572 lines
25 KiB
Python
572 lines
25 KiB
Python
# coding=utf-8
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# Copyright 2021 The OpenAI 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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"""TF IdeficsVision model: a copy of CLIPVisionModel using a simpler config object"""
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import math
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from dataclasses import dataclass
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from typing import Optional, Union
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import tensorflow as tf
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from ...activations_tf import get_tf_activation
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from ...modeling_tf_outputs import TFBaseModelOutput, TFBaseModelOutputWithPooling
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from ...modeling_tf_utils import TFPreTrainedModel, shape_list
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from ...tf_utils import flatten
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from ...utils import ModelOutput, logging
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from .configuration_idefics import IdeficsVisionConfig
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logger = logging.get_logger(__name__)
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@dataclass
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class TFIdeficsVisionModelOutput(ModelOutput):
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"""
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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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Args:
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image_embeds (`tf.Tensor` 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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last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
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Sequence of hidden-states at the output of the last layer of the model.
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hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `tf.Tensor` (one for the output of the embeddings, if the model has an embedding layer, +
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one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
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Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
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attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
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Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
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sequence_length)`.
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Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
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heads.
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"""
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image_embeds: Optional[tf.Tensor] = None
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last_hidden_state: Optional[tf.Tensor] = None
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hidden_states: Optional[tuple[tf.Tensor]] = None
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attentions: Optional[tuple[tf.Tensor]] = None
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class TFIdeficsVisionEmbeddings(tf.keras.layers.Layer):
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def __init__(self, config: IdeficsVisionConfig, **kwargs):
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super().__init__(**kwargs)
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self.config = config
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self.embed_dim = config.hidden_size
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self.image_size = config.image_size
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self.patch_size = config.patch_size
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self.patch_embedding = tf.keras.layers.Conv2D(
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filters=self.embed_dim,
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kernel_size=self.patch_size,
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strides=self.patch_size,
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use_bias=False,
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padding="valid",
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data_format="channels_last",
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name="patch_embedding",
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)
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self.num_patches = (self.image_size // self.patch_size) ** 2
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self.num_positions = self.num_patches + 1
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self.position_embedding = tf.keras.layers.Embedding(
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self.num_positions, self.embed_dim, name="position_embedding"
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)
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# self.position_ids = tf.range(self.num_positions)[tf.newaxis, :]
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def interpolate_pos_encoding(self, embeddings: tf.Tensor, height: int, width: int) -> tf.Tensor:
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num_patches = shape_list(embeddings)[1] - 1
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pos_embed = self.position_embedding(self.position_ids)
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num_positions = shape_list(pos_embed)[1] - 1
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if num_patches == num_positions and height == width:
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return pos_embed
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class_pos_embed = pos_embed[:, 0]
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patch_pos_embed = pos_embed[:, 1:]
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embed_dim = shape_list(embeddings)[-1]
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num_h_patches = height // self.config.patch_size
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num_w_patches = width // self.config.patch_size
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num_h_patches, num_w_patches = num_h_patches + 0.1, num_w_patches + 0.1
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sqrt_num_positions = math.sqrt(float(num_positions))
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patch_pos_embed = tf.reshape(patch_pos_embed, (1, int(sqrt_num_positions), int(sqrt_num_positions), embed_dim))
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scale_height = num_h_patches / sqrt_num_positions
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scale_width = num_w_patches / sqrt_num_positions
