Randeng-Deltalm-362M-En-Zh / modeling_deltalm.py

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	#!/usr/bin/env python
	# -- coding: utf-8 --


	import copy
	import math
	import random
	import torch
	import torch.nn as nn
	import torch.utils.checkpoint
	from torch.nn import CrossEntropyLoss
	from typing import List, Optional, Tuple, Union

	from transformers.modeling_utils import PreTrainedModel
	from transformers.activations import ACT2FN
	from transformers.modeling_outputs import (
	BaseModelOutput,
	BaseModelOutputWithPastAndCrossAttentions,
	CausalLMOutputWithCrossAttentions,
	Seq2SeqModelOutput,
	Seq2SeqLMOutput,
	)
	from transformers.file_utils import (
	add_end_docstrings,
	add_start_docstrings,
	add_start_docstrings_to_model_forward,
	replace_return_docstrings,
	)

	import logging
	from .configuration_deltalm import DeltalmConfig
	logger = logging.getLogger(__name__)

	_CHECKPOINT_FOR_DOC = "IDEA-CCNL/Randeng-Deltalm-362M-En-Zn"
	_CONFIG_FOR_DOC = "DeltalmConfig"
	_TOKENIZER_FOR_DOC = "DeltalmTokenizer"

	# Base model docstring
	_EXPECTED_OUTPUT_SHAPE = [1, 8, 768]


	def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int):
	"""
	Shift input ids one token to the right.
	"""
	shifted_input_ids = input_ids.new_zeros(input_ids.shape)
	shifted_input_ids[:, 1:] = input_ids[:, :-1].clone()
	shifted_input_ids[:, 0] = decoder_start_token_id

	if pad_token_id is None:
	raise ValueError("self.model.config.pad_token_id has to be defined.")
	# replace possible -100 values in labels by `pad_token_id`
	shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)

	return shifted_input_ids


	def _make_causal_mask(input_ids_shape: torch.Size, dtype: torch.dtype, past_key_values_length: int = 0):
	"""
	Make causal mask used for bi-directional self-attention.
	"""
	bsz, tgt_len = input_ids_shape
	mask = torch.full((tgt_len, tgt_len), torch.tensor(float("-inf")))
	mask_cond = torch.arange(mask.size(-1))
	mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
	mask = mask.to(dtype)

	if past_key_values_length > 0:
	mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype), mask], dim=-1)
	return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)


	def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
	"""
	Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
	"""
	bsz, src_len = mask.size()
	tgt_len = tgt_len if tgt_len is not None else src_len

	expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)

	inverted_mask = 1.0 - expanded_mask

	return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)


	class DeltalmLearnedPositionalEmbedding(nn.Embedding):
	"""
	This module learns positional embeddings up to a fixed maximum size.
	"""

	def __init__(self, num_embeddings: int, embedding_dim: int):
	# Deltalm 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_shape: torch.Size, past_key_values_length: int = 0):
	"""`input_ids_shape` is expected to be [bsz x seqlen]."""
	bsz, seq_len = input_ids_shape[:2]
	positions = torch.arange(
	past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device
	)
	return super().forward(positions + self.offset)


	class DeltalmAttention(nn.Module):
	"""Multi-headed attention from 'Attention Is All You Need' paper"""

	def __init__(
	self,
	embed_dim: int,
	num_heads: int,
	dropout: float = 0.0,
	is_decoder: bool = False,
	bias: bool = True,
	):
	super().__init__()
	self.embed_dim = embed_dim
	self.num_heads = num_heads
	self.dropout = dropout
	self.head_dim = embed_dim // num_heads

	if (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}"
	f" and `num_heads`: {num_heads})."
	)
	self.scaling = self.head_dim**-0.5
	self.is_decoder = is_decoder

	self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
	self.v_proj = nn.Linear(embed_dim, 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 _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
	return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()

	def forward(
	self,
	hidden_states: torch.Tensor,
	key_value_states: Optional[torch.Tensor] = None,
	past_key_value: Optional[Tuple[torch.Tensor]] = None,
	attention_mask: Optional[torch.Tensor] = None,
	layer_head_mask: Optional[torch.Tensor] = None,
	output_attentions: bool = False,
	) -> 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, _ = hidden_states.size()

