1453 lines
62 KiB
Python
1453 lines
62 KiB
Python
# coding=utf-8
|
|
# Copyright 2022 The BAAI Teams Authors and The HuggingFace Inc. team. All rights reserved.
|
|
#
|
|
# 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.
|
|
"""PyTorch AltCLIP model."""
|
|
|
|
import math
|
|
from dataclasses import dataclass
|
|
from typing import Any, Callable, Optional, Union
|
|
|
|
import torch
|
|
import torch.nn as nn
|
|
import torch.utils.checkpoint
|
|
|
|
from ...activations import ACT2FN
|
|
from ...modeling_layers import GradientCheckpointingLayer
|
|
from ...modeling_outputs import (
|
|
BaseModelOutput,
|
|
BaseModelOutputWithPooling,
|
|
BaseModelOutputWithPoolingAndCrossAttentions,
|
|
BaseModelOutputWithPoolingAndProjection,
|
|
)
|
|
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
|
|
from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
|
|
from ...utils import ModelOutput, auto_docstring, can_return_tuple, logging, torch_int
|
|
from ...utils.deprecation import deprecate_kwarg
|
|
from .configuration_altclip import AltCLIPConfig, AltCLIPTextConfig, AltCLIPVisionConfig
|
|
|
|
|
|
logger = logging.get_logger(__name__)
|
|
|
|
|
|
# contrastive loss function, adapted from
|
|
# https://sachinruk.github.io/blog/pytorch/pytorch%20lightning/loss%20function/gpu/2021/03/07/CLIP.html
|
|
def contrastive_loss(logits: torch.Tensor) -> torch.Tensor:
|
|
return nn.functional.cross_entropy(logits, torch.arange(len(logits), device=logits.device))
|
|
|
|
|
|
def clip_loss(similarity: torch.Tensor) -> torch.Tensor:
|
|
caption_loss = contrastive_loss(similarity)
|
|
image_loss = contrastive_loss(similarity.t())
|
|
return (caption_loss + image_loss) / 2.0
|
|
|
|
|
|
@dataclass
|
|
@auto_docstring
|
|
# Copied from transformers.models.clip.modeling_clip.CLIPOutput with CLIP->AltCLIP
|
|
class AltCLIPOutput(ModelOutput):
|
|
r"""
|
|
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`):
|
|
Contrastive loss for image-text similarity.
|
|
logits_per_image (`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`):
|
|
The scaled dot product scores between `image_embeds` and `text_embeds`. This represents the image-text
|
|
similarity scores.
|
|
logits_per_text (`torch.FloatTensor` of shape `(text_batch_size, image_batch_size)`):
|
|
The scaled dot product scores between `text_embeds` and `image_embeds`. This represents the text-image
|
|
similarity scores.
|
|
text_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim`):
|
|
The text embeddings obtained by applying the projection layer to the pooled output of [`AltCLIPTextModel`].
|
|
image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim`):
|
|
The image embeddings obtained by applying the projection layer to the pooled output of [`AltCLIPVisionModel`].
|
|
text_model_output (`BaseModelOutputWithPooling`):
|
|
The output of the [`AltCLIPTextModel`].
|
|
vision_model_output (`BaseModelOutputWithPooling`):
|
|
The output of the [`AltCLIPVisionModel`].
|
|
"""
|
|
|
|
loss: Optional[torch.FloatTensor] = None
|
|
logits_per_image: Optional[torch.FloatTensor] = None
|
|
logits_per_text: Optional[torch.FloatTensor] = None
|
|
text_embeds: Optional[torch.FloatTensor] = None
|
|
image_embeds: Optional[torch.FloatTensor] = None
|
|
text_model_output: BaseModelOutputWithPooling = None
|
|
vision_model_output: BaseModelOutputWithPooling = None
|
|
|
|
def to_tuple(self) -> tuple[Any]:
|
|
return tuple(
|
|
self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple()
|
|
for k in self.keys()
|
|
)
|
|
|
|
|
|
# Copied from transformers.models.roberta.modeling_roberta.RobertaEmbeddings with Roberta->AltRoberta
|
|
class AltRobertaEmbeddings(nn.Module):
|
|
"""
|
|
Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
|
|
"""
|
|
|
|
# Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
|
|
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
|
|
self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
|
|
|
|
# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
|
|
# any TensorFlow checkpoint file
|
|
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
|
|
self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
|
|
self.register_buffer(
|
|
"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
|
|
)
|
|
self.register_buffer(
|
|
"token_type_ids", torch.zeros(self.position_ids.size(), dtype=torch.long), persistent=False
|
|
)
|
|
|
|
# End copy
|
|
self.padding_idx = config.pad_token_id
|
|
self.position_embeddings = nn.Embedding(
|
|
config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx
|
|
)
|
|
|
|
def forward(
|
|
self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
|
|
):
|
|
if position_ids is None:
|
|
if input_ids is not None:
|
|
# Create the position ids from the input token ids. Any padded tokens remain padded.
|
|
position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx, past_key_values_length)
|
|
else:
|
|
position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)
|
|
|
|
if input_ids is not None:
|
|
input_shape = input_ids.size()
|
|
else:
|
|
input_shape = inputs_embeds.size()[:-1]
|
|
|
|
seq_length = input_shape[1]
|
|
|
|
# Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs
|
|
# when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves
|
|
# issue #5664
|
|
if token_type_ids is None:
|
|
if hasattr(self, "token_type_ids"):
|
|
buffered_token_type_ids = self.token_type_ids[:, :seq_length]
|
|
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length)
|
|
token_type_ids = buffered_token_type_ids_expanded
|
|
else:
|
|
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.word_embeddings(input_ids)
|
|
token_type_embeddings = self.token_type_embeddings(token_type_ids)
|
|
|
|
embeddings = inputs_embeds + token_type_embeddings
|
|
if self.position_embedding_type == "absolute":
|
|
position_embeddings = self.position_embeddings(position_ids)
|
|
embeddings += position_embeddings
|
|
embeddings = self.LayerNorm(embeddings)
|
|
embeddings = self.dropout(embeddings)
|
|
return embeddings
|
|
|
|
def create_position_ids_from_inputs_embeds(self, inputs_embeds):
|
|
"""
|
|
We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.
