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ai-content-maker/.venv/Lib/site-packages/transformers/models/flava/modeling_flava.py

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# coding=utf-8
# Copyright 2022 Meta Platforms authors and The HuggingFace 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 FLAVA model."""
import collections
import math
from collections import OrderedDict
from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Set, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
from ...modeling_utils import PreTrainedModel, find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import (
ModelOutput,
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from .configuration_flava import (
FlavaConfig,
FlavaImageCodebookConfig,
FlavaImageConfig,
FlavaMultimodalConfig,
FlavaTextConfig,
)
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "facebook/flava-full"
# Codebook docstring
_CHECKPOINT_FOR_CODEBOOK_DOC = "facebook/flava-image-codebook"
_CONFIG_CLASS_FOR_IMAGE_MODEL_DOC = "FlavaImageConfig"
_CONFIG_CLASS_FOR_TEXT_MODEL_DOC = "FlavaTextConfig"
_CONFIG_CLASS_FOR_MULTIMODAL_MODEL_DOC = "FlavaMultimodalConfig"
_EXPECTED_IMAGE_OUTPUT_SHAPE = [1, 197, 768]
from ..deprecated._archive_maps import FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402
FLAVA_CODEBOOK_PRETRAINED_MODEL_ARCHIVE_LIST = ["facebook/flava-image-codebook"]
LOGIT_SCALE_CLAMP_MIN = 0
LOGIT_SCALE_CLAMP_MAX = 4.6052
FlavaPossibleConfigs = Union[FlavaTextConfig, FlavaImageConfig, FlavaMultimodalConfig]
@dataclass
class FlavaModelOutput(ModelOutput):
"""
Output from FlavaModel containing embeddings and outputs from individual encoders.
Note that `image_embeddings` and `text_embeddigns` returned are similar to pooled output returned from a
transformer. If you want embeddings for contrastive loss or retrieval use a FLAVA model's `image_projection` and
`text_projection` layers on `image_embeddings` and `text_embeddings` respectively.
Args:
image_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `pixel_values` are present):
The image embeddings which are basically the pooled output of [`FlavaImageModel`].
image_output (`BaseModelOutputWithPooling`, *optional*, returned when `pixel_values` are present):
The output of the [`FlavaImageModel`].
text_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `input_ids` are present):
The text embeddings which are basically the pooled output of [`FlavaTextModel`].
text_output (`BaseModelOutputWithPooling`, *optional*, returned when `input_ids` are present):
The output of the [`FlavaTextModel`].
multimodal_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `input_ids` and `pixel_values` are present and `skip_multimodal_encoder` is `None` or `False`):
The multimodal embeddings which are basically the pooled output of [`FlavaTextModel`].
multimodal_output (`BaseModelOutputWithPooling`, returned when `input_ids` and `pixel_values` are present and `skip_multimodal_encoder` is `None` or `False`):
The output of the [`FlavaMultimodalModel`].
"""
image_embeddings: Optional[torch.FloatTensor] = None
image_output: Optional[BaseModelOutputWithPooling] = None
text_embeddings: Optional[torch.FloatTensor] = None
text_output: Optional[BaseModelOutputWithPooling] = None
multimodal_embeddings: Optional[torch.FloatTensor] = None
multimodal_output: Optional[BaseModelOutputWithPooling] = None
def to_tuple(self) -> Tuple[Any]:
return tuple(
self[k] if k not in ["text_output", "image_output", "multimodal_output"] else getattr(self, k).to_tuple()
for k in self.keys()
)
@dataclass
class FlavaLosses(ModelOutput):
"""Class representing pretraining losses from FLAVA model
Args:
mim (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `mim_labels` and `pixel_values` are present, `input_ids_masked` is absent and `mim_weight` > 0.:
Masked Image Modeling loss as used in BeIT calculated only for unimodal image data.
mlm (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `mlm_labels` and `input_ids_masked` are present, `pixel_values` is absent and `mlm_weight` > 0.:
Masked Language Modeling loss as used in BERT calculated only for unimodal text data.
itm (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `itm_labels`, `input_ids_masked`, `pixel_values` are present and `itm_weight` > 0.:
Image Text Matching (ITM) loss calculated for paired image-text data. Note that ITM loss is calculated on
masked pairs in FLAVA.
global_contrastive (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `input_ids` and `pixel_values` are present and `global_contrastive_weight` > 0.:
Contrastive loss for image-text similarity similar to CLIP but calculated globally for paired image-text
data. This is calculated on unmasked images and texts.
mmm_image (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `mim_labels`, `pixel_values` and `input_ids_masked` are present and `mmm_image_weight` > 0.:
Masked Multimodal Modeling loss's image component calculated on paired image-text data.
mmm_text (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `mlm_labels`, `pixel_values` and `input_ids_masked` are present and `mmm_text_weight` > 0.:
Masked Multimodal Modeling loss's text component calculated on paired image-text data.
"""
mim: Optional[torch.FloatTensor] = None
mlm: Optional[torch.FloatTensor] = None
itm: Optional[torch.FloatTensor] = None
global_contrastive: Optional[torch.FloatTensor] = None
mmm_image: Optional[torch.FloatTensor] = None
mmm_text: Optional[torch.FloatTensor] = None
def all_none(self) -> bool:
all_none = True
for v in self.values():
if v is not None:
all_none = False
break
return all_none
@dataclass
class FlavaForPreTrainingOutput(ModelOutput):
"""
Output from FlavaForPreTraining containing embeddings, and outputs from individual encoders.
Note that `image_embeddings` and `text_embeddings` returned are similar to pooled output returned from a
transformer. If you want embeddings for contrastive loss or retrieval use a FLAVA model's `image_projection` and
`text_projection` layers on `image_embeddings` and `text_embeddings` respectively.
Args:
loss (`torch.FloatTensor`, *optional*, returned when `return_loss` is True):
Total loss calculated for this model.
loss_info (`FlavaLosses`):
Detailed info for FLAVA Pretraining losses. Check `FlavaLosses` class description for the information on
the keys.
image_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `pixel_values` are present):
The image embeddings which are basically the pooled output of [`FlavaImageModel`].
image_output (`BaseModelOutputWithPooling`, *optional*, returned when `pixel_values` are present):
The output of the [`FlavaImageModel`].
text_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `input_ids` are present):
The text embeddings which are basically the pooled output of [`FlavaTextModel`].
text_output (`BaseModelOutputWithPooling`, *optional*, returned when `input_ids` are present):
The output of the [`FlavaTextModel`].
multimodal_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `input_ids` and `pixel_values` are present and `skip_unmasked_multimodal_encoder` is `None` or `False`):
The multimodal embeddings which are basically the pooled output of [`FlavaTextModel`].
multimodal_output (`BaseModelOutputWithPooling`, returned when `input_ids` and `pixel_values` are present and `skip_unmasked_multimodal_encoder` is `None` or `False`):
The output of the [`FlavaMultimodalModel`].
image_masked_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `pixel_values` are present):
The image embeddings which are basically the pooled output of [`FlavaImageModel`]. Uses `bool_masked_pos`
to create masked images.
image_masked_output (`BaseModelOutputWithPooling`, *optional*, returned when `pixel_values` are present):
The output of the [`FlavaImageModel`]. Uses `bool_masked_pos` to create masked images.
text_masked_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `input_ids_masked` are present):
The text embeddings which are basically the pooled output of [`FlavaTextModel`].
text_masked_output (`BaseModelOutputWithPooling`, *optional*, returned when `input_ids_masked` are present):
The output of the [`FlavaTextModel`].
multimodal_masked_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `input_ids` and `pixel_values` are present):
The multimodal embeddings which are basically the pooled output of [`FlavaTextModel`].