	# get query proj
	query_states = self.q_proj(hidden_states) * self.scaling
	# get key, value proj
	if is_cross_attention and past_key_value is not None:
	# reuse k,v, cross_attentions
	key_states = past_key_value[0]
	value_states = past_key_value[1]
	elif is_cross_attention:
	# cross_attentions
	key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
	value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
	elif past_key_value is not None:
	# reuse k, v, self_attention
	key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
	value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
	key_states = torch.cat([past_key_value[0], key_states], dim=2)
	value_states = torch.cat([past_key_value[1], value_states], dim=2)
	else:
	# self_attention
	key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
	value_states = self._shape(self.v_proj(hidden_states), -1, bsz)

	if self.is_decoder:
	# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
	# Further calls to cross_attention layer can then reuse all cross-attention
	# key/value_states (first "if" case)
	# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
	# all previous decoder key/value_states. Further calls to uni-directional self-attention
	# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
	# if encoder bi-directional self-attention `past_key_value` is always `None`
	past_key_value = (key_states, value_states)

	proj_shape = (bsz * self.num_heads, -1, self.head_dim)
	query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
	key_states = key_states.view(*proj_shape)
	value_states = value_states.view(*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)

	# Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
	# partitioned aross GPUs when using tensor-parallelism.
	attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)

	attn_output = self.out_proj(attn_output)

	return attn_output, attn_weights_reshaped, past_key_value


	class DeltalmEncoderLayer(nn.Module):
	def __init__(self, config: DeltalmConfig):
	super().__init__()
	self.embed_dim = config.d_model
	self.self_attn = DeltalmAttention(
	embed_dim=self.embed_dim,
	num_heads=config.encoder_attention_heads,
	dropout=config.attention_dropout,
	)
	self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
	self.dropout = config.dropout
	self.activation_fn = ACT2FN[config.activation_function]
	self.activation_dropout = config.activation_dropout
	self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
	self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
	self.final_layer_norm = nn.LayerNorm(self.embed_dim)

	def forward(
	self,
	hidden_states: torch.FloatTensor,
	attention_mask: torch.FloatTensor,
	layer_head_mask: torch.FloatTensor,
	output_attentions: Optional[bool] = False,
	) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]:
	"""
	Args:
	hidden_states (`torch.FloatTensor`): input to the layer of shape `(seq_len, batch, 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.
	layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
	`(encoder_attention_heads,)`.
	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
	hidden_states, attn_weights, _ = self.self_attn(
	hidden_states=hidden_states,
	attention_mask=attention_mask,
	layer_head_mask=layer_head_mask,
	output_attentions=output_attentions,
	)
	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)

	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)

	if hidden_states.dtype == torch.float16 and (
	torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any()
	):
	clamp_value = torch.finfo(hidden_states.dtype).max - 1000
	hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)

	outputs = (hidden_states,)

	if output_attentions:
	outputs += (attn_weights,)

	return outputs


	class DeltalmDecoderLayer(nn.Module):
	def __init__(self, config: DeltalmConfig):
	super().__init__()
	self.embed_dim = config.d_model

	self.self_attn = DeltalmAttention(
	embed_dim=self.embed_dim,
	num_heads=config.decoder_attention_heads,
	dropout=config.attention_dropout,
	is_decoder=True,
	)
	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)
	self.encoder_attn = DeltalmAttention(
	self.embed_dim,
	config.decoder_attention_heads,
	dropout=config.attention_dropout,
	is_decoder=True,
	)
	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.fc3 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
	self.fc4 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)

	self.ffn_layer_norm = nn.LayerNorm(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[Tuple[torch.Tensor]] = None,
	output_attentions: Optional[bool] = False,
	use_cache: Optional[bool] = True,
	) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
	"""
	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
	# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
	self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
	# add present self-attn cache to positions 1,2 of present_key_value tuple
	hidden_states, self_attn_weights, present_key_value = self.self_attn(
	hidden_states=hidden_states,
	past_key_value=self_attn_past_key_value,
	attention_mask=attention_mask,
	layer_head_mask=layer_head_mask,
	output_attentions=output_attentions,
	)
	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)

	# Add another ffn after self-attention to keep the structure same to encoder-layer
	residual = hidden_states
	hidden_states = self.activation_fn(self.fc3(hidden_states))
	hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
	hidden_states = self.fc4(hidden_states)
	hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
	hidden_states = residual + hidden_states
	hidden_states = self.ffn_layer_norm(hidden_states)

	# Cross-Attention Block
	cross_attn_present_key_value = None
	cross_attn_weights = None
	if encoder_hidden_states is not None:
	residual = hidden_states

	# cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple
	cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
	hidden_states, cross_attn_weights, cross_attn_present_key_value = 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=cross_attn_past_key_value,
	output_attentions=output_attentions,
	)
	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)

	# add cross-attn to positions 3,4 of present_key_value tuple
	present_key_value = present_key_value + cross_attn_present_key_value

