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==========================================================================================
Last updated: 08/14/2025.
This is a tutorial for data collection using the GRPO or DAPO algorithm
based on FSDP on Ascend devices.
Configuration
-------------
Leverage two levels of configuration to control data collection:
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.
Global collection control
~~~~~~~~~~~~~~~~~~~~~~~~~
Use parameters in ppo_trainer.yaml to control the collection mode
and steps.
- global_profiler: Control the ranks and mode of profiling
- 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".
Role collection control
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
In each role's ``profiler`` field, you can control the collection mode for that role.
- 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.
Use parameters in each role's ``profiler.tool_config.npu`` to control npu profiler behavior:
- level: Collection level—options are level_none, level0, level1, and
level2
- 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.
- 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.
- analysis: Enables automatic data parsing.
- discrete: Whether to enable discrete mode.
Examples
--------
Disabling collection
~~~~~~~~~~~~~~~~~~~~
.. code:: yaml
global_profiler:
steps: null # disable profile
End-to-End collection
~~~~~~~~~~~~~~~~~~~~~
.. code:: yaml
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
Discrete Mode Collection
~~~~~~~~~~~~~~~~~~~~~~~~
.. code:: yaml
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
Visualization
-------------
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.
If the analysis parameter is set to False, offline parsing is required after data collection:
.. code:: python
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) |