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| # | |
| # Pyserini: Reproducible IR research with sparse and dense representations | |
| # | |
| # 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 as np | |
| import torch | |
| if torch.cuda.is_available(): | |
| from torch.cuda.amp import autocast | |
| from transformers import BertModel, BertTokenizer, BertTokenizerFast | |
| from pyserini.encode import DocumentEncoder, QueryEncoder | |
| from onnxruntime import ExecutionMode, SessionOptions, InferenceSession | |
| class TctColBertDocumentEncoder(DocumentEncoder): | |
| def __init__(self, model_name: str, tokenizer_name=None, device='cuda:0'): | |
| self.device = device | |
| self.onnx = False | |
| if model_name.endswith('onnx'): | |
| options = SessionOptions() | |
| self.session = InferenceSession(model_name, options) | |
| self.onnx = True | |
| self.tokenizer = BertTokenizerFast.from_pretrained(tokenizer_name or model_name[:-5]) | |
| else: | |
| self.model = BertModel.from_pretrained(model_name) | |
| self.model.to(self.device) | |
| self.tokenizer = BertTokenizerFast.from_pretrained(tokenizer_name or model_name) | |
| def encode(self, texts, titles=None, fp16=False, max_length=512, **kwargs): | |
| if titles is not None: | |
| texts = [f'[CLS] [D] {title} {text}' for title, text in zip(titles, texts)] | |
| else: | |
| texts = ['[CLS] [D] ' + text for text in texts] | |
| inputs = self.tokenizer( | |
| texts, | |
| max_length=max_length, | |
| padding="longest", | |
| truncation=True, | |
| add_special_tokens=False, | |
| return_tensors='pt' | |
| ) | |
| if self.onnx: | |
| inputs_onnx = {name: np.atleast_2d(value) for name, value in inputs.items()} | |
| inputs.to(self.device) | |
| outputs, _ = self.session.run(None, inputs_onnx) | |
| outputs = torch.from_numpy(outputs).to(self.device) | |
| embeddings = self._mean_pooling(outputs[:, 4:, :], inputs['attention_mask'][:, 4:]) | |
| else: | |
| inputs.to(self.device) | |
| if fp16: | |
| with autocast(): | |
| with torch.no_grad(): | |
| outputs = self.model(**inputs) | |
| else: | |
| outputs = self.model(**inputs) | |
| embeddings = self._mean_pooling(outputs["last_hidden_state"][:, 4:, :], inputs['attention_mask'][:, 4:]) | |
| return embeddings.detach().cpu().numpy() | |
| class TctColBertQueryEncoder(QueryEncoder): | |
| def __init__(self, model_name: str, tokenizer_name: str = None, device: str = 'cpu'): | |
| self.device = device | |
| self.model = BertModel.from_pretrained(model_name) | |
| self.model.to(self.device) | |
| self.tokenizer = BertTokenizer.from_pretrained(tokenizer_name or model_name) | |
| def encode(self, query: str, **kwargs): | |
| max_length = 36 # hardcode for now | |
| inputs = self.tokenizer( | |
| '[CLS] [Q] ' + query + '[MASK]' * max_length, | |
| max_length=max_length, | |
| truncation=True, | |
| add_special_tokens=False, | |
| return_tensors='pt' | |
| ) | |
| inputs.to(self.device) | |
| outputs = self.model(**inputs) | |
| embeddings = outputs.last_hidden_state.detach().cpu().numpy() | |
| return np.average(embeddings[:, 4:, :], axis=-2).flatten() | |