|
|
|
|
Args:
|
|
inputs_embeds: torch.Tensor
|
|
|
|
Returns: torch.Tensor
|
|
"""
|
|
input_shape = inputs_embeds.size()[:-1]
|
|
sequence_length = input_shape[1]
|
|
|
|
position_ids = torch.arange(
|
|
self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device
|
|
)
|
|
return position_ids.unsqueeze(0).expand(input_shape)
|
|
|
|
|
|
class AltRobertaSelfAttention(nn.Module):
|
|
def __init__(self, config, position_embedding_type=None):
|
|
super().__init__()
|
|
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
|
|
raise ValueError(
|
|
f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
|
|
f"heads ({config.num_attention_heads})"
|
|
)
|
|
|
|
self.num_attention_heads = config.num_attention_heads
|
|
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
|
|
self.all_head_size = self.num_attention_heads * self.attention_head_size
|
|
|
|
self.query = nn.Linear(config.hidden_size, self.all_head_size)
|
|
self.key = nn.Linear(config.hidden_size, self.all_head_size)
|
|
self.value = nn.Linear(config.hidden_size, self.all_head_size)
|
|
|
|
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
|
|
self.position_embedding_type = position_embedding_type or getattr(
|
|
config, "position_embedding_type", "absolute"
|
|
)
|
|
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
|
|
self.max_position_embeddings = config.max_position_embeddings
|
|
self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
|
|
|
|
@deprecate_kwarg("encoder_hidden_states", version="4.54.0")
|
|
@deprecate_kwarg("encoder_attention_mask", version="4.54.0")
|
|
@deprecate_kwarg("past_key_value", version="4.54.0")
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
|
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
|
past_key_value: Optional[tuple[tuple[torch.FloatTensor]]] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
) -> tuple[torch.Tensor]:
|
|
input_shape = hidden_states.shape[:-1]
|
|
hidden_shape = (*input_shape, -1, self.attention_head_size)
|
|
|
|
query_layer = self.query(hidden_states).view(hidden_shape).transpose(1, 2)
|
|
key_layer = self.key(hidden_states).view(hidden_shape).transpose(1, 2)
|
|
value_layer = self.value(hidden_states).view(hidden_shape).transpose(1, 2)
|
|
|
|
# Take the dot product between "query" and "key" to get the raw attention scores.
|
|
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
|
|
|
|
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
|
|
query_length, key_length = query_layer.shape[2], key_layer.shape[2]
|
|
position_ids_l = torch.arange(query_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
|
|
position_ids_r = torch.arange(key_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
|
|
distance = position_ids_l - position_ids_r
|
|
|
|
positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
|
|
positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility
|
|
|
|
if self.position_embedding_type == "relative_key":
|
|
relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
|
|
attention_scores = attention_scores + relative_position_scores
|
|
elif self.position_embedding_type == "relative_key_query":
|
|
relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
|
|
relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
|
|
attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
|
|
|
|
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
|
|
if attention_mask is not None:
|
|
# Apply the attention mask is (precomputed for all layers in AltRobertaModel forward() function)
|
|
attention_scores = attention_scores + attention_mask
|
|
|
|
# Normalize the attention scores to probabilities.
|
|
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
|
|
|
|
# This is actually dropping out entire tokens to attend to, which might
|
|
# seem a bit unusual, but is taken from the original Transformer paper.
|
|
attention_probs = self.dropout(attention_probs)
|
|
|
|
# Mask heads if we want to
|
|
if head_mask is not None:
|
|
attention_probs = attention_probs * head_mask
|
|
|
|
context_layer = torch.matmul(attention_probs, value_layer)
|
|
|
|
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
|
|
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
|
|
context_layer = context_layer.view(new_context_layer_shape)
|
|
|
|
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
|
|
|
|
return outputs
|
|
|
|
|
|
# Copied from transformers.models.roberta.modeling_roberta.RobertaSelfOutput
|
|
class AltRobertaSelfOutput(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
|
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
|
|
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.dropout(hidden_states)
|
|
hidden_states = self.LayerNorm(hidden_states + input_tensor)
|
|
return hidden_states
|
|
|
|
|
|
ALT_ROBERTA_SELF_ATTENTION_CLASSES = {
|
|
"eager": AltRobertaSelfAttention,
|
|
}
|
|
|
|
|
|
class AltRobertaAttention(nn.Module):
|
|
def __init__(self, config, position_embedding_type=None):
|
|
super().__init__()
|
|
self.self = ALT_ROBERTA_SELF_ATTENTION_CLASSES[config._attn_implementation](
|
|
config, position_embedding_type=position_embedding_type
|
|
)
|
|
self.output = AltRobertaSelfOutput(config)
|
|
self.pruned_heads = set()
|
|
|
|
def prune_heads(self, heads):
|
|
if len(heads) == 0:
|
|
return
|
|
heads, index = find_pruneable_heads_and_indices(
|
|
heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
|
|
)
|
|
|
|
# Prune linear layers
|
|
self.self.query = prune_linear_layer(self.self.query, index)
|
|
self.self.key = prune_linear_layer(self.self.key, index)
|
|
