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from torch import nn
from transformers import Idefics2Model, Idefics2PreTrainedModel
class BiIdefics(Idefics2PreTrainedModel):
def __init__(self, config):
super(BiIdefics, self).__init__(config=config)
self.model: Idefics2Model = Idefics2Model(config)
self.pooling_strategy = "last"
self.main_input_name = "doc_input_ids"
def forward(self, *args, **kwargs):
"""
Forward pass through Llama and the linear layer for dimensionality reduction
Args:
- input_ids (torch.LongTensor): The input tokens tensor.
- attention_mask (torch.LongTensor): The attention mask tensor.
Returns:
- torch.Tensor: Embeddings of shape (batch_size, num_tokens, dim)
"""
outputs = self.model(*args, **kwargs)
last_hidden_states = outputs[0] # (batch_size, sequence_length, hidden_size)
# pooling - last token
proj = last_hidden_states[:, -1, :]
# normalize l2 norm
proj = proj / proj.norm(dim=-1, keepdim=True)
return proj
class ColIdefics(Idefics2PreTrainedModel):
def __init__(self, config):
super(ColIdefics, self).__init__(config=config)
self.model: Idefics2Model = Idefics2Model(config)
self.dim = 128
self.linear = nn.Linear(self.model.config.text_config.hidden_size, self.dim)
self.main_input_name = "doc_input_ids"
def forward(self, *args, **kwargs):
"""
Forward pass through Llama and the linear layer for dimensionality reduction
Args:
- input_ids (torch.LongTensor): The input tokens tensor.
- attention_mask (torch.LongTensor): The attention mask tensor.
Returns:
- torch.Tensor: Embeddings of shape (batch_size, num_tokens, dim)
"""
outputs = self.model(*args, **kwargs)
last_hidden_states = outputs[0] # (batch_size, sequence_length, hidden_size)
proj = self.linear(last_hidden_states)
# normalize l2 norm
proj = proj / proj.norm(dim=-1, keepdim=True)
proj = proj * kwargs["attention_mask"].unsqueeze(-1)
return proj
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