# coding=utf-8 # Copyright 2021 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """PyTorch TrOCR decoder model (based on RoBERTa).""" import math from typing import Optional, Union import torch from torch import nn from torch.nn import CrossEntropyLoss from ...activations import ACT2FN from ...cache_utils import Cache, EncoderDecoderCache from ...generation import GenerationMixin from ...modeling_attn_mask_utils import ( _prepare_4d_attention_mask, _prepare_4d_causal_attention_mask, ) from ...modeling_layers import GradientCheckpointingLayer from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions from ...modeling_utils import PreTrainedModel from ...utils import auto_docstring, logging from .configuration_trocr import TrOCRConfig logger = logging.get_logger(__name__) # Copied from transformers.models.bart.modeling_bart.BartLearnedPositionalEmbedding with Bart->TrOCR class TrOCRLearnedPositionalEmbedding(nn.Embedding): """ This module learns positional embeddings up to a fixed maximum size. """ def __init__(self, num_embeddings: int, embedding_dim: int): # TrOCR is set up so that if padding_idx is specified then offset the embedding ids by 2 # and adjust num_embeddings appropriately. Other models don't have this hack self.offset = 2 super().__init__(num_embeddings + self.offset, embedding_dim) def forward(self, input_ids: torch.Tensor, past_key_values_length: int = 0, position_ids: torch.Tensor = None): """`input_ids' shape is expected to be [bsz x seqlen].""" if position_ids is None: bsz, seq_len = input_ids.shape[:2] position_ids = torch.arange( past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device ).expand(bsz, -1) else: position_ids = position_ids.unsqueeze(0) return super().forward(position_ids + self.offset) # Copied from transformers.models.bart.modeling_bart.BartScaledWordEmbedding with Bart->TrOCR class TrOCRScaledWordEmbedding(nn.Embedding): """ This module overrides nn.Embeddings' forward by multiplying with embeddings scale. """ def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, embed_scale: Optional[float] = 1.0): super().__init__(num_embeddings, embedding_dim, padding_idx) self.embed_scale = embed_scale def forward(self, input_ids: torch.Tensor): return super().forward(input_ids) * self.embed_scale class TrOCRSinusoidalPositionalEmbedding(nn.Module): """This module produces sinusoidal positional embeddings of any length.""" def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None): super().__init__() self.offset = 2 self.embedding_dim = embedding_dim self.padding_idx = padding_idx self.weights = self.get_embedding(num_positions, embedding_dim, padding_idx) self.register_buffer("_float_tensor", torch.FloatTensor(1)) @staticmethod def get_embedding(num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None): """ Build sinusoidal embeddings. This matches the implementation in tensor2tensor, but differs slightly from the description in Section 3.5 of "Attention Is All You Need". """ half_dim = embedding_dim // 2 emb = math.log(10000) / (half_dim - 1) emb = torch.exp(torch.arange(half_dim, dtype=torch.int64).float() * -emb) emb = torch.arange(num_embeddings, dtype=torch.int64).float().unsqueeze(1) * emb.unsqueeze(0) emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(num_embeddings, -1) if embedding_dim % 2 == 1: # zero pad emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1) if padding_idx is not None: emb[padding_idx, :] = 0 return emb.to(torch.get_default_dtype()) @torch.no_grad() def forward(self, input_ids: torch.Tensor, past_key_values_length: int = 0): bsz, seq_len = input_ids.size() # Create the position ids from the input token ids. Any padded tokens remain padded. position_ids = self.create_position_ids_from_input_ids(input_ids, self.padding_idx, past_key_values_length).to( input_ids.device ) # expand embeddings if needed max_pos = self.padding_idx + 1 + seq_len if self.weights is None or max_pos > self.weights.size(0): # recompute/expand embeddings if needed self.weights = self.get_embedding(max_pos, self.embedding_dim, self.padding_idx) self.weights = self.weights.to(self._float_tensor) x = self.weights.index_select(0, position_ids.view(-1)).view(bsz, seq_len, -1).detach() return x def create_position_ids_from_input_ids( self, input_ids: torch.Tensor, padding_idx: int, past_key_values_length: Optional[int] = 0 ): """ Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols are ignored. This is modified from fairseq's `utils.make_positions`. """ # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. mask = input_ids.ne(padding_idx).int() incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask return incremental_indices.long() + padding_idx class TrOCRAttention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper.""" def __init__( self, config, embed_dim: int, num_heads: int, kdim: Optional[int] = None, vdim: Optional[int] = None, dropout: Optional[float] = 0.0, is_decoder: Optional[bool] = False, bias: Optional[bool] = True, is_cross_attention: Optional[bool] = False, layer_idx: Optional[bool] = None, ): super().