self.self.value = prune_linear_layer(self.self.value, index)
|
|
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
|
|
|
|
# Update hyper params and store pruned heads
|
|
self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
|
|
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
|
|
self.pruned_heads = self.pruned_heads.union(heads)
|
|
|
|
@deprecate_kwarg("encoder_hidden_states", version="4.54.0")
|
|
@deprecate_kwarg("encoder_attention_mask", version="4.54.0")
|
|
@deprecate_kwarg("past_key_value", version="4.54.0")
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
|
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
|
past_key_value: Optional[tuple[tuple[torch.FloatTensor]]] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
) -> tuple[torch.Tensor]:
|
|
self_outputs = self.self(
|
|
hidden_states,
|
|
attention_mask=attention_mask,
|
|
head_mask=head_mask,
|
|
output_attentions=output_attentions,
|
|
)
|
|
attention_output = self.output(self_outputs[0], hidden_states)
|
|
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
|
|
return outputs
|
|
|
|
|
|
# Copied from transformers.models.roberta.modeling_roberta.RobertaIntermediate with Roberta->AltRoberta
|
|
class AltRobertaIntermediate(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
|
|
if isinstance(config.hidden_act, str):
|
|
self.intermediate_act_fn = ACT2FN[config.hidden_act]
|
|
else:
|
|
self.intermediate_act_fn = config.hidden_act
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.intermediate_act_fn(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
# Copied from transformers.models.roberta.modeling_roberta.RobertaOutput
|
|
class AltRobertaOutput(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
|
|
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
|
|
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.dropout(hidden_states)
|
|
hidden_states = self.LayerNorm(hidden_states + input_tensor)
|
|
return hidden_states
|
|
|
|
|
|
# Copied from transformers.models.align.modeling_align.AlignTextLayer with AlignText->AltRoberta
|
|
class AltRobertaLayer(GradientCheckpointingLayer):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.chunk_size_feed_forward = config.chunk_size_feed_forward
|
|
self.seq_len_dim = 1
|
|
self.attention = AltRobertaAttention(config)
|
|
self.intermediate = AltRobertaIntermediate(config)
|
|
self.output = AltRobertaOutput(config)
|
|
|
|
@deprecate_kwarg("encoder_hidden_states", version="4.54.0")
|
|
@deprecate_kwarg("encoder_attention_mask", version="4.54.0")
|
|
@deprecate_kwarg("past_key_value", version="4.54.0")
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
|
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
|
past_key_value: Optional[tuple[tuple[torch.FloatTensor]]] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
**kwargs,
|
|
) -> tuple[torch.Tensor]:
|
|
self_attention_outputs = self.attention(
|
|
hidden_states,
|
|
attention_mask=attention_mask,
|
|
head_mask=head_mask,
|
|
output_attentions=output_attentions,
|
|
**kwargs,
|
|
)
|
|
attention_output = self_attention_outputs[0]
|
|
|
|
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
|
|
layer_output = apply_chunking_to_forward(
|
|
self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
|
|
)
|
|
outputs = (layer_output,) + outputs
|
|
|
|
return outputs
|
|
|
|
def feed_forward_chunk(self, attention_output):
|
|
intermediate_output = self.intermediate(attention_output)
|
|
layer_output = self.output(intermediate_output, attention_output)
|
|
return layer_output
|
|
|
|
|
|
# Copied from transformers.models.align.modeling_align.AlignTextEncoder with AlignText->AltRoberta
|
|
class AltRobertaEncoder(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.config = config
|
|
self.layer = nn.ModuleList([AltRobertaLayer(config) for i in range(config.num_hidden_layers)])
|
|
self.gradient_checkpointing = False
|
|
|
|
@deprecate_kwarg("encoder_hidden_states", version="4.54.0")
|
|
@deprecate_kwarg("encoder_attention_mask", version="4.54.0")
|
|
@deprecate_kwarg("past_key_values", version="4.54.0")
|
|
@deprecate_kwarg("use_cache", version="4.54.0")
|
|
@can_return_tuple
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
|
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
|
past_key_values: Optional[tuple[tuple[torch.FloatTensor]]] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
output_hidden_states: Optional[bool] = False,
|
|
return_dict: Optional[bool] = True,
|
|
**kwargs,
|
|
) -> Union[tuple[torch.Tensor], BaseModelOutput]:
|
|
all_hidden_states = () if output_hidden_states else None
|
|
all_self_attentions = () if output_attentions else None
|
|
|
|
for i, layer_module in enumerate(self.layer):
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
layer_head_mask = head_mask[i] if head_mask is not None else None
|
|
|
|
layer_outputs = layer_module(
|
|
hidden_states=hidden_states,
|
|
attention_mask=attention_mask,
|
|
head_mask=layer_head_mask,
|
|
output_attentions=output_attentions,
|
|
**kwargs,
|
|
)
|
|
|
|
hidden_states = layer_outputs[0]
|
|
if output_attentions:
|
|
all_self_attentions = all_self_attentions + (layer_outputs[1],)
|
|
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
return BaseModelOutput(
|
|
last_hidden_state=hidden_states,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attentions,
|
|
)
|
|
|
|
|
|
# Copied from transformers.models.roberta.modeling_roberta.RobertaPooler
|
|
class AltRobertaPooler(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
|
self.activation = nn.Tanh()
