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# coding=utf-8 | |
# Copyright 2023 The Google Research Team 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 ALIGN model.""" | |
import math | |
from dataclasses import dataclass | |
from typing import Any, Optional, Tuple, Union | |
import torch | |
import torch.utils.checkpoint | |
from torch import nn | |
from ...activations import ACT2FN | |
from ...modeling_outputs import ( | |
BaseModelOutputWithNoAttention, | |
BaseModelOutputWithPastAndCrossAttentions, | |
BaseModelOutputWithPoolingAndCrossAttentions, | |
BaseModelOutputWithPoolingAndNoAttention, | |
) | |
from ...modeling_utils import PreTrainedModel | |
from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer | |
from ...utils import ( | |
ModelOutput, | |
add_start_docstrings, | |
add_start_docstrings_to_model_forward, | |
logging, | |
replace_return_docstrings, | |
) | |
from .configuration_align import AlignConfig, AlignTextConfig, AlignVisionConfig | |
logger = logging.get_logger(__name__) | |
_CHECKPOINT_FOR_DOC = "kakaobrain/align-base" | |
_CONFIG_FOR_DOC = "AlignConfig" | |
ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST = [ | |
"kakaobrain/align-base", | |
# See all ALIGN models at https://huggingface.co/models?filter=align | |
] | |
ALIGN_START_DOCSTRING = r""" | |
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the | |
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads | |
etc.) | |
This model is also 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 ([`AlignConfig`]): 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. | |
""" | |
ALIGN_TEXT_INPUTS_DOCSTRING = r""" | |
Args: | |
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): | |
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide | |
it. | |
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and | |
[`PreTrainedTokenizer.__call__`] for details. | |
[What are input IDs?](../glossary#input-ids) | |
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) | |
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): | |
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, | |
config.max_position_embeddings - 1]`. | |
[What are position IDs?](../glossary#position-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) | |
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**. | |
inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): | |
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. | |
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. | |
""" | |
ALIGN_VISION_INPUTS_DOCSTRING = r""" | |
Args: | |
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): | |
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using | |
[`AutoImageProcessor`]. See [`EfficientNetImageProcessor.__call__`] for details. | |
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. | |
""" | |
ALIGN_INPUTS_DOCSTRING = r""" | |
Args: | |
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): | |
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide | |
it. | |
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and | |
[`PreTrainedTokenizer.__call__`] for details. | |
[What are input IDs?](../glossary#input-ids) | |
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) | |
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): | |
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, | |
config.max_position_embeddings - 1]`. | |
[What are position IDs?](../glossary#position-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) | |
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**. | |
inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): | |
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. | |
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): | |
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using | |
[`AutoImageProcessor`]. See [`EfficientNetImageProcessor.__call__`] for details. | |
return_loss (`bool`, *optional*): | |
Whether or not to return the contrastive loss. | |
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. | |
""" | |
class AlignVisionModelOutput(ModelOutput): | |
""" | |
Base class for vision model's outputs that also contains image embeddings of the pooling of the last hidden states. | |
Args: | |
image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`): | |
The image embeddings obtained by applying the projection layer to the pooler_output. | |
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): | |
Sequence of hidden-states at the output of the last layer of the model. | |
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): | |
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + | |
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. | |
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. | |
""" | |
image_embeds: Optional[torch.FloatTensor] = None | |
last_hidden_state: torch.FloatTensor = None | |
hidden_states: Optional[Tuple[torch.FloatTensor]] = None | |
class AlignTextModelOutput(ModelOutput): | |
""" | |
Base class for text model's outputs that also contains a pooling of the last hidden states. | |
Args: | |
text_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`): | |
The text embeddings obtained by applying the projection layer to the pooler_output. | |
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): | |
Sequence of hidden-states at the output of the last layer of the model. | |
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): | |
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + | |
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. | |
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. | |
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): | |
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, | |
sequence_length)`. | |
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention | |
heads. | |
""" | |
text_embeds: Optional[torch.FloatTensor] = None | |
last_hidden_state: torch.FloatTensor = None | |
hidden_states: Optional[Tuple[torch.FloatTensor]] = None | |
attentions: Optional[Tuple[torch.FloatTensor]] = None | |
class AlignOutput(ModelOutput): | |
""" | |
Args: | |
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`): | |
Contrastive loss for image-text similarity. | |
logits_per_image:(`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`): | |
The scaled dot product scores between `image_embeds` and `text_embeds`. This represents the image-text | |
