Collecting and Parsing Profile Data

Restrictions

• The Ascend PyTorch Profiler APIs support multiple profiling methods, but these methods cannot be enabled at the same time.
• Ensure that the Ascend PyTorch Profiler APIs are called in the same process as the service process to be profiled.
• The Ascend PyTorch Profiler APIs profile data by mapping processes to devices as follows:
  • Multi-process to multi-device: One profiling process for each device.
  • Single-process to multi-device: Supported. PyTorch 2.1.0post14, 2.5.1post2, 2.6.0, or later is required.
  • Multi-process to single-device: Ensure that the profiling processes are in serial sequence, that is, the profiling processes do not start at the same time, and each profiling process is complete from start to stop.
• Profile data occupies certain disk space. As a result, the server may be unavailable when the disk space is used up. Profile data size depends on model parameters, profiling settings, and the iteration count. Make sure the storage location has enough free disk space.

Prerequisites

• Ensure that operations in Before You Start have been completed.
• Prepare a model trained on PyTorch 2.1.0 or later and a matched dataset, and port the model to the Ascend AI Processor. For details, see "Model Porting" in PyTorch Training Model Porting and Tuning Guide.

Profile Data Collection and Parsing (torch_npu.profiler.profile)

1. Add the following sample code to the training script (for example, train_*.py)/online inference script to configure profiling parameters, and then start the training/online inference.
   

   • For details about the torch_npu.profiler.profile API in the following sample code, see Ascend PyTorch Profiler APIs.
   • The following provides two samples of calling the torch_npu.profiler.profile API:

   • Example 1: Use the WITH statement to call the torch_npu.profiler.profile API to automatically create a profiler and collect profile data within the range specified by WITH.

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     import torch import torch_npu ... # Add extended configuration parameters for profiling. For details, see the following parameter description. experimental_config = torch_npu.profiler._ExperimentalConfig( export_type=[ torch_npu.profiler.ExportType.Text ], profiler_level=torch_npu.profiler.ProfilerLevel.Level0, mstx=False, # The original parameter name msprof_tx is changed to mstx. The new version is compatible with the original parameter name msprof_tx. mstx_domain_include=[], mstx_domain_exclude=[], aic_metrics=torch_npu.profiler.AiCMetrics.AiCoreNone, l2_cache=False, op_attr=False, data_simplification=False, record_op_args=False, gc_detect_threshold=None, host_sys=[], sys_io=False, sys_interconnection=False ) # Add basic configuration parameters for profiling. For details, see the following parameter description. with torch_npu.profiler.profile( activities=[ torch_npu.profiler.ProfilerActivity.CPU, torch_npu.profiler.ProfilerActivity.NPU ], schedule=torch_npu.profiler.schedule(wait=0, warmup=0, active=1, repeat=1, skip_first=1), # Used with prof.step(). on_trace_ready=torch_npu.profiler.tensorboard_trace_handler("./result"), record_shapes=False, profile_memory=False, with_stack=False, with_modules=False, with_flops=False, experimental_config=experimental_config) as prof: # Start profiling. for step in range(steps): train_one_step(step, steps, train_loader, model, optimizer, criterion) prof.step() # Used with schedule.

   • Example 2: Create a torch_npu.profiler.profile object and use the start and stop APIs to control profiling. You can set a custom location to begin profiling.

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     import torch import torch_npu ... # Add extended configuration parameters for profiling. For details, see the following parameter description. experimental_config = torch_npu.profiler._ExperimentalConfig( export_type=[ torch_npu.profiler.ExportType.Text ], profiler_level=torch_npu.profiler.ProfilerLevel.Level0, mstx=False, # The original parameter name msprof_tx is changed to mstx. The new version is compatible with the original parameter name msprof_tx. aic_metrics=torch_npu.profiler.AiCMetrics.AiCoreNone, l2_cache=False, op_attr=False, data_simplification=False, record_op_args=False, gc_detect_threshold=None, host_sys=[], sys_io=False, sys_interconnection=False ) # Add basic configuration parameters for profiling. For details, see the following parameter description. prof = torch_npu.profiler.profile( activities=[ torch_npu.profiler.ProfilerActivity.CPU, torch_npu.profiler.ProfilerActivity.NPU ], schedule=torch_npu.profiler.schedule(wait=0, warmup=0, active=1, repeat=1, skip_first=1), on_trace_ready=torch_npu.profiler.tensorboard_trace_handler("./result"), record_shapes=False, profile_memory=False, with_stack=False, with_modules=False, with_flops=False, experimental_config=experimental_config) prof.start() # Start profiling. for step in range(steps): train_one_step() prof.step() # Used with schedule. prof.stop() # Stop profiling.