multimodal_masked_output (`BaseModelOutputWithPooling`, returned when `input_ids_masked` and `pixel_values` are present):
The output of the [`FlavaMultimodalModel`].
mim_logits (`torch.FloatTensor` of shape `(batch_size, num_image_patches, image_vocab_size)` or of shape `(total_masked_patches, image_vocab_size)` , *optional*, returned when `pixel_values` are present and `input_ids_masked` are not):
The logits for MIM unimodal loss. Uses `book_masked_pos` to get masked patches. The flattened output is
returned when `bool_masked_pos` has some of the patches masked.
mlm_logits (`torch.FloatTensor` of shape `(batch_size, text_seq_length, text_vocab_size)` or of shape `(total_masked_seq_length, text_vocab_size)`, *optional*, returned when `input_ids_masked` are present and `pixel_values` are not):
The logits for MLM unimodal loss. The flattened output is returned when `input_ids_masked` has some of
the tokens masked.
itm_logits (`torch.FloatTensor` of shape `(batch_size, 2)`, *optional*, returned when `input_ids_masked` and `pixel_values` are present):
The logits for ITM loss. Note that ITM loss is calculated on masked pairs in FLAVA.
mmm_image_logits (`torch.FloatTensor` of shape `(batch_size, num_image_patches, image_vocab_size)` or of shape`(total_masked_patches, image_vocab_size)`, *optional*, returned when `pixel_values` and `input_ids_masked` are present):
The logits for MMM image multimodal loss. Uses `book_masked_pos` to get masked patches. The flattened
output is returned when `bool_masked_pos` has some of the patches masked.
mmm_text_logits (`torch.FloatTensor` of shape `(batch_size, text_seq_length, text_vocab_size)` or of shape `(`(total_masked_seq_length, text_vocab_size)`), *optional*, returned when `pixel_values` and `input_ids_masked` are present):
The logits for MMM text multimodal loss. The flattened output is returned when `input_ids_masked` has
some of the tokens masked.
contrastive_logits_per_image (`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`):
The scaled dot product scores between `image_embeddings` and `text_embeddings` but passed through FLAVA's
`image_projection` and `text_projection` layers respectively. This represents the image-text similarity
scores. This is calculated on unmasked images and texts.
contrastive_logits_per_text (`torch.FloatTensor` of shape `(text_batch_size, image_batch_size)`):
The scaled dot product scores between `text_embeddings` and `image_embeddings` but passed through FLAVA's
`text_projection` and `image_projection` layers respectively. This is calculated on unmasked images and
texts.
"""
loss: Optional[torch.FloatTensor] = None
loss_info: FlavaLosses = None
image_embeddings: Optional[torch.FloatTensor] = None
image_output: Optional[BaseModelOutputWithPooling] = None
text_embeddings: Optional[torch.FloatTensor] = None
text_output: Optional[BaseModelOutputWithPooling] = None
multimodal_embeddings: Optional[torch.FloatTensor] = None
multimodal_output: Optional[BaseModelOutputWithPooling] = None
image_masked_embeddings: Optional[torch.FloatTensor] = None
image_masked_output: Optional[BaseModelOutputWithPooling] = None
text_masked_embeddings: Optional[torch.FloatTensor] = None
text_masked_output: Optional[BaseModelOutputWithPooling] = None
multimodal_masked_embeddings: Optional[torch.FloatTensor] = None
multimodal_masked_output: Optional[BaseModelOutputWithPooling] = None
mim_logits: Optional[torch.FloatTensor] = None
mlm_logits: Optional[torch.FloatTensor] = None
itm_logits: Optional[torch.FloatTensor] = None
contrastive_logits_per_image: Optional[torch.FloatTensor] = None
contrastive_logits_per_text: Optional[torch.FloatTensor] = None
mmm_image_logits: Optional[torch.FloatTensor] = None
mmm_text_logits: Optional[torch.FloatTensor] = None
def to_tuple(self) -> Tuple[Any]:
transformer_outputs = [
"text_output",
"image_output",
"multimodal_output",
"text_masked_output",
"image_masked_output",
"multimodal_masked_output",
]
return tuple(self[k] if k not in transformer_outputs else getattr(self, k).to_tuple() for k in self.keys())
# Based on timm implementation, which can be found here:
# https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/image_transformer.py
class FlavaImageEmbeddings(nn.Module):
"""
Construct the CLS token, position and patch embeddings. Optionally, also the mask token.
"""
def __init__(self, config: FlavaImageConfig, use_mask_token: bool = False) -> None:
super().__init__()
use_mask_token = use_mask_token or config.mask_token
self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
self.mask_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size)) if use_mask_token else None
self.patch_embeddings = PatchEmbeddings(
image_size=config.image_size,
patch_size=config.patch_size,
num_channels=config.num_channels,
embed_dim=config.hidden_size,
)
num_patches = self.patch_embeddings.num_patches
self.position_embeddings = nn.Parameter(torch.zeros(1, num_patches + 1, config.hidden_size))
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.config = config
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.
Source:
https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/image_transformer.py#L174
"""
npatch = embeddings.shape[1] - 1
num_pos = self.position_embeddings.shape[1] - 1
if npatch == num_pos and height == width:
return self.position_embeddings
class_pos_embed = self.position_embeddings[:, 0]
patch_pos_embed = self.position_embeddings[:, 1:]
dim = embeddings.shape[-1]
num_h_patches = height // self.config.patch_size
num_w_patches = width // self.config.patch_size
# we add a small number to avoid floating point error in the interpolation
# see discussion at https://github.com/facebookresearch/dino/issues/8
num_h_patches, num_w_patches = num_h_patches + 0.1, num_w_patches + 0.1
patch_pos_embed = nn.functional.interpolate(
patch_pos_embed.reshape(1, int(math.sqrt(num_pos)), int(math.sqrt(num_pos)), dim).permute(0, 3, 1, 2),
scale_factor=(num_h_patches / math.sqrt(num_pos), num_w_patches / math.sqrt(num_pos)),
mode="bicubic",
align_corners=False,
)
if int(num_h_patches) != patch_pos_embed.shape[-2] or int(num_w_patches) != patch_pos_embed.shape[-1]:
raise ValueError(
f"Number of patches for images ({int(num_h_patches), int(num_w_patches)}) don't match the "
f"shape of position embedding ({patch_pos_embed.shape[-2], patch_pos_embed.shape[-1]})"
)
patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1)
def forward(
self,
pixel_values: torch.Tensor,
bool_masked_pos: Optional[torch.BoolTensor] = None,
interpolate_pos_encoding: bool = False,
) -> torch.Tensor:
batch_size, num_channels, height, width = pixel_values.shape
embeddings = self.patch_embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
batch_size, seq_len, _ = embeddings.size()
if bool_masked_pos is not None:
mask_tokens = self.mask_token.expand(batch_size, seq_len, -1)
# B X H X W = B X HW
if bool_masked_pos.dim() == 3:
bool_masked_pos = bool_masked_pos.view(bool_masked_pos.size(0), -1)
# replace the masked visual tokens by mask_tokens
mask = bool_masked_pos.unsqueeze(-1).type_as(mask_tokens)
embeddings = embeddings * (1.0 - mask) + mask_tokens * mask
# add the [CLS] token to the embedded patch tokens
cls_tokens = self.cls_token.expand(batch_size, -1, -1)
embeddings = torch.cat((cls_tokens, embeddings), dim=1)
# add positional encoding to each token
if interpolate_pos_encoding:
embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
else:
embeddings = embeddings + self.position_embeddings
embeddings = self.dropout(embeddings)
return embeddings
# Based on timm implementation, which can be found here:
# https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/image_transformer.py
class PatchEmbeddings(nn.Module):
"""
Image to Patch Embedding.
