TF_SQ.md

Smooth Quant

• Smooth Quant
  • Introduction
  • Usage
    • Using a Fixed alpha
    • Determining the alpha through auto-tuning
  • Examples

Introduction

Quantization is a common compression operation to reduce memory and accelerate inference by converting the floating point matrix to an integer matrix. For large language models (LLMs) with gigantic parameters, the systematic outliers make quantification of activations difficult. SmoothQuant, a training free post-training quantization (PTQ) solution, offline migrates this difficulty from activations to weights with a mathematically equivalent transformation.

Please refer to the document of Smooth Quant for detailed fundamental knowledge.

Usage

There are two ways to apply smooth quantization: 1) using a fixed alpha for the entire model or 2) determining the alpha through auto-tuning.

Using a Fixed alpha

To set a fixed alpha for the entire model, users can follow this example:

from neural_compressor.tensorflow import SmoothQuantConfig, StaticQuantConfig

quant_config = [SmoothQuantConfig(alpha=0.5), StaticQuantConfig()]
q_model = quantize_model(output_graph_def, [sq_config, static_config], calib_dataloader)

The SmoothQuantConfig should be combined with StaticQuantConfig in a list because we still need to insert QDQ and apply pattern fusion after the smoothing process.

Determining the alpha through auto-tuning

Users can search for the best alpha for the entire model.The tuning process looks for the optimal alpha value from a list of alpha values provided by the user.

Here is an example:

from neural_compressor.tensorflow import StaticQuantConfig, SmoothQuantConfig

custom_tune_config = TuningConfig(config_set=[SmoothQuantConfig(alpha=[0.5, 0.6, 0.7]), StaticQuantConfig()])
best_model = autotune(
    model="fp32_model",
    tune_config=custom_tune_config,
    eval_fn=eval_fn_wrapper,
    calib_dataloader=calib_dataloader,
)

Please note that, it may a considerable amount of time as the tuning process applies each alpha to the entire model and uses the evaluation result on the entire dataset as the metric to determine the best alpha.

Examples

Users can also refer to examples on how to apply smooth quant to a TensorFlow model with neural_compressor.tensorflow.