   In the preceding examples, tensorboard_trace_handler is used to export profile data. You can also use prof.export_chrome_trace to export the profile data of a single file chrome_trace_{pid}.json. The profile data exported by tensorboard_trace_handler contains the profile data exported by prof.export_chrome_trace. You can select either method based on the actual requirements.

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   import torch import torch_npu ... with torch_npu.profiler.profile() as prof: # Start profiling. for step in range(steps): train_one_step(step, steps, train_loader, model, optimizer, criterion) prof.export_chrome_trace('./chrome_trace_14.json')

2. Parse profile data.

   Automatic parsing (see tensorboard_trace_handler and prof.export_chrome_trace in the preceding sample code) and offline parsing are supported.

3. View and analyze the profile data result files.

   For details about the profile data result files, see MindSpore & PyTorch Profile Data File References.

   For details about how to visualize and analyze the parsed profile data files, see MindStudio Insight User Guide.

   You can use the msprof-analyze to analyze the profile data.

Profile Data Collection and Parsing (dynamic_profile)

dynamic_profile is used to start the profiling process at any time during model training/online inference.

Use only one of the following methods to enable dynamic_profile.

Using environment variables

1. Configure the following environment variable:

   export PROF_CONFIG_PATH="/path/to/profiler_config_path"

   After this environment variable is configured and training is started, dynamic_profile automatically creates the template file profiler_config.json in profiler_config_path. You can modify configuration options based on the template file.

   

   • This method applies only to training scenarios.
   • In this method, dynamic_profile cannot sample data of the first iteration (step 0).
   • This method depends on the profiling steps in the training process of the Torch native Optimizer.step(). Custom optimizer is not supported.
   • The path specified by PROF_CONFIG_PATH can be customized (read and write permissions are required). The path can contain only letters, digits, underscores (_), and hyphens (-). Soft links are not supported, for example, "/home/xxx/profiler_config_path".
2. Start a training job.
3. Open a new CLI and modify the profiler_config.json configuration file to enable the profiling task.
   The configuration file contains the profile data collection parameters of Profiler. You can see profiler_config.json File to modify the parameters in the configuration file to execute different profiling tasks.

   

   • dynamic_profile determines whether the profiler_config.json file is modified by identifying the status of the file.
     • dynamic_profile polls every two seconds. If the profiler_config.json file is modified, the profiling process is started. Then, the running interval between steps is recorded and used as the new polling interval. The minimum interval is one second.
     • If the profiler_config.json file is modified during the dynamic_profile profiling process, the dynamic_profile profiling is started again after the profiling process ends.
   • You are advised to use shared storage to set profiler_config_path of dynamic_profile.
   • The dynamic_profile maintenance and test logs are automatically recorded in the profiler_config_path directory. For details, see Using dynamic_profile for Dynamic Profiling of Maintenance and Test Logs.
   • The value of start_step must be greater than the current training/online inference steps and cannot exceed the maximum steps. For example, if the total number of steps is 10 and step 3 has been executed, the value of start_step must be between 3 and 10. Since the training/online inference job is still being executed during the configuration, the value of start_step must be greater than step 3.
4. Parse profile data.

   Automatic parsing and manual parsing are supported. For details, see the analyse parameter in Table 6.

5. View and analyze the profile data result files.

   For details about the profile data result files, see MindSpore & PyTorch Profile Data File References.

   For details about how to visualize and analyze the parsed profile data files, see MindStudio Insight User Guide.

   You can use the msprof-analyze to analyze the profile data.

Modifying the user training/online inference script by adding the dynamic_profile API

1. Add the following sample code to the training script (for example, train_*.py)/online inference script:

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   # Load the dynamic_profile module. from torch_npu.profiler import dynamic_profile as dp # Set the profiling configuration file path. dp.init("profiler_config_path") ... for step in steps: train_one_step() # Divide steps. dp.step()

   During init, dynamic_profile automatically creates the template file profiler_config.json in profiler_config_path. You can modify configuration items based on the template file.

   profiler_config_path can contain only letters, digits, underscores (_), and hyphens (-). Soft links are not supported.