__init__() self.embed_dim = embed_dim self.kdim = kdim if kdim is not None else embed_dim self.vdim = vdim if vdim is not None else embed_dim self.num_heads = num_heads self.dropout = dropout self.head_dim = embed_dim // num_heads if not (self.head_dim * num_heads == self.embed_dim): raise ValueError( f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" f" {num_heads})." ) self.scaling = self.head_dim**-0.5 self.is_decoder = is_decoder self.layer_idx = layer_idx self.k_proj = nn.Linear(self.kdim, embed_dim, bias=bias) self.v_proj = nn.Linear(self.vdim, embed_dim, bias=bias) self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) def forward( self, hidden_states: torch.Tensor, key_value_states: Optional[torch.Tensor] = None, past_key_value: Optional[Cache] = None, attention_mask: Optional[torch.Tensor] = None, layer_head_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = False, cache_position: Optional[torch.Tensor] = None, ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]: """Input shape: Batch x Time x Channel""" # if key_value_states are provided this layer is used as a cross-attention layer # for the decoder is_cross_attention = key_value_states is not None bsz, tgt_len, embed_dim = hidden_states.size() # get query proj query_states = self.q_proj(hidden_states) * self.scaling if past_key_value is not None: if isinstance(past_key_value, EncoderDecoderCache): is_updated = past_key_value.is_updated.get(self.layer_idx) if is_cross_attention: # after the first generated id, we can subsequently re-use all key/value_states from cache curr_past_key_value = past_key_value.cross_attention_cache else: curr_past_key_value = past_key_value.self_attention_cache else: curr_past_key_value = past_key_value current_states = key_value_states if is_cross_attention else hidden_states if is_cross_attention and past_key_value is not None and is_updated: # reuse k,v, cross_attentions key_states = curr_past_key_value.key_cache[self.layer_idx] value_states = curr_past_key_value.value_cache[self.layer_idx] else: key_states = self.k_proj(current_states) value_states = self.v_proj(current_states) key_states = key_states.view(bsz, -1, self.num_heads, self.head_dim).transpose(1, 2) value_states = value_states.view(bsz, -1, self.num_heads, self.head_dim).transpose(1, 2) if past_key_value is not None: # save all key/value_states to cache to be re-used for fast auto-regressive generation cache_position = cache_position if not is_cross_attention else None key_states, value_states = curr_past_key_value.update( key_states, value_states, self.layer_idx, {"cache_position": cache_position} ) # set flag that curr layer for cross-attn is already updated so we can re-use in subsequent calls if is_cross_attention: past_key_value.is_updated[self.layer_idx] = True proj_shape = (bsz * self.num_heads, -1, self.head_dim) query_states = query_states.view(bsz, tgt_len, self.num_heads, self.head_dim).transpose(1, 2) query_states = query_states.reshape(*proj_shape) key_states = key_states.reshape(*proj_shape) value_states = value_states.reshape(*proj_shape) src_len = key_states.size(1) attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): raise ValueError( f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is" f" {attn_weights.size()}" ) if attention_mask is not None: if attention_mask.size() != (bsz, 1, tgt_len, src_len): raise ValueError( f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}" ) attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) attn_weights = nn.functional.softmax(attn_weights, dim=-1) if layer_head_mask is not None: if layer_head_mask.size() != (self.num_heads,): raise ValueError( f"Head mask for a single layer should be of size {(self.num_heads,)}, but is" f" {layer_head_mask.size()}" ) attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len) attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) if output_attentions: # this operation is a bit awkward, but it's required to # make sure that attn_weights keeps its gradient. # In order to do so, attn_weights have to be reshaped # twice and have to be reused in the following attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len) else: attn_weights_reshaped = None attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) attn_output = torch.bmm(attn_probs, value_states) if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim): raise ValueError( f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is" f" {attn_output.size()}" ) attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim) attn_output = attn_output.transpose(1, 2) attn_output = attn_output.reshape(bsz, tgt_len, embed_dim) attn_output = self.out_proj(attn_output) return attn_output, attn_weights_reshaped class TrOCRDecoderLayer(GradientCheckpointingLayer): def __init__(self, config: TrOCRConfig, layer_idx=None): super().