"""
def __init__(
self,
image_size: int = 224,
patch_size: Union[int, Tuple[int, int]] = 16,
num_channels: int = 3,
embed_dim: int = 768,
):
super().__init__()
if not isinstance(image_size, collections.abc.Iterable):
image_size = (image_size, image_size)
if not isinstance(patch_size, collections.abc.Iterable):
patch_size = (patch_size, patch_size)
num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.image_size = image_size
self.patch_size = patch_size
self.num_patches = num_patches
self.projection = nn.Conv2d(num_channels, embed_dim, kernel_size=patch_size, stride=patch_size)
def forward(self, pixel_values: torch.Tensor, interpolate_pos_encoding: bool = False) -> torch.Tensor:
batch_size, num_channels, height, width = pixel_values.shape
if not interpolate_pos_encoding:
if height != self.image_size[0] or width != self.image_size[1]:
raise ValueError(
f"Input image size ({height}*{width}) doesn't match model"
f" ({self.image_size[0]}*{self.image_size[1]})."
)
x = self.projection(pixel_values).flatten(2).transpose(1, 2)
return x
class FlavaTextEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
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
)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
):
input_shape = input_ids.size()
seq_length = input_shape[1]
if position_ids is None:
position_ids = self.position_ids[:, :seq_length]
# 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)
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
class FlavaSelfAttention(nn.Module):
def __init__(self, config: FlavaPossibleConfigs) -> 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, bias=config.qkv_bias)
self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
mixed_query_layer = self.query(hidden_states)
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
query_layer = self.transpose_for_scores(mixed_query_layer)
# 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))
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 BertModel forward() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
# 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
class FlavaSelfOutput(nn.Module):
"""
The residual connection is defined in FlavaLayer (same as ViTLayer) instead of here (as is the case with other
models), due to the layernorm applied before each block.
"""
def __init__(self, config: FlavaPossibleConfigs) -> None:
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
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)
return hidden_states
class FlavaAttention(nn.Module):
def __init__(self, config: FlavaPossibleConfigs) -> None:
super().__init__()
self.attention = FlavaSelfAttention(config)
self.output = FlavaSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads: Set[int]) -> None:
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(
heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads
)
# Prune linear layers
self.attention.query = prune_linear_layer(self.attention.query, index)
self.attention.key = prune_linear_layer(self.attention.key, index)
self.attention.value = prune_linear_layer(self.attention.value, index)
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
# Update hyper params and store pruned heads
self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads)
self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
self_outputs = self.attention(
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
class FlavaIntermediate(nn.Module):
def __init__(self, config: FlavaPossibleConfigs) -> None:
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
# Copied from transformers.models.vit.modeling_vit.ViTIntermediate.forward
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
class FlavaOutput(nn.Module):
def __init__(self, config: FlavaPossibleConfigs) -> None:
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
# Copied from transformers.models.vit.modeling_vit.ViTOutput.forward
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 = hidden_states + input_tensor
return hidden_states
class FlavaLayer(nn.Module):
"""This corresponds to the Block class in the timm implementation."""
def __init__(self, config: FlavaPossibleConfigs) -> None:
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = FlavaAttention(config)
self.intermediate = FlavaIntermediate(config)
self.output = FlavaOutput(config)
# TODO: Check fp32 layer norm possiblity
self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
self_attention_outputs = self.attention(
self.layernorm_before(hidden_states), # in ViT, layernorm is applied before self-attention
attention_mask=attention_mask,
head_mask=head_mask,
output_attentions=output_attentions,
)
attention_output = self_attention_outputs[0]
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
# first residual connection
hidden_states = attention_output + hidden_states
# in ViT, layernorm is also applied after self-attention
layer_output = self.layernorm_after(hidden_states)
layer_output = self.intermediate(layer_output)
# second residual connection is done here
layer_output = self.output(layer_output, hidden_states)
outputs = (layer_output,) + outputs
return outputs
class FlavaEncoder(nn.Module):
def __init__(self, config: FlavaConfig) -> None:
super().__init__()
self.config = config
self.layer = nn.ModuleList([FlavaLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
) -> Union[tuple, 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
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
layer_module.__call__,
hidden_states,
attention_mask,
layer_head_mask,
output_attentions,
)
else:
layer_outputs = layer_module(hidden_states, attention_mask, layer_head_mask, output_attentions)
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,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions
)
class FlavaPooler(nn.Module):
def __init__(self, config: FlavaPossibleConfigs):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states: 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
FLAVA_START_DOCSTRING = r"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`{config}`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
FLAVA_INPUTS_DOCSTRING_COMMON = r"""
attention_mask (`torch.FloatTensor` of shape `({0})`, *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)
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
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.
"""
FLAVA_IMAGE_INPUTS_DOCSTRING_BASE = r"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`FlavaImageProcessor.__call__`] for details.
bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, image_num_patches)`):
Boolean masked positions. Indicates which patches are masked (1) and which aren't (0).
interpolate_pos_encoding (`bool`, *optional*):
Whether to interpolate the pre-trained position encodings.
"""
FLAVA_IMAGE_INPUTS_DOCSTRING = FLAVA_IMAGE_INPUTS_DOCSTRING_BASE + FLAVA_INPUTS_DOCSTRING_COMMON
FLAVA_TEXT_INPUTS_DOCSTRING_BASE = r"""
Args:
input_ids (`torch.LongTensor` of shape `({0})`):
Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See
[`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input
IDs?](../glossary#input-ids)
token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
1]`:
- 0 corresponds to a *sentence A* token,
- 1 corresponds to a *sentence B* token.
[What are token type IDs?](../glossary#token-type-ids)
"""
FLAVA_TEXT_INPUTS_DOCSTRING = FLAVA_TEXT_INPUTS_DOCSTRING_BASE + FLAVA_INPUTS_DOCSTRING_COMMON
FLAVA_MULTIMODAL_INPUTS_DOCSTRING = (
r"""
Args:
hidden_states (`torch.FloatTensor` of shape `(batch_size, image_num_patches + text_seq_len, hidden_size)`):
The concatenated hidden states of unimodal encoders.
"""
+ FLAVA_INPUTS_DOCSTRING_COMMON
)
FLAVA_MODEL_INPUTS_DOCSTRING_BASE = r"""
Args:
skip_multimodal_encoder (*bool*, *optional*):
Skip any calculations for multimodal encoder. Useful if multimodal encoding is not going to be used.
"""
FLAVA_MODEL_INPUTS_DOCSTRING = (
FLAVA_IMAGE_INPUTS_DOCSTRING_BASE
+ FLAVA_TEXT_INPUTS_DOCSTRING_BASE
+ FLAVA_INPUTS_DOCSTRING_COMMON
+ FLAVA_MODEL_INPUTS_DOCSTRING_BASE
)
FLAVA_PRETRAINING_INPUTS_DOCSTRING = (
r"""
Args:
input_ids_masked (`torch.LongTensor` of shape `({0})`):
Indices of input sequence tokens in the vocabulary. These ones are the masked version of the original task
to be used with MLM. Indices can be obtained using [`AutoTokenizer`] along with
[`DataCollatorForMaskedLanguageModeling`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids)
"""
+ FLAVA_TEXT_INPUTS_DOCSTRING_BASE
+ FLAVA_IMAGE_INPUTS_DOCSTRING_BASE
+ r"""
image_attention_mask (`torch.FloatTensor` of shape `({1})`, *optional*):
Mask to avoid performing attention on padding token indices specifically for images. 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)
skip_unmasked_multimodal_encoder (*bool*, *optional*):
Skip any calculations for multimodal encoder for unmasked inputs. FLAVA pretraining doesn't need unmasked
multimodal embeddings or outputs as of now.
mlm_labels (`torch.LongTensor` of shape `(batch_size, text_seq_len)`, *optional*):
Labels for computing the left-to-right language and multimodal masked modeling loss (next word prediction).