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
# We "pool" the model by simply taking the hidden state corresponding
|
|
# to the first token.
|
|
first_token_tensor = hidden_states[:, 0]
|
|
pooled_output = self.dense(first_token_tensor)
|
|
pooled_output = self.activation(pooled_output)
|
|
return pooled_output
|
|
|
|
|
|
# Copied from transformers.models.siglip.modeling_siglip.eager_attention_forward
|
|
def eager_attention_forward(
|
|
module: nn.Module,
|
|
query: torch.Tensor,
|
|
key: torch.Tensor,
|
|
value: torch.Tensor,
|
|
attention_mask: Optional[torch.Tensor],
|
|
scaling: float,
|
|
dropout: float = 0.0,
|
|
**kwargs,
|
|
):
|
|
attn_weights = torch.matmul(query, key.transpose(-1, -2)) * scaling
|
|
if attention_mask is not None:
|
|
attn_weights = attn_weights + attention_mask
|
|
|
|
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
|
|
attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
|
|
|
|
attn_output = torch.matmul(attn_weights, value)
|
|
attn_output = attn_output.transpose(1, 2).contiguous()
|
|
|
|
return attn_output, attn_weights
|
|
|
|
|
|
class AltCLIPAttention(nn.Module):
|
|
"""Multi-headed attention from 'Attention Is All You Need' paper"""
|
|
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.config = config
|
|
self.embed_dim = config.hidden_size
|
|
self.num_heads = config.num_attention_heads
|
|
self.head_dim = self.embed_dim // self.num_heads
|
|
if self.head_dim * self.num_heads != self.embed_dim:
|
|
raise ValueError(
|
|
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
|
|
f" {self.num_heads})."
|
|
)
|
|
self.scale = self.head_dim**-0.5
|
|
self.dropout = config.attention_dropout
|
|
self.is_causal = False
|
|
|
|
self.k_proj = nn.Linear(self.embed_dim, self.embed_dim)
|
|
self.v_proj = nn.Linear(self.embed_dim, self.embed_dim)
|
|
self.q_proj = nn.Linear(self.embed_dim, self.embed_dim)
|
|
self.out_proj = nn.Linear(self.embed_dim, self.embed_dim)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
causal_attention_mask: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
|
|
"""Input shape: Batch x Time x Channel"""
|
|
|
|
batch_size, seq_length, embed_dim = hidden_states.shape
|
|
|
|
queries = self.q_proj(hidden_states)
|
|
keys = self.k_proj(hidden_states)
|
|
values = self.v_proj(hidden_states)
|
|
|
|
queries = queries.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2)
|
|
keys = keys.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2)
|
|
values = values.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2)
|
|
# CLIP text model uses both `causal_attention_mask` and `attention_mask`
|
|
# in case FA2 kernel is called, `is_causal` should be inferred from `causal_attention_mask`
|
|
if self.config._attn_implementation != "flash_attention_2":
|
|
if attention_mask is not None and causal_attention_mask is not None:
|
|
attention_mask = attention_mask + causal_attention_mask
|
|
elif causal_attention_mask is not None:
|
|
attention_mask = causal_attention_mask
|
|
else:
|
|
self.is_causal = causal_attention_mask is not None
|
|
|
|
attention_interface: Callable = eager_attention_forward
|
|
if self.config._attn_implementation != "eager":
|
|
if self.config._attn_implementation == "sdpa" and output_attentions:
|
|
logger.warning_once(
|
|
"`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to "
|
|
'eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
|
|
)
|
|
else:
|
|
attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
|
|
|
|
attn_output, attn_weights = attention_interface(
|
|
self,
|
|
queries,
|
|
keys,
|
|
values,
|
|
attention_mask,
|
|
is_causal=self.is_causal,
|
|
scaling=self.scale,
|
|
dropout=0.0 if not self.training else self.dropout,
|
|
)
|
|
|
|
attn_output = attn_output.reshape(batch_size, seq_length, embed_dim).contiguous()
|
|
attn_output = self.out_proj(attn_output)
|
|
if not output_attentions:
|
|
attn_weights = None
|
|
return attn_output, attn_weights
|
|
|
|
|
|
# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->AltCLIP
|
|
class AltCLIPMLP(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.config = config
|
|
self.activation_fn = ACT2FN[config.hidden_act]
|
|
self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
|
|
self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
hidden_states = self.fc1(hidden_states)
|
|
hidden_states = self.activation_fn(hidden_states)
|
|
hidden_states = self.fc2(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
class AltCLIPEncoderLayer(GradientCheckpointingLayer):
|
|
def __init__(self, config: AltCLIPConfig):
|
|
super().__init__()
|
|
self.embed_dim = config.hidden_size
|
|
self.self_attn = AltCLIPAttention(config)
|
|
self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
|
|
self.mlp = AltCLIPMLP(config)
|
|
self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: torch.Tensor,
|
|
causal_attention_mask: torch.Tensor,
|
|
output_attentions: Optional[bool] = False,
|
|
) -> tuple[torch.FloatTensor]:
|
|
"""
|
|
Args:
|
|
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
|
|
attention_mask (`torch.FloatTensor`): attention mask of size
|
|
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
|
|
`(config.encoder_attention_heads,)`.
|
|
output_attentions (`bool`, *optional*):
|
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
|
returned tensors for more detail.
|
|
"""
|
|
residual = hidden_states
|
|
|
|
hidden_states = self.layer_norm1(hidden_states)
|
|
hidden_states, attn_weights = self.self_attn(
|
|
hidden_states=hidden_states,
|
|
attention_mask=attention_mask,
|
|
causal_attention_mask=causal_attention_mask,
|
|
output_attentions=output_attentions,
|
|
)
|
|
hidden_states = residual + hidden_states
|
|
|
|
residual = hidden_states
|
|
hidden_states = self.layer_norm2(hidden_states)
|
|
hidden_states = self.mlp(hidden_states)
|
|
hidden_states = residual + hidden_states
|
|
|
|
outputs = (hidden_states,)
|
|
|
|
if output_attentions:
|
|
outputs += (attn_weights,)
|
|
|
|
return outputs
|
|
|
|
|
|
class AltCLIPEncoder(nn.Module):
|
|
"""
|
|
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
|
|
[`AltCLIPEncoderLayer`].
