Quantize NLP models with Post-Training Quantization in NNCF#

This Jupyter notebook can be launched on-line, opening an interactive environment in a browser window. You can also make a local installation. Choose one of the following options:

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:

Imports
Settings
Prepare the Model
Prepare the Dataset
Optimize model using NNCF Post-training Quantization API
Load and Test OpenVINO Model
- Select inference device
Compare F1-score of FP32 and INT8 models
Compare Performance of the Original, Converted and Quantized Models

Installation Instructions#

This is a self-contained example that relies solely on its own code.

We recommend running the notebook in a virtual environment. You only need a Jupyter server to start. For details, please refer to Installation Guide.

%pip install -q "nncf>=2.5.0"
%pip install -q torch transformers "torch>=2.1" datasets evaluate tqdm  --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 requests

r = requests.get(
    url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
)

open("notebook_utils.py", "w").write(r.text)
from notebook_utils import download_file, device_widget

2024-12-10 02:16:53.582571: 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.
2024-12-10 02:16:53.608080: 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.

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)

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)

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.
/opt/home/k8sworker/ci-ai/cibuilds/jobs/ov-notebook/jobs/OVNotebookOps/builds/835/archive/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:5006: FutureWarning: _is_quantized_training_enabled is going to be deprecated in transformers 4.39.0. Please use model.hf_quantizer.is_trainable instead
  warnings.warn(
loss_type=None was set in the config but it is unrecognised.Using the default loss: ForCausalLMLoss.

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:

Create a Dataset for quantization
Run nncf.quantize for getting an optimized model
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()

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

device = device_widget()

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: 0.068 seconds per sentence, SPS: 14.71
IR FP32 model in OpenVINO Runtime/AUTO: 0.020 seconds per sentence, SPS: 49.37
OpenVINO IR INT8 model in OpenVINO Runtime/AUTO: 0.009 seconds per sentence, SPS: 107.19

