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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
from transformers import PreTrainedModel, RobertaConfig, RobertaModel, RobertaTokenizer
from pyserini.encode import DocumentEncoder, QueryEncoder
class AnceEncoder(PreTrainedModel):
config_class = RobertaConfig
base_model_prefix = 'ance_encoder'
load_tf_weights = None
_keys_to_ignore_on_load_missing = [r'position_ids']
_keys_to_ignore_on_load_unexpected = [r'pooler', r'classifier']
def __init__(self, config: RobertaConfig):
super().__init__(config)
self.config = config
self.roberta = RobertaModel(config)
self.embeddingHead = torch.nn.Linear(config.hidden_size, 768)
self.norm = torch.nn.LayerNorm(768)
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.roberta.init_weights()
self.embeddingHead.apply(self._init_weights)
self.norm.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.roberta.config.pad_token_id)
)
outputs = self.roberta(input_ids=input_ids, attention_mask=attention_mask)
sequence_output = outputs.last_hidden_state
pooled_output = sequence_output[:, 0, :]
pooled_output = self.norm(self.embeddingHead(pooled_output))
return pooled_output
class AnceDocumentEncoder(DocumentEncoder):
def __init__(self, model_name, tokenizer_name=None, device='cuda:0'):
self.device = device
self.model = AnceEncoder.from_pretrained(model_name)
self.model.to(self.device)
self.tokenizer = RobertaTokenizer.from_pretrained(tokenizer_name or model_name)
def encode(self, texts, titles=None, max_length=256, **kwargs):
if titles is not None:
texts = [f'{title} {text}' for title, text in zip(titles, texts)]
inputs = self.tokenizer(
texts,
max_length=max_length,
padding='longest',
truncation=True,
add_special_tokens=True,
return_tensors='pt'
)
inputs.to(self.device)
return self.model(inputs["input_ids"]).detach().cpu().numpy()
class AnceQueryEncoder(QueryEncoder):
def __init__(self, model_name: str, tokenizer_name: str = None, device: str = 'cpu'):
self.device = device
self.model = AnceEncoder.from_pretrained(model_name)
self.model.to(self.device)
self.tokenizer = RobertaTokenizer.from_pretrained(tokenizer_name or tokenizer_name)
def encode(self, query: str, **kwargs):
inputs = self.tokenizer(
[query],
max_length=64,
padding='longest',
truncation=True,
add_special_tokens=True,
return_tensors='pt'
)
inputs.to(self.device)
embeddings = self.model(inputs["input_ids"]).detach().cpu().numpy()
return embeddings.flatten()
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