|
|
|
|
Args:
|
|
config: AltCLIPConfig
|
|
"""
|
|
|
|
def __init__(self, config: AltCLIPConfig):
|
|
super().__init__()
|
|
self.config = config
|
|
self.layers = nn.ModuleList([AltCLIPEncoderLayer(config) for _ in range(config.num_hidden_layers)])
|
|
self.gradient_checkpointing = False
|
|
|
|
@can_return_tuple
|
|
def forward(
|
|
self,
|
|
inputs_embeds,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
causal_attention_mask: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple, BaseModelOutput]:
|
|
r"""
|
|
Args:
|
|
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
|
|
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
|
|
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
|
|
than the model's internal embedding lookup matrix.
|
|
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 for tokens that are **not masked**,
|
|
- 0 for tokens that are **masked**.
|
|
|
|
[What are attention masks?](../glossary#attention-mask)
|
|
causal_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Causal mask for the text model. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 for tokens that are **not masked**,
|
|
- 0 for tokens that are **masked**.
|
|
|
|
[What are attention masks?](../glossary#attention-mask)
|
|
output_attentions (`bool`, *optional*):
|
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
|
returned tensors for more detail.
|
|
output_hidden_states (`bool`, *optional*):
|
|
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
|
|
for more detail.
|
|
return_dict (`bool`, *optional*):
|
|
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
|
"""
|
|
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
|
|
|
|
encoder_states = () if output_hidden_states else None
|
|
all_attentions = () if output_attentions else None
|
|
|
|
hidden_states = inputs_embeds
|
|
for idx, encoder_layer in enumerate(self.layers):
|
|
if output_hidden_states:
|
|
encoder_states = encoder_states + (hidden_states,)
|
|
layer_outputs = encoder_layer(
|
|
hidden_states,
|
|
attention_mask,
|
|
causal_attention_mask,
|
|
output_attentions=output_attentions,
|
|
)
|
|
|
|
hidden_states = layer_outputs[0]
|
|
|
|
if output_attentions:
|
|
all_attentions = all_attentions + (layer_outputs[1],)
|
|
|
|
if output_hidden_states:
|
|
encoder_states = encoder_states + (hidden_states,)
|
|
|
|
return BaseModelOutput(
|
|
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
|
|
)
|
|
|
|
|
|
# Copied from transformers.models.clip.modeling_clip.CLIPVisionEmbeddings with CLIP->AltCLIP
|
|
class AltCLIPVisionEmbeddings(nn.Module):
|
|
def __init__(self, config: AltCLIPVisionConfig):
|
|
super().__init__()
|
|
self.config = config
|
|
self.embed_dim = config.hidden_size
|
|
self.image_size = config.image_size
|
|
self.patch_size = config.patch_size
|
|
|
|
self.class_embedding = nn.Parameter(torch.randn(self.embed_dim))
|
|
|
|
self.patch_embedding = nn.Conv2d(
|
|
in_channels=config.num_channels,
|
|
out_channels=self.embed_dim,
|
|
kernel_size=self.patch_size,
|
|
stride=self.patch_size,
|
|
bias=False,
|
|
)
|
|
|
|
self.num_patches = (self.image_size // self.patch_size) ** 2
|
|
self.num_positions = self.num_patches + 1
|
|
self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
|
|
self.register_buffer("position_ids", torch.arange(self.num_positions).expand((1, -1)), persistent=False)
|
|
|
|
def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
|
|
"""
|
|
This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution
|
|
images. This method is also adapted to support torch.jit tracing.
|
|
|
|
Adapted from:
|
|
- https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174-L194, and
|
|
- https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/models/vision_transformer.py#L179-L211
|
|
"""
|
|
|
|
num_patches = embeddings.shape[1] - 1
|
|
position_embedding = self.position_embedding.weight.unsqueeze(0)
|
|
num_positions = position_embedding.shape[1] - 1
|
|
|
|
# always interpolate when tracing to ensure the exported model works for dynamic input shapes
|
|
if not torch.jit.is_tracing() and num_patches == num_positions and height == width:
|
|
return self.position_embedding(self.position_ids)
|
|
|
|
class_pos_embed = position_embedding[:, :1]
|
|
patch_pos_embed = position_embedding[:, 1:]
|
|
|
|
dim = embeddings.shape[-1]
|
|
|
|
new_height = height // self.patch_size
|
|
new_width = width // self.patch_size
|
|
|
|
sqrt_num_positions = torch_int(num_positions**0.5)
|
|
patch_pos_embed = patch_pos_embed.reshape(1, sqrt_num_positions, sqrt_num_positions, dim)
|
|
patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
|
|
|
|
patch_pos_embed = nn.functional.interpolate(
|
|
patch_pos_embed,
|
|
size=(new_height, new_width),
|
|
mode="bicubic",
|
|
align_corners=False,
|
|
)
|
|
|
|
patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
|
|
|
|
return torch.cat((class_pos_embed, patch_pos_embed), dim=1)
|
|
|
|
def forward(self, pixel_values: torch.FloatTensor, interpolate_pos_encoding=False) -> torch.Tensor:
|
|
batch_size, _, height, width = pixel_values.shape
|
|
if not interpolate_pos_encoding and (height != self.image_size or width != self.image_size):
|
|
raise ValueError(
|
|
f"Input image size ({height}*{width}) doesn't match model ({self.image_size}*{self.image_size})."
