Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/28837
The JIT code used in op bench is not compatibility with latest JIT code path. This diff aims to resolve that issue.
Test Plan:
```buck run mode/opt //caffe2/benchmarks/operator_benchmark/pt:add_test -- --use_jit
Building: finished in 02:29.8 min (100%) 7055/7055 jobs, 1 updated
Total time: 02:30.3 min
# ----------------------------------------
# PyTorch/Caffe2 Operator Micro-benchmarks
# ----------------------------------------
# Tag : short
# Benchmarking PyTorch: add
# Mode: JIT
# Name: add_M64_N64_K64_cpu
# Input: M: 64, N: 64, K: 64, device: cpu
Forward Execution Time (us) : 118.052
Reviewed By: hl475
Differential Revision: D18197057
fbshipit-source-id: 92edae8a48abc4115a558a91ba46cc9c3edb2eb8
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22309
This diff enables PT operators to run with JIT mode. Users can control eager and JIT mode using the `use_jit` flag.
In this diff, we are putting operators in a loop and passed it to JIT. One extra step which wraps the operator with the `_consume` op is introduced to avoid dead code elimination optimization in JIT. With that, the reported time includes the real operator execution time plus the `_consume` (directly return input, nothing else if happening inside) op.
Reviewed By: zheng-xq
Differential Revision: D16033082
fbshipit-source-id: e03be89fd5a505e44e81015dfc63db9cd76fb8a1
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21210
This diff introduces a new path to run op with JIT. There are two steps involved here:
1. Users need to script the op. This should happen in the `init` method.
2. The generated graph from step1 is passed to `jit_forward` which will be executed by the benchmark backend
Reviewed By: zheng-xq
Differential Revision: D15460831
fbshipit-source-id: 48441d9cd4be5d0acebab901f45544616e6ed2ee
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21206
This diff change the default test_name to be a globally unique value across tests. With that, users can list all the tests and choose to run a specific test.
Reviewed By: zheng-xq
Differential Revision: D15543508
fbshipit-source-id: 0814ef6a60d41637fed5245e30c282497cf21bb8
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21149
The diff modifies the interface for PyTorch operators in the benchmark suite
Reviewed By: zheng-xq
Differential Revision: D15433897
fbshipit-source-id: e858183431eb37d90313356716c2de8709372b58
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19518
Previous design needs to run the op benchmarks from PyTorch root directory which could lead to `module not found` error in OSS environment. This diff fixes that issue by making the benchmark to be launched in the `benchmarks` folder.
Reviewed By: ilia-cher
Differential Revision: D15020787
fbshipit-source-id: eb09814a33432a66cc857702bc86538cd17bea3b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19450
We want to make each operator benchmark as a separate binary. The previous way to run the benchmark is by collecting all operators into a single binary, it is unnecessary when we want to filter a specific operator. This diff aims to resolve that issue.
Reviewed By: ilia-cher
Differential Revision: D14808159
fbshipit-source-id: 43cd25b219c6e358d0cd2a61463b34596bf3bfac
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18740
Test utilities for writing Caffe2/PyTorch performance microbenchmarks. Brief description of the file structure
* benchmark_core.py : core utiltiites for running microbenchmark tests
* benchmark_caffe2.py : Caffe2 specific benchmark utilitites
* benchmark_pytorch.py: PyTorch specific benchmark utilities
* benchmark_runner.py : Main function. Currently it can run the microbenchmark tests in a stand-alone mode. The next step is to have this integrate with AI-PEP.
The utilities are located at https://github.com/pytorch/pytorch/tree/master/test to have access to both Caffe2/PyTorch Python's frontend.
Include two operator microbenchmarks; support both Caffe2/PyTorch:
* MatMul
* Add
Reference: PyTorch benchmarks : https://github.com/pytorch/benchmark/tree/master/timing/python. In this work, we start with two example binary operators MatMul and Add, but eventually we should to cover unary operators like in the PyTorch benchmark repo.
Reviewed By: zheng-xq
Differential Revision: D13887111
fbshipit-source-id: b7a56b95448c9ec3e674b0de0ffb96af4439bfce
