codet5p-16b / modeling_codet5p.py

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	# coding=utf-8
	# Copyright 2023 Salesforce authors, The EleutherAI, and HuggingFace Teams. All rights reserved.
	""" PyTorch CodeT5+ 2B 6B 16B models.
	The implementation is mainly based on transformers.models.codegen.modeling_codegen by adding cross-attention
	and transformers.models.encoder_decoder.modeling_encoder_decoder.EncoderDecoderModel.
	"""
	from typing import Optional, Tuple, Union
	import torch
	import torch.utils.checkpoint
	from torch import nn
	from torch.nn import CrossEntropyLoss

	from transformers.activations import ACT2FN
	from transformers.modeling_outputs import BaseModelOutput, Seq2SeqLMOutput, \
	BaseModelOutputWithPast, CausalLMOutputWithPast, \
	BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions
	from transformers.modeling_utils import PreTrainedModel
	from transformers.configuration_utils import PretrainedConfig
	from transformers.utils import add_code_sample_docstrings, add_start_docstrings, logging
	from .configuration_codet5p import CodeT5pConfig, CodeT5pModuleConfig

	logger = logging.get_logger(__name__)

	CODET5P_PRETRAINED_MODEL_ARCHIVE_LIST = [
	"Salesforce/codet5p-220m",
	"Salesforce/codet5p-770m",
	"Salesforce/codet5p-220m-py",
	"Salesforce/codet5p-770m-py",
	"Salesforce/codet5p-2b",
	"Salesforce/codet5p-6b",
	"Salesforce/codet5p-16b",
	"Salesforce/instructcodet5p-16b",
	# See all CodeT5+ models at https://huggingface.co/models?filter=codet5p
	]


	# Copied from transformers.models.gptj.modeling_gptj.fixed_pos_embedding
	def fixed_pos_embedding(x, seq_dim=1, seq_len=None):
	dim = x.shape[-1]
	if seq_len is None:
	seq_len = x.shape[seq_dim]
	inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2) / dim))
	sinusoid_inp = (
	torch.einsum("i , j -> i j", torch.arange(seq_len, dtype=torch.float), inv_freq).to(x.device).float()
	)
	return torch.sin(sinusoid_inp), torch.cos(sinusoid_inp)


	# Copied from transformers.models.gptj.modeling_gptj.rotate_every_two
	def rotate_every_two(x):
	x1 = x[:, :, :, ::2]
	x2 = x[:, :, :, 1::2]
	x = torch.stack((-x2, x1), dim=-1)
	return x.flatten(-2) # in einsum notation: rearrange(x, '... d j -> ... (d j)')


	# Copied from transformers.models.gptj.modeling_gptj.duplicate_interleave
	def duplicate_interleave(m):
	"""
	A simple version of `torch.repeat_interleave` for duplicating a matrix while interleaving the copy.
	"""
	dim0 = m.shape[0]
	m = m.view(-1, 1) # flatten the matrix
	m = m.repeat(1, 2) # repeat all elements into the 2nd dimension
	m = m.view(dim0, -1) # reshape into a matrix, interleaving the copy
	return m


	# Copied from transformers.models.gptj.modeling_gptj.apply_rotary_pos_emb
	def apply_rotary_pos_emb(x, sincos, offset=0):
	sin, cos = (duplicate_interleave(t)[None, offset: x.shape[1] + offset, None, :] for t in sincos)
	# einsum notation for lambda t: repeat(t[offset:x.shape[1]+offset,:], "n d -> () n () (d j)", j=2)
	return (x * cos) + (rotate_every_two(x) * sin)


	# Adapted from transformers.models.codegen.modeling_codegen.CodeGenAttention
	class CodeT5pAttention(nn.Module):
	def __init__(self, config, is_cross_attention=False, is_decoder=True):
	super().__init__()

	max_positions = config.max_position_embeddings
	self.register_buffer(
	"causal_mask",
	torch.tril(torch.ones((max_positions, max_positions), dtype=torch.uint8)).view(
	1, 1, max_positions, max_positions
	),
	)

	self.attn_dropout = nn.Dropout(config.attn_pdrop)
	self.resid_dropout = nn.Dropout(config.resid_pdrop)

	self.embed_dim = config.hidden_size
	self.num_attention_heads = config.num_attention_heads
	self.head_dim = self.embed_dim // self.num_attention_heads
	if self.head_dim * self.num_attention_heads != self.embed_dim:
	raise ValueError(
	f"embed_dim must be divisible by num_attention_heads (got `embed_dim`: {self.embed_dim} and"
	f" `num_attention_heads`: {self.num_attention_heads})."
	)