__init__() self.embed_dim = config.hidden_size self.self_attn = TrOCRAttention( config, embed_dim=self.embed_dim, num_heads=config.decoder_attention_heads, dropout=config.attention_dropout, is_decoder=True, layer_idx=layer_idx, ) self.dropout = config.dropout self.activation_fn = ACT2FN[config.activation_function] self.activation_dropout = config.activation_dropout self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) if config.is_decoder: self.encoder_attn = TrOCRAttention( config, embed_dim=self.embed_dim, num_heads=config.decoder_attention_heads, kdim=config.cross_attention_hidden_size, vdim=config.cross_attention_hidden_size, dropout=config.attention_dropout, is_decoder=True, is_cross_attention=True, layer_idx=layer_idx, ) self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim) self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim) self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim) self.final_layer_norm = nn.LayerNorm(self.embed_dim) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, layer_head_mask: Optional[torch.Tensor] = None, cross_attn_layer_head_mask: Optional[torch.Tensor] = None, past_key_value: Optional[Cache] = None, output_attentions: Optional[bool] = False, use_cache: Optional[bool] = True, cache_position: Optional[torch.Tensor] = None, ): """ Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (`torch.FloatTensor`): attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. encoder_hidden_states (`torch.FloatTensor`): cross attention input to the layer of shape `(batch, seq_len, embed_dim)` encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size `(encoder_attention_heads,)`. cross_attn_layer_head_mask (`torch.FloatTensor`): mask for cross-attention heads in a given layer of size *(decoder_attention_heads,)*. past_key_value (`Tuple(torch.FloatTensor)`): cached past key and value projection states output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. """ residual = hidden_states # Self Attention hidden_states, self_attn_weights = self.self_attn( hidden_states=hidden_states, past_key_value=past_key_value, attention_mask=attention_mask, layer_head_mask=layer_head_mask, output_attentions=output_attentions, cache_position=cache_position, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states hidden_states = self.self_attn_layer_norm(hidden_states) # Cross-Attention Block cross_attn_weights = None if encoder_hidden_states is not None: residual = hidden_states hidden_states, cross_attn_weights = self.encoder_attn( hidden_states=hidden_states, key_value_states=encoder_hidden_states, attention_mask=encoder_attention_mask, layer_head_mask=cross_attn_layer_head_mask, past_key_value=past_key_value, output_attentions=output_attentions, cache_position=cache_position, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states hidden_states = self.encoder_attn_layer_norm(hidden_states) # Fully Connected residual = hidden_states hidden_states = self.activation_fn(self.fc1(hidden_states)) hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) hidden_states = self.fc2(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states hidden_states = self.final_layer_norm(hidden_states) outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights, cross_attn_weights) return outputs @auto_docstring class TrOCRPreTrainedModel(PreTrainedModel): config: TrOCRConfig base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = ["TrOCRDecoderLayer"] def _init_weights(self, module): std = self.config.init_std if isinstance(module, (nn.Linear, nn.Conv1d)): module.weight.data.normal_(mean=0.0, std=std) if module.bias is not None: module.bias.data.zero_() 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_() class TrOCRDecoder(TrOCRPreTrainedModel): """ Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`TrOCRDecoderLayer`] Args: config: TrOCRConfig """ def __init__(self, config: TrOCRConfig): super().