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from typing import Optional, Tuple, Union
import torch
from transformers.modeling_outputs import BaseModelOutputWithPooling
from transformers.models.clip.configuration_clip import CLIPConfig
from transformers.models.clip.modeling_clip import CLIPModel, CLIPTextTransformer, _make_causal_mask, _expand_mask, clip_loss, CLIPOutput
class CLIPTextTransformerCanReceiveEmbed(CLIPTextTransformer):
def forward(self,
input_ids: Optional[torch.Tensor] = None,
input_embeds: Optional[torch.Tensor] = None, # NOTE
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,) -> Union[Tuple, BaseModelOutputWithPooling]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_embeds is None:
if input_ids is None:
raise ValueError("You have to specify input_ids")
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
hidden_states = self.embeddings(input_ids=input_ids, position_ids=position_ids)
else:
hidden_states = input_embeds
input_shape = torch.Size([hidden_states.size(0), hidden_states.size(1)])
# CLIP's text model uses causal mask, prepare it here.
# https://github.com/openai/CLIP/blob/cfcffb90e69f37bf2ff1e988237a0fbe41f33c04/clip/model.py#L324
# print(input_shape)
causal_attention_mask = _make_causal_mask(input_shape, hidden_states.dtype, device=hidden_states.device)
# expand attention_mask
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
attention_mask = _expand_mask(attention_mask, hidden_states.dtype)
encoder_outputs = self.encoder(
inputs_embeds=hidden_states,
attention_mask=attention_mask,
causal_attention_mask=causal_attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
last_hidden_state = encoder_outputs[0]
last_hidden_state = self.final_layer_norm(last_hidden_state)
# text_embeds.shape = [batch_size, sequence_length, transformer.width]
# take features from the eot embedding (eot_token is the highest number in each sequence)
# eot embedding pos: input_ids.to(dtype=torch.int, device=last_hidden_state.device).argmax(dim=-1)
# casting to torch.int for onnx compatibility: argmax doesn't support int64 inputs with opset 14
if input_ids is not None:
eos_embedding_pos = input_ids.to(dtype=torch.int, device=last_hidden_state.device).argmax(dim=-1)
# print(input_ids, eos_embedding_pos)
else:
# pass
# TODO: is there any exception?
eos_embedding_pos = torch.tensor([input_embeds.size(1) - 1] * input_embeds.size(0), device=last_hidden_state.device)
pooled_output = last_hidden_state[
torch.arange(last_hidden_state.shape[0], device=last_hidden_state.device),
eos_embedding_pos
]
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPooling(
last_hidden_state=last_hidden_state,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
class CLIPModelCanReceiveTextEmbeds(CLIPModel):
def __init__(self, config: CLIPConfig):
super().__init__(config)
self.text_model = CLIPTextTransformerCanReceiveEmbed(config.text_config)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
input_embeds: Optional[torch.LongTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
return_loss: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
only_return_logits_per_text = False,
no_grad_text = False
) -> Union[Tuple, CLIPOutput]:
# Use CLIP model's config for some fields (if specified) instead of those of vision & text components.
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
vision_outputs = self.vision_model(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
if no_grad_text:
with torch.no_grad():
text_outputs = self.text_model(
input_ids=input_ids,
input_embeds=input_embeds,
attention_mask=attention_mask,
position_ids=position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
else:
text_outputs = self.text_model(
input_ids=input_ids,
input_embeds=input_embeds,
attention_mask=attention_mask,
position_ids=position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
image_embeds = vision_outputs[1]
image_embeds = self.visual_projection(image_embeds)
text_embeds = text_outputs[1]
text_embeds = self.text_projection(text_embeds)
# normalized features
image_embeds = image_embeds / image_embeds.norm(p=2, dim=-1, keepdim=True)
text_embeds = text_embeds / text_embeds.norm(p=2, dim=-1, keepdim=True)
# cosine similarity as logits
logit_scale = self.logit_scale.exp()
logits_per_text = torch.matmul(text_embeds, image_embeds.t()) * logit_scale
logits_per_image = logits_per_text.t()
if only_return_logits_per_text:
return logits_per_text
loss = None
if return_loss:
loss = clip_loss(logits_per_text)
if not return_dict:
output = (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs)
return ((loss,) + output) if loss is not None else output
return CLIPOutput(
loss=loss,
logits_per_image=logits_per_image,
logits_per_text=logits_per_text,
text_embeds=text_embeds,
image_embeds=image_embeds,
text_model_output=text_outputs,
vision_model_output=vision_outputs,
) |