Quantize NLP models with Post-Training Quantization ​in NNCF

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This tutorial demonstrates how to apply INT8 quantization to the Natural Language Processing model known as BERT, using the Post-Training Quantization API (NNCF library). A fine-tuned HuggingFace BERT PyTorch model, trained on the Microsoft Research Paraphrase Corpus (MRPC), will be used. The tutorial is designed to be extendable to custom models and datasets. It consists of the following steps:

  • Download and prepare the BERT model and MRPC dataset.

  • Define data loading and accuracy validation functionality.

  • Prepare the model for quantization.

  • Run optimization pipeline.

  • Load and test quantized model.

  • Compare the performance of the original, converted and quantized models.

Table of contents:

%pip install -q "nncf>=2.5.0"
%pip install -q transformers datasets evaluate --extra-index-url https://download.pytorch.org/whl/cpu
%pip install -q "openvino>=2023.1.0"

Note: you may need to restart the kernel to use updated packages.

Note: you may need to restart the kernel to use updated packages.

Note: you may need to restart the kernel to use updated packages.

Imports

import os
import time
from pathlib import Path
from zipfile import ZipFile
from typing import Iterable
from typing import Any

import datasets
import evaluate
import numpy as np
import nncf
from nncf.parameters import ModelType
import openvino as ov
import torch
from transformers import BertForSequenceClassification, BertTokenizer

# Fetch `notebook_utils` module
import urllib.request
urllib.request.urlretrieve(
    url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/notebook_utils.py',
    filename='notebook_utils.py'
)
from notebook_utils import download_file

2024-02-09 22:38:10.763464: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable TF_ENABLE_ONEDNN_OPTS=0.
2024-02-09 22:38:10.797722: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-02-09 22:38:11.441310: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT

INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, tensorflow, onnx, openvino

Settings

# Set the data and model directories, source URL and the filename of the model.
DATA_DIR = "data"
MODEL_DIR = "model"
MODEL_LINK = "https://download.pytorch.org/tutorial/MRPC.zip"
FILE_NAME = MODEL_LINK.split("/")[-1]
PRETRAINED_MODEL_DIR = os.path.join(MODEL_DIR, "MRPC")

os.makedirs(DATA_DIR, exist_ok=True)
os.makedirs(MODEL_DIR, exist_ok=True)

Prepare the Model

Perform the following:

  • Download and unpack pre-trained BERT model for MRPC by PyTorch.

  • Convert the model to the OpenVINO Intermediate Representation (OpenVINO IR)

download_file(MODEL_LINK, directory=MODEL_DIR, show_progress=True)
with ZipFile(f"{MODEL_DIR}/{FILE_NAME}", "r") as zip_ref:
    zip_ref.extractall(MODEL_DIR)

model/MRPC.zip:   0%|          | 0.00/387M [00:00<?, ?B/s]

Convert the original PyTorch model to the OpenVINO Intermediate Representation.

From OpenVINO 2023.0, we can directly convert a model from the PyTorch format to the OpenVINO IR format using model conversion API. Following PyTorch model formats are supported:

  • torch.nn.Module

  • torch.jit.ScriptModule

  • torch.jit.ScriptFunction

MAX_SEQ_LENGTH = 128
input_shape = ov.PartialShape([1, -1])
ir_model_xml = Path(MODEL_DIR) / "bert_mrpc.xml"
core = ov.Core()

torch_model = BertForSequenceClassification.from_pretrained(PRETRAINED_MODEL_DIR)
torch_model.eval

input_info = [("input_ids", input_shape, np.int64),("attention_mask", input_shape, np.int64),("token_type_ids", input_shape, np.int64)]
default_input = torch.ones(1, MAX_SEQ_LENGTH, dtype=torch.int64)
inputs = {
    "input_ids": default_input,
    "attention_mask": default_input,
    "token_type_ids": default_input,
}

# Convert the PyTorch model to OpenVINO IR FP32.
if not ir_model_xml.exists():
    model = ov.convert_model(torch_model, example_input=inputs, input=input_info)
    ov.save_model(model, str(ir_model_xml))
else:
    model = core.read_model(ir_model_xml)

/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
  return self.fget.__get__(instance, owner)()

WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.base has been moved to tensorflow.python.trackable.base. The old module will be deleted in version 2.11.

[ WARNING ]  Please fix your imports. Module %s has been moved to %s. The old module will be deleted in version %s.

