1346 lines
53 KiB
Python
1346 lines
53 KiB
Python
# coding=utf-8
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# Copyright 2023 The Google Research Team Authors and The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""PyTorch ALIGN model."""
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import math
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from dataclasses import dataclass
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from typing import Any, Callable, Optional, Union
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import torch
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import torch.utils.checkpoint
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from torch import nn
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from ...activations import ACT2FN
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from ...modeling_layers import GradientCheckpointingLayer
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from ...modeling_outputs import (
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BaseModelOutput,
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BaseModelOutputWithNoAttention,
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BaseModelOutputWithPooling,
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BaseModelOutputWithPoolingAndNoAttention,
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)
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from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
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from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
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from ...utils import ModelOutput, auto_docstring, can_return_tuple, logging
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from ...utils.deprecation import deprecate_kwarg
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from .configuration_align import AlignConfig, AlignTextConfig, AlignVisionConfig
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logger = logging.get_logger(__name__)
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@dataclass
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@auto_docstring(
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custom_intro="""
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Base class for vision model's outputs that also contains image embeddings of the pooling of the last hidden states.
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"""
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)
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class AlignVisionModelOutput(ModelOutput):
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r"""
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image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):
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The image embeddings obtained by applying the projection layer to the pooler_output.
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"""
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image_embeds: Optional[torch.FloatTensor] = None
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last_hidden_state: Optional[torch.FloatTensor] = None
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hidden_states: Optional[tuple[torch.FloatTensor]] = None
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@dataclass
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@auto_docstring(
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custom_intro="""
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Base class for text model's outputs that also contains a pooling of the last hidden states.
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"""
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)
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class AlignTextModelOutput(ModelOutput):
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r"""
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text_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):
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The text embeddings obtained by applying the projection layer to the pooler_output.
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"""
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text_embeds: Optional[torch.FloatTensor] = None
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last_hidden_state: Optional[torch.FloatTensor] = None
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hidden_states: Optional[tuple[torch.FloatTensor]] = None
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attentions: Optional[tuple[torch.FloatTensor]] = None
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@dataclass
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@auto_docstring
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class AlignOutput(ModelOutput):
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r"""
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loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`):
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Contrastive loss for image-text similarity.
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logits_per_image (`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`):
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The scaled dot product scores between `image_embeds` and `text_embeds`. This represents the image-text
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similarity scores.
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logits_per_text (`torch.FloatTensor` of shape `(text_batch_size, image_batch_size)`):
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The scaled dot product scores between `text_embeds` and `image_embeds`. This represents the text-image
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similarity scores.
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text_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim`):
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The text embeddings obtained by applying the projection layer to the pooled output of [`AlignTextModel`].
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image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim`):
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The output of [`AlignVisionModel`].
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text_model_output (`BaseModelOutputWithPooling`):
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The output of the [`AlignTextModel`].
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vision_model_output (`BaseModelOutputWithPoolingAndNoAttention`):
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The output of the [`AlignVisionModel`].
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"""
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loss: Optional[torch.FloatTensor] = None
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logits_per_image: Optional[torch.FloatTensor] = None
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logits_per_text: Optional[torch.FloatTensor] = None
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text_embeds: Optional[torch.FloatTensor] = None
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image_embeds: Optional[torch.FloatTensor] = None
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text_model_output: BaseModelOutputWithPooling = None
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vision_model_output: BaseModelOutputWithPoolingAndNoAttention = None
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def to_tuple(self) -> tuple[Any]:
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return tuple(
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self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple()
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for k in self.keys()
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)
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# contrastive loss function, adapted from
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# https://sachinruk.github.io/blog/pytorch/pytorch%20lightning/loss%20function/gpu/2021/03/07/CLIP.html
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def contrastive_loss(logits: torch.Tensor) -> torch.Tensor:
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return nn.functional.cross_entropy(logits, torch.arange(len(logits), device=logits.device), label_smoothing=0.1)
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def align_loss(similarity: torch.Tensor) -> torch.Tensor:
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caption_loss = contrastive_loss(similarity)
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image_loss = contrastive_loss(similarity.t())
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return (caption_loss + image_loss) / 2.0
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# Copied from transformers.models.efficientnet.modeling_efficientnet.round_filters with EfficientNet->AlignVision
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def round_filters(config: AlignVisionConfig, num_channels: int):
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r"""
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Round number of filters based on depth multiplier.
