初始化项目，由ModelHub XC社区提供模型
Model: kotoba-tech/kotoba-whisper-v1.0 Source: Original Platform
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ModelHub XC
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create_student_model.py
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221
create_student_model.py
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#!/usr/bin/env python
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# coding=utf-8
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# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Initialise a student Whisper model from a pre-trained teacher model for
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teacher-student distillation.
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"""
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import argparse
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import copy
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import logging
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import os
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import numpy as np
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import torch
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from transformers import GenerationConfig, WhisperForConditionalGeneration, WhisperProcessor
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# https://stackoverflow.com/questions/71692354/facing-ssl-error-with-huggingface-pretrained-models
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os.environ['CURL_CA_BUNDLE'] = ''
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# disable warning message
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os.environ['TOKENIZERS_PARALLELISM'] = 'false'
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logger = logging.getLogger(__name__)
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def parse_args():
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parser = argparse.ArgumentParser(
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description="Initialise a student Whisper model from a teacher model, copying the relevant layer weights and adjusting the processor as necessary."
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)
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parser.add_argument(
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"--teacher_checkpoint",
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type=str,
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required=True,
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help="The HF Hub ID of the teacher checkpoint.",
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)
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parser.add_argument(
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"--subfolder",
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type=str,
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default="",
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help="In case the relevant teacher weights are located inside a subfolder of the model repo on huggingface.co, you "
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"can specify the folder name here.",
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)
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parser.add_argument(
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"--encoder_layers",
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type=int,
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default=None,
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help="Number of encoder layers to use in the student model. Defaults to all layers from the teacher.",
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)
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parser.add_argument(
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"--decoder_layers",
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type=int,
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default=2,
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help="Number of decoder layers to use in the student model. Defaults to 2 layers.",
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)
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parser.add_argument(
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"--save_dir",
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type=str,
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required=True,
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help="Where to save the student weights and processor.",
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)
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parser.add_argument(
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"--push_to_hub",
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type=bool,
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required=False,
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default=False,
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help="Whether to push the student weights and processor to the Hub.",
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)
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parser.add_argument(
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"--cache_dir",
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type=str,
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default=None,
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help="Where to store the pretrained models downloaded from huggingface.co",
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)
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args = parser.parse_args()
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return args
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def init_student_model_from_teacher(
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teacher_checkpoint,
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encoder_layers=None,
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decoder_layers=2,
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save_dir=None,
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push_to_hub=None,
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cache_dir=None,
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subfolder="",
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):
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teacher_model = WhisperForConditionalGeneration.from_pretrained(
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teacher_checkpoint,
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cache_dir=cache_dir,
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subfolder=subfolder,
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low_cpu_mem_usage=True,
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)
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processor = WhisperProcessor.from_pretrained(teacher_checkpoint)
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generation_config = GenerationConfig.from_pretrained(teacher_checkpoint)
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teacher_config = teacher_model.config
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teacher_encoder_layers = teacher_config.encoder_layers
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teacher_decoder_layers = teacher_config.decoder_layers
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student_config = copy.deepcopy(teacher_config)
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student_config.update(
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{
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"encoder_layers": encoder_layers if encoder_layers is not None else teacher_encoder_layers,
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"decoder_layers": decoder_layers,
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}
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)
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encoder_mapping = np.linspace(0, teacher_encoder_layers - 1, student_config.encoder_layers, dtype=int)
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encoder_mapping[-1] = teacher_encoder_layers - 1
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encoder_map = {}
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for student_layer, teacher_layer in enumerate(encoder_mapping):
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encoder_map[teacher_layer] = student_layer
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decoder_mapping = np.linspace(0, teacher_decoder_layers - 1, student_config.decoder_layers, dtype=int)
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decoder_mapping[-1] = teacher_decoder_layers - 1
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decoder_map = {}
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for student_layer, teacher_layer in enumerate(decoder_mapping):
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decoder_map[teacher_layer] = student_layer
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# init the student params from the teacher model
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student_model = WhisperForConditionalGeneration(student_config)
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missing_keys, unexpected_keys = student_model.load_state_dict(teacher_model.state_dict(), strict=False)
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if len(missing_keys) > 0:
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raise RuntimeError(
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"Error(s) in loading state_dict for WhisperForConditionalGeneration. \n"
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f"Missing key(s) in state_dict: {missing_keys}"
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)
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if decoder_layers == teacher_decoder_layers:
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decoder_keys = [key for key in unexpected_keys if "model.decoder.layers" in key]
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if len(decoder_keys) > 0:
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raise RuntimeError(
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"Error(s) in loading state_dict for WhisperForConditionalGeneration. \n"
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f"Unexpected key(s) in state_dict: {decoder_keys}"
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)
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if encoder_layers == teacher_encoder_layers:
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encoder_keys = [key for key in unexpected_keys if "model.encoder.layers" in key]
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if len(encoder_keys) > 0:
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raise RuntimeError(
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"Error(s) in loading state_dict for WhisperForConditionalGeneration. \n"
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f"Unexpected key(s) in state_dict: {encoder_keys}"
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)
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for layer in range(teacher_decoder_layers):
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if layer in decoder_map:
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# re-introduce pre-defined layers from the teacher
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student_model.model.decoder.layers[decoder_map[layer]].load_state_dict(
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teacher_model.model.decoder.layers[layer].state_dict()
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)
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if encoder_layers is not None:
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for layer in range(teacher_encoder_layers):
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if layer in encoder_map:
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# re-introduce pre-defined layers from the teacher
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student_model.model.encoder.layers[encoder_map[layer]].load_state_dict(
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teacher_model.model.encoder.layers[layer].state_dict()
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)
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# remove the teacher params and model
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del teacher_model
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# save the converted weights and model
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if save_dir is not None:
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student_model.save_pretrained(save_dir)
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# we also need to correctly save the processor and generation config
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processor.save_pretrained(save_dir)
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generation_config.save_pretrained(save_dir)
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# check we can do a forward pass with the saved model - first load the weights and processor
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logger.info("Checking we can load the saved model...")
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student_model = WhisperForConditionalGeneration.from_pretrained(
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save_dir,
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low_cpu_mem_usage=True,
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)
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processor = WhisperProcessor.from_pretrained(save_dir)
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# define some random inputs
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input_features = processor(np.ones(16000), sampling_rate=16000, return_tensors="pt").input_features
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decoder_start_token_id = student_model.config.decoder_start_token_id
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decoder_input_ids = torch.ones((input_features.shape[0], 1), dtype=torch.long) * decoder_start_token_id
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# do a forward pass - outputs will be gibberish for the initialised model so we can't check them
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# but we make can sure the model runs as expected
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logger.info("Checking we can run the converted model forward...")
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_ = student_model(input_features, decoder_input_ids=decoder_input_ids).logits
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logger.info("Conversion successful!")
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if push_to_hub:
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student_model.push_to_hub(save_dir)
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processor.push_to_hub(save_dir)
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generation_config.push_to_hub(save_dir)
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if __name__ == "__main__":
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args = parse_args()
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init_student_model_from_teacher(
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teacher_checkpoint=args.teacher_checkpoint,
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encoder_layers=args.encoder_layers,
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decoder_layers=args.decoder_layers,
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save_dir=args.save_dir,
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push_to_hub=args.push_to_hub,
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cache_dir=args.cache_dir,
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subfolder=args.subfolder,
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)
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