2. Start a training/online inference task.
3. Open a new CLI and modify the profiler_config.json configuration file to enable the profiling task.
   The configuration file contains the profile data collection parameters of Profiler. You can see profiler_config.json File to modify the parameters in the configuration file to execute different profiling tasks.

   

   • dynamic_profile determines whether the profiler_config.json file is modified by identifying the status of the file.
     • dynamic_profile polls every two seconds. If the profiler_config.json file is modified, the profiling process is started. Then, the running interval between steps is recorded and used as the new polling interval. The minimum interval is one second.
     • If the profiler_config.json file is modified during the dynamic_profile profiling process, the dynamic_profile profiling is started again after the profiling process ends.
   • You are advised to use shared storage to set profiler_config_path of dynamic_profile.
   • The dynamic_profile maintenance and test logs are automatically recorded in the profiler_config_path directory. For details, see Using dynamic_profile for Dynamic Profiling of Maintenance and Test Logs.
   • The value of start_step must be greater than the current training/online inference steps and cannot exceed the maximum steps. For example, if the total number of steps is 10 and step 3 has been executed, the value of start_step must be between 3 and 10. Since the training/online inference job is still being executed during the configuration, the value of start_step must be greater than step 3.
4. Automatic parsing and manual parsing are supported. For details, see the analyse parameter in Table 6.
5. View and analyze the profile data result files.

   For details about the profile data result files, see MindSpore & PyTorch Profile Data File References.

   For details about how to visualize and analyze the parsed profile data files, see MindStudio Insight User Guide.

   You can use the msprof-analyze to analyze the profile data.

Modifying the user training/online inference script by adding the dp.start() function of dynamic_profile

1. Add the following sample code to the training script (for example, train_*.py)/online inference script:

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   # Load the dynamic_profile module. from torch_npu.profiler import dynamic_profile as dp # Set the path to the profiling configuration file of the init API. dp.init("profiler_config_path") ... for step in steps: if step==5: # Set the path to the profiling configuration file of the start API. dp.start("start_config_path") train_one_step() # Divide steps. The code that requires profiling must be loaded between dp.start() and dp.step(). dp.step()

   start_config_path is also specified as the profiler_config.json path. However, you need to manually create a configuration file by referring to profiler_config.json File and set parameters based on your actual needs. The file name must be specified, for example, dp.start("/home/xx/start_config_path/profiler_config.json").

   profiler_config_path and start_config_path can contain only letters, digits, underscores (_), and hyphens (-). Soft links are not supported.

   

   • After the dp.start() function is added, when a training/online inference job proceeds to dp.start(), data is automatically profiled based on the profiler_config.json file specified by start_config_path. The dp.start() function does not detect the modification of the profiler_config.json file. It triggers a profiling task only during training/online inference.
   • After the dp.start() function is added and the training/online inference is started:
     • If the profiler_config.json configuration file is not specified in dp.start() or the configuration file does not take effect due to an error, sample data based on the profiler_config.json configuration file in the profiler_config_path directory when the task is proceeded to dp.start()
     • If the script proceeds to dp.start() when the dynamic_profile configured in dp.init() is valid, dp.start() does not take effect.
     • If the script proceeds to dp.start() after the dynamic_profile profiling finishes as specified in dp.init(), the script continues profiling with dp.start() and generates a new profile data file directory in the prof_dir directory.
     • If the profiler_config.json file in the profiler_config_path directory is modified while the dynamic_profile configured in dp.start() is valid, dp.init() starts after the dp.start() profiling finishes and a new profile data file is generated in the prof_dir directory.
   • You are advised to use shared storage to set profiler_config_path of dynamic_profile.
   • The value of start_step must be greater than the current training/online inference steps and cannot exceed the maximum steps. For example, if the total number of steps is 10 and step 3 has been executed, the value of start_step must be between 3 and 10. Since the training/online inference job is still being executed during the configuration, the value of start_step must be greater than step 3.
2. Start a training/online inference task.
3. Parse profile data.

   Automatic parsing and manual parsing are supported. For details, see the analyse parameter in Table 6.

4. View and analyze the profile data result files.

   For details about the profile data result files, see MindSpore & PyTorch Profile Data File References.

   For details about how to visualize and analyze the parsed profile data files, see MindStudio Insight User Guide.

   You can use the msprof-analyze to analyze the profile data.

