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FP8 Quantization

  1. Introduction
  2. Supported Parameters
  3. Get Start with FP8 Quantization
  4. Examples

Introduction

Float point 8 (FP8) is a promising data type for low precision quantization which provides a data distribution that is completely different from INT8 and it's shown as below.

Intel Gaudi2, also known as HPU, provides this data type capability for low precision quantization, which includes E4M3 and E5M2. For more information about these two data type, please refer to link.

Intel Neural Compressor provides general quantization APIs to leverage HPU FP8 capability. with simple with lower memory usage and lower compute cost, 8 bit model

Supported Parameters

Attribute Description Values
fp8_config The target data type of FP8 quantization. E4M3 (default) - As Fig. 2
E5M2 - As Fig. 1.
hp_dtype The high precision data type of non-FP8 operators. bf16 (default) - torch.bfloat16
fp16 - torch.float16.
fp32 - torch.float32.
observer The observer to measure the statistics. maxabs (default), saves all tensors to files.
allowlist List of nn.Module names or types to quantize. When setting an empty list, all the supported modules will be quantized by default. See Supported Modules. Not setting the list at all is not recommended as it will set the allowlist to these modules only: torch.nn.Linear, torch.nn.Conv2d, and BMM. Default = {'names': [], 'types': FP8_WHITE_LIST}
blocklist List of nn.Module names or types not to quantize. Defaults to empty list, so you may omit it from the config file. Default = {'names': [], 'types': ()}
mode The mode, measure or quantize, to run HQT with. MEASURE - Measure statistics of all modules and emit the results to dump_stats_path.
QUANTIZE - Quantize and run the model according to the provided measurements.
AUTO (default) - Select from [MEASURE, QUANTIZE] automatically.
dump_stats_path The path to save and load the measurements. The path is created up until the level before last "/". The string after the last / will be used as prefix to all the measurement files that will be created. Default = "./hqt_output/measure"
scale_method The method for calculating the scale from the measurement. - unit_scale - Always use scale of 1.
- hw_aligned_single_scale - Always use scale that's aligned to the corresponding HW accelerated scale.
- maxabs_hw (default) - Scale is calculated to stretch/compress the maxabs measurement to the full-scale of FP8 and then aligned to the corresponding HW accelerated scale.
- maxabs_pow2 - Scale is calculated to stretch/compress the maxabs measurement to the full-scale of FP8 and then rounded to the power of 2.
- maxabs_hw_opt_weight - Scale of model params (weights) is chosen as the scale that provides minimal mean-square-error between quantized and non-quantized weights, from all possible HW accelerated scales. Scale of activations is calculated the same as maxabs_hw.
- act_maxabs_pow2_weights_pcs_opt_pow2 - Scale of model params (weights) is calculated per-channel of the params tensor. The scale per-channel is calculated the same as maxabs_hw_opt_weight. Scale of activations is calculated the same as maxabs_pow2.
- act_maxabs_hw_weights_pcs_maxabs_pow2 - Scale of model params (weights) is calculated per-channel of the params tensor. The scale per-channel is calculated the same as maxabs_pow2. Scale of activations is calculated the same as maxabs_hw.
measure_exclude If this attribute is not defined, the default is OUTPUT. Since most models do not require measuring output tensors, you can exclude it to speed up the measurement process. NONE - All tensors are measured.
OUTPUT (default) - Excludes measurement of output tensors.

Get Start with FP8 Quantization

Demo Usage

from neural_compressor.torch.quantization import (
    FP8Config,
    prepare,
    convert,
)
import torchvision.models as models

model = models.resnet18()
qconfig = FP8Config(fp8_config="E4M3")
model = prepare(model, qconfig)
# customer defined calibration
calib_func(model)
model = convert(model)

Examples

Task Example
Computer Vision (CV) Link
Large Language Model (LLM) Link

Note: For LLM, Optimum-habana provides higher performance based on modified modeling files, so here the Link of LLM goes to Optimum-habana, which utilize Intel Neural Compressor for FP8 quantization internally.