mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-06 12:20:52 +01:00
60bda4d06b
Summary: The implementation of `PackedLinearWeightFp16::apply_dynamic_impl` [here](https://www.internalfb.com/code/fbsource/[b1ef7c31f022]/fbcode/caffe2/aten/src/ATen/native/quantized/cpu/qlinear_dynamic.cpp?lines=393) does not handle `relu`. It completely ignores the `ReluFused` boolean template parameter. At this point, callers of that function handle `relu` explicitly. While the correct thing to do would be to handle the `ReluFused` parameter in that implementation, it is not clear if that semantics is being followed in this code. So, we are handling this in SR's out-variant implementation, until the owner fixes that issue. This issue resulted in incorrect results when Static Runtime was enabled for the MRS video model. Test Plan: ``` buck run mode/opt //caffe2/benchmarks/static_runtime:static_runtime_cpptest -- --gtest_filter=StaticRuntime.QuantizedLinearReluDynamicFp16 ``` Reviewed By: mikeiovine Differential Revision: D35366309 fbshipit-source-id: e60126e3590d52681ceaee5583b81c4c0b5404d9 (cherry picked from commit cabeb96a792339e7dbfd16cb51a3ac9039812137) |
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| .. | ||
| cpp | ||
| distributed | ||
| fastrnns | ||
| framework_overhead_benchmark | ||
| functional_autograd_benchmark | ||
| fuser | ||
| instruction_counts | ||
| operator_benchmark | ||
| overrides_benchmark | ||
| profiler_benchmark | ||
| record_function_benchmark | ||
| serialization | ||
| sparse | ||
| static_runtime | ||
| tensorexpr | ||
| compare-fastrnn-results.py | ||
| compare.sh | ||
| README.md | ||
| upload_scribe.py | ||
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