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"""
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divisor = config.depth_divisor
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num_channels *= config.width_coefficient
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new_dim = max(divisor, int(num_channels + divisor / 2) // divisor * divisor)
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# Make sure that round down does not go down by more than 10%.
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if new_dim < 0.9 * num_channels:
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new_dim += divisor
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return int(new_dim)
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# Copied from transformers.models.efficientnet.modeling_efficientnet.correct_pad
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def correct_pad(kernel_size: Union[int, tuple], adjust: bool = True):
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r"""
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Utility function to get the tuple padding value for the depthwise convolution.
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Args:
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kernel_size (`int` or `tuple`):
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Kernel size of the convolution layers.
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adjust (`bool`, *optional*, defaults to `True`):
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Adjusts padding value to apply to right and bottom sides of the input.
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"""
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if isinstance(kernel_size, int):
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kernel_size = (kernel_size, kernel_size)
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correct = (kernel_size[0] // 2, kernel_size[1] // 2)
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if adjust:
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return (correct[1] - 1, correct[1], correct[0] - 1, correct[0])
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else:
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return (correct[1], correct[1], correct[0], correct[0])
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# Copied from transformers.models.efficientnet.modeling_efficientnet.EfficientNetEmbeddings with EfficientNet->AlignVision
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class AlignVisionEmbeddings(nn.Module):
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r"""
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A module that corresponds to the stem module of the original work.
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"""
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def __init__(self, config: AlignVisionConfig):
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super().__init__()
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self.out_dim = round_filters(config, 32)
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self.padding = nn.ZeroPad2d(padding=(0, 1, 0, 1))
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self.convolution = nn.Conv2d(
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config.num_channels, self.out_dim, kernel_size=3, stride=2, padding="valid", bias=False
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)
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self.batchnorm = nn.BatchNorm2d(self.out_dim, eps=config.batch_norm_eps, momentum=config.batch_norm_momentum)
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self.activation = ACT2FN[config.hidden_act]
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def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
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features = self.padding(pixel_values)
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features = self.convolution(features)
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features = self.batchnorm(features)
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features = self.activation(features)
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return features
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# Copied from transformers.models.efficientnet.modeling_efficientnet.EfficientNetDepthwiseConv2d with EfficientNet->AlignVision
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class AlignVisionDepthwiseConv2d(nn.Conv2d):
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def __init__(
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self,
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in_channels,
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depth_multiplier=1,
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kernel_size=3,
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stride=1,
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padding=0,
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dilation=1,
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bias=True,
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padding_mode="zeros",
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):
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out_channels = in_channels * depth_multiplier
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super().__init__(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=kernel_size,
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stride=stride,
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padding=padding,
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dilation=dilation,
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groups=in_channels,
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bias=bias,
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padding_mode=padding_mode,
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)
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# Copied from transformers.models.efficientnet.modeling_efficientnet.EfficientNetExpansionLayer with EfficientNet->AlignVision
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class AlignVisionExpansionLayer(nn.Module):
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r"""
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This corresponds to the expansion phase of each block in the original implementation.