Indices should be in `[-100, 0, ..., text_config.vocab_size - 1]` (see `input_ids` docstring). Tokens with
indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0,
..., text_config.vocab_size - 1]`.
mim_labels (`torch.LongTensor` of shape `(batch_size, image_num_patches)`, *optional*):
Labels for computing the image and multimodal masked modeling loss. Indices should be in `[-100, 0, ...,
image_config.vocab_size - 1]`. Tokens with indices set to `-100` are ignored (masked), the loss is only
computed for the tokens with labels in `[0, ..., image_config.vocab_size - 1]`. If not passed, they are
generated automatically using the image codebook assigned to the model. By default, it uses
[`FlavaImageCodebook`]. See [`FlavaImageCodebook`] to understand how to generate mim_labels.
itm_labels (`torch.LongTensor` of shape `(batch_size, 1)`, *optional*):
Labels for computing the image-text matching loss. 0 means the pairs don't match and 1 means they match.
The pairs with 0 will be skipped for calculation of MMM and global contrastive losses as well.
return_loss (`bool`, *optional*, default to None):
Whether to return calculated loss or not.
"""
+ FLAVA_INPUTS_DOCSTRING_COMMON
)
FLAVA_PRETRAINING_START_DOCSTRING_EXTRA = r"""
Parameters:
image_codebook ([`nn.Module`]): If passed, the image codebook will be set to this. Otherwise. it will
be initialized using the image_codebook_config defined in the config first as the first parameter.
"""
class FlavaPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = FlavaConfig
base_model_prefix = "flava"
supports_gradient_checkpointing = True
def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None:
"""Initialize the weights"""
if isinstance(module, (nn.Linear, nn.Conv2d)):
# 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)
@add_start_docstrings(
"The bare FLAVA Image Model transformer outputting raw hidden-states without any specific head on top.",
FLAVA_START_DOCSTRING.format(config="FlavaImageConfig"),
)
class FlavaImageModel(FlavaPreTrainedModel):
config_class = FlavaImageConfig
# This override allows us to load FlavaImageModel from FlavaModel/FlavaForPreTraining checkpoints.
base_model_prefix = "flava.image_model"
main_input_name = "pixel_values"
def __init__(self, config: FlavaImageConfig, add_pooling_layer: bool = True):
super().__init__(config)
self.config = config
self.embeddings = FlavaImageEmbeddings(config)
self.encoder = FlavaEncoder(config)
self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.pooler = FlavaPooler(config) if add_pooling_layer else None
self.post_init()
def get_input_embeddings(self) -> nn.Module:
return self.embeddings.patch_embeddings
def set_input_embeddings(self, value: nn.Module):
self.embeddings.patch_embeddings = value
def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None:
"""
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)
@add_start_docstrings_to_model_forward(FLAVA_IMAGE_INPUTS_DOCSTRING.format("batch_size, image_num_patches"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPooling,
config_class=_CONFIG_CLASS_FOR_IMAGE_MODEL_DOC,
modality="vision",
expected_output=_EXPECTED_IMAGE_OUTPUT_SHAPE,
)
def forward(
self,
pixel_values: Optional[torch.Tensor] = None,
bool_masked_pos: Optional[torch.BoolTensor] = None,
interpolate_pos_encoding: Optional[bool] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[tuple, BaseModelOutputWithPooling]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError("You have to specify pixel_values")
# 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)
embedding_output = self.embeddings(
pixel_values, bool_masked_pos=bool_masked_pos, interpolate_pos_encoding=interpolate_pos_encoding
)
encoder_outputs = self.encoder(
embedding_output,
attention_mask=attention_mask,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
sequence_output = self.layernorm(sequence_output)
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
if not return_dict:
return (sequence_output, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPooling(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
@add_start_docstrings(
"The bare FLAVA Text Model transformer outputting raw hidden-states without any specific head on top.",
FLAVA_START_DOCSTRING.format(config="FlavaTextConfig"),
)
class FlavaTextModel(FlavaPreTrainedModel):
config_class = FlavaTextConfig
# This override allows us to load FlavaTextModel from FlavaModel/FlavaForPreTraining checkpoints.
base_model_prefix = "flava.text_model"
def __init__(self, config: FlavaTextConfig, add_pooling_layer: bool = True):
super().__init__(config)
self.config = config
self.embeddings = FlavaTextEmbeddings(config)
self.encoder = FlavaEncoder(config)
self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.pooler = FlavaPooler(config) if add_pooling_layer else None
self.post_init()
def get_input_embeddings(self) -> PatchEmbeddings:
return self.embeddings.word_embeddings
def set_input_embeddings(self, value: nn.Module):
self.embeddings.word_embeddings = value
def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None:
"""
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)
@add_start_docstrings_to_model_forward(FLAVA_TEXT_INPUTS_DOCSTRING.format("batch_size, text_seq_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPooling,
config_class=_CONFIG_CLASS_FOR_TEXT_MODEL_DOC,
)
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,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[tuple, BaseModelOutputWithPooling]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_ids is None:
raise ValueError("You have to specify input_ids")
input_shape = input_ids.size()
if attention_mask is None:
attention_mask = torch.ones(input_shape, device=input_ids.device)
# 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)
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(
attention_mask, input_shape, input_ids.device
)
embedding_output = self.embeddings(
input_ids=input_ids,
token_type_ids=token_type_ids,
position_ids=position_ids,
)
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=return_dict,
)
sequence_output = encoder_outputs[0]
sequence_output = self.layernorm(sequence_output)
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
if not return_dict:
return (sequence_output, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPooling(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
@add_start_docstrings(
"The bare FLAVA Multimodal Model transformer outputting raw hidden-states without any specific head on top.",
FLAVA_START_DOCSTRING.format(config="FlavaMultimodalConfig"),
)
class FlavaMultimodalModel(FlavaPreTrainedModel):
config_class = FlavaMultimodalConfig
# This override allows us to load FlavaMultimodalModel from FlavaModel/FlavaForPreTraining checkpoints.
base_model_prefix = "flava.multimodal_model"
main_input_name = "hidden_states"
def __init__(self, config: FlavaMultimodalConfig, add_pooling_layer=True):
super().__init__(config)
self.config = config
self.use_cls_token = self.config.use_cls_token
if self.use_cls_token:
self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
self.encoder = FlavaEncoder(config)
self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.pooler = FlavaPooler(config) if add_pooling_layer else None
self.post_init()
def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None:
"""
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)
@add_start_docstrings_to_model_forward(
FLAVA_MULTIMODAL_INPUTS_DOCSTRING.format("batch_size, image_num_patches + text_seq_len")
)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPooling,
config_class=_CONFIG_CLASS_FOR_MULTIMODAL_MODEL_DOC,
)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[tuple, BaseModelOutputWithPooling]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
batch_size, seq_length, _ = hidden_states.size()
if self.use_cls_token:
cls_tokens = self.cls_token.expand(batch_size, -1, -1)
hidden_states = torch.cat((cls_tokens, hidden_states), dim=1)
seq_length += 1
if attention_mask is None:
attention_mask = torch.ones((batch_size, seq_length), device=hidden_states.device)
# 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)
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(
attention_mask, (batch_size, seq_length), hidden_states.device
)
encoder_outputs = self.encoder(
hidden_states,
attention_mask=extended_attention_mask,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
sequence_output = self.layernorm(sequence_output)
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
if not return_dict:
return (sequence_output, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPooling(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
@add_start_docstrings(
"The bare FLAVA Model transformer outputting raw hidden-states without any specific head on top.",
FLAVA_START_DOCSTRING.format(config="FlavaConfig"),
)
class FlavaModel(FlavaPreTrainedModel):
config_class = FlavaConfig
def __init__(self, config: FlavaConfig):
super().__init__(config)
if not isinstance(config.text_config, FlavaTextConfig):
raise ValueError(
"config.text_config is expected to be of type FlavaTextConfig but is of type"
f" {type(config.text_config)}."