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original_height = tf.cast(tf.shape(patch_pos_embed)[1], tf.float32)
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original_width = tf.cast(tf.shape(patch_pos_embed)[2], tf.float32)
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# Apply scaling
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new_height = tf.cast(original_height * scale_height, tf.int32)
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new_width = tf.cast(original_width * scale_width, tf.int32)
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patch_pos_embed = tf.image.resize(
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patch_pos_embed, size=[new_height, new_width], method=tf.image.ResizeMethod.BICUBIC
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)
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if (
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int(num_h_patches) != shape_list(patch_pos_embed)[-3]
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or int(num_w_patches) != shape_list(patch_pos_embed)[-2]
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):
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raise ValueError(
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f"Number of patches for images ({int(num_h_patches), int(num_w_patches)}) don't match the "
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f"shape of position embedding ({shape_list(patch_pos_embed)[-2], shape_list(patch_pos_embed)[-1]})"
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)
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patch_pos_embed = tf.reshape(patch_pos_embed, (1, -1, embed_dim))
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return tf.concat((class_pos_embed[tf.newaxis, :], patch_pos_embed), axis=1)
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def call(self, pixel_values: tf.Tensor, interpolate_pos_encoding: bool = False) -> tf.Tensor:
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# Input `pixel_values` is NCHW format which doesn't run on CPU so first thing we do is
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# transpose it to change it to NHWC. We don't care to transpose it back because
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# the Conv2D layer is only hit once for each query
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if isinstance(pixel_values, dict):
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pixel_values = pixel_values["pixel_values"]
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pixel_values = tf.transpose(pixel_values, perm=(0, 2, 3, 1))
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batch_size, height, width, num_channels = shape_list(pixel_values)
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if not interpolate_pos_encoding:
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if height != self.image_size or width != self.image_size:
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raise ValueError(
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f"Input image size ({height}*{width}) doesn't match model"
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f" ({self.image_size}*{self.image_size}). You should try to set `interpolate_pos_encoding=True`"
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)
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patch_embeds = self.patch_embedding(pixel_values) # shape = [*, width, grid, grid]
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# Change the 2D spatial dimensions to a single temporal dimension.
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# shape = (batch_size, num_patches, out_channels=embed_dim)
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patch_embeds = flatten(patch_embeds, 1, 2)
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class_embeds = tf.broadcast_to(
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self.class_embedding[tf.newaxis, tf.newaxis, :], [batch_size, 1, self.embed_dim]
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)
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embeddings = tf.concat([class_embeds, patch_embeds], axis=1)
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# add positional encoding to each token
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if interpolate_pos_encoding:
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embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
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else:
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embeddings = embeddings + self.position_embedding(self.position_ids)
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return embeddings
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def build(self, input_shape=None):
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if self.built:
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return
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self.built = True
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self.position_ids = tf.range(self.num_positions, name="self.position_ids")[tf.newaxis, :]
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self.class_embedding = self.add_weight(shape=(self.embed_dim,), name="class_embedding")
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if getattr(self, "patch_embedding", None) is not None:
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with tf.name_scope(self.patch_embedding.name):
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self.patch_embedding.build([None, None, None, self.config.num_channels])
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if getattr(self, "position_embedding", None) is not None:
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with tf.name_scope(self.position_embedding.name):
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self.position_embedding.build(None)
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class TFIdeficsVisionAttention(tf.keras.layers.Layer):
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"""Multi-headed attention from 'Attention Is All You Need' paper"""
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def __init__(self, config, **kwargs):
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super().__init__(**kwargs)
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self.config = config
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self.embed_dim = config.hidden_size
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self.num_heads = config.num_attention_heads
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self.head_dim = self.embed_dim // self.num_heads
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if self.head_dim * self.num_heads != self.embed_dim:
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raise ValueError(
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f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
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f" {self.num_heads})."
