Summary:
This PR adds a new quantization backend, ONEDNN, with quantized conv and linear kernels in the same code path as the FBGEMM backend
The ONEDNN backend is an alternative of FBGEMM and QNNPACK backends. It takes advantage of features of the latest Intel® CPU products. It supports VNNI on Cascade Lake and the AMX instruction set to be available on Sapphire Rapids which has 8X int8 peak TOPS over VNNI.
ONEDNN demonstrates better performance on conv kernels of popular CNN models than FBGEMM. It also supports more fused ops, such as convolution-add-ReLU, than FBGEMM and QNNPACK.
To use this backend, users only need to set the quantization backend to 'onednn' before any calculation without a single change to models.
```python
torch.backends.quantized.engine = 'onednn'
```
## Design docs
https://github.com/pytorch/pytorch/issues/21120#issuecomment-562371983https://github.com/pytorch/pytorch/pull/67177#issuecomment-963787096
## File changes
**Add ONEDNN to qengine list**
- aten/src/ATen/Context.cpp
- c10/core/QEngine.h
- torch/ao/quantization/qconfig.py
- torch/backends/quantized/\_\_init\_\_.py
**Implement qconv & qlinear for ONEDNN backend**
- aten/src/ATen/native/quantized/cpu/conv_serialization.h
- aten/src/ATen/native/quantized/cpu/fbgemm_utils.cpp
- aten/src/ATen/native/quantized/cpu/onednn_utils.h
- aten/src/ATen/native/quantized/cpu/qconv.cpp
- aten/src/ATen/native/quantized/cpu/qconv_dynamic.cpp
- aten/src/ATen/native/quantized/cpu/qconv_prepack.cpp
- aten/src/ATen/native/quantized/cpu/qconv_unpack.cpp
- aten/src/ATen/native/quantized/cpu/qlinear.cpp
- aten/src/ATen/native/quantized/cpu/qlinear_dynamic.cpp
- aten/src/ATen/native/quantized/cpu/qlinear_prepack.cpp
- aten/src/ATen/native/quantized/cpu/qlinear_unpack.cpp
**Skip tests that are not supported by ONEDNN**
- test/ao/sparsity/test_kernels.py
- test/quantization/core/test_quantized_module.py
- test/quantization/core/test_quantized_op.py
## Validation results
This PR has passed `test_quantization.py` and `test_mkldnn.py`.
Below are performance data of int8 2d convolution and linear on the Cascade Lake Xeon® platform:
(Note: Tested with single instance on single core. Using the latest oneDNN library.)
**Table 1. Performance comparison of int8 2d convolution operator**
|No.| Shape| FBGEMM| ONEDNN| Gain|
|-|-|-|-|-|
|1| IC=128, OC=128, kernel=3, stride=1, N=4, H=32, W=32, G=1, pad=0| 668.310us| 535.630us| 24.8%|
|2| IC=128, OC=128, kernel=3, stride=2, N=4, H=32, W=32, G=1, pad=0| 290.630us| 281.810us| 3.1%|
|3| IC=128, OC=256, kernel=3, stride=1, N=4, H=32, W=32, G=1, pad=0| 1.045ms| 893.010us| 17.0%|
|4| IC=128, OC=256, kernel=3, stride=2, N=4, H=32, W=32, G=1, pad=0| 385.320us| 373.720us| 3.1%|
|5| IC=256, OC=256, kernel=3, stride=1, N=4, H=32, W=32, G=1, pad=0| 1.876ms| 1.641ms| 14.3%|
|6| IC=256, OC=256, kernel=3, stride=2, N=4, H=32, W=32, G=1, pad=0| 660.460us| 638.470us| 3.4%|
**Table 2. Performance comparison of int8 linear operator**
|No.| Shape (m, n, k)| FBGEMM| ONEDNN| Gap|
|-|-|-|-|-|
|1| 64, 800, 320| 80.550us| 96.770us| 20.10%|
|2| 64, 768, 512| 101.230us| 130.720us| 29.10%|
|3| 16, 256, 512| 30.230us| 51.450us| 70.20%|
|4| 128, 128, 128| 33.810us| 50.480us| 49.30%|
|5| 256, 512, 256| 154.490us| 195.050us| 26.30%|
|6| 1024, 1024, 1024| 3.134ms| 3.514ms| 12.10%|
ONEDNN showed advantages over FBGEMM for convolution. However, it has performance gap to FBGEMM for Linear ops. The gap is a known issue and further optimization is in progress in the oneDNN library. On the latest platforms, better performance of ONEDNN is achieved for both conv and linear.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69820
Reviewed By: HDCharles
Differential Revision: D33716039
Pulled By: jerryzh168
fbshipit-source-id: 6f7bb807e85798142dfcffccfca8b8bd652fb3dd
(cherry picked from commit 91526b373560f42ba0ad307f9cccfc0eb5218b1f)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56830
Opt into formatting on GitHub and format everything. This is a trial run before turning on formatting for more and eventually all of the codebase.
Test Plan: CI
Reviewed By: zertosh
Differential Revision: D27979080
fbshipit-source-id: a80f0c48691c08ae8ca0af06377b87e6a2351151
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26620
This change updates torch.backend.quantized.engine to accept string ("fbgemm"/"qnnpack"/"none" for now).
set_qengine and get_qengine return an int which represents the at::QEngine enum
Test Plan:
python test/test_torch.py
Imported from OSS
Differential Revision: D17533582
fbshipit-source-id: 5103263d0d59ff37d43dec27243cb76ba8ba633f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25680
Add a runtime flag to choose between FBGEMM and QNNPACK when compiled with both.
The flag can be set by using torch.backends.quantized.engine = torch.fbgemm/torch.qnnpack or ctx::setPreferredQuantizedEngine(at::QEngine)
ghstack-source-id: 89935643
Test Plan: Verified torch.backends.quantized.engine works
Differential Revision: D17198233
fbshipit-source-id: e5449d06f4136385e0e6d18bd4237f8654a61672
