIVITY_COMMUNICATION_UINT32 = 9, + MSPTI_ACTIVITY_COMMUNICATION_FP64 = 10, + MSPTI_ACTIVITY_COMMUNICATION_BFP16 = 11, + MSPTI_ACTIVITY_COMMUNICATION_INT128 = 12, + MSPTI_ACTIVITY_COMMUNICATION_INVALID_TYPE = 0x0000FFFF +} msptiCommunicationDataType; + START_PACKED_ALIGNMENT typedef union PACKED_ALIGNMENT { @@ -232,6 +250,22 @@ typedef struct PACKED_ALIGNMENT { uint8_t isAsync; } msptiActivityMemcpy; +typedef struct PACKED_ALIGNMENT { + msptiActivityKind kind; + msptiCommunicationDataType dataType; + uint64_t count; + struct { + uint32_t deviceId; + uint32_t streamId; + } ds; + uint64_t start; + uint64_t end; + const char* algType; + const char* name; + const char* commName; + uint64_t correlationId; +} msptiActivityCommunication; + END_PACKED_ALIGNMENT typedef void(*msptiCallbackFunc)(void* userdata, msptiCallbackDomain domain, msptiCallbackId cbid, const msptiCallbackData *cbdata); -- Gitee From c776cf3741ebae5c3f5e9669076e26213b36ed41 Mon Sep 17 00:00:00 2001 From: wangchao426 Date: Wed, 25 Jun 2025 14:08:49 +0800 Subject: [PATCH 31/79] =?UTF-8?q?=E5=BC=BA=E5=88=B6=E6=9B=B4=E6=96=B0?= =?UTF-8?q?=E5=B7=B2=E5=AF=BC=E5=85=A5deallocate=5Foutput=5Ftensor?= =?UTF-8?q?=E7=9A=84=E6=A8=A1=E5=9D=97=E4=B8=AD=E5=87=BD=E6=95=B0=E7=9A=84?= =?UTF-8?q?=E5=BC=95=E7=94=A8?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../pytorch/dump/module_dump/module_processer.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/debug/accuracy_tools/msprobe/pytorch/dump/module_dump/module_processer.py b/debug/accuracy_tools/msprobe/pytorch/dump/module_dump/module_processer.py index c0467e349..c65c91d63 100644 --- a/debug/accuracy_tools/msprobe/pytorch/dump/module_dump/module_processer.py +++ b/debug/accuracy_tools/msprobe/pytorch/dump/module_dump/module_processer.py @@ -69,12 +69,15 @@ class ModuleProcesser: replace_checkpoint() try: from megatron.core.pipeline_parallel import schedules + origin_func_id = id(schedules.deallocate_output_tensor) schedules.deallocate_output_tensor = wrap_megatron_deallocate(schedules.deallocate_output_tensor) for module in list(sys.modules.values()): - if hasattr(module, 'deallocate_output_tensor'): - if module.__name__ == 'schedules': - continue - module.deallocate_output_tensor = schedules.deallocate_output_tensor + if module.__name__ == 'schedules': + continue + for func in module.__dict__: + if id(module.__dict__[func]) == origin_func_id: + module.__setattr__(func, schedules.deallocate_output_tensor) + logger.debug(f'patch {module.__name__}.{func}.') logger.info_on_rank_0("Patch megatron method success.") except ImportError: logger.info_on_rank_0("No megatron find.") -- Gitee From 0e364d9d5b040087f5c64b2c9ce67b5b37af4b56 Mon Sep 17 00:00:00 2001 From: jiandaobao Date: Wed, 25 Jun 2025 14:16:18 +0800 Subject: [PATCH 32/79] threshold zero --- .../pytorch/free_benchmark/result_handlers/base_handler.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/debug/accuracy_tools/msprobe/pytorch/free_benchmark/result_handlers/base_handler.py b/debug/accuracy_tools/msprobe/pytorch/free_benchmark/result_handlers/base_handler.py index 47f93ab7b..e0d583dd0 100644 --- a/debug/accuracy_tools/msprobe/pytorch/free_benchmark/result_handlers/base_handler.py +++ b/debug/accuracy_tools/msprobe/pytorch/free_benchmark/result_handlers/base_handler.py @@ -186,6 +186,8 @@ class FuzzHandler(ABC): ratio = self.ratio_calculate( origin_output, perturbed_output, norm_type=NormType.ENDLESS_NORM ) + if threshold == 0: + raise ValueError("Threshold cannot be zero. Check `get_threshold` implementation.") if ratio == ThresholdConfig.SYMBOL_FLIPPING: is_consistent = False else: -- Gitee From fbc7f6a546e2b5e7e521098402dd67978239f4ef Mon Sep 17 00:00:00 2001 From: qiangge Date: Wed, 25 Jun 2025 14:19:04 +0800 Subject: [PATCH 33/79] avoid divide zero --- debug/accuracy_tools/msprobe/pytorch/monitor/features.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/debug/accuracy_tools/msprobe/pytorch/monitor/features.py b/debug/accuracy_tools/msprobe/pytorch/monitor/features.py index 81c029d40..cfd0c1615 100644 --- a/debug/accuracy_tools/msprobe/pytorch/monitor/features.py +++ b/debug/accuracy_tools/msprobe/pytorch/monitor/features.py @@ -45,13 +45,18 @@ def get_max(x: torch.tensor): @torch.no_grad() def get_zeros(x: torch.tensor, eps: float): + if x.numel() == 0: + return torch.tensor(float('nan')) return torch.sum(torch.abs(x) < eps) / x.numel() @torch.no_grad() def get_sign_matches(x: torch.tensor, y: torch.tensor): + if y.numel() == 0: + return torch.tensor(1.) xs = x.sign() ys = y.sign() + try: same_direction_ratio = ((xs * ys).sum() / ys.numel() + 1) / 2 except RuntimeError as e: -- Gitee From 692c5205404faaf06b6645e1e4158a699dd9a41c Mon Sep 17 00:00:00 2001 From: pxp1 <958876660@qq.com> Date: Wed, 25 Jun 2025 11:11:35 +0800 Subject: [PATCH 34/79] =?UTF-8?q?=E5=A2=9E=E5=8A=A0zip=E5=8C=85=E5=AE=89?= =?UTF-8?q?=E5=85=A8=E5=A4=84=E7=90=86?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../msprobe/core/common/file_utils.py | 22 +++++++++++++++---- 1 file changed, 18 insertions(+), 4 deletions(-) diff --git a/debug/accuracy_tools/msprobe/core/common/file_utils.py b/debug/accuracy_tools/msprobe/core/common/file_utils.py index e51438147..424e3d08c 100644 --- a/debug/accuracy_tools/msprobe/core/common/file_utils.py +++ b/debug/accuracy_tools/msprobe/core/common/file_utils.py @@ -771,6 +771,13 @@ def create_file_with_content(data, filepath): change_mode(filepath, FileCheckConst.DATA_FILE_AUTHORITY) +def check_file_whether_exist_or_not(filepath): + if os.path.exists(filepath): + check_file_or_directory_path(filepath) + else: + check_path_before_create(filepath) + + def add_file_to_zip(zip_file_path, file_path, arc_path=None): """ Add a file to a ZIP archive, if zip does not exist, create one. @@ -779,12 +786,13 @@ def add_file_to_zip(zip_file_path, file_path, arc_path=None): :param file_path: Path to the file to add :param arc_path: Optional path inside the ZIP archive where the file should be added """ + check_file_or_directory_path(file_path) check_file_suffix(zip_file_path, FileCheckConst.ZIP_SUFFIX) + check_file_whether_exist_or_not(zip_file_path) check_file_size(file_path, FileCheckConst.MAX_FILE_IN_ZIP_SIZE) zip_size = os.path.getsize(zip_file_path) if os.path.exists(zip_file_path) else 0 if zip_size + os.path.getsize(file_path) > FileCheckConst.MAX_ZIP_SIZE: raise RuntimeError(f"ZIP file size exceeds the limit of {FileCheckConst.MAX_ZIP_SIZE} bytes") - check_path_before_create(zip_file_path) try: proc_lock.acquire() with zipfile.ZipFile(zip_file_path, 'a') as zip_file: @@ -806,7 +814,7 @@ def create_file_in_zip(zip_file_path, file_name, content): :param content: Content to write to the file """ check_file_suffix(zip_file_path, FileCheckConst.ZIP_SUFFIX) - check_path_before_create(zip_file_path) + check_file_whether_exist_or_not(zip_file_path) zip_size = os.path.getsize(zip_file_path) if os.path.exists(zip_file_path) else 0 if zip_size + sys.getsizeof(content) > FileCheckConst.MAX_ZIP_SIZE: raise RuntimeError(f"ZIP file size exceeds the limit of {FileCheckConst.MAX_ZIP_SIZE} bytes") @@ -833,6 +841,8 @@ def extract_zip(zip_file_path, extract_dir): :param extract_dir: Directory to extract the contents to """ check_file_suffix(zip_file_path, FileCheckConst.ZIP_SUFFIX) + check_file_or_directory_path(zip_file_path) + create_directory(extract_dir) try: proc_lock.acquire() check_zip_file(zip_file_path) @@ -841,8 +851,12 @@ def extract_zip(zip_file_path, extract_dir): raise RuntimeError(f"Save content to file {os.path.basename(zip_file_path)} failed.") from e finally: proc_lock.release() - with zipfile.ZipFile(zip_file_path, 'r') as zip_file: - zip_file.extractall(extract_dir) + try: + with zipfile.ZipFile(zip_file_path, 'r') as zip_file: + zip_file.extractall(extract_dir) + except Exception as e: + raise RuntimeError(f"extract zip file {os.path.basename(zip_file_path)} failed") from e + recursive_chmod(extract_dir) def split_zip_file_path(zip_file_path): -- Gitee From d87ec038f68a13fa25852e5b36108cd1034a0679 Mon Sep 17 00:00:00 2001 From: lichangwei Date: Wed, 25 Jun 2025 15:45:41 +0800 Subject: [PATCH 35/79] =?UTF-8?q?=E3=80=90bugfix=E3=80=91pytorch=E6=95=B0?= =?UTF-8?q?=E6=8D=AE=E9=87=87=E9=9B=86=E5=9C=BA=E6=99=AF=E9=99=90=E5=88=B6?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- debug/accuracy_tools/msprobe/docs/FAQ.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/debug/accuracy_tools/msprobe/docs/FAQ.md b/debug/accuracy_tools/msprobe/docs/FAQ.md index 252fc94e9..bc34e9b36 100644 --- a/debug/accuracy_tools/msprobe/docs/FAQ.md +++ b/debug/accuracy_tools/msprobe/docs/FAQ.md @@ -36,6 +36,9 @@ 该信息说明 module 挂载了被 PyTorch 框架废弃的 register_backward_hook，这与工具使用的 register_full_backward_hook 接口会产生冲突，故工具会跳过该 module 的反向数据采集。 - 如果您希望所有 module 数据都能采集下来，可以将模型中使用的 register_backward_hook 接口改为 PyTorch 框架推荐的 register_full_backward_pre_hook 或 register_full_backward_hook 接口。 + +5. 在使用 msprobe 进行 Pytorch 框架的数据采集功能时，请注意确认环境变量 NPU_ASD_ENABLE=0 ，即关闭特征值检测功能。 由于工具冲突， 在该功能开启的情况下可能导致某些 api 数据采集的缺失。 + # 2 精度预检(PyTorch) 1. 预检工具在 dump 和 run_ut 的过程中，是否需要同时开启或关闭 jit 编译（jit_compile）？ -- Gitee From 3cfad3f3e1d1d75c4f4fb3a70835208fd0984d0d Mon Sep 17 00:00:00 2001 From: zhouxianqi <13165993773@163.com> Date: Wed, 25 Jun 2025 11:20:29 +0800 Subject: [PATCH 36/79] update_mstx2commop_readme --- .../msprof_analyze/cluster_analyse/README.md | 73 +++++++++++++++---- 1 file changed, 57 insertions(+), 16 deletions(-) diff --git a/profiler/msprof_analyze/cluster_analyse/README.md b/profiler/msprof_analyze/cluster_analyse/README.md index 7e881d94d..2ab5cf646 100644 --- a/profiler/msprof_analyze/cluster_analyse/README.md +++ b/profiler/msprof_analyze/cluster_analyse/README.md @@ -91,22 +91,23 @@ experimental_config = torch_npu.profiler._ExperimentalConfig( --mode参数设置不同的数据解析模式，可分析生成cluster_analysis.db交付件，交付件详细内容请参见[cluster_analysis.db交付件表结构说明](#cluster_analysisdb交付件表结构说明)。 - | 参数名 | 说明 | 是否必选 | - |--------------------------| ------------------------------------------------------------ | -------- | - | communication_matrix | 解析通信矩阵数据。 | 否 | - | communication_time | 解析通信耗时数据。 | 否 | - | all | 解析内容包括：
    通信矩阵communication_matrix
    通信耗时数据communication_time
    汇总集群内的节点信息（基于ascend_pytorch_profiler_{rank_id}.db生成）
    --mode参数默认值为all。 | 否 | - | cann_api_sum | 集群API性能数据汇总分析，输入性能数据需要基于ascend_pytorch_profiler_{rank_id}.db文件。--export_type为db时，输出交付件cluster_analysis.db；--export_type为notebook时，在cluster_analysis_output/CannApiSum目录下输出交付件stats.ipynb。 | 否 | - | compute_op_sum | 集群场景性能数据的device运行算子信息汇总分析，输入性能数据需要基于ascend_pytorch_profiler_{rank_id}.db文件。--export_type为db时，输出交付件cluster_analysis.db；--export_type为notebook时，在cluster_analysis_output/ComputeOpSum目录下输出交付件stats.ipynb；可根据实际情况决定是否打开--exclude_op_name。 | 否 | - | hccl_sum | 集合通信算子耗时分析，输入性能数据需要基于ascend_pytorch_profiler_{rank_id}.db文件。--export_type为db时，输出交付件cluster_analysis.db；--export_type为notebook时，在cluster_analysis_output/HcclSum目录下输出交付件stats.ipynb。 | 否 | - | mstx_sum | 集群场景mstx打点信息汇总分析，输入性能数据需要基于ascend_pytorch_profiler_{rank_id}.db文件。--export_type为db时，输出交付件cluster_analysis.db；--export_type为notebook时，在cluster_analysis_output/MstxSum目录下输出交付件stats.ipynb。 | 否 | - | communication_group_map | 集群场景通信域与并行策略呈现，输入性能数据需要基于ascend_pytorch_profiler_{rank_id}.db文件和analysis.db。--export_type为db时，输出交付件cluster_analysis.db。 | 否 | - | communication_time_sum | 集群场景通信时间和带宽汇总分析，输入性能数据需要基于analysis.db。--export_type为db时，输出交付件cluster_analysis.db。| 否 | - | communication_matrix_sum | 集群场景通信矩阵汇总分析，输入性能数据需要基于analysis.db。--export_type为db时，输出交付件cluster_analysis.db。| 否 | - | freq_analysis | 集群场景aicore frequency信息汇总分析，输入性能数据需要基于ascend_pytorch_profiler_{rank_id}.db文件。打印输出是否aicore存在空闲（频率为800MHz）、异常（频率不为1800MHz或800MHz）的现象。如果有，则在输出交付件cluster_analysis.db增加对应的卡和频率信息。 | 否 | - | ep_load_balance | 集群场景moe负载信息汇总分析，输入性能数据需要基于ascend_pytorch_profiler_{rank_id}.db文件。输出交付件cluster_analysis.db增加EPTokensSummary, TopEPTokensInfo分析表格。 | 否 | - | slow_rank | 集群场景通信算子快慢卡汇总分析，输入性能数据需要基于ascend_pytorch_profiler_{rank_id}.db文件。输出交付件cluster_analysis.db中展示各个rank按照当前的快慢卡统计算法得出的快慢卡影响次数。|| - | 自定义分析参数 | 与cann_api_sum、compute_op_sum、hccl_sum等参数功能类似，用户可自定义一套性能数据的分析规则，要求用户开发者详细了解性能分析规则，具体开发指导请参见“[自定义分析规则开发指导](#自定义分析规则开发指导)”。 | 否 | +| 参数名 | 说明 | 是否必选 | +|--------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------| +| communication_matrix | 解析通信矩阵数据。 | 否 | +| communication_time | 解析通信耗时数据。 | 否 | +| all | 解析内容包括：
    通信矩阵communication_matrix
    通信耗时数据communication_time
    汇总集群内的节点信息（基于ascend_pytorch_profiler_{rank_id}.db生成）
    --mode参数默认值为all。 | 否 | +| cann_api_sum | 集群API性能数据汇总分析，输入性能数据需要基于ascend_pytorch_profiler_{rank_id}.db文件。--export_type为db时，输出交付件cluster_analysis.db；--export_type为notebook时，在cluster_analysis_output/CannApiSum目录下输出交付件stats.ipynb。 | 否 | +| compute_op_sum | 集群场景性能数据的device运行算子信息汇总分析，输入性能数据需要基于ascend_pytorch_profiler_{rank_id}.db文件。--export_type为db时，输出交付件cluster_analysis.db；--export_type为notebook时，在cluster_analysis_output/ComputeOpSum目录下输出交付件stats.ipynb；可根据实际情况决定是否打开--exclude_op_name。 | 否 | +| hccl_sum | 集合通信算子耗时分析，输入性能数据需要基于ascend_pytorch_profiler_{rank_id}.db文件。--export_type为db时，输出交付件cluster_analysis.db；--export_type为notebook时，在cluster_analysis_output/HcclSum目录下输出交付件stats.ipynb。 | 否 | +| mstx_sum | 集群场景mstx打点信息汇总分析，输入性能数据需要基于ascend_pytorch_profiler_{rank_id}.db文件。--export_type为db时，输出交付件cluster_analysis.db；--export_type为notebook时，在cluster_analysis_output/MstxSum目录下输出交付件stats.ipynb。 | 否 | +| communication_group_map | 集群场景通信域与并行策略呈现，输入性能数据需要基于ascend_pytorch_profiler_{rank_id}.db文件和analysis.db。--export_type为db时，输出交付件cluster_analysis.db。 | 否 | +| communication_time_sum | 集群场景通信时间和带宽汇总分析，输入性能数据需要基于analysis.db。--export_type为db时，输出交付件cluster_analysis.db。 | 否 | +| communication_matrix_sum | 集群场景通信矩阵汇总分析，输入性能数据需要基于analysis.db。--export_type为db时，输出交付件cluster_analysis.db。 | 否 | +| freq_analysis | 集群场景aicore frequency信息汇总分析，输入性能数据需要基于ascend_pytorch_profiler_{rank_id}.db文件。打印输出是否aicore存在空闲（频率为800MHz）、异常（频率不为1800MHz或800MHz）的现象。如果有，则在输出交付件cluster_analysis.db增加对应的卡和频率信息。 | 否 | +| ep_load_balance | 集群场景moe负载信息汇总分析，输入性能数据需要基于ascend_pytorch_profiler_{rank_id}.db文件。输出交付件cluster_analysis.db增加EPTokensSummary, TopEPTokensInfo分析表格。 | 否 | +| slow_rank | 集群场景通信算子快慢卡汇总分析，输入性能数据需要基于ascend_pytorch_profiler_{rank_id}.db文件。输出交付件cluster_analysis.db中展示各个rank按照当前的快慢卡统计算法得出的快慢卡影响次数。 | 否 | +| mstx2commop | 集群场景基于mstx打点信息生成通信算子信息，输入性能数据需要基于ascend_pytorch_profiler_{rank_id}.db文件。输出交付件ascend_pytorch_profiler_{rank_id}.db增加COMMUNICATION_OP, STRING_IDS分析表格。 | 否 | +| 自定义分析参数 | 与cann_api_sum、compute_op_sum、hccl_sum等参数功能类似，用户可自定义一套性能数据的分析规则，要求用户开发者详细了解性能分析规则，具体开发指导请参见“[自定义分析规则开发指导](#自定义分析规则开发指导)”。 | 否 | ### 交付件 @@ -699,6 +700,46 @@ msprof-analyze配置--mode参数时可分析并输出cluster_analysis.db交付 | rankId | INTEGER | 慢卡 | | slowAffectCount | INTEGER | 该rank影响了多少次通信 | +### mstx2commop + +设置-m mstx2commop时，会生成以下表。 + +#### COMMUNICATION_OP + +说明： + +基于db格式的集群通信算子数据。 + +格式： + +| 字段名 | 类型 | 含义 | +|--------------|---------|----------------------------------------------------------------------------| +| opName | INTEGER | 算子名，STRING_IDS(opName)，例：hcom_allReduce__428_0_1 | +| startNs | INTEGER | 通信大算子的开始时间，单位ns | +| endNs | INTEGER | 通信大算子的结束时间，单位ns | +| connectionId | INTEGER | 生成host-device连线 | +| groupName | INTEGER | 通信域，STRING_IDS(groupName)，例：10.170.22.98%enp67s0f5_60000_0_1708156014257149 | +| opId | INTEGER | 索引，通信大算子Id，用于关联COMMUNICATION_TASK_INFO表 | +| relay | INTEGER | 借轨通信标识 | +| retry | INTEGER | 重传标识 | +| dataType | INTEGER | 大算子传输的数据类型，如（INT8，FP32），ENUM_HCCL_DATA_TYPE(dataType) | +| algType | INTEGER | 通信算子使用的算法，可分为多个阶段，STRING_IDS(algType)，如（HD-MESH） | +| count | NUMERIC | 算子传输的dataType类型的数据量 | +| opType | INTEGER | 算子类型，STRING_IDS(opType)，例：hcom_broadcast_ | + +#### STRING_IDS + +说明： + +字符串映射表，COMMUNICATION_OP表opName字段可通过关联STRING_IDS表得到value。 + +格式： + +| 字段名 | 类型 | 含义 | +|-------|---------|--------------| +| id | INTEGER | 索引，string id | +| value | TEXT | string value | + ## 附录 ### 自定义分析规则开发指导 -- Gitee From c1a1ef85c6cd42f1b8e6ea96a0fc094ba698271e Mon Sep 17 00:00:00 2001 From: sunchao <1299792067@qq.com> Date: Thu, 26 Jun 2025 09:29:10 +0800 Subject: [PATCH 37/79] =?UTF-8?q?=F0=9F=90=9E=20fix:=20=E5=8F=96=E6=B6=88?= =?UTF-8?q?=E5=8C=B9=E9=85=8D=E6=9D=A1=E4=BB=B6=E6=9B=B4=E6=94=B9?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../server/app/controllers/match_nodes_controller.py | 12 ++++-------- plugins/tensorboard-plugins/tb_graph_ascend/setup.py | 2 +- 2 files changed, 5 insertions(+), 9 deletions(-) diff --git a/plugins/tensorboard-plugins/tb_graph_ascend/server/app/controllers/match_nodes_controller.py b/plugins/tensorboard-plugins/tb_graph_ascend/server/app/controllers/match_nodes_controller.py index e2e500c13..8704a93e9 100644 --- a/plugins/tensorboard-plugins/tb_graph_ascend/server/app/controllers/match_nodes_controller.py +++ b/plugins/tensorboard-plugins/tb_graph_ascend/server/app/controllers/match_nodes_controller.py @@ -270,10 +270,8 @@ class MatchNodesController: @staticmethod def process_md5_task_delete(graph_data, npu_node_name, bench_node_name): config_data = GraphState.get_global_value("config_data") - npu_match_nodes_list = config_data.get('npuMatchNodes', {}) - bench_match_nodes_list = config_data.get('benchMatchNodes', {}) - if npu_match_nodes_list.get(npu_node_name) != bench_node_name or bench_match_nodes_list.get( - bench_node_name) != npu_node_name: + manual_match_nodes = config_data.get('manualMatchNodes', {}) + if manual_match_nodes.get(npu_node_name) != bench_node_name: return { 'success': False, 'error': "操作失败：节点未匹配，请先匹配节点", @@ -294,10 +292,8 @@ class MatchNodesController: @staticmethod def process_summary_task_delete(graph_data, npu_node_name, bench_node_name): config_data = GraphState.get_global_value("config_data") - npu_match_nodes_list = config_data.get('npuMatchNodes', {}) - bench_match_nodes_list = config_data.get('benchMatchNodes', {}) - if npu_match_nodes_list.get(npu_node_name) != bench_node_name or bench_match_nodes_list.get( - bench_node_name) != npu_node_name: + manual_match_nodes = config_data.get('manualMatchNodes', {}) + if manual_match_nodes.get(npu_node_name) != bench_node_name: return { 'success': False, 'error': "操作失败：节点未匹配，请先匹配节点", diff --git a/plugins/tensorboard-plugins/tb_graph_ascend/setup.py b/plugins/tensorboard-plugins/tb_graph_ascend/setup.py index dd1b98bef..5c5cf240f 100644 --- a/plugins/tensorboard-plugins/tb_graph_ascend/setup.py +++ b/plugins/tensorboard-plugins/tb_graph_ascend/setup.py @@ -16,7 +16,7 @@ # --------------------------------------------------------------------------------------------# import setuptools -VERSION = '2.0.0' +VERSION = '8.1.0' INSTALL_REQUIRED = ["tensorboard >= 2.11.2"] setuptools.setup( -- Gitee From 86ae66c567c90d5a5240179c7808c592256903ea Mon Sep 17 00:00:00 2001 From: zhouxianqi <13165993773@163.com> Date: Thu, 26 Jun 2025 09:48:35 +0800 Subject: [PATCH 38/79] fix_for_mstx2commop --- profiler/msprof_analyze/README.md | 42 +++++++++---------- .../recipes/mstx2commop/mstx2commop.py | 5 ++- .../prof_exports/mstx2commop_export.py | 8 ++-- profiler/msprof_analyze/version.txt | 2 +- 4 files changed, 30 insertions(+), 27 deletions(-) diff --git a/profiler/msprof_analyze/README.md b/profiler/msprof_analyze/README.md index 290118b83..208fe4e29 100644 --- a/profiler/msprof_analyze/README.md +++ b/profiler/msprof_analyze/README.md @@ -115,27 +115,27 @@ Successfully installed msprof-analyze-{version} 请通过下表链接下载profiler工具whl包。 -| profiler版本 | 发布日期 | 下载链接 | 校验码 | -|------------|------------|-------------------------------------------------------------------------------------------------------------------------------------------| ------------------------------------------------------------ | -| 2.1.0 | 2025-06-25 | [msprof_analyze-2.0.2-py3-none-any.whl](https://ptdbg.obs.myhuaweicloud.com/profiler/package/2.1.0/msprof_analyze-2.1.0-py3-none-any.whl) | 5959ba8ac24a36c9a349274c773440e99cd7e50963189587ecfa0a1ddb8c8d60 | -| 2.0.2 | 2025-03-31 | [msprof_analyze-2.0.2-py3-none-any.whl](https://ptdbg.obs.myhuaweicloud.com/profiler/package/2.0.2/msprof_analyze-2.0.2-py3-none-any.whl) | 4227ff628187297b2f3bc14b9dd3a8765833ed25d527f750bc266a8d29f86935 | -| 2.0.1 | 2025-02-28 | [msprof_analyze-2.0.1-py3-none-any.whl](https://ptdbg.obs.myhuaweicloud.com/profiler/package/2.0.1/msprof_analyze-2.0.1-py3-none-any.whl) | 82dfe2c779dbab9015f61d36ea0c32d832b6d182454b3f7db68e6c0ed49c0423 | -| 2.0.0 | 2025-02-08 | [msprof_analyze-2.0.0-py3-none-any.whl](https://ptdbg.obs.myhuaweicloud.com/profiler/package/2.0.0/msprof_analyze-2.0.0-py3-none-any.whl) | 8e44e5f3e7681c377bb2657a600ad9841d3bed11061ddd7844c30e8a97242101 | -| 1.3.4 | 2025-01-20 | [msprof_analyze-1.3.4-py3-none-any.whl](https://ptdbg.obs.myhuaweicloud.com/profiler/package/1.3.4/msprof_analyze-1.3.4-py3-none-any.whl) | 8de92188d1a97105fb14cadcb0875ccd5f66629ee3bb25f37178da1906f4cce2 | -| 1.3.3 | 2024-12-26 | [msprof_analyze-1.3.3-py3-none-any.whl](https://ptdbg.obs.myhuaweicloud.com/profiler/package/1.3.3/msprof_analyze-1.3.3-py3-none-any.whl) | 27676f2eee636bd0c65243f81e292c7f9d30d7f985c772ac9cbaf10b54d3584e | -| 1.3.2 | 2024-12-20 | [msprof_analyze-1.3.2-py3-none-any.whl](https://ptdbg.obs.myhuaweicloud.com/profiler/package/1.3.2/msprof_analyze-1.3.2-py3-none-any.whl) | ceb227e751ec3a204135be13801f1deee6a66c347f1bb3cdaef596872874df06 | -| 1.3.1 | 2024-12-04 | [msprof_analyze-1.3.1-py3-none-any.whl](https://ptdbg.obs.myhuaweicloud.com/profiler/package/1.3.1/msprof_analyze-1.3.1-py3-none-any.whl) | eae5548804314110a649caae537f2c63320fc70ec41ce1167f67c1d674d8798e | -| 1.3.0 | 2024-10-12 | [msprof_analyze-1.3.0-py3-none-any.whl](https://ptdbg.obs.myhuaweicloud.com/profiler/package/1.3.0/msprof_analyze-1.3.0-py3-none-any.whl) | 8b09758c6b5181bb656a95857c32852f898c370e7f1041e5a08e4f10d5004d48 | -| 1.2.5 | 2024-09-25 | [msprof_analyze-1.2.5-py3-none-any.whl](https://ptdbg.obs.myhuaweicloud.com/profiler/package/1.2.5/msprof_analyze-1.2.5-py3-none-any.whl) | aea8ae8deac07b5b4980bd2240da27d0eec93b9ace9ea9eb2e3a05ae9072018b | -| 1.2.4 | 2024-09-19 | [msprof_analyze-1.2.4-py3-none-any.whl](https://ptdbg.obs.myhuaweicloud.com/profiler/package/1.2.4/msprof_analyze-1.2.4-py3-none-any.whl) | 7c392e72c3347c4034fd3fdfcccb1f7936c24d9c3eb217e2cc05bae1347e5ab7 | -| 1.2.3 | 2024-08-29 | [msprof_analyze-1.2.3-py3-none-any.whl](https://ptdbg.obs.myhuaweicloud.com/profiler/package/1.2.3/msprof_analyze-1.2.3-py3-none-any.whl) | 354a55747f64ba1ec6ee6fe0f05a53e84e1b403ee0341ec40cc216dd25fda14c | -| 1.2.2 | 2024-08-23 | [msprof_analyze-1.2.2-py3-none-any.whl](https://ptdbg.obs.myhuaweicloud.com/profiler/package/1.2.2/msprof_analyze-1.2.2-py3-none-any.whl) | ed92a8e4eaf5ada8a2b4079072ec0cc42501b1b1f2eb00c8fdcb077fecb4ae02 | -| 1.2.1 | 2024-08-14 | [msprof_analyze-1.2.1-py3-none-any.whl](https://ptdbg.obs.myhuaweicloud.com/profiler/package/1.2.1/msprof_analyze-1.2.1-py3-none-any.whl) | 7acd477417bfb3ea29029dadf175d019ad3212403b7e11dc1f87e84c2412c078 | -| 1.2.0 | 2024-07-25 | [msprof_analyze-1.2.0-py3-none-any.whl](https://ptdbg.obs.myhuaweicloud.com/profiler/package/1.2.0/msprof_analyze-1.2.0-py3-none-any.whl) | 6a4366e3beca40b4a8305080e6e441d6ecafb5c05489e5905ac0265787555f37 | -| 1.1.2 | 2024-07-12 | [msprof_analyze-1.1.2-py3-none-any.whl](https://ptdbg.obs.myhuaweicloud.com/profiler/package/1.1.2/msprof_analyze-1.1.2-py3-none-any.whl) | af62125b1f9348bf491364e03af712fc6d0282ccee3fb07458bc9bbef82dacc6 | -| 1.1.1 | 2024-06-20 | [msprof_analyze-1.1.1-py3-none-any.whl](https://ptdbg.obs.myhuaweicloud.com/profiler/package/1.1.1/msprof_analyze-1.1.1-py3-none-any.whl) | 76aad967a3823151421153d368d4d2f8e5cfbcb356033575e0b8ec5acea8e5e4 | -| 1.1.0 | 2024-05-28 | [msprof_analyze-1.1.0-py3-none-any.whl](https://ptdbg.obs.myhuaweicloud.com/profiler/package/1.1.0/msprof_analyze-1.1.0-py3-none-any.whl) | b339f70e7d1e45e81f289332ca64990a744d0e7ce6fdd84a8d82e814fa400698 | -| 1.0 | 2024-05-10 | [msprof_analyze-1.0-py3-none-any.whl](https://ptdbg.obs.myhuaweicloud.com/profiler/package/1.0/msprof_analyze-1.0-py3-none-any.whl) | 95b2f41c8c8e8afe4887b738c8cababcb4f412e1874483b6adae4a025fcbb7d4 | +| profiler版本 | 发布日期 | 下载链接 | 校验码 | +|------------|------------|-------------------------------------------------------------------------------------------------------------------------------------------------| ------------------------------------------------------------ | +| 8.1.0a1 | 2025-06-26 | [msprof_analyze-8.1.0a1-py3-none-any.whl](https://ptdbg.obs.myhuaweicloud.com/profiler/package/8.1.0a1/msprof_analyze-8.1.0a1-py3-none-any.whl) | d694b8e1318f346b647f13e9185d3fdefb88a124f9b0e07b74b769a292001886 | +| 2.0.2 | 2025-03-31 | [msprof_analyze-2.0.2-py3-none-any.whl](https://ptdbg.obs.myhuaweicloud.com/profiler/package/2.0.2/msprof_analyze-2.0.2-py3-none-any.whl) | 4227ff628187297b2f3bc14b9dd3a8765833ed25d527f750bc266a8d29f86935 | +| 2.0.1 | 2025-02-28 | [msprof_analyze-2.0.1-py3-none-any.whl](https://ptdbg.obs.myhuaweicloud.com/profiler/package/2.0.1/msprof_analyze-2.0.1-py3-none-any.whl) | 82dfe2c779dbab9015f61d36ea0c32d832b6d182454b3f7db68e6c0ed49c0423 | +| 2.0.0 | 2025-02-08 | [msprof_analyze-2.0.0-py3-none-any.whl](https://ptdbg.obs.myhuaweicloud.com/profiler/package/2.0.0/msprof_analyze-2.0.0-py3-none-any.whl) | 8e44e5f3e7681c377bb2657a600ad9841d3bed11061ddd7844c30e8a97242101 | +| 1.3.4 | 2025-01-20 | [msprof_analyze-1.3.4-py3-none-any.whl](https://ptdbg.obs.myhuaweicloud.com/profiler/package/1.3.4/msprof_analyze-1.3.4-py3-none-any.whl) | 8de92188d1a97105fb14cadcb0875ccd5f66629ee3bb25f37178da1906f4cce2 | +| 1.3.3 | 2024-12-26 | [msprof_analyze-1.3.3-py3-none-any.whl](https://ptdbg.obs.myhuaweicloud.com/profiler/package/1.3.3/msprof_analyze-1.3.3-py3-none-any.whl) | 27676f2eee636bd0c65243f81e292c7f9d30d7f985c772ac9cbaf10b54d3584e | +| 1.3.2 | 2024-12-20 | [msprof_analyze-1.3.2-py3-none-any.whl](https://ptdbg.obs.myhuaweicloud.com/profiler/package/1.3.2/msprof_analyze-1.3.2-py3-none-any.whl) | ceb227e751ec3a204135be13801f1deee6a66c347f1bb3cdaef596872874df06 | +| 1.3.1 | 2024-12-04 | [msprof_analyze-1.3.1-py3-none-any.whl](https://ptdbg.obs.myhuaweicloud.com/profiler/package/1.3.1/msprof_analyze-1.3.1-py3-none-any.whl) | eae5548804314110a649caae537f2c63320fc70ec41ce1167f67c1d674d8798e | +| 1.3.0 | 2024-10-12 | [msprof_analyze-1.3.0-py3-none-any.whl](https://ptdbg.obs.myhuaweicloud.com/profiler/package/1.3.0/msprof_analyze-1.3.0-py3-none-any.whl) | 8b09758c6b5181bb656a95857c32852f898c370e7f1041e5a08e4f10d5004d48 | +| 1.2.5 | 2024-09-25 | [msprof_analyze-1.2.5-py3-none-any.whl](https://ptdbg.obs.myhuaweicloud.com/profiler/package/1.2.5/msprof_analyze-1.2.5-py3-none-any.whl) | aea8ae8deac07b5b4980bd2240da27d0eec93b9ace9ea9eb2e3a05ae9072018b | +| 1.2.4 | 2024-09-19 | [msprof_analyze-1.2.4-py3-none-any.whl](https://ptdbg.obs.myhuaweicloud.com/profiler/package/1.2.4/msprof_analyze-1.2.4-py3-none-any.whl) | 7c392e72c3347c4034fd3fdfcccb1f7936c24d9c3eb217e2cc05bae1347e5ab7 | +| 1.2.3 | 2024-08-29 | [msprof_analyze-1.2.3-py3-none-any.whl](https://ptdbg.obs.myhuaweicloud.com/profiler/package/1.2.3/msprof_analyze-1.2.3-py3-none-any.whl) | 354a55747f64ba1ec6ee6fe0f05a53e84e1b403ee0341ec40cc216dd25fda14c | +| 1.2.2 | 2024-08-23 | [msprof_analyze-1.2.2-py3-none-any.whl](https://ptdbg.obs.myhuaweicloud.com/profiler/package/1.2.2/msprof_analyze-1.2.2-py3-none-any.whl) | ed92a8e4eaf5ada8a2b4079072ec0cc42501b1b1f2eb00c8fdcb077fecb4ae02 | +| 1.2.1 | 2024-08-14 | [msprof_analyze-1.2.1-py3-none-any.whl](https://ptdbg.obs.myhuaweicloud.com/profiler/package/1.2.1/msprof_analyze-1.2.1-py3-none-any.whl) | 7acd477417bfb3ea29029dadf175d019ad3212403b7e11dc1f87e84c2412c078 | +| 1.2.0 | 2024-07-25 | [msprof_analyze-1.2.0-py3-none-any.whl](https://ptdbg.obs.myhuaweicloud.com/profiler/package/1.2.0/msprof_analyze-1.2.0-py3-none-any.whl) | 6a4366e3beca40b4a8305080e6e441d6ecafb5c05489e5905ac0265787555f37 | +| 1.1.2 | 2024-07-12 | [msprof_analyze-1.1.2-py3-none-any.whl](https://ptdbg.obs.myhuaweicloud.com/profiler/package/1.1.2/msprof_analyze-1.1.2-py3-none-any.whl) | af62125b1f9348bf491364e03af712fc6d0282ccee3fb07458bc9bbef82dacc6 | +| 1.1.1 | 2024-06-20 | [msprof_analyze-1.1.1-py3-none-any.whl](https://ptdbg.obs.myhuaweicloud.com/profiler/package/1.1.1/msprof_analyze-1.1.1-py3-none-any.whl) | 76aad967a3823151421153d368d4d2f8e5cfbcb356033575e0b8ec5acea8e5e4 | +| 1.1.0 | 2024-05-28 | [msprof_analyze-1.1.0-py3-none-any.whl](https://ptdbg.obs.myhuaweicloud.com/profiler/package/1.1.0/msprof_analyze-1.1.0-py3-none-any.whl) | b339f70e7d1e45e81f289332ca64990a744d0e7ce6fdd84a8d82e814fa400698 | +| 1.0 | 2024-05-10 | [msprof_analyze-1.0-py3-none-any.whl](https://ptdbg.obs.myhuaweicloud.com/profiler/package/1.0/msprof_analyze-1.0-py3-none-any.whl) | 95b2f41c8c8e8afe4887b738c8cababcb4f412e1874483b6adae4a025fcbb7d4 | 2. whl包校验。 diff --git a/profiler/msprof_analyze/cluster_analyse/recipes/mstx2commop/mstx2commop.py b/profiler/msprof_analyze/cluster_analyse/recipes/mstx2commop/mstx2commop.py index f2b9c01b1..0b53798f5 100644 --- a/profiler/msprof_analyze/cluster_analyse/recipes/mstx2commop/mstx2commop.py +++ b/profiler/msprof_analyze/cluster_analyse/recipes/mstx2commop/mstx2commop.py @@ -64,12 +64,13 @@ class Mstx2Commop(BaseRecipeAnalysis): def _mapper_func(self, data_map, analysis_class): profiler_db_path = data_map.get(Constant.PROFILER_DB_PATH) - data_service = DatabaseService(profiler_db_path) + step_range = data_map.get(Constant.STEP_RANGE) + data_service = DatabaseService(profiler_db_path, step_range) data_service.add_table_for_query("ENUM_HCCL_DATA_TYPE", ["id", "name"]) data_service.add_table_for_query("STRING_IDS", ["id", "value"]) df_dict = data_service.query_data() - df = Mstx2CommopExport(profiler_db_path, analysis_class).read_export_db() + df = Mstx2CommopExport(profiler_db_path, analysis_class, step_range).read_export_db() if df is None or df.empty: logger.warning(f"There is no stats data in {profiler_db_path}.") diff --git a/profiler/msprof_analyze/prof_exports/mstx2commop_export.py b/profiler/msprof_analyze/prof_exports/mstx2commop_export.py index 5ed239603..f84b5e714 100644 --- a/profiler/msprof_analyze/prof_exports/mstx2commop_export.py +++ b/profiler/msprof_analyze/prof_exports/mstx2commop_export.py @@ -35,11 +35,13 @@ WHERE AND si.value LIKE '%"dataType":%' AND si.value LIKE '%"groupName":%' AND si.value LIKE '%"opName":%' + {} """ class Mstx2CommopExport(BaseStatsExport): - def __init__(self, db_path, recipe_name): - super().__init__(db_path, recipe_name) - self._query = QUERY + def __init__(self, db_path, recipe_name, step_range): + super().__init__(db_path, recipe_name, step_range) + filter_stat = "AND ta.startNs >= ? and ta.startNs <= ?" if step_range else "" + self._query = QUERY.format(filter_stat) diff --git a/profiler/msprof_analyze/version.txt b/profiler/msprof_analyze/version.txt index 50aea0e7a..c069720a9 100644 --- a/profiler/msprof_analyze/version.txt +++ b/profiler/msprof_analyze/version.txt @@ -1 +1 @@ -2.1.0 \ No newline at end of file +8.1.0.alpha1 \ No newline at end of file -- Gitee From d009e3b39f0c047d174080f1dcb9d842fda69bcd Mon Sep 17 00:00:00 2001 From: sunchao <1299792067@qq.com> Date: Thu, 26 Jun 2025 11:15:32 +0800 Subject: [PATCH 39/79] =?UTF-8?q?=F0=9F=90=9E=20fix:=20stackInfo=E9=97=AE?= =?UTF-8?q?=E9=A2=98=E7=A7=80=E4=BF=AE=E5=A4=8D?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../tb_graph_ascend/fe/src/graph_info_board/useNodeInfo.ts | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/plugins/tensorboard-plugins/tb_graph_ascend/fe/src/graph_info_board/useNodeInfo.ts b/plugins/tensorboard-plugins/tb_graph_ascend/fe/src/graph_info_board/useNodeInfo.ts index 4d1b74901..9ea8877b2 100644 --- a/plugins/tensorboard-plugins/tb_graph_ascend/fe/src/graph_info_board/useNodeInfo.ts +++ b/plugins/tensorboard-plugins/tb_graph_ascend/fe/src/graph_info_board/useNodeInfo.ts @@ -158,10 +158,10 @@ const useNodeInfo = (): UseNodeInfoType => { const benchstackInfo = benchNode?.stackData; const title = 'title'; if (!isEmpty(npustackInfo)) { - stackInfo[nodeName] = safeJSONParse(npustackInfo.replace(/'/g, '"'))?.join('\n'); + stackInfo[nodeName] = safeJSONParse(npustackInfo)?.join('\n'); } if (!isEmpty(benchstackInfo)) { - stackInfo[benchNodeName] = safeJSONParse(benchstackInfo.replace(/'/g, '"')).join('\n'); + stackInfo[benchNodeName] = safeJSONParse(benchstackInfo).join('\n'); } if (!isEmpty(stackInfo)) { stackInfo[title] = 'stackInfo'; -- Gitee From 666eb89fb62c429776039456e3567f9d0d62401c Mon Sep 17 00:00:00 2001 From: gitee Date: Thu, 26 Jun 2025 19:42:35 +0800 Subject: [PATCH 40/79] remove temp file when save json failed --- .../api_accuracy_checker/run_ut/multi_run_ut.py | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/debug/accuracy_tools/msprobe/pytorch/api_accuracy_checker/run_ut/multi_run_ut.py b/debug/accuracy_tools/msprobe/pytorch/api_accuracy_checker/run_ut/multi_run_ut.py index 56f5dd7ac..6fb0b00aa 100644 --- a/debug/accuracy_tools/msprobe/pytorch/api_accuracy_checker/run_ut/multi_run_ut.py +++ b/debug/accuracy_tools/msprobe/pytorch/api_accuracy_checker/run_ut/multi_run_ut.py @@ -76,8 +76,17 @@ def split_json_file(input_file, num_splits, filter_api): } } split_filename = os.path.join(input_dir, f"temp_part{i}.json") - save_json(split_filename, temp_data) split_files.append(split_filename) + try: + save_json(split_filename, temp_data) + except Exception as e: + logger.error(f"An error occurred while saving split file: {e}") + for file in split_files: + check_link(file) + try: + remove_path(file) + except FileNotFoundError: + logger.error(f"File not found and could not be deleted: {file}") return split_files, total_items -- Gitee From 68dc8974666715a965e048352edd4446c03a87c4 Mon Sep 17 00:00:00 2001 From: gitee Date: Thu, 26 Jun 2025 20:10:39 +0800 Subject: [PATCH 41/79] add ] --- .../accuracy_tools/msprobe/pytorch/online_dispatch/dispatch.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/debug/accuracy_tools/msprobe/pytorch/online_dispatch/dispatch.py b/debug/accuracy_tools/msprobe/pytorch/online_dispatch/dispatch.py index 916a68aec..4615f4554 100644 --- a/debug/accuracy_tools/msprobe/pytorch/online_dispatch/dispatch.py +++ b/debug/accuracy_tools/msprobe/pytorch/online_dispatch/dispatch.py @@ -104,7 +104,7 @@ class PtdbgDispatch(TorchDispatchMode): if not is_npu: return - logger.info(f'start write compare csv: Rank[{self.device_id}], Pid[{os.getpid()}') + logger.info(f'start write compare csv: Rank[{self.device_id}], Pid[{os.getpid()}]') if self.process_num > 0: self.pool.close() -- Gitee From ba121af733040b48381b7a7f13a211dfd8fcc78d Mon Sep 17 00:00:00 2001 From: pxp1 <958876660@qq.com> Date: Thu, 26 Jun 2025 20:17:53 +0800 Subject: [PATCH 42/79] =?UTF-8?q?=E5=A4=96=E9=83=A8=E8=BE=93=E5=85=A5?= =?UTF-8?q?=E5=AE=89=E5=85=A8=E5=8A=A0=E5=9B=BA?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- debug/accuracy_tools/msprobe/core/common/const.py | 3 +++ debug/accuracy_tools/msprobe/core/common/utils.py | 15 +++++++++++++++ .../checkers/hyperparameter_checker.py | 9 +++++---- .../config_check/ckpt_compare/ckpt_comparator.py | 5 ++++- .../msprobe/core/single_save/single_comparator.py | 2 ++ .../msprobe/pytorch/monitor/csv2tb.py | 10 +--------- 6 files changed, 30 insertions(+), 14 deletions(-) diff --git a/debug/accuracy_tools/msprobe/core/common/const.py b/debug/accuracy_tools/msprobe/core/common/const.py index 4af7b5a85..59c3a354f 100644 --- a/debug/accuracy_tools/msprobe/core/common/const.py +++ b/debug/accuracy_tools/msprobe/core/common/const.py @@ -56,6 +56,9 @@ class Const: CPU_QUARTER = 4 DUMP_MAX_DEPTH = 50 + EXTERN_INPUT_LIST_MAX_LEN = 100 + MAX_PROCESS_NUM = 128 + # dump mode ALL = "all" LIST = "list" diff --git a/debug/accuracy_tools/msprobe/core/common/utils.py b/debug/accuracy_tools/msprobe/core/common/utils.py index 4635679c4..e65a707d4 100644 --- a/debug/accuracy_tools/msprobe/core/common/utils.py +++ b/debug/accuracy_tools/msprobe/core/common/utils.py @@ -685,3 +685,18 @@ def analyze_api_call_stack(name): else: stack_str.append(Const.WITHOUT_CALL_STACK) return "".join(stack_str) + + +def check_extern_input_list(input_list): + if not isinstance(input_list, list): + raise Exception("input is not a list") + if len(input_list) > Const.EXTERN_INPUT_LIST_MAX_LEN: + raise Exception(f"input list exceed max length {Const.EXTERN_INPUT_LIST_MAX_LEN}") + + +def check_process_num(process_num): + if not is_int(process_num) or process_num <= 0: + raise ValueError(f"process_num({process_num}) is not a positive integer") + if process_num > Const.MAX_PROCESS_NUM: + raise ValueError(f"The maximum supported process_num is {Const.MAX_PROCESS_NUM}, current value: {process_num}.") + diff --git a/debug/accuracy_tools/msprobe/core/config_check/checkers/hyperparameter_checker.py b/debug/accuracy_tools/msprobe/core/config_check/checkers/hyperparameter_checker.py index 774abef48..bd23f326b 100644 --- a/debug/accuracy_tools/msprobe/core/config_check/checkers/hyperparameter_checker.py +++ b/debug/accuracy_tools/msprobe/core/config_check/checkers/hyperparameter_checker.py @@ -20,12 +20,13 @@ from difflib import SequenceMatcher from typing import Union, List, Dict, Any import pandas as pd +from msprobe.core.common.utils import check_extern_input_list from msprobe.core.config_check.checkers.base_checker import BaseChecker from msprobe.core.config_check.config_checker import register_checker_item from msprobe.core.config_check.utils.utils import compare_dict, config_checking_print, update_dict from msprobe.core.config_check.utils.hyperparameter_parser import ParserFactory -from msprobe.core.common.file_utils import (os_walk_for_files, create_file_in_zip, load_json, create_file_with_list, - FileOpen, load_yaml) +from msprobe.core.common.file_utils import (check_file_or_directory_path, create_file_in_zip, load_json, + load_yaml) from msprobe.core.common.const import Const @@ -47,13 +48,13 @@ class HyperparameterChecker(BaseChecker): output_zip_path = pack_input.output_zip_path if shell_path: - if not isinstance(shell_path, list): - raise TypeError("shell_path should be a list of file paths.") + check_extern_input_list(shell_path) hyperparameters = {} parser_factory = ParserFactory() for script_path in shell_path: if os.path.isfile(script_path): + check_file_or_directory_path(script_path) parser = parser_factory.get_parser(os.path.splitext(script_path)[1]) update_dict(hyperparameters, parser.run(os.path.realpath(script_path))) else: diff --git a/debug/accuracy_tools/msprobe/core/config_check/ckpt_compare/ckpt_comparator.py b/debug/accuracy_tools/msprobe/core/config_check/ckpt_compare/ckpt_comparator.py index 3c088c249..42e2dcd53 100644 --- a/debug/accuracy_tools/msprobe/core/config_check/ckpt_compare/ckpt_comparator.py +++ b/debug/accuracy_tools/msprobe/core/config_check/ckpt_compare/ckpt_comparator.py @@ -16,7 +16,8 @@ from typing import Dict from tqdm import tqdm -from msprobe.core.common.file_utils import save_json, check_path_before_create, check_path_not_exists +from msprobe.core.common.file_utils import save_json, check_path_before_create, check_path_not_exists, \ + check_file_or_directory_path from msprobe.core.common.log import logger from msprobe.core.config_check.ckpt_compare.megatron_loader import load_megatron_weights from msprobe.core.config_check.ckpt_compare.metrics import METRIC_FUNC @@ -44,6 +45,8 @@ def compare_checkpoints(ckpt_path1, ckpt_path2, output_path) -> Dict: """ # Load both checkpoints + check_file_or_directory_path(ckpt_path1, isdir=True) + check_file_or_directory_path(ckpt_path2, isdir=True) check_path_before_create(output_path) check_path_not_exists(output_path) weights1 = load_megatron_weights(ckpt_path1) diff --git a/debug/accuracy_tools/msprobe/core/single_save/single_comparator.py b/debug/accuracy_tools/msprobe/core/single_save/single_comparator.py index 5aae155bf..e1cf68dfe 100644 --- a/debug/accuracy_tools/msprobe/core/single_save/single_comparator.py +++ b/debug/accuracy_tools/msprobe/core/single_save/single_comparator.py @@ -23,6 +23,7 @@ from tqdm import tqdm from msprobe.core.common.file_utils import check_file_or_directory_path, create_directory, save_excel from msprobe.core.common.log import logger +from msprobe.core.common.utils import check_process_num @dataclass @@ -57,6 +58,7 @@ class SingleComparator: data_dir2 = os.path.join(dir2, "data") check_file_or_directory_path(data_dir1, isdir=True) check_file_or_directory_path(data_dir2, isdir=True) + check_process_num(num_processes) # 确保输出目录存在，如果不存在则创建 if not os.path.exists(output_path): create_directory(output_path) diff --git a/debug/accuracy_tools/msprobe/pytorch/monitor/csv2tb.py b/debug/accuracy_tools/msprobe/pytorch/monitor/csv2tb.py index ebedae69d..d2a6d95cb 100644 --- a/debug/accuracy_tools/msprobe/pytorch/monitor/csv2tb.py +++ b/debug/accuracy_tools/msprobe/pytorch/monitor/csv2tb.py @@ -23,7 +23,7 @@ from tqdm import tqdm from msprobe.core.common.const import MonitorConst from msprobe.core.common.file_utils import read_csv, create_directory, remove_path, recursive_chmod -from msprobe.core.common.utils import is_int +from msprobe.core.common.utils import check_process_num from msprobe.core.common.decorator import recursion_depth_decorator from msprobe.pytorch.common.log import logger from msprobe.pytorch.monitor.utils import get_target_output_dir @@ -33,7 +33,6 @@ all_data_type_list = [ "grad_unreduced", "grad_reduced", "param_origin", "param_updated" ] CSV_FILE_SUFFIX = r"_\d+-\d+\.csv" -MAX_PROCESS_NUM = 128 def parse_step_line(line, ops): @@ -119,13 +118,6 @@ def csv2tb_by_step_work(target_output_dirs, output_dirpath, data_type_list): write_step(output_dirpath, all_step_result, rank, data_type) -def check_process_num(process_num): - if not is_int(process_num) or process_num <= 0: - raise ValueError(f"process_num({process_num}) is not a positive integer") - if process_num > MAX_PROCESS_NUM: - raise ValueError(f"The maximum supported process_num is {MAX_PROCESS_NUM}, current value: {process_num}.") - - def check_data_type_list(data_type_list): if data_type_list is None: logger.info(f"data_type_list is None, use default all_data_type_list: {all_data_type_list}") -- Gitee From ff8a65908baeb2dcd332063cfbcb769b6c43c443 Mon Sep 17 00:00:00 2001 From: pxp1 <958876660@qq.com> Date: Thu, 26 Jun 2025 20:41:38 +0800 Subject: [PATCH 43/79] =?UTF-8?q?