	# 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)

	if use_cache:
	outputs += (present_key_value,)

	return outputs


	class DeltalmPretrainedModel(PreTrainedModel):
	config_class = DeltalmConfig
	base_model_prefix = "model"
	supports_gradient_checkpointing = True

	def _init_weights(self, module):
	std = self.config.init_std
	if isinstance(module, nn.Linear):
	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_()

	def _set_gradient_checkpointing(self, module, value=False):
	if isinstance(module, (DeltalmDecoder, DeltalmEncoder)):
	module.gradient_checkpointing = value


	class DeltalmDecoder(DeltalmPretrainedModel):
	"""
	Transformer decoder consisting of config.decoder_layers layers. Each layer is a [`DeltalmDecoderLayer`]
	Args:
	config: DeltalmConfig
	embed_tokens (nn.Embedding): output embedding
	"""

	def __init__(self, config: DeltalmConfig, embed_tokens: Optional[nn.Embedding] = None):
	super().__init__(config)
	self.dropout = config.dropout
	self.layerdrop = config.decoder_layerdrop
	self.padding_idx = config.pad_token_id
	self.max_target_positions = config.max_position_embeddings
	self.embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0

	if embed_tokens is not None:
	self.embed_tokens = embed_tokens
	else:
	self.embed_tokens = nn.Embedding(config.vocab_size, config.d_model, self.padding_idx)

	self.embed_positions = DeltalmLearnedPositionalEmbedding(
	config.max_position_embeddings,
	config.d_model,
	)
	self.layers = nn.ModuleList([DeltalmDecoderLayer(config) for _ in range(config.decoder_layers)])
	self.layernorm_embedding = nn.LayerNorm(config.d_model)

	self.gradient_checkpointing = False
	# Initialize weights and apply final processing
	self.post_init()

	# fairseq实现了一个 nn.init.normal_(self.output_projection.weight, mean=0, std=self.output_embed_dim ** -0.5) 对最后的output权重做正态分布转换？

	def get_input_embeddings(self):
	return self.embed_tokens

	def set_input_embeddings(self, value):
	self.embed_tokens = value

	def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
	# create causal mask
	# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
	combined_attention_mask = None
	if input_shape[-1] > 1:
	combined_attention_mask = _make_causal_mask(
	input_shape, inputs_embeds.dtype, past_key_values_length=past_key_values_length
	).to(inputs_embeds.device)

	if attention_mask is not None:
	# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
	expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1])
	combined_attention_mask = (
	expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
	)

	return combined_attention_mask

	def forward(
	self,
	input_ids: 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[List[torch.FloatTensor]] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	use_cache: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]:
	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 [`DeltalmTokenizer`]. 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 cross-attention modules in the decoder 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_shape = input_ids.size()
	input_ids = input_ids.view(-1, input_shape[-1])
	elif inputs_embeds is not None:
	input_shape = inputs_embeds.size()[:-1]
	else:
	raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")

	# past_key_values_length
	past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0

	if inputs_embeds is None:
	inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale

	attention_mask = self._prepare_decoder_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 = _expand_mask(encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1])

	# embed positions
	positions = self.embed_positions(input_shape, past_key_values_length)

	hidden_states = inputs_embeds + positions
	hidden_states = self.layernorm_embedding(hidden_states)

	hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)

	# 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
	next_decoder_cache = () if use_cache 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://arxiv.org/abs/1909.11556 for description)
	if output_hidden_states:
	all_hidden_states += (hidden_states,)
	dropout_probability = random.uniform(0, 1)
	if self.training and (dropout_probability < self.layerdrop):
	continue

	past_key_value = past_key_values[idx] if past_key_values is not None else None

	if self.gradient_checkpointing and self.training:

	if use_cache:
	logger.warning(
	"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
	)
	use_cache = False

	def create_custom_forward(module):
	def custom_forward(*inputs):
	# None for past_key_value
	return module(*inputs, output_attentions, use_cache)

	return custom_forward

	layer_outputs = torch.utils.checkpoint.checkpoint(
	create_custom_forward(decoder_layer),
	hidden_states,
	attention_mask,
	encoder_hidden_states,
	encoder_attention_mask,
	head_mask[idx] if head_mask is not None else None,
	cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None,
	None,
	)
	else:

	layer_outputs = decoder_layer(
	hidden_states,
	attention_mask=attention_mask,
	encoder_hidden_states=encoder_hidden_states,
	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_value,
	output_attentions=output_attentions,
	use_cache=use_cache,
	)
	hidden_states = layer_outputs[0]

	if use_cache:
	next_decoder_cache += (layer_outputs[3 if output_attentions else 1],)