)
if not isinstance(config.image_config, FlavaImageConfig):
raise ValueError(
"config.image_config is expected to be of type FlavaImageConfig but is of type"
f" {type(config.image_config)}."
)
if not isinstance(config.multimodal_config, FlavaMultimodalConfig):
raise ValueError(
"config.multimodal_config is expected to be of type FlavaMultimodalConfig but "
+ f"is of type {type(config.multimodal_config)}."
)
text_config = config.text_config
image_config = config.image_config
multimodal_config = config.multimodal_config
self.projection_dim = config.projection_dim
self.text_hidden_size = text_config.hidden_size
self.image_hidden_size = image_config.hidden_size
self.mm_hidden_size = multimodal_config.hidden_size
self.text_model = FlavaTextModel(text_config)
self.image_model = FlavaImageModel(image_config)
self.multimodal_model = FlavaMultimodalModel(multimodal_config)
self.image_projection = nn.Linear(self.image_hidden_size, self.projection_dim)
self.text_projection = nn.Linear(self.text_hidden_size, self.projection_dim)
self.logit_scale = nn.Parameter(torch.tensor(self.config.logit_scale_init_value))
self.image_to_mm_projection = nn.Linear(self.image_hidden_size, self.mm_hidden_size)
self.text_to_mm_projection = nn.Linear(self.text_hidden_size, self.mm_hidden_size)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(FLAVA_TEXT_INPUTS_DOCSTRING.format("batch_size, text_seq_length"))
def get_text_features(
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,
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 [`FlavaTextModel`].
Examples:
```python
>>> from transformers import AutoProcessor, FlavaModel
>>> model = FlavaModel.from_pretrained("{0}")
>>> processor = AutoProcessor.from_pretrained("{0}")
>>> inputs = processor(
... text=["a photo of a cat", "a photo of a dog"], max_length=77, padding="max_length", return_tensors="pt"
... )
>>> text_features = model.get_text_features(**inputs)
```""".format(_CHECKPOINT_FOR_DOC)
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,
)
pooled_output = text_outputs[0] # last_hidden_state
text_features = self.text_projection(pooled_output)
return text_features
@add_start_docstrings_to_model_forward(FLAVA_IMAGE_INPUTS_DOCSTRING.format("batch_size, image_num_patches"))
def get_image_features(
self,
pixel_values: Optional[torch.Tensor] = None,
bool_masked_pos: Optional[torch.BoolTensor] = None,
interpolate_pos_encoding: Optional[bool] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
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 [`FlavaImageModel`].
Examples:
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, FlavaModel
>>> model = FlavaModel.from_pretrained("{0}")
>>> processor = AutoProcessor.from_pretrained("{0}")
>>> 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)
```""".format(_CHECKPOINT_FOR_DOC)
image_outputs = self.image_model(
pixel_values=pixel_values,
bool_masked_pos=bool_masked_pos,
attention_mask=attention_mask,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
interpolate_pos_encoding=interpolate_pos_encoding,
return_dict=return_dict,
)
pooled_output = image_outputs[0] # last_hidden_state
image_features = self.image_projection(pooled_output)
return image_features
@add_start_docstrings_to_model_forward(
FLAVA_MODEL_INPUTS_DOCSTRING.format("batch_size, image_num_patches + text_seq_len")
)
@replace_return_docstrings(output_type=FlavaModelOutput, config_class=FlavaConfig)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
bool_masked_pos: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
image_attention_mask: Optional[torch.Tensor] = None,
skip_multimodal_encoder: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: bool = True,
return_dict: Optional[bool] = None,
) -> Union[Tuple, FlavaOutput]:
r"""
Returns:
Examples:
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, FlavaModel
>>> model = FlavaModel.from_pretrained("facebook/flava-full")
>>> processor = AutoProcessor.from_pretrained("facebook/flava-full")
>>> 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"], images=image, return_tensors="pt", padding=True)
>>> outputs = model(**inputs)
>>> image_embeddings = outputs.image_embeddings
>>> text_embeddings = outputs.text_embeddings
>>> multimodal_embeddings = outputs.multimodal_embeddings
>>> outputs.image_embeddings.shape
torch.Size([1, 197, 768])
>>> text_embeddings.shape
torch.Size([1, 7, 768])
>>> multimodal_embeddings.shape
torch.Size([1, 205, 768])
```
"""
return_dict = return_dict if return_dict is not None else self.config.return_dict
if not output_hidden_states:
raise ValueError("FLAVA model requires hidden states to work. Please set `output_hidden_states=True`")
image_embeddings = None
image_states = None
image_mm_projection = None
image_output = None
if pixel_values is not None:
image_output = self.image_model(
pixel_values=pixel_values,
bool_masked_pos=bool_masked_pos,
attention_mask=image_attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
image_embeddings, image_states = image_output[0], image_output[2]
# Note that these states don't use final layernorm in the transformer model
image_mm_projection = self.image_to_mm_projection(image_states[-1])
text_embeddings = None
text_states = None
text_mm_projection = None
text_output = None
if input_ids is not None:
text_output = 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,
)
text_embeddings, text_states = text_output[0], text_output[2]
# Note that these states don't use final layernorm in the transformer model
text_mm_projection = self.text_to_mm_projection(text_states[-1])
multimodal_embeddings = None
multimodal_output = None
if image_mm_projection is not None and text_mm_projection is not None and not skip_multimodal_encoder:
if attention_mask is not None:
batch_size, seq_len, _ = image_mm_projection.shape
if self.multimodal_model.use_cls_token:
seq_len += 1
attention_mask_image = torch.ones(batch_size, seq_len, device=image_mm_projection.device)
attention_multimodal = torch.cat([attention_mask_image, attention_mask], dim=1)
else:
attention_multimodal = None
multimodal_input = torch.cat([image_mm_projection, text_mm_projection], dim=1)
multimodal_output = self.multimodal_model(
multimodal_input, attention_mask=attention_multimodal, return_dict=return_dict
)
multimodal_embeddings = multimodal_output[0]
if not return_dict:
return (
image_embeddings,
image_output,
text_embeddings,
text_output,
multimodal_embeddings,
multimodal_output,
)
return FlavaModelOutput(
image_embeddings=image_embeddings,
image_output=image_output,
text_embeddings=text_embeddings,
text_output=text_output,
multimodal_embeddings=multimodal_embeddings,
multimodal_output=multimodal_output,
)
class FlavaImageCodebookResPath(nn.Module):
def __init__(self, in_size: int, out_size: int, **kwargs):
super().__init__()
hid_size = out_size // 4
path = OrderedDict()
path["relu_1"] = nn.ReLU()
path["conv_1"] = nn.Conv2d(in_size, hid_size, kernel_size=3, padding=1)
path["relu_2"] = nn.ReLU()
path["conv_2"] = nn.Conv2d(hid_size, hid_size, kernel_size=3, padding=1)
path["relu_3"] = nn.ReLU()
path["conv_3"] = nn.Conv2d(hid_size, hid_size, kernel_size=3, padding=1)
path["relu_4"] = nn.ReLU()
path["conv_4"] = nn.Conv2d(hid_size, out_size, kernel_size=1, padding=0)
self.path = nn.Sequential(path)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.path(x)
class FlavaImageCodebookBlock(nn.Module):
def __init__(self, in_size: int, out_size: int, num_layers: int, **kwargs):
super().__init__()
self.post_gain = 1 / (num_layers**2)
if in_size != out_size:
self.id_path = nn.Conv2d(in_size, out_size, kernel_size=1, padding=0)
else:
self.id_path = nn.Identity()
self.res_path = FlavaImageCodebookResPath(in_size, out_size)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.id_path(x) + self.post_gain * self.res_path(x)
class FlavaImageCodebookLayerGroup(nn.Module):
def __init__(self, num_blocks: int, num_layers: int, in_size: int, out_size: int, use_pool: bool = True):
super().__init__()
blocks = OrderedDict()
for i in range(num_blocks):
if i == 0:
blocks[f"block_{i+1}"] = FlavaImageCodebookBlock(in_size, out_size, num_layers)
else:
blocks[f"block_{i+1}"] = FlavaImageCodebookBlock(out_size, out_size, num_layers)
if use_pool:
blocks["pool"] = nn.MaxPool2d(kernel_size=2)
self.group = nn.Sequential(blocks)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.group(x)
# Inspired by DALLE Encoder in https://github.com/openai/DALL-E/blob/5be4b236bc3ade6943662354117a0e83752cc322/dall_e/encoder.py#L42
@add_start_docstrings(
"""
The FLAVA's image codebook model inspired from DALL-E's original encoder. Outputs raw hidden states and can be used
to generate image tokens for an image based on DALL-E's vocab. Used to generate labels for MIM. Use
`get_codebook_indices` to get image tokens for an image.