=E4=BF=AE=E5=A4=8D=E6=95=B0=E6=8D=AE?= =?UTF-8?q?=E4=B8=AD=E5=AD=98=E5=9C=A8int64=E5=AF=BC=E8=87=B4json=E5=BA=8F?= =?UTF-8?q?=E5=88=97=E5=8C=96=E5=A4=B1=E8=B4=A5=E7=9A=84=E9=97=AE=E9=A2=98?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../msprobe/core/data_dump/data_processor/pytorch_processor.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/debug/accuracy_tools/msprobe/core/data_dump/data_processor/pytorch_processor.py b/debug/accuracy_tools/msprobe/core/data_dump/data_processor/pytorch_processor.py index 2cd93b3ca..c06aabc85 100644 --- a/debug/accuracy_tools/msprobe/core/data_dump/data_processor/pytorch_processor.py +++ b/debug/accuracy_tools/msprobe/core/data_dump/data_processor/pytorch_processor.py @@ -181,7 +181,7 @@ class PytorchDataProcessor(BaseDataProcessor): @staticmethod def _analyze_torch_size(arg): - return {"type": "torch.Size", "value": list(arg)} + return {"type": "torch.Size", "value": [int(x) for x in list(arg)]} @staticmethod def _analyze_memory_format(arg): -- Gitee From a1ceef7cd10a83c93bdf6ea6a1c38e8da1a44ae1 Mon Sep 17 00:00:00 2001 From: yangchen <947098055@qq.com> Date: Thu, 26 Jun 2025 12:49:43 +0000 Subject: [PATCH 44/79] =?UTF-8?q?cleancode=E9=97=AE=E9=A2=98?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: yangchen <947098055@qq.com> --- debug/accuracy_tools/msprobe/ccsrc/utils/FileUtils.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/debug/accuracy_tools/msprobe/ccsrc/utils/FileUtils.cpp b/debug/accuracy_tools/msprobe/ccsrc/utils/FileUtils.cpp index 112462e86..9b1cdb75d 100644 --- a/debug/accuracy_tools/msprobe/ccsrc/utils/FileUtils.cpp +++ b/debug/accuracy_tools/msprobe/ccsrc/utils/FileUtils.cpp @@ -465,8 +465,8 @@ DebuggerErrno OpenFile(const std::string& path, std::ofstream& ofs, std::ios::op } } - if (!IsPathExist(path)) { - int fd = open(path.c_str(), O_CREAT | O_WRONLY, permission); + if (!IsPathExist(realPath)) { + int fd = open(realPath.c_str(), O_CREAT | O_WRONLY, permission); if (fd < 0) { return DebuggerErrno::ERROR_FAILED_TO_OPEN_FILE; } -- Gitee From 4e199c899971843b7238e00353cc5cbb6a4d499c Mon Sep 17 00:00:00 2001 From: Linwei-Ying Date: Fri, 27 Jun 2025 09:04:04 +0800 Subject: [PATCH 45/79] compare log bugfix --- debug/accuracy_tools/msprobe/core/common/file_utils.py | 2 +- debug/accuracy_tools/msprobe/core/common/utils.py | 5 +++-- .../accuracy_tools/msprobe/test/core_ut/common/test_utils.py | 2 +- 3 files changed, 5 insertions(+), 4 deletions(-) diff --git a/debug/accuracy_tools/msprobe/core/common/file_utils.py b/debug/accuracy_tools/msprobe/core/common/file_utils.py index 424e3d08c..1b848791f 100644 --- a/debug/accuracy_tools/msprobe/core/common/file_utils.py +++ b/debug/accuracy_tools/msprobe/core/common/file_utils.py @@ -384,7 +384,7 @@ def check_file_type(path): elif os.path.isfile(path): return FileCheckConst.FILE else: - logger.error(f'{path} does not exist, please check!') + logger.error(f'path does not exist, please check!') raise FileCheckException(FileCheckException.INVALID_FILE_ERROR) diff --git a/debug/accuracy_tools/msprobe/core/common/utils.py b/debug/accuracy_tools/msprobe/core/common/utils.py index 4635679c4..82311b2cb 100644 --- a/debug/accuracy_tools/msprobe/core/common/utils.py +++ b/debug/accuracy_tools/msprobe/core/common/utils.py @@ -233,9 +233,10 @@ def check_compare_param(input_param, output_path, dump_mode, stack_mode): def check_configuration_param(stack_mode=False, auto_analyze=True, fuzzy_match=False, is_print_compare_log=True): arg_list = [stack_mode, auto_analyze, fuzzy_match, is_print_compare_log] - for arg in arg_list: + arg_names = ['stack_mode', 'auto_analyze', 'fuzzy_match', 'is_print_compare_log'] + for arg, name in zip(arg_list, arg_names): if not isinstance(arg, bool): - logger.error(f"Invalid input parameter, {arg} which should be only bool type.") + logger.error(f"Invalid input parameter, {name} which should be only bool type.") raise CompareException(CompareException.INVALID_PARAM_ERROR) diff --git a/debug/accuracy_tools/msprobe/test/core_ut/common/test_utils.py b/debug/accuracy_tools/msprobe/test/core_ut/common/test_utils.py index 2c9d1dd86..7a62d9f02 100644 --- a/debug/accuracy_tools/msprobe/test/core_ut/common/test_utils.py +++ b/debug/accuracy_tools/msprobe/test/core_ut/common/test_utils.py @@ -140,7 +140,7 @@ class TestUtils(TestCase): check_configuration_param(stack_mode="False", auto_analyze=True, fuzzy_match=False, is_print_compare_log=True) self.assertEqual(context.exception.code, CompareException.INVALID_PARAM_ERROR) - mock_error.assert_called_with("Invalid input parameter, False which should be only bool type.") + mock_error.assert_called_with("Invalid input parameter, stack_mode which should be only bool type.") @patch.object(logger, "error") def test__check_json(self, mock_error): -- Gitee From e792135c5c96e93e49f53762084f674d89542201 Mon Sep 17 00:00:00 2001 From: RanZheng <364167184@qq.com> Date: Fri, 27 Jun 2025 10:27:22 +0800 Subject: [PATCH 46/79] =?UTF-8?q?=E3=80=90=E5=AE=89=E5=85=A8=E3=80=91parse?= =?UTF-8?q?=5Ftool=E5=8E=BB=E9=99=A4=E5=A4=96=E9=83=A8=E4=BC=A0=E5=85=A5?= =?UTF-8?q?=E8=B7=AF=E5=BE=84=E6=89=93=E5=8D=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- debug/accuracy_tools/msprobe/pytorch/parse_tool/lib/utils.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/debug/accuracy_tools/msprobe/pytorch/parse_tool/lib/utils.py b/debug/accuracy_tools/msprobe/pytorch/parse_tool/lib/utils.py index 2cdfe6f51..ff294ad67 100644 --- a/debug/accuracy_tools/msprobe/pytorch/parse_tool/lib/utils.py +++ b/debug/accuracy_tools/msprobe/pytorch/parse_tool/lib/utils.py @@ -226,7 +226,7 @@ class Util: def check_path_valid(self, path): path = self.path_strip(path) if not path or not os.path.exists(path): - self.log.error("The path %s does not exist." % path) + self.log.error("The path does not exist.") raise ParseException(ParseException.PARSE_INVALID_PATH_ERROR) isdir = check_file_type(path) == FileCheckConst.DIR check_file_or_directory_path(path, isdir=isdir) @@ -234,7 +234,7 @@ class Util: def check_files_in_path(self, path): if os.path.isdir(path) and len(os.listdir(path)) == 0: - self.log.error("No files in %s." % path) + self.log.error("No files found in path dir.") raise ParseException(ParseException.PARSE_INVALID_PATH_ERROR) def npy_info(self, source_data): -- Gitee From 276465470e2f12cbea5c3dc03bad9caa119b89e7 Mon Sep 17 00:00:00 2001 From: RanZheng <364167184@qq.com> Date: Fri, 27 Jun 2025 10:39:05 +0800 Subject: [PATCH 47/79] =?UTF-8?q?=E6=9B=B4=E6=96=B0=E6=8F=90=E7=A4=BA?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- debug/accuracy_tools/msprobe/pytorch/parse_tool/lib/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/debug/accuracy_tools/msprobe/pytorch/parse_tool/lib/utils.py b/debug/accuracy_tools/msprobe/pytorch/parse_tool/lib/utils.py index ff294ad67..a3886641e 100644 --- a/debug/accuracy_tools/msprobe/pytorch/parse_tool/lib/utils.py +++ b/debug/accuracy_tools/msprobe/pytorch/parse_tool/lib/utils.py @@ -234,7 +234,7 @@ class Util: def check_files_in_path(self, path): if os.path.isdir(path) and len(os.listdir(path)) == 0: - self.log.error("No files found in path dir.") + self.log.error("No files found in path.") raise ParseException(ParseException.PARSE_INVALID_PATH_ERROR) def npy_info(self, source_data): -- Gitee From 93667491d07358ea5bbb631d8a37a44e41a79610 Mon Sep 17 00:00:00 2001 From: gitee Date: Fri, 27 Jun 2025 11:11:55 +0800 Subject: [PATCH 48/79] =?UTF-8?q?=E5=A2=9E=E5=8A=A0=E5=88=A0=E9=99=A4?= =?UTF-8?q?=E4=B8=B4=E6=97=B6=E6=96=87=E4=BB=B6?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../msprobe/pytorch/online_dispatch/dump_compare.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/debug/accuracy_tools/msprobe/pytorch/online_dispatch/dump_compare.py b/debug/accuracy_tools/msprobe/pytorch/online_dispatch/dump_compare.py index dbf7626a2..adc0c3a82 100644 --- a/debug/accuracy_tools/msprobe/pytorch/online_dispatch/dump_compare.py +++ b/debug/accuracy_tools/msprobe/pytorch/online_dispatch/dump_compare.py @@ -21,7 +21,7 @@ from datetime import datetime, timezone import torch from msprobe.core.common.const import Const from msprobe.core.common.decorator import recursion_depth_decorator -from msprobe.core.common.file_utils import FileOpen, save_npy, save_json +from msprobe.core.common.file_utils import FileOpen, save_npy, save_json, remove_path, check_link from msprobe.pytorch.common.log import logger @@ -113,6 +113,13 @@ def save_temp_summary(api_index, single_api_summary, path, lock): try: data = [api_index, single_api_summary] save_json(summary_path, data, mode='a') + except Exception as e: + logger.error(f'save temp summary error:{e}') + check_link(summary_path) + try: + remove_path(summary_path) + except Exception as e: + logger.error(f'remove temp summary error:{e}') finally: lock.release() -- Gitee From 56610acd50bd17df5a1b43079eb0f55cfd9ce023 Mon Sep 17 00:00:00 2001 From: fanglanyue Date: Fri, 27 Jun 2025 10:23:41 +0800 Subject: [PATCH 49/79] delete urllib3 requirement --- profiler/msprof_analyze/requirements/build.txt | 2 -- profiler/msprof_analyze/requirements/tests.txt | 2 -- 2 files changed, 4 deletions(-) diff --git a/profiler/msprof_analyze/requirements/build.txt b/profiler/msprof_analyze/requirements/build.txt index 3ef20e787..7c05f4dab 100644 --- a/profiler/msprof_analyze/requirements/build.txt +++ b/profiler/msprof_analyze/requirements/build.txt @@ -6,10 +6,8 @@ PyYaml tqdm prettytable ijson -requests xlsxwriter>=3.0.6 sqlalchemy -urllib3<2.0 numpy<=1.26.4 pandas psutil \ No newline at end of file diff --git a/profiler/msprof_analyze/requirements/tests.txt b/profiler/msprof_analyze/requirements/tests.txt index 6ef8754a2..bdaea7bb5 100644 --- a/profiler/msprof_analyze/requirements/tests.txt +++ b/profiler/msprof_analyze/requirements/tests.txt @@ -11,9 +11,7 @@ PyYaml tqdm prettytable ijson -requests xlsxwriter sqlalchemy -urllib3<2.0 beautifulsoup4 openpyxl \ No newline at end of file -- Gitee From de6fe64bd04abf03ab2310952af23812e8ae5ab8 Mon Sep 17 00:00:00 2001 From: l30036321 Date: Thu, 26 Jun 2025 16:10:42 +0800 Subject: [PATCH 50/79] save doc --- .../msprobe/docs/05.data_dump_PyTorch.md | 2 + .../msprobe/docs/06.data_dump_MindSpore.md | 1 + .../docs/28.debugger_save_instruction.md | 68 +++++++++++++++++-- 3 files changed, 67 insertions(+), 4 deletions(-) diff --git a/debug/accuracy_tools/msprobe/docs/05.data_dump_PyTorch.md b/debug/accuracy_tools/msprobe/docs/05.data_dump_PyTorch.md index db745f420..e152710ca 100644 --- a/debug/accuracy_tools/msprobe/docs/05.data_dump_PyTorch.md +++ b/debug/accuracy_tools/msprobe/docs/05.data_dump_PyTorch.md @@ -238,6 +238,8 @@ save(variable, name, save_backward=True) | name | 指定的名称 | str | 是 | | save_backward | 是否保存反向数据 | boolean | 否 | +具体使用样例可参考：[单点保存工具使用介绍](./28.debugger_save_instruction.md)。 + ### 1.10 set_init_step **功能说明**：设置起始step数，step数默认从0开始计数，使用该接口后step从指定值开始计数。该函数需要写在训练迭代的循环开始前，不能写在循环内。 diff --git a/debug/accuracy_tools/msprobe/docs/06.data_dump_MindSpore.md b/debug/accuracy_tools/msprobe/docs/06.data_dump_MindSpore.md index 4019c87ac..fa0a3e891 100644 --- a/debug/accuracy_tools/msprobe/docs/06.data_dump_MindSpore.md +++ b/debug/accuracy_tools/msprobe/docs/06.data_dump_MindSpore.md @@ -153,6 +153,7 @@ save(variable, name, save_backward=True) | name | 指定的名称 | str | 是 | | save_backward | 是否保存反向数据 | boolean | 否 | +具体使用样例可参考：[单点保存工具使用介绍](./28.debugger_save_instruction.md)。 #### 6.1.6 set_init_step diff --git a/debug/accuracy_tools/msprobe/docs/28.debugger_save_instruction.md b/debug/accuracy_tools/msprobe/docs/28.debugger_save_instruction.md index db4b20d24..3b85320fa 100644 --- a/debug/accuracy_tools/msprobe/docs/28.debugger_save_instruction.md +++ b/debug/accuracy_tools/msprobe/docs/28.debugger_save_instruction.md @@ -79,12 +79,72 @@ for data, label in data_loader: ``` -调用保存接口 +调用保存接口示例（以PyTorch代码为例，MindSpore使用方法相同） ```python -# 训练过程中被调用py文件 +import torch +import torch.nn as nn +import torch.nn.functional as F + +from msprobe.pytorch import PrecisionDebugger, seed_all +# 在模型训练开始前实例化PrecisionDebugger +debugger = PrecisionDebugger(dump_path="dump_path", level="debug") + +# 定义网络 +class ModuleOP(nn.Module): + def __init__(self) -> None: + super().__init__() + self.linear_1 = nn.Linear(in_features=8, out_features=4) + self.linear_2 = nn.Linear(in_features=4, out_features=2) + + def forward(self, x): + x1 = self.linear_1(x) + x2 = self.linear_2(x1) + debugger.save(x2, "x2", save_backward=True) # 调用save接口 + r1 = F.relu(x2) + return r1 + +if __name__ == "__main__": + module = ModuleOP() + + x = torch.randn(10, 8) + out = module(x) + loss = out.sum() + loss.backward() +``` + +分step保存数据（以PyTorch代码为例，MindSpore使用方法相同） +```python +import torch +import torch.nn as nn +import torch.nn.functional as F + from msprobe.pytorch import PrecisionDebugger -dict_variable = {"key1": "value1", "key2": [1, 2]} -PrecisionDebugger.save(dict_variable, "dict_variable", save_backward=False) +# 在模型训练开始前实例化PrecisionDebugger +debugger = PrecisionDebugger(dump_path="dump_path", level="debug") + +# 定义网络 +class ModuleOP(nn.Module): + def __init__(self) -> None: + super().__init__() + self.linear_1 = nn.Linear(in_features=8, out_features=4) + self.linear_2 = nn.Linear(in_features=4, out_features=2) + + def forward(self, x): + x1 = self.linear_1(x) + x2 = self.linear_2(x1) + debugger.save(x2, "x2", save_backward=True) # 调用save接口 + r1 = F.relu(x2) + return r1 + +if __name__ == "__main__": + module = ModuleOP() + train_iter = 10 + for i in range(train_iter): + x = torch.randn(10, 8) + out = module(x) + loss = out.sum() + loss.backward() + debugger.step() # 调用debugger.step用于区分step保存 ``` -- Gitee From 3dd42b380d44949c0890b64a06c6e04ac9c71381 Mon Sep 17 00:00:00 2001 From: gitee Date: Fri, 27 Jun 2025 14:11:10 +0800 Subject: [PATCH 51/79] fix cleancode --- .../msprobe/pytorch/online_dispatch/dump_compare.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/debug/accuracy_tools/msprobe/pytorch/online_dispatch/dump_compare.py b/debug/accuracy_tools/msprobe/pytorch/online_dispatch/dump_compare.py index adc0c3a82..8d6c1f7ac 100644 --- a/debug/accuracy_tools/msprobe/pytorch/online_dispatch/dump_compare.py +++ b/debug/accuracy_tools/msprobe/pytorch/online_dispatch/dump_compare.py @@ -118,8 +118,8 @@ def save_temp_summary(api_index, single_api_summary, path, lock): check_link(summary_path) try: remove_path(summary_path) - except Exception as e: - logger.error(f'remove temp summary error:{e}') + except FileNotFoundError: + logger.error(f'file not found:{summary_path}') finally: lock.release() -- Gitee From 985cc614c2f5efc4749066294d0ebb0a5576668a Mon Sep 17 00:00:00 2001 From: l30036321 Date: Thu, 26 Jun 2025 15:44:49 +0800 Subject: [PATCH 52/79] fix model list out of range --- debug/accuracy_tools/msprobe/core/service.py | 3 ++- .../mindspore/debugger/debugger_config.py | 14 +++++++++----- .../msprobe/pytorch/debugger/debugger_config.py | 17 ++++++++++++----- 3 files changed, 23 insertions(+), 11 deletions(-) diff --git a/debug/accuracy_tools/msprobe/core/service.py b/debug/accuracy_tools/msprobe/core/service.py index 5f0fb49b0..f435549d1 100644 --- a/debug/accuracy_tools/msprobe/core/service.py +++ b/debug/accuracy_tools/msprobe/core/service.py @@ -303,7 +303,8 @@ class BaseService(ABC): self.logger.info( f"Current token id: {self.cur_token_id}, exceed token_range, early stop dump infer token.") self.cur_token_id += 1 - if isinstance(root_model, list): + # 此处root_model可以保证为 Module/Cell类型 或 [Module/Cell]类型 + if root_model and isinstance(root_model, list): root_model = root_model[0] self.logger.warning("Infer model can only input one to support token_range, choose the first one.") if self._is_online_run_ut: diff --git a/debug/accuracy_tools/msprobe/mindspore/debugger/debugger_config.py b/debug/accuracy_tools/msprobe/mindspore/debugger/debugger_config.py index b56273160..90b84c900 100644 --- a/debug/accuracy_tools/msprobe/mindspore/debugger/debugger_config.py +++ b/debug/accuracy_tools/msprobe/mindspore/debugger/debugger_config.py @@ -81,18 +81,22 @@ class DebuggerConfig: target_module_type = (torch.nn.Module, "torch.nn.Module") if is_mindtorch() else (nn.Cell, "mindspore.nn.Cell") if models is None or isinstance(models, target_module_type[0]): return models - error_model = None if isinstance(models, (list, tuple)): + error_model = None for model in models: if not isinstance(model, target_module_type[0]): error_model = model break - else: - error_model = models + if error_model is not None: + error_info = ( + f"The 'model' parameter must be a {target_module_type[1]} or list[{target_module_type[1]}] " + f"type, currently there is a {type(error_model)} type.") + raise MsprobeException( + MsprobeException.INVALID_PARAM_ERROR, error_info) - if error_model is not None: + else: error_info = (f"The 'model' parameter must be a {target_module_type[1]} or list[{target_module_type[1]}] " - f"type, currently there is a {type(error_model)} type.") + f"type, currently there is a {type(models)} type.") raise MsprobeException( MsprobeException.INVALID_PARAM_ERROR, error_info) return models diff --git a/debug/accuracy_tools/msprobe/pytorch/debugger/debugger_config.py b/debug/accuracy_tools/msprobe/pytorch/debugger/debugger_config.py index 2ed2b3a08..ed5204c0d 100644 --- a/debug/accuracy_tools/msprobe/pytorch/debugger/debugger_config.py +++ b/debug/accuracy_tools/msprobe/pytorch/debugger/debugger_config.py @@ -98,6 +98,11 @@ class DebuggerConfig: def check_model(self, instance, start_model, token_range=None): instance.model = start_model if start_model is not None else instance.model + + if token_range and not instance.model: + error_info = "The 'model' parameter must be provided when token_range is not None" + raise MsprobeException(MsprobeException.INVALID_PARAM_ERROR, error_info) + if self.level not in [Const.LEVEL_L0, Const.LEVEL_MIX] and token_range is None: return @@ -110,18 +115,20 @@ class DebuggerConfig: if is_torch_nn_module(instance.model): return - error_model = None if isinstance(instance.model, (list, tuple)): + error_model = None for model in instance.model: if not is_torch_nn_module(model): error_model = model break + if error_model is not None: + error_info = (f"The 'model' parameter must be a torch.nn.Module or list[torch.nn.Module] " + f"type, currently there is an unsupported {type(error_model)} type.") + raise MsprobeException( + MsprobeException.INVALID_PARAM_ERROR, error_info) else: - error_model = instance.model - - if error_model is not None: error_info = (f"The 'model' parameter must be a torch.nn.Module or list[torch.nn.Module] " - f"type, currently there is an unsupported {type(error_model)} type.") + f"type, currently there is an unsupported {type(instance.model)} type.") raise MsprobeException( MsprobeException.INVALID_PARAM_ERROR, error_info) -- Gitee From fab2a5a139e854211a80b7795efc4ddc4d790577 Mon Sep 17 00:00:00 2001 From: gitee Date: Fri, 27 Jun 2025 15:25:46 +0800 Subject: [PATCH 53/79] fix review --- .../pytorch/api_accuracy_checker/run_ut/multi_run_ut.py | 5 +++-- .../msprobe/pytorch/online_dispatch/dump_compare.py | 2 +- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/debug/accuracy_tools/msprobe/pytorch/api_accuracy_checker/run_ut/multi_run_ut.py b/debug/accuracy_tools/msprobe/pytorch/api_accuracy_checker/run_ut/multi_run_ut.py index 6fb0b00aa..bfda1daf5 100644 --- a/debug/accuracy_tools/msprobe/pytorch/api_accuracy_checker/run_ut/multi_run_ut.py +++ b/debug/accuracy_tools/msprobe/pytorch/api_accuracy_checker/run_ut/multi_run_ut.py @@ -82,12 +82,13 @@ def split_json_file(input_file, num_splits, filter_api): except Exception as e: logger.error(f"An error occurred while saving split file: {e}") for file in split_files: - check_link(file) try: + check_link(file) remove_path(file) except FileNotFoundError: logger.error(f"File not found and could not be deleted: {file}") - + msg = 'ERROR: Split json file failed, please check the input file and try again.' + raise CompareException(CompareException.PARSE_FILE_ERROR, msg) return split_files, total_items diff --git a/debug/accuracy_tools/msprobe/pytorch/online_dispatch/dump_compare.py b/debug/accuracy_tools/msprobe/pytorch/online_dispatch/dump_compare.py index 8d6c1f7ac..2568ff69c 100644 --- a/debug/accuracy_tools/msprobe/pytorch/online_dispatch/dump_compare.py +++ b/debug/accuracy_tools/msprobe/pytorch/online_dispatch/dump_compare.py @@ -115,8 +115,8 @@ def save_temp_summary(api_index, single_api_summary, path, lock): save_json(summary_path, data, mode='a') except Exception as e: logger.error(f'save temp summary error:{e}') - check_link(summary_path) try: + check_link(summary_path) remove_path(summary_path) except FileNotFoundError: logger.error(f'file not found:{summary_path}') -- Gitee From 06c3d96ee42f983b39d0e377c7b90a74ae974f86 Mon Sep 17 00:00:00 2001 From: gitee Date: Fri, 27 Jun 2025 15:52:01 +0800 Subject: [PATCH 54/79] fix bug --- .../pytorch/api_accuracy_checker/run_ut/multi_run_ut.