1460 lines
61 KiB
Python
1460 lines
61 KiB
Python
# coding=utf-8
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# Copyright 2022 Microsoft Research and The HuggingFace Inc. team.
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# All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""PyTorch GIT model."""
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import math
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from dataclasses import dataclass
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from typing import Callable, Optional, Union
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import torch
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import torch.utils.checkpoint
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from torch import nn
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from ...activations import ACT2FN
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from ...cache_utils import Cache, DynamicCache
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from ...generation import GenerationMixin
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from ...modeling_attn_mask_utils import _prepare_4d_attention_mask
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from ...modeling_layers import GradientCheckpointingLayer
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from ...modeling_outputs import (
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BaseModelOutput,
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BaseModelOutputWithPast,
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BaseModelOutputWithPooling,
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CausalLMOutputWithPast,
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)
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from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
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from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
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from ...utils import (
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ModelOutput,
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auto_docstring,
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can_return_tuple,
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logging,
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torch_int,
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)
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from .configuration_git import GitConfig, GitVisionConfig
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logger = logging.get_logger(__name__)
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@dataclass
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@auto_docstring(
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custom_intro="""
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Base class for vision model's outputs that also contains image embeddings of the pooling of the last hidden states.
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"""
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)
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# Copied from transformers.models.clip.modeling_clip.CLIPVisionModelOutput with CLIP->Git
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class GitVisionModelOutput(ModelOutput):
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r"""
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image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):
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The image embeddings obtained by applying the projection layer to the pooler_output.
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"""
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image_embeds: Optional[torch.FloatTensor] = None
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last_hidden_state: Optional[torch.FloatTensor] = None
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hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
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attentions: Optional[tuple[torch.FloatTensor, ...]] = None
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class GitEmbeddings(nn.Module):
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"""Construct the embeddings from word and position embeddings."""
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def __init__(self, config):
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super().__init__()
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self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
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self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
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# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
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# any TensorFlow checkpoint file
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self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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# position_ids (1, len position emb) is contiguous in memory and exported when serialized
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self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
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self.register_buffer(
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"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
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)
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def forward(
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self,
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input_ids: Optional[torch.LongTensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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past_key_values_length: int = 0,
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) -> torch.Tensor:
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if input_ids is not None:
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input_shape = input_ids.size()
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else:
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input_shape = inputs_embeds.size()[:-1]
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seq_length = input_shape[1]
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if position_ids is None:
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position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]
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if inputs_embeds is None:
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embeddings = self.word_embeddings(input_ids)
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else:
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embeddings = inputs_embeds
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if self.position_embedding_type == "absolute":
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position_embeddings = self.position_embeddings(position_ids)
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embeddings += position_embeddings
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embeddings = self.LayerNorm(embeddings)
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embeddings = self.dropout(embeddings)
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return embeddings
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class GitSelfAttention(nn.Module):
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def __init__(self, config, position_embedding_type=None, layer_idx=None):
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super().__init__()
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if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
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raise ValueError(
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f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
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f"heads ({config.num_attention_heads})"
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)
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self.layer_idx = layer_idx
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if layer_idx is None:
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logger.warning_once(
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f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
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"lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
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"when creating this class."
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)
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self.num_attention_heads = config.num_attention_heads
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self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
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self.all_head_size = self.num_attention_heads * self.attention_head_size
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self.image_patch_tokens = int((config.vision_config.image_size / config.vision_config.patch_size) ** 2 + 1)
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if config.num_image_with_embedding is not None:
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self.image_patch_tokens *= config.num_image_with_embedding
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self.query = nn.Linear(config.hidden_size, self.all_head_size)
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self.key = nn.Linear(config.hidden_size, self.all_head_size)
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self.value = nn.Linear(config.hidden_size, self.all_head_size)
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self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
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self.position_embedding_type = position_embedding_type or getattr(
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config, "position_embedding_type", "absolute"
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)
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if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
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self.max_position_embeddings = config.max_position_embeddings
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self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.FloatTensor] = None,
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head_mask: Optional[torch.FloatTensor] = None,
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past_key_value: Optional[Cache] = None,
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output_attentions: Optional[bool] = False,
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pixel_values_present: Optional[bool] = False,
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) -> tuple[torch.Tensor]:
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batch_size, seq_length, _ = hidden_states.shape
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query_layer = (
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self.query(hidden_states)
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.view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
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.transpose(1, 2)
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)
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cutoff = self.image_patch_tokens if pixel_values_present else 0
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key_layer = (
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self.key(hidden_states)
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.view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
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.transpose(1, 2)
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)
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value_layer = (
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self.value(hidden_states)
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.view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
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.transpose(1, 2)
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)
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if past_key_value is not None:
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# NOTE: like in other caches, we store the text component. In GIT it means we discard the image component.
