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Mixed Precision

  1. Introduction
  2. Mixed Precision Support Matrix
  3. Get Started with Mixed Precision API
  4. Examples

Introduction

The recent growth of Deep Learning has driven the development of more complex models that require significantly more compute and memory capabilities. Several low precision numeric formats have been proposed to address the problem. Google's bfloat16 and the FP16: IEEE half-precision format are two of the most widely used sixteen bit formats. Mixed precision training and inference using low precision formats have been developed to reduce compute and bandwidth requirements.

The recently launched 3rd Gen Intel® Xeon® Scalable processor (codenamed Cooper Lake), featuring Intel® Deep Learning Boost, is the first general-purpose x86 CPU to support the bfloat16 format. Specifically, three new bfloat16 instructions are added as a part of the AVX512_BF16 extension within Intel Deep Learning Boost: VCVTNE2PS2BF16, VCVTNEPS2BF16, and VDPBF16PS. The first two instructions allow converting to and from bfloat16 data type, while the last one performs a dot product of bfloat16 pairs. Further details can be found in the hardware numerics document published by Intel.

Architecture

Mixed Precision Support Matrix

Framework Backend Backend Library Backend Value Support Device(cpu as default) Support BF16 Support FP16
PyTorch FX FBGEMM "default" cpu ✔ ❌
IPEX OneDNN "ipex" cpu ✔ ❌
ONNX Runtime CPUExecutionProvider MLAS "default" cpu ❌ ❌
TensorrtExecutionProvider TensorRT "onnxrt_trt_ep" gpu ❌ ❌
CUDAExecutionProvider CUDA "onnxrt_cuda_ep" gpu ✔ ✔
DnnlExecutionProvider OneDNN "onnxrt_dnnl_ep" cpu ✔ ❌
Tensorflow Tensorflow OneDNN "default" cpu ✔ ❌
ITEX OneDNN "itex" cpu | gpu ✔ ❌
MXNet OneDNN OneDNN "default" cpu ✔ ❌

Hardware and Software requests for BF16

  • TensorFlow
    1. Hardware: CPU supports avx512_bf16 instruction set.
    2. Software: intel-tensorflow >= 2.3.0.
  • PyTorch
    1. Hardware: CPU supports avx512_bf16 instruction set.
    2. Software: torch >= 1.11.0.
  • ONNX Runtime
    1. Hardware: GPU, set 'device' of config to 'gpu' and 'backend' to 'onnxrt_cuda_ep'.
    2. Software: onnxruntime-gpu.

Hardware and Software requests for FP16

  • ONNX Runtime
    1. Hardware: GPU, set 'device' of config to 'gpu' and 'backend' to 'onnxrt_cuda_ep'.
    2. Software: onnxruntime-gpu.

During quantization mixed precision

During quantization, if the hardware support BF16, the conversion is default enabled. So you may get an INT8/BF16/FP32 mixed precision model on those hardware. FP16 can be executed if 'device' of config is 'gpu'. Please refer to this document for its workflow.

Accuracy-driven mixed precision

BF16/FP16 conversion may lead to accuracy drop. Intel® Neural Compressor provides an accuracy-driven tuning function to reduce accuracy loss, which will fallback converted ops to FP32 automatically to get better accuracy. To enable this function, users only to provide evaluation function or (evaluation dataloader plus evaluation metric) for mixed precision inputs.
To be noticed, IPEX backend doesn't support accuracy-driven mixed precision.

Get Started with Mixed Precision API

To get a bf16/fp16 model, users can use the Mixed Precision API as follows.

  • BF16:
from neural_compressor import mix_precision
from neural_compressor.config import MixedPrecisionConfig

conf = MixedPrecisionConfig()  # default precision is bf16
converted_model = mix_precision.fit(model, conf=conf)
converted_model.save("./path/to/save/")
  • FP16:
from neural_compressor import mix_precision
from neural_compressor.config import MixedPrecisionConfig

conf = MixedPrecisionConfig(
    backend="onnxrt_cuda_ep",
    device="gpu",
    precisions="fp16",
)
converted_model = mix_precision.fit(model, conf=conf)
converted_model.save("./path/to/save/")

Examples