| Data collection based on FSDP backend on Ascend devices(en) |
| ========================================================================================== |
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| Last updated: 08/14/2025. |
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| This is a tutorial for data collection using the GRPO or DAPO algorithm |
| based on FSDP on Ascend devices. |
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| Configuration |
| ------------- |
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| Leverage two levels of configuration to control data collection: |
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| 1. **Global profiler control**: Use parameters in ``ppo_trainer.yaml`` to control the collection mode and steps. |
| 2. **Role profile control**: Use parameters in each role's ``profile`` field to control the collection mode for each role. |
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| Global collection control |
| ~~~~~~~~~~~~~~~~~~~~~~~~~ |
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| Use parameters in ppo_trainer.yaml to control the collection mode |
| and steps. |
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| - global_profiler: Control the ranks and mode of profiling |
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| - tool: The profiling tool to use, options are nsys, npu, torch, |
| torch_memory. |
| - steps: This parameter can be set as a list that has |
| collection steps, such as [2, 4], which means it will collect steps 2 |
| and 4. If set to null, no collection occurs. |
| - save_path: The path to save the collected data. Default is |
| "outputs/profile". |
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| Role collection control |
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ |
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| In each role's ``profiler`` field, you can control the collection mode for that role. |
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| - enable: Whether to enable profiling for this role. |
| - all_ranks: Whether to collect data from all ranks. |
| - ranks: A list of ranks to collect data from. If empty, no data is collected. |
| - tool_config: Configuration for the profiling tool used by this role. |
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| Use parameters in each role's ``profiler.tool_config.npu`` to control npu profiler behavior: |
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| - level: Collection level—options are level_none, level0, level1, and |
| level2 |
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| - level_none: Disables all level-based data collection (turns off |
| profiler_level). |
| - level0: Collect high-level application data, underlying NPU data, |
| and operator execution details on NPU. |
| - level1: Extends level0 by adding CANN-layer AscendCL data and AI |
| Core performance metrics on NPU. |
| - level2: Extends level1 by adding CANN-layer Runtime data and AI |
| CPU metrics. |
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| - contents: A list of options to control the collection content, such as |
| npu, cpu, memory, shapes, module, stack. |
| |
| - npu: Whether to collect device-side performance data. |
| - cpu: Whether to collect host-side performance data. |
| - memory: Whether to enable memory analysis. |
| - shapes: Whether to record tensor shapes. |
| - module: Whether to record framework-layer Python call stack |
| information. |
| - stack: Whether to record operator call stack information. |
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| - analysis: Enables automatic data parsing. |
| - discrete: Whether to enable discrete mode. |
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| Examples |
| -------- |
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| Disabling collection |
| ~~~~~~~~~~~~~~~~~~~~ |
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| .. code:: yaml |
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| global_profiler: |
| steps: null # disable profile |
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| End-to-End collection |
| ~~~~~~~~~~~~~~~~~~~~~ |
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| .. code:: yaml |
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| global_profiler: |
| steps: [1, 2, 5] |
| actor_rollout_ref: |
| actor: |
| profiler: |
| enable: True |
| all_ranks: True |
| tool_config: |
| npu: |
| discrete: False |
| # rollout & ref follow actor settings |
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| Discrete Mode Collection |
| ~~~~~~~~~~~~~~~~~~~~~~~~ |
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| .. code:: yaml |
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| global_profiler: |
| steps: [1, 2, 5] |
| actor_rollout_ref: |
| actor: |
| profiler: |
| enable: True |
| all_ranks: True |
| tool_config: |
| npu: |
| discrete: True |
| # rollout & ref follow actor settings |
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| Visualization |
| ------------- |
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| Collected data is stored in the user-defined save_path and can be |
| visualized by using the `MindStudio Insight <https://www.hiascend.com/document/detail/zh/mindstudio/80RC1/GUI_baseddevelopmenttool/msascendinsightug/Insight_userguide_0002.html>`_ tool. |
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| If the analysis parameter is set to False, offline parsing is required after data collection: |
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| .. code:: python |
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| import torch_npu |
| # Set profiler_path to the parent directory of the "localhost.localdomain_<PID>_<timestamp>_ascend_pt" folder |
| torch_npu.profiler.profiler.analyse(profiler_path=profiler_path) |