	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,)

	next_cache = next_decoder_cache if use_cache else None
	if not return_dict:
	return tuple(
	v
	for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions]
	if v is not None
	)
	return BaseModelOutputWithPastAndCrossAttentions(
	last_hidden_state=hidden_states,
	past_key_values=next_cache,
	hidden_states=all_hidden_states,
	attentions=all_self_attns,
	cross_attentions=all_cross_attentions,
	)


	DELTALM_START_DOCSTRING = r"""
	This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
	library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
	etc.)
	This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
	Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
	and behavior.
	Parameters:
	config ([`DeltalmConfig`]):
	Model configuration class with all the parameters of the model. Initializing with a config file does not
	load the weights associated with the model, only the configuration. Check out the
	[`~PreTrainedModel.from_pretrained`] method to load the model weights.
	"""

	DELTALM_GENERATION_EXAMPLE = r"""
	Summarization example:
	```python
	>>> from transformers import DeltalmTokenizer, DeltalmForConditionalGeneration
	>>> model = DeltalmForConditionalGeneration.from_pretrained("facebook/deltalm-large-cnn")
	>>> tokenizer = DeltalmTokenizer.from_pretrained("facebook/deltalm-large-cnn")
	>>> ARTICLE_TO_SUMMARIZE = (
	... "PG&E stated it scheduled the blackouts in response to forecasts for high winds "
	... "amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were "
	... "scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow."
	... )
	>>> inputs = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=1024, return_tensors="pt")
	>>> # Generate Summary
	>>> summary_ids = model.generate(inputs["input_ids"], num_beams=2, min_length=0, max_length=20)
	>>> tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
	'PG&E scheduled the blackouts in response to forecasts for high winds amid dry conditions'
	```
	Mask filling example:
	```python
	>>> from transformers import DeltalmTokenizer, DeltalmForConditionalGeneration
	>>> tokenizer = DeltalmTokenizer.from_pretrained("facebook/deltalm-base")
	>>> model = DeltalmForConditionalGeneration.from_pretrained("facebook/deltalm-base")
	>>> TXT = "My friends are <mask> but they eat too many carbs."
	>>> input_ids = tokenizer([TXT], return_tensors="pt")["input_ids"]
	>>> logits = model(input_ids).logits
	>>> masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero().item()
	>>> probs = logits[0, masked_index].softmax(dim=0)
	>>> values, predictions = probs.topk(5)
	>>> tokenizer.decode(predictions).split()
	['not', 'good', 'healthy', 'great', 'very']
	```
	"""

	DELTALM_INPUTS_DOCSTRING = r"""
	Args:
	input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
	Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
	it.
	Indices can be obtained using [`DeltalmTokenizer`]. 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)
	decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, optional):
	Indices of decoder input sequence tokens in the vocabulary.
	Indices can be obtained using [`DeltalmTokenizer`]. See [`PreTrainedTokenizer.encode`] and
	[`PreTrainedTokenizer.__call__`] for details.
	[What are decoder input IDs?](../glossary#decoder-input-ids)
	Deltalm uses the `eos_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values`
	is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`).
	For translation and summarization training, `decoder_input_ids` should be provided. If no
	`decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right
	for denoising pre-training following the paper.
	decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, optional):
	Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
	be used by default.
	If you want to change padding behavior, you should read [`modeling_deltalm._prepare_decoder_attention_mask`]
	and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
	information on the default strategy.
	head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, optional):
	Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:
	- 1 indicates the head is not masked,
	- 0 indicates the head is masked.
	decoder_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, optional):
	Mask to nullify selected heads of the attention modules in the decoder. 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 cross-attention modules in the decoder. Mask values selected in `[0,
	1]`:
	- 1 indicates the head is not masked,
	- 0 indicates the head is masked.
	encoder_outputs (`tuple(tuple(torch.FloatTensor)`, optional):
	Tuple consists of (`last_hidden_state`, optional: `hidden_states`, optional: `attentions`)
	`last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, optional) is a sequence of
	hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
	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.
	decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, optional):
	Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded
	representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be
	input (see `past_key_values`). This is useful if you want more control over how to convert
	`decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix.
	If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value
	of `inputs_embeds`.
	use_cache (`bool`, optional):
	If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
	`past_key_values`).
	output_attentions (`bool`, optional):
	Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
	tensors for more detail.
	output_hidden_states (`bool`, optional):
	Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
	more detail.
	return_dict (`bool`, optional):
	Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
	"""