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/debug/accuracy_tools/msprobe/pytorch/api_accuracy_checker/run_ut/multi_run_ut.py b/debug/accuracy_tools/msprobe/pytorch/api_accuracy_checker/run_ut/multi_run_ut.py index bfda1daf5..c99ebbdec 100644 --- a/debug/accuracy_tools/msprobe/pytorch/api_accuracy_checker/run_ut/multi_run_ut.py +++ b/debug/accuracy_tools/msprobe/pytorch/api_accuracy_checker/run_ut/multi_run_ut.py @@ -87,8 +87,8 @@ def split_json_file(input_file, num_splits, filter_api): remove_path(file) except FileNotFoundError: logger.error(f"File not found and could not be deleted: {file}") - msg = 'ERROR: Split json file failed, please check the input file and try again.' - raise CompareException(CompareException.PARSE_FILE_ERROR, msg) + msg = 'ERROR: Split json file failed, please check the input file and try again.' + raise CompareException(CompareException.PARSE_FILE_ERROR, msg) from e return split_files, total_items -- Gitee From cd516f9f35e0607fae3a8fe57ed4dea2f3a23df1 Mon Sep 17 00:00:00 2001 From: gitee Date: Fri, 27 Jun 2025 16:16:01 +0800 Subject: [PATCH 55/79] fix review --- .../msprobe/pytorch/api_accuracy_checker/run_ut/multi_run_ut.py | 1 - .../msprobe/pytorch/online_dispatch/dump_compare.py | 1 - 2 files changed, 2 deletions(-) diff --git a/debug/accuracy_tools/msprobe/pytorch/api_accuracy_checker/run_ut/multi_run_ut.py b/debug/accuracy_tools/msprobe/pytorch/api_accuracy_checker/run_ut/multi_run_ut.py index c99ebbdec..15e18fe85 100644 --- a/debug/accuracy_tools/msprobe/pytorch/api_accuracy_checker/run_ut/multi_run_ut.py +++ b/debug/accuracy_tools/msprobe/pytorch/api_accuracy_checker/run_ut/multi_run_ut.py @@ -83,7 +83,6 @@ def split_json_file(input_file, num_splits, filter_api): logger.error(f"An error occurred while saving split file: {e}") for file in split_files: try: - check_link(file) remove_path(file) except FileNotFoundError: logger.error(f"File not found and could not be deleted: {file}") diff --git a/debug/accuracy_tools/msprobe/pytorch/online_dispatch/dump_compare.py b/debug/accuracy_tools/msprobe/pytorch/online_dispatch/dump_compare.py index 2568ff69c..a254599f6 100644 --- a/debug/accuracy_tools/msprobe/pytorch/online_dispatch/dump_compare.py +++ b/debug/accuracy_tools/msprobe/pytorch/online_dispatch/dump_compare.py @@ -116,7 +116,6 @@ def save_temp_summary(api_index, single_api_summary, path, lock): except Exception as e: logger.error(f'save temp summary error:{e}') try: - check_link(summary_path) remove_path(summary_path) except FileNotFoundError: logger.error(f'file not found:{summary_path}') -- Gitee From 76621817b05402361f02ac96dc626771f0324534 Mon Sep 17 00:00:00 2001 From: Linwei-Ying Date: Wed, 25 Jun 2025 09:42:40 +0800 Subject: [PATCH 56/79] add transfer_to_npu quick main.py add transfer_to_npu quick main.py add transfer_to_npu quick main.py add transfer_to_npu quick main.py add transfer_to_npu quick main.py --- sample/transfer_to_npu/main.py | 533 +++++++++++++++++++++++++++++++++ 1 file changed, 533 insertions(+) create mode 100644 sample/transfer_to_npu/main.py diff --git a/sample/transfer_to_npu/main.py b/sample/transfer_to_npu/main.py new file mode 100644 index 000000000..00c2a0c2d --- /dev/null +++ b/sample/transfer_to_npu/main.py @@ -0,0 +1,533 @@ +# BSD 3-Clause License +# +# Copyright (c) 2017, +# All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +#* Redistributions of source code must retain the above copyright notice, this +# list of conditions and the following disclaimer. +# +#* Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +#* Neither the name of the copyright holder nor the names of its +# contributors may be used to endorse or promote products derived from +# this software without specific prior written permission. + +import argparse +import os +import random +import shutil +import time +import warnings +from enum import Enum + +import torch +import torch.backends.cudnn as cudnn +import torch.distributed as dist +import torch.multiprocessing as mp +import torch.nn as nn +import torch.nn.parallel +import torch.optim +import torch.utils.data +import torch.utils.data.distributed +import torchvision.datasets as datasets +import torchvision.models as models +import torchvision.transforms as transforms +from torch.optim.lr_scheduler import StepLR +from torch.utils.data import Subset + +model_names = sorted(name for name in models.__dict__ + if name.islower() and not name.startswith("__") + and callable(models.__dict__[name])) + +parser = argparse.ArgumentParser(description='PyTorch ImageNet Training') +parser.add_argument('data', metavar='DIR', nargs='?', default='imagenet', + help='path to dataset (default: imagenet)') +parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet18', + choices=model_names, + help='model architecture: ' + + ' | '.join(model_names) + + ' (default: resnet18)') +parser.add_argument('-j', '--workers', default=4, type=int, metavar='N', + help='number of data loading workers (default: 4)') +parser.add_argument('--epochs', default=90, type=int, metavar='N', + help='number of total epochs to run') +parser.add_argument('--start-epoch', default=0, type=int, metavar='N', + help='manual epoch number (useful on restarts)') +parser.add_argument('-b', '--batch-size', default=256, type=int, + metavar='N', + help='mini-batch size (default: 256), this is the total ' + 'batch size of all GPUs on the current node when ' + 'using Data Parallel or Distributed Data Parallel') +parser.add_argument('--lr', '--learning-rate', default=0.1, type=float, + metavar='LR', help='initial learning rate', dest='lr') +parser.add_argument('--momentum', default=0.9, type=float, metavar='M', + help='momentum') +parser.add_argument('--wd', '--weight-decay', default=1e-4, type=float, + metavar='W', help='weight decay (default: 1e-4)', + dest='weight_decay') +parser.add_argument('-p', '--print-freq', default=10, type=int, + metavar='N', help='print frequency (default: 10)') +parser.add_argument('--resume', default='', type=str, metavar='PATH', + help='path to latest checkpoint (default: none)') +parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true', + help='evaluate model on validation set') +parser.add_argument('--pretrained', dest='pretrained', action='store_true', + help='use pre-trained model') +parser.add_argument('--world-size', default=-1, type=int, + help='number of nodes for distributed training') +parser.add_argument('--rank', default=-1, type=int, + help='node rank for distributed training') +parser.add_argument('--dist-url', default='tcp://224.66.41.62:23456', type=str, + help='url used to set up distributed training') +parser.add_argument('--dist-backend', default='nccl', type=str, + help='distributed backend') +parser.add_argument('--seed', default=None, type=int, + help='seed for initializing training. ') +parser.add_argument('--gpu', default=None, type=int, + help='GPU id to use.') +parser.add_argument('--multiprocessing-distributed', action='store_true', + help='Use multi-processing distributed training to launch ' + 'N processes per node, which has N GPUs. This is the ' + 'fastest way to use PyTorch for either single node or ' + 'multi node data parallel training') +parser.add_argument('--dummy', action='store_true', help="use fake data to benchmark") + +best_acc1 = 0 + + +def main(): + args = parser.parse_args() + + if args.seed is not None: + random.seed(args.seed) + torch.manual_seed(args.seed) + cudnn.deterministic = True + cudnn.benchmark = False + warnings.warn('You have chosen to seed training. ' + 'This will turn on the CUDNN deterministic setting, ' + 'which can slow down your training considerably! ' + 'You may see unexpected behavior when restarting ' + 'from checkpoints.') + + if args.gpu is not None: + warnings.warn('You have chosen a specific GPU. This will completely ' + 'disable data parallelism.') + + if args.dist_url == "env://" and args.world_size == -1: + args.world_size = int(os.environ["WORLD_SIZE"]) + + args.distributed = args.world_size > 1 or args.multiprocessing_distributed + + if torch.cuda.is_available(): + ngpus_per_node = torch.cuda.device_count() + if ngpus_per_node == 1 and args.dist_backend == "nccl": + warnings.warn("nccl backend >=2.5 requires GPU count>1, see https://github.com/NVIDIA/nccl/issues/103 perhaps use 'gloo'") + else: + ngpus_per_node = 1 + + if args.multiprocessing_distributed: + # Since we have ngpus_per_node processes per node, the total world_size + # needs to be adjusted accordingly + args.world_size = ngpus_per_node * args.world_size + # Use torch.multiprocessing.spawn to launch distributed processes: the + # main_worker process function + mp.spawn(main_worker, nprocs=ngpus_per_node, args=(ngpus_per_node, args)) + else: + # Simply call main_worker function + main_worker(args.gpu, ngpus_per_node, args) + + +def main_worker(gpu, ngpus_per_node, args): + global best_acc1 + args.gpu = gpu + + if args.gpu is not None: + print("Use GPU: {} for training".format(args.gpu)) + + if args.distributed: + if args.dist_url == "env://" and args.rank == -1: + args.rank = int(os.environ["RANK"]) + if args.multiprocessing_distributed: + # For multiprocessing distributed training, rank needs to be the + # global rank among all the processes + args.rank = args.rank * ngpus_per_node + gpu + dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url, + world_size=args.world_size, rank=args.rank) + # create model + if args.pretrained: + print("=> using pre-trained model '{}'".format(args.arch)) + model = models.__dict__[args.arch](pretrained=True) + else: + print("=> creating model '{}'".format(args.arch)) + model = models.__dict__[args.arch]() + + if not torch.cuda.is_available() and not torch.backends.mps.is_available(): + print('using CPU, this will be slow') + elif args.distributed: + # For multiprocessing distributed, DistributedDataParallel constructor + # should always set the single device scope, otherwise, + # DistributedDataParallel will use all available devices. + if torch.cuda.is_available(): + if args.gpu is not None: + torch.cuda.set_device(args.gpu) + model.cuda(args.gpu) + # When using a single GPU per process and per + # DistributedDataParallel, we need to divide the batch size + # ourselves based on the total number of GPUs of the current node. + args.batch_size = int(args.batch_size / ngpus_per_node) + args.workers = int((args.workers + ngpus_per_node - 1) / ngpus_per_node) + model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu]) + else: + model.cuda() + # DistributedDataParallel will divide and allocate batch_size to all + # available GPUs if device_ids are not set + model = torch.nn.parallel.DistributedDataParallel(model) + elif args.gpu is not None and torch.cuda.is_available(): + torch.cuda.set_device(args.gpu) + model = model.cuda(args.gpu) + elif torch.backends.mps.is_available(): + device = torch.device("mps") + model = model.to(device) + else: + # DataParallel will divide and allocate batch_size to all available GPUs + if args.arch.startswith('alexnet') or args.arch.startswith('vgg'): + model.features = torch.nn.DataParallel(model.features) + model.cuda() + else: + model = torch.nn.DataParallel(model).cuda() + + if torch.cuda.is_available(): + if args.gpu: + device = torch.device('cuda:{}'.format(args.gpu)) + else: + device = torch.device("cuda") + elif torch.backends.mps.is_available(): + device = torch.device("mps") + else: + device = torch.device("cpu") + # define loss function (criterion), optimizer, and learning rate scheduler + criterion = nn.CrossEntropyLoss().to(device) + + optimizer = torch.optim.SGD(model.parameters(), args.lr, + momentum=args.momentum, + weight_decay=args.weight_decay) + + """Sets the learning rate to the initial LR decayed by 10 every 30 epochs""" + scheduler = StepLR(optimizer, step_size=30, gamma=0.1) + + # optionally resume from a checkpoint + if args.resume: + if os.path.isfile(args.resume): + print("=> loading checkpoint '{}'".format(args.resume)) + if args.gpu is None: + checkpoint = torch.load(args.resume) + elif torch.cuda.is_available(): + # Map model to be loaded to specified single gpu. + loc = 'cuda:{}'.format(args.gpu) + checkpoint = torch.load(args.resume, map_location=loc) + args.start_epoch = checkpoint['epoch'] + best_acc1 = checkpoint['best_acc1'] + if args.gpu is not None: + # best_acc1 may be from a checkpoint from a different GPU + best_acc1 = best_acc1.to(args.gpu) + model.load_state_dict(checkpoint['state_dict']) + optimizer.load_state_dict(checkpoint['optimizer']) + scheduler.load_state_dict(checkpoint['scheduler']) + print("=> loaded checkpoint '{}' (epoch {})" + .format(args.resume, checkpoint['epoch'])) + else: + print("=> no checkpoint found at '{}'".format(args.resume)) + + + # Data loading code + if args.dummy: + print("=> Dummy data is used!") + train_dataset = datasets.FakeData(1281167, (3, 224, 224), 1000, transforms.ToTensor()) + val_dataset = datasets.FakeData(50000, (3, 224, 224), 1000, transforms.ToTensor()) + else: + traindir = os.path.join(args.data, 'train') + valdir = os.path.join(args.data, 'val') + normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225]) + + train_dataset = datasets.ImageFolder( + traindir, + transforms.Compose([ + transforms.RandomResizedCrop(224), + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + normalize, + ])) + + val_dataset = datasets.ImageFolder( + valdir, + transforms.Compose([ + transforms.Resize(256), + transforms.CenterCrop(224), + transforms.ToTensor(), + normalize, + ])) + + if args.distributed: + train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset) + val_sampler = torch.utils.data.distributed.DistributedSampler(val_dataset, shuffle=False, drop_last=True) + else: + train_sampler = None + val_sampler = None + + train_loader = torch.utils.data.DataLoader( + train_dataset, batch_size=args.batch_size, shuffle=(train_sampler is None), + num_workers=args.workers, pin_memory=True, sampler=train_sampler) + + val_loader = torch.utils.data.DataLoader( + val_dataset, batch_size=args.batch_size, shuffle=False, + num_workers=args.workers, pin_memory=True, sampler=val_sampler) + + if args.evaluate: + validate(val_loader, model, criterion, args) + return + + for epoch in range(args.start_epoch, args.epochs): + if args.distributed: + train_sampler.set_epoch(epoch) + + # train for one epoch + train(train_loader, model, criterion, optimizer, epoch, device, args) + + # evaluate on validation set + acc1 = validate(val_loader, model, criterion, args) + + scheduler.step() + + # remember best acc@1 and save checkpoint + is_best = acc1 > best_acc1 + best_acc1 = max(acc1, best_acc1) + + if not args.multiprocessing_distributed or (args.multiprocessing_distributed + and args.rank % ngpus_per_node == 0): + save_checkpoint({ + 'epoch': epoch + 1, + 'arch': args.arch, + 'state_dict': model.state_dict(), + 'best_acc1': best_acc1, + 'optimizer' : optimizer.state_dict(), + 'scheduler' : scheduler.state_dict() + }, is_best) + + +def train(train_loader, model, criterion, optimizer, epoch, device, args): + batch_time = AverageMeter('Time', ':6.3f') + data_time = AverageMeter('Data', ':6.3f') + losses = AverageMeter('Loss', ':.4e') + top1 = AverageMeter('Acc@1', ':6.2f') + top5 = AverageMeter('Acc@5', ':6.2f') + progress = ProgressMeter( + len(train_loader), + [batch_time, data_time, losses, top1, top5], + prefix="Epoch: [{}]".format(epoch)) + + # switch to train mode + model.train() + + end = time.time() + for i, (images, target) in enumerate(train_loader): + # measure data loading time + data_time.update(time.time() - end) + + # move data to the same device as model + images = images.to(device, non_blocking=True) + target = target.to(device, non_blocking=True) + + # compute output + output = model(images) + loss = criterion(output, target) + + # measure accuracy and record loss + acc1, acc5 = accuracy(output, target, topk=(1, 5)) + losses.update(loss.item(), images.size(0)) + top1.update(acc1[0], images.size(0)) + top5.update(acc5[0], images.size(0)) + + # compute gradient and do SGD step + optimizer.zero_grad() + loss.backward() + optimizer.step() + + # measure elapsed time + batch_time.update(time.time() - end) + end = time.time() + + if i % args.print_freq == 0: + progress.display(i + 1) + + +def validate(val_loader, model, criterion, args): + + def run_validate(loader, base_progress=0): + with torch.no_grad(): + end = time.time() + for i, (images, target) in enumerate(loader): + i = base_progress + i + if args.gpu is not None and torch.cuda.is_available(): + images = images.cuda(args.gpu, non_blocking=True) + if torch.backends.mps.is_available(): + images = images.to('mps') + target = target.to('mps') + if torch.cuda.is_available(): + target = target.cuda(args.gpu, non_blocking=True) + + # compute output + output = model(images) + loss = criterion(output, target) + + # measure accuracy and record loss + acc1, acc5 = accuracy(output, target, topk=(1, 5)) + losses.update(loss.item(), images.size(0)) + top1.update(acc1[0], images.size(0)) + top5.update(acc5[0], images.size(0)) + + # measure elapsed time + batch_time.update(time.time() - end) + end = time.time() + + if i % args.print_freq == 0: + progress.display(i + 1) + + batch_time = AverageMeter('Time', ':6.3f', Summary.NONE) + losses = AverageMeter('Loss', ':.4e', Summary.NONE) + top1 = AverageMeter('Acc@1', ':6.2f', Summary.AVERAGE) + top5 = AverageMeter('Acc@5', ':6.2f', Summary.AVERAGE) + progress = ProgressMeter( + len(val_loader) + (args.distributed and (len(val_loader.sampler) * args.world_size < len(val_loader.dataset))), + [batch_time, losses, top1, top5], + prefix='Test: ') + + # switch to evaluate mode + model.eval() + + run_validate(val_loader) + if args.distributed: + top1.all_reduce() + top5.all_reduce() + + if args.distributed and (len(val_loader.sampler) * args.world_size < len(val_loader.dataset)): + aux_val_dataset = Subset(val_loader.dataset, + range(len(val_loader.sampler) * args.world_size, len(val_loader.dataset))) + aux_val_loader = torch.utils.data.DataLoader( + aux_val_dataset, batch_size=args.batch_size, shuffle=False, + num_workers=args.workers, pin_memory=True) + run_validate(aux_val_loader, len(val_loader)) + + progress.display_summary() + + return top1.avg + + +def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'): + torch.save(state, filename) + if is_best: + shutil.copyfile(filename, 'model_best.pth.tar') + +class Summary(Enum): + NONE = 0 + AVERAGE = 1 + SUM = 2 + COUNT = 3 + +class AverageMeter(object): + """Computes and stores the average and current value""" + def __init__(self, name, fmt=':f', summary_type=Summary.AVERAGE): + self.name = name + self.fmt = fmt + self.summary_type = summary_type + self.reset() + + def reset(self): + self.val = 0 + self.avg = 0 + self.sum = 0 + self.count = 0 + + def update(self, val, n=1): + self.val = val + self.sum += val * n + self.count += n + self.avg = self.sum / self.count + + def all_reduce(self): + if torch.cuda.is_available(): + device = torch.device("cuda") + elif torch.backends.mps.is_available(): + device = torch.device("mps") + else: + device = torch.device("cpu") + total = torch.tensor([self.sum, self.count], dtype=torch.float32, device=device) + dist.all_reduce(total, dist.ReduceOp.SUM, async_op=False) + self.sum, self.count = total.tolist() + self.avg = self.sum / self.count + + def __str__(self): + fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})' + return fmtstr.format(**self.