|
|
)
|
|
target_dtype = self.patch_embedding.weight.dtype
|
|
patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype)) # shape = [*, width, grid, grid]
|
|
patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
|
|
|
|
class_embeds = self.class_embedding.expand(batch_size, 1, -1)
|
|
embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
|
|
if interpolate_pos_encoding:
|
|
embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
|
|
else:
|
|
embeddings = embeddings + self.position_embedding(self.position_ids)
|
|
return embeddings
|
|
|
|
|
|
@auto_docstring
|
|
class AltCLIPPreTrainedModel(PreTrainedModel):
|
|
config: AltCLIPConfig
|
|
base_model_prefix = "altclip"
|
|
supports_gradient_checkpointing = True
|
|
_no_split_module = []
|
|
|
|
def _init_weights(self, module):
|
|
"""Initialize the weights"""
|
|
factor = self.config.initializer_factor
|
|
if isinstance(module, AltCLIPVisionEmbeddings):
|
|
factor = self.config.initializer_factor
|
|
nn.init.normal_(module.class_embedding, mean=0.0, std=module.embed_dim**-0.5 * factor)
|
|
nn.init.normal_(module.patch_embedding.weight, std=module.config.initializer_range * factor)
|
|
nn.init.normal_(module.position_embedding.weight, std=module.config.initializer_range * factor)
|
|
elif isinstance(module, AltCLIPAttention):
|
|
factor = self.config.initializer_factor
|
|
in_proj_std = (module.embed_dim**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor
|
|
out_proj_std = (module.embed_dim**-0.5) * factor
|
|
nn.init.normal_(module.q_proj.weight, std=in_proj_std)
|
|
nn.init.normal_(module.k_proj.weight, std=in_proj_std)
|
|
nn.init.normal_(module.v_proj.weight, std=in_proj_std)
|
|
nn.init.normal_(module.out_proj.weight, std=out_proj_std)
|
|
elif isinstance(module, AltCLIPMLP):
|
|
factor = self.config.initializer_factor
|
|
in_proj_std = (module.config.hidden_size**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor
|
|
fc_std = (2 * module.config.hidden_size) ** -0.5 * factor
|
|
nn.init.normal_(module.fc1.weight, std=fc_std)
|
|
nn.init.normal_(module.fc2.weight, std=in_proj_std)
|
|
elif isinstance(module, AltCLIPModel):
|
|
nn.init.normal_(
|
|
module.text_projection.weight,
|
|
std=module.text_embed_dim**-0.5 * self.config.initializer_factor,
|
|
)
|
|
module.text_projection._is_hf_initialized = True
|
|
nn.init.normal_(
|
|
module.visual_projection.weight,
|
|
std=module.vision_embed_dim**-0.5 * self.config.initializer_factor,
|
|
)
|
|
module.visual_projection._is_hf_initialized = True
|
|
elif isinstance(module, nn.LayerNorm):
|
|
module.bias.data.zero_()
|
|
module.weight.data.fill_(1.0)
|
|
elif isinstance(module, nn.Linear):
|
|
module.weight.data.normal_(mean=0.0, std=self.config.initializer_factor)
|
|
if module.bias is not None:
|
|
module.bias.data.zero_()
|
|
elif isinstance(module, nn.Embedding):
|
|
module.weight.data.normal_(mean=0.0, std=self.config.initializer_factor)
|
|
if module.padding_idx is not None:
|
|
module.weight.data[module.padding_idx].zero_()
|
|
|
|
|
|
class AltCLIPVisionTransformer(nn.Module):
|
|
def __init__(self, config: AltCLIPVisionConfig):
|
|
super().__init__()
|
|
self.config = config
|
|
embed_dim = config.hidden_size
|
|
|
|
self.embeddings = AltCLIPVisionEmbeddings(config)
|
|
self.pre_layrnorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
|
|
self.encoder = AltCLIPEncoder(config)
|
|
self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
pixel_values: Optional[torch.FloatTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
interpolate_pos_encoding: Optional[bool] = False,
|
|
) -> 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 pixel_values is None:
|
|
raise ValueError("You have to specify pixel_values")
|
|
|
|
hidden_states = self.embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
|
|
hidden_states = self.pre_layrnorm(hidden_states)
|
|
|
|
encoder_outputs = self.encoder(
|
|
inputs_embeds=hidden_states,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=True,
|
|
)
|
|
|
|
last_hidden_state = encoder_outputs[0]
|
|
pooled_output = last_hidden_state[:, 0, :]
|
|
pooled_output = self.post_layernorm(pooled_output)
|
|
|
|
return BaseModelOutputWithPooling(
|
|
last_hidden_state=last_hidden_state,
|
|
pooler_output=pooled_output,
|
|
hidden_states=encoder_outputs.hidden_states,
|
|
attentions=encoder_outputs.attentions,
|
|
)
|
|
|
|
|
|
class AltCLIPVisionModel(AltCLIPPreTrainedModel):
|
|
config: AltCLIPVisionConfig
|
|
main_input_name = "pixel_values"
|
|
|
|
def __init__(self, config: AltCLIPVisionConfig):
|
|
super().__init__(config)
|
|
self.vision_model = AltCLIPVisionTransformer(config)
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self) -> nn.Module:
|
|
return self.vision_model.embeddings.patch_embedding
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
pixel_values: Optional[torch.FloatTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
interpolate_pos_encoding: bool = False,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple, BaseModelOutputWithPooling]:
|
|
r"""
|
|
Examples:
|
|
|
|
```python
|
|
>>> from PIL import Image
|
|
>>> import requests
|
|
>>> from transformers import AutoProcessor, AltCLIPVisionModel
|
|
|
|
>>> model = AltCLIPVisionModel.from_pretrained("BAAI/AltCLIP")
|
|
>>> processor = AutoProcessor.from_pretrained("BAAI/AltCLIP")
|
|
|
|
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
|
|
>>> image = Image.open(requests.get(url, stream=True).raw)
|
|
|
|
>>> inputs = processor(images=image, return_tensors="pt")
|
|
|
|
>>> outputs = model(**inputs)
|
|
>>> last_hidden_state = outputs.last_hidden_state
|
|
>>> pooled_output = outputs.pooler_output # pooled CLS states
|
|
```"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
return self.vision_model(
|
|
pixel_values=pixel_values,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
interpolate_pos_encoding=interpolate_pos_encoding,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
The model behaves as an encoder following the architecture described in *Attention is
|
|
all you need*_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz
|
|
Kaiser and Illia Polosukhin.