	class DeltalmEncoder(DeltalmPretrainedModel):
	"""
	Transformer encoder consisting of config.encoder_layers self attention layers. Each layer is a
	[`DeltalmEncoderLayer`].
	Args:
	config: DeltalmConfig
	embed_tokens (nn.Embedding): output embedding
	"""

	def __init__(self, config: DeltalmConfig, embed_tokens: Optional[nn.Embedding] = None):
	super().__init__(config)

	self.dropout = config.dropout
	self.layerdrop = config.encoder_layerdrop

	embed_dim = config.d_model
	self.padding_idx = config.pad_token_id
	self.max_source_positions = config.max_position_embeddings
	self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0

	if embed_tokens is not None:
	self.embed_tokens = embed_tokens
	else:
	self.embed_tokens = nn.Embedding(config.vocab_size, embed_dim, self.padding_idx)

	self.embed_positions = DeltalmLearnedPositionalEmbedding(
	config.max_position_embeddings,
	embed_dim,
	)
	self.layers = nn.ModuleList([DeltalmEncoderLayer(config) for _ in range(config.encoder_layers)])
	self.layernorm_embedding = nn.LayerNorm(embed_dim)

	self.gradient_checkpointing = False
	if config.encoder_normalize_before:
	self.layer_norm = nn.LayerNorm(embed_dim)
	else:
	self.layer_norm = None
	# Initialize weights and apply final processing
	self.post_init()

	def get_input_embeddings(self):
	return self.embed_tokens

	def set_input_embeddings(self, value):
	self.embed_tokens = value

	def forward(
	self,
	input_ids: torch.LongTensor = None,
	attention_mask: Optional[torch.Tensor] = None,
	head_mask: Optional[torch.Tensor] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, BaseModelOutput]:
	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 [`DeltalmTokenizer`]. 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)
	head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_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.
	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
	)
	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 input_ids and inputs_embeds at the same time")
	elif input_ids is not None:
	input_shape = input_ids.size()
	input_ids = input_ids.view(-1, input_shape[-1])
	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")

	if inputs_embeds is None:
	inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale

	embed_pos = self.embed_positions(input_shape)

	hidden_states = inputs_embeds + embed_pos
	hidden_states = self.layernorm_embedding(hidden_states)
	hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)

	# expand attention_mask
	if attention_mask is not None:
	# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
	attention_mask = _expand_mask(attention_mask, inputs_embeds.dtype)

	encoder_states = () if output_hidden_states else None
	all_attentions = () if output_attentions else None

	# check if head_mask has a correct number of layers specified if desired
	if head_mask is not None:
	if head_mask.size()[0] != (len(self.layers)):
	raise ValueError(
	f"The head_mask should be specified for {len(self.layers)} layers, but it is for"
	f" {head_mask.size()[0]}."
	)

	for idx, encoder_layer in enumerate(self.layers):
	if output_hidden_states:
	encoder_states = encoder_states + (hidden_states,)
	# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
	dropout_probability = random.uniform(0, 1)
	if self.training and (dropout_probability < self.layerdrop): # skip the layer
	layer_outputs = (None, None)
	else:
	if self.gradient_checkpointing and self.training:

	def create_custom_forward(module):
	def custom_forward(*inputs):
	return module(*inputs, output_attentions)

	return custom_forward

	layer_outputs = torch.utils.checkpoint.checkpoint(
	create_custom_forward(encoder_layer),
	hidden_states,
	attention_mask,
	(head_mask[idx] if head_mask is not None else None),
	)
	else:
	layer_outputs = encoder_layer(
	hidden_states,
	attention_mask,
	layer_head_mask=(head_mask[idx] if head_mask is not None else None),
	output_attentions=output_attentions,
	)

	hidden_states = layer_outputs[0]

	if output_attentions:
	all_attentions = all_attentions + (layer_outputs[1],)

	if self.layer_norm is not None:
	hidden_states = self.layer_norm(hidden_states)
	# hidden_states = self.layernorm_embedding(hidden_states)

	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 BaseModelOutput(
	last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
	)


	class DeltalmModel(DeltalmPretrainedModel):
	def __init__(self, config: DeltalmConfig):
	super().__init__(config)

	padding_idx, vocab_size = config.pad_token_id, config.vocab_size
	self.shared = nn.Embedding(vocab_size, config.d_model, padding_idx)

	self.encoder = DeltalmEncoder(config, self.shared)
	self.decoder = DeltalmDecoder(config, self.shared)

