Accelerate Inference of Hugging Face Transformer Models with Optimum Intel and OpenVINO™
Authors: Xiake Sun, Kunda Xu
Hugging Face is a large open-source community that quickly became an enticing hub for pre-trained deep learning models across Natural Language Processing (NLP), Automatic Speech Recognition(ASR), and Computer Vision (CV) domains.
Optimum Intel provides a simple interface to optimize Transformer models and convert them to OpenVINO™ Intermediate Representation (IR) format to accelerate end-to-end pipelines on Intel® architectures using OpenVINO™ runtime.
Sentimental classification, as one of the popular NLP tasks, is the automated process of identifying opinions in the text and labeling them as positive or negative. In this blog, we use DistilBERT for the sentimental classification task as an example to show how Optimum Intel helps to optimize the model with Neural Network Compression Framework (NNCF) and accelerate inference with OpenVINO™ runtime.
2. Setup Environment
Install optimum-intel and its dependency in a new python virtual environment as follow:
3. Model Inference with OpenVINO™ Runtime
The Optimum inference models are API compatible with Hugging Face Transformers models; which means you could simply replace Hugging Face Transformer “AutoModelXXX” class with the “OVModelXXX” class to switch model inference with OpenVINO™ runtime. You could set “from_transformers=True” when loading the model with the from_pretrained() method, the loaded model will be automatically converted to an OpenVINO™ IR for inference with OpenVINO™ runtime.
Here is an example of how to perform inference with OpenVINO™ runtime for a sentimental classification task, the output of the pipeline consists of classification label (positive/negative) and corresponding confidence.
4. Model Quantization with NNCF framework
Most deep learning models are built using 32 bits floating-point precision (FP32). Quantization is the process to represent the model using less memory with minimal accuracy loss. To further optimize model performance on Intel® architecture via Intel® Deep Learning Boost, model quantization as 8 bits integer precision (INT8) is required.
Optimum Intel enables you to apply quantization on Hugging Face Transformer Models using the NNCF. NNCF provides two mainstream quantization methods - Post-Training Quantization (PTQ) and Quantization-Aware Training (QAT).
- Post-Training Quantization (PTQ) refers to quantizing a model with a representative calibration dataset without fine-tuning.
- Quantization-Aware Training (QAT) is applied to simulate the effects of quantization during training to mitigate its effect on the model’s accuracy
4.1. Model Quantization with NNCF PTQ
NNCF Post-training static quantization introduces an additional calibration step where data is fed through the network to compute the activations quantization parameters. Here is how to apply static quantization on a pre-trained DistilBERT using General Language Understanding Evaluation (GLUE) dataset as the calibration dataset:
The quantize() method applies post-training static quantization and export the resulting quantized model to the OpenVINO™ Intermediate Representation (IR), which can be deployed on any target Intel® architecture.
4.2. Model Quantization with NNCF QAT
Quantization-Aware Training (QAT) aims to mitigate model accuracy issue by simulating the effects of quantization during training. If post-training quantization results in accuracy degradation, QAT can be used instead.
NNCF provides an “OVTrainer” class to replace Hugging Face Transformer’s “Trainer” class to enable quantization during training with additional quantization configuration. Here is an example on how to fine-tune a DistilBERT with Stanford Sentiment Treebank (SST) dataset while applying quantization aware training (QAT):
4.3. Comparison of FP32 and INT8 model outputs
“OVModelForXXX” class provided the same API to load FP32 and quantized INT8 OpenVINO™ models by setting “from_transformers=False”. Here is an example of how to load quantized INT8 models optimized by NNCF and inference with OpenVINO™ runtime.
Here is an example for sentimental classification output of FP32 and INT8 models:
5. Mitigation of accuracy issue cause by saturation
8-bit instructions of old CPU generations (based on SSE,AVX-2, AVX-512 instruction sets) are prone to so-called saturation(overflow) of the intermediate buffer when calculating the dot product, which is an essential part of Convolutional or MatMul operations. This saturation can lead to a drop in accuracy when running inference of 8-bit quantized models on the mentioned architectures. The problem does not occur on GPUs or CPUs with Intel® Deep Learning Boost (VNNI) technology and further generations.
In the case a significant difference in accuracy (>1%) occurs after quantization with NNCF default quantization configuration, here is an example code to check if deployed platform supports Intel® Deep Learning Boost (VNNI) and further generations:
While quantizing activations use the full range of 8-bit data types, there is a workaround using only 7 bits to represent weights (of Convolutional or Fully-Connected layers) to mitigate saturation issue for many models on old CPU platform.
NNCF provides three options to deal with the saturation issue. The options can be enabled in the NNCF quantization configuration using the “overflow_fix” parameter:
- "disable": (default) option do not apply saturation fix at all
- "enable": option to apply for all layers in the model
- "first_layer_only": option to fix saturation issue for the first layer
Here is an example to enable overflow fix in quantization configuration to mitigate accuracy issue on old CPU platform:
After model quantization with updated quantization configuration with NNCF PTQ/NNCF, you can repeat step 4.3 to verify if quantized INT8 model inference results are consistent with FP32 model outputs.
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