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Add feature of distillation for quantization (#1219)
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...classification/optimization_pipeline/distillation_for_quantization/fx/README.md
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Step-by-Step | ||
============ | ||
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This document is used to illustrate how to run the distillation for quantization examples. | ||
<br> | ||
These examples will take a NLP model fine tuned on the down stream task, use its copy as a teacher model, and do distillation during the process of quantization aware training. | ||
<br> | ||
For more informations of this algorithm, please refer to the paper [ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers](https://arxiv.org/abs/2206.01861) | ||
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# Prerequisite | ||
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## Python Version | ||
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Recommend python 3.7 or higher version. | ||
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## Install dependency | ||
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```shell | ||
pip install -r requirements.txt | ||
``` | ||
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# Start running neural_compressor implementation of distillation for quantization | ||
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Below are example NLP tasks of distillation for quantization to quantize the fine tuned BERT model on the specific tasks. | ||
<br> | ||
It requires the pre-trained task specific model such as `yoshitomo-matsubara/bert-base-uncased-sst2` from yoshitomo-matsubara Huggingface portal as the teacher model for distillation, as well as for quantization. | ||
<br> | ||
The distillation configuration is specified in yaml file i.e. distillation.yaml, the quantization aware training configuration is specified in yaml file i.e. qat.yaml. | ||
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## SST-2 task | ||
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```bash | ||
python run_glue_no_trainer.py --task_name sst2 --model_name_or_path yoshitomo-matsubara/bert-base-uncased-sst2 --teacher_model_name_or_path yoshitomo-matsubara/bert-base-uncased-sst2 --batch_size 32 --do_eval --do_quantization --do_distillation --pad_to_max_length --num_train_epochs 9 --output_dir /path/to/output_dir | ||
``` | ||
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## MNLI task | ||
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```bash | ||
python run_glue_no_trainer.py --task_name mnli --model_name_or_path yoshitomo-matsubara/bert-base-uncased-mnli --teacher_model_name_or_path yoshitomo-matsubara/bert-base-uncased-mnli --batch_size 32 --do_eval --do_quantization --do_distillation --pad_to_max_length --num_train_epochs 9 --output_dir /path/to/output_dir | ||
``` | ||
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## QQP task | ||
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```bash | ||
python run_glue_no_trainer.py --task_name qqp --model_name_or_path yoshitomo-matsubara/bert-base-uncased-qqp --teacher_model_name_or_path yoshitomo-matsubara/bert-base-uncased-qqp --batch_size 32 --do_eval --do_quantization --do_distillation --pad_to_max_length --num_train_epochs 9 --output_dir /path/to/output_dir | ||
``` | ||
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## QNLI task | ||
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```bash | ||
python run_glue_no_trainer.py --task_name qnli --model_name_or_path yoshitomo-matsubara/bert-base-uncased-qnli --teacher_model_name_or_path yoshitomo-matsubara/bert-base-uncased-qnli --batch_size 32 --do_eval --do_quantization --do_distillation --pad_to_max_length --num_train_epochs 9 --output_dir /path/to/output_dir | ||
``` | ||
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# Results | ||
We listed the results on 4 distillation for quantization experiments, for comparison, we also listed the results of QAT as well as the baselie metrics of the FP32 model. These experiments use a fine-tuned BERT-Base model on the 4 GLUE task (SST-2, QNLI, QQP and MNLI), data in the column of FP32 is the metrics of the 4 fine-tuned BERT-Base model, data in the column of INT8 (QAT) is the metrics of the 4 INT8 BERT-Base model from QAT process, data in the column of INT8 (Distillation for Quantization) is the metrics of the 4 INT8 BERT-Base model from distillation for quantization process. | ||
| | FP32 | INT8 (QAT) | INT8 (Distillation for Quantization) | | ||
|---------------|----------------|--------------------------|--------------------------| | ||
| SST-2 (ACC) | 92.48% | 91.90% | 92.01% | | ||