|
|
|
|
.. _*Attention is all you need*: https://arxiv.org/abs/1706.03762
|
|
"""
|
|
)
|
|
class AltRobertaModel(AltCLIPPreTrainedModel):
|
|
config: AltCLIPTextConfig
|
|
|
|
# Copied from transformers.models.clap.modeling_clap.ClapTextModel.__init__ with ClapText->AltRoberta
|
|
def __init__(self, config, add_pooling_layer=True):
|
|
r"""
|
|
add_pooling_layer (bool, *optional*, defaults to `True`):
|
|
Whether to add a pooling layer
|
|
"""
|
|
super().__init__(config)
|
|
self.config = config
|
|
|
|
self.embeddings = AltRobertaEmbeddings(config)
|
|
self.encoder = AltRobertaEncoder(config)
|
|
|
|
self.pooler = AltRobertaPooler(config) if add_pooling_layer else None
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.embeddings.word_embeddings
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.embeddings.word_embeddings = value
|
|
|
|
def _prune_heads(self, heads_to_prune):
|
|
"""
|
|
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
|
|
class PreTrainedModel
|
|
"""
|
|
for layer, heads in heads_to_prune.items():
|
|
self.encoder.layer[layer].attention.prune_heads(heads)
|
|
|
|
@deprecate_kwarg("encoder_hidden_states", version="4.54.0")
|
|
@deprecate_kwarg("encoder_attention_mask", version="4.54.0")
|
|
@deprecate_kwarg("past_key_values", version="4.54.0")
|
|
@deprecate_kwarg("use_cache", version="4.54.0")
|
|
@auto_docstring
|
|
# Copied from transformers.models.clap.modeling_clap.ClapTextModel.forward
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
encoder_hidden_states: Optional[torch.Tensor] = None,
|
|
encoder_attention_mask: Optional[torch.Tensor] = None,
|
|
past_key_values: Optional[list[torch.FloatTensor]] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
|
|
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 not None and inputs_embeds is not None:
|
|
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
|
|
elif input_ids is not None:
|
|
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
|
|
input_shape = input_ids.size()
|
|
elif inputs_embeds is not None:
|
|
input_shape = inputs_embeds.size()[:-1]
|
|
else:
|
|
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
|
|
|
batch_size, seq_length = input_shape
|
|
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
|
|
|
if attention_mask is None:
|
|
attention_mask = torch.ones(((batch_size, seq_length)), device=device)
|
|
|
|
if token_type_ids is None:
|
|
if hasattr(self.embeddings, "token_type_ids"):
|
|
buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
|
|
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length)
|
|
token_type_ids = buffered_token_type_ids_expanded
|
|
else:
|
|
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
|
|
|
|
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
|
|
# ourselves in which case we just need to make it broadcastable to all heads.
|
|
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
|
|
|
|
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
|
|
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
|
|
|
|
embedding_output = self.embeddings(
|
|
input_ids=input_ids,
|
|
position_ids=position_ids,
|
|
token_type_ids=token_type_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
)
|
|
encoder_outputs = self.encoder(
|
|
embedding_output,
|
|
attention_mask=extended_attention_mask,
|
|
head_mask=head_mask,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=True,
|
|
)
|
|
sequence_output = encoder_outputs[0]
|
|
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
|
|
|
|
return BaseModelOutputWithPooling(
|
|
last_hidden_state=sequence_output,
|
|
pooler_output=pooled_output,
|
|
hidden_states=encoder_outputs.hidden_states,
|
|
attentions=encoder_outputs.attentions,
|
|
)
|
|
|
|
|
|
class AltCLIPTextModel(AltCLIPPreTrainedModel):
|
|
config: AltCLIPTextConfig
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.roberta = AltRobertaModel(config, add_pooling_layer=False)
|
|
self.transformation = nn.Linear(config.hidden_size, config.project_dim)
|
|
self.pre_LN = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self) -> nn.Module:
|
|
return self.roberta.embeddings.word_embeddings
|
|
|
|
def set_input_embeddings(self, value: nn.Embedding) -> None:
|
|
self.roberta.embeddings.word_embeddings = value
|
|
|
|
def resize_token_embeddings(self, new_num_tokens: Optional[int] = None) -> nn.Embedding:
|
|
return super().resize_token_embeddings(new_num_tokens)
|
|
|
|
@deprecate_kwarg("encoder_hidden_states", version="4.54.0")
|
|
@deprecate_kwarg("encoder_attention_mask", version="4.54.0")
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
encoder_hidden_states: Optional[torch.Tensor] = None,
|
|
encoder_attention_mask: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
) -> Union[tuple, BaseModelOutputWithPoolingAndProjection]:
|
|
r"""
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import AutoProcessor, AltCLIPTextModel
|
|
|
|
>>> model = AltCLIPTextModel.from_pretrained("BAAI/AltCLIP")
|
|
>>> processor = AutoProcessor.from_pretrained("BAAI/AltCLIP")
|
|
|
|
>>> texts = ["it's a cat", "it's a dog"]
|
|
|
|
>>> inputs = processor(text=texts, padding=True, return_tensors="pt")
|
|
|
|
>>> outputs = model(**inputs)
|
|
>>> last_hidden_state = outputs.last_hidden_state
|
|
>>> pooled_output = outputs.pooler_output # pooled CLS states
|
|
```"""
|
|
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.roberta(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=True,
|
|
)
|
|
|
|
# last module outputs
|
|
sequence_output = outputs[0]
|
|
|
|
# project every module
|
|
sequence_output = self.pre_LN(sequence_output)
|
|
|
|
# pooler
|
|
projection_state = self.transformation(sequence_output)
|
|
pooler_output = projection_state[:, 0]
|
|
|
|
return BaseModelOutputWithPoolingAndProjection(
|
|
last_hidden_state=projection_state,
|
|
pooler_output=pooler_output,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
class AltCLIPModel(AltCLIPPreTrainedModel):
|
|
config: AltCLIPConfig
|
|
|
|
def __init__(self, config: AltCLIPConfig):
|
|
super().__init__(config)
|
|
|
|
if not isinstance(config.vision_config, AltCLIPVisionConfig):
|
|
raise TypeError(
|
|
"config.vision_config is expected to be of type AltCLIPVisionConfig but is of type"
|
|
f" {type(config.vision_config)}."