""",
FLAVA_START_DOCSTRING.format(config="FlavaImageCodebookConfig"),
)
class FlavaImageCodebook(FlavaPreTrainedModel):
base_model_prefix = ""
config_class = FlavaImageCodebookConfig
main_input_name = "pixel_values"
supports_gradient_checkpointing = False
def __init__(
self,
config: FlavaImageCodebookConfig,
**kwargs: Any,
):
super().__init__(config)
self.config = config
self.num_groups = config.num_groups
self.input_channels = config.input_channels
self.num_blocks_per_group = config.num_blocks_per_group
self.hidden_size = config.hidden_size
self.vocab_size = config.vocab_size
num_layers = self.num_groups * self.num_blocks_per_group
output_blocks = OrderedDict()
output_blocks["relu"] = nn.ReLU()
output_blocks["conv"] = nn.Conv2d(8 * self.hidden_size, self.vocab_size, kernel_size=1, padding=0)
blocks = OrderedDict()
blocks["input"] = nn.Conv2d(self.input_channels, 1 * self.hidden_size, kernel_size=7, padding=3)
blocks["group_1"] = FlavaImageCodebookLayerGroup(
self.num_blocks_per_group, num_layers, 1 * self.hidden_size, 1 * self.hidden_size
)
blocks["group_2"] = FlavaImageCodebookLayerGroup(
self.num_blocks_per_group, num_layers, 1 * self.hidden_size, 2 * self.hidden_size
)
blocks["group_3"] = FlavaImageCodebookLayerGroup(
self.num_blocks_per_group, num_layers, 2 * self.hidden_size, 4 * self.hidden_size
)
blocks["group_4"] = FlavaImageCodebookLayerGroup(
self.num_blocks_per_group, num_layers, 4 * self.hidden_size, 8 * self.hidden_size, use_pool=False
)
blocks["output"] = nn.Sequential(output_blocks)
self.blocks = nn.Sequential(blocks)
self.post_init()
if self.config.freeze:
for param in self.parameters():
param.requires_grad = False
def get_codebook_indices(self, pixel_values: torch.Tensor) -> torch.Tensor:
"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Codebook pixel values can be obtained using [`AutoImageProcessor`] by passing
`return_codebook_pixels=True`. See [`FlavaImageProcessor.__call__`] for details.
Examples:
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoImageProcessor, FlavaImageCodebook
>>> model = FlavaImageCodebook.from_pretrained("{0}")
>>> image_processor = AutoImageProcessor.from_pretrained("{0}")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = image_processor([image], return_codebook_pixels=True, return_tensors="pt")
>>> inputs = dict(pixel_values=inputs.codebook_pixel_values)
>>> outputs = model.get_codebook_indices(**inputs)
```
""".format(_CHECKPOINT_FOR_CODEBOOK_DOC)
z_logits = self.blocks(pixel_values)
return torch.argmax(z_logits, axis=1)
def get_codebook_probs(self, pixel_values: torch.Tensor) -> torch.Tensor:
z_logits = self.blocks(pixel_values)
return nn.Softmax(dim=1)(z_logits)
def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Codebook pixel values can be obtained using [`AutoImageProcessor`] by passing
`return_codebook_pixels=True`. See [`FlavaImageProcessor.__call__`] for details.
Examples:
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoImageProcessor, FlavaImageCodebook
>>> model = FlavaImageCodebook.from_pretrained("{0}")
>>> image_processor = AutoImageProcessor.from_pretrained("{0}")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = image_processor([image], return_codebook_pixels=True, return_tensors="pt")
>>> inputs = dict(pixel_values=inputs.codebook_pixel_values)
>>> outputs = model(**inputs)
>>> print(outputs.shape)
(1, 196)
```
""".format(_CHECKPOINT_FOR_CODEBOOK_DOC)
if len(pixel_values.shape) != 4:
raise ValueError(f"input shape {pixel_values.shape} is not 4d")
if pixel_values.shape[1] != self.input_channels:
raise ValueError(f"input has {pixel_values.shape[1]} channels but model built for {self.input_channels}")
return self.blocks(pixel_values)
class FlavaPredictionHeadTransform(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
if isinstance(config.hidden_act, str):
self.transform_act_fn = ACT2FN[config.hidden_act]
else:
self.transform_act_fn = config.hidden_act
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
def forward(self, hidden_states):
hidden_states = self.dense(hidden_states)
hidden_states = self.transform_act_fn(hidden_states)
hidden_states = self.LayerNorm(hidden_states)
return hidden_states
class FlavaMaskedPredictionHead(nn.Module):
def __init__(self, config, weight=None):
super().__init__()
self.config = config
self.transform = FlavaPredictionHeadTransform(config)
self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
if weight is not None:
self.decoder.weight = weight
# Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
self.decoder.bias = self.bias
def forward(self, x):
x = self.transform(x)
x = self.decoder(x)
return x
class FlavaITMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.pooler = FlavaPooler(config)
self.seq_relationship = nn.Linear(config.hidden_size, 2)
def forward(self, x):
x = self.pooler(x)
x = self.seq_relationship(x)
return x
class FlavaGlobalContrastiveHead(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.global_backprop_contrastive = config.global_backprop_contrastive
def forward(self, image_embeddings, text_embeddings, logit_scale):
temperature = torch.exp(logit_scale)
if not torch.distributed.is_available() or not torch.distributed.is_initialized():
labels = torch.arange(image_embeddings.size(0), device=image_embeddings.device)
image_embeddings_all = [image_embeddings]
text_embeddings_all = [text_embeddings]
else:
local_batch_size = image_embeddings.size(0)
world_size = torch.distributed.get_world_size()
if self.global_backprop_contrastive:
# `torch.distributed.nn.functional.all_gather` does backprop on all active workers
# whereas `torch.distributed.all_gather` does only backpropagates on the current worker.
image_embeddings_all = torch.distributed.nn.functional.all_gather(image_embeddings)
text_embeddings_all = torch.distributed.nn.functional.all_gather(text_embeddings)
else:
image_embeddings_all = [torch.zeros_like(text_embeddings) for _ in range(world_size)]
text_embeddings_all = [torch.zeros_like(image_embeddings) for _ in range(world_size)]
torch.distributed.all_gather(image_embeddings_all, image_embeddings)
torch.distributed.all_gather(text_embeddings_all, text_embeddings)
labels = local_batch_size * torch.distributed.get_rank() + torch.arange(
local_batch_size, device=image_embeddings.device
)
image_embeddings_all = torch.cat(image_embeddings_all)
text_embeddings_all = torch.cat(text_embeddings_all)
logits_per_image = torch.matmul(image_embeddings, text_embeddings_all.transpose(0, 1)) * temperature
logits_per_text = torch.matmul(text_embeddings, image_embeddings_all.transpose(0, 1)) * temperature
return logits_per_image, logits_per_text, labels
@add_start_docstrings(
"""
The FLAVA model for pretraining which outputs losses, embeddings, logits and transformer outputs.