WARNING:nncf:NNCF provides best results with torch==2.1.2, while current torch version is 2.1.0+cpu. If you encounter issues, consider switching to torch==2.1.2

No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'

Prepare the Dataset

We download the General Language Understanding Evaluation (GLUE) dataset for the MRPC task from HuggingFace datasets. Then, we tokenize the data with a pre-trained BERT tokenizer from HuggingFace.

def create_data_source():
    raw_dataset = datasets.load_dataset('glue', 'mrpc', split='validation')
    tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_DIR)

    def _preprocess_fn(examples):
        texts = (examples['sentence1'], examples['sentence2'])
        result = tokenizer(*texts, padding='max_length', max_length=MAX_SEQ_LENGTH, truncation=True)
        result['labels'] = examples['label']
        return result
    processed_dataset = raw_dataset.map(_preprocess_fn, batched=True, batch_size=1)

    return processed_dataset

data_source = create_data_source()

Optimize model using NNCF Post-training Quantization API

NNCF provides a suite of advanced algorithms for Neural Networks inference optimization in OpenVINO with minimal accuracy drop. We will use 8-bit quantization in post-training mode (without the fine-tuning pipeline) to optimize BERT.

The optimization process contains the following steps:

  1. Create a Dataset for quantization

  2. Run nncf.quantize for getting an optimized model

  3. Serialize OpenVINO IR model using openvino.save_model function

INPUT_NAMES = [key for key in inputs.keys()]

def transform_fn(data_item):
    """
    Extract the model's input from the data item.
    The data item here is the data item that is returned from the data source per iteration.
    This function should be passed when the data item cannot be used as model's input.
    """
    inputs = {
        name: np.asarray([data_item[name]], dtype=np.int64) for name in INPUT_NAMES
    }
    return inputs

calibration_dataset = nncf.Dataset(data_source, transform_fn)
# Quantize the model. By specifying model_type, we specify additional transformer patterns in the model.
quantized_model = nncf.quantize(model, calibration_dataset,
                                model_type=ModelType.TRANSFORMER)

Output()

Output()





INFO:nncf:36 ignored nodes were found by name in the NNCFGraph





INFO:nncf:50 ignored nodes were found by name in the NNCFGraph





Output()





Output()





compressed_model_xml = Path(MODEL_DIR) / "quantized_bert_mrpc.xml"
ov.save_model(quantized_model, compressed_model_xml)









Load and Test OpenVINO Model


To load and test converted model, perform the following:


    

  • Load the model and compile it for selected device.
    • 

  • Prepare the input.
    • 

  • Run the inference.
    • 

  • Get the answer from the model output.
    • 





Select inference device


select device from dropdown list for running inference using OpenVINO


import ipywidgets as widgets

device = widgets.Dropdown(
    options=core.available_devices + ["AUTO"],
    value='AUTO',
    description='Device:',
    disabled=False,
)

device





Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')





# Compile the model for a specific device.
compiled_quantized_model = core.compile_model(model=quantized_model, device_name=device.value)
output_layer = compiled_quantized_model.outputs[0]





The Data Source returns a pair of sentences (indicated by
sample_idx) and the inference compares these sentences and outputs
whether their meaning is the same. You can test other sentences by
changing sample_idx to another value (from 0 to 407).


sample_idx = 5
sample = data_source[sample_idx]
inputs = {k: torch.unsqueeze(torch.tensor(sample[k]), 0) for k in ['input_ids', 'token_type_ids', 'attention_mask']}

result = compiled_quantized_model(inputs)[output_layer]
result = np.argmax(result)

print(f"Text 1: {sample['sentence1']}")
print(f"Text 2: {sample['sentence2']}")
print(f"The same meaning: {'yes' if result == 1 else 'no'}")





Text 1: Wal-Mart said it would check all of its million-plus domestic workers to ensure they were legally employed .
Text 2: It has also said it would review all of its domestic employees more than 1 million to ensure they have legal status .
The same meaning: yes











Compare F1-score of FP32 and INT8 models


def validate(model: ov.Model, dataset: Iterable[Any]) -> float:
    """
    Evaluate the model on GLUE dataset.
    Returns F1 score metric.
    """
    compiled_model = core.compile_model(model, device_name=device.value)
    output_layer = compiled_model.output(0)

    metric = evaluate.load('glue', 'mrpc')
    for batch in dataset:
        inputs = [
            np.expand_dims(np.asarray(batch[key], dtype=np.int64), 0) for key in INPUT_NAMES
        ]
        outputs = compiled_model(inputs)[output_layer]
        predictions = outputs[0].argmax(axis=-1)
        metric.add_batch(predictions=[predictions], references=[batch['labels']])
    metrics = metric.compute()
    f1_score = metrics['f1']

    return f1_score


print('Checking the accuracy of the original model:')
metric = validate(model, data_source)
print(f'F1 score: {metric:.4f}')

print('Checking the accuracy of the quantized model:')
metric = validate(quantized_model, data_source)
print(f'F1 score: {metric:.4f}')





Checking the accuracy of the original model:





F1 score: 0.9019
Checking the accuracy of the quantized model:





F1 score: 0.8969









Compare Performance of the Original, Converted and Quantized Models


Compare the original PyTorch model with OpenVINO converted and quantized
models (FP32, INT8) to see the difference in performance. It is
expressed in Sentences Per Second (SPS) measure, which is the same as
Frames Per Second (FPS) for images.


