mirror of https://github.com/pytorch/pytorch.git synced 2025-10-20 21:14:14 +08:00

Files

Mikayla Gawarecki 19207b9183 Allow more backend worker threads with each using a separate cuda stream (#116190 )

Added a `--num_workers` option to `server.py` that allows more than 1 worker in the `ThreadPoolWorker` used for model predictions. Each worker uses its own `cuda.Stream()` that is created when the worker thread is initialized.

Ran benchmark for 2-4 workers with `compile=False` (since compile is not thread-safe)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/116190
Approved by: https://github.com/albanD
ghstack dependencies: #115286, #116187, #116188, #116189

2023-12-20 22:08:29 +00:00

cpp

[BE]: Remove useless lambdas (#113602 )

2023-11-14 20:06:48 +00:00

distributed

[BE]: Apply FURB118 (prev): replaces unnecessary lambdas with operator. (#116027 )

2023-12-20 19:35:08 +00:00

dynamo

Run eager adam optimizer in benchmarks where possible (#115445 )

2023-12-18 18:28:23 +00:00

fastrnns

Apply UFMT to all files in benchmarks/ (#105928 )

2023-07-26 01:18:48 +00:00

framework_overhead_benchmark

Apply UFMT to all files in benchmarks/ (#105928 )

2023-07-26 01:18:48 +00:00

functional_autograd_benchmark

[BE]: Update ruff to 0.285 (#107519 )

2023-08-22 23:16:38 +00:00

fuser

Apply UFMT to all files in benchmarks/ (#105928 )

2023-07-26 01:18:48 +00:00

inference

Allow more backend worker threads with each using a separate cuda stream (#116190 )

2023-12-20 22:08:29 +00:00

instruction_counts

Fix some typos, mostly "that that" (#106901 )

2023-08-10 19:46:53 +00:00

nested

Apply UFMT to all files in benchmarks/ (#105928 )

2023-07-26 01:18:48 +00:00

operator_benchmark

[BE]: Update ruff to 0.285 (#107519 )

2023-08-22 23:16:38 +00:00

overrides_benchmark

[BE]: Update ruff to 0.285 (#107519 )

2023-08-22 23:16:38 +00:00

profiler_benchmark

Apply UFMT to all files in benchmarks/ (#105928 )

2023-07-26 01:18:48 +00:00

record_function_benchmark

Apply UFMT to all files in benchmarks/ (#105928 )

2023-07-26 01:18:48 +00:00

serialization

Apply UFMT to all files in benchmarks/ (#105928 )

2023-07-26 01:18:48 +00:00

sparse

Use weakref in storing tensors as keys (follow-up to #111470 ) (#112076 )

2023-10-30 19:16:05 +00:00

static_runtime

fix some typos (#106018 )

2023-07-26 18:14:44 +00:00

tensorexpr

[BE]: Apply FURB118 (prev): replaces unnecessary lambdas with operator. (#116027 )

2023-12-20 19:35:08 +00:00

transformer

Update the sdpa benchmark to measure forward backward time in isolation (#115986 )

2023-12-18 22:40:47 +00:00

compare-fastrnn-results.py

Apply UFMT to all files in benchmarks/ (#105928 )

2023-07-26 01:18:48 +00:00

compare.sh

Benchmarks: add scripts for FastRNNs results comparison. (#44134 )

2020-09-03 13:44:42 -07:00

README.md

Add more child links to benchmark readme (#104627 )

2023-07-06 12:11:00 +00:00

upload_scribe.py

Apply UFMT to all files in benchmarks/ (#105928 )

2023-07-26 01:18:48 +00:00

README.md

PyTorch Benchmarks

This folder contains scripts that produce reproducible timings of various PyTorch features.

It also provides mechanisms to compare PyTorch with other frameworks.

Setup environment

Make sure you're on a machine with CUDA, torchvision, and pytorch installed. Install in the following order:

# Install torchvision. It comes with the pytorch stable release binary
conda install pytorch torchvision -c pytorch

# Install the latest pytorch master from source.
# It should supersede the installation from the release binary.
cd $PYTORCH_HOME
python setup.py build develop

# Check the pytorch installation version
python -c "import torch; print(torch.__version__)"

Benchmark List

Please refer to each subfolder to discover each benchmark suite. Links are provided where descriptions exist: