pytorch/benchmarks/profiler_benchmark
Ilia Cherniavskii f7a8bf2855 Use libkineto in profiler (#46470)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46470

Adding ability to use Kineto (CUPTI) to profile CUDA kernels

Test Plan:
USE_KINETO=1 USE_CUDA=1 USE_MKLDNN=1 BLAS=MKL BUILD_BINARY=1 python setup.py develop install
python test/test_profiler.py

python test/test_autograd.py -k test_profile
python test/test_autograd.py -k test_record

```
-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------
                                                   Name    Self CPU %      Self CPU   CPU total %     CPU total  CPU time avg     Self CUDA   Self CUDA %    CUDA total  CUDA time avg    # of Calls
-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------
                       Memcpy HtoD (Pageable -> Device)         0.00%       0.000us         0.00%       0.000us       0.000us       2.000us        33.33%       2.000us       1.000us             2
                                      sgemm_32x32x32_NN         0.00%       0.000us         0.00%       0.000us       0.000us       2.000us        33.33%       2.000us       2.000us             1
void at::native::vectorized_elementwise_kernel<4, at...         0.00%       0.000us         0.00%       0.000us       0.000us       1.000us        16.67%       1.000us       1.000us             1
                       Memcpy DtoH (Device -> Pageable)         0.00%       0.000us         0.00%       0.000us       0.000us       1.000us        16.67%       1.000us       1.000us             1
                                            aten::randn         5.17%      74.000us         6.71%      96.000us      48.000us       0.000us         0.00%       0.000us       0.000us             2
                                            aten::empty         1.33%      19.000us         1.33%      19.000us       4.750us       0.000us         0.00%       0.000us       0.000us             4
                                          aten::normal_         1.05%      15.000us         1.05%      15.000us       7.500us       0.000us         0.00%       0.000us       0.000us             2
                                               aten::to        77.90%       1.114ms        91.61%       1.310ms     436.667us       0.000us         0.00%       3.000us       1.000us             3
                                    aten::empty_strided         2.52%      36.000us         2.52%      36.000us      12.000us       0.000us         0.00%       0.000us       0.000us             3
                                            aten::copy_         2.73%      39.000us        11.19%     160.000us      53.333us       0.000us         0.00%       3.000us       1.000us             3
                                        cudaMemcpyAsync         4.34%      62.000us         4.34%      62.000us      20.667us       0.000us         0.00%       0.000us       0.000us             3
                                  cudaStreamSynchronize         1.61%      23.000us         1.61%      23.000us       7.667us       0.000us         0.00%       0.000us       0.000us             3
                                               aten::mm         0.21%       3.000us         7.20%     103.000us     103.000us       0.000us         0.00%       2.000us       2.000us             1
                                           aten::stride         0.21%       3.000us         0.21%       3.000us       1.000us       0.000us         0.00%       0.000us       0.000us             3
                                       cudaLaunchKernel         2.45%      35.000us         2.45%      35.000us      17.500us       0.000us         0.00%       0.000us       0.000us             2
                                              aten::add         0.49%       7.000us         4.27%      61.000us      61.000us       0.000us         0.00%       1.000us       1.000us             1
-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------
```

benchmark: https://gist.github.com/ilia-cher/a5a9eb6b68504542a3cad5150fc39b1a

Reviewed By: Chillee

Differential Revision: D25142223

Pulled By: ilia-cher

fbshipit-source-id: b0dff46c28da5fb0a8e01cf548aa4f2b723fde80
2020-11-25 04:32:16 -08:00
..
profiler_bench.py Use libkineto in profiler (#46470) 2020-11-25 04:32:16 -08:00
resnet_memory_profiler.py Memory profiling (#37775) 2020-05-19 15:48:48 -07:00