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key_layer_past, value_layer_past = past_key_value.update(
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key_layer[:, :, cutoff:, :], value_layer[:, :, cutoff:, :], self.layer_idx
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)
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key_layer = torch.cat([key_layer[:, :, :cutoff, :], key_layer_past], dim=2)
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value_layer = torch.cat([value_layer[:, :, :cutoff, :], value_layer_past], dim=2)
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# Take the dot product between "query" and "key" to get the raw attention scores.
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attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
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if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
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query_length, key_length = query_layer.shape[2], key_layer.shape[2]
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if past_key_value is not None:
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position_ids_l = torch.tensor(key_length - 1, dtype=torch.long, device=hidden_states.device).view(
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-1, 1
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)
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else:
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position_ids_l = torch.arange(query_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
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position_ids_r = torch.arange(key_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
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distance = position_ids_l - position_ids_r
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positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
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positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility
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if self.position_embedding_type == "relative_key":
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relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
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attention_scores = attention_scores + relative_position_scores
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elif self.position_embedding_type == "relative_key_query":
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relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
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relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
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attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
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attention_scores = attention_scores / math.sqrt(self.attention_head_size)
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if attention_mask is not None:
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# Apply the attention mask is (precomputed for all layers in GitModel forward() function)
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attention_scores = attention_scores + attention_mask
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# Normalize the attention scores to probabilities.
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attention_probs = nn.functional.softmax(attention_scores, dim=-1)
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# This is actually dropping out entire tokens to attend to, which might
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# seem a bit unusual, but is taken from the original Transformer paper.
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attention_probs = self.dropout(attention_probs)
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# Mask heads if we want to
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if head_mask is not None:
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attention_probs = attention_probs * head_mask
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context_layer = torch.matmul(attention_probs, value_layer)
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context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
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new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
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context_layer = context_layer.view(new_context_layer_shape)
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return context_layer, attention_probs
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# Copied from transformers.models.bert.modeling_bert.BertSelfOutput
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class GitSelfOutput(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.dense = nn.Linear(config.hidden_size, config.hidden_size)
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self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
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hidden_states = self.dense(hidden_states)
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hidden_states = self.dropout(hidden_states)
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hidden_states = self.LayerNorm(hidden_states + input_tensor)
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return hidden_states
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GIT_SELF_ATTENTION_CLASSES = {
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"eager": GitSelfAttention,
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}
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class GitAttention(nn.Module):
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def __init__(self, config, position_embedding_type=None, layer_idx=None):
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super().__init__()
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self.self = GIT_SELF_ATTENTION_CLASSES[config._attn_implementation](
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config, position_embedding_type=position_embedding_type, layer_idx=layer_idx
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)
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self.output = GitSelfOutput(config)
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self.pruned_heads = set()
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# Copied from transformers.models.bert.modeling_bert.BertAttention.prune_heads
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def prune_heads(self, heads):
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if len(heads) == 0:
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return
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heads, index = find_pruneable_heads_and_indices(
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heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
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)
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# Prune linear layers
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self.self.query = prune_linear_layer(self.self.query, index)
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self.self.key = prune_linear_layer(self.self.key, index)
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self.self.value = prune_linear_layer(self.self.value, index)
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self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
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# Update hyper params and store pruned heads
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self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
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self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
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self.pruned_heads = self.pruned_heads.union(heads)
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.FloatTensor] = None,
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head_mask: Optional[torch.FloatTensor] = None,
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past_key_value: Optional[Cache] = None,
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output_attentions: Optional[bool] = False,
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pixel_values_present: Optional[bool] = False,
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) -> tuple[torch.Tensor]:
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attn_output, self_attn_weights = self.self(
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hidden_states,
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attention_mask,
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head_mask,
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past_key_value,
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output_attentions,
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pixel_values_present,
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)
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attention_output = self.output(attn_output, hidden_states)
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return attention_output, self_attn_weights
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# Copied from transformers.models.bert.modeling_bert.BertIntermediate
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class GitIntermediate(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
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if isinstance(config.hidden_act, str):
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self.intermediate_act_fn = ACT2FN[config.hidden_act]
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else:
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self.intermediate_act_fn = config.hidden_act
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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hidden_states = self.dense(hidden_states)
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hidden_states = self.intermediate_act_fn(hidden_states)
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return hidden_states
