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from typing import Optional, Tuple, Union
import torch
from torch import Tensor, nn
from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
from transformers.models.clip import CLIPPreTrainedModel, CLIPTextConfig, CLIPTextModel
from transformers.models.clip.modeling_clip import (
CLIP_TEXT_INPUTS_DOCSTRING,
CLIPTextTransformer,
_expand_mask,
_make_causal_mask,
)
from transformers.utils import add_start_docstrings_to_model_forward, replace_return_docstrings
CLIP_SKIP_TEXT_INPUTS_DOCSTRING = (
CLIP_TEXT_INPUTS_DOCSTRING
+ r"""
clip_skip (`int`, *optional*, defaults to 1):
Skip the final N layers of the CLIP text encoder. Some Diffusion models were trained
using the hidden states from the 2nd-last layer of the CLIP text encoder (ie clip_skip=2),
so we reproduce that behavior here for use with those models.
"""
)
class CLIPSkipTextTransformer(CLIPTextTransformer):
@add_start_docstrings_to_model_forward(CLIP_SKIP_TEXT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=CLIPTextConfig)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
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,
clip_skip: int = 1,
) -> Union[Tuple, BaseModelOutputWithPooling]:
r"""
Returns:
"""
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_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)
# CLIP's text model uses causal mask, prepare it here.
# https://github.com/openai/CLIP/blob/cfcffb90e69f37bf2ff1e988237a0fbe41f33c04/clip/model.py#L324
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: BaseModelOutput = self.encoder(
inputs_embeds=hidden_states,
attention_mask=attention_mask,
causal_attention_mask=causal_attention_mask,
output_attentions=output_attentions,
output_hidden_states=True,
return_dict=True,
)
# take the hidden state from the Nth-to-last layer of the encoder, where N = clip_skip
# clip_skip=1 means take the hidden state from the last layer as with CLIPTextTransformer
last_hidden_state = encoder_outputs.hidden_states[-clip_skip]
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)
# casting to torch.int for onnx compatibility: argmax doesn't support int64 inputs with opset 14
pooled_output = last_hidden_state[
torch.arange(last_hidden_state.shape[0], device=last_hidden_state.device),
input_ids.to(dtype=torch.int, device=last_hidden_state.device).argmax(dim=-1),
]
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,
)
def _build_causal_attention_mask(self, bsz, seq_len, dtype):
# lazily create causal attention mask, with full attention between the vision tokens
# pytorch uses additive attention mask; fill with -inf
mask = torch.empty(bsz, seq_len, seq_len, dtype=dtype)
mask.fill_(torch.tensor(torch.finfo(dtype).min))
mask.triu_(1) # zero out the lower diagonal
mask = mask.unsqueeze(1) # expand mask
return mask
class CLIPSkipTextModel(CLIPTextModel):
config_class = CLIPTextConfig
_no_split_modules = ["CLIPEncoderLayer"]
def __init__(self, config: CLIPTextConfig):
super().__init__(config)
self.text_model = CLIPSkipTextTransformer(config)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(CLIP_SKIP_TEXT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=CLIPTextConfig)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
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,
clip_skip: int = 1,
) -> Union[Tuple, BaseModelOutputWithPooling]:
r"""
Returns:
Examples:
```python
>>> from transformers import AutoTokenizer, CLIPSkipTextModel
>>> model = CLIPSkipTextModel.from_pretrained("openai/clip-vit-base-patch32")
>>> tokenizer = AutoTokenizer.from_pretrained("openai/clip-vit-base-patch32")
>>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt")
>>> outputs = model(**inputs)
>>> last_hidden_state = outputs.last_hidden_state
>>> pooled_output = outputs.pooler_output # pooled (EOS token) states
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
return self.text_model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
clip_skip=clip_skip,
)
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