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"""
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def __init__(self, config: AlignVisionConfig, in_dim: int, out_dim: int, stride: int):
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super().__init__()
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self.expand_conv = nn.Conv2d(
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in_channels=in_dim,
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out_channels=out_dim,
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kernel_size=1,
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padding="same",
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bias=False,
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)
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self.expand_bn = nn.BatchNorm2d(num_features=out_dim, eps=config.batch_norm_eps)
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self.expand_act = ACT2FN[config.hidden_act]
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def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor:
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# Expand phase
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hidden_states = self.expand_conv(hidden_states)
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hidden_states = self.expand_bn(hidden_states)
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hidden_states = self.expand_act(hidden_states)
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return hidden_states
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# Copied from transformers.models.efficientnet.modeling_efficientnet.EfficientNetDepthwiseLayer with EfficientNet->AlignVision
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class AlignVisionDepthwiseLayer(nn.Module):
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r"""
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This corresponds to the depthwise convolution phase of each block in the original implementation.
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"""
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def __init__(
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self,
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config: AlignVisionConfig,
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in_dim: int,
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stride: int,
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kernel_size: int,
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adjust_padding: bool,
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):
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super().__init__()
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self.stride = stride
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conv_pad = "valid" if self.stride == 2 else "same"
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padding = correct_pad(kernel_size, adjust=adjust_padding)
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self.depthwise_conv_pad = nn.ZeroPad2d(padding=padding)
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self.depthwise_conv = AlignVisionDepthwiseConv2d(
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in_dim, kernel_size=kernel_size, stride=stride, padding=conv_pad, bias=False
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)
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self.depthwise_norm = nn.BatchNorm2d(
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num_features=in_dim, eps=config.batch_norm_eps, momentum=config.batch_norm_momentum
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)
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self.depthwise_act = ACT2FN[config.hidden_act]
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def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor:
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# Depthwise convolution
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if self.stride == 2:
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hidden_states = self.depthwise_conv_pad(hidden_states)
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hidden_states = self.depthwise_conv(hidden_states)
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hidden_states = self.depthwise_norm(hidden_states)
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hidden_states = self.depthwise_act(hidden_states)
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return hidden_states
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# Copied from transformers.models.efficientnet.modeling_efficientnet.EfficientNetSqueezeExciteLayer with EfficientNet->AlignVision
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class AlignVisionSqueezeExciteLayer(nn.Module):
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r"""
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This corresponds to the Squeeze and Excitement phase of each block in the original implementation.
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"""
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def __init__(self, config: AlignVisionConfig, in_dim: int, expand_dim: int, expand: bool = False):
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super().__init__()
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self.dim = expand_dim if expand else in_dim
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self.dim_se = max(1, int(in_dim * config.squeeze_expansion_ratio))
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self.squeeze = nn.AdaptiveAvgPool2d(output_size=1)
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self.reduce = nn.Conv2d(
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in_channels=self.dim,
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out_channels=self.dim_se,
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kernel_size=1,
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padding="same",
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)
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self.expand = nn.Conv2d(
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in_channels=self.dim_se,
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out_channels=self.dim,
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kernel_size=1,
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padding="same",
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)
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self.act_reduce = ACT2FN[config.hidden_act]
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self.act_expand = nn.Sigmoid()
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def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor:
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inputs = hidden_states
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hidden_states = self.squeeze(hidden_states)
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hidden_states = self.reduce(hidden_states)
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hidden_states = self.act_reduce(hidden_states)
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hidden_states = self.expand(hidden_states)
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hidden_states = self.act_expand(hidden_states)
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hidden_states = torch.mul(inputs, hidden_states)
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return hidden_states
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class AlignVisionFinalBlockLayer(nn.Module):
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r"""
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This corresponds to the final phase of each block in the original implementation.
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"""
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def __init__(
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self, config: AlignVisionConfig, in_dim: int, out_dim: int, stride: int, drop_rate: float, id_skip: bool
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):
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super().__init__()
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self.apply_dropout = stride == 1 and not id_skip
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self.project_conv = nn.Conv2d(
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in_channels=in_dim,
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out_channels=out_dim,
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kernel_size=1,
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padding="same",
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bias=False,
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)
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self.project_bn = nn.BatchNorm2d(
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num_features=out_dim, eps=config.batch_norm_eps, momentum=config.batch_norm_momentum
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)
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self.dropout = nn.Dropout(p=drop_rate)
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def forward(self, embeddings: torch.FloatTensor, hidden_states: torch.FloatTensor) -> torch.Tensor:
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hidden_states = self.project_conv(hidden_states)
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hidden_states = self.project_bn(hidden_states)
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if self.apply_dropout:
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hidden_states = self.dropout(hidden_states)
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hidden_states = hidden_states + embeddings
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return hidden_states
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class AlignVisionBlock(nn.Module):
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r"""
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This corresponds to the block module of original the EfficientNet vision encoder implementation.