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# Copied from transformers.models.bert.modeling_bert.BertOutput
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class GitOutput(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
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self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
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hidden_states = self.dense(hidden_states)
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hidden_states = self.dropout(hidden_states)
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hidden_states = self.LayerNorm(hidden_states + input_tensor)
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return hidden_states
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class GitLayer(GradientCheckpointingLayer):
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def __init__(self, config, layer_idx=None):
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super().__init__()
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self.chunk_size_feed_forward = config.chunk_size_feed_forward
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self.seq_len_dim = 1
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self.attention = GitAttention(config, layer_idx=layer_idx)
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self.intermediate = GitIntermediate(config)
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self.output = GitOutput(config)
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.FloatTensor] = None,
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head_mask: Optional[torch.FloatTensor] = None,
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past_key_value: Optional[Cache] = None,
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output_attentions: Optional[bool] = False,
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pixel_values_present: Optional[bool] = False,
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) -> tuple[torch.Tensor]:
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# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
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attention_output, self_attention_weights = self.attention(
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hidden_states,
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attention_mask,
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head_mask,
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output_attentions=output_attentions,
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past_key_value=past_key_value,
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pixel_values_present=pixel_values_present,
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)
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layer_output = apply_chunking_to_forward(
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self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
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)
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return layer_output, self_attention_weights
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def feed_forward_chunk(self, attention_output):
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intermediate_output = self.intermediate(attention_output)
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layer_output = self.output(intermediate_output, attention_output)
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return layer_output
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class GitEncoder(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.config = config
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self.layer = nn.ModuleList([GitLayer(config, i) for i in range(config.num_hidden_layers)])
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self.gradient_checkpointing = False
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.FloatTensor] = None,
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head_mask: Optional[torch.FloatTensor] = None,
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past_key_values: Optional[Union[Cache, tuple[tuple[torch.FloatTensor]]]] = None,
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use_cache: Optional[bool] = None,
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output_attentions: Optional[bool] = False,
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output_hidden_states: Optional[bool] = False,
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pixel_values_present: Optional[bool] = False,
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return_dict: Optional[bool] = True,
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) -> Union[tuple[torch.Tensor], BaseModelOutputWithPast]:
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if self.gradient_checkpointing and self.training:
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if use_cache:
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logger.warning_once(
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"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
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)
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use_cache = False
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# TODO (joao): remove this exception in v4.56 -- it exists for users that try to pass a legacy cache
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if not isinstance(past_key_values, (type(None), Cache)):
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raise ValueError("The `past_key_values` should be either a `Cache` object or `None`.")
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if use_cache and past_key_values is None:
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past_key_values = DynamicCache()
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all_hidden_states = () if output_hidden_states else None
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all_self_attentions = () if output_attentions else None
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for i, layer_module in enumerate(self.layer):
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if output_hidden_states:
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all_hidden_states = all_hidden_states + (hidden_states,)
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layer_head_mask = head_mask[i] if head_mask is not None else None
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layer_outputs = layer_module(
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hidden_states,
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attention_mask,
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layer_head_mask,
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past_key_values,
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output_attentions,
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pixel_values_present,
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)
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hidden_states = layer_outputs[0]
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if output_attentions:
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all_self_attentions = all_self_attentions + (layer_outputs[1],)
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if output_hidden_states:
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all_hidden_states = all_hidden_states + (hidden_states,)
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if not return_dict:
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return tuple(
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v
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for v in [
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hidden_states,
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past_key_values,
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all_hidden_states,
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|
all_self_attentions,
|
|
]
|
|
if v is not None
|
|
)
|
|
return BaseModelOutputWithPast(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=past_key_values,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring
|
|
class GitPreTrainedModel(PreTrainedModel):
|
|
config: GitConfig
|
|
base_model_prefix = "git"
|
|
supports_gradient_checkpointing = True
|
|
|
|
def _init_weights(self, module):
|
|
"""Initialize the weights"""
|
|
if isinstance(module, GitVisionEmbeddings):
|
|
nn.init.normal_(module.class_embedding, mean=0.0, std=self.config.initializer_range)
|
|
nn.init.normal_(module.patch_embedding.weight, std=self.config.initializer_range)
|
|
nn.init.normal_(module.position_embedding.weight, std=self.config.initializer_range)
|
|
if isinstance(module, nn.Linear):
|
|
# 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)
|
|
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_range)
|
|
if module.padding_idx is not None:
|
|
module.weight.data[module.padding_idx].zero_()
|
|
elif isinstance(module, nn.LayerNorm):
|
|
module.bias.data.zero_()
|
|
module.weight.data.fill_(1.0)
|
|
|
|
|
|
# Copied from transformers.models.clip.modeling_clip.CLIPVisionEmbeddings with CLIP->Git
|
|
class GitVisionEmbeddings(nn.Module):
|
|
def __init__(self, config: GitVisionConfig):
|
|
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
|
|
|
|
|
|
class GitVisionMLP(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
|
|
|
|
|
|
# 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 GitVisionAttention(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.altclip.modeling_altclip.AltCLIPEncoderLayer with AltCLIP->GitVision
|
|
class GitVisionEncoderLayer(GradientCheckpointingLayer):
|
|
def __init__(self, config: GitVisionConfig):
|
|
super().__init__()
|
|
self.embed_dim = config.hidden_size
|
|
self.self_attn = GitVisionAttention(config)
|
|
self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
|
|
self.mlp = GitVisionMLP(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
|
|
|
|
|
|
# Copied from transformers.models.altclip.modeling_altclip.AltCLIPEncoder with AltCLIP->GitVision, CLIPConfig
|
|
class GitVisionEncoder(nn.Module):
|
|
"""
|
|
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
|
|
[`GitVisionEncoderLayer`].