|
|
)
|
|
if not isinstance(config.text_config, AltCLIPTextConfig):
|
|
raise TypeError(
|
|
"config.text_config is expected to be of type AltCLIPTextConfig but is of type"
|
|
f" {type(config.text_config)}."
|
|
)
|
|
|
|
text_config = config.text_config
|
|
vision_config = config.vision_config
|
|
# The module using it is not a PreTrainedModel subclass so we need this
|
|
vision_config._attn_implementation = config._attn_implementation
|
|
|
|
self.projection_dim = config.projection_dim
|
|
self.text_embed_dim = text_config.project_dim
|
|
self.vision_embed_dim = vision_config.hidden_size
|
|
|
|
self.text_model = AltCLIPTextModel(text_config)
|
|
self.vision_model = AltCLIPVisionTransformer(vision_config)
|
|
|
|
self.visual_projection = nn.Linear(self.vision_embed_dim, self.projection_dim, bias=False)
|
|
self.text_projection = nn.Linear(self.text_embed_dim, self.projection_dim, bias=False)
|
|
self.logit_scale = nn.Parameter(torch.tensor(self.config.logit_scale_init_value))
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@auto_docstring
|
|
def get_text_features(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
token_type_ids=None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> torch.FloatTensor:
|
|
r"""
|
|
Returns:
|
|
text_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The text embeddings obtained by
|
|
applying the projection layer to the pooled output of [`AltCLIPTextModel`].
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import AutoProcessor, AltCLIPModel
|
|
|
|
>>> model = AltCLIPModel.from_pretrained("BAAI/AltCLIP")
|
|
>>> processor = AutoProcessor.from_pretrained("BAAI/AltCLIP")
|
|
>>> inputs = processor(text=["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt")
|
|
>>> text_features = model.get_text_features(**inputs)
|
|
```"""
|
|
# Use AltCLIP 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
|
|
|
|
text_outputs = self.text_model(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
token_type_ids=token_type_ids,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
pooled_output = text_outputs[1]
|
|
text_features = self.text_projection(pooled_output)
|
|
|
|
return text_features
|
|
|
|
@auto_docstring
|
|
def get_image_features(
|
|
self,
|
|
pixel_values: Optional[torch.FloatTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
interpolate_pos_encoding: bool = False,
|
|
return_dict: Optional[bool] = None,
|
|
) -> torch.FloatTensor:
|
|
r"""
|
|
Returns:
|
|
image_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The image embeddings obtained by
|
|
applying the projection layer to the pooled output of [`AltCLIPVisionModel`].
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from PIL import Image
|
|
>>> import requests
|
|
>>> from transformers import AutoProcessor, AltCLIPModel
|
|
|
|
>>> model = AltCLIPModel.from_pretrained("BAAI/AltCLIP")
|
|
>>> processor = AutoProcessor.from_pretrained("BAAI/AltCLIP")
|
|
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
|
|
>>> image = Image.open(requests.get(url, stream=True).raw)
|
|
>>> inputs = processor(images=image, return_tensors="pt")
|
|
>>> image_features = model.get_image_features(**inputs)
|
|
```"""
|
|
# Use AltCLIP 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,
|
|
interpolate_pos_encoding=interpolate_pos_encoding,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
pooled_output = vision_outputs[1] # pooled_output
|
|
image_features = self.visual_projection(pooled_output)
|
|
|
|
return image_features
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
pixel_values: Optional[torch.FloatTensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
return_loss: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
interpolate_pos_encoding: bool = False,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple, AltCLIPOutput]:
|
|
r"""
|
|
return_loss (`bool`, *optional*):
|
|
Whether or not to return the contrastive loss.
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from PIL import Image
|
|
>>> import requests
|
|
>>> from transformers import AutoProcessor, AltCLIPModel
|
|
|
|
>>> model = AltCLIPModel.from_pretrained("BAAI/AltCLIP")
|
|
>>> processor = AutoProcessor.from_pretrained("BAAI/AltCLIP")
|
|
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
|
|
>>> image = Image.open(requests.get(url, stream=True).raw)
|
|
>>> inputs = processor(
|
|
... text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True
|
|
... )
|
|
>>> outputs = model(**inputs)
|
|
>>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score
|
|
>>> probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities
|
|
```"""
|
|
# Use AltCLIP 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
|
|
|
|
text_outputs = self.text_model(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
vision_outputs = self.vision_model(
|
|
pixel_values=pixel_values,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
interpolate_pos_encoding=interpolate_pos_encoding,
|
|
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
|
|
|
|
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 AltCLIPOutput(
|
|
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,
|
|
)
|
|
|
|
|
|
# Copied from transformers.models.roberta.modeling_roberta.create_position_ids_from_input_ids
|
|
def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0):
|
|
"""
|
|
Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
|
|
are ignored. This is modified from fairseq's `utils.make_positions`.
|
|
|
|
Args:
|
|
x: torch.Tensor x:
|
|
|
|
Returns: torch.Tensor
|
|
"""
|
|
# The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
|
|
mask = input_ids.ne(padding_idx).int()
|
|
incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask
|
|
return incremental_indices.long() + padding_idx
|
|
|
|
|
|
__all__ = ["AltCLIPPreTrainedModel", "AltCLIPVisionModel", "AltCLIPTextModel", "AltCLIPModel"]
|