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)
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self.scale = self.head_dim**-0.5
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self.dropout = config.attention_dropout
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self.k_proj = tf.keras.layers.Dense(self.embed_dim, name="k_proj")
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self.v_proj = tf.keras.layers.Dense(self.embed_dim, name="v_proj")
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self.q_proj = tf.keras.layers.Dense(self.embed_dim, name="q_proj")
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self.out_proj = tf.keras.layers.Dense(self.embed_dim, name="out_proj")
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def _shape(self, tensor: tf.Tensor, seq_len: int, bsz: int):
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return tf.transpose(tf.reshape(tensor, (bsz, seq_len, self.num_heads, self.head_dim)), perm=[0, 2, 1, 3])
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def call(
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self,
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hidden_states: tf.Tensor,
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attention_mask: Optional[tf.Tensor] = None,
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causal_attention_mask: Optional[tf.Tensor] = None,
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output_attentions: Optional[bool] = False,
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) -> tuple[tf.Tensor, Optional[tf.Tensor], Optional[tuple[tf.Tensor]]]:
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"""Input shape: Batch x Time x Channel"""
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bsz, tgt_len, embed_dim = shape_list(hidden_states)
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# get query proj
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query_states = self.q_proj(hidden_states) * self.scale
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key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
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value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
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proj_shape = (bsz * self.num_heads, -1, self.head_dim)
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query_states = tf.reshape(self._shape(query_states, tgt_len, bsz), proj_shape)
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key_states = tf.reshape(key_states, proj_shape)
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value_states = tf.reshape(value_states, proj_shape)
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src_len = shape_list(key_states)[1]
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attn_weights = tf.linalg.matmul(query_states, key_states, transpose_b=True)
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tf.debugging.assert_equal(
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tf.shape(attn_weights),
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[bsz * self.num_heads, tgt_len, src_len],
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message=f"Attention weights should be of size {[bsz * self.num_heads, tgt_len, src_len]}, but is {tf.shape(attn_weights)}",
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)
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# apply the causal_attention_mask first
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if causal_attention_mask is not None:
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if shape_list(causal_attention_mask) != [bsz, 1, tgt_len, src_len]:
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raise ValueError(
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f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is"
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f" {shape_list(causal_attention_mask)}"
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)
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attn_weights = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len)) + causal_attention_mask
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attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len))
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if attention_mask is not None:
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if shape_list(attention_mask) != [bsz, 1, tgt_len, src_len]:
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raise ValueError(
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f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {shape_list(attention_mask)}"
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)
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attn_weights = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len)) + attention_mask
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attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len))
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attn_weights = tf.nn.softmax(attn_weights, axis=-1)
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if output_attentions:
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# this operation is a bit akward, but it's required to
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# make sure that attn_weights keeps its gradient.
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# In order to do so, attn_weights have to reshaped
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# twice and have to be reused in the following
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attn_weights_reshaped = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len))
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attn_weights = tf.reshape(attn_weights_reshaped, (bsz * self.num_heads, tgt_len, src_len))
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else:
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attn_weights_reshaped = None
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attn_probs = tf.nn.dropout(attn_weights, rate=self.dropout)
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attn_output = tf.linalg.matmul(attn_probs, value_states)
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tf.debugging.assert_equal(
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tf.shape(attn_output),
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[bsz * self.num_heads, tgt_len, self.head_dim],
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message=f"Attention weights should be of size {[bsz * self.num_heads, tgt_len, self.head_dim]}, but is {tf.shape(attn_output)}",
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)
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attn_output = tf.reshape(attn_output, (bsz, self.num_heads, tgt_len, self.head_dim))
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attn_output = tf.transpose(attn_output, perm=[0, 2, 1, 3])
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attn_output = tf.reshape(attn_output, (bsz, tgt_len, embed_dim))
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attn_output = self.out_proj(attn_output)
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return attn_output, attn_weights_reshaped
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def build(self, input_shape=None):
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if self.built:
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return
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self.built = True
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if getattr(self, "k_proj", None) is not None:
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with tf.name_scope(self.k_proj.name):
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self.k_proj.build((self.embed_dim, self.embed_dim))
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if getattr(self, "v_proj", None) is not None:
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with tf.name_scope(self.v_proj.name):
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self.v_proj.build((self.embed_dim, self.embed_dim))
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if getattr(self, "q_proj", None) is not None:
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with tf.name_scope(self.q_proj.name):
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self.q_proj.build((self.embed_dim, self.embed_dim))
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if getattr(self, "out_proj", None) is not None:
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with tf.name_scope(self.out_proj.name):
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self.out_proj.build((self.embed_dim, self.embed_dim))
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class TFIdeficsVisionMLP(tf.keras.layers.Layer):
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def __init__(self, config, **kwargs):
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super().__init__(**kwargs)
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self.config = config
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self.activation_fn = get_tf_activation(config.hidden_act)
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self.fc1 = tf.keras.layers.Dense(config.intermediate_size, name="fc1")
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self.fc2 = tf.keras.layers.Dense(config.hidden_size, name="fc2")
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def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
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hidden_states = self.fc1(hidden_states)
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hidden_states = self.activation_fn(hidden_states)
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hidden_states = self.fc2(hidden_states)
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return hidden_states
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def build(self, input_shape=None):
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if self.built:
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return
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self.built = True
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if getattr(self, "fc1", None) is not None:
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with tf.name_scope(self.fc1.name):
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self.fc1.build(self.config.hidden_size)
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if getattr(self, "fc2", None) is not None:
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with tf.name_scope(self.fc2.name):
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self.fc2.build(self.config.intermediate_size)
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class TFIdeficsVisionEncoderLayer(tf.keras.layers.Layer):
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def __init__(self, config: IdeficsVisionConfig, **kwargs):
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super().__init__(**kwargs)
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self.embed_dim = config.hidden_size
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self.self_attn = TFIdeficsVisionAttention(config, name="self_attn")
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self.layer_norm1 = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm1")
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self.mlp = TFIdeficsVisionMLP(config, name="mlp")
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self.layer_norm2 = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm2")
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def call(
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self,
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hidden_states: tf.Tensor,
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attention_mask: tf.Tensor,
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causal_attention_mask: tf.Tensor,
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output_attentions: Optional[bool] = False,
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) -> tuple[tf.Tensor]:
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"""
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Args:
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hidden_states (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
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attention_mask (`tf.Tensor`): attention mask of size
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`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
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`(config.encoder_attention_heads,)`.