|
|
|
|
Args:
|
|
config: GitVisionConfig
|
|
"""
|
|
|
|
def __init__(self, config: GitVisionConfig):
|
|
super().__init__()
|
|
self.config = config
|
|
self.layers = nn.ModuleList([GitVisionEncoderLayer(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
|
|
)
|
|
|
|
|
|
class GitVisionTransformer(nn.Module):
|
|
# Copied from transformers.models.altclip.modeling_altclip.AltCLIPVisionTransformer.__init__ with AltCLIPEncoder->GitVisionEncoder, AltCLIP->Git
|
|
def __init__(self, config: GitVisionConfig):
|
|
super().__init__()
|
|
self.config = config
|
|
embed_dim = config.hidden_size
|
|
|
|
self.embeddings = GitVisionEmbeddings(config)
|
|
self.pre_layrnorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
|
|
self.encoder = GitVisionEncoder(config)
|
|
self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
|
|
|
|
@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: Optional[bool] = False,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple, BaseModelOutput]:
|
|
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=return_dict,
|
|
)
|
|
|
|
last_hidden_state = encoder_outputs[0]
|
|
|
|
last_hidden_state = self.post_layernorm(last_hidden_state)
|
|
|
|
if not return_dict:
|
|
return (last_hidden_state,) + encoder_outputs[1:]
|
|
|
|
return BaseModelOutput(
|
|
last_hidden_state=last_hidden_state,
|
|
hidden_states=encoder_outputs.hidden_states,
|
|
attentions=encoder_outputs.attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
The vision model from CLIP, used in GIT, without any head or projection on top.
|
|
"""
|
|
)
|
|
class GitVisionModel(GitPreTrainedModel):
|
|
config: GitVisionConfig
|
|
main_input_name = "pixel_values"
|
|
|
|
# Copied from transformers.models.clip.modeling_clip.CLIPVisionModel.__init__ with CLIP->Git
|
|
def __init__(self, config: GitVisionConfig):
|
|
super().__init__(config)
|
|
self.vision_model = GitVisionTransformer(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, BaseModelOutput]:
|
|
r"""
|
|
Examples:
|
|
|
|
```python
|
|
>>> from PIL import Image
|
|
>>> import requests
|
|
>>> from transformers import AutoProcessor, GitVisionModel
|
|
|
|
>>> processor = AutoProcessor.from_pretrained("microsoft/git-base")
|
|
>>> model = GitVisionModel.from_pretrained("microsoft/git-base")
|
|
|
|
>>> 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
|
|
```"""
|
|
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,
|
|
)
|
|
|
|
|
|
class GitProjection(nn.Module):
|
|
def __init__(self, config: GitConfig):
|
|
super().__init__()
|
|
self.config = config
|
|
self.visual_projection = nn.Sequential(
|
|
nn.Linear(config.vision_config.hidden_size, config.hidden_size),
|
|
nn.LayerNorm(config.hidden_size, eps=config.vision_config.layer_norm_eps),
|
|
)
|
|
|
|
def forward(self, embeddings: torch.Tensor) -> torch.Tensor:
|
|
return self.visual_projection(embeddings)
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
The bare GIT Model transformer consisting of a CLIP image encoder and text decoder outputting raw hidden-states
|
|
"""
|
|
)
|
|
class GitModel(GitPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.config = config
|
|
|
|
self.embeddings = GitEmbeddings(config)
|
|
self.image_encoder = GitVisionModel(config.vision_config)
|
|
self.encoder = GitEncoder(config)
|
|
|
|
self.visual_projection = GitProjection(config)
|
|
|
|
if config.num_image_with_embedding is not None:
|
|
self.img_temperal_embedding = nn.ParameterList(
|
|
nn.Parameter(torch.zeros(1, 1, config.vision_config.hidden_size))
|
|
for _ in range(config.num_image_with_embedding)
|
|
)
|
|
|
|
# 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)
|
|
|
|
def _generate_future_mask(self, size: int, dtype: torch.dtype, device: torch.device) -> torch.Tensor:
|
|
# Default mask is for forward direction. Flip for backward direction.
|
|
mask = torch.triu(torch.ones(size, size, device=device, dtype=dtype), diagonal=1)
|
|
mask = mask.masked_fill(mask == 1, float("-inf"))
|
|
return mask
|
|
|
|
def create_attention_mask(self, tgt, memory, tgt_mask, past_key_values_length, memory_key_padding_mask=None):
|
|
num_tgt = tgt.shape[1]
|
|
num_memory = memory.shape[1]
|
|
device = tgt.device
|
|
dtype = tgt.dtype
|
|
top_left = torch.zeros((num_memory, num_memory), device=device, dtype=dtype)
|
|
top_right = torch.full(
|
|
(num_memory, num_tgt + past_key_values_length),
|
|
float("-inf"),
|
|
device=tgt.device,
|
|
dtype=dtype,
|
|
)
|
|
bottom_left = torch.zeros(
|
|
(num_tgt, num_memory),
|
|
dtype=dtype,
|
|
device=tgt_mask.device,
|
|
)
|
|
|
|
if past_key_values_length > 0:
|
|
tgt_mask = torch.zeros(
|
|
(tgt_mask.shape[0], tgt_mask.shape[0] + past_key_values_length),
|
|
dtype=dtype,
|
|
device=tgt_mask.device,
|
|
)
|
|
|
|
left = torch.cat((top_left, bottom_left), dim=0)
|
|
right = torch.cat((top_right, tgt_mask.to(dtype)), dim=0)
|
|
|
|
full_attention_mask = torch.cat((left, right), dim=1)[None, :]
|
|
|
|
if memory_key_padding_mask is None:
|
|
memory_key_padding_mask = torch.full((memory.shape[0], memory.shape[1]), fill_value=False, device=device)
|
|
# if it is False, it means valid. That is, it is not a padding
|
|
if memory_key_padding_mask.dtype != torch.bool:
|
|
raise ValueError("Memory key padding mask must be a boolean tensor.")
|
|
zero_negative_infinity = torch.zeros_like(memory_key_padding_mask, dtype=tgt.dtype)
|
|
zero_negative_infinity[memory_key_padding_mask] = float("-inf")
|
|
full_attention_mask = full_attention_mask.expand(
|
|
(memory_key_padding_mask.shape[0], num_memory + num_tgt, num_memory + past_key_values_length + num_tgt)
|
|
)
|
|
full_attention_mask = full_attention_mask.clone()
|
|
origin_left = full_attention_mask[:, :, :num_memory]
|
|
update = zero_negative_infinity[:, None, :]
|
|
full_attention_mask[:, :, :num_memory] = origin_left + update
|
|
|
|
# add axis for multi-head
|
|
full_attention_mask = full_attention_mask[:, None, :, :]
|
|
|
|
return full_attention_mask
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
pixel_values: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
past_key_values: Optional[Union[Cache, list[torch.FloatTensor]]] = None,
|
|
use_cache: 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[torch.Tensor], BaseModelOutputWithPooling]:
|
|
r"""
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import AutoProcessor, AutoModel
|
|
>>> import requests
|
|
>>> from PIL import Image
|
|
|
|
>>> processor = AutoProcessor.from_pretrained("microsoft/git-base")
|
|
>>> model = AutoModel.from_pretrained("microsoft/git-base")
|
|
|
|
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
|
|
>>> image = Image.open(requests.get(url, stream=True).raw)
|
|
|
|
>>> text = "this is an image of two cats"
|
|
|
|
>>> inputs = processor(images=image, text=text, return_tensors="pt")
|
|
|
|
>>> outputs = model(**inputs)
|
|
>>> last_hidden_state = outputs.last_hidden_state
|
|
```"""
|
|
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
|
|
)
|
|
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
|
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")
|
|
|
|
seq_length = input_shape[1]
|
|
|
|
# past_key_values_length
|
|
past_key_values_length = 0
|
|
if past_key_values is not None:
|
|
past_key_values_length = (
|
|
past_key_values.get_seq_length()
|
|
if not isinstance(past_key_values, Cache)
|
|
else past_key_values.get_seq_length()
|
|
)
|
|
|
|
# Prepare head mask if needed
|
|
# 1.0 in head_mask indicate we keep the head
|
|
# attention_probs has shape bsz x n_heads x N x N
|
|
