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12 Commits

Author SHA1 Message Date
Sam Estep
8c798e0622 Forbid trailing whitespace (#53406) 
Summary:
Context: https://github.com/pytorch/pytorch/pull/53299#discussion_r587882857

These are the only hand-written parts of this diff:
- the addition to `.github/workflows/lint.yml`
- the file endings changed in these four files (to appease FB-internal land-blocking lints):
  - `GLOSSARY.md`
  - `aten/src/ATen/core/op_registration/README.md`
  - `scripts/README.md`
  - `torch/csrc/jit/codegen/fuser/README.md`

The rest was generated by running this command (on macOS):
```
git grep -I -l ' $' -- . ':(exclude)**/contrib/**' ':(exclude)third_party' | xargs gsed -i 's/ *$//'
```

I looked over the auto-generated changes and didn't see anything that looked problematic.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/53406

Test Plan:
This run (after adding the lint but before removing existing trailing spaces) failed:
- https://github.com/pytorch/pytorch/runs/2043032377

This run (on the tip of this PR) succeeded:
- https://github.com/pytorch/pytorch/runs/2043296348

Reviewed By: walterddr, seemethere

Differential Revision: D26856620

Pulled By: samestep

fbshipit-source-id: 3f0de7f7c2e4b0f1c089eac9b5085a58dd7e0d97
 2021-03-05 17:22:55 -08:00
Xiang Gao
20ac736200 Remove py2 compatible future imports (#44735) 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/44735

Reviewed By: mruberry

Differential Revision: D23731306

Pulled By: ezyang

fbshipit-source-id: 0ba009a99e475ddbe22981be8ac636f8a1c8b02f
 2020-09-16 12:55:57 -07:00
Stanislau Hlebik
b774ce54f8 remediation of S205607 
fbshipit-source-id: 798decc90db4f13770e97cdce3c0df7d5421b2a3
 2020-07-17 17:19:47 -07:00
Stanislau Hlebik
8fdea489af remediation of S205607 
fbshipit-source-id: 5113fe0c527595e4227ff827253b7414abbdf7ac
 2020-07-17 17:17:03 -07:00
Mingzhe Li
ab15584dce add random sample function to generate list of inputs (#23174) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23174

This diff introduces a new function to random generates inputs based on the weights.

Test Plan:
buck run mode/dev-nosan //caffe2/benchmarks/operator_benchmark/common/tests:random_sample_test -- --iterations 3

```
# ----------------------------------------
# PyTorch/Caffe2 Operator Micro-benchmarks
# ----------------------------------------
# Tag : short

# Benchmarking PyTorch: add
# Mode: Eager
# Name: add_M1_N5_K7
# Input: M: 1, N: 5, K: 7
Forward Execution Time (us) : 82.923

# Benchmarking PyTorch: add
# Mode: Eager
# Name: add_M1_N6_K8
# Input: M: 1, N: 6, K: 8
Forward Execution Time (us) : 79.535

# Benchmarking PyTorch: add
# Mode: Eager
# Name: add_M2_N6_K7
# Input: M: 2, N: 6, K: 7
Forward Execution Time (us) : 83.471

# Benchmarking PyTorch: add
# Mode: Eager
# Name: add_M1_N4_K7
# Input: M: 1, N: 4, K: 7
Forward Execution Time (us) : 84.410

# Benchmarking PyTorch: add
# Mode: Eager
# Name: add_M1_N6_K7
# Input: M: 1, N: 6, K: 7
Forward Execution Time (us) : 82.399
```

Reviewed By: zheng-xq

Differential Revision: D15791723

fbshipit-source-id: 730e34d455e962ddf594a491d7c81c3f99fafa86
 2019-10-09 11:24:14 -07:00
Mingzhe Li
a750a1a2b4 modify config_list to support cross product of attributes (#23399) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23399

This diff enables config_list function to support cross product of inputs besides the shapes.

The following is an example using the update interface. The same input shapes can run on different devices and dtypes.
```
add_short_configs = op_bench.config_list(
    attr_names=['M', 'N', 'K'],
    attrs=[
        [8, 16, 32],
        [16, 16, 64],
        [64, 64, 128],
    ],
    cross_product_configs={
        'device': ['cpu', 'cuda'],
        'dtype': [torch.float, torch.float64],
    },
    tags=['short'],
)
```

Test Plan:
buck run mode/dev-nosan caffe2/benchmarks/operator_benchmark/common/tests:pt_configs_list_test -- --iterations 3

```
# ----------------------------------------
# PyTorch/Caffe2 Operator Micro-benchmarks
# ----------------------------------------
# Tag : short

# Benchmarking PyTorch: add
# Mode: Eager
# Name: add_M8_N16_K32_devicecpu_dtypetorch.float32
# Input: M: 8, N: 16, K: 32, device: cpu, dtype: torch.float32
Forward Execution Time (us) : 164.489

# Benchmarking PyTorch: add
# Mode: Eager
# Name: add_M8_N16_K32_devicecpu_dtypetorch.float64
# Input: M: 8, N: 16, K: 32, device: cpu, dtype: torch.float64
Forward Execution Time (us) : 158.677

# Benchmarking PyTorch: add
# Mode: Eager
# Name: add_M8_N16_K32_devicecuda_dtypetorch.float32
# Input: M: 8, N: 16, K: 32, device: cuda, dtype: torch.float32
Forward Execution Time (us) : 103.866

# Benchmarking PyTorch: add
# Mode: Eager
# Name: add_M8_N16_K32_devicecuda_dtypetorch.float64
# Input: M: 8, N: 16, K: 32, device: cuda, dtype: torch.float64
Forward Execution Time (us) : 106.027

# Benchmarking PyTorch: add
# Mode: Eager
# Name: add_M16_N16_K64_devicecpu_dtypetorch.float32
# Input: M: 16, N: 16, K: 64, device: cpu, dtype: torch.float32
Forward Execution Time (us) : 451.016
...
```

