mirror of https://github.com/InternLM/InternLM
165 lines
8.0 KiB
ReStructuredText
165 lines
8.0 KiB
ReStructuredText
性能分析
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========
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.. Mainly about the usage of torch profiler and memory profiler
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Torch Profiler
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-----------------
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InternLM 使用 ``internlm.train.initialize_llm_profile()`` 来收集和分析模型训练或推理期间的性能数据，如 CPU/CUDA/memory 等性能数据。这个实现基于 `torch.profiler <https://pytorch.org/docs/stable/profiler.html>`_ ，输出的性能分析 trace 文件可以使用 `tensorboard <https://www.tensorflow.org>`_ 进行可视化。
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用户如果想使用这个 torch 性能分析工具，需要在启动训练时传递 ``--profiling`` 参数以启用性能分析。完成 torch 性能分析后，用户可以在 ``{JOB_NAME}/{start_time}/traces/rank{}_dp{}_tp{}_pp{}`` 文件夹中看到性能分析结果。
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实际运行生成的 ``Torch Profiler`` 目录结构如下：
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.. code-block:: bash
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# tree ./7b_train/Sep08_11-00-51/traces -L 2
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./7b_train/Sep08_11-00-51/traces/
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└── rank0_dp0_tp0_pp0
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└── SH-IDC1-10-140-1-78_238619.1694142354680.pt.trace.json
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其中， ``traces`` 可以通过 ``TensorBoard`` 可视化，运行命令
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.. code-block:: bash
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# visualize traces with tensorboard and custom port
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tensorboard --logdir rank0_dp0_tp0_pp0 --port 10088
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在打开的 ``TensorBoard -> PyTorch Profiler -> Views -> Trace`` 页面可以看到Operator和GPU Kernel的性能分析时间线如下，更多的功能请参考 `torch profiler with tensorboard <https://pytorch.org/tutorials/intermediate/tensorboard_profiler_tutorial.html#pytorch-profiler-with-tensorboard>`_
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.. figure:: ../../imgs/torch_profiler_trace.png
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:scale: 45%
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:class: with-border
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.. autofunction:: internlm.train.initialize_llm_profile
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Memory Profiler
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-----------------
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InternLM 提供了一个实用的内存分析工具 ``internlm.utils.simple_memory_profiler.SimpleMemoryProfiler`` 来监控实际的 GPU 内存使用情况。在实现中，会对模型数据（包括模型参数、模型梯度和优化器状态）和非模型数据（包括激活值）分别进行详细的统计。
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要使用这个内存分析工具，用户需要在启动训练时传递 ``--profiling`` 参数以启用内存分析。完成内存分析后，用户可以在 ``memory_trace/rank{}_dp{}_tp{}`` 文件夹中找到特定 rank 对应的内存分析结果（包括不同时间点的内存使用日志和显示总体内存使用情况的太阳图表）。
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实际运行生成的 ``memory_trace`` 目录结构如下：
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.. code-block:: bash
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# tree ./memory_trace -L 2
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./memory_trace
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├── rank0_dp0_tp0 # Profiling results for a specific rank device
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│ ├── activation_memory_sunburst.html # Sunburst chart showing activation memory usage
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│ ├── grads_memory_sunburst.html # Sunburst chart showing gradient memory usage
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│ ├── memory.log # Log of GPU memory usage at different time points
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│ ├── os_memory_sunburst.html # Sunburst chart showing optimizer state memory usage
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│ ├── params_memory_sunburst.html # Sunburst chart showing parameter memory usage
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│ └── summary_sunburst.html # Sunburst chart showing overall memory usage
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├── rank1_dp1_tp0
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│ ├── activation_memory_sunburst.html
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│ ├── grads_memory_sunburst.html
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│ ├── memory.log
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│ ├── os_memory_sunburst.html
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│ ├── params_memory_sunburst.html
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│ └── summary_sunburst.html
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├── rank2_dp2_tp0
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│ ├── activation_memory_sunburst.html
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│ ├── grads_memory_sunburst.html
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│ ├── memory.log
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│ ├── os_memory_sunburst.html
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│ ├── params_memory_sunburst.html
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│ └── summary_sunburst.html
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├── rank3_dp3_tp0
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│ ├── activation_memory_sunburst.html
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│ ├── grads_memory_sunburst.html
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│ ├── memory.log
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│ ├── os_memory_sunburst.html
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│ ├── params_memory_sunburst.html
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│ └── summary_sunburst.html
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├── rank4_dp4_tp0
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│ ├── activation_memory_sunburst.html
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│ ├── grads_memory_sunburst.html
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│ ├── memory.log
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│ ├── os_memory_sunburst.html
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│ ├── params_memory_sunburst.html
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│ └── summary_sunburst.html
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├── rank5_dp5_tp0
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│ ├── activation_memory_sunburst.html
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│ ├── grads_memory_sunburst.html