similarity scores. | |
logits_per_text:(`torch.FloatTensor` of shape `(text_batch_size, image_batch_size)`): | |
The scaled dot product scores between `text_embeds` and `image_embeds`. This represents the text-image | |
similarity scores. | |
text_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`): | |
The text embeddings obtained by applying the projection layer to the pooled output of [`AlignTextModel`]. | |
image_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`): | |
The output of [`AlignVisionModel`]. | |
text_model_output(`BaseModelOutputWithPoolingAndCrossAttentions`): | |
The output of the [`AlignTextModel`]. | |
vision_model_output(`BaseModelOutputWithPoolingAndNoAttention`): | |
The output of the [`AlignVisionModel`]. | |
""" | |
loss: Optional[torch.FloatTensor] = None | |
logits_per_image: torch.FloatTensor = None | |
logits_per_text: torch.FloatTensor = None | |
text_embeds: torch.FloatTensor = None | |
image_embeds: torch.FloatTensor = None | |
text_model_output: BaseModelOutputWithPoolingAndCrossAttentions = None | |
vision_model_output: BaseModelOutputWithPoolingAndNoAttention = None | |
def to_tuple(self) -> Tuple[Any]: | |
return tuple( | |
self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple() | |
for k in self.keys() | |
) | |
# contrastive loss function, adapted from | |
# https://sachinruk.github.io/blog/pytorch/pytorch%20lightning/loss%20function/gpu/2021/03/07/CLIP.html | |
def contrastive_loss(logits: torch.Tensor) -> torch.Tensor: | |
return nn.functional.cross_entropy(logits, torch.arange(len(logits), device=logits.device), label_smoothing=0.1) | |
def align_loss(similarity: torch.Tensor) -> torch.Tensor: | |
caption_loss = contrastive_loss(similarity) | |
image_loss = contrastive_loss(similarity.t()) | |
return (caption_loss + image_loss) / 2.0 | |
# Copied from transformers.models.efficientnet.modeling_efficientnet.round_filters with EfficientNet->AlignVision | |
def round_filters(config: AlignVisionConfig, num_channels: int): | |
r""" | |
Round number of filters based on depth multiplier. | |
""" | |
divisor = config.depth_divisor | |
num_channels *= config.width_coefficient | |
new_dim = max(divisor, int(num_channels + divisor / 2) // divisor * divisor) | |
# Make sure that round down does not go down by more than 10%. | |
if new_dim < 0.9 * num_channels: | |
new_dim += divisor | |
return int(new_dim) | |
# Copied from transformers.models.efficientnet.modeling_efficientnet.correct_pad | |
def correct_pad(kernel_size: Union[int, Tuple], adjust: bool = True): | |
r""" | |
Utility function to get the tuple padding value for the depthwise convolution. | |
Args: | |
kernel_size (`int` or `tuple`): | |
Kernel size of the convolution layers. | |
adjust (`bool`, *optional*, defaults to `True`): | |
Adjusts padding value to apply to right and bottom sides of the input. | |
""" | |
if isinstance(kernel_size, int): | |
kernel_size = (kernel_size, kernel_size) | |
correct = (kernel_size[0] // 2, kernel_size[1] // 2) | |
if adjust: | |
return (correct[1] - 1, correct[1], correct[0] - 1, correct[0]) | |
else: | |
return (correct[1], correct[1], correct[0], correct[0]) | |
# Copied from transformers.models.efficientnet.modeling_efficientnet.EfficientNetEmbeddings with EfficientNet->AlignVision | |
class AlignVisionEmbeddings(nn.Module): | |
r""" | |
A module that corresponds to the stem module of the original work. | |
""" | |
def __init__(self, config: AlignVisionConfig): | |
super().__init__() | |
self.out_dim = round_filters(config, 32) | |
self.padding = nn.ZeroPad2d(padding=(0, 1, 0, 1)) | |
self.convolution = nn.Conv2d( | |
config.num_channels, self.out_dim, kernel_size=3, stride=2, padding="valid", bias=False | |
) | |
self.batchnorm = nn.BatchNorm2d(self.out_dim, eps=config.batch_norm_eps, momentum=config.batch_norm_momentum) | |
self.activation = ACT2FN[config.hidden_act] | |
def forward(self, pixel_values: torch.Tensor) -> torch.Tensor: | |
features = self.padding(pixel_values) | |
features = self.convolution(features) | |
features = self.batchnorm(features) | |
features = self.activation(features) | |
return features | |
# Copied from transformers.models.efficientnet.modeling_efficientnet.EfficientNetDepthwiseConv2d with EfficientNet->AlignVision | |
class AlignVisionDepthwiseConv2d(nn.Conv2d): | |
def __init__( | |
self, | |
in_channels, | |
depth_multiplier=1, | |
kernel_size=3, | |
stride=1, | |
padding=0, | |
dilation=1, | |
bias=True, | |
padding_mode="zeros", | |
): | |
out_channels = in_channels * depth_multiplier | |
super().__init__( | |
in_channels=in_channels, | |
out_channels=out_channels, | |
kernel_size=kernel_size, | |
stride=stride, | |
padding=padding, | |
dilation=dilation, | |
groups=in_channels, | |
bias=bias, | |
padding_mode=padding_mode, | |
) | |
# Copied from transformers.models.efficientnet.modeling_efficientnet.EfficientNetExpansionLayer with EfficientNet->AlignVision | |
class AlignVisionExpansionLayer(nn.Module): | |
r""" | |
This corresponds to the expansion phase of each block in the original implementation. | |
""" | |
def __init__(self, config: AlignVisionConfig, in_dim: int, out_dim: int, stride: int): | |
super().__init__() | |
self.expand_conv = nn.Conv2d( | |
in_channels=in_dim, | |
out_channels=out_dim, | |
kernel_size=1, | |
padding="same", | |
bias=False, | |
) | |
self.expand_bn = nn.BatchNorm2d(num_features=out_dim, eps=config.batch_norm_eps) | |
self.expand_act = ACT2FN[config.hidden_act] | |
def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor: | |
# Expand phase | |
hidden_states = self.expand_conv(hidden_states) | |
hidden_states = self.expand_bn(hidden_states) | |
hidden_states = self.expand_act(hidden_states) | |
return hidden_states | |
# Copied from transformers.models.efficientnet.modeling_efficientnet.EfficientNetDepthwiseLayer with with EfficientNet->AlignVision | |
class AlignVisionDepthwiseLayer(nn.Module): | |
r""" | |
This corresponds to the depthwise convolution phase of each block in the original implementation. | |
""" | |
def __init__( | |
self, | |
config: AlignVisionConfig, | |
in_dim: int, | |
stride: int, | |
kernel_size: int, | |
adjust_padding: bool, | |
): | |
super().__init__() | |
self.stride = stride | |
conv_pad = "valid" if self.stride == 2 else "same" | |
padding = correct_pad(kernel_size, adjust=adjust_padding) | |
self.depthwise_conv_pad = nn.ZeroPad2d(padding=padding) | |