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output_attentions (`bool`, *optional*):
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Whether or not to return the attentions tensors of all attention layers. See `attentions` under
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returned tensors for more detail.
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"""
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residual = hidden_states
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hidden_states = self.layer_norm1(hidden_states)
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hidden_states, attn_weights = self.self_attn(
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hidden_states=hidden_states,
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attention_mask=attention_mask,
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causal_attention_mask=causal_attention_mask,
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output_attentions=output_attentions,
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)
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hidden_states = residual + hidden_states
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residual = hidden_states
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hidden_states = self.layer_norm2(hidden_states)
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hidden_states = self.mlp(hidden_states)
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hidden_states = residual + hidden_states
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outputs = (hidden_states,)
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if output_attentions:
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outputs += (attn_weights,)
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return outputs
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def build(self, input_shape=None):
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if self.built:
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return
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self.built = True
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if getattr(self, "layer_norm1", None) is not None:
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with tf.name_scope(self.layer_norm1.name):
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self.layer_norm1.build([None, None, self.embed_dim])
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if getattr(self, "layer_norm2", None) is not None:
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with tf.name_scope(self.layer_norm2.name):
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self.layer_norm2.build([None, None, self.embed_dim])
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class TFIdeficsVisionEncoder(tf.keras.layers.Layer):
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"""
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Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
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[`TFIdeficsVisionEncoderLayer`].
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Args:
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config: IdeficsVisionConfig
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"""
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def __init__(self, config: IdeficsVisionConfig, **kwargs):
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super().__init__(**kwargs)
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self.config = config
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self.layers = [
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TFIdeficsVisionEncoderLayer(config, name=f"layers.{i}") for i in range(config.num_hidden_layers)
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]
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self.gradient_checkpointing = False
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def call(
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self,
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inputs_embeds,
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attention_mask: Optional[tf.Tensor] = None,
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causal_attention_mask: Optional[tf.Tensor] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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training: Optional[bool] = None,
|
|
) -> Union[tuple, TFBaseModelOutput]:
|
|
r"""
|
|
Args:
|
|
inputs_embeds (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
|
|
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
|
|
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
|
|
than the model's internal embedding lookup matrix.
|
|
attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 for tokens that are **not masked**,
|
|
- 0 for tokens that are **masked**.
|
|
|
|
[What are attention masks?](../glossary#attention-mask)
|
|
causal_attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Causal mask for the text model. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 for tokens that are **not masked**,
|
|
- 0 for tokens that are **masked**.
|
|
|
|
[What are attention masks?](../glossary#attention-mask)
|
|
output_attentions (`bool`, *optional*):
|
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
|
returned tensors for more detail.
|
|
output_hidden_states (`bool`, *optional*):
|
|
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
|
|
for more detail.