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Args:
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config ([`AlignVisionConfig`]):
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Model configuration class.
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in_dim (`int`):
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Number of input channels.
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out_dim (`int`):
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Number of output channels.
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stride (`int`):
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Stride size to be used in convolution layers.
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expand_ratio (`int`):
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Expand ratio to set the output dimensions for the expansion and squeeze-excite layers.
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kernel_size (`int`):
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Kernel size for the depthwise convolution layer.
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drop_rate (`float`):
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Dropout rate to be used in the final phase of each block.
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id_skip (`bool`):
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Whether to apply dropout and sum the final hidden states with the input embeddings during the final phase
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of each block. Set to `True` for the first block of each stage.
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adjust_padding (`bool`):
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Whether to apply padding to only right and bottom side of the input kernel before the depthwise convolution
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operation, set to `True` for inputs with odd input sizes.
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"""
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def __init__(
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self,
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config: AlignVisionConfig,
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in_dim: int,
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out_dim: int,
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stride: int,
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expand_ratio: int,
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kernel_size: int,
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drop_rate: float,
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id_skip: bool,
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adjust_padding: bool,
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):
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super().__init__()
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self.expand_ratio = expand_ratio
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self.expand = True if self.expand_ratio != 1 else False
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expand_in_dim = in_dim * expand_ratio
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if self.expand:
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self.expansion = AlignVisionExpansionLayer(
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config=config, in_dim=in_dim, out_dim=expand_in_dim, stride=stride
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)
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self.depthwise_conv = AlignVisionDepthwiseLayer(
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config=config,
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in_dim=expand_in_dim if self.expand else in_dim,
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stride=stride,
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kernel_size=kernel_size,
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adjust_padding=adjust_padding,
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)
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self.squeeze_excite = AlignVisionSqueezeExciteLayer(
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config=config, in_dim=in_dim, expand_dim=expand_in_dim, expand=self.expand
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)
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self.projection = AlignVisionFinalBlockLayer(
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config=config,
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in_dim=expand_in_dim if self.expand else in_dim,
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out_dim=out_dim,
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stride=stride,
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drop_rate=drop_rate,
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id_skip=id_skip,
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)
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def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor:
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embeddings = hidden_states
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# Expansion and depthwise convolution phase
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if self.expand_ratio != 1:
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hidden_states = self.expansion(hidden_states)
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hidden_states = self.depthwise_conv(hidden_states)
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# Squeeze and excite phase
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hidden_states = self.squeeze_excite(hidden_states)
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hidden_states = self.projection(embeddings, hidden_states)
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return hidden_states
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class AlignVisionEncoder(nn.Module):
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r"""
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Forward propagates the embeddings through each vision encoder (EfficientNet) block.
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Args:
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config ([`AlignVisionConfig`]):
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Model configuration class.
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"""
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def __init__(self, config: AlignVisionConfig):
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super().__init__()
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self.depth_coefficient = config.depth_coefficient
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def round_repeats(repeats):
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# Round number of block repeats based on depth multiplier.