Profile Data Collection and Parsing (torch_npu.profiler._KinetoProfile)

1. Add the following sample code to the training script (for example, train_*.py)/online inference script to configure profiling parameters, and then start the training/online inference.
   

   For details about the torch_npu.profiler._KinetoProfile API in the following sample code, see Ascend PyTorch Profiler APIs.

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   import torch import torch_npu ... prof = torch_npu.profiler._KinetoProfile(activities=None, record_shapes=False, profile_memory=False, with_stack=False, with_flops=False, with_modules=False, experimental_config=None) for epoch in range(epochs): train_model_step() if epoch == 0: prof.start() if epoch == 1: prof.stop() prof.export_chrome_trace("result_dir/trace.json")

   In this method, schedule and tensorboard_trace_handler cannot be used to export profile data.

2. Parse profile data.

   Automatic parsing is supported. For details, see prof.export_chrome_trace in the preceding sample code.

3. View and analyze the profile data result files.

   For details about the profile data result files, see MindSpore & PyTorch Profile Data File References.

   For details about how to visualize and analyze the parsed profile data files, see MindStudio Insight User Guide.

   You can use the msprof-analyze to analyze the profile data.

(Optional) mstx Data Collection and Parsing

In large cluster scenarios, traditional profiling involves a large amount of data and complex analysis process. You can use the mstx parameter of experimental_config to enable custom instrumentation, customize the profiling period or the start and end times of key functions, and identify key functions or iterations to quickly demarcate performance issues.

The usage and sample code are as follows:

1. Enable torch_npu.profiler and mstx, set profiler_level to Level_none (the level can be configured as required), and set mstx_domain_include or mstx_domain_exclude to profile data.
2. In the PyTorch script, call the marker APIs torch_npu.npu.mstx, torch_npu.npu.mstx.mark, torch_npu.npu.mstx.range_start, torch_npu.npu.mstx.range_end, torch_npu.npu.mstx.mstx_range to profile required events. For details about the APIs, see "Python APIs" > "torch_npu.npu" > "profiler" in Ascend Extension for PyTorch Custom API Reference.

Only the range duration on the host is recorded.

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id = torch_npu.npu.mstx.range_start("dataloader", None)    # If the second input parameter is set to None or not set, only the range duration on the host is recorded.
dataloader()
torch_npu.npu.mstx.range_end(id)

Mark IDs on compute streams to record the range durations on the host and devices.

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stream = torch_npu.npu.current_stream()
id = torch_npu.npu.mstx.range_start("matmul", stream)    # Set the second input parameter to a valid stream, record the range durations on the host and devices.
torch.matmul()    # Compute stream operations.
torch_npu.npu.mstx.range_end(id)

Mark IDs on the collective communication stream:

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from torch.distributed.distributed_c10d import _world

if (torch.__version__ != '1.11.0') :
    stream_id = _world.default_pg._get_backend(torch.device('npu'))._get_stream_id(False)
    collective_stream = torch.npu.Stream(stream_id=collective_stream_id, device_type=20, device_index=device_id)    #  Use device_index to specify device IDs of actual services.
else:
    stream_id = _world.default_pg._get_stream_id(False)
    current_stream = torch.npu.current_stream()
    cdata = current_stream._cdata & 0xffff000000000000
    collective_stream = torch.npu.Stream(_cdata=( stream_id + cdata), device_index=device_id)    # Use device_index to specify device IDs of actual services.
id = torch_npu.npu.mstx.range_start("allreduce", collective_stream)    # Set the second input parameter to a valid stream, record the range durations on the host and devices.
torch.allreduce()    # Collective communication stream operations.
torch_npu.npu.mstx.range_end(id)

Mark IDs on the P2P communication stream:

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from torch.distributed.distributed_c10d import _world
 
if (torch.__version__ != '1.11.0') :
    stream_id = _world.default_pg._get_backend(torch.device('npu'))._get_stream_id(True)
    p2p_stream = torch.npu.Stream(stream_id=collective_stream_id, device_type=20, device_index=device_id)    # Use device_index to specify device IDs of actual services.
else:
    stream_id = _world.default_pg._get_stream_id(True)
    current_stream = torch.npu.current_stream()
    cdata = current_stream._cdata & 0xffff000000000000
    p2p_stream = torch.npu.Stream(_cdata=( stream_id + cdata), device_index=device_id)    # Use device_index to specify device IDs of actual services.
id = torch_npu.npu.mstx.range_start("send", p2p_stream)    # Set the second input parameter to a valid stream, record the range durations on the host and devices.
torch.send()
torch_npu.npu.mstx.range_end(id)