	# Initialize weights and apply final processing
	self.post_init()

	def get_input_embeddings(self):
	return self.shared

	def set_input_embeddings(self, value):
	self.shared = value
	self.encoder.embed_tokens = self.shared
	self.decoder.embed_tokens = self.shared

	def get_encoder(self):
	return self.encoder

	def get_decoder(self):
	return self.decoder

	@add_start_docstrings_to_model_forward(DELTALM_INPUTS_DOCSTRING)
	# @add_code_sample_docstrings(
	# processor_class=_TOKENIZER_FOR_DOC,
	# checkpoint=_CHECKPOINT_FOR_DOC,
	# output_type=Seq2SeqModelOutput,
	# config_class=_CONFIG_FOR_DOC,
	# expected_output=_EXPECTED_OUTPUT_SHAPE,
	# )
	def forward(
	self,
	input_ids: torch.LongTensor = None,
	attention_mask: Optional[torch.Tensor] = None,
	decoder_input_ids: Optional[torch.LongTensor] = None,
	decoder_attention_mask: Optional[torch.LongTensor] = None,
	head_mask: Optional[torch.Tensor] = None,
	decoder_head_mask: Optional[torch.Tensor] = None,
	cross_attn_head_mask: Optional[torch.Tensor] = None,
	encoder_outputs: Optional[List[torch.FloatTensor]] = None,
	past_key_values: Optional[List[torch.FloatTensor]] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
	use_cache: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, Seq2SeqModelOutput]:

	# different to other models, Deltalm automatically creates decoder_input_ids from
	# input_ids if no decoder_input_ids are provided
	if decoder_input_ids is None and decoder_inputs_embeds is None:
	if input_ids is None:
	raise ValueError(
	"If no `decoder_input_ids` or `decoder_inputs_embeds` are "
	"passed, `input_ids` cannot be `None`. Please pass either "
	"`input_ids` or `decoder_input_ids` or `decoder_inputs_embeds`."
	)

	decoder_input_ids = shift_tokens_right(
	input_ids, self.config.pad_token_id, self.config.decoder_start_token_id
	)

	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

	if encoder_outputs is None:
	encoder_outputs = self.encoder(
	input_ids=input_ids,
	attention_mask=attention_mask,
	head_mask=head_mask,
	inputs_embeds=inputs_embeds,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)
	# If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True
	elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
	encoder_outputs = BaseModelOutput(
	last_hidden_state=encoder_outputs[0],
	hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
	attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
	)

	# decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn)
	decoder_outputs = self.decoder(
	input_ids=decoder_input_ids,
	attention_mask=decoder_attention_mask,
	encoder_hidden_states=encoder_outputs[0],
	encoder_attention_mask=attention_mask,
	head_mask=decoder_head_mask,
	cross_attn_head_mask=cross_attn_head_mask,
	past_key_values=past_key_values,
	inputs_embeds=decoder_inputs_embeds,
	use_cache=use_cache,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	if not return_dict:
	return decoder_outputs + encoder_outputs

	logger.debug("last_hidden_state.size: %s", decoder_outputs.last_hidden_state)
	return Seq2SeqModelOutput(
	last_hidden_state=decoder_outputs.last_hidden_state,
	past_key_values=decoder_outputs.past_key_values,
	decoder_hidden_states=decoder_outputs.hidden_states,
	decoder_attentions=decoder_outputs.attentions,
	cross_attentions=decoder_outputs.cross_attentions,
	encoder_last_hidden_state=encoder_outputs.last_hidden_state,
	encoder_hidden_states=encoder_outputs.hidden_states,
	encoder_attentions=encoder_outputs.attentions,
	)


	@add_start_docstrings(
	"The DELTALM Model with a language modeling head. Can be used for translation.", DELTALM_START_DOCSTRING
	)
	class DeltalmForConditionalGeneration(DeltalmPretrainedModel):
	base_model_prefix = "model"
	_keys_to_ignore_on_load_missing = [r"final_logits_bias", r"lm_head.weight"]

	def __init__(self, config: DeltalmConfig):
	super().__init__(config)
	self.model = DeltalmModel(config)
	self.register_buffer("final_logits_bias", torch.zeros((1, self.model.shared.num_embeddings)))
	self.lm_head = nn.Linear(config.d_model, self.model.shared.num_embeddings, bias=False)