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return int(math.ceil(self.depth_coefficient * repeats))
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num_base_blocks = len(config.in_channels)
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num_blocks = sum(round_repeats(n) for n in config.num_block_repeats)
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curr_block_num = 0
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blocks = []
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for i in range(num_base_blocks):
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in_dim = round_filters(config, config.in_channels[i])
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out_dim = round_filters(config, config.out_channels[i])
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stride = config.strides[i]
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kernel_size = config.kernel_sizes[i]
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expand_ratio = config.expand_ratios[i]
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for j in range(round_repeats(config.num_block_repeats[i])):
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id_skip = True if j == 0 else False
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stride = 1 if j > 0 else stride
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in_dim = out_dim if j > 0 else in_dim
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adjust_padding = False if curr_block_num in config.depthwise_padding else True
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drop_rate = config.drop_connect_rate * curr_block_num / num_blocks
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block = AlignVisionBlock(
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config=config,
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in_dim=in_dim,
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out_dim=out_dim,
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stride=stride,
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kernel_size=kernel_size,
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expand_ratio=expand_ratio,
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|
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,
|
|
)
|
|
|
|
|
|
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,
|
|
) -> 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[:, :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)
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.word_embeddings(input_ids)
|
|
token_type_embeddings = self.token_type_embeddings(token_type_ids)
|
|
|
|
embeddings = inputs_embeds + token_type_embeddings
|
|
if self.position_embedding_type == "absolute":
|
|
position_embeddings = self.position_embeddings(position_ids)
|
|
embeddings += position_embeddings
|
|
embeddings = self.LayerNorm(embeddings)
|
|
embeddings = self.dropout(embeddings)
|
|
return embeddings
|
|
|
|
|
|
def eager_attention_forward(
|
|
module: nn.Module,
|
|
query: torch.Tensor,
|
|
key: torch.Tensor,
|
|
value: torch.Tensor,
|
|
attention_mask: Optional[torch.Tensor],
|
|
scaling: float,
|
|
dropout: float = 0.0,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
**kwargs,
|
|
):
|
|
attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling
|
|
if attention_mask is not None:
|
|
causal_mask = attention_mask[:, :, :, : key.shape[-2]]
|
|
attn_weights = attn_weights + causal_mask
|
|
|
|
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
|
|
attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
|
|
|
|
if head_mask is not None:
|
|
attn_weights = attn_weights * head_mask.view(1, -1, 1, 1)
|
|
|
|
attn_output = torch.matmul(attn_weights, value)
|
|
attn_output = attn_output.transpose(1, 2).contiguous()
|
|
return attn_output, attn_weights
|
|
|
|
|
|
class AlignTextSelfAttention(nn.Module):
|
|
def __init__(self, config):
|
|
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.config = config
|
|
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.attention_dropout = config.attention_probs_dropout_prob
|
|
self.scaling = self.attention_head_size**-0.5
|
|
|
|
@deprecate_kwarg("encoder_hidden_states", version="4.54.0")
|
|
@deprecate_kwarg("encoder_attention_mask", version="4.54.0")
|
|
@deprecate_kwarg("past_key_value", version="4.54.0")
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
|
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
|
past_key_value: Optional[tuple[tuple[torch.FloatTensor]]] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
**kwargs,
|
|
) -> tuple[torch.Tensor]:
|
|
input_shape = hidden_states.shape[:-1]
|
|
hidden_shape = (*input_shape, -1, self.attention_head_size)
|
|
|
|
query_states = self.query(hidden_states).view(hidden_shape).transpose(1, 2)
|
|
key_states = self.key(hidden_states).view(hidden_shape).transpose(1, 2)
|
|
value_states = self.value(hidden_states).view(hidden_shape).transpose(1, 2)
|
|
|
|
attention_interface: Callable = eager_attention_forward
|
|
if self.config._attn_implementation != "eager":
|
|
attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
|
|
|
|
attn_output, attn_weights = attention_interface(
|
|
self,
|
|
query_states,
|
|
key_states,
|
|
value_states,
|
|
attention_mask,
|
|