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│ ├── memory.log
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│ ├── os_memory_sunburst.html
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│ ├── params_memory_sunburst.html
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│ └── summary_sunburst.html
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├── rank6_dp6_tp0
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│ ├── activation_memory_sunburst.html
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│ ├── grads_memory_sunburst.html
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│ ├── memory.log
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│ ├── os_memory_sunburst.html
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│ ├── params_memory_sunburst.html
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│ └── summary_sunburst.html
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└── rank7_dp7_tp0
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├── activation_memory_sunburst.html
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├── grads_memory_sunburst.html
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├── memory.log
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├── os_memory_sunburst.html
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├── params_memory_sunburst.html
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└── summary_sunburst.html
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其中， ``memory.log`` 的内容示例如下：
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.. code-block:: bash
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Memory State:
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time: 37.56313228607178
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---summary---
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total_memory: 55953.56 MB
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params_memory: 13965.51 MB, grads_memory: 13965.51 MB, os_params_memory: 3461.52 MB, os_state_memory: 6923.03 MB, activation_memory: 17638.00 MB
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Memory State:
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time: 38.46969723701477
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---summary---
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total_memory: 38315.56 MB
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params_memory: 13965.51 MB, grads_memory: 13965.51 MB, os_params_memory: 3461.52 MB, os_state_memory: 6923.03 MB, activation_memory: 0.00 MB
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---Layout---
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params_layout:
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layer: param_mem, layer_mem: 0.00 MB, total_mem: 13965.51 MB
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layer: param_mem.embedding, layer_mem: 0.00 MB, total_mem: 806.00 MB
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layer: param_mem.embedding.weight, layer_mem: 806.00 MB, total_mem: 806.00 MB
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layer: param_mem.blocks, layer_mem: 0.00 MB, total_mem: 12353.50 MB
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layer: param_mem.blocks.0, layer_mem: 0.00 MB, total_mem: 386.05 MB
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layer: param_mem.blocks.0.mixer, layer_mem: 0.00 MB, total_mem: 128.03 MB
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layer: param_mem.blocks.0.mixer.Wqkv, layer_mem: 0.00 MB, total_mem: 96.02 MB
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layer: param_mem.blocks.0.mixer.Wqkv.weight, layer_mem: 96.00 MB, total_mem: 96.00 MB
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layer: param_mem.blocks.0.mixer.Wqkv.bias, layer_mem: 0.02 MB, total_mem: 0.02 MB
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layer: param_mem.blocks.0.mixer.out_proj, layer_mem: 0.00 MB, total_mem: 32.01 MB
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layer: param_mem.blocks.0.mixer.out_proj.weight, layer_mem: 32.00 MB, total_mem: 32.00 MB
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layer: param_mem.blocks.0.mixer.out_proj.bias, layer_mem: 0.01 MB, total_mem: 0.01 MB
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layer: param_mem.blocks.0.norm1, layer_mem: 0.00 MB, total_mem: 0.01 MB
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layer: param_mem.blocks.0.norm1.weight, layer_mem: 0.01 MB, total_mem: 0.01 MB
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layer: param_mem.blocks.0.norm2, layer_mem: 0.00 MB, total_mem: 0.01 MB
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layer: param_mem.blocks.0.norm2.weight, layer_mem: 0.01 MB, total_mem: 0.01 MB
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layer: param_mem.blocks.0.mlp, layer_mem: 0.00 MB, total_mem: 258.00 MB
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layer: param_mem.blocks.0.mlp.w1, layer_mem: 0.00 MB, total_mem: 86.00 MB
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layer: param_mem.blocks.0.mlp.w1.weight, layer_mem: 86.00 MB, total_mem: 86.00 MB
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layer: param_mem.blocks.0.mlp.w2, layer_mem: 0.00 MB, total_mem: 86.00 MB
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layer: param_mem.blocks.0.mlp.w2.weight, layer_mem: 86.00 MB, total_mem: 86.00 MB
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layer: param_mem.blocks.0.mlp.w3, layer_mem: 0.00 MB, total_mem: 86.00 MB
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layer: param_mem.blocks.0.mlp.w3.weight, layer_mem: 86.00 MB, total_mem: 86.00 MB
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......
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grads_layout:
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......
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os_params_layout:
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......
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os_state_layout:
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......
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activation_base_layout:
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......
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模型参数的太阳图示例如下：
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.. figure:: ../../imgs/params_memory_sunburst.png
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:scale: 50%
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:class: with-border
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.. autoclass:: internlm.utils.simple_memory_profiler.SimpleMemoryProfiler
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:members:
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