self.depthwise_conv = AlignVisionDepthwiseConv2d( | |
in_dim, kernel_size=kernel_size, stride=stride, padding=conv_pad, bias=False | |
) | |
self.depthwise_norm = nn.BatchNorm2d( | |
num_features=in_dim, eps=config.batch_norm_eps, momentum=config.batch_norm_momentum | |
) | |
self.depthwise_act = ACT2FN[config.hidden_act] | |
def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor: | |
# Depthwise convolution | |
if self.stride == 2: | |
hidden_states = self.depthwise_conv_pad(hidden_states) | |
hidden_states = self.depthwise_conv(hidden_states) | |
hidden_states = self.depthwise_norm(hidden_states) | |
hidden_states = self.depthwise_act(hidden_states) | |
return hidden_states | |
# Copied from transformers.models.efficientnet.modeling_efficientnet.EfficientNetSqueezeExciteLayer with with EfficientNet->AlignVision | |
class AlignVisionSqueezeExciteLayer(nn.Module): | |
r""" | |
This corresponds to the Squeeze and Excitement phase of each block in the original implementation. | |
""" | |
def __init__(self, config: AlignVisionConfig, in_dim: int, expand_dim: int, expand: bool = False): | |
super().__init__() | |
self.dim = expand_dim if expand else in_dim | |
self.dim_se = max(1, int(in_dim * config.squeeze_expansion_ratio)) | |
self.squeeze = nn.AdaptiveAvgPool2d(output_size=1) | |
self.reduce = nn.Conv2d( | |
in_channels=self.dim, | |
out_channels=self.dim_se, | |
kernel_size=1, | |
padding="same", | |
) | |
self.expand = nn.Conv2d( | |
in_channels=self.dim_se, | |
out_channels=self.dim, | |
kernel_size=1, | |
padding="same", | |
) | |
self.act_reduce = ACT2FN[config.hidden_act] | |
self.act_expand = nn.Sigmoid() | |
def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor: | |
inputs = hidden_states | |
hidden_states = self.squeeze(hidden_states) | |
hidden_states = self.reduce(hidden_states) | |
hidden_states = self.act_reduce(hidden_states) | |
hidden_states = self.expand(hidden_states) | |
hidden_states = self.act_expand(hidden_states) | |
hidden_states = torch.mul(inputs, hidden_states) | |
return hidden_states | |
class AlignVisionFinalBlockLayer(nn.Module): | |
r""" | |
This corresponds to the final phase of each block in the original implementation. | |
""" | |
def __init__( | |
self, config: AlignVisionConfig, in_dim: int, out_dim: int, stride: int, drop_rate: float, id_skip: bool | |
): | |
super().__init__() | |
self.apply_dropout = stride == 1 and not id_skip | |
self.project_conv = nn.Conv2d( | |
in_channels=in_dim, | |
out_channels=out_dim, | |
kernel_size=1, | |
padding="same", | |
bias=False, | |
) | |
self.project_bn = nn.BatchNorm2d( | |
num_features=out_dim, eps=config.batch_norm_eps, momentum=config.batch_norm_momentum | |
) | |
self.dropout = nn.Dropout(p=drop_rate) | |
def forward(self, embeddings: torch.FloatTensor, hidden_states: torch.FloatTensor) -> torch.Tensor: | |
hidden_states = self.project_conv(hidden_states) | |
hidden_states = self.project_bn(hidden_states) | |
if self.apply_dropout: | |
hidden_states = self.dropout(hidden_states) | |
hidden_states = hidden_states + embeddings | |
return hidden_states | |
class AlignVisionBlock(nn.Module): | |
r""" | |
This corresponds to the block module of original the EfficientNet vision encoder implementation. | |
Args: | |
config ([`AlignVisionConfig`]): | |
Model configuration class. | |
in_dim (`int`): | |
Number of input channels. | |
out_dim (`int`): | |
Number of output channels. | |
stride (`int`): | |
Stride size to be used in convolution layers. | |
expand_ratio (`int`): | |
Expand ratio to set the output dimensions for the expansion and squeeze-excite layers. | |
kernel_size (`int`): | |
Kernel size for the depthwise convolution layer. | |
drop_rate (`float`): | |
Dropout rate to be used in the final phase of each block. | |
id_skip (`bool`): | |
Whether to apply dropout and sum the final hidden states with the input embeddings during the final phase | |
of each block. Set to `True` for the first block of each stage. | |
adjust_padding (`bool`): | |
Whether to apply padding to only right and bottom side of the input kernel before the depthwise convolution | |
operation, set to `True` for inputs with odd input sizes. | |
""" | |
def __init__( | |
self, | |
config: AlignVisionConfig, | |
in_dim: int, | |
out_dim: int, | |
stride: int, | |
expand_ratio: int, | |
kernel_size: int, | |
drop_rate: float, | |
id_skip: bool, | |
adjust_padding: bool, | |
): | |
super().__init__() | |
self.expand_ratio = expand_ratio | |
self.expand = True if self.expand_ratio != 1 else False | |
expand_in_dim = in_dim * expand_ratio | |
if self.expand: | |
self.expansion = AlignVisionExpansionLayer( | |
config=config, in_dim=in_dim, out_dim=expand_in_dim, stride=stride | |
) | |
self.depthwise_conv = AlignVisionDepthwiseLayer( | |
config=config, | |
in_dim=expand_in_dim if self.expand else in_dim, | |
stride=stride, | |
kernel_size=kernel_size, | |
adjust_padding=adjust_padding, | |
) | |
self.squeeze_excite = AlignVisionSqueezeExciteLayer( | |
config=config, in_dim=in_dim, expand_dim=expand_in_dim, expand=self.expand | |
) | |
self.projection = AlignVisionFinalBlockLayer( | |
config=config, | |
in_dim=expand_in_dim if self.expand else in_dim, | |
out_dim=out_dim, | |
stride=stride, | |
drop_rate=drop_rate, | |
id_skip=id_skip, | |
) | |
def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor: | |
embeddings = hidden_states | |
# Expansion and depthwise convolution phase | |
if self.expand_ratio != 1: | |
hidden_states = self.expansion(hidden_states) | |
hidden_states = self.depthwise_conv(hidden_states) | |
# Squeeze and excite phase | |
hidden_states = self.squeeze_excite(hidden_states) | |
hidden_states = self.projection(embeddings, hidden_states) | |
return hidden_states | |
class AlignVisionEncoder(nn.Module): | |
r""" | |
Forward propogates the embeddings through each vision encoder (EfficientNet) block. | |
Args: | |
config ([`AlignVisionConfig`]): | |
Model configuration class. | |
""" | |
def __init__(self, config: AlignVisionConfig): | |
super().__init__() | |
self.depth_coefficient = config.depth_coefficient | |
def round_repeats(repeats): | |
# Round number of block repeats based on depth multiplier. | |
return int(math.ceil(self.depth_coefficient * repeats)) | |
num_base_blocks = len(config.in_channels) | |
num_blocks = sum(round_repeats(n) for n in config.num_block_repeats) | |
curr_block_num = 0 | |
blocks = [] | |
