TorchProfiling

Build And Install

For Cpu

// build with cpp extension
// try to hook some function
bash scripts/build.sh

// build without cpp extension
// just profiling cpu kernel, and analysis the log
bash scripts/build_regular.sh

For Gpu

export CUDA_DEV=true
bash scripts/build.sh

User Guide

1. Get Profiling Data

step 1: Profiling

Env

export ENABLE_PROFILING=True

Mode 1: just profiling the aten op

import module_logging as ml

with ml.combined_context():
    model()

Mode 2: profiling both the nn.Module and aten op

m = model()
import module_logging as ml

m = model
with ml.PerformanceLogger(m):
    m()

from  module_logging import PerformanceLogger as PL
pl = PL()
m = model()
pl.config(model=m)

pl.__enter__()
for i in range(100):
    m()
pl.__exit__()

step 2: Post-Processing

# for default print the total time table
python -m module_logging --path 7.log

# print summary table
python -m module_logging --path 7.log --summary

# print the detail table
python -m module_logging --path 7.log --detail

# print all 3 kinds table
python -m module_logging --path 7.log --all

# write table to csv: /tmp/total.csv
python -m module_logging --path 7.log --csv

#compare mode, must profiling with Mode 2
python -m module_logging --compare --lhs_path 0.log --rhs_path 1.log

# compare mode and write to csv: /tmp/compare.csv
# must profiling with Mode 2
python -m module_logging --compare --lhs_path 0.log --rhs_path 1.log --csv

# analysis the  distribution op
python -m module_logging --dist --path 7.log 

# compare the two nn.Module inputs/outputs/parameters or torch.Tensor(s)
python -m module_logging --percision --lhs_path 0.h5f --rhs_path 1.h5f

2. 统计C函数调用次数

export ENABLE_HOOK_TRACE=true

import module_logging
module_logging.Hook.install_hook()


python test.py

3. 打印C函数的调用栈

export ENABLE_HOOK_TRACE=true
export PRINT_BACKTRACE=true

import module_logging
module_logging.Hook.install_hook()

python test.py

example:

/root/miniconda/envs/python38_torch201_cuda/lib/python3.8/site-packages/module_logging/Hook.cpython-38-x86_64-linux-gnu.so(_ZN5trace6Tracer5traceEv+0x39) [0x7fb56afa46d9]
/root/miniconda/envs/python38_torch201_cuda/lib/python3.8/site-packages/module_logging/Hook.cpython-38-x86_64-linux-gnu.so(_ZN5trace6TracerC1ENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE+0x92) [0x7fb56afa4942]
/root/miniconda/envs/python38_torch201_cuda/lib/python3.8/site-packages/module_logging/Hook.cpython-38-x86_64-linux-gnu.so(_ZN14CpuHookWrapper20local_launch_arg_setEPKvmm+0x99) [0x7fb56afa2b69]
/root/miniconda/envs/python38_torch201_cuda/lib/python3.8/site-packages/torch_xmlir/libxdnn_pytorch.so(_ZN14xpukernel_xpu310calc_basicILi2EfEEvPKT0_S3_PS1_x+0x46) [0x7fb69f724076]
/root/miniconda/envs/python38_torch201_cuda/lib/python3.8/site-packages/torch_xmlir/libxdnn_pytorch.so(+0x3c44692) [0x7fb6a23d4692]
/root/miniconda/envs/python38_torch201_cuda/lib/python3.8/site-packages/torch_xmlir/libxdnn_pytorch.so(_ZN8xpytorch3xpu3api13broadcast_mulIfEEiPNS1_7ContextEPKT_S7_PS5_RKSt6vectorIlSaIlEESD_+0x4b) [0x7fb6a23d26db]
/root/miniconda/envs/python38_torch201_cuda/lib/python3.8/site-packages/torch_xmlir/libxdnn_pytorch.so(+0x1a139ca) [0x7fb6a01a39ca]
/root/miniconda/envs/python38_torch201_cuda/lib/python3.8/site-packages/torch_xmlir/libxdnn_pytorch.so(_ZN12xdnn_pytorch10mul_tensorEPN8xpytorch3xpu3api7ContextERKNS_6TensorES7_RS5_+0x1f5) [0x7fb6a01a0685]
/root/miniconda/envs/python38_torch201_cuda/lib/python3.8/site-packages/torch_xmlir/_XMLIRC.cpython-38-x86_64-linux-gnu.so(+0xc5a1d4) [0x7fb6ed9761d4]
/root/miniconda/envs/python38_torch201_cuda/lib/python3.8/site-packages/torch_xmlir/_XMLIRC.cpython-38-x86_64-linux-gnu.so(+0xe4ae6e) [0x7fb6edb66e6e]
/root/miniconda/envs/python38_torch201_cuda/lib/python3.8/site-packages/torch/lib/libtorch_cpu.so(_ZN2at4_ops10mul_Tensor10redispatchEN3c1014DispatchKeySetERKNS_6TensorES6_+0x8a) [0x7fb7ce23204a]
/root/miniconda/envs/python38_torch201_cuda/lib/python3.8/site-packages/torch/lib/libtorch_cpu.so(+0x3d09390) [0x7fb7cffeb390]
/root/miniconda/envs/python38_torch201_cuda/lib/python3.8/site-packages/torch/lib/libtorch_cpu.so(+0x3d09e9b) [0x7fb7cffebe9b]
/root/miniconda/envs/python38_torch201_cuda/lib/python3.8/site-packages/torch/lib/libtorch_cpu.so(_ZN2at4_ops10mul_Tensor4callERKNS_6TensorES4_+0x175) [0x7fb7ce29b715]
/root/miniconda/envs/python38_torch201_cuda/lib/python3.8/site-packages/torch/lib/libtorch_cpu.so(+0x526184b) [0x7fb7d154384b]
/root/miniconda/envs/python38_torch201_cuda/lib/python3.8/site-packages/torch/lib/libtorch_cpu.so(_ZN5torch8autograd9generated12PowBackward05applyEOSt6vectorIN2at6TensorESaIS5_EE+0x144) [0x7fb7cfee50c4]
/root/miniconda/envs/python38_torch201_cuda/lib/python3.8/site-packages/torch/lib/libtorch_cpu.so(+0x48d9d8b) [0x7fb7d0bbbd8b]