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
|
|
# 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)
|
|
|
|
projected_visual_features = None
|
|
if pixel_values is not None:
|
|
if pixel_values.ndim == 4:
|
|
# here we assume pixel_values is of shape (batch_size, num_channels, height, width)
|
|
visual_features = self.image_encoder(
|
|
pixel_values, interpolate_pos_encoding=interpolate_pos_encoding
|
|
).last_hidden_state
|
|
|
|
elif pixel_values.ndim == 5:
|
|
# here we assume pixel_values is of shape (batch_size, num_frames, num_channels, height, width)
|
|
visual_features = []
|
|
for frame_idx in range(pixel_values.shape[1]):
|
|
visual_features_frame = self.image_encoder(
|
|
pixel_values[:, frame_idx, :, :], interpolate_pos_encoding=interpolate_pos_encoding
|
|
).last_hidden_state
|
|
visual_features_frame += self.img_temperal_embedding[frame_idx]
|
|
visual_features.append(visual_features_frame)
|
|
|
|
# finally, concatenate all features along sequence dimension
|
|
visual_features = torch.cat(visual_features, dim=1)
|
|
|
|
else:
|
|
raise ValueError("pixel_values must be of rank 4 or 5")
|
|
|
|
projected_visual_features = self.visual_projection(visual_features)
|
|
|
|
embedding_output = self.embeddings(
|
|
input_ids=input_ids,
|
|
position_ids=position_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
past_key_values_length=past_key_values_length,
|
|
)
|
|
|
|
if projected_visual_features is None:
|
|
projected_visual_features = torch.zeros(
|
|
(embedding_output.shape[0], 0, embedding_output.shape[2]),
|
|
dtype=embedding_output.dtype,
|
|
device=embedding_output.device,
|
|
)
|
|
|
|
# Repeat visual features to match embedding batch size.
|
|
projected_visual_features = projected_visual_features.repeat(
|
|
embedding_output.size(0) // projected_visual_features.size(0), 1, 1
|
|
)
|
|
|
|
# concatenate patch token and text token embeddings
|
|
hidden_states = torch.cat((projected_visual_features, embedding_output), dim=1)
|
|
|
|
# By default, an additive causal mask is created
|
|
# for masking the future (one direction).
|
|
tgt_mask = self._generate_future_mask(seq_length, embedding_output.dtype, embedding_output.device)
|
|
|
|
# Create an attention mask of shape (batch_size, 1, tgt_seq_len, src_seq_len)
|
|
combined_attention_mask = self.create_attention_mask(
|
|
tgt=embedding_output,
|
|
memory=projected_visual_features,
|
|
tgt_mask=tgt_mask,
|
|
past_key_values_length=past_key_values_length,
|
|
)
|
|
|
|
if attention_mask is not None:
|
|
# if the user provides an attention mask, we add it to the default one
|
|
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
|
|
expanded_attn_mask = _prepare_4d_attention_mask(
|
|
attention_mask, embedding_output.dtype, tgt_len=input_shape[-1]
|
|
).to(embedding_output.device)
|
|
if past_key_values_length > 0:
|
|
expanded_attn_mask = expanded_attn_mask[:, :, -past_key_values_length:, :]
|
|
else:
|
|
combined_attention_mask[:, :, -input_shape[1] :, -input_shape[1] :] += expanded_attn_mask
|
|
|
|
encoder_outputs = self.encoder(
|
|
hidden_states,
|
|
attention_mask=combined_attention_mask,
|
|
head_mask=head_mask,
|
|
past_key_values=past_key_values,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
pixel_values_present=pixel_values is not None,
|
|
)
|
|
sequence_output = encoder_outputs[0]
|
|
|
|
if not return_dict:
|
|
return (sequence_output,) + encoder_outputs[1:]
|
|
|
|
return BaseModelOutputWithPast(
|
|
last_hidden_state=sequence_output,
|
|
past_key_values=encoder_outputs.past_key_values,
|
|
hidden_states=encoder_outputs.hidden_states,
|
|
attentions=encoder_outputs.attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
GIT Model with a `language modeling` head on top for autoregressive language modeling.