|
|
return_dict (`bool`, *optional*):
|
|
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
|
"""
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
encoder_states = () if output_hidden_states else None
|
|
all_attentions = () if output_attentions else None
|
|
|
|
hidden_states = inputs_embeds
|
|
for idx, encoder_layer in enumerate(self.layers):
|
|
if output_hidden_states:
|
|
encoder_states = encoder_states + (hidden_states,)
|
|
if self.gradient_checkpointing and training:
|
|
|
|
def create_custom_forward(module):
|
|
def custom_forward(*inputs):
|
|
return module(*inputs, output_attentions)
|
|
|
|
return custom_forward
|
|
|
|
layer_outputs = tf.recompute_grad(
|
|
create_custom_forward(encoder_layer),
|
|
hidden_states,
|
|
attention_mask,
|
|
causal_attention_mask,
|
|
)
|
|
else:
|
|
layer_outputs = encoder_layer(
|
|
hidden_states,
|
|
attention_mask,
|
|
causal_attention_mask,
|
|
output_attentions=output_attentions,
|
|
)
|
|
|
|
hidden_states = layer_outputs[0]
|
|
|
|
if output_attentions:
|
|
all_attentions = all_attentions + (layer_outputs[1],)
|
|
|
|
if output_hidden_states:
|
|
encoder_states = encoder_states + (hidden_states,)
|
|
|
|
if not return_dict:
|
|
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
|
|
return TFBaseModelOutput(
|
|
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
|
|
)
|
|
|
|
def build(self, input_shape=None):
|
|
if self.built:
|
|
return
|
|
self.built = True
|
|
if getattr(self, "layers", None) is not None:
|
|
for layer in self.layers:
|
|
with tf.name_scope(layer.name):
|
|
layer.build(None)
|
|
|
|
|
|
class TFIdeficsVisionTransformer(TFPreTrainedModel):
|
|
def __init__(self, config: IdeficsVisionConfig, **kwargs):
|
|
super().__init__(config, **kwargs)
|
|
self.config = config
|
|
self.embed_dim = config.hidden_size
|
|
|
|
self.embeddings = TFIdeficsVisionEmbeddings(config, name="embeddings")
|
|
self.pre_layrnorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="pre_layrnorm")
|
|
self.encoder = TFIdeficsVisionEncoder(config, name="encoder")
|
|
self.post_layernorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="post_layernorm")
|
|
|
|
# Adapted from transformers.models.clip.modeling_clip.CLIPVisionTransformer.forward
|
|
def call(
|
|
self,
|
|
pixel_values: Optional[tf.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
interpolate_pos_encoding: Optional[bool] = False,
|
|
return_dict: Optional[bool] = None,
|
|
training: Optional[bool] = False,
|
|
) -> Union[tuple, TFBaseModelOutputWithPooling]:
|
|
r"""
|
|
Returns:
|
|
|
|
"""
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
if pixel_values is None:
|
|
raise ValueError("You have to specify pixel_values")
|
|
|
|
hidden_states = self.embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
|
|
hidden_states = self.pre_layrnorm(hidden_states)
|
|
encoder_outputs = self.encoder(
|
|
inputs_embeds=hidden_states,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
training=training,
|
|
)
|
|
|
|
last_hidden_state = encoder_outputs[0]
|
|
pooled_output = last_hidden_state[:, 0, :]
|
|
pooled_output = self.post_layernorm(pooled_output)
|
|
|
|
if not return_dict:
|
|
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
|
|
|
|
return TFBaseModelOutputWithPooling(
|
|
last_hidden_state=last_hidden_state,
|
|
pooler_output=pooled_output,
|
|
hidden_states=encoder_outputs.hidden_states,
|
|
attentions=encoder_outputs.attentions,
|
|
)
|
|
|
|
def build(self, input_shape=None):
|
|
if self.built:
|
|
return
|
|
self.built = True
|
|
if getattr(self, "embeddings", None) is not None:
|
|
with tf.name_scope(self.embeddings.name):
|
|
self.embeddings.build(None)
|
|
if getattr(self, "pre_layrnorm", None) is not None:
|
|
with tf.name_scope(self.pre_layrnorm.name):
|
|
self.pre_layrnorm.build([None, None, self.embed_dim])
|
|
if getattr(self, "encoder", None) is not None:
|
|
with tf.name_scope(self.encoder.name):
|
|
self.encoder.build(None)
|
|
if getattr(self, "post_layernorm", None) is not None:
|
|
with tf.name_scope(self.post_layernorm.name):
|
|
self.post_layernorm.build([None, self.embed_dim])
|