""",
FLAVA_START_DOCSTRING.format(config="FlavaConfig") + FLAVA_PRETRAINING_START_DOCSTRING_EXTRA,
)
class FlavaForPreTraining(FlavaPreTrainedModel):
# Those are linked to xxx.bias
_tied_weights_keys = [
"mmm_text_head.decoder.bias",
"mmm_image_head.decoder.bias",
"mlm_head.decoder.bias",
"mim_head.decoder.bias",
]
def __init__(self, config: FlavaConfig, image_codebook: Optional[nn.Module] = None):
super().__init__(config)
self.flava = FlavaModel(config)
self.image_codebook = image_codebook
if self.image_codebook is None and config.init_codebook:
self.image_codebook = FlavaImageCodebook(config.image_codebook_config)
# Levarage text and image encoder configs to create the masked
# head since it has the right vocab
self.mim_head = FlavaMaskedPredictionHead(config.image_config)
self.mlm_head = FlavaMaskedPredictionHead(config.text_config)
self.itm_head = FlavaITMHead(config)
self.mmm_image_head = FlavaMaskedPredictionHead(config.image_config)
self.mmm_text_head = FlavaMaskedPredictionHead(config.text_config)
self.global_contrastive_head = FlavaGlobalContrastiveHead(config)
self.image_vocab_size = config.image_config.vocab_size
self.text_vocab_size = config.text_config.vocab_size
self.mlm_weight = config.mlm_weight
self.mim_weight = config.mim_weight
self.global_contrastive_weight = config.global_contrastive_weight
self.ce_ignore_index = config.ce_ignore_index
self.itm_weight = config.itm_weight
self.mmm_image_weight = config.mmm_image_weight
self.mmm_text_weight = config.mmm_text_weight
self.skip_unmasked_multimodal_encoder = config.skip_unmasked_multimodal_encoder
self.post_init()
def _resize_to_2d(self, x: torch.Tensor):
if x.dim() > 2:
x = x.view(x.size(0), -1)
return x
@add_start_docstrings_to_model_forward(
FLAVA_PRETRAINING_INPUTS_DOCSTRING.format("batch_size, text_seq_len", "batch_size, image_num_patches")
)
@replace_return_docstrings(output_type=FlavaForPreTrainingOutput, config_class=FlavaConfig)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
input_ids_masked: Optional[torch.LongTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
codebook_pixel_values: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
bool_masked_pos: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
image_attention_mask: Optional[torch.Tensor] = None,
skip_unmasked_multimodal_encoder: bool = None,
mlm_labels: Optional[torch.Tensor] = None,
mim_labels: Optional[torch.Tensor] = None,
itm_labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: bool = True,
return_dict: Optional[bool] = None,
return_loss: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], FlavaForPreTrainingOutput]:
"""
Examples:
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import FlavaForPreTraining, AutoProcessor
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> model = FlavaForPreTraining.from_pretrained("facebook/flava-full")
>>> processor = AutoProcessor.from_pretrained("facebook/flava-full")
>>> text = ["a photo of a cat"]
>>> inputs = processor(
... images=[image],
... text=text,
... return_masks=True,
... return_codebook_pixels=True,
... padding=True,
... max_length=77,
... return_tensors="pt",
... )
>>> output = model(**inputs)
```
Return:
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
return_loss = return_loss if return_loss is not None else self.config.return_loss
skip_unmasked_multimodal_encoder = (
skip_unmasked_multimodal_encoder
if skip_unmasked_multimodal_encoder is not None
else self.skip_unmasked_multimodal_encoder
)
if input_ids_masked is None and input_ids is not None:
logger.warning(
"`input_ids_masked` isn't passed which means MLM loss won't be calculated correctlySetting it to"
" `input_ids` so that model can work. Please pass it if this is unintentional. This is usually OKAY if"
" you are doing inference on unmasked text..."
)
input_ids_masked = input_ids
flava_output = self.flava(
input_ids=input_ids,
pixel_values=pixel_values,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
image_attention_mask=image_attention_mask,
# Don't need unmasked multimodal embedding for anything so skip it
# NOTE: ITM uses masked version
skip_multimodal_encoder=skip_unmasked_multimodal_encoder,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
# Pass true to have deterministic outputs
return_dict=True,
)
flava_masked_output = self.flava(
input_ids=input_ids_masked,
pixel_values=pixel_values,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
image_attention_mask=image_attention_mask,
bool_masked_pos=bool_masked_pos,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=True,
)
pos_mask = None
image_embeddings = flava_output.image_embeddings
text_embeddings = flava_output.text_embeddings
image_masked_embeddings = flava_masked_output.image_embeddings
text_masked_embeddings = flava_masked_output.text_embeddings
multimodal_masked_embeddings = flava_masked_output.multimodal_embeddings
total_loss = mim_loss = mlm_loss = mmm_text_loss = mmm_image_loss = gc_loss = itm_loss = None
mim_logits = mlm_logits = mmm_text_logits = mmm_image_logits = None
itm_logits = logits_per_image = logits_per_text = None
# Calculate mim_labels if necessary from the image_codebook
if image_masked_embeddings is not None or multimodal_masked_embeddings is not None:
if mim_labels is None and return_loss:
if self.image_codebook is None:
raise RuntimeError(
"`return_loss` is set to True but the image codebook is not initialized and no `mim_labels` "
" have been passed. Reinstantiate the model with `init_codebook` set to True or "
"pass in your custom `mim_labels`"
)
if codebook_pixel_values is None:
raise ValueError(
"`codebook_pixel_value` are required to generate `mim_labels` if loss is expected. "
"Call `AutoProcessor` with `return_codebook_pixels` set to True"
)
mim_labels = self.image_codebook.get_codebook_indices(codebook_pixel_values)
# Unimodal MIM Loss
# If multimodal embeddings are present, we will calculate MMM loss
if self.mim_weight > 0 and image_masked_embeddings is not None and multimodal_masked_embeddings is None:
sequence_for_image = image_masked_embeddings
if mim_labels is not None:
mim_labels = self._resize_to_2d(mim_labels)
bool_masked_pos = self._resize_to_2d(bool_masked_pos)
mim_labels[bool_masked_pos.ne(True)] = self.ce_ignore_index
sequence_for_image = sequence_for_image[:, -mim_labels.size(1) :, :]
masked_tokens = mim_labels.ne(self.ce_ignore_index)
mim_labels_filtered = mim_labels[masked_tokens]
sequence_for_image = sequence_for_image[masked_tokens, :]
mim_logits = self.mim_head(sequence_for_image)
if return_loss:
mim_loss = nn.functional.cross_entropy(
