quantization_mixed_precision.md

Tensorflow

Intel has worked with the TensorFlow development team to enhance TensorFlow to include bfloat16 data support for CPUs. For more information about BF16 in TensorFlow, please read Accelerating AI performance on 3rd Gen Intel® Xeon® Scalable processors with TensorFlow and Bfloat16.

• BF16 conversion during quantization in TensorFlow

• Three steps

1. Convert to a FP32 + INT8 mixed precision Graph

   In this steps, TF adaptor will regard all fallback datatype as FP32. According to the per op datatype in tuning config passed by strategy, TF adaptor will generate a FP32 + INT8 mixed precision graph.

2. Convert to a BF16 + FP32 + INT8 mixed precision Graph

   In this phase, adaptor will convert some FP32 ops to BF16 according to bf16_ops list in tuning config.

3. Optimize the BF16 + FP32 + INT8 mixed precision Graph

   After the mixed precision graph generated, there are still some optimization need to be applied to improved the performance, for example Cast + Cast and so on. The BF16Convert transformer also apply a depth-first method to make it possible to take the ops use BF16 which can support BF16 datatype to reduce the insertion of Cast op.

PyTorch

Intel has also worked with the PyTorch development team to enhance PyTorch to include bfloat16 data support for CPUs.

• BF16 conversion during quantization in PyTorch

• Two steps

1. Convert to a FP32 + INT8 mixed precision Graph or Module

   In this steps, PT adaptor will combine the INT8 ops and all fallback ops to FP32 + INT8 mixed precision Graph or Module no matter in Eager mode or Fx Graph mode.

2. Convert to a BF16 + FP32 + INT8 mixed precision Graph or Module

   In this phase, adaptor will according to BF16 op list from strategy tune config to wrapper the FP32 module with BF16Wrapper to realize the BF16 + FP32 + INT8 mixed precision Graph or Module. adaptor will do retrace the GraphModule again if using Fx Graph mode.