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PaddleAPEX is an accuracy and performance expansion pack for PaddlePaddle, supporting operator accuracy checking & operator performance profiling and run-time device memory cost analysis. PaddleAPEX is designed to help developers achieve auto accuracy checking and performance profiling for various devices on paddlepaddle.

Api_tracer

Accuracy auto-checker, which can grasp target operators in training models.

Before run: Let us check our global config

Step1: Set up your config.

Accuracy tool need some configuration before start, you need set target_step, dump_mode. If you set dump_mode=real_data, you need set dump_root_path.(This path can be a local path or a remote path)

Advanced usage: You can set Async_data=True to dump real_data asynchronously. Apex will work better when you set a remote path. Profile_mode = True will enable profile_mode, which is used to analyze tensors on devices. Apex will use paddle.max, paddle.min to analyze tensors in profile_mode. Which will cause a little loss of accuracy. But can get higher training performance. For more details, please refer to PaddleAPEX/paddleapex/api_tracer/configs/tool_config.yaml.

Step2: Set config path.

If you use default config file, you can modify specific variable in this file, such as target_step, dump_root_path.

Advanced usage: You can also set your own configuration file by setting environment variable via: export APEX_CONFIG_PATH=your_own_path/tool_config.yaml

Step3: Install into your python environment.

    # If you want to use paddleapex out of this repository please add the following environment variable.
    export PYTHONPATH=[abs_path to PaddleAPEX]:$PYTHONPATH

Step4: Record your target operators.

1. Take demo.py as example.

   import paddle
   from paddleapex import Tracer
   
   if __name__ == "__main__":
       a = paddle.randn([2,2])
       b = paddle.randn([2,2])
   
       apex = Tracer()
       apex.start()
       y = paddle.add(a,a)
       y = paddle.add(a,a)
       apex.stop()

2. Take Llama2-13b traning as example: For more details, please refer to Llama2-13b

3. Run your code, and get a json file:

   After running code above, our tool can dump real_data or tensor satistical data asynchronously.
   Here, we can get dumped json file and tensor(Optional).
       |-- dump_info
           |-- rank0_step5
           |-- rank0_step20
               |-- forward_rank0.json
               |-- Paddle*add*0.0.pt
               |-- Paddle*add*0.1.pt
               |-- Paddle*add*1.0.pt
               |-- Paddle*add*1.1.pt
   

4. Advanced Usage: If you have specific api which you want to trace(e.g. layer_norm), you can add its api call stack in paddleapex/api_tracer/configs/op_target.yaml like:

  target op:
  -  paddle.add
  -  paddle.mul
  -  paddle._C_ops.layer_norm
  -  paddlenlp.transformers.fusion_ops.xxx

Please note that paddleapex only support paddle apis which contain regular types, not suppport custom object instance.

Step5: Accuracy comparision.

1. Directly comparision:

cd paddleapex/apex
python run_paddle.py -json [json_path] -backend [gpu/npu/cpu] -out[local_path/remote_path] -dtype FP32,FP16,BF16 -mode all -op <op_name>
# mode can combine mem, acc, pro arbitary. E.g.:-mode mem,acc or -mode all
# -op is optional args, if you want to run specific op.
E.g.:
python run_paddle.py -json ./dump_info/rank0_step2/forward_rank0.json -backend gpu -out ./ -dtype FP32 -mode acc

This script will generate a repository, which contains api fwd/bwd outputs results. The sturcture is as follows:

|-- local_path
    |-- backend_output
    |-- backend_output_backward
    |-- Warning_list.txt

UT Runtime errors and warnings will be recorded in Warning_list.txt. After runing this script twice on different backends, you can run comparision tool to get accuracy result:

python acc_direct_cmp.py --benchmark [gpu_dump_repo] --device [npu_dump_repo] -o [result_path]

This script will generate two csv files, which contains accuracy result and details.

2. Multi-end precision comparision.

    # We use run_paddle.py to run the same operator on different devices and generate corresponding outputs.
    python run_paddle.py -json [json_path] -backend [gpu/npu/cpu] -out[local_path/remote_path] --dtype FP32,FP16,BF16 -mode all -op <op_name>
    python run_paddle.py -json [json_path] -backend [gpu/npu/cpu] -out[local_path/remote_path] --dtype FP32,FP16,BF16 -mode all -op <op_name>
    # This script will generate a repository, which contains api fwd/bwd outputs results.
    # Then we need to execute two times directly comparision tool.
    python acc_direct_cmp.py --benchmark [gpufp32_dump_repo] --device [gpubf16_dump_repo] -o [result_path]
    python acc_direct_cmp.py --benchmark [gpufp32_dump_repo] --device [npubf16_dump_repo] -o [result_path]
    python acc_multi_cmp.py --benchmark [gpufp32_gpubf16] --device [gpufp32_npubf16] -o [third_party_cmp_path]

We provide a flow chart for Multi-end precision comparision.

3. For cross framework comparision is in WIP, it will coming soon!

Step6: Performance/Memory comparision.

1. Test cases running:

    cd paddleapex/apex
    python run_paddle.py -json [json_path] -backend [gpu/npu/cpu] -out[local_path/remote_path] --dtype [dtype] -mode mem,pro
    # exec code above on different devices, and generate corresponding outputs.

2. Test cases comparision:

    cd paddleapex/apex
    python prof_cmp.py --benchmark [gpu_repo] --device [npu_repo] -o [result_path]
    python mem_cmp.py --benchmark [gpu_repo] --device [npu_repo] -o [result_path]   

3. Generate performance/accuracy summary:

    cd paddleapex/apex
    python summary_generator.py -acc [acc_result] -prof [prof_detail] 

4. Directly comparision standard: We provide a logic flow chart for Directly comparision between devices.

How to run the test cases

    cd PaddleAPEX/paddleapex/test
    bash test.sh <device_platform>