__dict__) + + def summary(self): + fmtstr = '' + if self.summary_type is Summary.NONE: + fmtstr = '' + elif self.summary_type is Summary.AVERAGE: + fmtstr = '{name} {avg:.3f}' + elif self.summary_type is Summary.SUM: + fmtstr = '{name} {sum:.3f}' + elif self.summary_type is Summary.COUNT: + fmtstr = '{name} {count:.3f}' + else: + raise ValueError('invalid summary type %r' % self.summary_type) + + return fmtstr.format(**self.__dict__) + + +class ProgressMeter(object): + def __init__(self, num_batches, meters, prefix=""): + self.batch_fmtstr = self._get_batch_fmtstr(num_batches) + self.meters = meters + self.prefix = prefix + + def display(self, batch): + entries = [self.prefix + self.batch_fmtstr.format(batch)] + entries += [str(meter) for meter in self.meters] + print('\t'.join(entries)) + + def display_summary(self): + entries = [" *"] + entries += [meter.summary() for meter in self.meters] + print(' '.join(entries)) + + def _get_batch_fmtstr(self, num_batches): + num_digits = len(str(num_batches // 1)) + fmt = '{:' + str(num_digits) + 'd}' + return '[' + fmt + '/' + fmt.format(num_batches) + ']' + +def accuracy(output, target, topk=(1,)): + """Computes the accuracy over the k top predictions for the specified values of k""" + with torch.no_grad(): + maxk = max(topk) + batch_size = target.size(0) + + _, pred = output.topk(maxk, 1, True, True) + pred = pred.t() + correct = pred.eq(target.view(1, -1).expand_as(pred)) + + res = [] + for k in topk: + correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True) + res.append(correct_k.mul_(100.0 / batch_size)) + return res + + +if __name__ == '__main__': + main() -- Gitee From 3873a083f23a06841e8105d77ca8b53ef588f538 Mon Sep 17 00:00:00 2001 From: Linwei-Ying Date: Fri, 27 Jun 2025 17:39:42 +0800 Subject: [PATCH 57/79] add transfer_to_npu quick main.py --- sample/transfer_to_npu/README.md | 45 ++++++++++++++++++++++++++++++++ sample/transfer_to_npu/main.py | 16 +----------- 2 files changed, 46 insertions(+), 15 deletions(-) create mode 100644 sample/transfer_to_npu/README.md diff --git a/sample/transfer_to_npu/README.md b/sample/transfer_to_npu/README.md new file mode 100644 index 000000000..167bc1ffa --- /dev/null +++ b/sample/transfer_to_npu/README.md @@ -0,0 +1,45 @@ +# 迁移工具快速入门样例介绍 + +## 说明 + +本样例选用ResNet50模型。 + +## 环境准备 + +1. 准备一台基于Atlas 训练系列产品的训练服务器，并[安装NPU驱动和固件](https://www.hiascend.com/document/detail/zh/canncommercial/81RC1/softwareinst/instg/instg_0005.html?Mode=PmIns&InstallType=local&OS=Ubuntu&Software=cannToolKit)。 + +2. 安装开发套件包Ascend-cann-toolkit，具体请参考[安装CANN软件包](https://www.hiascend.com/document/detail/zh/canncommercial/81RC1/softwareinst/instg/instg_0008.html?Mode=PmIns&InstallType=local&OS=Ubuntu&Software=cannToolKit)。 + +3. 以安装PyTorch 2.1.0版本为例，具体操作请参考适配插件开发（PyTorch框架)。 + +4. 配置环境变量。
    +安装CANN软件后，使用CANN运行用户进行编译、运行时，需要以CANN运行用户登录环境，执行source ${install_path}/set_env.sh命令设置环境变量。其中${install_path}为CANN软件的安装目录，例如：/usr/local/Ascend/ascend-toolkit。 + +5. 下载[main.py](main.py)文件，并上传至训练服务器的个人目录下。 + + +## 执行迁移 +1. 在训练脚本（main.py文件）中导入自动迁移的库代码。 +```Python + 42 + 43 import torch_npu + 44 from torch_npu.contrib import transfer_to_npu + 45 +# 在43 44行插入的代码为自动迁移的库代码，可以在NPU环境下直接执行训练 +``` + +2. 迁移完成后的训练脚本可在NPU上运行，执行以下训练命令。 +```Python +python main.py -a resnet50 -b 32 --gpu 1 --dummy +``` +如果训练正常进行，开始打印迭代日志，说明训练功能迁移成功，如下所示。 +```Python +Use GPU: 1 for training +=> creating model 'resnet50' +=> Dummy data is used! +Epoch: [0][ 1/40037] Time 8.287 ( 8.287) Data 0.504 ( 0.504) Loss 7.0919e+00 (7.0919e+00) Acc@1 0.00 ( 0.00) Acc@5 0.00 ( 0.00) +Epoch: [0][ 11/40037] Time 0.097 ( 1.268) Data 0.000 ( 0.479) Loss 1.5627e+01 (1.8089e+01) Acc@1 0.00 ( 0.00) Acc@5 3.12 ( 0.57) +Epoch: [0][ 21/40037] Time 0.096 ( 0.710) Data 0.000 ( 0.253) Loss 7.7462e+00 (1.4883e+01) Acc@1 0.00 ( 0.00) +``` + +3. 成功保存权重，说明保存权重功能迁移成功。 diff --git a/sample/transfer_to_npu/main.py b/sample/transfer_to_npu/main.py index 00c2a0c2d..7d4d994d4 100644 --- a/sample/transfer_to_npu/main.py +++ b/sample/transfer_to_npu/main.py @@ -82,8 +82,6 @@ parser.add_argument('--world-size', default=-1, type=int, help='number of nodes for distributed training') parser.add_argument('--rank', default=-1, type=int, help='node rank for distributed training') -parser.add_argument('--dist-url', default='tcp://224.66.41.62:23456', type=str, - help='url used to set up distributed training') parser.add_argument('--dist-backend', default='nccl', type=str, help='distributed backend') parser.add_argument('--seed', default=None, type=int, @@ -118,15 +116,12 @@ def main(): warnings.warn('You have chosen a specific GPU. This will completely ' 'disable data parallelism.') - if args.dist_url == "env://" and args.world_size == -1: - args.world_size = int(os.environ["WORLD_SIZE"]) - args.distributed = args.world_size > 1 or args.multiprocessing_distributed if torch.cuda.is_available(): ngpus_per_node = torch.cuda.device_count() if ngpus_per_node == 1 and args.dist_backend == "nccl": - warnings.warn("nccl backend >=2.5 requires GPU count>1, see https://github.com/NVIDIA/nccl/issues/103 perhaps use 'gloo'") + warnings.warn("nccl backend >=2.5 requires GPU count>1, perhaps use 'gloo'") else: ngpus_per_node = 1 @@ -149,15 +144,6 @@ def main_worker(gpu, ngpus_per_node, args): if args.gpu is not None: print("Use GPU: {} for training".format(args.gpu)) - if args.distributed: - if args.dist_url == "env://" and args.rank == -1: - args.rank = int(os.environ["RANK"]) - if args.multiprocessing_distributed: - # For multiprocessing distributed training, rank needs to be the - # global rank among all the processes - args.rank = args.rank * ngpus_per_node + gpu - dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url, - world_size=args.world_size, rank=args.rank) # create model if args.pretrained: print("=> using pre-trained model '{}'".format(args.arch)) -- Gitee From da6e55d4a8da91db15d1e3ee7cdac1698fdc8320 Mon Sep 17 00:00:00 2001 From: l30036321 Date: Sat, 28 Jun 2025 09:44:14 +0800 Subject: [PATCH 58/79] add l2 level restrictions --- .../msprobe/mindspore/debugger/precision_debugger.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/debug/accuracy_tools/msprobe/mindspore/debugger/precision_debugger.py b/debug/accuracy_tools/msprobe/mindspore/debugger/precision_debugger.py index 6d7ee60b5..420f15470 100644 --- a/debug/accuracy_tools/msprobe/mindspore/debugger/precision_debugger.py +++ b/debug/accuracy_tools/msprobe/mindspore/debugger/precision_debugger.py @@ -87,7 +87,7 @@ class PrecisionDebugger(BasePrecisionDebugger): Runtime.step_count = 0 Runtime.is_running = False - if enable_dynamic_kbyk_dump: + if enable_dynamic_kbyk_dump and self.config.level_ori == Const.LEVEL_L2: _dump_set_dynamic() @staticmethod -- Gitee From 8fdc83ed5c076d97ffa5e43ce2cf870c6b828f00 Mon Sep 17 00:00:00 2001 From: Linwei-Ying Date: Sat, 28 Jun 2025 11:19:39 +0800 Subject: [PATCH 59/79] compare indexerror bugfix --- .../msprobe/core/compare/acc_compare.py | 42 ++++++++++--------- 1 file changed, 23 insertions(+), 19 deletions(-) diff --git a/debug/accuracy_tools/msprobe/core/compare/acc_compare.py b/debug/accuracy_tools/msprobe/core/compare/acc_compare.py index f0ca6bfc9..435cd25bf 100644 --- a/debug/accuracy_tools/msprobe/core/compare/acc_compare.py +++ b/debug/accuracy_tools/msprobe/core/compare/acc_compare.py @@ -210,25 +210,29 @@ class ParseData: # 遍历单个API的所有item for index, op_name in enumerate(op_name_reorder): result[CompareConst.OP_NAME].append(op_name) - if (CompareConst.INPUT_PATTERN in op_name) or (CompareConst.KWARGS_PATTERN in op_name): - struct = merge_list[CompareConst.INPUT_STRUCT].pop(0) - elif CompareConst.OUTPUT_PATTERN in op_name: - struct = merge_list[CompareConst.OUTPUT_STRUCT].pop(0) - elif CompareConst.PARAMS_PATTERN in op_name: - struct = merge_list[CompareConst.PARAMS_STRUCT].pop(0) - elif CompareConst.PARAMS_GRAD_PATTERN in op_name: - struct = merge_list[CompareConst.PARAMS_GRAD_STRUCT].pop(0) - else: - struct = merge_list[CompareConst.DEBUG_STRUCT].pop(0) - result[Const.DTYPE].append(struct[0]) - result[Const.SHAPE].append(struct[1]) - if self.mode_config.dump_mode == Const.MD5: - result[Const.MD5].append(struct[2]) - result[Const.SUMMARY].append(summary_reorder.pop(0)) - result[Const.STACK_INFO].append( - merge_list[Const.STACK_INFO][0] if index == 0 and self.mode_config.stack_mode else None) - if self.mode_config.dump_mode == Const.ALL: - result['data_name'].append(data_name_reorder.pop(0)) + try: + if (CompareConst.INPUT_PATTERN in op_name) or (CompareConst.KWARGS_PATTERN in op_name): + struct = merge_list[CompareConst.INPUT_STRUCT].pop(0) + elif CompareConst.OUTPUT_PATTERN in op_name: + struct = merge_list[CompareConst.OUTPUT_STRUCT].pop(0) + elif CompareConst.PARAMS_PATTERN in op_name: + struct = merge_list[CompareConst.PARAMS_STRUCT].pop(0) + elif CompareConst.PARAMS_GRAD_PATTERN in op_name: + struct = merge_list[CompareConst.PARAMS_GRAD_STRUCT].pop(0) + else: + struct = merge_list[CompareConst.DEBUG_STRUCT].pop(0) + result[Const.DTYPE].append(struct[0]) + result[Const.SHAPE].append(struct[1]) + if self.mode_config.dump_mode == Const.MD5: + result[Const.MD5].append(struct[2]) + result[Const.SUMMARY].append(summary_reorder.pop(0)) + result[Const.STACK_INFO].append( + merge_list[Const.STACK_INFO][0] if index == 0 and self.mode_config.stack_mode else None) + if self.mode_config.dump_mode == Const.ALL: + result['data_name'].append(data_name_reorder.pop(0)) + except IndexError as e: + logger.error(f'Index out of bounds error, please check info of api: {op_name}.') + raise CompareException(CompareException.INDEX_OUT_OF_BOUNDS_ERROR) from e progress_bar.update(1) progress_bar.close() -- Gitee From 46de8690a4137c5c48419dda8908f1d968b8431b Mon Sep 17 00:00:00 2001 From: mei-feiyao <1332490378@qq.com> Date: Sat, 28 Jun 2025 12:46:39 +0800 Subject: [PATCH 60/79] close socket fd before throw --- msmonitor/dynolog_npu/dynolog/src/rpc/SimpleJsonServer.cpp | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/msmonitor/dynolog_npu/dynolog/src/rpc/SimpleJsonServer.cpp b/msmonitor/dynolog_npu/dynolog/src/rpc/SimpleJsonServer.cpp index ea4c3bdc1..c1a765c5e 100644 --- a/msmonitor/dynolog_npu/dynolog/src/rpc/SimpleJsonServer.cpp +++ b/msmonitor/dynolog_npu/dynolog/src/rpc/SimpleJsonServer.cpp @@ -41,6 +41,10 @@ SimpleJsonServerBase::SimpleJsonServerBase(int port) : port_(port) } } catch (const std::exception& e) { LOG(ERROR) << "Failed to initialize server: " << e.what(); + if (sock_fd_ != -1) { + close(sock_fd_); + sock_fd_ = -1; + } throw; } } -- Gitee From 4eb847d38b23e0567ed158dbb8e51bde33019591 Mon Sep 17 00:00:00 2001 From: sunchao <1299792067@qq.com> Date: Sat, 28 Jun 2025 15:13:38 +0800 Subject: [PATCH 61/79] =?UTF-8?q?=F0=9F=A6=84=20refactor:=20=E8=AF=BB?= =?UTF-8?q?=E6=96=87=E4=BB=B6=E5=AE=89=E5=85=A8=E5=8A=A0=E5=9B=BA?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../server/app/service/graph_service.py | 4 +- .../server/app/utils/graph_utils.py | 153 +++++++++++------- 2 files changed, 101 insertions(+), 56 deletions(-) diff --git a/plugins/tensorboard-plugins/tb_graph_ascend/server/app/service/graph_service.py b/plugins/tensorboard-plugins/tb_graph_ascend/server/app/service/graph_service.py index 70ef0b217..36c3aa157 100644 --- a/plugins/tensorboard-plugins/tb_graph_ascend/server/app/service/graph_service.py +++ b/plugins/tensorboard-plugins/tb_graph_ascend/server/app/service/graph_service.py @@ -135,7 +135,7 @@ class GraphService: config['colors'] = config_data_run.get('colors') # 读取目录下配置文件列表 config_files = GraphUtils.find_config_files(run) - config['matchedConfigFiles'] = config_files + config['matchedConfigFiles'] = config_files or [] config['task'] = graph_data.get('task') return {'success': True, 'data': config} except Exception as e: @@ -329,7 +329,7 @@ class GraphService: config_data_run['colors'] = colors config_data[run] = config_data_run GraphState.set_global_value("config_data", config_data) - GraphUtils.safe_save_data(first_file_data, run, first_run_tag) + GraphUtils.safe_save_data(first_file_data, run, f"{first_run_tag}.vis") return {'success': True, 'error': None, 'data': {}} except Exception as e: return {'success': False, 'error': str(e), 'data': None} diff --git a/plugins/tensorboard-plugins/tb_graph_ascend/server/app/utils/graph_utils.py b/plugins/tensorboard-plugins/tb_graph_ascend/server/app/utils/graph_utils.py index 41a86a45b..3f1af3503 100644 --- a/plugins/tensorboard-plugins/tb_graph_ascend/server/app/utils/graph_utils.py +++ b/plugins/tensorboard-plugins/tb_graph_ascend/server/app/utils/graph_utils.py @@ -19,12 +19,14 @@ import os import json import re import stat +import sys from functools import cmp_to_key from pathlib import Path from tensorboard.util import tb_logging from .global_state import GraphState, FILE_NAME_REGEX, MAX_FILE_SIZE logger = tb_logging.get_logger() +FILE_PATH_MAX_LENGTH = 4096 class GraphUtils: @@ -210,6 +212,30 @@ class GraphUtils: return f"{size_bytes:.{decimal_places}f} {units[unit_index]}" + @staticmethod + def has_group_permission(path, permission): + """ + 检查指定路径是否对其所属组具有指定权限。 + + :param path: 要检查的文件或目录的路径。 + :param permission: 要检查的权限，使用 stat 中定义的常量，如 stat.S_IWGRP 表示写权限。 + :return: 如果所属组有指定权限，则返回 True；否则返回 False。 + """ + try: + # 获取文件的状态信息 + st = os.stat(path) + except FileNotFoundError: + return False + + # 使用位运算检查权限 + group_permissions = st.st_mode & (stat.S_IRGRP | stat.S_IWGRP | stat.S_IXGRP) + + # 判断指定的权限是否存在 + if group_permissions & permission: + return True + else: + return False + @staticmethod def safe_save_data(data, run_name, tag): runs = GraphState.get_global_value('runs', {}) @@ -222,13 +248,13 @@ class GraphUtils: # 构建文件路径并标准化 file_path = os.path.join(run, tag) file_path = os.path.normpath(file_path) - # 权限校验：检查目录是否有写权限 - if not os.access(run, os.W_OK): - raise PermissionError(f"No write permission for directory: {run}\n") # 检查 run 目录是否存在，如果不存在则创建 if not os.path.exists(run): os.makedirs(run, exist_ok=True) os.chmod(run, 0o750) + # 权限校验：检查目录所属组是否有写权限 + if not os.stat(file_path).st_mode and not GraphUtils.has_group_permission(file_path, stat.stat.S_IWGRP): + raise PermissionError(f"No write permission for directory: {run}\n") # 检查 tag 是否为合法文件名 if not re.match(FILE_NAME_REGEX, tag): raise ValueError(f"Invalid tag: {tag}.") @@ -262,7 +288,6 @@ class GraphUtils: @staticmethod def safe_load_data(run_name, tag, only_check=False): - runs = GraphState.get_global_value('runs', {}) run_dir = runs.get(str(run_name)) or run_name """Load a single .vis file from a given directory based on the tag.""" @@ -271,33 +296,14 @@ class GraphUtils: return None, error_message try: file_path = os.path.join(run_dir, tag) - file_path = os.path.normpath(file_path) # 标准化路径 - # 解析真实路径（包含符号链接跟踪） - real_path = os.path.realpath(file_path) - safe_base_dir = GraphState.get_global_value('logdir') - # 安全验证1：路径归属检查（防止越界访问） - if not GraphUtils.is_relative_to(file_path, safe_base_dir): - raise RuntimeError(f"Path out of bounds:") - # 安全验证2：禁止符号链接文件 - if os.path.islink(file_path): - raise RuntimeError(f"Detected symbolic link file") - if os.path.islink(run_dir): - raise RuntimeError(f"Parent directory contains a symbolic link") - # 安全验证3：二次文件类型检查（防御TOCTOU攻击） - if not os.path.isfile(real_path): - raise RuntimeError(f"Path is not a regular file") - # 安全检查4：文件存在性验证 - if not os.path.exists(real_path): - raise FileNotFoundError(f"File does not exist") - # 权限验证 - if not os.stat(real_path).st_mode & stat.S_IRUSR: - raise PermissionError(f"File has no read permissions") - # 文件大小验证 - if os.path.getsize(real_path) > MAX_FILE_SIZE: - file_size = GraphUtils.bytes_to_human_readable(os.path.getsize(real_path)) - max_size = GraphUtils.bytes_to_human_readable(MAX_FILE_SIZE) - raise RuntimeError( - f"File size exceeds limit ({file_size} > {max_size})") + # 目录安全校验 + success, error = GraphUtils.safe_check_load_file_path(run_dir, True) + if not success: + raise RuntimeError(error) + # 文件安全校验 + success, error = GraphUtils.safe_check_load_file_path(file_path) + if not success: + raise RuntimeError(error) # 读取文件比较耗时，支持onlyCheck参数，仅进行安全校验 if only_check: return True, None @@ -309,8 +315,64 @@ class GraphUtils: return None, "File is not a valid JSON file!" except Exception as e: logger.error(f'Error: File "{file_path}" is not accessible. Error: {e}') - return None, 'failed to load file' + return None, e + @staticmethod + def safe_check_load_file_path(file_path, isDir=False): + # 权限常量定义 + PERM_GROUP_WRITE = 0o020 + PERM_OTHER_WRITE = 0o002 + PERM_OTHER_READ = 0o004 + file_path = os.path.normpath(file_path) # 标准化路径 + real_path = os.path.realpath(file_path) + safe_base_dir = GraphState.get_global_value('logdir') + st = os.stat(real_path) + try: + # 安全验证：路径长度检查 + if len(real_path) > FILE_PATH_MAX_LENGTH: + raise RuntimeError(f"Path length exceeds limit") + # 安全验证：路径归属检查（防止越界访问） + if not GraphUtils.is_relative_to(file_path, safe_base_dir): + raise RuntimeError(f"Path out of bounds") + # 安全检查：文件存在性验证 + if not os.path.exists(real_path): + raise FileNotFoundError(f"File does not exist") + # 安全验证：禁止符号链接文件 + if os.path.islink(file_path): + raise RuntimeError(f"Detected symbolic link file") + # 安全验证：文件类型检查（防御TOCTOU攻击） + # 文件类型 + if not isDir and not os.path.isfile(real_path): + raise RuntimeError(f"Path is not a regular file") + # 目录类型 + if isDir and not Path(real_path).is_dir(): + raise RuntimeError(f"Directory does not exist") + # 可读性检查 + if not st.st_mode & PERM_OTHER_READ: + raise RuntimeError(f"Directory lacks read permission for others") + # 文件大小校验 + if not isDir and os.path.getsize(file_path) > MAX_FILE_SIZE: + file_size = GraphUtils.bytes_to_human_readable(os.path.getsize(file_path)) + max_size = GraphUtils.bytes_to_human_readable(MAX_FILE_SIZE) + raise RuntimeError( + f"File size exceeds limit ({file_size} > {max_size})") + # 非windows系统下，属主检查 + if os.name != 'nt': + current_uid = os.getuid() + # 如果是root用户，跳过后续权限检查 + if current_uid == 0: + return True + # 属主检查 + if st.st_uid != current_uid: + raise RuntimeError(f"Directory is not owned by the current user") + # group和其他用户不可写检查 + if st.st_mode & PERM_GROUP_WRITE or st.st_mode & PERM_OTHER_WRITE: + raise RuntimeError(f"Directory has group or other write permission") + return True, None + except Exception as e: + logger.error(e) + return False, e + @staticmethod def find_config_files(run_name): """ @@ -322,28 +384,11 @@ class GraphUtils: run = runs.get(run_name) dir_path = Path(run) try: - file_path = os.path.normpath(run) # 标准化路径 - # 解析真实路径（包含符号链接跟踪） - real_path = os.path.realpath(file_path) - safe_base_dir = GraphState.get_global_value('logdir') - # 安全验证1：路径归属检查（防止越界访问） - if not GraphUtils.is_relative_to(file_path, safe_base_dir): - raise RuntimeError(f"Path out of bounds:") - # 安全验证2：禁止符号链接文件 - if os.path.islink(file_path): - raise RuntimeError(f"Detected symbolic link file") - # 安全检查3：文件存在性验证 - if not os.path.exists(real_path): - raise FileNotFoundError(f"File does not exist") - # 权限验证 - if not os.stat(real_path).st_mode & stat.S_IRUSR: - raise PermissionError(f"File has no read permissions") - if not dir_path.is_dir(): - raise ValueError(f"The provided path '{run}' is not a valid directory.") - return [ - file.name for file in dir_path.iterdir() - if file.is_file() and file.name.endswith('.vis.config') - ] + if GraphUtils.safe_check_load_file_path(run, True): + return [ + file.name for file in dir_path.iterdir() + if file.is_file() and file.name.endswith('.vis.config') + ] except Exception as e: logger.error(e) return [] -- Gitee From dec6d1dc7a31cc4fa2d926c4a9097fb815f12960 Mon Sep 17 00:00:00 2001 From: sunchao <1299792067@qq.com> Date: Sat, 28 Jun 2025 16:07:49 +0800 Subject: [PATCH 62/79] =?UTF-8?q?