# Copyright 2021 Zilliz. All rights reserved. # # 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. import numpy import os import requests import torch import shutil from pathlib import Path from typing import Union from collections import OrderedDict from transformers import AutoTokenizer, AutoConfig, AutoModel, AutoModelForMaskedLM, AutoModelForCausalLM from towhee.operator import NNOperator from towhee import register # from towhee.dc2 import accelerate import warnings import logging from transformers import logging as t_logging from .train_mlm_with_hf_trainer import train_mlm_with_hf_trainer from .train_clm_with_hf_trainer import train_clm_with_hf_trainer log = logging.getLogger('run_op') warnings.filterwarnings('ignore') os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' log.setLevel(logging.ERROR) t_logging.set_verbosity_error() def create_model(model_name, checkpoint_path, device): model = AutoModel.from_pretrained(model_name).to(device) if hasattr(model, 'pooler') and model.pooler: model.pooler = None if checkpoint_path: try: state_dict = torch.load(checkpoint_path, map_location=device) model.load_state_dict(state_dict) except Exception: log.error(f'Fail to load weights from {checkpoint_path}') model.eval() return model # @accelerate class Model: def __init__(self, model_name, checkpoint_path, device): self.model = create_model(model_name, checkpoint_path, device) def __call__(self, *args, **kwargs): outs = self.model(*args, **kwargs, return_dict=True) return outs['last_hidden_state'] @register(output_schema=['vec']) class AutoTransformers(NNOperator): """ NLP embedding operator that uses the pretrained transformers model gathered by huggingface. Args: model_name (`str`): The model name to load a pretrained model from transformers. checkpoint_path (`str`): The local checkpoint path. tokenizer (`object`): The tokenizer to tokenize input text as model inputs. """ def __init__(self, model_name: str = None, checkpoint_path: str = None, tokenizer: object = None, device: str = None, norm: bool = False ): super().__init__() if device: self.device = device else: self.device = 'cuda' if torch.cuda.is_available() else 'cpu' if model_name in s_list: self.model_name = 'sentence-transformers/' + model_name else: self.model_name = model_name self.norm = norm self.checkpoint_path = checkpoint_path if self.model_name: # model_list = self.supported_model_names() # assert model_name in model_list, f"Invalid model name: {model_name}. Supported model names: {model_list}" self.model = Model(model_name=self.model_name, checkpoint_path=self.checkpoint_path, device=self.device) if tokenizer: self.tokenizer = tokenizer else: self.tokenizer = AutoTokenizer.from_pretrained(self.model_name) if not self.tokenizer.pad_token: self.tokenizer.pad_token = '[PAD]' else: log.warning('The operator is initialized without specified model.') pass def __call__(self, data: Union[str, list]) -> numpy.ndarray: if isinstance(data, str): txt = [data] else: txt = data try: inputs = self.tokenizer(txt, padding=True, truncation=True, return_tensors='pt').to(self.device) except Exception as e: log.error(f'Fail to tokenize inputs: {e}') raise e try: outs = self.model(**inputs) except Exception as e: log.error(f'Invalid input for the model: {self.model_name}') raise e outs = self.post_proc(outs, inputs) if self.norm: outs = torch.nn.functional.normalize(outs, ) features = outs.cpu().detach().numpy() if isinstance(data, str): features = features.squeeze(0) else: features = list(features) return features @property def _model(self): model = self.model.model return model @property def model_config(self): configs = AutoConfig.from_pretrained(self.model_name) return configs @property def onnx_config(self): from transformers.onnx.features import FeaturesManager try: model_kind, model_onnx_config = FeaturesManager.check_supported_model_or_raise( self._model, feature='default') old_config = model_onnx_config(self.model_config) onnx_config = { 'inputs': dict(old_config.inputs), 'outputs': {'last_hidden_state': old_config.outputs['last_hidden_state']} } except Exception: input_dict = {} for k in self.tokenizer.model_input_names: input_dict[k] = {0: 'batch_size', 1: 'sequence_length'} onnx_config = { 'inputs': input_dict, 'outputs': {'last_hidden_state': {0: 'batch_size', 1: 'sequence_length'}} } return onnx_config def post_proc(self, token_embeddings, inputs): token_embeddings = token_embeddings.to(self.device) attention_mask = inputs['attention_mask'].to(self.device) input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() sentence_embs = torch.sum( token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) return sentence_embs