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 AUTO
[ INFO ] OpenVINO:
[ INFO ] Build ................................. 2024.4.0-16579-c3152d32c9c-releases/2024/4
[ INFO ]
[ INFO ] Device info:
[ INFO ] AUTO
[ INFO ] Build ................................. 2024.4.0-16579-c3152d32c9c-releases/2024/4
[ INFO ]
[ INFO ]
[Step 3/11] Setting device configuration
[ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.LATENCY.
[Step 4/11] Reading model files
[ INFO ] Loading model files
[ INFO ] Read model took 19.10 ms
[ INFO ] Original model I/O parameters:
[ INFO ] Model inputs:
[ INFO ]     input_ids (node: input_ids) : i64 / [...] / [1,?]
[ INFO ]     63 , attention_mask (node: attention_mask) : i64 / [...] / [1,?]
[ INFO ]     token_type_ids (node: token_type_ids) : i64 / [...] / [1,?]
[ INFO ] Model outputs:
[ INFO ]     logits (node: __module.classifier/aten::linear/Add) : f32 / [...] / [1,2]
[Step 5/11] Resizing model to match image sizes and given batch
[ INFO ] Model batch size: 1
[ INFO ] Reshaping model: 'input_ids': [1,128], '63': [1,128], 'token_type_ids': [1,128]
[ INFO ] Reshape model took 5.45 ms
[Step 6/11] Configuring input of the model
[ INFO ] Model inputs:
[ INFO ]     input_ids (node: input_ids) : i64 / [...] / [1,128]
[ INFO ]     63 , attention_mask (node: attention_mask) : i64 / [...] / [1,128]
[ INFO ]     token_type_ids (node: token_type_ids) : i64 / [...] / [1,128]
[ INFO ] Model outputs:
[ INFO ]     logits (node: __module.classifier/aten::linear/Add) : f32 / [...] / [1,2]
[Step 7/11] Loading the model to the device
[ INFO ] Compile model took 353.22 ms
[Step 8/11] Querying optimal runtime parameters
[ INFO ] Model:
[ INFO ]   NETWORK_NAME: Model0
[ INFO ]   EXECUTION_DEVICES: ['CPU']
[ INFO ]   PERFORMANCE_HINT: PerformanceMode.LATENCY
[ INFO ]   OPTIMAL_NUMBER_OF_INFER_REQUESTS: 1
[ INFO ]   MULTI_DEVICE_PRIORITIES: CPU
[ INFO ]   CPU:
[ INFO ]     AFFINITY: Affinity.CORE
[ INFO ]     CPU_DENORMALS_OPTIMIZATION: False
[ INFO ]     CPU_SPARSE_WEIGHTS_DECOMPRESSION_RATE: 1.0
[ INFO ]     DYNAMIC_QUANTIZATION_GROUP_SIZE: 32
[ INFO ]     ENABLE_CPU_PINNING: True
[ INFO ]     ENABLE_HYPER_THREADING: False
[ INFO ]     EXECUTION_DEVICES: ['CPU']
[ INFO ]     EXECUTION_MODE_HINT: ExecutionMode.PERFORMANCE
[ INFO ]     INFERENCE_NUM_THREADS: 12
[ INFO ]     INFERENCE_PRECISION_HINT: <Type: 'float32'>
[ INFO ]     KV_CACHE_PRECISION: <Type: 'float16'>
[ INFO ]     LOG_LEVEL: Level.NO
[ INFO ]     MODEL_DISTRIBUTION_POLICY: set()
[ INFO ]     NETWORK_NAME: Model0
[ INFO ]     NUM_STREAMS: 1
[ INFO ]     OPTIMAL_NUMBER_OF_INFER_REQUESTS: 1
[ INFO ]     PERFORMANCE_HINT: LATENCY
[ INFO ]     PERFORMANCE_HINT_NUM_REQUESTS: 0
[ INFO ]     PERF_COUNT: NO
[ INFO ]     SCHEDULING_CORE_TYPE: SchedulingCoreType.ANY_CORE
[ INFO ]   MODEL_PRIORITY: Priority.MEDIUM
[ INFO ]   LOADED_FROM_CACHE: False
[ INFO ]   PERF_COUNT: False
[Step 9/11] Creating infer requests and preparing input tensors
[ WARNING ] No input files were given for input 'input_ids'!. This input will be filled with random values!
[ WARNING ] No input files were given for input '63'!. This input will be filled with random values!
[ WARNING ] No input files were given for input 'token_type_ids'!. This input will be filled with random values!
[ INFO ] Fill input 'input_ids' with random values
[ INFO ] Fill input '63' with random values
[ INFO ] Fill input 'token_type_ids' with random values
[Step 10/11] Measuring performance (Start inference synchronously, limits: 120000 ms duration)
[ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
[ INFO ] First inference took 24.05 ms
[Step 11/11] Dumping statistics report
[ INFO ] Execution Devices:['CPU']
[ INFO ] Count:            6455 iterations
[ INFO ] Duration:         120010.71 ms
[ INFO ] Latency:
[ INFO ]    Median:        18.02 ms
[ INFO ]    Average:       18.50 ms
[ INFO ]    Min:           17.25 ms
[ INFO ]    Max:           25.45 ms
[ INFO ] Throughput:   53.79 FPS