4. Trace And Visualization

Step 1:

import module_logging as ml
with ml.trace.Tracer(model=m, path="/tmp/profiling.log", print_module_info=False, ranks=[0, 1, 2]):
    m()

• model: optional, set the nn.Module to profiling, [nn.Module] or nn.Module
• path: optional a file path to save the profiling result
• print_module_info: optional, if True, will record the profiling info and write to /tmp/logs/
• ranks: the ranks to trace and profiling. Default is None, means all ranks.

Step 2:

open the json file with: chrome://tracing/

Step 3:

# print summary table
python -m module_logging --path 7.log --summary

# print the detail table
python -m module_logging --path 7.log --detail

# print all 3 kinds table
python -m module_logging --path 7.log --all

5. Percision

Step 1: Get nn.Module's input/output/parameters/grad Tensor(s)

from module_logging import percision_debugger

m = model()

percision_debugger.config(m, path="/tmp/", steps=[0, 1], ranks=[0])
percision_debugger.__enter__()
for iter in range(100):
    inputs = []
    m(inputs)
    ......
    optimizer.step()
    percision_debugger.update_step()

percision_debugger.__exit__()

from module_logging import percision_debugger
m = model()
percision_debugger.config(m, path="/tmp/", steps=[0, 1], ranks=[0])

with persion_debugger:
    for iter in range(100):
        inputs = []
        m(inputs)
        ......
        optimizer.step()
        percision_debugger.update_step()

Step 2: Compare Two Files

# compare the two nn.Module inputs/outputs/parameters or torch.Tensor(s)
python -m module_logging --percision --lhs_path 0.h5f --rhs_path 1.h5f

6. Tensor Tracing

In training, due to some kernel implementation error, some kernel may write data over range. This action is Secretive and diffcult to debug. There is neccesary to trace the Tensor and record the action which modified the inner data.

Usage

Example

from  module_logging import tensor_tracer
tensor1 = torch.tensor([1, 2, 3], device='cpu').float()
tensor2 = torch.tensor([4, 5, 6], device='cpu').float()
tensor_tracer.__enter__()

# begin to trace the tensor
tensor_tracer.trace("tensor1", tensor1)

# tensor1 will be modified in add
tensor1.add_(tensor2)

tensor_tracer.__exit__()

Result

[aten op name]: aten.add_.Tensor
|  Tensor  | Status |  Max  |  Min  | Mean | Std  |
|----------|--------|-------|-------|------|------|
| tensor1  |  old   |  3.0  |  1.0  |  2.0 |  1.0 |
| tensor1  |  new   |  9.0  |  5.0  |  7.0 |  2.0 |

Disadvantage

The traced tensor will not be released until the end of program.