To profile data in these scenarios, configure the torch_npu.profiler.profile API and enable the mstx switch. The following is an example:

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import torch
import torch_npu

experimental_config = torch_npu.profiler._ExperimentalConfig(
    profiler_level=torch_npu.profiler.ProfilerLevel.Level_none,
    mstx=True,    # The original parameter name msprof_tx is changed to mstx. The new name is compatible with the original name.
    export_type=[
        torch_npu.profiler.ExportType.Db
        ])
with torch_npu.profiler.profile(
    schedule=torch_npu.profiler.schedule(wait=1, warmup=1, active=2, repeat=1, skip_first=1),
    on_trace_ready=torch_npu.profiler.tensorboard_trace_handler("./result"),
    experimental_config=experimental_config) as prof:
       
    for step in range(steps):
        train_one_step()    # User code, including the mstx call
        prof.step()

Profile by domain:

import torch
import torch_npu
import time
experimental_config = torch_npu.profiler._ExperimentalConfig(
    data_simplification=False,
    # Enable mstx and configure mstx_domain_include or mstx_domain_exclude.
    mstx=True,
    mstx_domain_include=['default','domain1']    # Profile 'default' and 'domain1'.
    # mstx_domain_exclude=['domain2']    #Do not profile 'domain2'. This parameter cannot be configured together with mstx_domain_include.
)
with torch_npu.profiler.profile(
    activities=[torch_npu.profiler.ProfilerActivity.CPU, torch_npu.profiler.ProfilerActivity.NPU],
    schedule=torch_npu.profiler.schedule(wait=1, warmup=0, active=1, repeat=1, skip_first=1),
    on_trace_ready=torch_npu.profiler.tensorboard_trace_handler("./result"),
    experimental_config=experimental_config) as prof:
    for i in range(5):
        # Mark the default domain.
        torch_npu.npu.mstx.mark("mark_with_default_domain")
        range_id = torch_npu.npu.mstx.range_start("range_with_default_domain")
        time.sleep(1)    # Simulate user code.
        torch_npu.npu.mstx.range_end(range_id)
        ...    # User code.
        # Mark the custom domain1.
        torch_npu.npu.mstx.mark("mark_with_domain1", domain = "domain1")
        range_id1 = torch_npu.npu.mstx.range_start("range_with_domain1", domain="domain1")
        time.sleep(1)    # Simulate user code.
        torch_npu.npu.mstx.range_end(range_id1, domain="domain1")
        ...    # User code.
        # Mark the custom domain2.
        torch_npu.npu.mstx.mark("mark_with_domain2", domain = "domain2")
        range_id2 = torch_npu.npu.mstx.range_start("range_with_domain2", domain="domain2")
        time.sleep(1)    # Simulate user code.
        torch_npu.npu.mstx.range_end(range_id2, domain="domain2")
        prof.step()

Use MindStudio Insight to open the marker data. The following figure shows the visualization view.

Figure 1 Example marker results

By default, mstx profiles communication operators, DataLoader processing time, and checkpoint saving duration.

• Format: {"streamId": "{pg streamId}","count": "{count}","dataType": "{dataType}",["srcRank": "{srcRank}"],["destRank": "{destRank}"],"groupName": "{groupName}","opName": "{opName}"}

  Example: {"streamId": "32","count": "25701386","dataType": "fp16","groupName": "group_name_43","opName": "HcclAllreduce"}

  • streamId: Stream ID for data instrumentation.
  • count: Number of input data records.
  • dataType: Input data type.
  • srcRank: Rank ID of the data sender in the communicator. This parameter applies exclusively to the hcclRecv operator.
  • destRank: Rank ID of the data receiver in the communicator. This parameter applies exclusively to the hcclSend operator.
  • groupName: Communicator name.
  • opName: Operator name.
• dataloader
• save_checkpoint

In addition, with the mstx function, you can use the mstx_torch_plugin to obtain the profile data of dataloader, forward, step, and save_checkpoint in the PyTorch model. For details, see the mstx_torch_plugin.

This function allows you to view the execution and scheduling status of custom markers from the framework to the CANN layer and then to the NPU, helping you identify key functions or events to be observed and demarcate performance issues.