# Compile the model for a specific device.
compiled_model = core.compile_model(model=model, device_name=device.value)





num_samples = 50
sample = data_source[0]
inputs = {k: torch.unsqueeze(torch.tensor(sample[k]), 0) for k in ['input_ids', 'token_type_ids', 'attention_mask']}

with torch.no_grad():
    start = time.perf_counter()
    for _ in range(num_samples):
        torch_model(torch.vstack(list(inputs.values())))
    end = time.perf_counter()
    time_torch = end - start
print(
    f"PyTorch model on CPU: {time_torch / num_samples:.3f} seconds per sentence, "
    f"SPS: {num_samples / time_torch:.2f}"
)

start = time.perf_counter()
for _ in range(num_samples):
    compiled_model(inputs)
end = time.perf_counter()
time_ir = end - start
print(
    f"IR FP32 model in OpenVINO Runtime/{device.value}: {time_ir / num_samples:.3f} "
    f"seconds per sentence, SPS: {num_samples / time_ir:.2f}"
)

start = time.perf_counter()
for _ in range(num_samples):
    compiled_quantized_model(inputs)
end = time.perf_counter()
time_ir = end - start
print(
    f"OpenVINO IR INT8 model in OpenVINO Runtime/{device.value}: {time_ir / num_samples:.3f} "
    f"seconds per sentence, SPS: {num_samples / time_ir:.2f}"
)





We strongly recommend passing in an attention_mask since your input_ids may be padded. See https://huggingface.co/docs/transformers/troubleshooting#incorrect-output-when-padding-tokens-arent-masked.


PyTorch model on CPU: 0.073 seconds per sentence, SPS: 13.77





IR FP32 model in OpenVINO Runtime/AUTO: 0.021 seconds per sentence, SPS: 47.89





OpenVINO IR INT8 model in OpenVINO Runtime/AUTO: 0.009 seconds per sentence, SPS: 109.72





Finally, measure the inference performance of OpenVINO FP32 and
INT8 models. For this purpose, use Benchmark
Tool
in OpenVINO.




NOTE: The benchmark_app tool is able to measure the
performance of the OpenVINO Intermediate Representation (OpenVINO IR)
models only. For more accurate performance, run benchmark_app in
a terminal/command prompt after closing other applications. Run
benchmark_app -m model.xml -d CPU to benchmark async inference on
CPU for one minute. Change CPU to GPU to benchmark on GPU.
Run benchmark_app --help to see an overview of all command-line
options.




# Inference FP32 model (OpenVINO IR)
!benchmark_app -m $ir_model_xml -shape [1,128],[1,128],[1,128] -d device.value -api sync





[Step 1/11] Parsing and validating input arguments
[ INFO ] Parsing input parameters
[Step 2/11] Loading OpenVINO Runtime
[ WARNING ] Default duration 120 seconds is used for unknown device device.value
[ INFO ] OpenVINO:
[ INFO ] Build ................................. 2023.3.0-13775-ceeafaf64f3-releases/2023/3
[ INFO ]
[ INFO ] Device info:
[ INFO ]
[ INFO ]
[Step 3/11] Setting device configuration
[ ERROR ] Exception from src/inference/src/core.cpp:228:
Exception from src/inference/src/dev/core_impl.cpp:560:
Device with "device" name is not registered in the OpenVINO Runtime

Traceback (most recent call last):
  File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/openvino/tools/benchmark/main.py", line 166, in main
    supported_properties = benchmark.core.get_property(device, properties.supported_properties())
RuntimeError: Exception from src/inference/src/core.cpp:228:
Exception from src/inference/src/dev/core_impl.cpp:560:
Device with "device" name is not registered in the OpenVINO Runtime





# Inference INT8 model (OpenVINO IR)
! benchmark_app -m $compressed_model_xml -shape [1,128],[1,128],[1,128] -d device.value -api sync





[Step 1/11] Parsing and validating input arguments
[ INFO ] Parsing input parameters
[Step 2/11] Loading OpenVINO Runtime
[ WARNING ] Default duration 120 seconds is used for unknown device device.value
[ INFO ] OpenVINO:
[ INFO ] Build ................................. 2023.3.0-13775-ceeafaf64f3-releases/2023/3
[ INFO ]
[ INFO ] Device info:
[ INFO ]
[ INFO ]
[Step 3/11] Setting device configuration
[ ERROR ] Exception from src/inference/src/core.cpp:228:
Exception from src/inference/src/dev/core_impl.cpp:560:
Device with "device" name is not registered in the OpenVINO Runtime

Traceback (most recent call last):
  File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/openvino/tools/benchmark/main.py", line 166, in main
    supported_properties = benchmark.core.get_property(device, properties.supported_properties())
RuntimeError: Exception from src/inference/src/core.cpp:228:
Exception from src/inference/src/dev/core_impl.cpp:560:
Device with "device" name is not registered in the OpenVINO Runtime