	# Initialize weights and apply final processing
	self.post_init()

	def get_encoder(self):
	return self.model.get_encoder()

	def get_decoder(self):
	return self.model.get_decoder()

	def resize_token_embeddings(self, new_num_tokens: int) -> nn.Embedding:
	new_embeddings = super().resize_token_embeddings(new_num_tokens)
	self._resize_final_logits_bias(new_num_tokens)
	return new_embeddings

	def _resize_final_logits_bias(self, new_num_tokens: int) -> None:
	logger.debug("Debug: coming to _resize_final_logits_bias")
	old_num_tokens = self.final_logits_bias.shape[-1]
	if new_num_tokens <= old_num_tokens:
	new_bias = self.final_logits_bias[:, :new_num_tokens]
	else:
	extra_bias = torch.zeros((1, new_num_tokens - old_num_tokens), device=self.final_logits_bias.device)
	new_bias = torch.cat([self.final_logits_bias, extra_bias], dim=1)
	self.register_buffer("final_logits_bias", new_bias)

	def get_output_embeddings(self):
	return self.lm_head

	def set_output_embeddings(self, new_embeddings):
	self.lm_head = new_embeddings

	@add_start_docstrings_to_model_forward(DELTALM_INPUTS_DOCSTRING)
	@replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
	@add_end_docstrings(DELTALM_GENERATION_EXAMPLE)
	def forward(
	self,
	input_ids: torch.LongTensor = None,
	attention_mask: Optional[torch.Tensor] = None,
	decoder_input_ids: Optional[torch.LongTensor] = None,
	decoder_attention_mask: Optional[torch.LongTensor] = None,
	head_mask: Optional[torch.Tensor] = None,
	decoder_head_mask: Optional[torch.Tensor] = None,
	cross_attn_head_mask: Optional[torch.Tensor] = None,
	encoder_outputs: Optional[List[torch.FloatTensor]] = None,
	past_key_values: Optional[List[torch.FloatTensor]] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	decoder_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,
	) -> Union[Tuple, Seq2SeqLMOutput]:
	r"""
	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]`.
	Returns:
	"""
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	logger.debug("Comming to Generation!")

	if labels is not None:
	logger.debug("Debug: ************* Before label *************** ")
	logger.debug("Debug: %s", labels.size())
	if use_cache:
	logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.")
	use_cache = False
	if decoder_input_ids is None and decoder_inputs_embeds is None:
	decoder_input_ids = shift_tokens_right(
	labels, self.config.pad_token_id, self.config.decoder_start_token_id
	)

	logger.debug("Debug: ********** After labels **********")
	logger.debug("Debug: %s", labels.size())

	outputs = self.model(
	input_ids,
	attention_mask=attention_mask,
	decoder_input_ids=decoder_input_ids,
	encoder_outputs=encoder_outputs,
	decoder_attention_mask=decoder_attention_mask,
	head_mask=head_mask,
	decoder_head_mask=decoder_head_mask,
	cross_attn_head_mask=cross_attn_head_mask,
	past_key_values=past_key_values,
	inputs_embeds=inputs_embeds,
	decoder_inputs_embeds=decoder_inputs_embeds,
	use_cache=use_cache,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)
	lm_logits = self.lm_head(outputs[0]) + self.final_logits_bias
	# print(self.lm_head)
	logger.debug("Debug: logit_size: %s", lm_logits.size())

	# logger.debug("Debug: change logit size: ", lm_logits.view(-1, self.config.vocab_size).size())
	# logger.debug("Debug: change label size: ", labels.view(-1).size())
	masked_lm_loss = None

	if labels is not None:
	# logger.debug("Debug: model label_size: %s", labels.size())
	# loss_fct = CrossEntropyLoss()
	# masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1))
	loss_fct = CrossEntropyLoss()
	masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1))
	# label_smoothing = self.config.label_smoothing
	# # logger.debug("Debug: label.size: ", )
	# if label_smoothing == 0:
	# # compute label smoothed loss
	# loss_fct = CrossEntropyLoss()
	# masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1))
	# else:
	# m = torch.nn.LogSoftmax(dim=-1)
	# lprobs = m(lm_logits.float())
	# # lprobs = m(lm_logits)
	# # # torch.set_printoptions(linewidth=200)
	# loss_fn = label_smoothed_nll_loss
	# masked_lm_loss, _ = loss_fn(lprobs.view(-1, lprobs.size(-1)), labels.view(-1), label_smoothing, self.config.pad_token_id)

	if not return_dict:
	logger.debug("Debug: not return dict")
	output = (lm_logits,) + outputs[1:]
	return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output

	return Seq2SeqLMOutput(
	loss=masked_lm_loss,
	logits=lm_logits,
	past_key_values=outputs.past_key_values,
	decoder_hidden_states=outputs.decoder_hidden_states,
	decoder_attentions=outputs.decoder_attentions,
	cross_attentions=outputs.cross_attentions,
	encoder_last_hidden_state=outputs.encoder_last_hidden_state,
	encoder_hidden_states=outputs.encoder_hidden_states,
	encoder_attentions=outputs.encoder_attentions,
	)

