File size: 7,867 Bytes
62977bb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 |
#
# 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.
#
from typing import Optional
import numpy as np
import torch
from torch import Tensor
import torch.nn as nn
if torch.cuda.is_available():
from torch.cuda.amp import autocast
from transformers import DistilBertConfig, BertConfig
from transformers import AutoModelForMaskedLM, AutoTokenizer, PreTrainedModel
from pyserini.encode import DocumentEncoder, QueryEncoder
class BERTAggretrieverEncoder(PreTrainedModel):
config_class = BertConfig
base_model_prefix = 'encoder'
load_tf_weights = None
def __init__(self, config: BertConfig):
super().__init__(config)
self.config = config
self.softmax = nn.Softmax(dim=-1)
self.encoder = AutoModelForMaskedLM.from_config(config)
self.tok_proj = torch.nn.Linear(config.hidden_size, 1)
self.cls_proj = torch.nn.Linear(config.hidden_size, 128)
self.init_weights()
# Copied from https://github.com/castorini/dhr/blob/main/tevatron/Aggretriever/utils.py
def cal_remove_dim(self, dims, vocab_size=30522):
remove_dims = vocab_size % dims
if remove_dims > 1000: # the first 1000 tokens in BERT are useless
remove_dims -= dims
return remove_dims
# Copied from https://github.com/castorini/dhr/blob/main/tevatron/Aggretriever/utils.py
def aggregate(self,
lexical_reps: Tensor,
dims: int = 640,
remove_dims: int = -198,
full: bool = True
):
if full:
remove_dims = self.cal_remove_dim(dims*2)
batch_size = lexical_reps.shape[0]
if remove_dims >= 0:
lexical_reps = lexical_reps[:, remove_dims:].view(batch_size, -1, dims*2)
else:
lexical_reps = torch.nn.functional.pad(lexical_reps, (0, -remove_dims), "constant", 0).view(batch_size, -1, dims*2)
tok_reps, _ = lexical_reps.max(1)
positive_tok_reps = tok_reps[:, 0:2*dims:2]
negative_tok_reps = tok_reps[:, 1:2*dims:2]
positive_mask = positive_tok_reps > negative_tok_reps
negative_mask = positive_tok_reps <= negative_tok_reps
tok_reps = positive_tok_reps * positive_mask - negative_tok_reps * negative_mask
else:
remove_dims = self.cal_remove_dim(dims)
batch_size = lexical_reps.shape[0]
lexical_reps = lexical_reps[:, remove_dims:].view(batch_size, -1, dims)
tok_reps, index_reps = lexical_reps.max(1)
return tok_reps
# Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights
def _init_weights(self, module):
""" Initialize the weights """
if isinstance(module, (torch.nn.Linear, torch.nn.Embedding)):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
elif isinstance(module, torch.nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
if isinstance(module, torch.nn.Linear) and module.bias is not None:
module.bias.data.zero_()
def init_weights(self):
self.encoder.init_weights()
self.tok_proj.apply(self._init_weights)
self.cls_proj.apply(self._init_weights)
def forward(
self,
input_ids: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: torch.Tensor = None,
skip_mlm: bool = False
):
seq_out = self.encoder(input_ids=input_ids, attention_mask=attention_mask, return_dict=True)
seq_hidden = seq_out.hidden_states[-1]
cls_hidden = seq_hidden[:,0] # get [CLS] embeddings
term_weights = self.tok_proj(seq_hidden[:,1:]) # batch, seq, 1
if not skip_mlm:
logits = seq_out.logits[:,1:] # batch, seq-1, vocab
logits = self.softmax(logits)
attention_mask = attention_mask[:,1:].unsqueeze(-1)
lexical_reps = torch.max((logits * term_weights) * attention_mask, dim=-2).values
else:
# w/o MLM
lexical_reps = torch.zeros(seq_hidden.shape[0], seq_hidden.shape[1], 30522, dtype=seq_hidden.dtype, device=seq_hidden.device) # (batch, len, vocab)
lexical_reps = torch.scatter(lexical_reps, dim=-1, index=input_ids[:,1:,None], src=term_weights)
lexical_reps = lexical_reps.max(-2).values
lexical_reps = self.aggregate(lexical_reps, 640)
semantic_reps = self.cls_proj(cls_hidden)
return torch.cat((semantic_reps, lexical_reps), -1)
class DistlBERTAggretrieverEncoder(BERTAggretrieverEncoder):
config_class = DistilBertConfig
base_model_prefix = 'encoder'
load_tf_weights = None
class AggretrieverDocumentEncoder(DocumentEncoder):
def __init__(self, model_name: str, tokenizer_name=None, device='cuda:0'):
self.device = device
if 'distilbert' in model_name.lower():
self.model = DistlBERTAggretrieverEncoder.from_pretrained(model_name)
else:
self.model = BERTAggretrieverEncoder.from_pretrained(model_name)
self.model.to(self.device)
self.tokenizer = AutoTokenizer.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'{title} {text}' for title, text in zip(titles, texts)]
else:
texts = [text for text in texts]
inputs = self.tokenizer(
texts,
max_length=max_length,
padding="longest",
truncation=True,
add_special_tokens=True,
return_tensors='pt'
)
inputs.to(self.device)
if fp16:
with autocast():
with torch.no_grad():
outputs = self.model(**inputs)
else:
outputs = self.model(**inputs)
return outputs.detach().cpu().numpy()
class AggretrieverQueryEncoder(QueryEncoder):
def __init__(self, model_name: str, tokenizer_name=None, device='cuda:0'):
self.device = device
if 'distilbert' in model_name.lower():
self.model = DistlBERTAggretrieverEncoder.from_pretrained(model_name)
else:
self.model = BERTAggretrieverEncoder.from_pretrained(model_name)
self.model.to(self.device)
self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name or model_name)
def encode(self, texts, fp16=False, max_length=32, **kwargs):
texts = [text for text in texts]
inputs = self.tokenizer(
texts,
max_length=max_length,
padding="longest",
truncation=True,
add_special_tokens=True,
return_tensors='pt'
)
inputs.to(self.device)
if fp16:
with autocast():
with torch.no_grad():
outputs = self.model(**inputs)
else:
outputs = self.model(**inputs)
return outputs.detach().cpu().numpy() |