File size: 3,050 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 |
#
# 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
from transformers import PreTrainedModel, RobertaConfig, RobertaModel
from transformers.file_utils import is_torch_available, requires_backends
if is_torch_available():
import torch
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):
requires_backends(self, 'torch')
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
|