=F0=9F=A6=84=20refactor:=20=E5=86=99?= =?UTF-8?q?=E6=96=87=E4=BB=B6=E5=AE=89=E5=85=A8=E6=A0=A1=E9=AA=8C?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../server/app/utils/graph_utils.py | 132 ++++++++++-------- 1 file changed, 72 insertions(+), 60 deletions(-) diff --git a/plugins/tensorboard-plugins/tb_graph_ascend/server/app/utils/graph_utils.py b/plugins/tensorboard-plugins/tb_graph_ascend/server/app/utils/graph_utils.py index 3f1af3503..03cf11bc3 100644 --- a/plugins/tensorboard-plugins/tb_graph_ascend/server/app/utils/graph_utils.py +++ b/plugins/tensorboard-plugins/tb_graph_ascend/server/app/utils/graph_utils.py @@ -212,63 +212,28 @@ class GraphUtils: return f"{size_bytes:.{decimal_places}f} {units[unit_index]}" - @staticmethod - def has_group_permission(path, permission): - """ - 检查指定路径是否对其所属组具有指定权限。 - - :param path: 要检查的文件或目录的路径。 - :param permission: 要检查的权限，使用 stat 中定义的常量，如 stat.S_IWGRP 表示写权限。 - :return: 如果所属组有指定权限，则返回 True；否则返回 False。 - """ - try: - # 获取文件的状态信息 - st = os.stat(path) - except FileNotFoundError: - return False - - # 使用位运算检查权限 - group_permissions = st.st_mode & (stat.S_IRGRP | stat.S_IWGRP | stat.S_IXGRP) - - # 判断指定的权限是否存在 - if group_permissions & permission: - return True - else: - return False - @staticmethod def safe_save_data(data, run_name, tag): runs = GraphState.get_global_value('runs', {}) run = runs.get(run_name) or run_name - safe_base_dir = GraphState.get_global_value('logdir') if run is None or tag is None: error_message = 'The query parameters "run" and "tag" are required' return None, error_message try: - # 构建文件路径并标准化 - file_path = os.path.join(run, tag) - file_path = os.path.normpath(file_path) - # 检查 run 目录是否存在，如果不存在则创建 - if not os.path.exists(run): - os.makedirs(run, exist_ok=True) - os.chmod(run, 0o750) - # 权限校验：检查目录所属组是否有写权限 - if not os.stat(file_path).st_mode and not GraphUtils.has_group_permission(file_path, stat.stat.S_IWGRP): - raise PermissionError(f"No write permission for directory: {run}\n") # 检查 tag 是否为合法文件名 if not re.match(FILE_NAME_REGEX, tag): raise ValueError(f"Invalid tag: {tag}.") - # 检查文件路径是否合法，防止路径遍历攻击 - if not file_path.startswith(os.path.abspath(run)): - raise ValueError(f"Invalid file path: {file_path}. Potential path traversal attack.\n") - # 基础路径校验 - if not GraphUtils.is_relative_to(file_path, safe_base_dir): - raise ValueError(f"Path out of bounds: {file_path}") - if os.path.islink(file_path): - raise RuntimeError("The target file is a symbolic link") - if os.path.islink(run): - raise RuntimeError(f"Parent directory contains a symbolic link") - # # 尝试写入文件 + # 构建文件路径并标准化 + file_path = os.path.join(run, tag) + # 目录安全校验 + success, error = GraphUtils.safe_check_save_file_path(run, True) + if not success: + raise PermissionError(error) + # 文件安全校验 + success, error = GraphUtils.safe_check_save_file_path(file_path) + if not success: + raise PermissionError(error) + # 尝试写入文件 with open(file_path, "w", encoding="utf-8") as file: json.dump(data, file, ensure_ascii=False, indent=4) os.chmod(file_path, 0o640) @@ -281,7 +246,7 @@ class GraphUtils: return None, 'Invalid data' except OSError as e: logger.error(f"Failed to create directory: {run}. Error: {e}\n") - return None, 'failed to create directory {run}' + return None, 'failed to create directory ' except Exception as e: logger.error(f'Error: File "{file_path}" is not accessible. Error: {e}') return None, 'failed to save file' @@ -299,11 +264,11 @@ class GraphUtils: # 目录安全校验 success, error = GraphUtils.safe_check_load_file_path(run_dir, True) if not success: - raise RuntimeError(error) + raise PermissionError(error) # 文件安全校验 success, error = GraphUtils.safe_check_load_file_path(file_path) if not success: - raise RuntimeError(error) + raise PermissionError(error) # 读取文件比较耗时，支持onlyCheck参数，仅进行安全校验 if only_check: return True, None @@ -317,12 +282,59 @@ class GraphUtils: logger.error(f'Error: File "{file_path}" is not accessible. Error: {e}') return None, e + @staticmethod + def safe_check_save_file_path(file_path, isDir=False): + PERM_GROUP_WRITE = 0o020 + PERM_OTHER_WRITE = 0o002 + st = os.stat(file_path) + file_path = os.path.normpath(file_path) # 标准化路径 + real_path = os.path.realpath(file_path) + safe_base_dir = GraphState.get_global_value('logdir') + try: + # 安全验证：路径长度检查 + if len(file_path) > FILE_PATH_MAX_LENGTH: + raise PermissionError(f"Path length exceeds limit") + # 安全验证：基础路径校验 + if not GraphUtils.is_relative_to(file_path, safe_base_dir): + raise ValueError(f"Path out of bounds: {file_path}") + # 安全验证：禁止符号链接文件 + if os.path.islink(file_path): + raise RuntimeError("The target file is a symbolic link") + # 安全验证：文件大小校验 + if not isDir and os.path.getsize(file_path) > MAX_FILE_SIZE: + file_size = GraphUtils.bytes_to_human_readable(os.path.getsize(file_path)) + max_size = GraphUtils.bytes_to_human_readable(MAX_FILE_SIZE) + raise PermissionError( + f"File size exceeds limit ({file_size} > {max_size})") + # 安全验证：检查目录是否存在，如果不存在则创建 + if isDir and not os.path.exists(real_path): + os.makedirs(real_path, exist_ok=True) + os.chmod(file_path, 0o640) + # 权限校验：检查是否有写权限 + if not os.stat(file_path).st_mode & stat.S_IWGRP: + raise PermissionError(f"No write permission for directory\n") + # 安全验证： 非windows系统下，属主检查 + if os.name != 'nt': + current_uid = os.getuid() + # 如果是root用户，跳过后续权限检查 + if current_uid == 0: + return True + # 属主检查 + if st.st_uid != current_uid: + raise PermissionError(f"Directory is not owned by the current user") + # group和其他用户不可写检查 + if st.st_mode & PERM_GROUP_WRITE or st.st_mode & PERM_OTHER_WRITE: + raise PermissionError(f"Directory has group or other write permission") + return True, None + except Exception as e: + logger.error(e) + return False, e + @staticmethod def safe_check_load_file_path(file_path, isDir=False): # 权限常量定义 PERM_GROUP_WRITE = 0o020 PERM_OTHER_WRITE = 0o002 - PERM_OTHER_READ = 0o004 file_path = os.path.normpath(file_path) # 标准化路径 real_path = os.path.realpath(file_path) safe_base_dir = GraphState.get_global_value('logdir') @@ -330,31 +342,31 @@ class GraphUtils: try: # 安全验证：路径长度检查 if len(real_path) > FILE_PATH_MAX_LENGTH: - raise RuntimeError(f"Path length exceeds limit") + raise PermissionError(f"Path length exceeds limit") # 安全验证：路径归属检查（防止越界访问） if not GraphUtils.is_relative_to(file_path, safe_base_dir): - raise RuntimeError(f"Path out of bounds") + raise PermissionError(f"Path out of bounds") # 安全检查：文件存在性验证 if not os.path.exists(real_path): raise FileNotFoundError(f"File does not exist") # 安全验证：禁止符号链接文件 if os.path.islink(file_path): - raise RuntimeError(f"Detected symbolic link file") + raise PermissionError(f"Detected symbolic link file") # 安全验证：文件类型检查（防御TOCTOU攻击） # 文件类型 if not isDir and not os.path.isfile(real_path): - raise RuntimeError(f"Path is not a regular file") + raise PermissionError(f"Path is not a regular file") # 目录类型 if isDir and not Path(real_path).is_dir(): - raise RuntimeError(f"Directory does not exist") + raise PermissionError(f"Directory does not exist") # 可读性检查 - if not st.st_mode & PERM_OTHER_READ: - raise RuntimeError(f"Directory lacks read permission for others") + if not st.st_mode & stat.S_IRGRP: + raise PermissionError(f"Directory lacks read permission for others") # 文件大小校验 if not isDir and os.path.getsize(file_path) > MAX_FILE_SIZE: file_size = GraphUtils.bytes_to_human_readable(os.path.getsize(file_path)) max_size = GraphUtils.bytes_to_human_readable(MAX_FILE_SIZE) - raise RuntimeError( + raise PermissionError( f"File size exceeds limit ({file_size} > {max_size})") # 非windows系统下，属主检查 if os.name != 'nt': @@ -364,10 +376,10 @@ class GraphUtils: return True # 属主检查 if st.st_uid != current_uid: - raise RuntimeError(f"Directory is not owned by the current user") + raise PermissionError(f"Directory is not owned by the current user") # group和其他用户不可写检查 if st.st_mode & PERM_GROUP_WRITE or st.st_mode & PERM_OTHER_WRITE: - raise RuntimeError(f"Directory has group or other write permission") + raise PermissionError(f"Directory has group or other write permission") return True, None except Exception as e: logger.error(e) -- Gitee From e9e0e3463374db4c790832e582630ec984efcafe Mon Sep 17 00:00:00 2001 From: sunchao <1299792067@qq.com> Date: Sat, 28 Jun 2025 17:51:48 +0800 Subject: [PATCH 63/79] =?UTF-8?q?=F0=9F=90=9E=20fix:=20=E5=86=99=E5=85=A5?= =?UTF-8?q?=E6=96=87=E4=BB=B6=E4=BC=98=E5=8C=96?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../server/app/utils/graph_utils.py | 16 ++++++---------- 1 file changed, 6 insertions(+), 10 deletions(-) diff --git a/plugins/tensorboard-plugins/tb_graph_ascend/server/app/utils/graph_utils.py b/plugins/tensorboard-plugins/tb_graph_ascend/server/app/utils/graph_utils.py index 03cf11bc3..45dea6105 100644 --- a/plugins/tensorboard-plugins/tb_graph_ascend/server/app/utils/graph_utils.py +++ b/plugins/tensorboard-plugins/tb_graph_ascend/server/app/utils/graph_utils.py @@ -286,7 +286,6 @@ class GraphUtils: def safe_check_save_file_path(file_path, isDir=False): PERM_GROUP_WRITE = 0o020 PERM_OTHER_WRITE = 0o002 - st = os.stat(file_path) file_path = os.path.normpath(file_path) # 标准化路径 real_path = os.path.realpath(file_path) safe_base_dir = GraphState.get_global_value('logdir') @@ -297,21 +296,18 @@ class GraphUtils: # 安全验证：基础路径校验 if not GraphUtils.is_relative_to(file_path, safe_base_dir): raise ValueError(f"Path out of bounds: {file_path}") + if not os.path.exists(file_path): + return True, None + st = os.stat(file_path) # 安全验证：禁止符号链接文件 if os.path.islink(file_path): - raise RuntimeError("The target file is a symbolic link") - # 安全验证：文件大小校验 - if not isDir and os.path.getsize(file_path) > MAX_FILE_SIZE: - file_size = GraphUtils.bytes_to_human_readable(os.path.getsize(file_path)) - max_size = GraphUtils.bytes_to_human_readable(MAX_FILE_SIZE) - raise PermissionError( - f"File size exceeds limit ({file_size} > {max_size})") + raise PermissionError("The target file is a symbolic link") # 安全验证：检查目录是否存在，如果不存在则创建 if isDir and not os.path.exists(real_path): os.makedirs(real_path, exist_ok=True) os.chmod(file_path, 0o640) # 权限校验：检查是否有写权限 - if not os.stat(file_path).st_mode & stat.S_IWGRP: + if not os.stat(file_path).st_mode & stat.S_IWUSR: raise PermissionError(f"No write permission for directory\n") # 安全验证： 非windows系统下，属主检查 if os.name != 'nt': @@ -360,7 +356,7 @@ class GraphUtils: if isDir and not Path(real_path).is_dir(): raise PermissionError(f"Directory does not exist") # 可读性检查 - if not st.st_mode & stat.S_IRGRP: + if not st.st_mode & stat.S_IRUSR: raise PermissionError(f"Directory lacks read permission for others") # 文件大小校验 if not isDir and os.path.getsize(file_path) > MAX_FILE_SIZE: -- Gitee From 8c8760ce2f80b583575bed337f65c858374a3c8d Mon Sep 17 00:00:00 2001 From: sunchao <1299792067@qq.com> Date: Mon, 30 Jun 2025 10:02:20 +0800 Subject: [PATCH 64/79] =?UTF-8?q?=F0=9F=90=9E=20fix:=20=20=E6=B3=A8?= =?UTF-8?q?=E9=87=8A=E8=AF=AF=E5=88=A4?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../tb_graph_ascend/server/app/controllers/hierarchy.py | 4 ---- 1 file changed, 4 deletions(-) diff --git a/plugins/tensorboard-plugins/tb_graph_ascend/server/app/controllers/hierarchy.py b/plugins/tensorboard-plugins/tb_graph_ascend/server/app/controllers/hierarchy.py index b5870f298..acb60aeae 100644 --- a/plugins/tensorboard-plugins/tb_graph_ascend/server/app/controllers/hierarchy.py +++ b/plugins/tensorboard-plugins/tb_graph_ascend/server/app/controllers/hierarchy.py @@ -53,15 +53,11 @@ class Hierarchy: def extract_label_name(node_name, node_type): splited_subnode_name = node_name.split('.') splited_label = [] - # Module.layer1.1.relu.ReLU.forward.1 =>relu.ReLU.forward.1 - # Module.layer1.1.relu.ReLU.forward.1 =>relu.ReLU.forward.1 if node_type == MODULE: if len(splited_subnode_name) < 4: return node_name splited_label = splited_subnode_name[-4:] if not splited_subnode_name[ -4].isdigit() else splited_subnode_name[-5:] - # Module.layer1.1.ApiList.1 =>ApiList.1 - # Module.layer1.1.ApiList.0.1 =>ApiList.0.1 else: if len(splited_subnode_name) < 2: return node_name -- Gitee From 0392f16c9e35b84a3e022a8ef74897f6fdbf5760 Mon Sep 17 00:00:00 2001 From: sunchao <1299792067@qq.com> Date: Mon, 30 Jun 2025 10:02:20 +0800 Subject: [PATCH 65/79] =?UTF-8?q?=F0=9F=90=9E=20fix:=20=20=E6=B3=A8?= =?UTF-8?q?=E9=87=8A=E8=AF=AF=E5=88=A4?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../tb_graph_ascend/server/app/controllers/hierarchy.py | 4 ---- 1 file changed, 4 deletions(-) diff --git a/plugins/tensorboard-plugins/tb_graph_ascend/server/app/controllers/hierarchy.py b/plugins/tensorboard-plugins/tb_graph_ascend/server/app/controllers/hierarchy.py index b5870f298..acb60aeae 100644 --- a/plugins/tensorboard-plugins/tb_graph_ascend/server/app/controllers/hierarchy.py +++ b/plugins/tensorboard-plugins/tb_graph_ascend/server/app/controllers/hierarchy.py @@ -53,15 +53,11 @@ class Hierarchy: def extract_label_name(node_name, node_type): splited_subnode_name = node_name.split('.') splited_label = [] - # Module.layer1.1.relu.ReLU.forward.1 =>relu.ReLU.forward.1 - # Module.layer1.1.relu.ReLU.forward.1 =>relu.ReLU.forward.1 if node_type == MODULE: if len(splited_subnode_name) < 4: return node_name splited_label = splited_subnode_name[-4:] if not splited_subnode_name[ -4].isdigit() else splited_subnode_name[-5:] - # Module.layer1.1.ApiList.1 =>ApiList.1 - # Module.layer1.1.ApiList.0.1 =>ApiList.0.1 else: if len(splited_subnode_name) < 2: return node_name -- Gitee From ec08b06847f0e6586eadfc851735a04b3a6a7661 Mon Sep 17 00:00:00 2001 From: sunchao <1299792067@qq.com> Date: Mon, 30 Jun 2025 11:03:36 +0800 Subject: [PATCH 66/79] =?UTF-8?q?=E2=9C=A8=20feat:=20=E6=96=87=E4=BB=B6?= =?UTF-8?q?=E4=B8=8D=E5=AD=98=E5=9C=A8=E4=B8=8D=E5=9C=A8=E8=BF=9B=E8=A1=8C?= =?UTF-8?q?=E4=B8=8B=E5=B1=9E=E5=AE=89=E5=85=A8=E6=93=8D=E4=BD=9C?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../tb_graph_ascend/server/app/controllers/hierarchy.py | 6 ++++++ .../tb_graph_ascend/server/app/utils/graph_utils.py | 2 +- 2 files changed, 7 insertions(+), 1 deletion(-) diff --git a/plugins/tensorboard-plugins/tb_graph_ascend/server/app/controllers/hierarchy.py b/plugins/tensorboard-plugins/tb_graph_ascend/server/app/controllers/hierarchy.py index acb60aeae..1a8393200 100644 --- a/plugins/tensorboard-plugins/tb_graph_ascend/server/app/controllers/hierarchy.py +++ b/plugins/tensorboard-plugins/tb_graph_ascend/server/app/controllers/hierarchy.py @@ -53,11 +53,17 @@ class Hierarchy: def extract_label_name(node_name, node_type): splited_subnode_name = node_name.split('.') splited_label = [] + # 在展开层级时，将父级层级名称相关去除，仅保留子节点本身名称信息 + # 如Module.layer1.1.relu.ReLU.forward.0中的父级名称Module.layer1.1去除，仅保留子级的relu.ReLU.forward.1 + # 如Module.layer4.0.BasicBlock.forward.0中的父级名称Module.1去除，仅保留子级的layer4.0.BasicBlock.forward.0 if node_type == MODULE: if len(splited_subnode_name) < 4: return node_name splited_label = splited_subnode_name[-4:] if not splited_subnode_name[ -4].isdigit() else splited_subnode_name[-5:] + # 在展开层级时，将父级层级名称相关去除，仅保留API子节点本身名称信息， + # 如 Module.layer1.1.ApiList.1 中的父级名称Module.layer1.1去除，仅保留子级的ApiList.1 + # 如 Module.layer1.1.ApiList.0.1 中的父级名称Module.layer1.1去除，仅保留子级的ApiList.0.1 else: if len(splited_subnode_name) < 2: return node_name diff --git a/plugins/tensorboard-plugins/tb_graph_ascend/server/app/utils/graph_utils.py b/plugins/tensorboard-plugins/tb_graph_ascend/server/app/utils/graph_utils.py index 45dea6105..89b6f3f82 100644 --- a/plugins/tensorboard-plugins/tb_graph_ascend/server/app/utils/graph_utils.py +++ b/plugins/tensorboard-plugins/tb_graph_ascend/server/app/utils/graph_utils.py @@ -296,7 +296,7 @@ class GraphUtils: # 安全验证：基础路径校验 if not GraphUtils.is_relative_to(file_path, safe_base_dir): raise ValueError(f"Path out of bounds: {file_path}") - if not os.path.exists(file_path): + if not isDir and not os.path.exists(file_path): return True, None st = os.stat(file_path) # 安全验证：禁止符号链接文件 -- Gitee From ea491a1a95e80fbfb249a830885cfcf9658d5e87 Mon Sep 17 00:00:00 2001 From: sunchao <1299792067@qq.com> Date: Mon, 30 Jun 2025 15:50:17 +0800 Subject: [PATCH 67/79] =?UTF-8?q?=F0=9F=90=9E=20fix:=20code=20check?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../server/app/utils/global_state.py | 3 +++ .../server/app/utils/graph_utils.py | 24 +++++++++---------- 2 files changed, 14 insertions(+), 13 deletions(-) diff --git a/plugins/tensorboard-plugins/tb_graph_ascend/server/app/utils/global_state.py b/plugins/tensorboard-plugins/tb_graph_ascend/server/app/utils/global_state.py index 979e5b4f3..6e4fcabae 100644 --- a/plugins/tensorboard-plugins/tb_graph_ascend/server/app/utils/global_state.py +++ b/plugins/tensorboard-plugins/tb_graph_ascend/server/app/utils/global_state.py @@ -36,6 +36,9 @@ SINGLE = 'Single' # 前端节点类型 EXPAND_MODULE = 0 UNEXPAND_NODE = 1 +# 权限码 +PERM_GROUP_WRITE = 0o020 +PERM_OTHER_WRITE = 0o002 # 后端节点类型 MODULE = 0 diff --git a/plugins/tensorboard-plugins/tb_graph_ascend/server/app/utils/graph_utils.py b/plugins/tensorboard-plugins/tb_graph_ascend/server/app/utils/graph_utils.py index 89b6f3f82..b5efe347b 100644 --- a/plugins/tensorboard-plugins/tb_graph_ascend/server/app/utils/graph_utils.py +++ b/plugins/tensorboard-plugins/tb_graph_ascend/server/app/utils/graph_utils.py @@ -23,7 +23,7 @@ import sys from functools import cmp_to_key from pathlib import Path from tensorboard.util import tb_logging -from .global_state import GraphState, FILE_NAME_REGEX, MAX_FILE_SIZE +from .global_state import GraphState, FILE_NAME_REGEX, MAX_FILE_SIZE, PERM_GROUP_WRITE, PERM_OTHER_WRITE logger = tb_logging.get_logger() FILE_PATH_MAX_LENGTH = 4096 @@ -283,9 +283,7 @@ class GraphUtils: return None, e @staticmethod - def safe_check_save_file_path(file_path, isDir=False): - PERM_GROUP_WRITE = 0o020 - PERM_OTHER_WRITE = 0o002 + def safe_check_save_file_path(file_path, is_dir=False): file_path = os.path.normpath(file_path) # 标准化路径 real_path = os.path.realpath(file_path) safe_base_dir = GraphState.get_global_value('logdir') @@ -296,14 +294,14 @@ class GraphUtils: # 安全验证：基础路径校验 if not GraphUtils.is_relative_to(file_path, safe_base_dir): raise ValueError(f"Path out of bounds: {file_path}") - if not isDir and not os.path.exists(file_path): - return True, None + if not is_dir and not os.path.exists(file_path): + return True, None st = os.stat(file_path) # 安全验证：禁止符号链接文件 if os.path.islink(file_path): raise PermissionError("The target file is a symbolic link") # 安全验证：检查目录是否存在，如果不存在则创建 - if isDir and not os.path.exists(real_path): + if is_dir and not os.path.exists(real_path): os.makedirs(real_path, exist_ok=True) os.chmod(file_path, 0o640) # 权限校验：检查是否有写权限 @@ -327,10 +325,8 @@ class GraphUtils: return False, e @staticmethod - def safe_check_load_file_path(file_path, isDir=False): + def safe_check_load_file_path(file_path, is_dir=False): # 权限常量定义 - PERM_GROUP_WRITE = 0o020 - PERM_OTHER_WRITE = 0o002 file_path = os.path.normpath(file_path) # 标准化路径 real_path = os.path.realpath(file_path) safe_base_dir = GraphState.get_global_value('logdir') @@ -350,16 +346,16 @@ class GraphUtils: raise PermissionError(f"Detected symbolic link file") # 安全验证：文件类型检查（防御TOCTOU攻击） # 文件类型 - if not isDir and not os.path.isfile(real_path): + if not is_dir and not os.path.isfile(real_path): raise PermissionError(f"Path is not a regular file") # 目录类型 - if isDir and not Path(real_path).is_dir(): + if is_dir and not Path(real_path).is_dir(): raise PermissionError(f"Directory does not exist") # 可读性检查 if not st.st_mode & stat.S_IRUSR: raise PermissionError(f"Directory lacks read permission for others") # 文件大小校验 - if not isDir and os.path.getsize(file_path) > MAX_FILE_SIZE: + if not is_dir and os.path.getsize(file_path) > MAX_FILE_SIZE: file_size = GraphUtils.bytes_to_human_readable(os.path.getsize(file_path)) max_size = GraphUtils.bytes_to_human_readable(MAX_FILE_SIZE) raise PermissionError( @@ -397,6 +393,8 @@ class GraphUtils: file.name for file in dir_path.iterdir() if file.is_file() and file.name.endswith('.vis.config') ] + else: + return [] except Exception as e: logger.error(e) return [] -- Gitee From bd9e18ca32c3cb00577c215e77b47f1ce197cc26 Mon Sep 17 00:00:00 2001 From: sunchao <1299792067@qq.com> Date: Tue, 1 Jul 2025 15:06:47 +0800 Subject: [PATCH 68/79] =?UTF-8?q?