dropout=0.0 if not self.training else self.attention_dropout,
|
|
scaling=self.scaling,
|
|
head_mask=head_mask,
|
|
**kwargs,
|
|
)
|
|
|
|
attn_output = attn_output.reshape(*input_shape, -1).contiguous()
|
|
outputs = (attn_output, attn_weights) if output_attentions else (attn_output,)
|
|
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
|
|
|
|
|
|
class AlignTextAttention(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.self = AlignTextSelfAttention(config)
|
|
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)
|
|
|
|
@deprecate_kwarg("encoder_hidden_states", version="4.54.0")
|
|
@deprecate_kwarg("encoder_attention_mask", version="4.54.0")
|
|
@deprecate_kwarg("past_key_value", version="4.54.0")
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
|
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
|
past_key_value: Optional[tuple[tuple[torch.FloatTensor]]] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
**kwargs,
|
|
) -> tuple[torch.Tensor]:
|
|
self_outputs = self.self(
|
|
hidden_states,
|
|
attention_mask=attention_mask,
|
|
head_mask=head_mask,
|
|
output_attentions=output_attentions,
|
|
**kwargs,
|
|
)
|
|
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
|
|
|
|
|
|
class AlignTextLayer(GradientCheckpointingLayer):
|
|
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.intermediate = AlignTextIntermediate(config)
|
|
self.output = AlignTextOutput(config)
|
|
|
|
@deprecate_kwarg("encoder_hidden_states", version="4.54.0")
|
|
@deprecate_kwarg("encoder_attention_mask", version="4.54.0")
|
|
@deprecate_kwarg("past_key_value", version="4.54.0")
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
|
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
|
past_key_value: Optional[tuple[tuple[torch.FloatTensor]]] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
**kwargs,
|
|
) -> tuple[torch.Tensor]:
|
|
self_attention_outputs = self.attention(
|
|
hidden_states,
|
|
attention_mask=attention_mask,
|
|
head_mask=head_mask,
|
|
output_attentions=output_attentions,
|
|
**kwargs,
|
|
)
|
|
attention_output = self_attention_outputs[0]
|
|
|
|
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
|
|
layer_output = apply_chunking_to_forward(
|
|
self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
|
|
)
|
|
outputs = (layer_output,) + outputs
|
|
|
|
return outputs
|
|
|
|
def feed_forward_chunk(self, attention_output):
|
|
intermediate_output = self.intermediate(attention_output)
|
|
layer_output = self.output(intermediate_output, attention_output)
|
|
return layer_output
|
|
|
|
|
|
class AlignTextEncoder(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.config = config
|
|
self.layer = nn.ModuleList([AlignTextLayer(config) for i in range(config.num_hidden_layers)])
|
|
self.gradient_checkpointing = False
|
|
|
|
@deprecate_kwarg("encoder_hidden_states", version="4.54.0")
|
|
@deprecate_kwarg("encoder_attention_mask", version="4.54.0")
|
|
@deprecate_kwarg("past_key_values", version="4.54.0")
|
|
@deprecate_kwarg("use_cache", version="4.54.0")
|
|
@can_return_tuple
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
|
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
|
past_key_values: Optional[tuple[tuple[torch.FloatTensor]]] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
output_hidden_states: Optional[bool] = False,
|
|
return_dict: Optional[bool] = True,
|
|
**kwargs,
|
|
) -> Union[tuple[torch.Tensor], BaseModelOutput]:
|
|
all_hidden_states = () if output_hidden_states else None
|
|
all_self_attentions = () if output_attentions else None
|
|
|
|
for i, layer_module in enumerate(self.layer):
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
layer_head_mask = head_mask[i] if head_mask is not None else None
|
|
|
|
layer_outputs = layer_module(
|
|
hidden_states=hidden_states,
|
|
attention_mask=attention_mask,
|
|
head_mask=layer_head_mask,
|
|
output_attentions=output_attentions,
|
|
**kwargs,
|
|
)
|
|
|
|
hidden_states = layer_outputs[0]
|
|
if output_attentions:
|
|
all_self_attentions = all_self_attentions + (layer_outputs[1],)
|
|
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
return BaseModelOutput(
|
|
last_hidden_state=hidden_states,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attentions,
|
|
)
|
|
|
|
|
|
# Copied from transformers.models.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
|
|
|
|
|
|
@auto_docstring
|
|
class AlignPreTrainedModel(PreTrainedModel):
|
|
config: AlignConfig
|
|
base_model_prefix = "align"
|
|
supports_gradient_checkpointing = True
|
|
|
|
def _init_weights(self, module: nn.Module):
|
|
"""Initialize the weights"""
|
|
std = self.config.initializer_range
|
|
if isinstance(module, (nn.Linear, nn.Conv2d)):
|
|
module.weight.data.normal_(mean=0.0, std=std)
|
|
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.temperature.data.fill_(self.config.temperature_init_value)
|
|
elif isinstance(module, nn.Embedding):
|
|
module.weight.data.normal_(mean=0.0, std=std)
|
|
if module.padding_idx is not None:
|
|
module.weight.data[module.padding_idx].zero_()
|
|
if isinstance(module, (nn.LayerNorm, nn.BatchNorm2d)):
|
|
module.bias.data.zero_()
|
|
module.weight.data.fill_(1.0)
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
The text model from ALIGN without any head or projection on top.