|
|
"""
|
|
)
|
|
class GitForCausalLM(GitPreTrainedModel, GenerationMixin):
|
|
_tied_weights_keys = ["output.weight"]
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
self.git = GitModel(config)
|
|
self.output = nn.Linear(config.hidden_size, config.vocab_size)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_output_embeddings(self):
|
|
return self.output
|
|
|
|
def set_output_embeddings(self, new_embeddings):
|
|
self.output = new_embeddings
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
pixel_values: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
labels: Optional[torch.Tensor] = None,
|
|
past_key_values: Optional[Union[Cache, list[torch.Tensor]]] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
interpolate_pos_encoding: bool = False,
|
|
return_dict: Optional[bool] = None,
|
|
**kwargs,
|
|
) -> Union[tuple[torch.Tensor], CausalLMOutputWithPast]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
|
|
`[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are
|
|
ignored (masked), the loss is only computed for the tokens with labels n `[0, ..., config.vocab_size]`
|
|
|
|
Examples:
|
|
|
|
Image captioning example:
|
|
|
|
```python
|
|
>>> from transformers import AutoProcessor, AutoModelForCausalLM
|
|
>>> import requests
|
|
>>> from PIL import Image
|
|
|
|
>>> processor = AutoProcessor.from_pretrained("microsoft/git-base-coco")
|
|
>>> model = AutoModelForCausalLM.from_pretrained("microsoft/git-base-coco")
|
|
|
|
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
|
|
>>> image = Image.open(requests.get(url, stream=True).raw)
|
|
|
|
>>> pixel_values = processor(images=image, return_tensors="pt").pixel_values
|
|
|
|
>>> generated_ids = model.generate(pixel_values=pixel_values, max_length=50)
|
|
>>> generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
|
|
>>> print(generated_caption)
|
|
two cats sleeping on a pink blanket next to remotes.
|
|
```
|
|
|
|
Visual question answering (VQA) example:
|
|
|
|
```python
|
|
>>> from transformers import AutoProcessor, AutoModelForCausalLM
|
|
>>> from huggingface_hub import hf_hub_download
|
|
>>> from PIL import Image
|
|
|
|
>>> processor = AutoProcessor.from_pretrained("microsoft/git-base-textvqa")
|
|
>>> model = AutoModelForCausalLM.from_pretrained("microsoft/git-base-textvqa")
|
|
|
|
>>> file_path = hf_hub_download(repo_id="nielsr/textvqa-sample", filename="bus.png", repo_type="dataset")
|
|
>>> image = Image.open(file_path).convert("RGB")
|
|
|
|
>>> pixel_values = processor(images=image, return_tensors="pt").pixel_values
|
|
|
|
>>> question = "what does the front of the bus say at the top?"
|
|
|
|
>>> input_ids = processor(text=question, add_special_tokens=False).input_ids
|
|
>>> input_ids = [processor.tokenizer.cls_token_id] + input_ids
|
|
>>> input_ids = torch.tensor(input_ids).unsqueeze(0)
|
|
|
|
>>> generated_ids = model.generate(pixel_values=pixel_values, input_ids=input_ids, max_length=50)
|
|
>>> print(processor.batch_decode(generated_ids, skip_special_tokens=True))
|
|
['what does the front of the bus say at the top? special']
|
|
```
|
|
|
|
Video captioning example:
|
|
|
|
```python
|
|
>>> import av
|
|
>>> import numpy as np
|
|
>>> from PIL import Image
|
|
>>> from huggingface_hub import hf_hub_download
|
|
>>> from transformers import AutoProcessor, AutoModelForCausalLM
|
|
|
|
>>> processor = AutoProcessor.from_pretrained("microsoft/git-base-vatex")
|
|
>>> model = AutoModelForCausalLM.from_pretrained("microsoft/git-base-vatex")
|
|
|
|
>>> # set seed for reproducibility
|
|
>>> np.random.seed(45)
|
|
|
|
|
|
>>> def read_video_pyav(container, indices):
|
|
... '''
|
|
... Decode the video with PyAV decoder.
|
|
... Args:
|
|
... container (`av.container.input.InputContainer`): PyAV container.
|
|
... indices (`list[int]`): List of frame indices to decode.
|
|
... Returns:
|
|
... result (np.ndarray): np array of decoded frames of shape (num_frames, height, width, 3).
|
|
... '''
|
|
... frames = []
|
|
... container.seek(0)
|
|
... start_index = indices[0]
|
|
... end_index = indices[-1]
|
|
... for i, frame in enumerate(container.decode(video=0)):
|
|
... if i > end_index:
|
|
... break
|
|
... if i >= start_index and i in indices:
|
|
... frames.append(frame)
|
|
... return np.stack([x.to_ndarray(format="rgb24") for x in frames])
|
|
|
|
|
|
>>> def sample_frame_indices(clip_len, frame_sample_rate, seg_len):
|
|
... '''
|
|
... Sample a given number of frame indices from the video.