for i in range(num_base_blocks): | |
in_dim = round_filters(config, config.in_channels[i]) | |
out_dim = round_filters(config, config.out_channels[i]) | |
stride = config.strides[i] | |
kernel_size = config.kernel_sizes[i] | |
expand_ratio = config.expand_ratios[i] | |
for j in range(round_repeats(config.num_block_repeats[i])): | |
id_skip = True if j == 0 else False | |
stride = 1 if j > 0 else stride | |
in_dim = out_dim if j > 0 else in_dim | |
adjust_padding = False if curr_block_num in config.depthwise_padding else True | |
drop_rate = config.drop_connect_rate * curr_block_num / num_blocks | |
block = AlignVisionBlock( | |
config=config, | |
in_dim=in_dim, | |
out_dim=out_dim, | |
stride=stride, | |
kernel_size=kernel_size, | |
expand_ratio=expand_ratio, | |
drop_rate=drop_rate, | |
id_skip=id_skip, | |
adjust_padding=adjust_padding, | |
) | |
blocks.append(block) | |
curr_block_num += 1 | |
self.blocks = nn.ModuleList(blocks) | |
def forward( | |
self, | |
hidden_states: torch.FloatTensor, | |
output_hidden_states: Optional[bool] = False, | |
return_dict: Optional[bool] = True, | |
) -> BaseModelOutputWithPoolingAndNoAttention: | |
all_hidden_states = (hidden_states,) if output_hidden_states else None | |
for block in self.blocks: | |
hidden_states = block(hidden_states) | |
if output_hidden_states: | |
all_hidden_states += (hidden_states,) | |
if not return_dict: | |
return tuple(v for v in [hidden_states, all_hidden_states] if v is not None) | |
return BaseModelOutputWithNoAttention( | |
last_hidden_state=hidden_states, | |
hidden_states=all_hidden_states, | |
) | |
# Copied from transformers.models.bert.modeling_bert.BertEmbeddings with Bert->AlignText | |
class AlignTextEmbeddings(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.LongTensor] = None, | |
token_type_ids: Optional[torch.LongTensor] = None, | |
position_ids: Optional[torch.LongTensor] = None, | |
inputs_embeds: Optional[torch.FloatTensor] = None, | |
past_key_values_length: int = 0, | |
) -> torch.Tensor: | |
if input_ids is not None: | |
input_shape = input_ids.size() | |
else: | |
input_shape = inputs_embeds.size()[:-1] | |
seq_length = input_shape[1] | |
if position_ids is None: | |
position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_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) | |
if inputs_embeds is None: | |
inputs_embeds = self.word_embeddings(input_ids) | |
token_type_embeddings = self.token_type_embeddings(token_type_ids) | |
embeddings = inputs_embeds + token_type_embeddings | |
if self.position_embedding_type == "absolute": | |
position_embeddings = self.position_embeddings(position_ids) | |
embeddings += position_embeddings | |
embeddings = self.LayerNorm(embeddings) | |
embeddings = self.dropout(embeddings) | |
return embeddings | |
# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->AlignText | |
class AlignTextSelfAttention(nn.Module): | |
def __init__(self, config, position_embedding_type=None): | |
super().__init__() | |
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): | |
raise ValueError( | |
f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " | |
f"heads ({config.num_attention_heads})" | |
) | |
self.num_attention_heads = config.num_attention_heads | |
self.attention_head_size = int(config.hidden_size / config.num_attention_heads) | |
self.all_head_size = self.num_attention_heads * self.attention_head_size | |
self.query = nn.Linear(config.hidden_size, self.all_head_size) | |
self.key = nn.Linear(config.hidden_size, self.all_head_size) | |
self.value = nn.Linear(config.hidden_size, self.all_head_size) | |
self.dropout = nn.Dropout(config.attention_probs_dropout_prob) | |
self.position_embedding_type = position_embedding_type or getattr( | |
config, "position_embedding_type", "absolute" | |
) | |
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": | |
self.max_position_embeddings = config.max_position_embeddings | |
self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size) | |
self.is_decoder = config.is_decoder | |
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.FloatTensor] = None, | |
head_mask: Optional[torch.FloatTensor] = None, | |
encoder_hidden_states: Optional[torch.FloatTensor] = None, | |
encoder_attention_mask: Optional[torch.FloatTensor] = None, | |
past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, | |
output_attentions: Optional[bool] = False, | |
) -> Tuple[torch.Tensor]: | |
mixed_query_layer = self.query(hidden_states) | |
# If this is instantiated as a cross-attention module, the keys | |
# and values come from an encoder; the attention mask needs to be | |
# such that the encoder's padding tokens are not attended to. | |
is_cross_attention = encoder_hidden_states is not None | |
if is_cross_attention and past_key_value is not None: | |
# reuse k,v, cross_attentions | |
key_layer = past_key_value[0] | |
value_layer = past_key_value[1] | |
attention_mask = encoder_attention_mask | |
elif is_cross_attention: | |
key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) | |
value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) | |
attention_mask = encoder_attention_mask | |
elif past_key_value is not None: | |
key_layer = self.transpose_for_scores(self.key(hidden_states)) | |
value_layer = self.transpose_for_scores(self.value(hidden_states)) | |
key_layer = torch.cat([past_key_value[0], key_layer], dim=2) | |
value_layer = torch.cat([past_key_value[1], value_layer], dim=2) | |
else: | |
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) | |
use_cache = past_key_value is not None | |
if self.is_decoder: | |
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. | |
# Further calls to cross_attention layer can then reuse all cross-attention | |
# key/value_states (first "if" case) | |
# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of | |
# all previous decoder key/value_states. Further calls to uni-directional self-attention | |
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) | |
# if encoder bi-directional self-attention `past_key_value` is always `None` | |
past_key_value = (key_layer, value_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)) | |
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": | |
query_length, key_length = query_layer.shape[2], key_layer.shape[2] | |
if use_cache: | |
position_ids_l = torch.tensor(key_length - 1, dtype=torch.long, device=hidden_states.device).view( | |
-1, 1 | |
) | |
