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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.
#
from typing import Optional
import torch
if torch.cuda.is_available():
from torch.cuda.amp import autocast
from transformers import BertConfig, BertModel, BertTokenizer, PreTrainedModel
from pyserini.encode import DocumentEncoder, QueryEncoder
class UniCoilEncoder(PreTrainedModel):
config_class = BertConfig
base_model_prefix = 'coil_encoder'
load_tf_weights = None
def __init__(self, config: BertConfig):
super().__init__(config)
self.config = config
self.bert = BertModel(config)
self.tok_proj = torch.nn.Linear(config.hidden_size, 1)
self.init_weights()
# 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.bert.init_weights()
self.tok_proj.apply(self._init_weights)
def forward(
self,
input_ids: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
):
input_shape = input_ids.size()
device = input_ids.device
if attention_mask is None:
attention_mask = (
torch.ones(input_shape, device=device)
if input_ids is None
else (input_ids != self.bert.config.pad_token_id)
)
outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)
sequence_output = outputs.last_hidden_state
tok_weights = self.tok_proj(sequence_output)
tok_weights = torch.relu(tok_weights)
return tok_weights
class UniCoilDocumentEncoder(DocumentEncoder):
def __init__(self, model_name, tokenizer_name=None, device='cuda:0'):
self.device = device
self.model = UniCoilEncoder.from_pretrained(model_name)
self.model.to(self.device)
self.tokenizer = BertTokenizer.from_pretrained(tokenizer_name or model_name)
def encode(self, texts, titles=None, expands=None, fp16=False, max_length=512, **kwargs):
if titles:
texts = [f'{title} {text}' for title, text in zip(titles, texts)]
if expands:
input_ids = self._tokenize_with_injects(texts, expands)
else:
input_ids = self.tokenizer(texts, max_length=max_length, padding='longest',
truncation=True, add_special_tokens=True,
return_tensors='pt').to(self.device)["input_ids"]
if fp16:
with autocast():
with torch.no_grad():
batch_weights = self.model(input_ids).cpu().detach().numpy()
else:
batch_weights = self.model(input_ids).cpu().detach().numpy()
batch_token_ids = input_ids.cpu().detach().numpy()
return self._output_to_weight_dicts(batch_token_ids, batch_weights)
def _output_to_weight_dicts(self, batch_token_ids, batch_weights):
to_return = []
for i in range(len(batch_token_ids)):
weights = batch_weights[i].flatten()
tokens = self.tokenizer.convert_ids_to_tokens(batch_token_ids[i])
tok_weights = {}
for j in range(len(tokens)):
tok = str(tokens[j])
weight = float(weights[j])
if tok == '[CLS]':
continue
if tok == '[PAD]':
break
if tok not in tok_weights:
tok_weights[tok] = weight
elif weight > tok_weights[tok]:
tok_weights[tok] = weight
to_return.append(tok_weights)
return to_return
def _tokenize_with_injects(self, texts, expands):
tokenized = []
max_len = 0
for text, expand in zip(texts, expands):
text_ids = self.tokenizer.encode(text, add_special_tokens=False, max_length=400, truncation=True)
expand_ids = self.tokenizer.encode(expand, add_special_tokens=False, max_length=100, truncation=True)
injects = set()
for tok_id in expand_ids:
if tok_id not in text_ids:
injects.add(tok_id)
all_tok_ids = [101] + text_ids + [102] + list(injects) + [102] # 101: CLS, 102: SEP
tokenized.append(all_tok_ids)
cur_len = len(all_tok_ids)
if cur_len > max_len:
max_len = cur_len
for i in range(len(tokenized)):
tokenized[i] += [0] * (max_len - len(tokenized[i]))
return torch.tensor(tokenized, device=self.device)
class UniCoilQueryEncoder(QueryEncoder):
def __init__(self, model_name_or_path, tokenizer_name=None, device='cpu'):
self.device = device
self.model = UniCoilEncoder.from_pretrained(model_name_or_path)
self.model.to(self.device)
self.tokenizer = BertTokenizer.from_pretrained(tokenizer_name or model_name_or_path)
def encode(self, text, **kwargs):
max_length = 128 # hardcode for now
input_ids = self.tokenizer([text], max_length=max_length, padding='longest',
truncation=True, add_special_tokens=True,
return_tensors='pt').to(self.device)["input_ids"]
batch_weights = self.model(input_ids).cpu().detach().numpy()
batch_token_ids = input_ids.cpu().detach().numpy()
return self._output_to_weight_dicts(batch_token_ids, batch_weights)[0]
def _output_to_weight_dicts(self, batch_token_ids, batch_weights):
to_return = []
for i in range(len(batch_token_ids)):
weights = batch_weights[i].flatten()
tokens = self.tokenizer.convert_ids_to_tokens(batch_token_ids[i])
tok_weights = {}
for j in range(len(tokens)):
tok = str(tokens[j])
weight = float(weights[j])
if tok == '[CLS]':
continue
if tok == '[PAD]':
break
if tok not in tok_weights:
tok_weights[tok] = weight
else:
tok_weights[tok] += weight
to_return.append(tok_weights)
return to_return
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