Quantize Speech Recognition Models with accuracy control using NNCF PTQ API¶
This tutorial is also available as a Jupyter notebook that can be cloned directly from GitHub. See the installation guide for instructions to run this tutorial locally on Windows, Linux or macOS.
This tutorial demonstrates how to apply
INT8 quantization with
accuracy control to the speech recognition model, known as
using the NNCF (Neural Network Compression Framework) 8-bit quantization
with accuracy control in post-training mode (without the fine-tuning
pipeline). This notebook uses a fine-tuned
PyTorch model trained on the LibriSpeech ASR
corpus. The tutorial is designed to be
extendable to custom models and datasets. It consists of the following
Download and prepare the Wav2Vec2 model and LibriSpeech dataset.
Define data loading and accuracy validation functionality.
Model quantization with accuracy control.
Compare Accuracy of original PyTorch model, OpenVINO FP16 and INT8 models.
Compare performance of the original and quantized models.
The advanced quantization flow allows to apply 8-bit quantization to the model with control of accuracy metric. This is achieved by keeping the most impactful operations within the model in the original precision. The flow is based on the Basic 8-bit quantization and has the following differences:
Besides the calibration dataset, a validation dataset is required to compute the accuracy metric. Both datasets can refer to the same data in the simplest case.
Validation function, used to compute accuracy metric is required. It can be a function that is already available in the source framework or a custom function.
Since accuracy validation is run several times during the quantization process, quantization with accuracy control can take more time than the Basic 8-bit quantization flow.
The resulted model can provide smaller performance improvement than the Basic 8-bit quantization flow because some of the operations are kept in the original precision.
NOTE: Currently, 8-bit quantization with accuracy control in NNCF is available only for models in OpenVINO representation.
The steps for the quantization with accuracy control are described below.
Table of contents:
%pip install -q "openvino>=2023.1.0" %pip install -q "nncf>=2.6.0" %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu soundfile librosa transformers torch datasets torchmetrics
import numpy as np import torch from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
2023-10-10 09:32:06.465943: 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. 2023-10-10 09:32:06.505459: 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. 2023-10-10 09:32:07.113533: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
Prepare the Model¶
For instantiating PyTorch model class,
we should use
Wav2Vec2ForCTC.from_pretrained method with providing
model ID for downloading from HuggingFace hub. Model weights and
configuration files will be downloaded automatically in first time
usage. Keep in mind that downloading the files can take several minutes
and depends on your internet connection.
Additionally, we can create processor class which is responsible for model specific pre- and post-processing steps.
BATCH_SIZE = 1 MAX_SEQ_LENGTH = 30480 torch_model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h", ctc_loss_reduction="mean") processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
Some weights of Wav2Vec2ForCTC were not initialized from the model checkpoint at facebook/wav2vec2-base-960h and are newly initialized: ['wav2vec2.masked_spec_embed'] You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
Convert it to the OpenVINO Intermediate Representation (OpenVINO IR)
import openvino as ov default_input = torch.zeros([1, MAX_SEQ_LENGTH], dtype=torch.float) ov_model = ov.convert_model(torch_model, example_input=default_input)
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. /home/ea/work/ov_venv/lib/python3.8/site-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:595: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): /home/ea/work/ov_venv/lib/python3.8/site-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:634: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
Prepare LibriSpeech Dataset¶
For demonstration purposes, we will use short dummy version of
LibriSpeech dataset -
speed up model evaluation. Model accuracy can be different from reported
in the paper. For reproducing original accuracy, use
from datasets import load_dataset dataset = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation") test_sample = dataset["audio"] # define preprocessing function for converting audio to input values for model def map_to_input(batch): preprocessed_signal = processor(batch["audio"]["array"], return_tensors="pt", padding="longest", sampling_rate=batch['audio']['sampling_rate']) input_values = preprocessed_signal.input_values batch['input_values'] = input_values return batch # apply preprocessing function to dataset and remove audio column, to save memory as we do not need it anymore dataset = dataset.map(map_to_input, batched=False, remove_columns=["audio"])
Found cached dataset librispeech_asr_dummy (/home/ea/.cache/huggingface/datasets/patrickvonplaten___librispeech_asr_dummy/clean/2.1.0/f2c70a4d03ab4410954901bde48c54b85ca1b7f9bf7d616e7e2a72b5ee6ddbfc) Loading cached processed dataset at /home/ea/.cache/huggingface/datasets/patrickvonplaten___librispeech_asr_dummy/clean/2.1.0/f2c70a4d03ab4410954901bde48c54b85ca1b7f9bf7d616e7e2a72b5ee6ddbfc/cache-dcb48242e67b91b1.arrow
Prepare calibration dataset¶
import nncf 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. """ return np.array(data_item["input_values"]) calibration_dataset = nncf.Dataset(dataset, transform_fn)
INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, tensorflow, onnx, openvino
Prepare validation function¶
Define the validation function.