For details about mstx profiling results, see msproftx Data Description.

(Optional) Environment Variable Profiling

The Ascend PyTorch Profiler APIs profile environment variable information by default. The following environment variables can be profiled:

• "ASCEND_GLOBAL_LOG_LEVEL"
• "HCCL_RDMA_TC"
• "HCCL_RDMA_SL"
• "ACLNN_CACHE_LIMIT"

Procedure:

1. Configure environment variables. The following is an example:

   export ASCEND_GLOBAL_LOG_LEVEL=1
   export HCCL_RDMA_TC=0
   export HCCL_RDMA_SL=0
   export ACLNN_CACHE_LIMIT=4096

   Set the environment variables based on the actual requirements.

2. Call the Ascend PyTorch Profiler API for profiling.
3. View the result data.
   • When export_type of experimental_config is set to torch_npu.profiler.ExportType.Text, the environment variables configured in the preceding steps are stored in the profiler_metadata.json file in the {worker_name}_{timestamp}_ascend_pt directory and the META_DATA table in the ascend_pytorch_profiler_{Rank_ID}.db file.
   • When export_type of experimental_config is set to torch_npu.profiler.ExportType.Db, the environment variable information is written to the META_DATA table in the ascend_pytorch_profiler_{Rank_ID}.db file.

(Optional) Marking the Profiling Process With Custom Character String Keys and Values

• Example 1

  1 2	with torch_npu.profiler.profile(...) as prof: prof.add_metadata(key, value)

• Example 2

  1 2	with torch_npu.profiler._KinetoProfile(...) as prof: prof.add_metadata_json(key, value)

add_metadata and add_metadata_json can be configured under torch_npu.profiler.profile and torch_npu.profiler._KinetoProfile. They need to be added in the code of the profile data collection process, after profiler initialization and before finalization.

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prof.add_metadata("test_key1", "test_value1")

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prof.add_metadata_json("test_key2", json.dumps({"key1": test_value1, "key2": test_value2}))

The metadata passed by calling this API is written to the profiler_metadata.json file in the root directory of the collection results of the Ascend PyTorch Profiler APIs.

(Optional) Memory Visualization

The function classifies and displays the occupied data when the training process occupies the storage space during model training. Export the visualization file memory_timeline.html through export_memory_timeline. To output an HTML file, you need to install matplotlib in the Python environment and set torch_npu.profiler.profile to True. In addition, if you use this function, an ascend_pt data file is generated in the current directory. The following is an example:

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import torch
import torch_npu
...

def trace_handler(prof: torch_npu.profiler.profile):
    prof.export_memory_timeline(output_path="./memory_timeline.html", device="npu:0")

with torch_npu.profiler.profile(
    activities=[
        torch_npu.profiler.ProfilerActivity.CPU,
        torch_npu.profiler.ProfilerActivity.NPU
    ],
    schedule=torch_npu.profiler.schedule(wait=0, warmup=0, active=4, repeat=1, skip_first=0),
    on_trace_ready=trace_handler,
    record_shapes=True,           # Set it to True.
    profile_memory=True,          # Set it to True.
    with_stack=True,              # Set either with_stack or with_modules to True.
    with_modules=True
) as prof:
    for _ in range(steps):
        ...
        prof.step()

After profiling, the memory_timeline.html file is exported, with the following visualization effect:

Figure 2 memory_timeline

• Time (ms): Horizontal coordinate, indicating the memory occupation time of the tensors (unit: ms).
• Memory (GB): Vertical coordinate, indicating the memory size occupied by the tensors, in GB.
• Max memory allocated: Allocated maximum memory size, in GB.
• Max memory reserved: Reserved maximum memory size, in GB.
• PARAMETER: Model parameters and model weights.
• OPTIMIZER_STATE: Optimizer status. For example, the Adam optimizer records specific status during model training.
• INPUT: Input data.
• TEMPORARY: Temporarily occupied. It is defined as tensors that are allocated and then released for a single operator. Generally, these tensors store intermediate values.
• ACTIVATION: Activation values obtained in forward propagation.
• GRADIENT: Gradient value.
• AUTOGRAD_DETAIL: Memory usage generated during backward propagation.
• UNKNOWN: Unknown type.

