diff --git a/README.md b/README.md index 96394cb1e2457054e8e2018760eb1c701f015d0b..899f32e5c0c2b67774c6c36eb16211f12b15e698 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/debug/accuracy_tools/msprobe/core/data_dump/data_collector.py b/debug/accuracy_tools/msprobe/core/data_dump/data_collector.py index 5a79ef34cb98c988b81c07f32216b4ba685557f2..42a8d8e9cf8d4813fdb58fc6ee6f533806f2dcdd 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 fe256defcd997feed216147b22c60162612451c1..5226a7b2e222b03dfaef1cda52eb37363f049005 100644 --- a/debug/accuracy_tools/msprobe/core/hook_manager.py +++ b/debug/accuracy_tools/msprobe/core/hook_manager.py @@ -120,6 +120,8 @@ 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/docs/01.installation.md b/debug/accuracy_tools/msprobe/docs/01.installation.md index 2610852e2c00c3f53635e9431e65ef6428e980d1..d247400026d57d9e86d4f877a170659a32739d58 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。 +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[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 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 db745f42058c00069057e010337cf7a4c55752f7..e152710cafd875951ed68e72d8f8aca1fdbb74e2 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 4019c87acfb516366d71ef7962e94e58139cfdf7..fa0a3e891a68c93d4f7add65e6d628d002198f27 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 db4b20d24e3100833625d74c853bf3bdca1709cb..3b85320fa35f7ce151889e14f9d2b934b725ffa5 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 ``` diff --git a/debug/accuracy_tools/msprobe/mindspore/debugger/precision_debugger.py b/debug/accuracy_tools/msprobe/mindspore/debugger/precision_debugger.py index 6d7ee60b5c8969531084cfcdc1c2b4ecf9f08823..420f15470c4d1ac0d58e64f5e30c49fed0841ee7 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 diff --git a/debug/accuracy_tools/msprobe/nan_analyze/graph.py b/debug/accuracy_tools/msprobe/nan_analyze/graph.py index 5a4f8fb87296a39796b5124854ba7060be71d53a..37989cfa67b7984f6e7af0ed4be23c835355cd0d 100644 --- a/debug/accuracy_tools/msprobe/nan_analyze/graph.py +++ b/debug/accuracy_tools/msprobe/nan_analyze/graph.py @@ -18,6 +18,7 @@ from msprobe.core.common.const import Const from msprobe.core.common.log import logger from msprobe.core.common.exceptions import MsprobeException from msprobe.nan_analyze.utils import FileCache, RankPath, is_ignore_op, check_item_anomaly, NanAnalyseConst +from msprobe.core.common.exceptions import MsprobeException @dataclass 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 9bb4c3c8aafeb2984f8d4acfbdafd4b2fb929489..552a62a7a57562bb2fc3ca65a3748d3d59eb2a79 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 aad49a90d95ae7bccc56576f8c34a98de7f55ca6..1368e4341d20f26933aabc1fa4b915fe0f9b60ea 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 b93a122ac6d57a848ac27bce449a78f41c48ba22..67ea481a9b6c983c29f80913820deb07e0b2219c 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 f03a7d889645539d8a935454a6033aae23e62d0e..acab05db44dc02c3626fb69569ed1385f8ceaf17 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 bb2091c342b7d2036a96c1ccabeb1f81a49b65b2..da6e21049bf76e0389795aa816dbee8218bc87be 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) diff --git a/msmonitor/dynolog_npu/cli/Cargo.toml b/msmonitor/dynolog_npu/cli/Cargo.toml index 2f9703700622a0c31750ee900d2922efa4663ed8..65b8d94af52eaa9188ad107732060c4e6e727f0f 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 d1dd1dd4919b25362948c05be0a2a55e6f2cc617..7ee9d1be02657e711a2984c3a69ca78b02b34845 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() diff --git a/plugins/tensorboard-plugins/tb_graph_ascend/README.md b/plugins/tensorboard-plugins/tb_graph_ascend/README.md index 3c92c12c2453da71aad14988719d63c326f5d354..737292598ecfdf0fb39d676ce3352fb4b246076e 100644 --- a/plugins/tensorboard-plugins/tb_graph_ascend/README.md +++ b/plugins/tensorboard-plugins/tb_graph_ascend/README.md @@ -121,7 +121,10 @@ ![输入图片说明](./doc/images/vis_unmatch_info.png) ### 3.6 手动选择节点匹配 可通过浏览器界面，通过鼠标选择两个待匹配的灰色节点进行匹配。当前暂不支持真实数据模式。
-点击匹配后会将两个节点的子节点按照Module名称依次匹配，取消匹配后会将子节点的匹配关系清除。
+如果选中"操作选中节点及其子节点"：