else: | |
position_ids_l = torch.arange(query_length, dtype=torch.long, device=hidden_states.device).view(-1, 1) | |
position_ids_r = torch.arange(key_length, dtype=torch.long, device=hidden_states.device).view(1, -1) | |
distance = position_ids_l - position_ids_r | |
positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1) | |
positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility | |
if self.position_embedding_type == "relative_key": | |
relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) | |
attention_scores = attention_scores + relative_position_scores | |
elif self.position_embedding_type == "relative_key_query": | |
relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) | |
relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding) | |
attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key | |
attention_scores = attention_scores / math.sqrt(self.attention_head_size) | |
if attention_mask is not None: | |
# Apply the attention mask is (precomputed for all layers in AlignTextModel forward() function) | |
attention_scores = attention_scores + attention_mask | |
# Normalize the attention scores to probabilities. | |
attention_probs = nn.functional.softmax(attention_scores, dim=-1) | |
# This is actually dropping out entire tokens to attend to, which might | |
# seem a bit unusual, but is taken from the original Transformer paper. | |
attention_probs = self.dropout(attention_probs) | |
# Mask heads if we want to | |
if head_mask is not None: | |
attention_probs = attention_probs * head_mask | |
context_layer = torch.matmul(attention_probs, value_layer) | |
context_layer = context_layer.permute(0, 2, 1, 3).contiguous() | |
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) | |
context_layer = context_layer.view(new_context_layer_shape) | |
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) | |
if self.is_decoder: | |
outputs = outputs + (past_key_value,) | |
return outputs | |
# Copied from transformers.models.bert.modeling_bert.BertSelfOutput with Bert->AlignText | |
class AlignTextSelfOutput(nn.Module): | |
def __init__(self, config): | |
super().__init__() | |
self.dense = nn.Linear(config.hidden_size, config.hidden_size) | |
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) | |
self.dropout = nn.Dropout(config.hidden_dropout_prob) | |
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: | |
hidden_states = self.dense(hidden_states) | |
hidden_states = self.dropout(hidden_states) | |
hidden_states = self.LayerNorm(hidden_states + input_tensor) | |
return hidden_states | |
# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->AlignText | |
class AlignTextAttention(nn.Module): | |
def __init__(self, config, position_embedding_type=None): | |
super().__init__() | |
self.self = AlignTextSelfAttention(config, position_embedding_type=position_embedding_type) | |
self.output = AlignTextSelfOutput(config) | |
self.pruned_heads = set() | |
def prune_heads(self, heads): | |
if len(heads) == 0: | |
return | |
heads, index = find_pruneable_heads_and_indices( | |
heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads | |
) | |
# Prune linear layers | |
self.self.query = prune_linear_layer(self.self.query, index) | |
self.self.key = prune_linear_layer(self.self.key, index) | |
self.self.value = prune_linear_layer(self.self.value, index) | |
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) | |
# Update hyper params and store pruned heads | |
self.self.num_attention_heads = self.self.num_attention_heads - len(heads) | |
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads | |
self.pruned_heads = self.pruned_heads.union(heads) | |
def forward( | |
self, | |
hidden_states: torch.Tensor, | |
attention_mask: Optional[torch.FloatTensor] = None, | |
head_mask: Optional[torch.FloatTensor] = None, | |
encoder_hidden_states: Optional[torch.FloatTensor] = None, | |
encoder_attention_mask: Optional[torch.FloatTensor] = None, | |
past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, | |
output_attentions: Optional[bool] = False, | |
) -> Tuple[torch.Tensor]: | |
self_outputs = self.self( | |
hidden_states, | |
attention_mask, | |
head_mask, | |
encoder_hidden_states, | |
encoder_attention_mask, | |
past_key_value, | |
output_attentions, | |
) | |
attention_output = self.output(self_outputs[0], hidden_states) | |
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them | |
return outputs | |
# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->AlignText | |
class AlignTextIntermediate(nn.Module): | |
def __init__(self, config): | |
super().__init__() | |
self.dense = nn.Linear(config.hidden_size, config.intermediate_size) | |
if isinstance(config.hidden_act, str): | |
self.intermediate_act_fn = ACT2FN[config.hidden_act] | |
else: | |
self.intermediate_act_fn = config.hidden_act | |
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: | |
hidden_states = self.dense(hidden_states) | |
hidden_states = self.intermediate_act_fn(hidden_states) | |
return hidden_states | |
# Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->AlignText | |
class AlignTextOutput(nn.Module): | |
def __init__(self, config): | |
super().__init__() | |
self.dense = nn.Linear(config.intermediate_size, config.hidden_size) | |
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) | |
self.dropout = nn.Dropout(config.hidden_dropout_prob) | |
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: | |
hidden_states = self.dense(hidden_states) | |
hidden_states = self.dropout(hidden_states) | |
hidden_states = self.LayerNorm(hidden_states + input_tensor) | |
return hidden_states | |
# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->AlignText | |
class AlignTextLayer(nn.Module): | |
def __init__(self, config): | |
super().__init__() | |
self.chunk_size_feed_forward = config.chunk_size_feed_forward | |
self.seq_len_dim = 1 | |
self.attention = AlignTextAttention(config) | |
self.is_decoder = config.is_decoder | |
self.add_cross_attention = config.add_cross_attention | |
if self.add_cross_attention: | |
if not self.is_decoder: | |
raise ValueError(f"{self} should be used as a decoder model if cross attention is added") | |
self.crossattention = AlignTextAttention(config, position_embedding_type="absolute") | |
self.intermediate = AlignTextIntermediate(config) | |
self.output = AlignTextOutput(config) | |
def forward( | |
self, | |
hidden_states: torch.Tensor, | |