| QNLI (ACC) | 91.58% | 89.49% | 90.33% | | ||
| QQP (ACC/F1) | 90.95%/87.83% | 89.60%/86.56% | 91.07%/87.91% | | ||
| MNLI (ACC) | 84.20% | 78.67% | 84.42% | |
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...t-classification/optimization_pipeline/distillation_for_quantization/fx/distillation.yaml
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# | ||
# Copyright (c) 2021 Intel Corporation | ||
# | ||
# Licensed under the Apache License, Version 2.0 (the "License"); | ||
# you may not use this file except in compliance with the License. | ||
# You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. | ||
version: 1.0 | ||
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model: | ||
name: bert_distillation | ||
framework: pytorch_fx | ||
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distillation: | ||
train: | ||
optimizer: | ||
SGD: | ||
learning_rate: 0.001 | ||
criterion: | ||
IntermediateLayersKnowledgeDistillationLoss: | ||
layer_mappings: [ | ||
['bert.encoder.layer.0.output', 'bert.encoder.layer.0.output'], | ||
['bert.encoder.layer.0.attention', '1', 'bert.encoder.layer.0.attention', '1'], | ||
['bert.encoder.layer.1.output', 'bert.encoder.layer.1.output'], | ||
['bert.encoder.layer.1.attention', '1', 'bert.encoder.layer.1.attention', '1'], | ||
['bert.encoder.layer.2.output', 'bert.encoder.layer.2.output'], | ||
['bert.encoder.layer.2.attention', '1', 'bert.encoder.layer.2.attention', '1'], | ||
['bert.encoder.layer.3.output', 'bert.encoder.layer.3.output'], | ||
['bert.encoder.layer.3.attention', '1', 'bert.encoder.layer.3.attention', '1'], | ||
['bert.encoder.layer.4.output', 'bert.encoder.layer.4.output'], | ||
['bert.encoder.layer.4.attention', '1', 'bert.encoder.layer.4.attention', '1'], | ||
['bert.encoder.layer.5.output', 'bert.encoder.layer.5.output'], | ||
['bert.encoder.layer.5.attention', '1', 'bert.encoder.layer.5.attention', '1'], | ||
['bert.encoder.layer.6.output', 'bert.encoder.layer.6.output'], | ||
['bert.encoder.layer.6.attention', '1', 'bert.encoder.layer.6.attention', '1'], | ||
['bert.encoder.layer.7.output', 'bert.encoder.layer.7.output'], | ||
['bert.encoder.layer.7.attention', '1', 'bert.encoder.layer.7.attention', '1'], | ||
['bert.encoder.layer.8.output', 'bert.encoder.layer.8.output'], | ||
['bert.encoder.layer.8.attention', '1', 'bert.encoder.layer.8.attention', '1'], | ||
['bert.encoder.layer.9.output', 'bert.encoder.layer.9.output'], | ||
['bert.encoder.layer.9.attention', '1', 'bert.encoder.layer.9.attention', '1'], | ||
['bert.encoder.layer.10.output', 'bert.encoder.layer.10.output'], | ||
['bert.encoder.layer.10.attention', '1', 'bert.encoder.layer.10.attention', '1'], | ||
['bert.encoder.layer.11.output', 'bert.encoder.layer.11.output'], | ||
['bert.encoder.layer.11.attention', '1', 'bert.encoder.layer.11.attention', '1'], | ||
['classifier', 'classifier'], | ||
] | ||
loss_weights: [1, 1, 1, 1, 1, | ||
1, 1, 1, 1, 1, | ||
1, 1, 1, 1, 1, | ||
1, 1, 1, 1, 1, | ||
1, 1, 1, 1, 1] | ||
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tuning: | ||
accuracy_criterion: | ||
relative: 0.01 # the tuning target of accuracy loss percentage: 1% | ||
exit_policy: | ||
timeout: 0 # tuning timeout (seconds) | ||
random_seed: 9527 # random seed |
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...odels/text-classification/optimization_pipeline/distillation_for_quantization/fx/qat.yaml
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# | ||
# Copyright (c) 2021 Intel Corporation | ||
# | ||
# Licensed under the Apache License, Version 2.0 (the "License"); | ||
# you may not use this file except in compliance with the License. | ||
# You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. | ||
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version: 1.0 | ||
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model: # mandatory. used to specify model specific information. | ||
name: bert_qat | ||
framework: pytorch_fx # mandatory. supported values are tensorflow, pytorch, pytorch_ipex, onnxrt_integer, onnxrt_qlinear or mxnet; allow new framework backend extension. | ||
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quantization: # optional. required for QAT and PTQ. | ||
approach: quant_aware_training | ||
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tuning: | ||
random_seed: 9527 # optional. random seed for deterministic tuning. |
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...xt-classification/optimization_pipeline/distillation_for_quantization/fx/requirements.txt
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--find-links https://download.pytorch.org/whl/torch_stable.html | ||
transformers | ||
torch | ||
datasets | ||
accelerate |
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