(Optional) Creating Child Profiler Threads

In inference scenarios, it is common to call the torch operator in a single process with multiple threads. In this case, the profiler cannot detect the child threads created by users. As a result, it cannot profile framework data such as torch operators delivered by these child threads. In this case, you can call the torch_npu.profiler.profile.enable_profiler_in_child_thread and torch_npu.profiler.profile.disable_profiler_in_child_thread APIs in the child threads created by users to register the profiler callback function and profile framework data such as torch operators delivered by the child threads.

Example:

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import threading
import torch
import torch_npu

# Define the inference model.
...

def infer(device, child_thread):
    torch.npu.set_device(device)

    if child_thread:
        # Start to profile framework data such as the torch operators of child threads.
        torch_npu.profiler.profile.enable_profiler_in_child_thread(with_modules=True)

    for _ in range(5):
        outputs = model(input_data)

    if child_thread:
        # Stop to profile framework data such as the torch operators of child threads.
        torch_npu.profiler.profile.disable_profiler_in_child_thread()


if __name__ == "__main__":
    experimental_config = torch_npu.profiler._ExperimentalConfig(
        aic_metrics=torch_npu.profiler.AiCMetrics.PipeUtilization,
        profiler_level=torch_npu.profiler.ProfilerLevel.Level1
    )

    prof = torch_npu.profiler.profile(
        activities=[torch_npu.profiler.ProfilerActivity.CPU, torch_npu.profiler.ProfilerActivity.NPU],
        on_trace_ready=torch_npu.profiler.tensorboard_trace_handler("./result"),
        record_shapes=True,
        profile_memory=True,
        with_stack=False,
        with_flops=False,
        with_modules=True,
        experimental_config=experimental_config)

    prof.start()

    threads = []
    for i in range(1, 3):
        # Create two child threads and run an inference job on devices 1 and 2 respectively.
        t = threading.Thread(target=infer, args=(i, True))
        t.start()
        threads.append(t)

    # Run an inference job on device 0 in the main thread. Data is profiled by the profiler instead of enable_profiler_in_child_thread.
    infer(0, False)

    for t in threads:
        t.join()

    prof.stop()

After the child thread profiling is complete, the generated child thread profile data is as follows:

Figure 3 Child thread profile data

In the preceding figure, Thread 455385 is the main thread, which can be properly collected by profiler. The timeline prefixed by aten in the other two threads is the profiled torch operator data.

Ascend PyTorch Profiler APIs

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pid = os.getpid()
prof.export_chrome_trace(f'./chrome_trace_{pid}.json')

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export_stacks('result_dir/stack.log', metric='self_npu_time_total')

git clone https://github.com/brendangregg/FlameGraph
cd FlameGraph
./flamegraph.pl –title "NPU time" –countname "us." profiler.stacks > perf_viz.svg

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export_memory_timeline(output_path="./memory_timeline.html", device="npu:0")

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prof = torch_npu.profiler.profile(
on_trace_ready=torch_npu.profiler.tensorboard_trace_handler("./result"))
for step in range(steps):
    if step == 5:
        prof.start()
    train_one_step()
    if step == 5:
        prof.stop()

profiler_config.json File

The content of the profiler_config.json file is as follows (the default settings are used as an example):

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{
    "activities": ["CPU", "NPU"],
    "prof_dir": "./",
    "analyse": false,
    "record_shapes": false,
    "profile_memory": false,
    "with_stack": false,
    "with_flops": false,
    "with_modules": false,
    "active": 1,
    "warmup": 0,
    "start_step": 0,
    "is_rank": false,
    "rank_list": [],
    "experimental_config": {
        "profiler_level": "Level0",
        "aic_metrics": "AiCoreNone",
        "l2_cache": false,
        "op_attr": false,
        "gc_detect_threshold": null,
        "data_simplification": true,
        "record_op_args": false,
        "export_type": ["text"],
        "mstx": false,
        "mstx_domain_include": [],
        "mstx_domain_exclude": [],
        "host_sys": [],
        "sys_io": false,
        "sys_interconnection": false
    }
}

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    "metadata": {
        "distributed_args":{
            "tp":2,
            "pp":4,
            "dp":8
        }
    }

experimental_config Parameter Description (dynamic_profile Scenario)

All experimental_config parameters are optional. The following table lists the profiling items that can be extended.

experimental_config Parameter Description

All experimental_config parameters are optional. The following table lists the profiling items that can be extended.

torch_npu.profiler.schedule Parameter Description

The torch_npu.profiler.schedule class parameters are used to set the profiling behavior in different steps in the profiling process. Prototype

torch_npu.profiler.schedule(wait, active, warmup = 0, repeat = 0, skip_first = 0)

The following figure shows the relationships between torch_npu.profiler.schedule, step, and on_trace_ready.