buck test caffe2/benchmarks/operator_benchmark/fb:operator_benchmark_test

```
Building: finished in 2.4 sec (100%) 6882/6882 jobs, 2 updated
  Total time: 2.8 sec
Trace available for this run at /tmp/testpilot.20190730-160519.3952794.log
TestPilot test runner for Facebook. See https://fburl.com/testpilot for details.
Testpilot build revision 203f0104fbfcec4128be2c482c64736309ae39c9 fbpkg a4b2a9897a0c45069bd07d83e5981052 at Sun Jul 28 01:22:13 2019 by twsvcscm from /data/fbprojects/packages/testinfra.testpilot/667/t.par
Discovering tests
Running 3 tests
Started new test run: https://our.intern.facebook.com/intern/testinfra/testrun/5910974514382830
      ✓ caffe2/benchmarks/operator_benchmark/fb:operator_benchmark_test - test_config_list_impl (operator_benchmark_test.TestConsumeOp) 0.011 1/3 (passed)
      ✓ caffe2/benchmarks/operator_benchmark/fb:operator_benchmark_test - test_list_of_ops (operator_benchmark_test.TestConsumeOp) 19.920 2/3 (passed)
      ✓ caffe2/benchmarks/operator_benchmark/fb:operator_benchmark_test - test_single_op (operator_benchmark_test.TestConsumeOp) 23.418 3/3 (passed)
      ✓ caffe2/benchmarks/operator_benchmark/fb:operator_benchmark_test - main 0.000 (passed)
Finished test run: https://our.intern.facebook.com/intern/testinfra/testrun/5910974514382830
Summary (total time 29.90s):
  PASS: 4
  FAIL: 0
  SKIP: 0
  FATAL: 0
  TIMEOUT: 0
  OMIT: 0
```

Reviewed By: zheng-xq

Differential Revision: D16501272

fbshipit-source-id: d92b5cf50b0f37d5b3a79d423acb521366b4e8db
 2019-10-09 11:24:06 -07:00
Mingzhe Li
3c986dff77 introduce auto_set to simplify benchmarking the backward path of operators (#23276) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23276

This diff introduces a new feature to simplify benchmarking the backward path of ops. Here is an example:

```
...
self.input_one = torch.rand(M, N, K, requires_grad=self.auto_set())
self.input_two = torch.rand(M, N, K, requires_grad=self.auto_set())
...
```

In this way, the benchmark will generate three different test cases.
1. input_one requires grad
2. input_two requires grad
3. both inputs require grad

Here is a sample output:
```
# Benchmarking PyTorch: add
# Mode: Eager
# Name: add_M1_N8_K8_bwdall
# Input: M: 1, N: 8, K: 8
Backward Execution Time (us) : 863.744

# Benchmarking PyTorch: add
# Mode: Eager
# Name: add_M1_N8_K8_bwd1
# Input: M: 1, N: 8, K: 8
Backward Execution Time (us) : 727.915

# Benchmarking PyTorch: add
# Mode: Eager
# Name: add_M1_N8_K8_bwd2
# Input: M: 1, N: 8, K: 8
Backward Execution Time (us) : 687.626
```

Reviewed By: zheng-xq

Differential Revision: D16450355

fbshipit-source-id: 50ae0916e81c3ff9f0c482ed6d386319eb15b305
 2019-07-29 15:58:41 -07:00
Mingzhe Li
7eb0319339 add new tests to benchmark_all_test (#22787) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22787

as title

Reviewed By: hl475

Differential Revision: D16219329

fbshipit-source-id: 097ee73e7644d5ca482ad044d0fd2c3e7dc2c10b
 2019-07-11 22:50:55 -07:00
Mingzhe Li
3cf5f22f02 Enable C2 operators running with {cpu, gpu} * {forward, backward} (#22664) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22664

This diff enables c2 operators to run the combination of {cpu, gpu} * {forward, backward}.

Reviewed By: hl475

Differential Revision: D15781789

fbshipit-source-id: e9843e3c46ea144042829860638d406f6a33792b
 2019-07-09 16:41:53 -07:00
Mingzhe Li
007fd01e9b Enable PT operators running with {cpu, gpu} * {forward, backward} (#22416) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22416

This diff tests the combination of cpu/gpu and forward/backward path for PT add operator.

Reviewed By: hl475

Differential Revision: D15770792

fbshipit-source-id: 38cc648361d2501d774db407f988c3cb5115b2ae
 2019-07-01 16:30:58 -07:00
Mingzhe Li
4e3c97a0be add separate path for op with JIT (#21210) 
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
 2019-06-10 19:53:58 -07:00
Mingzhe Li
a5cf6d5100 reorganize op bench directory (#21543) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21543

No code change in this diff.

Reviewed By: hl475

Differential Revision: D15721419

fbshipit-source-id: 06212cc882f5297064153417dc4d80bce9ec2667
 2019-06-07 16:06:51 -07:00