=F0=9F=90=9E=20fix:=20=E8=87=AA=E5=8A=A8?= =?UTF-8?q?=E5=8C=B9=E9=85=8D=E8=8A=82=E7=82=B9=E6=94=AF=E6=8C=81=E5=8F=96?= =?UTF-8?q?=E6=B6=88=E5=8C=B9=E9=85=8D?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../server/app/controllers/match_nodes_controller.py | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/plugins/tensorboard-plugins/tb_graph_ascend/server/app/controllers/match_nodes_controller.py b/plugins/tensorboard-plugins/tb_graph_ascend/server/app/controllers/match_nodes_controller.py index 8704a93e9..e2e500c13 100644 --- a/plugins/tensorboard-plugins/tb_graph_ascend/server/app/controllers/match_nodes_controller.py +++ b/plugins/tensorboard-plugins/tb_graph_ascend/server/app/controllers/match_nodes_controller.py @@ -270,8 +270,10 @@ class MatchNodesController: @staticmethod def process_md5_task_delete(graph_data, npu_node_name, bench_node_name): config_data = GraphState.get_global_value("config_data") - manual_match_nodes = config_data.get('manualMatchNodes', {}) - if manual_match_nodes.get(npu_node_name) != bench_node_name: + npu_match_nodes_list = config_data.get('npuMatchNodes', {}) + bench_match_nodes_list = config_data.get('benchMatchNodes', {}) + if npu_match_nodes_list.get(npu_node_name) != bench_node_name or bench_match_nodes_list.get( + bench_node_name) != npu_node_name: return { 'success': False, 'error': "操作失败：节点未匹配，请先匹配节点", @@ -292,8 +294,10 @@ class MatchNodesController: @staticmethod def process_summary_task_delete(graph_data, npu_node_name, bench_node_name): config_data = GraphState.get_global_value("config_data") - manual_match_nodes = config_data.get('manualMatchNodes', {}) - if manual_match_nodes.get(npu_node_name) != bench_node_name: + npu_match_nodes_list = config_data.get('npuMatchNodes', {}) + bench_match_nodes_list = config_data.get('benchMatchNodes', {}) + if npu_match_nodes_list.get(npu_node_name) != bench_node_name or bench_match_nodes_list.get( + bench_node_name) != npu_node_name: return { 'success': False, 'error': "操作失败：节点未匹配，请先匹配节点", -- Gitee From 0d726d0917f78bb3619c2e86c4f1b66cd0883201 Mon Sep 17 00:00:00 2001 From: sunchao <1299792067@qq.com> Date: Tue, 1 Jul 2025 16:01:36 +0800 Subject: [PATCH 69/79] =?UTF-8?q?=E2=9C=A8=20feat:=20=E6=96=87=E6=9C=AC?= =?UTF-8?q?=E6=A1=86=E9=AB=98=E5=BA=A6=E5=A2=9E=E5=8A=A0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../components/tf_vaddin_text_table/index.ts | 2 +- .../fe/src/graph_info_board/useNodeInfo.ts | 14 -------------- 2 files changed, 1 insertion(+), 15 deletions(-) diff --git a/plugins/tensorboard-plugins/tb_graph_ascend/fe/src/graph_info_board/components/tf_vaddin_text_table/index.ts b/plugins/tensorboard-plugins/tb_graph_ascend/fe/src/graph_info_board/components/tf_vaddin_text_table/index.ts index 9f6d663fc..7e97498e6 100644 --- a/plugins/tensorboard-plugins/tb_graph_ascend/fe/src/graph_info_board/components/tf_vaddin_text_table/index.ts +++ b/plugins/tensorboard-plugins/tb_graph_ascend/fe/src/graph_info_board/components/tf_vaddin_text_table/index.ts @@ -39,7 +39,7 @@ class TfVaadinTable extends PolymerElement { .copyable-input { gap: 8px; width: 100%; - height: 160px; + height: 260px; font-family: Roboto, sans-serif; font-weight: 400; } diff --git a/plugins/tensorboard-plugins/tb_graph_ascend/fe/src/graph_info_board/useNodeInfo.ts b/plugins/tensorboard-plugins/tb_graph_ascend/fe/src/graph_info_board/useNodeInfo.ts index 9ea8877b2..d9557cec4 100644 --- a/plugins/tensorboard-plugins/tb_graph_ascend/fe/src/graph_info_board/useNodeInfo.ts +++ b/plugins/tensorboard-plugins/tb_graph_ascend/fe/src/graph_info_board/useNodeInfo.ts @@ -167,20 +167,6 @@ const useNodeInfo = (): UseNodeInfoType => { stackInfo[title] = 'stackInfo'; detailData.push(stackInfo); } - // 获取suggestions - const suggestion: Record = {}; - const npusuggestion = npuNode?.suggestions; - const benchsuggestion = benchNode?.suggestions; - if (!isEmpty(npusuggestion)) { - suggestion[nodeName] = converObjectToString(npusuggestion); - } - if (!isEmpty(benchsuggestion)) { - suggestion[benchNodeName] = converObjectToString(benchsuggestion); - } - if (!isEmpty(suggestion)) { - suggestion[title] = 'suggestions'; - detailData.push(suggestion); - } // 获取parallel_merge_info const parallelMergeInfo: Record = {}; const npuparallelMergeInfo = npuNode?.parallelMergeInfo; -- Gitee From 7d5e39bdd93ed9c5234b51f3be13416185a3e9e1 Mon Sep 17 00:00:00 2001 From: sunchao <1299792067@qq.com> Date: Tue, 1 Jul 2025 17:29:04 +0800 Subject: [PATCH 70/79] =?UTF-8?q?=E2=9C=A8=20feat:=20=E5=8F=96=E6=B6=88?= =?UTF-8?q?=E5=8C=B9=E9=85=8D=E5=A2=9E=E5=8A=A0=E5=8F=AF=E9=80=89?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../tb_graph_ascend/fe/package-lock.json | 204 ++++++++++++++---- .../tb_graph_ascend/fe/package.json | 1 + .../components/tf_manual_match/index.ts | 18 +- .../components/tf_manual_match/useMatched.ts | 13 +- .../src/graph_controls_board/type/index.d.ts | 4 +- .../server/app/service/graph_service.py | 37 +++- .../server/app/views/graph_views.py | 6 +- 7 files changed, 225 insertions(+), 58 deletions(-) diff --git a/plugins/tensorboard-plugins/tb_graph_ascend/fe/package-lock.json b/plugins/tensorboard-plugins/tb_graph_ascend/fe/package-lock.json index 15f1931fe..0e2e1896e 100644 --- a/plugins/tensorboard-plugins/tb_graph_ascend/fe/package-lock.json +++ b/plugins/tensorboard-plugins/tb_graph_ascend/fe/package-lock.json @@ -29,6 +29,7 @@ "@polymer/polymer": "^3.5.1", "@types/lodash": "^4.17.1", "@vaadin/button": "24.6.5", + "@vaadin/checkbox": "^24.8.2", "@vaadin/combo-box": "24.6.5", "@vaadin/context-menu": "24.6.5", "@vaadin/details": "24.6.5", @@ -1217,14 +1218,14 @@ } }, "node_modules/@vaadin/a11y-base": { - "version": "24.6.7", - "resolved": "https://registry.npmmirror.com/@vaadin/a11y-base/-/a11y-base-24.6.7.tgz", - "integrity": "sha512-CJYYTWPBEEaVt4AvBE8RzEn3hqUZbGUGLzqs6NGBFTw0c5cfkqoO2ZMkKhz5Z52QF+2mCXpEtyg6s+t0h171Qg==", + "version": "24.6.11", + "resolved": "https://registry.npmmirror.com/@vaadin/a11y-base/-/a11y-base-24.6.11.tgz", + "integrity": "sha512-yBZ0QGPngbItIJQx3FRIa9IXDW2Ftf6SFFPGhbdAZafJPBlFi6FElP9cVtL3qjJlI5KKBp/UXEcC8ehPK207gw==", "license": "Apache-2.0", "dependencies": { "@open-wc/dedupe-mixin": "^1.3.0", "@polymer/polymer": "^3.0.0", - "@vaadin/component-base": "~24.6.7", + "@vaadin/component-base": "~24.6.11", "lit": "^3.0.0" } }, @@ -1245,22 +1246,108 @@ } }, "node_modules/@vaadin/checkbox": { - "version": "24.6.7", - "resolved": "https://registry.npmmirror.com/@vaadin/checkbox/-/checkbox-24.6.7.tgz", - "integrity": "sha512-/Vl5codokNdN5ku1l/iAkdjUmYTUZGKyAleHjM7V3ZFpwkK2IoWN4HrbWyhPuf1gL3T85bKMLSPuYoOX/ymrFw==", + "version": "24.8.2", + "resolved": "https://registry.npmmirror.com/@vaadin/checkbox/-/checkbox-24.8.2.tgz", + "integrity": "sha512-sQPrahkVyK1Mm0t5tGKLzkKLnkhnLgBOQTnCUNQDThyhldcAFmzModnzkl/6rf8dFz0ti35AwUaV0mebfAUsUw==", "license": "Apache-2.0", "dependencies": { "@open-wc/dedupe-mixin": "^1.3.0", "@polymer/polymer": "^3.0.0", - "@vaadin/a11y-base": "~24.6.7", - "@vaadin/component-base": "~24.6.7", - "@vaadin/field-base": "~24.6.7", - "@vaadin/vaadin-lumo-styles": "~24.6.7", - "@vaadin/vaadin-material-styles": "~24.6.7", - "@vaadin/vaadin-themable-mixin": "~24.6.7", + "@vaadin/a11y-base": "~24.8.2", + "@vaadin/component-base": "~24.8.2", + "@vaadin/field-base": "~24.8.2", + "@vaadin/vaadin-lumo-styles": "~24.8.2", + "@vaadin/vaadin-material-styles": "~24.8.2", + "@vaadin/vaadin-themable-mixin": "~24.8.2", + "lit": "^3.0.0" + } + }, + "node_modules/@vaadin/checkbox/node_modules/@vaadin/a11y-base": { + "version": "24.8.2", + "resolved": "https://registry.npmmirror.com/@vaadin/a11y-base/-/a11y-base-24.8.2.tgz", + "integrity": "sha512-Cp2raqWoe/sgzuvpSo17ROxEvTMj+3gYfW8uAkCEC2bVmV542oMiPzLPyC0GP2GjJGzrvz7y9eQ3sq1ATZxLkA==", + "license": "Apache-2.0", + "dependencies": { + "@open-wc/dedupe-mixin": "^1.3.0", + "@polymer/polymer": "^3.0.0", + "@vaadin/component-base": "~24.8.2", + "lit": "^3.0.0" + } + }, + "node_modules/@vaadin/checkbox/node_modules/@vaadin/component-base": { + "version": "24.8.2", + "resolved": "https://registry.npmmirror.com/@vaadin/component-base/-/component-base-24.8.2.tgz", + "integrity": "sha512-pzM49fDKm4erK5GDIvakb0utCm8Xm7oBqkxyGJfgS3y9w8MGvJ6foUL/QD+13nW5zL6toZ+kwdIw7PvIF96pKQ==", + "license": "Apache-2.0", + "dependencies": { + "@open-wc/dedupe-mixin": "^1.3.0", + "@polymer/polymer": "^3.0.0", + "@vaadin/vaadin-development-mode-detector": "^2.0.0", + "@vaadin/vaadin-usage-statistics": "^2.1.0", "lit": "^3.0.0" } }, + "node_modules/@vaadin/checkbox/node_modules/@vaadin/field-base": { + "version": "24.8.2", + "resolved": "https://registry.npmmirror.com/@vaadin/field-base/-/field-base-24.8.2.tgz", + "integrity": "sha512-a74pBeaPI6E/bvwLCOlF8LyU0TcOqen1KSbERifPsPECRQq0ODif2pfOEiEatD1P/DM0jh8fdaiPjq2BTocNbw==", + "license": "Apache-2.0", + "dependencies": { + "@open-wc/dedupe-mixin": "^1.3.0", + "@polymer/polymer": "^3.0.0", + "@vaadin/a11y-base": "~24.8.2", + "@vaadin/component-base": "~24.8.2", + "lit": "^3.0.0" + } + }, + "node_modules/@vaadin/checkbox/node_modules/@vaadin/icon": { + "version": "24.8.2", + "resolved": "https://registry.npmmirror.com/@vaadin/icon/-/icon-24.8.2.tgz", + "integrity": "sha512-0IhbiikTDJzpn9NQplKIIW2lK5tK6q2pLHXT+O41KA2ZcwTWoyAoKQyjU40vpHK+9EOGhwqX7LncugnWSO6iwg==", + "license": "Apache-2.0", + "dependencies": { + "@open-wc/dedupe-mixin": "^1.3.0", + "@polymer/polymer": "^3.0.0", + "@vaadin/component-base": "~24.8.2", + "@vaadin/vaadin-lumo-styles": "~24.8.2", + "@vaadin/vaadin-themable-mixin": "~24.8.2", + "lit": "^3.0.0" + } + }, + "node_modules/@vaadin/checkbox/node_modules/@vaadin/vaadin-lumo-styles": { + "version": "24.8.2", + "resolved": "https://registry.npmmirror.com/@vaadin/vaadin-lumo-styles/-/vaadin-lumo-styles-24.8.2.tgz", + "integrity": "sha512-DanIV4ZHjmb/1dF+oyj33IZVxEXLa1Sjb3ZDWrTlu6I59/veDsFE7g6y6ZYjNLE/O8YlE1oz8yHEUViSXmBwfw==", + "license": "Apache-2.0", + "dependencies": { + "@polymer/polymer": "^3.0.0", + "@vaadin/component-base": "~24.8.2", + "@vaadin/icon": "~24.8.2", + "@vaadin/vaadin-themable-mixin": "~24.8.2" + } + }, + "node_modules/@vaadin/checkbox/node_modules/@vaadin/vaadin-material-styles": { + "version": "24.8.2", + "resolved": "https://registry.npmmirror.com/@vaadin/vaadin-material-styles/-/vaadin-material-styles-24.8.2.tgz", + "integrity": "sha512-l0WMqPeH0bfoviZqu96h9CFZ92Y7nQwG9gb1mr9tkXd7XCbt/DEmCcvVaAJSpxVp+Qi7/J8z8Wn9855GExTdHg==", + "license": "Apache-2.0", + "dependencies": { + "@polymer/polymer": "^3.0.0", + "@vaadin/component-base": "~24.8.2", + "@vaadin/vaadin-themable-mixin": "~24.8.2" + } + }, + "node_modules/@vaadin/checkbox/node_modules/@vaadin/vaadin-themable-mixin": { + "version": "24.8.2", + "resolved": "https://registry.npmmirror.com/@vaadin/vaadin-themable-mixin/-/vaadin-themable-mixin-24.8.2.tgz", + "integrity": "sha512-i0MLfbGU+B1fWEPxAQzrQOdQ2JfrYLyagOfe8FOKFAMm03xURtbD7hZKxFchULudphD+/NvHh2i++Vl3mGMfsA==", + "license": "Apache-2.0", + "dependencies": { + "@open-wc/dedupe-mixin": "^1.3.0", + "lit": "^3.0.0", + "style-observer": "^0.0.8" + } + }, "node_modules/@vaadin/combo-box": { "version": "24.6.5", "resolved": "https://registry.npmmirror.com/@vaadin/combo-box/-/combo-box-24.6.5.tgz", @@ -1283,9 +1370,9 @@ } }, "node_modules/@vaadin/component-base": { - "version": "24.6.7", - "resolved": "https://registry.npmmirror.com/@vaadin/component-base/-/component-base-24.6.7.tgz", - "integrity": "sha512-LcZQZEwouPDHBoXfXRREb1mRScsPSPeKTUZdgrXh180Piy57VzpNzslIMrdfVFSye9lLMs2/g2o8HCUDgnY/OQ==", + "version": "24.6.11", + "resolved": "https://registry.npmmirror.com/@vaadin/component-base/-/component-base-24.6.11.tgz", + "integrity": "sha512-7jR6vcJeCBgY2CNbAPLOcUTsxYspqdkA0slUGk3GwfgsRDD5FLkzqQDSM5+yE6O2+4Wah2Tk+kG/GsKGtlUlwg==", "license": "Apache-2.0", "dependencies": { "@open-wc/dedupe-mixin": "^1.3.0", @@ -1333,15 +1420,15 @@ } }, "node_modules/@vaadin/field-base": { - "version": "24.6.7", - "resolved": "https://registry.npmmirror.com/@vaadin/field-base/-/field-base-24.6.7.tgz", - "integrity": "sha512-5MXpAQGZA15/hRdnZrJK5q5Mv8rgOraSyBpC/gjRJ1W1IQ5DrCcb3ltvPATguv0K3vpJwunXGXrGqm/+SGEk0w==", + "version": "24.6.11", + "resolved": "https://registry.npmmirror.com/@vaadin/field-base/-/field-base-24.6.11.tgz", + "integrity": "sha512-dRjxKzbW3xQAau1xuO8uZepVWaImS2wEyKDK9Oh+y8iiu4smYEmo9e4aqMqQN/sOHU6OSa4YtbyJZlvD1sBXrA==", "license": "Apache-2.0", "dependencies": { "@open-wc/dedupe-mixin": "^1.3.0", "@polymer/polymer": "^3.0.0", - "@vaadin/a11y-base": "~24.6.7", - "@vaadin/component-base": "~24.6.7", + "@vaadin/a11y-base": "~24.6.11", + "@vaadin/component-base": "~24.6.11", "lit": "^3.0.0" } }, @@ -1364,6 +1451,23 @@ "lit": "^3.0.0" } }, + "node_modules/@vaadin/grid/node_modules/@vaadin/checkbox": { + "version": "24.6.11", + "resolved": "https://registry.npmmirror.com/@vaadin/checkbox/-/checkbox-24.6.11.tgz", + "integrity": "sha512-Uvd6gZ3xQQrZTtCJL6f4uLbg6mXsAKjiZto7Je39yJwUHz8r5MIQr+4mLF4zc6mYVSH/Ihj/a4n9FOuTwSEuQw==", + "license": "Apache-2.0", + "dependencies": { + "@open-wc/dedupe-mixin": "^1.3.0", + "@polymer/polymer": "^3.0.0", + "@vaadin/a11y-base": "~24.6.11", + "@vaadin/component-base": "~24.6.11", + "@vaadin/field-base": "~24.6.11", + "@vaadin/vaadin-lumo-styles": "~24.6.11", + "@vaadin/vaadin-material-styles": "~24.6.11", + "@vaadin/vaadin-themable-mixin": "~24.6.11", + "lit": "^3.0.0" + } + }, "node_modules/@vaadin/icon": { "version": "24.6.5", "resolved": "https://registry.npmmirror.com/@vaadin/icon/-/icon-24.6.5.tgz", @@ -1625,46 +1729,46 @@ "license": "Apache-2.0" }, "node_modules/@vaadin/vaadin-lumo-styles": { - "version": "24.6.7", - "resolved": "https://registry.npmmirror.com/@vaadin/vaadin-lumo-styles/-/vaadin-lumo-styles-24.6.7.tgz", - "integrity": "sha512-DNamU8cVxbaVn3HfRm3pN8ul95xvaem92ByVeEQwdvKaHwLI4m7AdSWKEA+13ST9TdBtCeDW6DjmtGcoEqbqiw==", + "version": "24.6.11", + "resolved": "https://registry.npmmirror.com/@vaadin/vaadin-lumo-styles/-/vaadin-lumo-styles-24.6.11.tgz", + "integrity": "sha512-WRluczao8lZgImdtl66v09YjFULb1iLAhcU48aiR9igAT7h6aLeHYBvRH3AA/gBlUNwHd4xlBSl89p4HP2GGog==", "license": "Apache-2.0", "dependencies": { "@polymer/polymer": "^3.0.0", - "@vaadin/component-base": "~24.6.7", - "@vaadin/icon": "~24.6.7", - "@vaadin/vaadin-themable-mixin": "~24.6.7" + "@vaadin/component-base": "~24.6.11", + "@vaadin/icon": "~24.6.11", + "@vaadin/vaadin-themable-mixin": "~24.6.11" } }, "node_modules/@vaadin/vaadin-lumo-styles/node_modules/@vaadin/icon": { - "version": "24.6.7", - "resolved": "https://registry.npmmirror.com/@vaadin/icon/-/icon-24.6.7.tgz", - "integrity": "sha512-+Cv3hLyFSXJAhnuGuPQ+hQcv9/ijZpIprJ6rqWeChvFk+bQOoPgUPx/tj67mOiTcrmV5hYt+dYs4QM7JZ//dGg==", + "version": "24.6.11", + "resolved": "https://registry.npmmirror.com/@vaadin/icon/-/icon-24.6.11.tgz", + "integrity": "sha512-CKOh+I84+GZRfMHrhtATtrw3bSW5eUArgGT4cKsOY3asoCZXUdTObPD/PqKfP4e2uAA1bgLl27kOc+W8dmibJA==", "license": "Apache-2.0", "dependencies": { "@open-wc/dedupe-mixin": "^1.3.0", "@polymer/polymer": "^3.0.0", - "@vaadin/component-base": "~24.6.7", - "@vaadin/vaadin-lumo-styles": "~24.6.7", - "@vaadin/vaadin-themable-mixin": "~24.6.7", + "@vaadin/component-base": "~24.6.11", + "@vaadin/vaadin-lumo-styles": "~24.6.11", + "@vaadin/vaadin-themable-mixin": "~24.6.11", "lit": "^3.0.0" } }, "node_modules/@vaadin/vaadin-material-styles": { - "version": "24.6.7", - "resolved": "https://registry.npmmirror.com/@vaadin/vaadin-material-styles/-/vaadin-material-styles-24.6.7.tgz", - "integrity": "sha512-7ecHOEZrFEbUz5UVSGapOt/uC7lSYV05RADCNhG16c+WsuN+oxkGIIaThMMCdBcclg5ej/BeTxZlZha8JoNO3g==", + "version": "24.6.11", + "resolved": "https://registry.npmmirror.com/@vaadin/vaadin-material-styles/-/vaadin-material-styles-24.6.11.tgz", + "integrity": "sha512-tDumwlaDp/s9u++MPi64I1o2ls/drWOZf4xVPhztUjt3NwYJUeVXtwu39q0wBRIeRM7UBrs06kug2CVT72U4qQ==", "license": "Apache-2.0", "dependencies": { "@polymer/polymer": "^3.0.0", - "@vaadin/component-base": "~24.6.7", - "@vaadin/vaadin-themable-mixin": "~24.6.7" + "@vaadin/component-base": "~24.6.11", + "@vaadin/vaadin-themable-mixin": "~24.6.11" } }, "node_modules/@vaadin/vaadin-themable-mixin": { - "version": "24.6.7", - "resolved": "https://registry.npmmirror.com/@vaadin/vaadin-themable-mixin/-/vaadin-themable-mixin-24.6.7.tgz", - "integrity": "sha512-fiVBvJWInNBq/oXeE0UAQmzadQ7UJE3ns768D1taKOwTMOxiio1UMoUXcVGwni9ASzXrd96S7F6c4aIaVqNx6A==", + "version": "24.6.11", + "resolved": "https://registry.npmmirror.com/@vaadin/vaadin-themable-mixin/-/vaadin-themable-mixin-24.6.11.tgz", + "integrity": "sha512-xCmn3X+2C7nI9LQn2OqLLkLw7VeJOCo99DlHwnxeLZpJJ/s8bjDXcIWflS+IOChzHixgEFkDSoLcNYoCR1RvYg==", "license": "Apache-2.0", "dependencies": { "@open-wc/dedupe-mixin": "^1.3.0", @@ -5977,6 +6081,22 @@ "webpack": "^5.27.0" } }, + "node_modules/style-observer": { + "version": "0.0.8", + "resolved": "https://registry.npmmirror.com/style-observer/-/style-observer-0.0.8.tgz", + "integrity": "sha512-UaIPn33Sx4BJ+goia51Q++VFWoplWK1995VdxQYzwwbFa+FUNLKlG+aiIdG2Vw7VyzIUBi8tqu8mTyg0Ppu6Yg==", + "funding": [ + { + "type": "individual", + "url": "https://github.com/sponsors/LeaVerou" + }, + { + "type": "opencollective", + "url": "https://opencollective.com/leaverou" + } + ], + "license": "MIT" + }, "node_modules/supports-color": { "version": "7.2.0", "resolved": "https://registry.npmmirror.com/supports-color/-/supports-color-7.2.0.tgz", @@ -6569,4 +6689,4 @@ } } } -} \ No newline at end of file +} diff --git a/plugins/tensorboard-plugins/tb_graph_ascend/fe/package.json b/plugins/tensorboard-plugins/tb_graph_ascend/fe/package.json index d5bf73218..6bb4470b3 100644 --- a/plugins/tensorboard-plugins/tb_graph_ascend/fe/package.json +++ b/plugins/tensorboard-plugins/tb_graph_ascend/fe/package.json @@ -50,6 +50,7 @@ "@polymer/polymer": "^3.5.1", "@types/lodash": "^4.17.1", "@vaadin/button": "24.6.5", + "@vaadin/checkbox": "^24.8.2", "@vaadin/combo-box": "24.6.5", "@vaadin/context-menu": "24.6.5", "@vaadin/details": "24.6.5", diff --git a/plugins/tensorboard-plugins/tb_graph_ascend/fe/src/graph_controls_board/components/tf_manual_match/index.ts b/plugins/tensorboard-plugins/tb_graph_ascend/fe/src/graph_controls_board/components/tf_manual_match/index.ts index 72d6ca4a8..9f6b98968 100644 --- a/plugins/tensorboard-plugins/tb_graph_ascend/fe/src/graph_controls_board/components/tf_manual_match/index.ts +++ b/plugins/tensorboard-plugins/tb_graph_ascend/fe/src/graph_controls_board/components/tf_manual_match/index.ts @@ -19,6 +19,7 @@ import '@vaadin/details'; import '@vaadin/combo-box'; import '@vaadin/tooltip'; import '@vaadin/progress-bar'; +import '@vaadin/checkbox'; import { isEmpty } from 'lodash'; import { Notification } from '@vaadin/notification'; import { PolymerElement, html } from '@polymer/polymer'; @@ -63,7 +64,12 @@ class Legend extends PolymerElement { tf-search-combox.matched-node::part(arraw-button) { margin-top: 28px !important; } - + .match-checkbox{ + font-size: 14px; + } + vaadin-checkbox::part(checkbox) { + + } .vaadin-details-title { font-size: 14px; color: #333333; @@ -160,6 +166,7 @@ class Legend extends PolymerElement { +