attention_mask: Optional[torch.FloatTensor] = None, | |
head_mask: Optional[torch.FloatTensor] = None, | |
encoder_hidden_states: Optional[torch.FloatTensor] = None, | |
encoder_attention_mask: Optional[torch.FloatTensor] = None, | |
past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, | |
output_attentions: Optional[bool] = False, | |
) -> Tuple[torch.Tensor]: | |
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2 | |
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None | |
self_attention_outputs = self.attention( | |
hidden_states, | |
attention_mask, | |
head_mask, | |
output_attentions=output_attentions, | |
past_key_value=self_attn_past_key_value, | |
) | |
attention_output = self_attention_outputs[0] | |
# if decoder, the last output is tuple of self-attn cache | |
if self.is_decoder: | |
outputs = self_attention_outputs[1:-1] | |
present_key_value = self_attention_outputs[-1] | |
else: | |
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights | |
cross_attn_present_key_value = None | |
if self.is_decoder and encoder_hidden_states is not None: | |
if not hasattr(self, "crossattention"): | |
raise ValueError( | |
f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers" | |
" by setting `config.add_cross_attention=True`" | |
) | |
# cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple | |
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None | |
cross_attention_outputs = self.crossattention( | |
attention_output, | |
attention_mask, | |
head_mask, | |
encoder_hidden_states, | |
encoder_attention_mask, | |
cross_attn_past_key_value, | |
output_attentions, | |
) | |
attention_output = cross_attention_outputs[0] | |
outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights | |
# add cross-attn cache to positions 3,4 of present_key_value tuple | |
cross_attn_present_key_value = cross_attention_outputs[-1] | |
present_key_value = present_key_value + cross_attn_present_key_value | |
layer_output = apply_chunking_to_forward( | |
self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output | |
) | |
outputs = (layer_output,) + outputs | |
# if decoder, return the attn key/values as the last output | |
if self.is_decoder: | |
outputs = outputs + (present_key_value,) | |
return outputs | |
def feed_forward_chunk(self, attention_output): | |
intermediate_output = self.intermediate(attention_output) | |
layer_output = self.output(intermediate_output, attention_output) | |
return layer_output | |
# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->AlignText | |
class AlignTextEncoder(nn.Module): | |
def __init__(self, config): | |
super().__init__() | |
self.config = config | |
self.layer = nn.ModuleList([AlignTextLayer(config) for _ in range(config.num_hidden_layers)]) | |
self.gradient_checkpointing = False | |
def forward( | |
self, | |
hidden_states: torch.Tensor, | |
attention_mask: Optional[torch.FloatTensor] = None, | |
head_mask: Optional[torch.FloatTensor] = None, | |
encoder_hidden_states: Optional[torch.FloatTensor] = None, | |
encoder_attention_mask: Optional[torch.FloatTensor] = None, | |
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, | |
use_cache: Optional[bool] = None, | |
output_attentions: Optional[bool] = False, | |
output_hidden_states: Optional[bool] = False, | |
return_dict: Optional[bool] = True, | |
) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]: | |
all_hidden_states = () if output_hidden_states else None | |
all_self_attentions = () if output_attentions else None | |
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None | |
if self.gradient_checkpointing and self.training: | |
if use_cache: | |
logger.warning_once( | |
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." | |
) | |
use_cache = False | |
next_decoder_cache = () if use_cache 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 | |
past_key_value = past_key_values[i] if past_key_values is not None else None | |
if self.gradient_checkpointing and self.training: | |
def create_custom_forward(module): | |
def custom_forward(*inputs): | |
return module(*inputs, past_key_value, output_attentions) | |
return custom_forward | |
layer_outputs = torch.utils.checkpoint.checkpoint( | |
create_custom_forward(layer_module), | |
hidden_states, | |
attention_mask, | |
layer_head_mask, | |
encoder_hidden_states, | |
encoder_attention_mask, | |
) | |
else: | |
layer_outputs = layer_module( | |
hidden_states, | |
attention_mask, | |
layer_head_mask, | |
encoder_hidden_states, | |
encoder_attention_mask, | |
past_key_value, | |
output_attentions, | |
) | |
hidden_states = layer_outputs[0] | |
if use_cache: | |
next_decoder_cache += (layer_outputs[-1],) | |
if output_attentions: | |
all_self_attentions = all_self_attentions + (layer_outputs[1],) | |
if self.config.add_cross_attention: | |
all_cross_attentions = all_cross_attentions + (layer_outputs[2],) | |
if output_hidden_states: | |
all_hidden_states = all_hidden_states + (hidden_states,) | |
if not return_dict: | |
return tuple( | |
v | |
for v in [ | |
hidden_states, | |
next_decoder_cache, | |
all_hidden_states, | |
all_self_attentions, | |
all_cross_attentions, | |
] | |
if v is not None | |
) | |
return BaseModelOutputWithPastAndCrossAttentions( | |
last_hidden_state=hidden_states, | |
past_key_values=next_decoder_cache, | |
hidden_states=all_hidden_states, | |
attentions=all_self_attentions, | |
cross_attentions=all_cross_attentions, | |
) | |
# Copied from transformers.models.bert.modeling_bert.BertPooler with Bert -> AlignText | |
class AlignTextPooler(nn.Module): | |
def __init__(self, config): | |
super().__init__() | |
self.dense = nn.Linear(config.hidden_size, config.hidden_size) | |
self.activation = nn.Tanh() | |
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: | |
# We "pool" the model by simply taking the hidden state corresponding | |
# to the first token. | |
first_token_tensor = hidden_states[:, 0] | |
pooled_output = self.dense(first_token_tensor) | |
pooled_output = self.activation(pooled_output) | |
return pooled_output | |
class AlignPreTrainedModel(PreTrainedModel): | |
""" | |
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained | |
models. | |
""" | |
config_class = AlignConfig | |
base_model_prefix = "align" | |
supports_gradient_checkpointing = True | |
def _init_weights(self, module): | |
"""Initialize the weights""" | |
if isinstance(module, (nn.Linear, nn.Conv2d)): | |