def save_model(self, model_type: str = 'pytorch', output_file: str = 'default'): if output_file == 'default': output_file = str(Path(__file__).parent) output_file = os.path.join(output_file, 'saved', model_type) os.makedirs(output_file, exist_ok=True) name = self.model_name.replace('/', '-') output_file = os.path.join(output_file, name) if model_type in ['pytorch', 'torchscript']: output_file = output_file + '.pt' elif model_type == 'onnx': output_file = output_file + '.onnx' else: raise AttributeError('Unsupported model_type.') dummy_input = 'test sentence' inputs = self.tokenizer(dummy_input, padding=True, truncation=True, return_tensors='pt').to(self.device) if model_type == 'pytorch': torch.save(self._model, output_file) elif model_type == 'torchscript': inputs = list(inputs.values()) try: try: jit_model = torch.jit.script(self._model) except Exception: jit_model = torch.jit.trace(self._model, inputs, strict=False) torch.jit.save(jit_model, output_file) except Exception as e: log.error(f'Fail to save as torchscript: {e}.') raise RuntimeError(f'Fail to save as torchscript: {e}.') elif model_type == 'onnx': dynamic_axes = {} for k, v in self.onnx_config['inputs'].items(): dynamic_axes[k] = v for k, v in self.onnx_config['outputs'].items(): dynamic_axes[k] = v if hasattr(self._model.config, 'use_cache'): self._model.config.use_cache = False torch.onnx.export( self._model, tuple(inputs.values()), output_file, input_names=list(self.onnx_config['inputs'].keys()), output_names=list(self.onnx_config['outputs'].keys()), dynamic_axes=dynamic_axes, opset_version=torch.onnx.constant_folding_opset_versions[-1] if hasattr( torch.onnx, 'constant_folding_opset_versions') else 14, do_constant_folding=True, ) # todo: elif format == 'tensorrt': else: log.error(f'Unsupported format "{format}".') return Path(output_file).resolve() @property def supported_formats(self): return ['onnx'] @staticmethod def supported_model_names(format: str = None): add_models = [ 'bert-base-uncased', 'bert-large-uncased', 'bert-large-uncased-whole-word-masking', 'distilbert-base-uncased', 'facebook/bart-large', 'gpt2-xl', 'microsoft/deberta-xlarge', 'microsoft/deberta-xlarge-mnli', ] full_list = s_list + add_models full_list.sort() if format is None: model_list = full_list elif format == 'pytorch': to_remove = [] assert set(to_remove).issubset(set(full_list)) model_list = list(set(full_list) - set(to_remove)) elif format == 'torchscript': to_remove = [] assert set(to_remove).issubset(set(full_list)) model_list = list(set(full_list) - set(to_remove)) elif format == 'onnx': to_remove = ['gpt2-xl'] assert set(to_remove).issubset(set(full_list)) model_list = list(set(full_list) - set(to_remove)) # todo: elif format == 'tensorrt': else: log.error(f'Invalid format "{format}". Currently supported formats: "pytorch", "torchscript".') return model_list def train(self, training_config=None, train_dataset=None, eval_dataset=None, resume_checkpoint_path=None, **kwargs): task = kwargs.pop('task', None) data_args = kwargs.pop('data_args', None) training_args = kwargs.pop('training_args', None) prepare_model_weights_f = kwargs.pop('prepare_model_weights_f', None) if task == 'mlm' or task is None: model_with_head = AutoModelForMaskedLM.from_pretrained(self.model_name) if prepare_model_weights_f is not None: model_with_head = prepare_model_weights_f(self._model, model_with_head, **kwargs) train_mlm_with_hf_trainer( model_with_head, self.tokenizer, data_args, training_args, **kwargs ) elif task == 'clm': model_with_head = AutoModelForCausalLM.from_pretrained(self.model_name) if prepare_model_weights_f is not None: model_with_head = prepare_model_weights_f(self._model, model_with_head, **kwargs) train_clm_with_hf_trainer( model_with_head, self.tokenizer, data_args, training_args, **kwargs ) s_list = [ 'paraphrase-MiniLM-L3-v2', 'paraphrase-MiniLM-L6-v2', 'paraphrase-MiniLM-L12-v2', 'paraphrase-distilroberta-base-v2', 'paraphrase-TinyBERT-L6-v2', 'paraphrase-mpnet-base-v2', 'paraphrase-albert-small-v2', 'paraphrase-multilingual-mpnet-base-v2', 'paraphrase-multilingual-MiniLM-L12-v2', 'distiluse-base-multilingual-cased-v1', 'distiluse-base-multilingual-cased-v2', 'all-distilroberta-v1', 'all-MiniLM-L6-v1', 'all-MiniLM-L6-v2', 'all-MiniLM-L12-v1', 'all-MiniLM-L12-v2', 'all-mpnet-base-v1', 'all-mpnet-base-v2', 'all-roberta-large-v1', 'multi-qa-MiniLM-L6-dot-v1', 'multi-qa-MiniLM-L6-cos-v1', 'multi-qa-distilbert-dot-v1', 'multi-qa-distilbert-cos-v1', 'multi-qa-mpnet-base-dot-v1', 'multi-qa-mpnet-base-cos-v1', 'msmarco-distilbert-dot-v5', 'msmarco-bert-base-dot-v5', 'msmarco-distilbert-base-tas-b', 'bert-base-nli-mean-tokens', 'msmarco-distilbert-base-v4' ]