# 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 AUTO
[ INFO ] OpenVINO:
[ INFO ] Build ................................. 2024.4.0-16579-c3152d32c9c-releases/2024/4
[ INFO ]
[ INFO ] Device info:
[ INFO ] AUTO
[ INFO ] Build ................................. 2024.4.0-16579-c3152d32c9c-releases/2024/4
[ INFO ]
[ INFO ]
[Step 3/11] Setting device configuration
[ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.LATENCY.
[Step 4/11] Reading model files
[ INFO ] Loading model files
[ INFO ] Read model took 25.14 ms
[ INFO ] Original model I/O parameters:
[ INFO ] Model inputs:
[ INFO ]     input_ids (node: input_ids) : i64 / [...] / [1,?]
[ INFO ]     attention_mask , 63 (node: attention_mask) : i64 / [...] / [1,?]
[ INFO ]     token_type_ids (node: token_type_ids) : i64 / [...] / [1,?]
[ INFO ] Model outputs:
[ INFO ]     logits (node: __module.classifier/aten::linear/Add) : f32 / [...] / [1,2]
[Step 5/11] Resizing model to match image sizes and given batch
[ INFO ] Model batch size: 1
[ INFO ] Reshaping model: 'input_ids': [1,128], '63': [1,128], 'token_type_ids': [1,128]
[ INFO ] Reshape model took 7.24 ms
[Step 6/11] Configuring input of the model
[ INFO ] Model inputs:
[ INFO ]     input_ids (node: input_ids) : i64 / [...] / [1,128]
[ INFO ]     attention_mask , 63 (node: attention_mask) : i64 / [...] / [1,128]
[ INFO ]     token_type_ids (node: token_type_ids) : i64 / [...] / [1,128]
[ INFO ] Model outputs:
[ INFO ]     logits (node: __module.classifier/aten::linear/Add) : f32 / [...] / [1,2]
[Step 7/11] Loading the model to the device
[ INFO ] Compile model took 1122.87 ms
[Step 8/11] Querying optimal runtime parameters
[ INFO ] Model:
[ INFO ]   NETWORK_NAME: Model0
[ INFO ]   EXECUTION_DEVICES: ['CPU']
[ INFO ]   PERFORMANCE_HINT: PerformanceMode.LATENCY
[ INFO ]   OPTIMAL_NUMBER_OF_INFER_REQUESTS: 1
[ INFO ]   MULTI_DEVICE_PRIORITIES: CPU
[ INFO ]   CPU:
[ INFO ]     AFFINITY: Affinity.CORE
[ INFO ]     CPU_DENORMALS_OPTIMIZATION: False
[ INFO ]     CPU_SPARSE_WEIGHTS_DECOMPRESSION_RATE: 1.0
[ INFO ]     DYNAMIC_QUANTIZATION_GROUP_SIZE: 32
[ INFO ]     ENABLE_CPU_PINNING: True
[ INFO ]     ENABLE_HYPER_THREADING: False
[ INFO ]     EXECUTION_DEVICES: ['CPU']
[ INFO ]     EXECUTION_MODE_HINT: ExecutionMode.PERFORMANCE
[ INFO ]     INFERENCE_NUM_THREADS: 12
[ INFO ]     INFERENCE_PRECISION_HINT: <Type: 'float32'>
[ INFO ]     KV_CACHE_PRECISION: <Type: 'float16'>
[ INFO ]     LOG_LEVEL: Level.NO
[ INFO ]     MODEL_DISTRIBUTION_POLICY: set()
[ INFO ]     NETWORK_NAME: Model0
[ INFO ]     NUM_STREAMS: 1
[ INFO ]     OPTIMAL_NUMBER_OF_INFER_REQUESTS: 1
[ INFO ]     PERFORMANCE_HINT: LATENCY
[ INFO ]     PERFORMANCE_HINT_NUM_REQUESTS: 0
[ INFO ]     PERF_COUNT: NO
[ INFO ]     SCHEDULING_CORE_TYPE: SchedulingCoreType.ANY_CORE
[ INFO ]   MODEL_PRIORITY: Priority.MEDIUM
[ INFO ]   LOADED_FROM_CACHE: False
[ INFO ]   PERF_COUNT: False
[Step 9/11] Creating infer requests and preparing input tensors
[ WARNING ] No input files were given for input 'input_ids'!. This input will be filled with random values!
[ WARNING ] No input files were given for input '63'!. This input will be filled with random values!
[ WARNING ] No input files were given for input 'token_type_ids'!. This input will be filled with random values!
[ INFO ] Fill input 'input_ids' with random values
[ INFO ] Fill input '63' with random values
[ INFO ] Fill input 'token_type_ids' with random values
[Step 10/11] Measuring performance (Start inference synchronously, limits: 120000 ms duration)
[ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
[ INFO ] First inference took 15.91 ms
[Step 11/11] Dumping statistics report
[ INFO ] Execution Devices:['CPU']
[ INFO ] Count:            13320 iterations
[ INFO ] Duration:         120008.45 ms
[ INFO ] Latency:
[ INFO ]    Median:        8.91 ms
[ INFO ]    Average:       8.92 ms
[ INFO ]    Min:           7.66 ms
[ INFO ]    Max:           13.50 ms
[ INFO ] Throughput:   110.99 FPS

Quantize NLP models with Post-Training Quantization ​in NNCF#