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, AlignModel): | |
nn.init.xavier_uniform_(module.text_projection.weight) | |
module.text_projection.bias.data.zero_() | |
module.text_projection._is_hf_initialized = True | |
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_() | |
if isinstance(module, nn.LayerNorm): | |
module.bias.data.zero_() | |
module.weight.data.fill_(1.0) | |
def _set_gradient_checkpointing(self, module, value=False): | |
if isinstance(module, (AlignTextModel, AlignVisionModel)): | |
module.gradient_checkpointing = value | |
class AlignTextModel(AlignPreTrainedModel): | |
config_class = AlignTextConfig | |
def __init__(self, config: AlignTextConfig, add_pooling_layer: bool = True): | |
super().__init__(config) | |
self.config = config | |
self.embeddings = AlignTextEmbeddings(config) | |
self.encoder = AlignTextEncoder(config) | |
self.pooler = AlignTextPooler(config) if add_pooling_layer else None | |
# Initialize weights and apply final processing | |
self.post_init() | |
def get_input_embeddings(self): | |
return self.embeddings.word_embeddings | |
def set_input_embeddings(self, value): | |
self.embeddings.word_embeddings = value | |
def forward( | |
self, | |
input_ids: Optional[torch.Tensor] = None, | |
attention_mask: Optional[torch.Tensor] = None, | |
token_type_ids: Optional[torch.Tensor] = None, | |
position_ids: Optional[torch.Tensor] = None, | |
head_mask: Optional[torch.Tensor] = None, | |
inputs_embeds: Optional[torch.Tensor] = None, | |
output_attentions: Optional[bool] = None, | |
output_hidden_states: Optional[bool] = None, | |
return_dict: Optional[bool] = None, | |
) -> Union[Tuple, BaseModelOutputWithPoolingAndCrossAttentions]: | |
r""" | |
Returns: | |
Examples: | |
```python | |
>>> from transformers import AutoTokenizer, AlignTextModel | |
>>> model = AlignTextModel.from_pretrained("kakaobrain/align-base") | |
>>> tokenizer = AutoTokenizer.from_pretrained("kakaobrain/align-base") | |
>>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt") | |
>>> outputs = model(**inputs) | |
>>> last_hidden_state = outputs.last_hidden_state | |
>>> pooled_output = outputs.pooler_output # pooled (EOS token) states | |
```""" | |
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions | |
output_hidden_states = ( | |
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states | |
) | |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | |
if input_ids is not None and inputs_embeds is not None: | |
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") | |
elif input_ids is not None: | |
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask) | |
input_shape = input_ids.size() | |
elif inputs_embeds is not None: | |
input_shape = inputs_embeds.size()[:-1] | |
else: | |
raise ValueError("You have to specify either input_ids or inputs_embeds") | |
batch_size, seq_length = input_shape | |
device = input_ids.device if input_ids is not None else inputs_embeds.device | |
if attention_mask is None: | |
attention_mask = torch.ones(((batch_size, seq_length)), device=device) | |
if token_type_ids is None: | |
if hasattr(self.embeddings, "token_type_ids"): | |
buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length] | |
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length) | |
token_type_ids = buffered_token_type_ids_expanded | |
else: | |
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) | |
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] | |
# ourselves in which case we just need to make it broadcastable to all heads. | |
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape) | |
# 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( | |
input_ids=input_ids, | |
position_ids=position_ids, | |
token_type_ids=token_type_ids, | |
inputs_embeds=inputs_embeds, | |
) | |
encoder_outputs = self.encoder( | |
embedding_output, | |
attention_mask=extended_attention_mask, | |
head_mask=head_mask, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
) | |
sequence_output = encoder_outputs[0] | |
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 BaseModelOutputWithPoolingAndCrossAttentions( | |
last_hidden_state=sequence_output, | |
pooler_output=pooled_output, | |
hidden_states=encoder_outputs.hidden_states, | |
attentions=encoder_outputs.attentions, | |
cross_attentions=encoder_outputs.cross_attentions, | |
) | |
class AlignVisionModel(AlignPreTrainedModel): | |
config_class = AlignVisionConfig | |
main_input_name = "pixel_values" | |
def __init__(self, config: AlignVisionConfig): | |
super().__init__(config) | |
self.config = config | |
self.embeddings = AlignVisionEmbeddings(config) | |
self.encoder = AlignVisionEncoder(config) | |
# Final pooling layer | |
if config.pooling_type == "mean": | |
self.pooler = nn.AvgPool2d(config.hidden_dim, ceil_mode=True) | |
elif config.pooling_type == "max": | |
self.pooler = nn.MaxPool2d(config.hidden_dim, ceil_mode=True) | |
else: | |
raise ValueError(f"config.pooling must be one of ['mean', 'max'] got {config.pooling}") | |
# Initialize weights and apply final processing | |
self.post_init() | |
def get_input_embeddings(self) -> nn.Module: | |
return self.vision_model.embeddings.convolution | |
def forward( | |
self, | |
pixel_values: Optional[torch.FloatTensor] = None, | |
output_hidden_states: Optional[bool] = None, | |
return_dict: Optional[bool] = None, | |
) -> Union[Tuple, BaseModelOutputWithPoolingAndNoAttention]: | |
r""" | |
Returns: | |
Examples: | |
```python | |
>>> from PIL import Image | |
>>> import requests | |
>>> from transformers import AutoProcessor, AlignVisionModel | |
>>> model = AlignVisionModel.from_pretrained("kakaobrain/align-base") | |
>>> processor = AutoProcessor.from_pretrained("kakaobrain/align-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 | |
>>> pooled_output = outputs.pooler_output # pooled CLS states | |
```""" | |
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") | |
embedding_output = self.embeddings(pixel_values) | |
encoder_outputs = self.encoder( | |
embedding_output, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
) | |
# Apply pooling | |
last_hidden_state = encoder_outputs[0] | |
pooled_output = self.pooler(last_hidden_state) | |
# Reshape (batch_size, projection_dim, 1 , 1) -> (batch_size, projection_dim) | |
pooled_output = pooled_output.reshape(pooled_output.shape[:2]) | |
if not return_dict: | |
return (last_hidden_state, pooled_output) + encoder_outputs[1:] | |
