192 lines
7.0 KiB
Python
192 lines
7.0 KiB
Python
# These codes are copied from modelscope revision c58451baead80d83281f063d12fb377fad415257
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# Copyright 2021-2022 The Alibaba DAMO Team Authors. All rights reserved.
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# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
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# Copyright (c) 2018, NVIDIA CORPORATION. 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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from __future__ import absolute_import, division, print_function
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import os.path
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import torch
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import torch.nn.functional as F
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from torch import nn
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from torch.utils.checkpoint import checkpoint
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from transformers import (AutoConfig, DPRConfig, DPRQuestionEncoder,
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MT5ForConditionalGeneration, RagTokenForGeneration,
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XLMRobertaForSequenceClassification, XLMRobertaModel,
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XLMRobertaTokenizer)
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from modelscope.utils.logger import get_logger
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logger = get_logger()
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class Wrapper(nn.Module):
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def __init__(self, encoder):
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super(Wrapper, self).__init__()
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self.encoder = encoder
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def forward(self, input_ids, attention_mask, dummy_tensor):
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return self.encoder(input_ids, attention_mask).pooler_output
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class DPRModel(nn.Module):
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def __init__(self, model_dir, config):
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super().__init__()
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self.config = config
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qry_encoder = XLMRobertaModel(
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config=AutoConfig.from_pretrained(
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os.path.join(model_dir, 'qry_encoder')))
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ctx_encoder = XLMRobertaModel(
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config=AutoConfig.from_pretrained(
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os.path.join(model_dir, 'ctx_encoder')))
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self.qry_encoder = Wrapper(qry_encoder)
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self.ctx_encoder = Wrapper(ctx_encoder)
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self.loss_fct = nn.CrossEntropyLoss()
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@staticmethod
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def encode(model, input_ids, attention_mask, gck_segment=32):
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dummy_tensor = torch.ones(1, dtype=torch.float32, requires_grad=True)
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pooled_output = []
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for mini_batch in range(0, input_ids.shape[0], gck_segment):
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mini_batch_input_ids = input_ids[mini_batch:mini_batch
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+ gck_segment]
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mini_batch_attention_mask = attention_mask[mini_batch:mini_batch
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+ gck_segment]
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mini_batch_pooled_output = checkpoint(model, mini_batch_input_ids,
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mini_batch_attention_mask,
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dummy_tensor)
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pooled_output.append(mini_batch_pooled_output)
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return torch.cat(pooled_output, dim=0)
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def forward(self,
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query_input_ids,
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query_attention_mask,
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context_input_ids,
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context_attention_mask,
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labels,
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gck_segment=32):
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query_vector = self.encode(self.qry_encoder, query_input_ids,
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query_attention_mask, gck_segment)
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context_vector = self.encode(self.ctx_encoder, context_input_ids,
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context_attention_mask, gck_segment)
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logits = torch.matmul(query_vector, context_vector.T)
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loss = self.loss_fct(logits, labels)
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return loss, logits
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class ClassifyRerank(nn.Module):
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def __init__(self, model_dir):
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super().__init__()
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self.base_model = XLMRobertaForSequenceClassification.from_pretrained(
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model_dir)
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def forward(self,
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input_ids=None,
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attention_mask=None,
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token_type_ids=None,
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position_ids=None,
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head_mask=None,
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inputs_embeds=None,
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labels=None,
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output_attentions=None,
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output_hidden_states=None,
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return_dict=None,
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*args,
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**kwargs):
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outputs = self.base_model.forward(
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input_ids=input_ids,
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attention_mask=attention_mask,
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token_type_ids=token_type_ids,
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position_ids=position_ids,
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head_mask=head_mask,
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inputs_embeds=inputs_embeds,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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return_dict=return_dict)
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return outputs
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class Rerank(nn.Module):
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def __init__(self, encoder, top_k):
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super().__init__()
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self.encoder = encoder
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self.top_k = top_k
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def forward(self, inputs):
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model = self.encoder
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logits = F.log_softmax(model(**inputs)[0], dim=-1)[:, 1]
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logits = logits.view(-1, self.top_k)
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logprobs = F.log_softmax(logits, dim=-1)
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return logprobs
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class Re2GModel(nn.Module):
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def __init__(self, model_dir, config):
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super(Re2GModel, self).__init__()
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self.config = config
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self.top_k = self.config['top_k']
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encoder = XLMRobertaForSequenceClassification(
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config=AutoConfig.from_pretrained(
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os.path.join(model_dir, 'rerank')))
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generator = MT5ForConditionalGeneration(
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config=AutoConfig.from_pretrained(
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os.path.join(model_dir, 'generation')))
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self.rerank = Rerank(encoder, self.top_k)
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dpr_config = DPRConfig()
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dpr_config.vocab_size = encoder.config.vocab_size
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rag_model = RagTokenForGeneration(
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question_encoder=DPRQuestionEncoder(dpr_config),
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generator=generator)
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rag_model.rag.question_encoder = None
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self.generator = rag_model
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def forward(self, rerank_input_ids, input_ids, attention_mask, label_ids):
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doc_scores = self.rerank(rerank_input_ids)
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outputs = self.generator(
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labels=label_ids,
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context_input_ids=input_ids,
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context_attention_mask=attention_mask,
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doc_scores=doc_scores,
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n_docs=self.top_k)
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return outputs
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def generate(self, rerank_input_ids, input_ids, attention_mask):
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doc_scores = self.rerank(rerank_input_ids)
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beam_search_output = self.generator.generate(
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n_docs=self.top_k,
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encoder_input_ids=input_ids,
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context_input_ids=input_ids,
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context_attention_mask=attention_mask,
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doc_scores=doc_scores,
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num_beams=self.config['num_beams'],
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max_length=self.config['target_sequence_length'],
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early_stopping=True,
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no_repeat_ngram_size=self.config['no_repeat_ngram_size'],
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return_dict_in_generate=True,
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output_scores=True)
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generated_ids = beam_search_output.detach().cpu().numpy()
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return generated_ids
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