Figure 4 Relationships between torch_npu.profiler.schedule, step, and on_trace_ready

A code example of the configuration:

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with torch_npu.profiler.profile(
    activities=[
        torch_npu.profiler.ProfilerActivity.CPU,
        torch_npu.profiler.ProfilerActivity.NPU,
    ],
    schedule=torch_npu.profiler.schedule(
        wait=1,                        # Waiting phase. One step is skipped.
        warmup=1,                      # Warm-up phase. One step is skipped.
        active=2,                      # Record the activity data of two steps and call on_trace_ready.
        repeat=2,                      # Repeat the wait+warmup+active process twice.
        skip_first=1                   # Skip one step.
    ),
    on_trace_ready=torch_npu.profiler.tensorboard_trace_handler('./result')
    ) as prof:
        for _ in range(9):
            train_one_step()
            prof.step()                # Notify the profiler to finish a step.

Using dynamic_profile for Dynamic Profiling of Maintenance and Test Logs

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profiler_config_path/
├── log
│    ├── dp_ubuntu_xxxxxx_rank_*.log
│    ├── dp_ubuntu_xxxxxx_rank_*.log.1
│    ├── monitor_dp_ubuntu_xxxxxx_rank_*.log
│    ├── monitor_dp_ubuntu_xxxxxx_rank_*.log.1
├── profiler_config.json
└── shm

• dp_ubuntu_xxxxxx.log: Execution log of dynamic_profile, which records all actions (INFO), warnings (WARNING), and errors (ERROR) during dynamic profiling. File naming format: dp_{Operating system}_{AI task process ID}_{Rank_ID}.log.

  When an AI task is started, each Rank will initiate an AI task process. The dynamic_profile generates log files for each AI task process based on the process ID of each task.

• dp_ubuntu_xxxxxx.log.1: This is a log aging backup file. The storage limit for the dp_ubuntu_xxxxxx.log file is 200 KB. Once the limit is reached, the earliest log entries are moved to dp_ubuntu_xxxxxx.log.1. The storage limit for the dp_ubuntu_xxxxxx.log.1 file is also 200 KB, and once the limit is reached, the earliest log entries are deleted through aging.
• monitor_dp_ubuntu_xxxxxx.log: This is the log for the profiler_config.json file modifications. After dynamic_profile is enabled for dynamic profiling, it records the modification time of the profiler_config.json file, whether the modifications take effect, and the end of the dynamic_profile process in real time. An example is shown below:

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  2024-08-21 15:51:46,392 [INFO] [2127856] _dynamic_profiler_monitor.py: Dynamic profiler process load json success 2024-08-21 15:51:58,406 [INFO] [2127856] _dynamic_profiler_monitor.py: Dynamic profiler process load json success 2024-08-21 15:58:16,795 [INFO] [2127856] _dynamic_profiler_monitor.py: Dynamic profiler process done

  File naming format: monitor_dp_{Operating system}_{monitor process ID}_{Rank_ID}.log.

• monitor_dp_ubuntu_xxxxxx.log.1: This is a log aging backup file. The storage limit for the monitor_dp_ubuntu_xxxxxx.log file is 200 KB. Once the limit is reached, the earliest log entries are moved to monitor_dp_ubuntu_xxxxxx.log.1. The storage limit for the monitor_dp_ubuntu_xxxxxx.log.1 file is also 200 KB, and once the limit is reached, the earliest log entries are deleted through aging.
• shm directory: To support Python 3.7, dynamic_profile will generate the shm directory in the environment. A binary file (DynamicProfileNpuShm+Timestamp) is created in this directory to map shared memory. The file will be automatically cleaned up when the program ends normally. However, when the program is terminated using pkill, it cannot release resources due to the abnormal termination, and you need to manually clean up this file. Otherwise, if dynamic_profile is started again within a short period of time (< 1 hour) using the same configuration path, dynamic_profile will fail. For Python 3.8 or later, the binary file (DynamicProfileNpuShm+Timestamp) is stored in the /dev/shm directory. When the program is terminated using pkill, the file still needs to be manually cleaned up.