+点击匹配后会将两个节点及其子节点按照Module名称依次匹配，取消匹配后会将子节点的匹配关系清除。
+否则：
+点击匹配后只会将两个节点进行匹配，取消匹配后会将节点的匹配关系清除 注意：匹配结束之后，需要点击保存才能持久化到源文件里面 ![输入图片说明](./doc/images/vis_match_info.png) @@ -140,6 +143,10 @@ #### 4.1.1 免责声明 本工具为基于 TensorBoard 底座开发的插件，使用本插件需要基于 TensorBoard 运行，请自行关注 TensorBoard 相关安全配置和安全风险。 + +打开本工具时，本工具会对logdir目录下的vis文件以及其父目录进行安全检查，如果存在安全风险，本工具会展示如下提示信息，询问用户是否继续执行，用户选择继续执行后，可以操作未通过安全检查的文件和目录，用户需要自行承担操作风险。如果用户选择不继续执行，则用户只能操作通过安全检查的文件。 + +![输入图片说明](./doc/images/saFe_warning.png) #### 4.1.2 TensorBoard版本说明 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 diff --git a/plugins/tensorboard-plugins/tb_graph_ascend/doc/images/safe_warning.png b/plugins/tensorboard-plugins/tb_graph_ascend/doc/images/safe_warning.png new file mode 100644 index 0000000000000000000000000000000000000000..15b14eab1fd7a5710cc540e1126c346f5291fb8a Binary files /dev/null and b/plugins/tensorboard-plugins/tb_graph_ascend/doc/images/safe_warning.png differ diff --git a/plugins/tensorboard-plugins/tb_graph_ascend/doc/images/vis_match_info.png b/plugins/tensorboard-plugins/tb_graph_ascend/doc/images/vis_match_info.png index 71e7d74991303036f02529c47dee545022647379..5785c81a2f308a4fb87db1c8528262e3b1821932 100644 Binary files a/plugins/tensorboard-plugins/tb_graph_ascend/doc/images/vis_match_info.png and b/plugins/tensorboard-plugins/tb_graph_ascend/doc/images/vis_match_info.png differ 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 15f1931fe9b6ac4de59e7abf2ef953aac12ab9ed..bf84218031fe0e5fc1de5fd7f2ea4d1ffebda948 100644 --- a/plugins/tensorboard-plugins/tb_graph_ascend/fe/package-lock.json +++ b/plugins/tensorboard-plugins/tb_graph_ascend/fe/package-lock.json @@ -29,7 +29,9 @@ "@polymer/polymer": "^3.5.1", "@types/lodash": "^4.17.1", "@vaadin/button": "24.6.5", + "@vaadin/checkbox": "24.6.5", "@vaadin/combo-box": "24.6.5", + "@vaadin/confirm-dialog": "24.6.5", "@vaadin/context-menu": "24.6.5", "@vaadin/details": "24.6.5", "@vaadin/grid": "24.6.5", @@ -1217,14 +1219,14 @@ } }, "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" } }, @@ -1245,19 +1247,19 @@ } }, "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": "24.6.5", + "resolved": "https://registry.npmmirror.com/@vaadin/checkbox/-/checkbox-24.6.5.tgz", + "integrity": "sha512-XYhiEr9PMnonyMot7y8PIK/9GlNNCxupFg7ZFsrGvsDpfD5fCfpsnv+c3kUwVN6yFze3NVQEXOyp/UGoZNoNmQ==", "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": "~24.6.5", + "@vaadin/component-base": "~24.6.5", + "@vaadin/field-base": "~24.6.5", + "@vaadin/vaadin-lumo-styles": "~24.6.5", + "@vaadin/vaadin-material-styles": "~24.6.5", + "@vaadin/vaadin-themable-mixin": "~24.6.5", "lit": "^3.0.0" } }, @@ -1283,9 +1285,9 @@ } }, "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", @@ -1295,6 +1297,24 @@ "lit": "^3.0.0" } }, + "node_modules/@vaadin/confirm-dialog": { + "version": "24.6.5", + "resolved": "https://registry.npmmirror.com/@vaadin/confirm-dialog/-/confirm-dialog-24.6.5.tgz", + "integrity": "sha512-YkEFCj4psD/+K50fpqoJIUYRvdyzoRfWdC3ZDaDcliWND7m50cNZBxj1psZ+iOP32MVK+4Ke16fFbEY0tKoyWg==", + "license": "Apache-2.0", + "dependencies": { + "@open-wc/dedupe-mixin": "^1.3.0", + "@polymer/polymer": "^3.0.0", + "@vaadin/button": "~24.6.5", + "@vaadin/component-base": "~24.6.5", + "@vaadin/dialog": "~24.6.5", + "@vaadin/overlay": "~24.6.5", + "@vaadin/vaadin-lumo-styles": "~24.6.5", + "@vaadin/vaadin-material-styles": "~24.6.5", + "@vaadin/vaadin-themable-mixin": "~24.6.5", + "lit": "^3.0.0" + } + }, "node_modules/@vaadin/context-menu": { "version": "24.6.5", "resolved": "https://registry.npmmirror.com/@vaadin/context-menu/-/context-menu-24.6.5.tgz", @@ -1332,16 +1352,33 @@ "lit": "^3.0.0" } }, + "node_modules/@vaadin/dialog": { + "version": "24.6.11", + "resolved": "https://registry.npmmirror.com/@vaadin/dialog/-/dialog-24.6.11.tgz", + "integrity": "sha512-EWMkf8yH6sFEG4e9zTpPW20sL8vTz5TpBvJmD4rUUqvxEphHCUI+uO0N05/XFa2k7xZS6q8G38hc1BcCYr5Syw==", + "license": "Apache-2.0", + "dependencies": { + "@open-wc/dedupe-mixin": "^1.3.0", + "@polymer/polymer": "^3.0.0", + "@vaadin/component-base": "~24.6.11", + "@vaadin/lit-renderer": "~24.6.11", + "@vaadin/overlay": "~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/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 +1401,23 @@ "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", @@ -1436,9 +1490,9 @@ } }, "node_modules/@vaadin/lit-renderer": { - "version": "24.6.7", - "resolved": "https://registry.npmmirror.com/@vaadin/lit-renderer/-/lit-renderer-24.6.7.tgz", - "integrity": "sha512-S9daJnGW/X+HBhOriENRYNf8hCFYABmea756onaLS0QoWLkaU3QVPKrhHjZtzNVf/15UcIeAx4C5JlIas2osFA==", + "version": "24.6.11", + "resolved": "https://registry.npmmirror.com/@vaadin/lit-renderer/-/lit-renderer-24.6.11.tgz", + "integrity": "sha512-JugFumbBQP4r28+HcbdDUVVGs5VRsqanLsifjkVrz/xb4saWv460lEYco5ES+StH+xZ2IuJZmEjEFUBSrVR/tA==", "license": "Apache-2.0", "dependencies": { "lit": "^3.0.0" @@ -1462,18 +1516,18 @@ } }, "node_modules/@vaadin/overlay": { - "version": "24.6.7", - "resolved": "https://registry.npmmirror.com/@vaadin/overlay/-/overlay-24.6.7.tgz", - "integrity": "sha512-3HZ2+Ld/ktOzFt3Ug3EoZeMqX//uKh9rsXd1d3lQl18bwVtSvG81lY7NI6tEQ2dSuniM0yy2tM+mVnV4lZq9Gw==", + "version": "24.6.11", + "resolved": "https://registry.npmmirror.com/@vaadin/overlay/-/overlay-24.6.11.tgz", + "integrity": "sha512-RrN16YWKpg2pxAu6RxlUxBbovf+1+RYRmVvUk0WKBuaA4EBli0mN1vkZbK0XEsPvC2bGHPMWWqwmUzaIXv+1bw==", "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/vaadin-lumo-styles": "~24.6.7", - "@vaadin/vaadin-material-styles": "~24.6.7", - "@vaadin/vaadin-themable-mixin": "~24.6.7", + "@vaadin/a11y-base": "~24.6.11", + "@vaadin/component-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" } }, @@ -1625,46 +1679,46 @@ "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", @@ -6569,4 +6623,4 @@ } } } -} \ 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 d5bf7321825ef2b085828176392addd974620162..fb9c1d48f45c89c57c4c9bc821db640bfc328178 100644 --- a/plugins/tensorboard-plugins/tb_graph_ascend/fe/package.json +++ b/plugins/tensorboard-plugins/tb_graph_ascend/fe/package.json @@ -50,7 +50,9 @@ "@polymer/polymer": "^3.5.1", "@types/lodash": "^4.17.1", "@vaadin/button": "24.6.5", + "@vaadin/checkbox": "24.6.5", "@vaadin/combo-box": "24.6.5", + "@vaadin/confirm-dialog": "24.6.5", "@vaadin/context-menu": "24.6.5", "@vaadin/details": "24.6.5", "@vaadin/grid": "24.6.5", @@ -73,4 +75,4 @@ "prettier": "^3.4.2", "style-loader": "^4.0.0" } -} +} \ No newline at end of file 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 8a3cff935c05614cd66ba101409e7ee05823be01..a0f6a8653759ee8b210a4b4c1255f8f0fbfb5555 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 +87,26 @@ class TfGraphDashboard extends LegacyElementMixin(PolymerElement) { - + +
+

如果您仍坚持继续，请知悉以下风险：

+
非授权路径访问可能存在信息泄露和文件内容篡改。 文件过大或格式异常，可能导致性能问题或服务中断。路径中存在软链接或权限不当，可能存在越权访问和数据篡改风险。
+

继续操作将由您自行承担相关后果。如非明确知晓风险，请取消操作并联系管理员处理。

+
+ +
+
+ - - - - - - - diff --git a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/static/trace_script.js b/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/static/trace_script.js deleted file mode 100644 index 039aef359e030dbace4161538dd1191cc6fa1ab9..0000000000000000000000000000000000000000 --- a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/static/trace_script.js +++ /dev/null @@ -1,27 +0,0 @@ -/** - * @license - * Copyright (c) 2014 The Polymer Project Authors. 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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; 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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()); 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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"; 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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 diff --git a/profiler/msprof_analyze/README.md b/profiler/msprof_analyze/README.md index 13e32a14a59f7676904e4723c146764adf856f19..208fe4e295e817e0a8c0c6bb6100d6c480cab96b 100644 --- a/profiler/msprof_analyze/README.md +++ b/profiler/msprof_analyze/README.md @@ -115,26 +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 | -|------------|------------|-------------------------------------------------------------------------------------------------------------------------------------------| ------------------------------------------------------------ | -| 2.0.2 | 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/README.md b/profiler/msprof_analyze/cluster_analyse/README.md index 7e881d94d3c8bce3ba78fe81f3480b7588c93093..2ab5cf646112153626163920f5b004a8dc1fcef4 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 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 2765ba263c809a0b0c4bf0d42da5d60b6066e182..31cf1ad9ff184d840ec16b9b636fbc3555728980 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 diff --git a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/io/__init__.py b/profiler/msprof_analyze/cluster_analyse/recipes/mstx2commop/__init__.py similarity index 42% rename from plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/io/__init__.py rename to profiler/msprof_analyze/cluster_analyse/recipes/mstx2commop/__init__.py index 296f53b7c813b2c97b498469f49b973438d9f3ae..7101187a2c2619f3b1c20dded14b433950b4c662 100644 --- a/plugins/tensorboard-plugins/tb_plugin/torch_tb_profiler/io/__init__.py +++ b/profiler/msprof_analyze/cluster_analyse/recipes/mstx2commop/__init__.py @@ -1,9 +1,7 @@ -# ------------------------------------------------------------------------- -# Copyright (c) Microsoft Corporation. -# Copyright(c) 2023 Huawei Technologies. -# All rights reserved +# Copyright (c) 2024, Huawei Technologies Co., Ltd. +# All rights reserved. # -# Licensed under the Apache License, Version 2.0 (the "License"); +# 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 # @@ -14,10 +12,3 @@ # 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/profiler/msprof_analyze/cluster_analyse/recipes/mstx2commop/mstx2commop.py b/profiler/msprof_analyze/cluster_analyse/recipes/mstx2commop/mstx2commop.py new file mode 100644 index 0000000000000000000000000000000000000000..0b53798f515c0ce70df7776f8f80eeb743ca7d1d --- /dev/null +++ b/profiler/msprof_analyze/cluster_analyse/recipes/mstx2commop/mstx2commop.py @@ -0,0 +1,200 @@ +# 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 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 +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 + self.set_output = Constant.CLUSTER_ANALYSIS_OUTPUT_PATH in params # 是否设置了output_path参数 + + @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) + 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, step_range).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))) + + 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="", 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 + 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 0000000000000000000000000000000000000000..f84b5e71407dfd1f5acb8594963177ebd863387e --- /dev/null +++ b/profiler/msprof_analyze/prof_exports/mstx2commop_export.py @@ -0,0 +1,47 @@ +# 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, 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/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 0000000000000000000000000000000000000000..5485f99da38f65fb916221aae1945ccf7ca6be0b --- /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") diff --git a/profiler/msprof_analyze/version.txt b/profiler/msprof_analyze/version.txt index f93ea0ca333052aa92d755e06d2a672b7f895426..c069720a95833aa20c1f985f38362ed236e4a1ad 100644 --- a/profiler/msprof_analyze/version.txt +++ b/profiler/msprof_analyze/version.txt @@ -1 +1 @@ -2.0.2 \ No newline at end of file +8.1.0.alpha1 \ No newline at end of file diff --git a/sample/transfer_to_npu/README.md b/sample/transfer_to_npu/README.md new file mode 100644 index 0000000000000000000000000000000000000000..167bc1ffaf7b1ffcc3e1264cb4cbdeeaa9149be9 --- /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 new file mode 100644 index 0000000000000000000000000000000000000000..7d4d994d425e2af7b6361a99235af970ca5a9480 --- /dev/null +++ b/sample/transfer_to_npu/main.py @@ -0,0 +1,519 @@ +# 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-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.') + + 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, 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)) + + # 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()