__init__(config) self.dropout = config.dropout self.layerdrop = config.decoder_layerdrop self.padding_idx = config.pad_token_id embed_scale = math.sqrt(config.hidden_size) if config.scale_embedding else 1.0 self.embed_tokens = TrOCRScaledWordEmbedding( config.vocab_size, config.hidden_size, self.padding_idx, embed_scale=embed_scale ) if config.use_learned_position_embeddings: self.embed_positions = TrOCRLearnedPositionalEmbedding(config.max_position_embeddings, config.hidden_size) else: self.embed_positions = TrOCRSinusoidalPositionalEmbedding( config.max_position_embeddings + self.padding_idx + 1, config.hidden_size, self.padding_idx, ) if config.layernorm_embedding: self.layernorm_embedding = nn.LayerNorm(config.hidden_size) else: self.layernorm_embedding = None self.layers = nn.ModuleList([TrOCRDecoderLayer(config, layer_idx=i) for i in range(config.decoder_layers)]) self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() def forward( self, input_ids=None, attention_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, head_mask=None, cross_attn_head_mask=None, past_key_values=None, inputs_embeds=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, cache_position=None, ): r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*): Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the attention modules in encoder to avoid performing cross-attention on hidden heads. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) use_cache = use_cache if use_cache is not None else self.config.use_cache return_dict = return_dict if return_dict is not None else self.config.use_return_dict # retrieve input_ids and inputs_embeds if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") elif input_ids is not None: input = input_ids input_ids = input_ids.view(-1, input.shape[-1]) elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] input = inputs_embeds[:, :, -1] else: raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") if self.gradient_checkpointing and self.training: if use_cache: logger.warning_once( "`use_cache = True` is incompatible with gradient checkpointing. Setting `use_cache = False`..." ) use_cache = False return_legacy_cache = False if use_cache and not isinstance(past_key_values, Cache): logger.warning_once( "Passing a tuple of `past_key_values` is deprecated and will be removed in Transformers v4.58.0. " "You should pass an instance of `EncoderDecoderCache` instead, e.g. " "`past_key_values=EncoderDecoderCache.from_legacy_cache(past_key_values)`." ) return_legacy_cache = True past_key_values = EncoderDecoderCache.from_legacy_cache(past_key_values) past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0 if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) if self.config.use_learned_position_embeddings: embed_pos = self.embed_positions(input, past_key_values_length=past_key_values_length) else: embed_pos = self.embed_positions(input_ids, past_key_values_length=past_key_values_length) hidden_states = inputs_embeds + embed_pos if self.layernorm_embedding is not None: hidden_states = self.layernorm_embedding(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) input_shape = input.shape attention_mask = _prepare_4d_causal_attention_mask( attention_mask, input_shape, inputs_embeds, past_key_values_length ) # expand encoder attention mask if encoder_hidden_states is not None and encoder_attention_mask is not None: # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] encoder_attention_mask = _prepare_4d_attention_mask( encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1] ) # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None # check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]): if attn_mask is not None: if attn_mask.size()[0] != (len(self.layers)): raise ValueError( f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for" f" {head_mask.size()[0]}." ) for idx, decoder_layer in enumerate(self.layers): # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description) if output_hidden_states: all_hidden_states += (hidden_states,) if self.training: dropout_probability = torch.rand([]) if dropout_probability < self.layerdrop: continue layer_outputs = decoder_layer( hidden_states, attention_mask, encoder_hidden_states, # as a positional argument for gradient checkpointing encoder_attention_mask=encoder_attention_mask, layer_head_mask=(head_mask[idx] if head_mask is not None else None), cross_attn_layer_head_mask=(cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None), past_key_value=past_key_values, output_attentions=output_attentions, use_cache=use_cache, cache_position=cache_position, ) hidden_states = layer_outputs[0] if output_attentions: all_self_attns += (layer_outputs[1],) if encoder_hidden_states is not None: all_cross_attentions += (layer_outputs[2],) # add hidden states from the last decoder layer if output_hidden_states: all_hidden_states += (hidden_states,) if return_legacy_cache: past_key_values = past_key_values.to_legacy_cache() if not return_dict: return tuple( v for v in [hidden_states, past_key_values, all_hidden_states, all_self_attns, all_cross_attentions] if v is not None ) return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=past_key_values, hidden_states=all_hidden_states, attentions=all_self_attns, cross_attentions=all_cross_attentions, ) @auto_docstring( custom_intro=""" The TrOCR Model with a language modeling head. Can be used for summarization. This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is used in combination with the [`EncoderDecoderModel`] framework. """ ) class TrOCRDecoderWrapper(TrOCRPreTrainedModel): def __init__(self, config): super().__init__(config) self.decoder = TrOCRDecoder(config) def forward(self, *args, **kwargs): return self.decoder(*args, **kwargs) @auto_docstring( custom_intro=""" The TrOCR Decoder with a language modeling head. Can be used as the decoder part of [`EncoderDecoderModel`] and """ ) class TrOCRForCausalLM(TrOCRPreTrainedModel, GenerationMixin): _tied_weights_keys = ["output_projection.weight"] def __init__(self, config): config.is_decoder = True config.is_encoder_decoder = False super().__init__(config) self.model = TrOCRDecoderWrapper(config) self.output_projection = nn.Linear(config.hidden_size, config.vocab_size, bias=False) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.model.decoder.embed_tokens def set_input_embeddings(self, value): self.model.decoder.embed_tokens = value def get_output_embeddings(self): return self.output_projection def set_output_embeddings(self, new_embeddings): self.output_projection = new_embeddings def set_decoder(self, decoder): self.model.decoder = decoder def get_decoder(self): return self.model.decoder @auto_docstring def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.LongTensor] = None, head_mask: Optional[torch.Tensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, past_key_values: Optional[tuple[tuple[torch.FloatTensor]]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, cache_position: Optional[torch.Tensor] = None, ) -> Union[tuple, CausalLMOutputWithCrossAttentions]: r""" cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. Example: ```python >>> from transformers import ( ... TrOCRConfig, ... TrOCRProcessor, ... TrOCRForCausalLM, ... ViTConfig, ... ViTModel, ... VisionEncoderDecoderModel, ... ) >>> import requests >>> from PIL import Image >>> # TrOCR is a decoder model and should be used within a VisionEncoderDecoderModel >>> # init vision2text model with random weights >>> encoder = ViTModel(ViTConfig()) >>> decoder = TrOCRForCausalLM(TrOCRConfig()) >>> model = VisionEncoderDecoderModel(encoder=encoder, decoder=decoder) >>> # If you want to start from the pretrained model, load the checkpoint with `VisionEncoderDecoderModel` >>> processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten") >>> model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-handwritten") >>> # load image from the IAM dataset >>> url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg" >>> image = Image.open(requests.get(url, stream=True).raw).convert("RGB") >>> pixel_values = processor(image, return_tensors="pt").pixel_values >>> text = "industry, ' Mr. Brown commented icily. ' Let us have a" >>> # training >>> model.config.decoder_start_token_id = processor.tokenizer.eos_token_id >>> model.config.pad_token_id = processor.tokenizer.pad_token_id >>> model.config.vocab_size = model.config.decoder.vocab_size >>> labels = processor.tokenizer(text, return_tensors="pt").input_ids >>> outputs = model(pixel_values, labels=labels) >>> loss = outputs.loss >>> round(loss.item(), 2) 5.30 >>> # inference >>> generated_ids = model.generate(pixel_values) >>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] >>> generated_text 'industry, " Mr. Brown commented icily. " Let us have a' ```""" 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 # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) outputs = self.model.decoder( input_ids=input_ids, attention_mask=attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, head_mask=head_mask, cross_attn_head_mask=cross_attn_head_mask, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, cache_position=cache_position, ) logits = self.output_projection(outputs[0]) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1)) if not return_dict: output = (logits,) + outputs[1:] return (loss,) + output if loss is not None else output return CausalLMOutputWithCrossAttentions( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, cross_attentions=outputs.cross_attentions, ) __all__ = ["TrOCRForCausalLM", "TrOCRPreTrainedModel"]