|
|
"""
|
|
)
|
|
class AlignTextModel(AlignPreTrainedModel):
|
|
config: AlignTextConfig
|
|
_no_split_modules = ["AlignTextEmbeddings"]
|
|
|
|
def __init__(self, config: AlignTextConfig, add_pooling_layer: bool = True):
|
|
r"""
|
|
add_pooling_layer (bool, *optional*, defaults to `True`):
|
|
Whether to add a pooling layer
|
|
"""
|
|
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
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
**kwargs,
|
|
) -> Union[tuple, BaseModelOutputWithPooling]:
|
|
r"""
|
|
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=True,
|
|
**kwargs,
|
|
)
|
|
sequence_output = encoder_outputs[0]
|
|
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
|
|
|
|
return BaseModelOutputWithPooling(
|
|
last_hidden_state=sequence_output,
|
|
pooler_output=pooled_output,
|
|
hidden_states=encoder_outputs.hidden_states,
|
|
attentions=encoder_outputs.attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
The vision model from ALIGN without any head or projection on top.
|
|
"""
|
|
)
|
|
class AlignVisionModel(AlignPreTrainedModel):
|
|
config: AlignVisionConfig
|
|
main_input_name = "pixel_values"
|
|
supports_gradient_checkpointing = False
|
|
|
|
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
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
pixel_values: Optional[torch.FloatTensor] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple, BaseModelOutputWithPoolingAndNoAttention]:
|
|
r"""
|
|
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=True,
|
|
)
|
|
# 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])
|
|
|
|
return BaseModelOutputWithPoolingAndNoAttention(
|
|
last_hidden_state=last_hidden_state,
|
|
pooler_output=pooled_output,
|
|
hidden_states=encoder_outputs.hidden_states,
|
|
)
|
|
|
|
|
|
@auto_docstring
|
|
class AlignModel(AlignPreTrainedModel):
|
|
config: AlignConfig
|
|
|
|
def __init__(self, config: AlignConfig):
|
|
super().__init__(config)
|
|
|
|
if not isinstance(config.text_config, AlignTextConfig):
|
|
raise TypeError(
|
|
"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 TypeError(
|
|
"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()
|
|
|
|
@auto_docstring
|
|
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
|
|
|
|
@auto_docstring
|
|
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
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
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"""
|
|
return_loss (`bool`, *optional*):
|
|
Whether or not to return the contrastive loss.
|
|
|
|
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, text=["a photo of a cat", "a photo of a dog"], 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=True,
|
|
)
|
|
|
|
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=True,
|
|
)
|
|
|
|
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)
|
|
|
|
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,
|
|
)
|
|
|
|
|
|
__all__ = ["AlignPreTrainedModel", "AlignTextModel", "AlignVisionModel", "AlignModel"]
|