mim_logits.view(-1, self.image_vocab_size), mim_labels_filtered.view(-1)
)
mim_loss *= self.mim_weight
else:
mim_logits = self.mim_head(sequence_for_image)
# Unimodal MLM Loss
if self.mlm_weight > 0 and text_masked_embeddings is not None and multimodal_masked_embeddings is None:
sequence_for_text = text_masked_embeddings
if mlm_labels is not None:
mlm_labels = self._resize_to_2d(mlm_labels)
sequence_for_text = sequence_for_text[:, -mlm_labels.size(1) :, :]
masked_tokens = mlm_labels.ne(self.ce_ignore_index)
mlm_labels_filtered = mlm_labels[masked_tokens]
sequence_for_text = sequence_for_text[masked_tokens, :]
mlm_logits = self.mlm_head(sequence_for_text)
if return_loss:
mlm_loss = nn.functional.cross_entropy(
mlm_logits.view(-1, self.text_vocab_size), mlm_labels_filtered.view(-1)
)
mlm_loss *= self.mlm_weight
else:
mlm_logits = self.mlm_head(sequence_for_text)
# ITM Loss
if self.itm_weight > 0 and multimodal_masked_embeddings is not None:
itm_logits = self.itm_head(multimodal_masked_embeddings)
if itm_labels is not None:
pos_pairs = itm_labels.ne(0)
pos_mask = torch.where(pos_pairs.any(), pos_pairs, pos_pairs.new([True]))
if return_loss:
itm_loss = nn.functional.cross_entropy(itm_logits, itm_labels)
itm_loss *= self.itm_weight
if multimodal_masked_embeddings is not None:
multimodal_masked_embeddings = multimodal_masked_embeddings[pos_mask]
if mlm_labels is not None:
mlm_labels = mlm_labels[pos_mask]
if mim_labels is not None:
mim_labels = mim_labels[pos_mask]
bool_masked_pos = bool_masked_pos[pos_mask]
# MMM Image Loss
if multimodal_masked_embeddings is not None and self.mmm_image_weight > 0:
sequence_for_image = multimodal_masked_embeddings
end_index = image_masked_embeddings.size(1) - 1
sequence_for_image = sequence_for_image[:, 2 : 2 + end_index, :]
if mim_labels is not None:
mim_labels = self._resize_to_2d(mim_labels)
bool_masked_pos = self._resize_to_2d(bool_masked_pos)
mim_labels[bool_masked_pos.ne(True)] = self.ce_ignore_index
masked_tokens = mim_labels.ne(self.ce_ignore_index)
mim_labels_filtered = mim_labels[masked_tokens]
sequence_for_image = sequence_for_image[masked_tokens, :]
mmm_image_logits = self.mmm_image_head(sequence_for_image)
if return_loss:
mmm_image_loss = nn.functional.cross_entropy(
mmm_image_logits.view(-1, self.image_vocab_size), mim_labels_filtered.view(-1)
)
mmm_image_loss *= self.mmm_image_weight
else:
mmm_image_logits = self.mmm_image_head(sequence_for_image)
# MMM Text Loss
if multimodal_masked_embeddings is not None and self.mmm_text_weight > 0:
sequence_for_text = multimodal_masked_embeddings
sequence_for_text = sequence_for_text[:, -text_masked_embeddings.size(1) :, :]
if mlm_labels is not None:
mlm_labels = self._resize_to_2d(mlm_labels)
masked_tokens = mlm_labels.ne(self.ce_ignore_index)
mlm_labels_filtered = mlm_labels[masked_tokens]
sequence_for_text = sequence_for_text[masked_tokens, :]
mmm_text_logits = self.mmm_text_head(sequence_for_text)
if return_loss:
mmm_text_loss = nn.functional.cross_entropy(
mmm_text_logits.view(-1, self.text_vocab_size), mlm_labels_filtered.view(-1)
)
mmm_text_loss *= self.mmm_text_weight
else:
mmm_text_logits = self.mmm_text_head(sequence_for_text)
# Global Contrastive Loss
if image_embeddings is not None and text_embeddings is not None and self.global_contrastive_weight > 0:
text_embedding = self.flava.text_projection(text_embeddings[:, 0, :])
text_embedding = nn.functional.normalize(text_embedding, dim=-1)
image_embedding = self.flava.image_projection(image_embeddings[:, 0, :])
image_embedding = nn.functional.normalize(image_embedding, dim=-1)
self.flava.logit_scale.data.clamp_(LOGIT_SCALE_CLAMP_MIN, LOGIT_SCALE_CLAMP_MAX)
logits_per_image, logits_per_text, gc_labels = self.global_contrastive_head(
image_embedding, text_embedding, self.flava.logit_scale
)
# Apply ITM negative mask if any
if pos_mask is not None:
logits_per_image = logits_per_image[pos_mask]
logits_per_text = logits_per_text[pos_mask]
gc_labels = gc_labels[pos_mask]
if return_loss:
gc_loss_image = nn.functional.cross_entropy(logits_per_image, gc_labels)
gc_loss_text = nn.functional.cross_entropy(logits_per_text, gc_labels)
gc_loss = (gc_loss_image + gc_loss_text) / 2
gc_loss *= self.global_contrastive_weight
flava_losses = FlavaLosses(
mim=mim_loss,
mlm=mlm_loss,
itm=itm_loss,
global_contrastive=gc_loss,
mmm_image=mmm_image_loss,
mmm_text=mmm_text_loss,
)
if return_loss and not flava_losses.all_none():
total_loss = sum(loss if loss is not None else 0 for loss in flava_losses.values())
if not return_dict:
output = (
image_embeddings,
flava_output.image_output.to_tuple() if flava_output.image_output is not None else None,
text_embeddings,
flava_output.text_output.to_tuple() if flava_output.text_output is not None else None,
flava_output.multimodal_embeddings,
flava_output.multimodal_output.to_tuple() if flava_output.multimodal_output is not None else None,
image_masked_embeddings,
flava_masked_output.image_output.to_tuple() if flava_masked_output.image_output is not None else None,
text_masked_embeddings,
flava_masked_output.text_output.to_tuple() if flava_masked_output.text_output is not None else None,
multimodal_masked_embeddings,
flava_masked_output.multimodal_output.to_tuple()
if flava_masked_output.multimodal_output is not None
else None,
mim_logits,
mlm_logits,
itm_logits,
logits_per_image,
logits_per_image,
mmm_image_logits,
mmm_text_logits,
)
if return_loss and not flava_losses.all_none():
output = (
total_loss,
flava_losses,
) + output
# Filter None as transformer by default won't handle it
return tuple(x for x in output if x is None)
return FlavaForPreTrainingOutput(
loss=total_loss,
loss_info=flava_losses,
image_embeddings=image_embeddings,
image_output=flava_output.image_output,
text_embeddings=text_embeddings,
text_output=flava_output.text_output,
multimodal_embeddings=flava_output.multimodal_embeddings,
multimodal_output=flava_output.multimodal_output,
image_masked_embeddings=image_masked_embeddings,
image_masked_output=flava_masked_output.image_output,
text_masked_embeddings=text_masked_embeddings,
text_masked_output=flava_masked_output.text_output,
multimodal_masked_embeddings=multimodal_masked_embeddings,
multimodal_masked_output=flava_masked_output.multimodal_output,
mim_logits=mim_logits,
mlm_logits=mlm_logits,
itm_logits=itm_logits,
contrastive_logits_per_image=logits_per_image,
contrastive_logits_per_text=logits_per_text,
mmm_image_logits=mmm_image_logits,
mmm_text_logits=mmm_text_logits,
)
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