    点击匹配
    @@ -183,6 +190,7 @@ class Legend extends PolymerElement { +
    取消匹配
    @@ -285,6 +293,12 @@ class Legend extends PolymerElement { @property({ type: String }) selectedConfigFile: string = ''; + @property({ type: Boolean }) + isMatchChildren: boolean = true; + + @property({ type: Boolean }) + isUnMatchChildren: boolean = true; + useMatched: UseMatchedType = useMatched(); npuMatchedNodeList = {}; benchMatchedNodeList = {}; @@ -414,6 +428,7 @@ class Legend extends PolymerElement { this.selectedNpuMatchedNode, this.selectedBenchMatchedNode, this.selection, + this.isUnMatchChildren ); this.set('unmatchLoading', false); if (success) { @@ -534,6 +549,7 @@ class Legend extends PolymerElement { this.selectedNpuUnMatchedNode, this.selectedBenchUnMatchedNode, this.selection, + this.isMatchChildren, ); this.set('matchLoading', false); if (success) { diff --git a/plugins/tensorboard-plugins/tb_graph_ascend/fe/src/graph_controls_board/components/tf_manual_match/useMatched.ts b/plugins/tensorboard-plugins/tb_graph_ascend/fe/src/graph_controls_board/components/tf_manual_match/useMatched.ts index efceeb4bc..3358e257a 100644 --- a/plugins/tensorboard-plugins/tb_graph_ascend/fe/src/graph_controls_board/components/tf_manual_match/useMatched.ts +++ b/plugins/tensorboard-plugins/tb_graph_ascend/fe/src/graph_controls_board/components/tf_manual_match/useMatched.ts @@ -20,21 +20,23 @@ import { safeJSONParse } from '../../../utils'; import request from '../../../utils/request'; import { UseMatchedType, MatchResultType } from '../../type'; const useMatched = (): UseMatchedType => { - const requestAddMatchNodes = async (npuNodeName: string, benchNodeName: string, metaData: any): Promise => { + const requestAddMatchNodes = async (npuNodeName: string, benchNodeName: string, metaData: any, isMatchChildren: boolean): Promise => { const params = { npuNodeName: npuNodeName, benchNodeName: benchNodeName, metaData: JSON.stringify(metaData), + isMatchChildren }; const mactchResult = await request({ url: 'addMatchNodes', method: 'GET', params: params }); return mactchResult; }; - const requestDeleteMatchNodes = async (npuNodeName: string, benchNodeName: string, metaData: any): Promise => { + const requestDeleteMatchNodes = async (npuNodeName: string, benchNodeName: string, metaData: any, isUnMatchChildren: boolean): Promise => { const params = { npuNodeName: npuNodeName, benchNodeName: benchNodeName, metaData: JSON.stringify(metaData), + isUnMatchChildren }; const mactchResult = await request({ url: 'deleteMatchNodes', method: 'GET', params: params }); return mactchResult; @@ -89,6 +91,7 @@ const useMatched = (): UseMatchedType => { npuNodeName: string, benchNodeName: string, selection: any, + isMatchChildren: boolean ): Promise => { if (isEmpty(npuNodeName) || isEmpty(benchNodeName)) { return { @@ -100,11 +103,11 @@ const useMatched = (): UseMatchedType => { run: selection.run, tag: selection.tag, }; - const matchResult: MatchResultType = await requestAddMatchNodes(npuNodeName, benchNodeName, metaData); + const matchResult: MatchResultType = await requestAddMatchNodes(npuNodeName, benchNodeName, metaData, isMatchChildren); return matchResult; }; - const deleteMatchedNodesLink = async (npuNodeName: string, benchNodeName: string, selection: any): Promise => { + const deleteMatchedNodesLink = async (npuNodeName: string, benchNodeName: string, selection: any, isUnMatchChildren: boolean): Promise => { if (isEmpty(npuNodeName) || isEmpty(benchNodeName)) { return { success: false, @@ -115,7 +118,7 @@ const useMatched = (): UseMatchedType => { run: selection.run, tag: selection.tag, }; - const matchResult: MatchResultType = await requestDeleteMatchNodes(npuNodeName, benchNodeName, metaData); + const matchResult: MatchResultType = await requestDeleteMatchNodes(npuNodeName, benchNodeName, metaData, isUnMatchChildren); return matchResult; }; diff --git a/plugins/tensorboard-plugins/tb_graph_ascend/fe/src/graph_controls_board/type/index.d.ts b/plugins/tensorboard-plugins/tb_graph_ascend/fe/src/graph_controls_board/type/index.d.ts index 94b2d6698..e4339fb2a 100644 --- a/plugins/tensorboard-plugins/tb_graph_ascend/fe/src/graph_controls_board/type/index.d.ts +++ b/plugins/tensorboard-plugins/tb_graph_ascend/fe/src/graph_controls_board/type/index.d.ts @@ -23,8 +23,8 @@ export type MetaDirType = Record>; export interface UseMatchedType { saveMatchedNodesLink: (selection: any) => Promise; - addMatchedNodesLink: (npuNodeName: string, benchNodeName: string, selection: any) => Promise; - deleteMatchedNodesLink: (npuNodeName: string, benchNodeName: string, selection: any) => Promise; + addMatchedNodesLink: (npuNodeName: string, benchNodeName: string, selection: any, isMatchChildren: boolean) => Promise; + deleteMatchedNodesLink: (npuNodeName: string, benchNodeName: string, selection: any, isUnMatchChildren: boolean) => Promise; saveMatchedRelations: (selection: any) => Promise; addMatchedNodesLinkByConfigFile: (condfigFile: string, selection: any) => Promise; } diff --git a/plugins/tensorboard-plugins/tb_graph_ascend/server/app/service/graph_service.py b/plugins/tensorboard-plugins/tb_graph_ascend/server/app/service/graph_service.py index 70ef0b217..202707c85 100644 --- a/plugins/tensorboard-plugins/tb_graph_ascend/server/app/service/graph_service.py +++ b/plugins/tensorboard-plugins/tb_graph_ascend/server/app/service/graph_service.py @@ -262,17 +262,30 @@ class GraphService: return {'success': False, 'error': '获取节点信息失败:' + str(e), 'data': None} @staticmethod - def add_match_nodes(npu_node_name, bench_node_name, meta_data): + def add_match_nodes(npu_node_name, bench_node_name, meta_data, is_match_children): graph_data, error_message = GraphUtils.get_graph_data(meta_data) if error_message: return {'success': False, 'error': error_message} task = graph_data.get('task') + result = {} try: # 根据任务类型计算误差 if task == 'md5' or task == 'summary': - result = MatchNodesController.process_task_add_child_layer(graph_data, - npu_node_name, bench_node_name, task) - return result + if(is_match_children): + result = MatchNodesController.process_task_add_child_layer(graph_data, + npu_node_name, bench_node_name, task) + return result + else: + result = MatchNodesController.process_task_add(graph_data, npu_node_name, bench_node_name, task) + if result.get('success'): + config_data = GraphState.get_global_value("config_data") + result['data'] = { + 'npuMatchNodes': config_data.get('npuMatchNodes', {}), + 'benchMatchNodes': config_data.get('benchMatchNodes', {}), + 'npuUnMatchNodes': config_data.get('npuUnMatchNodes', []), + 'benchUnMatchNodes': config_data.get('benchUnMatchNodes', []) + } + return result else: return {'success': False, 'error': '任务类型不支持(Task type not supported) '} except Exception as e: @@ -298,16 +311,28 @@ class GraphService: return {'success': False, 'error': '操作失败', 'data': None} @staticmethod - def delete_match_nodes(npu_node_name, bench_node_name, meta_data): + def delete_match_nodes(npu_node_name, bench_node_name, meta_data, is_unmatch_children): graph_data, error_message = GraphUtils.get_graph_data(meta_data) if error_message: return {'success': False, 'error': error_message} task = graph_data.get('task') + result = {} try: # 根据任务类型计算误差 if task == 'md5' or task == 'summary': - result = MatchNodesController.process_task_delete_child_layer(graph_data, npu_node_name, + if(is_unmatch_children): + result = MatchNodesController.process_task_delete_child_layer(graph_data, npu_node_name, bench_node_name, task) + else: + result = MatchNodesController.process_task_delete(graph_data, npu_node_name, bench_node_name, task) + if result.get('success'): + config_data = GraphState.get_global_value("config_data") + result['data'] = { + 'npuMatchNodes': config_data.get('npuMatchNodes', {}), + 'benchMatchNodes': config_data.get('benchMatchNodes', {}), + 'npuUnMatchNodes': config_data.get('npuUnMatchNodes', []), + 'benchUnMatchNodes': config_data.get('benchUnMatchNodes', []) + } return result else: return {'success': False, 'error': '任务类型不支持(Task type not supported) '} diff --git a/plugins/tensorboard-plugins/tb_graph_ascend/server/app/views/graph_views.py b/plugins/tensorboard-plugins/tb_graph_ascend/server/app/views/graph_views.py index 1ef7130e8..85762222b 100644 --- a/plugins/tensorboard-plugins/tb_graph_ascend/server/app/views/graph_views.py +++ b/plugins/tensorboard-plugins/tb_graph_ascend/server/app/views/graph_views.py @@ -135,7 +135,8 @@ class GraphView: npu_node_name = request.args.get("npuNodeName") bench_node_name = request.args.get("benchNodeName") meta_data = GraphUtils.safe_json_loads(request.args.get("metaData")) - match_result = GraphService.add_match_nodes(npu_node_name, bench_node_name, meta_data) + is_match_children = GraphUtils.safe_json_loads(request.args.get("isMatchChildren")) + match_result = GraphService.add_match_nodes(npu_node_name, bench_node_name, meta_data, is_match_children) return http_util.Respond(request, json.dumps(match_result), "application/json") # 取消节点匹配 @@ -145,7 +146,8 @@ class GraphView: npu_node_name = request.args.get("npuNodeName") bench_node_name = request.args.get("benchNodeName") meta_data = GraphUtils.safe_json_loads(request.args.get("metaData")) - match_result = GraphService.delete_match_nodes(npu_node_name, bench_node_name, meta_data) + is_unmatch_children = GraphUtils.safe_json_loads(request.args.get("isUnMatchChildren")) + match_result = GraphService.delete_match_nodes(npu_node_name, bench_node_name, meta_data, is_unmatch_children) return http_util.Respond(request, json.dumps(match_result), "application/json") # 保存匹配节点列表 -- Gitee From c980a7d64b8f946faf8a64111f6c5d428c9ab917 Mon Sep 17 00:00:00 2001 From: sunchao <1299792067@qq.com> Date: Wed, 2 Jul 2025 11:41:28 +0800 Subject: [PATCH 71/79] =?UTF-8?q?=F0=9F=90=9E=20fix:=20=E5=85=BC=E9=A1=BE?= =?UTF-8?q?=E6=98=93=E7=94=A8=E6=80=A7=E9=97=AE=E9=A2=98=E5=92=8C=E5=AE=89?= =?UTF-8?q?=E5=85=A8=E6=80=A7?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../tb_graph_ascend/fe/package-lock.json | 239 ++++++++++++++++++ .../tb_graph_ascend/fe/package.json | 1 + .../fe/src/graph_ascend/index.ts | 51 +++- .../fe/src/graph_ascend/type/index.d.ts | 13 + .../fe/src/graph_ascend/useGraphAscend.ts | 21 +- .../tb_graph_ascend/fe/src/index.css | 5 + .../server/app/service/graph_service.py | 16 +- .../server/app/views/graph_views.py | 3 +- 8 files changed, 336 insertions(+), 13 deletions(-) diff --git a/plugins/tensorboard-plugins/tb_graph_ascend/fe/package-lock.json b/plugins/tensorboard-plugins/tb_graph_ascend/fe/package-lock.json index 0e2e1896e..d58f92bf2 100644 --- a/plugins/tensorboard-plugins/tb_graph_ascend/fe/package-lock.json +++ b/plugins/tensorboard-plugins/tb_graph_ascend/fe/package-lock.json @@ -31,6 +31,7 @@ "@vaadin/button": "24.6.5", "@vaadin/checkbox": "^24.8.2", "@vaadin/combo-box": "24.6.5", + "@vaadin/confirm-dialog": "^24.8.2", "@vaadin/context-menu": "24.6.5", "@vaadin/details": "24.6.5", "@vaadin/grid": "24.6.5", @@ -1382,6 +1383,129 @@ "lit": "^3.0.0" } }, + "node_modules/@vaadin/confirm-dialog": { + "version": "24.8.2", + "resolved": "https://registry.npmmirror.com/@vaadin/confirm-dialog/-/confirm-dialog-24.8.2.tgz", + "integrity": "sha512-JIAhOYvhSlJbPDZ+EKbKENa2xsYy8fTlUO8cWJxJvDs1p/MbPCGvc9rhmHlKCHJJEGXyN23sS08ef7NHX7d9CA==", + "license": "Apache-2.0", + "dependencies": { + "@open-wc/dedupe-mixin": "^1.3.0", + "@polymer/polymer": "^3.0.0", + "@vaadin/button": "~24.8.2", + "@vaadin/component-base": "~24.8.2", + "@vaadin/dialog": "~24.8.2", + "@vaadin/overlay": "~24.8.2", + "@vaadin/vaadin-lumo-styles": "~24.8.2", + "@vaadin/vaadin-material-styles": "~24.8.2", + "@vaadin/vaadin-themable-mixin": "~24.8.2", + "lit": "^3.0.0" + } + }, + "node_modules/@vaadin/confirm-dialog/node_modules/@vaadin/a11y-base": { + "version": "24.8.2", + "resolved": "https://registry.npmmirror.com/@vaadin/a11y-base/-/a11y-base-24.8.2.tgz", + "integrity": "sha512-Cp2raqWoe/sgzuvpSo17ROxEvTMj+3gYfW8uAkCEC2bVmV542oMiPzLPyC0GP2GjJGzrvz7y9eQ3sq1ATZxLkA==", + "license": "Apache-2.0", + "dependencies": { + "@open-wc/dedupe-mixin": "^1.3.0", + "@polymer/polymer": "^3.0.0", + "@vaadin/component-base": "~24.8.2", + "lit": "^3.0.0" + } + }, + "node_modules/@vaadin/confirm-dialog/node_modules/@vaadin/button": { + "version": "24.8.2", + "resolved": "https://registry.npmmirror.com/@vaadin/button/-/button-24.8.2.tgz", + "integrity": "sha512-wuJn+aa4QfWC1KW2D4S6B8Qbh39MZ+/GoBVqjldU5KSksJEZFFePym0SmpRodvuSuoxrFKote7CxtthOY4TwXw==", + "license": "Apache-2.0", + "dependencies": { + "@open-wc/dedupe-mixin": "^1.3.0", + "@polymer/polymer": "^3.0.0", + "@vaadin/a11y-base": "~24.8.2", + "@vaadin/component-base": "~24.8.2", + "@vaadin/vaadin-lumo-styles": 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"node_modules/@vaadin/confirm-dialog/node_modules/@vaadin/vaadin-themable-mixin": { + "version": "24.8.2", + "resolved": "https://registry.npmmirror.com/@vaadin/vaadin-themable-mixin/-/vaadin-themable-mixin-24.8.2.tgz", + "integrity": "sha512-i0MLfbGU+B1fWEPxAQzrQOdQ2JfrYLyagOfe8FOKFAMm03xURtbD7hZKxFchULudphD+/NvHh2i++Vl3mGMfsA==", + "license": "Apache-2.0", + "dependencies": { + "@open-wc/dedupe-mixin": "^1.3.0", + "lit": "^3.0.0", + "style-observer": "^0.0.8" + } + }, "node_modules/@vaadin/context-menu": { "version": "24.6.5", "resolved": "https://registry.npmmirror.com/@vaadin/context-menu/-/context-menu-24.6.5.tgz", @@ -1419,6 +1543,121 @@ "lit": "^3.0.0" } }, + "node_modules/@vaadin/dialog": { + "version": "24.8.2", + "resolved": "https://registry.npmmirror.com/@vaadin/dialog/-/dialog-24.8.2.tgz", + "integrity": "sha512-DLUt4nPKQtTVBqQFwq4nz+TRht0uHFfNh/u/uR/7WdUISR7EwJ/L8XztiJO2tbv16toYl3FrX7Us8ybsHZIqKA==", + "license": "Apache-2.0", + "dependencies": { + 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"https://registry.npmmirror.com/@vaadin/component-base/-/component-base-24.8.2.tgz", + "integrity": "sha512-pzM49fDKm4erK5GDIvakb0utCm8Xm7oBqkxyGJfgS3y9w8MGvJ6foUL/QD+13nW5zL6toZ+kwdIw7PvIF96pKQ==", + "license": "Apache-2.0", + "dependencies": { + "@open-wc/dedupe-mixin": "^1.3.0", + "@polymer/polymer": "^3.0.0", + "@vaadin/vaadin-development-mode-detector": "^2.0.0", + "@vaadin/vaadin-usage-statistics": "^2.1.0", + "lit": "^3.0.0" + } + }, + "node_modules/@vaadin/dialog/node_modules/@vaadin/icon": { + "version": "24.8.2", + "resolved": "https://registry.npmmirror.com/@vaadin/icon/-/icon-24.8.2.tgz", + "integrity": "sha512-0IhbiikTDJzpn9NQplKIIW2lK5tK6q2pLHXT+O41KA2ZcwTWoyAoKQyjU40vpHK+9EOGhwqX7LncugnWSO6iwg==", + "license": "Apache-2.0", + "dependencies": { + "@open-wc/dedupe-mixin": "^1.3.0", + "@polymer/polymer": "^3.0.0", + "@vaadin/component-base": "~24.8.2", + "@vaadin/vaadin-lumo-styles": "~24.8.2", + "@vaadin/vaadin-themable-mixin": "~24.8.2", + "lit": "^3.0.0" + } + }, + "node_modules/@vaadin/dialog/node_modules/@vaadin/lit-renderer": { + "version": "24.8.2", + "resolved": "https://registry.npmmirror.com/@vaadin/lit-renderer/-/lit-renderer-24.8.2.tgz", + "integrity": "sha512-5xNRhD6ZPWLpvbCZ4mg/V6W93v3ohUyP/ULKk/6Hzxe05w6iYsel2xNEzoycmdQKIb/k24xZQMz53KDT8fyroQ==", + "license": "Apache-2.0", + "dependencies": { + "lit": "^3.0.0" + } + }, + "node_modules/@vaadin/dialog/node_modules/@vaadin/overlay": { + "version": "24.8.2", + "resolved": "https://registry.npmmirror.com/@vaadin/overlay/-/overlay-24.8.2.tgz", + "integrity": "sha512-DsmynLnU2KCNNRQe/0qHfRHQoSe/oZtrbzTGp7xyjwmU2sPLH4fXNShUbXi7MegLJs/YOH1QoIh8ejXNzxNuCA==", + "license": "Apache-2.0", + "dependencies": { + "@open-wc/dedupe-mixin": "^1.3.0", + "@polymer/polymer": "^3.0.0", + "@vaadin/a11y-base": "~24.8.2", + "@vaadin/component-base": "~24.8.2", + "@vaadin/vaadin-lumo-styles": "~24.8.2", + "@vaadin/vaadin-material-styles": "~24.8.2", + "@vaadin/vaadin-themable-mixin": "~24.8.2", + "lit": "^3.0.0" + } + }, + "node_modules/@vaadin/dialog/node_modules/@vaadin/vaadin-lumo-styles": { + "version": "24.8.2", + "resolved": "https://registry.npmmirror.com/@vaadin/vaadin-lumo-styles/-/vaadin-lumo-styles-24.8.2.tgz", + "integrity": "sha512-DanIV4ZHjmb/1dF+oyj33IZVxEXLa1Sjb3ZDWrTlu6I59/veDsFE7g6y6ZYjNLE/O8YlE1oz8yHEUViSXmBwfw==", + "license": "Apache-2.0", + "dependencies": { + "@polymer/polymer": "^3.0.0", + "@vaadin/component-base": "~24.8.2", + "@vaadin/icon": "~24.8.2", + "@vaadin/vaadin-themable-mixin": "~24.8.2" + } + }, + "node_modules/@vaadin/dialog/node_modules/@vaadin/vaadin-material-styles": { + "version": "24.8.2", + "resolved": "https://registry.npmmirror.com/@vaadin/vaadin-material-styles/-/vaadin-material-styles-24.8.2.tgz", + "integrity": "sha512-l0WMqPeH0bfoviZqu96h9CFZ92Y7nQwG9gb1mr9tkXd7XCbt/DEmCcvVaAJSpxVp+Qi7/J8z8Wn9855GExTdHg==", + "license": "Apache-2.0", + "dependencies": { + "@polymer/polymer": "^3.0.0", + "@vaadin/component-base": "~24.8.2", + "@vaadin/vaadin-themable-mixin": "~24.8.2" + } + }, + "node_modules/@vaadin/dialog/node_modules/@vaadin/vaadin-themable-mixin": { + "version": "24.8.2", + "resolved": "https://registry.npmmirror.com/@vaadin/vaadin-themable-mixin/-/vaadin-themable-mixin-24.8.2.tgz", + "integrity": "sha512-i0MLfbGU+B1fWEPxAQzrQOdQ2JfrYLyagOfe8FOKFAMm03xURtbD7hZKxFchULudphD+/NvHh2i++Vl3mGMfsA==", + "license": "Apache-2.0", + "dependencies": { + "@open-wc/dedupe-mixin": "^1.3.0", + "lit": "^3.0.0", + "style-observer": "^0.0.8" + } + }, "node_modules/@vaadin/field-base": { "version": "24.6.11", "resolved": "https://registry.npmmirror.com/@vaadin/field-base/-/field-base-24.6.11.tgz", diff --git a/plugins/tensorboard-plugins/tb_graph_ascend/fe/package.json b/plugins/tensorboard-plugins/tb_graph_ascend/fe/package.json index 6bb4470b3..3aacd5be1 100644 --- a/plugins/tensorboard-plugins/tb_graph_ascend/fe/package.json +++ b/plugins/tensorboard-plugins/tb_graph_ascend/fe/package.json @@ -52,6 +52,7 @@ "@vaadin/button": "24.6.5", "@vaadin/checkbox": "^24.8.2", "@vaadin/combo-box": "24.6.5", + "@vaadin/confirm-dialog": "^24.8.2", "@vaadin/context-menu": "24.6.5", "@vaadin/details": "24.6.5", "@vaadin/grid": "24.6.5", diff --git a/plugins/tensorboard-plugins/tb_graph_ascend/fe/src/graph_ascend/index.ts b/plugins/tensorboard-plugins/tb_graph_ascend/fe/src/graph_ascend/index.ts index 8a3cff935..171bb15d2 100644 --- a/plugins/tensorboard-plugins/tb_graph_ascend/fe/src/graph_ascend/index.ts +++ b/plugins/tensorboard-plugins/tb_graph_ascend/fe/src/graph_ascend/index.ts @@ -17,12 +17,17 @@ import { customElement, observe, property } from '@polymer/decorators'; import { html, PolymerElement } from '@polymer/polymer'; import { LegacyElementMixin } from '../polymer/legacy_element_mixin'; +import { loadGraphFileInfoListType } from './type'; import useGraphAscend from './useGraphAscend'; import { formatBytes, safeJSONParse } from '../utils'; +import { isEmpty } from 'lodash'; import '../graph_board/index'; import '../graph_info_board/index'; import '../graph_controls_board/index'; import '../common/graph-board-layout'; +import '@vaadin/confirm-dialog' +import { Notification } from '@vaadin/notification'; + import type { SelectionType, ProgressType, GraphConfigType, GraphAllNodeType, NodeListType, UnmatchedNodeType } from './type'; @customElement('graph-ascend') @@ -82,7 +87,21 @@ class TfGraphDashboard extends LegacyElementMixin(PolymerElement) { - + +
    + +
    +

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    Total: 187948us
    Kernel: 98598us
    Percentage: 52.46%", 1941, "