Commit Graph

8 Commits

Author SHA1 Message Date
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
Alexander Sidorov
1e15063761 ThroughputBenchmark: integration with Autograd Profiler (#36282)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36282

The reason to do this explicitly in the tool is that we don't want to capture warmup in profiling (as well as input cloning). So instead we make the benchmarking code explicitly aware of the profiler.

Example output:

```
I0408 16:06:40.300040 85516 throughput_benchmark-inl.h:106] Using Autograd profiler. Trace will be saved to /tmp/tmpt0gsz85y
I0408 16:06:40.302232 85516 throughput_benchmark-inl.h:111] Starting threads
I0408 16:06:40.302258 85524 throughput_benchmark-inl.h:78] Starting forward thread 1
I0408 16:06:40.302259 85525 throughput_benchmark-inl.h:78] Starting forward thread 2
I0408 16:06:40.302261 85523 throughput_benchmark-inl.h:78] Starting forward thread 0
I0408 16:06:40.302259 85526 throughput_benchmark-inl.h:78] Starting forward thread 3
I0408 16:06:40.412879 85525 throughput_benchmark-inl.h:88] Shutting down forward thread 2. Total number of finished threads: 1
I0408 16:06:40.412971 85523 throughput_benchmark-inl.h:88] Shutting down forward thread 0. Total number of finished threads: 2
I0408 16:06:40.412989 85526 throughput_benchmark-inl.h:88] Shutting down forward thread 3. Total number of finished threads: 3
I0408 16:06:40.413033 85524 throughput_benchmark-inl.h:88] Shutting down forward thread 1. Total number of finished threads: 4
I0408 16:06:40.413056 85516 throughput_benchmark-inl.h:123] Finished benchmark
Average latency per example: 443.256us
Total number of iterations: 1000
Total number of iterations per second (across all threads): 9024.12
Total time: 110.814ms
```

Test Plan: Imported from OSS

Differential Revision: D20987125

Pulled By: ezyang

fbshipit-source-id: 1f8980c3a5a0abdc268c7a16c99aa9ea868689eb
2020-04-13 08:53:40 -07:00
vfdev
b248e23de0 Docs fix: Added missing indent (#35017)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/35017

Differential Revision: D20552491

Pulled By: yf225

fbshipit-source-id: 4481e7aecb9dc4a54ef95dfdddacbe3ff48f1c5f
2020-03-22 09:57:11 -07:00
Hong Xu
5bd97be309 Fix lint error in format_time() in throughput_benchmark.py and clean it up a bit. (#22424)
Summary:
The `assert False` lint error has been causing CI to fail:

    ./torch/utils/throughput_benchmark.py:14:13: B011 Do not call assert False since python -O removes these calls. Instead callers should raise AssertionError().
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22424

Differential Revision: D16083464

Pulled By: bddppq

fbshipit-source-id: 6d96e36c8fcbb391d071b75fe79c22d526c1ba3c
2019-07-01 22:15:37 -07:00
Alexander Sidorov
d0348c0ef9 ThroughputBenchmark: improve formatting for ExecutionStats (#22293)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22293

Just wrapping C class with nicer python interface which now
ust print dirrectly to get all the data. Later we can add various
visualizations there

Differential Revision: D16023999

fbshipit-source-id: 8436e37e36965821a690035617784dcdc352dcd1
2019-07-01 14:24:34 -07:00
Alexander Sidorov
f51de8b61a Back out "Revert D15435461: [pytorch][PR] PyTorch ThroughputBenchmark" (#22185)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22185

Original commit changeset: 72a0eac1658b

Differential Revision: D15981928

fbshipit-source-id: d2455d79e81c26ee90d41414cde8ac0f9b703bc3
2019-06-26 16:05:51 -07:00
Soumith Chintala
08060e898b Revert D15435461: [pytorch][PR] PyTorch ThroughputBenchmark
Differential Revision:
D15435461

Original commit changeset: db08829dc3f4

fbshipit-source-id: 72a0eac1658b2d3f885bc9a21c49fcc23030ae3e
2019-06-23 22:55:05 -07:00
Alexander Sidorov
9b45237618 PyTorch ThroughputBenchmark (#20766)
Summary:
This is useful for measuring inference performance of your
models. This is a very basic benchmark for now. We don't support
batching on the benchmark side, no inter and intra op parallelizm is
supported yet, just caller based parallelizm.

Main phylosophy here is that user should be able to provide inputs
from python and just stack them within the benchmark. API should be
exactly the same as passing inputs to module.forward.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20766

Test Plan: Added a new unit test

Differential Revision: D15435461

Pulled By: salexspb

fbshipit-source-id: db08829dc3f4398bb1d8aa16cc4a58b6c72f16c6
2019-06-23 13:03:18 -07:00