from torchmetrics import WordErrorRate from tqdm.notebook import tqdm def validation_fn(model, dataset): """ Calculate and returns a metric for the model. """ wer = WordErrorRate() for sample in dataset: # run infer function on sample output = model.output(0) logits = model(np.array(sample['input_values']))[output] predicted_ids = np.argmax(logits, axis=-1) transcription = processor.batch_decode(torch.from_numpy(predicted_ids)) # update metric on sample result wer.update(transcription, [sample['text']]) result = wer.compute() return 1 - result
Run quantization with accuracy control¶
You should provide the calibration dataset and the validation dataset. It can be the same dataset.
max_dropdefines the accuracy drop threshold. The quantization process stops when the degradation of accuracy metric on the validation dataset is less than the
max_drop. The default value is 0.01. NNCF will stop the quantization and report an error if the
max_dropvalue can’t be reached.
drop_typedefines how the accuracy drop will be calculated: ABSOLUTE (used by default) or RELATIVE.
ranking_subset_size- size of a subset that is used to rank layers by their contribution to the accuracy drop. Default value is 300, and the more samples it has the better ranking, potentially. Here we use the value 25 to speed up the execution.
NOTE: Execution can take tens of minutes and requires up to 10 GB of free memory
from nncf.quantization.advanced_parameters import AdvancedAccuracyRestorerParameters from nncf.parameters import ModelType quantized_model = nncf.quantize_with_accuracy_control( ov_model, calibration_dataset=calibration_dataset, validation_dataset=calibration_dataset, validation_fn=validation_fn, max_drop=0.01, drop_type=nncf.DropType.ABSOLUTE, model_type=ModelType.TRANSFORMER, advanced_accuracy_restorer_parameters=AdvancedAccuracyRestorerParameters( ranking_subset_size=25 ), )
Statistics collection: 24%|███████████████████████████████████▎ | 73/300 [00:12<00:37, 5.98it/s] Applying Smooth Quant: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 50/50 [00:01<00:00, 41.01it/s]
INFO:nncf:36 ignored nodes was found by name in the NNCFGraph
Statistics collection: 24%|███████████████████████████████████▎ | 73/300 [00:22<01:08, 3.31it/s] Applying Fast Bias correction: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 74/74 [00:23<00:00, 3.09it/s]
INFO:nncf:Validation of initial model was started
INFO:nncf:Elapsed Time: 00:00:00 INFO:nncf:Elapsed Time: 00:00:11 INFO:nncf:Metric of initial model: 0.9469565153121948 INFO:nncf:Collecting values for each data item using the initial model INFO:nncf:Elapsed Time: 00:00:09 INFO:nncf:Validation of quantized model was started INFO:nncf:Elapsed Time: 00:00:22 INFO:nncf:Elapsed Time: 00:00:11 INFO:nncf:Metric of quantized model: 0.5 INFO:nncf:Collecting values for each data item using the quantized model INFO:nncf:Elapsed Time: 00:00:06 INFO:nncf:Accuracy drop: 0.4469565153121948 (DropType.ABSOLUTE) INFO:nncf:Accuracy drop: 0.4469565153121948 (DropType.ABSOLUTE) INFO:nncf:Total number of quantized operations in the model: 94 INFO:nncf:Number of parallel processes to rank quantized operations: 14 INFO:nncf:ORIGINAL metric is used to rank quantizers INFO:nncf:Calculating ranking score for groups of quantizers INFO:nncf:Elapsed Time: 00:04:36 INFO:nncf:Changing the scope of quantizer nodes was started INFO:nncf:Reverted 1 operations to the floating-point precision: __module.wav2vec2.feature_extractor.conv_layers.2.conv/aten::_convolution/Convolution_11 INFO:nncf:Accuracy drop with the new quantization scope is 0.06173914670944214 (DropType.ABSOLUTE) INFO:nncf:Reverted 1 operations to the floating-point precision: __module.wav2vec2.feature_extractor.conv_layers.1.conv/aten::_convolution/Convolution_10 INFO:nncf:Accuracy drop with the new quantization scope is 0.010434746742248535 (DropType.ABSOLUTE) INFO:nncf:Reverted 1 operations to the floating-point precision: __module.wav2vec2.feature_extractor.conv_layers.3.conv/aten::_convolution/Convolution_12 INFO:nncf:Algorithm completed: achieved required accuracy drop 0.006956517696380615 (DropType.ABSOLUTE) INFO:nncf:3 out of 94 were reverted back to the floating-point precision: __module.wav2vec2.feature_extractor.conv_layers.2.conv/aten::_convolution/Convolution_11 __module.wav2vec2.feature_extractor.conv_layers.1.conv/aten::_convolution/Convolution_10 __module.wav2vec2.feature_extractor.conv_layers.3.conv/aten::_convolution/Convolution_12
Model Usage Example¶
import IPython.display as ipd ipd.Audio(test_sample["array"], rate=16000)