	def prepare_inputs_for_generation(
	self,
	decoder_input_ids,
	past=None,
	attention_mask=None,
	head_mask=None,
	decoder_head_mask=None,
	cross_attn_head_mask=None,
	use_cache=None,
	encoder_outputs=None,
	**kwargs
	):
	# cut decoder_input_ids if past is used
	if past is not None:
	decoder_input_ids = decoder_input_ids[:, -1:]

	return {
	"input_ids": None, # encoder_outputs is defined. input_ids not needed
	"encoder_outputs": encoder_outputs,
	"past_key_values": past,
	"decoder_input_ids": decoder_input_ids,
	"attention_mask": attention_mask,
	"head_mask": head_mask,
	"decoder_head_mask": decoder_head_mask,
	"cross_attn_head_mask": cross_attn_head_mask,
	"use_cache": use_cache, # change this to avoid caching (presumably for debugging)
	}

	def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
	return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)

	@staticmethod
	def _reorder_cache(past, beam_idx):
	reordered_past = ()
	for layer_past in past:
	# cached cross_attention states don't have to be reordered -> they are always the same
	reordered_past += (
	tuple(past_state.index_select(0, beam_idx) for past_state in layer_past[:2]) + layer_past[2:],
	)
	return reordered_past


	class DeltalmDecoderWrapper(DeltalmPretrainedModel):
	"""
	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.
	"""

	def __init__(self, config):
	super().__init__(config)
	self.decoder = DeltalmDecoder(config)

	def forward(self, args, *kwargs):
	return self.decoder(args, *kwargs)


	class DeltalmForCausalLM(DeltalmPretrainedModel):
	def __init__(self, config):
	config = copy.deepcopy(config)
	config.is_decoder = True
	config.is_encoder_decoder = False
	super().__init__(config)
	self.model = DeltalmDecoderWrapper(config)

	self.lm_head = 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.lm_head

	def set_output_embeddings(self, new_embeddings):
	self.lm_head = new_embeddings

	def set_decoder(self, decoder):
	self.model.decoder = decoder

	def get_decoder(self):
	return self.model.decoder

	@replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
	def forward(
	self,
	input_ids: torch.LongTensor = None,
	attention_mask: Optional[torch.Tensor] = None,
	encoder_hidden_states: Optional[torch.FloatTensor] = None,
	encoder_attention_mask: Optional[torch.FloatTensor] = None,
	head_mask: Optional[torch.Tensor] = None,
	cross_attn_head_mask: Optional[torch.Tensor] = None,
	past_key_values: Optional[List[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,
	) -> Union[Tuple, CausalLMOutputWithCrossAttentions]:
	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 [`DeltalmTokenizer`]. 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, sequence_length, hidden_size)`, optional):
	Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
	if the model is configured as a decoder.
	encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, optional):
	Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used
	in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
	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 cross-attention modules. 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)`. The two additional
	tensors are only required when the model is used as a decoder in a Sequence to Sequence model.
	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)`.
	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]`.
	use_cache (`bool`, optional):
	If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
	(see `past_key_values`).
	- 1 for tokens that are not masked,
	- 0 for tokens that are masked.
	output_attentions (`bool`, optional):
	Whether or not to return the attentions tensors of all attention layers. See `attentions` under
	returned tensors for more detail.
	output_hidden_states (`bool`, optional):
	Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
	for more detail.
	return_dict (`bool`, optional):
	Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
	Returns:
	Example:
	```python
	>>> from transformers import DeltalmTokenizer, DeltalmForCausalLM
	>>> tokenizer = DeltalmTokenizer.from_pretrained("facebook/deltalm-base")
	>>> model = DeltalmForCausalLM.from_pretrained("facebook/deltalm-base", add_cross_attention=False)
	>>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder."
	>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
	>>> outputs = model(**inputs)
	>>> logits = outputs.logits
	>>> expected_shape = [1, inputs.input_ids.shape[-1], model.config.vocab_size]
	>>> list(logits.shape) == expected_shape
	True
	```"""

	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,
	)

	logits = self.lm_head(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,
	)

	def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, use_cache=None, **kwargs):
	# if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
	if attention_mask is None:
	attention_mask = input_ids.new_ones(input_ids.shape)

	if past:
	input_ids = input_ids[:, -1:]
	# first step, decoder_cached_states are empty
	return {
	"input_ids": input_ids, # encoder_outputs is defined. input_ids not needed
	"attention_mask": attention_mask,
	"past_key_values": past,
	"use_cache": use_cache,
	}

	@staticmethod
	def _reorder_cache(past, beam_idx):
	reordered_past = ()
	for layer_past in past:
	reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
	return reordered_past