|
|
... Args:
|
|
... clip_len (`int`): Total number of frames to sample.
|
|
... frame_sample_rate (`int`): Sample every n-th frame.
|
|
... seg_len (`int`): Maximum allowed index of sample's last frame.
|
|
... Returns:
|
|
... indices (`list[int]`): List of sampled frame indices
|
|
... '''
|
|
... converted_len = int(clip_len * frame_sample_rate)
|
|
... end_idx = np.random.randint(converted_len, seg_len)
|
|
... start_idx = end_idx - converted_len
|
|
... indices = np.linspace(start_idx, end_idx, num=clip_len)
|
|
... indices = np.clip(indices, start_idx, end_idx - 1).astype(np.int64)
|
|
... return indices
|
|
|
|
|
|
>>> # load video
|
|
>>> file_path = hf_hub_download(
|
|
... repo_id="nielsr/video-demo", filename="eating_spaghetti.mp4", repo_type="dataset"
|
|
... )
|
|
>>> container = av.open(file_path)
|
|
|
|
>>> # sample frames
|
|
>>> num_frames = model.config.num_image_with_embedding
|
|
>>> indices = sample_frame_indices(
|
|
... clip_len=num_frames, frame_sample_rate=4, seg_len=container.streams.video[0].frames
|
|
... )
|
|
>>> frames = read_video_pyav(container, indices)
|
|
|
|
>>> pixel_values = processor(images=list(frames), return_tensors="pt").pixel_values
|
|
|
|
>>> generated_ids = model.generate(pixel_values=pixel_values, max_length=50)
|
|
|
|
>>> print("Generated caption:", processor.batch_decode(generated_ids, skip_special_tokens=True))
|
|
Generated caption: ['a woman is sitting at a table and she is talking about the food she is holding.']
|
|
```
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
if labels is not None:
|
|
use_cache = False
|
|
|
|
outputs = self.git(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
pixel_values=pixel_values,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
past_key_values=past_key_values,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
interpolate_pos_encoding=interpolate_pos_encoding,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
sequence_output = outputs[0]
|
|
logits = self.output(sequence_output)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
# we are doing next-token prediction; shift prediction scores and input ids by one
|
|
num_image_tokens = self.git.encoder.layer[0].attention.self.image_patch_tokens
|
|
shifted_logits = logits[:, num_image_tokens:-1, :].contiguous()
|
|
labels = labels[:, 1:].contiguous()
|
|
loss = self.loss_function(
|
|
shifted_logits.view(-1, self.config.vocab_size),
|
|
labels.view(-1),
|
|
vocab_size=self.config.vocab_size,
|
|
**kwargs,
|
|
)
|
|
|
|
if not return_dict:
|
|
output = (logits,) + outputs[1:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return CausalLMOutputWithPast(
|
|
loss=loss,
|
|
logits=logits,
|
|
past_key_values=outputs.past_key_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
def prepare_inputs_for_generation(
|
|
self, input_ids, past_key_values=None, attention_mask=None, use_cache=None, **kwargs
|
|
):
|
|
# Overwritten -- `git` has special cache handling and doesn't support generating from `inputs_embeds` atm
|
|
|
|
# cut decoder_input_ids if past_key_values is used
|
|
if past_key_values is not None:
|
|
past_length = past_key_values.get_seq_length()
|
|
|
|
# Some generation methods already pass only the last input ID
|
|
if input_ids.shape[1] > past_length:
|
|
remove_prefix_length = past_length
|
|
else:
|
|
# Default to old behavior: keep only final ID
|
|
remove_prefix_length = input_ids.shape[1] - 1
|
|
|
|
input_ids = input_ids[:, remove_prefix_length:]
|
|
|
|
# if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
|
|
input_shape = input_ids.shape
|
|
if attention_mask is None:
|
|
attention_mask = input_ids.new_ones(input_shape)
|
|
|
|
return {
|
|
"input_ids": input_ids,
|
|
"attention_mask": attention_mask,
|
|
"pixel_values": kwargs.get("pixel_values", None),
|
|
"past_key_values": past_key_values,
|
|
"use_cache": use_cache,
|
|
}
|
|
|
|
|
|
__all__ = ["GitForCausalLM", "GitModel", "GitPreTrainedModel", "GitVisionModel"]
|