return BaseModelOutputWithPoolingAndNoAttention( | |
last_hidden_state=last_hidden_state, | |
pooler_output=pooled_output, | |
hidden_states=encoder_outputs.hidden_states, | |
) | |
class AlignModel(AlignPreTrainedModel): | |
config_class = AlignConfig | |
def __init__(self, config: AlignConfig): | |
super().__init__(config) | |
if not isinstance(config.text_config, AlignTextConfig): | |
raise ValueError( | |
"config.text_config is expected to be of type AlignTextConfig but is of type" | |
f" {type(config.text_config)}." | |
) | |
if not isinstance(config.vision_config, AlignVisionConfig): | |
raise ValueError( | |
"config.vision_config is expected to be of type AlignVisionConfig but is of type" | |
f" {type(config.vision_config)}." | |
) | |
text_config = config.text_config | |
vision_config = config.vision_config | |
self.projection_dim = config.projection_dim | |
self.text_embed_dim = text_config.hidden_size | |
self.text_model = AlignTextModel(text_config) | |
self.vision_model = AlignVisionModel(vision_config) | |
self.text_projection = nn.Linear(self.text_embed_dim, self.projection_dim) | |
self.temperature = nn.Parameter(torch.tensor(self.config.temperature_init_value)) | |
# Initialize weights and apply final processing | |
self.post_init() | |
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, | |
head_mask: Optional[torch.Tensor] = None, | |
inputs_embeds: 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 [`AlignTextModel`]. | |
Examples: | |
```python | |
>>> from transformers import AutoTokenizer, AlignModel | |
>>> model = AlignModel.from_pretrained("kakaobrain/align-base") | |
>>> tokenizer = AutoTokenizer.from_pretrained("kakaobrain/align-base") | |
>>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt") | |
>>> text_features = model.get_text_features(**inputs) | |
```""" | |
# Use ALIGN model's config for some fields (if specified) instead of those of vision & text components. | |
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions | |
output_hidden_states = ( | |
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states | |
) | |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | |
text_outputs = self.text_model( | |
input_ids=input_ids, | |
attention_mask=attention_mask, | |
token_type_ids=token_type_ids, | |
position_ids=position_ids, | |
head_mask=head_mask, | |
inputs_embeds=inputs_embeds, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
) | |
last_hidden_state = text_outputs[0][:, 0, :] | |
text_features = self.text_projection(last_hidden_state) | |
return text_features | |
def get_image_features( | |
self, | |
pixel_values: Optional[torch.FloatTensor] = 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 [`AlignVisionModel`]. | |
Examples: | |
```python | |
>>> from PIL import Image | |
>>> import requests | |
>>> from transformers import AutoProcessor, AlignModel | |
>>> model = AlignModel.from_pretrained("kakaobrain/align-base") | |
>>> processor = AutoProcessor.from_pretrained("kakaobrain/align-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") | |
>>> image_features = model.get_image_features(**inputs) | |
```""" | |
# Use ALIGN model's config for some fields (if specified) instead of those of vision & text components. | |
output_hidden_states = ( | |
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states | |
) | |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | |
vision_outputs = self.vision_model( | |
pixel_values=pixel_values, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
) | |
image_features = vision_outputs[1] # pooled_output | |
return image_features | |
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, | |
position_ids: Optional[torch.Tensor] = None, | |
head_mask: Optional[torch.Tensor] = None, | |
inputs_embeds: Optional[torch.Tensor] = None, | |
return_loss: Optional[bool] = None, | |
output_attentions: Optional[bool] = None, | |
output_hidden_states: Optional[bool] = None, | |
return_dict: Optional[bool] = None, | |
) -> Union[Tuple, AlignOutput]: | |
r""" | |
Returns: | |
Examples: | |
```python | |
>>> from PIL import Image | |
>>> import requests | |
>>> from transformers import AutoProcessor, AlignModel | |
>>> model = AlignModel.from_pretrained("kakaobrain/align-base") | |
>>> processor = AutoProcessor.from_pretrained("kakaobrain/align-base") | |
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" | |
>>> image = Image.open(requests.get(url, stream=True).raw) | |
>>> inputs = processor( | |
... text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True | |
... ) | |
>>> outputs = model(**inputs) | |
>>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score | |
>>> probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities | |
```""" | |
# Use ALIGN model's config for some fields (if specified) instead of those of vision & text components. | |
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions | |
output_hidden_states = ( | |
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states | |
) | |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | |
vision_outputs = self.vision_model( | |
pixel_values=pixel_values, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
) | |
text_outputs = self.text_model( | |
input_ids=input_ids, | |
attention_mask=attention_mask, | |
token_type_ids=token_type_ids, | |
position_ids=position_ids, | |
head_mask=head_mask, | |
inputs_embeds=inputs_embeds, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
) | |
image_embeds = vision_outputs[1] | |
text_embeds = text_outputs[0][:, 0, :] | |
text_embeds = self.text_projection(text_embeds) | |
# normalized features | |
image_embeds = image_embeds / image_embeds.norm(p=2, dim=-1, keepdim=True) | |
text_embeds = text_embeds / text_embeds.norm(p=2, dim=-1, keepdim=True) | |
# cosine similarity as logits | |
logits_per_text = torch.matmul(text_embeds, image_embeds.t()) / self.temperature | |
logits_per_image = logits_per_text.t() | |
loss = None | |
if return_loss: | |
loss = align_loss(logits_per_text) | |
if not return_dict: | |
output = (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs) | |
return ((loss,) + output) if loss is not None else output | |
return AlignOutput( | |
loss=loss, | |
logits_per_image=logits_per_image, | |
logits_per_text=logits_per_text, | |
text_embeds=text_embeds, | |
image_embeds=image_embeds, | |
text_model_output=text_outputs, | |
vision_model_output=vision_outputs, | |
) | |