v1.0.2

• Rebase torch CCL patch to PyTorch 1.5.0-rc3

v1.0.1-alpha Release

• Static link oneDNN library
• Check AVX512 build option
• Fix the issue that cannot normally invoke enable_auto_optimization

v1.0.0-alpha Release

What's New

• Auto Operator Optimization

  Intel Extension for PyTorch will automatically optimize the operators of PyTorch when importing its python package. It will significantly improve the computation performance if the input tensor and the model is converted to the extension device.

• Auto Mixed Precision

  Currently, the extension has supported bfloat16. It streamlines the work to enable a bfloat16 model. The feature is controlled by enable_auto_mix_precision. If you enable it, the extension will run the operator with bfloat16 automatically to accelerate the operator computation.

Performance Result

We collected the performance data of some models on the Intel Cooper Lake platform with 1 socket and 28 cores. Intel Cooper Lake introduced AVX512 BF16 instructions which could improve the bfloat16 computation significantly. The detail is as follows (The data is the speedup ratio and the baseline is upstream PyTorch).

	Imperative - Operator Injection	Imperative - Mixed Precision	JIT- Operator Injection	JIT - Mixed Precision
RN50	2.68	5.01	5.14	9.66
ResNet3D	3.00	4.67	5.19	8.39
BERT-LARGE	0.99	1.40	N/A	N/A

We also measured the performance of ResNeXt101, Transformer-FB, DLRM, and YOLOv3 with the extension. We observed that the performance could be significantly improved by the extension as expected.

Known Issues

#10 All data types have not been registered for DPCPP
#37 MaxPool can't get nan result when input's value is nan

NOTE
The extension supported PyTorch v1.5.0-rc3. Support for other PyTorch versions is working in progress.