	self.scale_attn = torch.sqrt(torch.tensor(self.head_dim, dtype=torch.float32)).to(torch.get_default_dtype())
	self.is_decoder = is_decoder
	self.is_cross_attention = is_cross_attention
	if self.is_cross_attention:
	self.qkv_proj = nn.Linear(self.embed_dim, self.embed_dim * 2, bias=False)
	self.q_attn = nn.Linear(self.embed_dim, self.embed_dim, bias=False)
	else:
	self.qkv_proj = nn.Linear(self.embed_dim, self.embed_dim * 3, bias=False)

	self.out_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False)
	self.rotary_dim = None
	if config.rotary_dim is not None:
	self.rotary_dim = config.rotary_dim

	def _split_heads(self, x, n_head, dim_head, mp_num):
	reshaped = x.reshape(x.shape[:-1] + (n_head // mp_num, dim_head))
	reshaped = reshaped.reshape(x.shape[:-2] + (-1,) + reshaped.shape[-1:])
	return reshaped

	def _merge_heads(self, tensor, num_attention_heads, attn_head_size):
	"""
	Merges attn_head_size dim and num_attn_heads dim into n_ctx
	"""
	if len(tensor.shape) == 5:
	tensor = tensor.permute(0, 1, 3, 2, 4).contiguous()
	elif len(tensor.shape) == 4:
	tensor = tensor.permute(0, 2, 1, 3).contiguous()
	else:
	raise ValueError(f"Input tensor rank should be one of [4, 5], but is: {len(tensor.shape)}")
	new_shape = tensor.size()[:-2] + (num_attention_heads * attn_head_size,)
	return tensor.view(new_shape)

	def _attn(
	self,
	query,
	key,
	value,
	attention_mask=None,
	head_mask=None,
	):
	# Keep the attention weights computation in fp32 to avoid overflow issues
	query = query.to(torch.float32)
	key = key.to(torch.float32)

	attn_weights = torch.matmul(query, key.transpose(-1, -2))
	attn_weights = attn_weights / self.scale_attn

	if not self.is_cross_attention and self.is_decoder:
	# compute causal mask from causal mask buffer
	query_length, key_length = query.size(-2), key.size(-2)
	causal_mask = self.causal_mask[:, :, key_length - query_length: key_length, :key_length]
	mask_value = torch.finfo(attn_weights.dtype).min
	# Need to be a tensor, otherwise we get error: `RuntimeError: expected scalar type float but found double`.
	# Need to be on the same device, otherwise `RuntimeError: ..., x and y to be on the same device`
	mask_value = torch.tensor(mask_value, dtype=attn_weights.dtype).to(attn_weights.device)
	attn_weights = torch.where(causal_mask.bool(), attn_weights, mask_value)

	if attention_mask is not None:
	# Apply the attention mask
	attn_weights = attn_weights + attention_mask

	attn_weights = nn.Softmax(dim=-1)(attn_weights)
	attn_weights = attn_weights.to(value.dtype)
	attn_weights = self.attn_dropout(attn_weights)

	# Mask heads if we want to
	if head_mask is not None:
	attn_weights = attn_weights * head_mask

	attn_output = torch.matmul(attn_weights, value)

	return attn_output, attn_weights

	def forward(
	self,
	hidden_states: Optional[torch.FloatTensor],
	attention_mask: Optional[torch.FloatTensor] = None,
	layer_past: Optional[Tuple[torch.Tensor]] = None,
	head_mask: Optional[torch.FloatTensor] = None,
	encoder_hidden_states: Optional[torch.Tensor] = None,
	encoder_attention_mask: Optional[torch.FloatTensor] = None,
	use_cache: Optional[bool] = False,
	output_attentions: Optional[bool] = False,
	) -> Union[
	Tuple[torch.Tensor, Tuple[torch.Tensor]],
	Optional[Tuple[torch.Tensor, Tuple[torch.Tensor], Tuple[torch.Tensor, ...]]],
	]:

	if encoder_hidden_states is not None:
	if not hasattr(self, "q_attn"):
	raise ValueError(
	"If class is used as cross attention, the weights `q_attn` have to be defined. "
	"Please make sure to instantiate class with `GPT2Attention(..., is_cross_attention=True)`."
	)

	mp_num = 4
	local_dim = self.head_dim * self.num_attention_heads // mp_num
	q = self.q_attn(hidden_states)
	q_split = q.reshape(q.shape[:-1] + (mp_num, -1))
	query = torch.split(q_split, local_dim, dim=-1)[0]

	qkv = self.qkv_proj(encoder_hidden_states)
	qkv_split = qkv.reshape(qkv.shape[:-1] + (mp_num, -1))
	value, key = torch.split(qkv_split, local_dim, dim=-1)

	attention_mask = encoder_attention_mask
	else:
	qkv = self.qkv_proj(hidden_states)
	mp_num = 4
	qkv_split = qkv.reshape(qkv.shape[:-1] + (mp_num, -1))

	local_dim = self.head_dim * self.num_attention_heads // mp_num
	query, value, key = torch.split(qkv_split, local_dim, dim=-1)

	query = self._split_heads(query, self.num_attention_heads, self.head_dim, mp_num=mp_num)
	key = self._split_heads(key, self.num_attention_heads, self.head_dim, mp_num=mp_num)

	value = self._split_heads(value, self.num_attention_heads, self.head_dim, mp_num=mp_num)
	value = value.permute(0, 2, 1, 3)

	seq_len = key.shape[1]
	offset = 0

	if layer_past is not None:
	offset = layer_past[0].shape[-2]
	seq_len += offset

	if self.rotary_dim is not None:
	k_rot = key[:, :, :, : self.rotary_dim]
	k_pass = key[:, :, :, self.rotary_dim:]

	q_rot = query[:, :, :, : self.rotary_dim]
	q_pass = query[:, :, :, self.rotary_dim:]

	sincos = fixed_pos_embedding(k_rot, 1, seq_len=seq_len)
	k_rot = apply_rotary_pos_emb(k_rot, sincos, offset=offset)
	seq_len_q = query.shape[1]
	sincos_q = fixed_pos_embedding(q_rot, 1, seq_len=seq_len_q)
	q_rot = apply_rotary_pos_emb(q_rot, sincos_q, offset=offset)

	key = torch.cat([k_rot, k_pass], dim=-1)
	query = torch.cat([q_rot, q_pass], dim=-1)
	else:
	sincos = fixed_pos_embedding(key, 1, seq_len=seq_len)
	key = apply_rotary_pos_emb(key, sincos, offset=offset)
	query = apply_rotary_pos_emb(query, sincos, offset=offset)

	key = key.permute(0, 2, 1, 3)
	query = query.permute(0, 2, 1, 3)

	if layer_past is not None:
	past_key = layer_past[0]
	past_value = layer_past[1]
	key = torch.cat((past_key, key), dim=-2)
	value = torch.cat((past_value, value), dim=-2)

	if use_cache is True:
	present = (key, value)
	else:
	present = None

	# compute self-attention: V x Softmax(QK^T)
	attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask)

	attn_output = self._merge_heads(attn_output, self.num_attention_heads, self.head_dim)
	attn_output = self.out_proj(attn_output)
	attn_output = self.resid_dropout(attn_output)

	outputs = (attn_output, present)
	if output_attentions:
	outputs += (attn_weights,)

	return outputs # a, present, (attentions)


	# Adapted from transformers.models.codegen.modeling_codegen.CodeGenMLP
	class CodeT5pMLP(nn.Module):
	def __init__(self, intermediate_size, config): # in MLP: intermediate_size= 4 * embed_dim
	super().__init__()
	embed_dim = config.n_embd

	self.fc_in = nn.Linear(embed_dim, intermediate_size)
	self.fc_out = nn.Linear(intermediate_size, embed_dim)

	self.act = ACT2FN[config.activation_function]
	self.dropout = nn.Dropout(config.resid_pdrop)

	def forward(self, hidden_states: Optional[torch.FloatTensor]) -> torch.FloatTensor:
	hidden_states = self.fc_in(hidden_states)
	hidden_states = self.act(hidden_states)
	hidden_states = self.fc_out(hidden_states)
	hidden_states = self.dropout(hidden_states)
	return hidden_states


	# Adapted from transformers.models.codegen.modeling_codegen.CodeGenBlock
	class CodeT5pBlock(nn.Module):
	def __init__(self, config, layer_idx=None):
	super().__init__()
	inner_dim = config.n_inner if config.n_inner is not None else 4 * config.n_embd
	self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)

	if config.is_decoder is False:
	self.attn = CodeT5pAttention(config, is_cross_attention=False, is_decoder=False)
	else:
	self.attn = CodeT5pAttention(config)
	self.mlp = CodeT5pMLP(inner_dim, config)

	# Adding 1 cross-attention layer at the final decoder layer
	self.add_cross_attention_by_layer = True \
	if config.add_cross_attention and layer_idx == config.n_layer - 1 else False

	if config.add_cross_attention and self.add_cross_attention_by_layer:
	self.crossattention = CodeT5pAttention(config, is_cross_attention=True)

	def forward(
	self,
	hidden_states: Optional[torch.FloatTensor],
	layer_past: Optional[Tuple[torch.Tensor]] = None,
	attention_mask: Optional[torch.FloatTensor] = None,
	head_mask: Optional[torch.FloatTensor] = None,
	encoder_hidden_states: Optional[torch.Tensor] = None,
	encoder_attention_mask: Optional[torch.FloatTensor] = None,
	use_cache: Optional[bool] = False,
	output_attentions: Optional[bool] = False,
	) -> Union[Tuple[torch.Tensor], Optional[Tuple[torch.Tensor, Tuple[torch.FloatTensor, ...]]]]:
	residual = hidden_states
	hidden_states = self.ln_1(hidden_states)
	attn_outputs = self.attn(
	hidden_states,
	layer_past=layer_past,
	attention_mask=attention_mask,
	head_mask=head_mask,
	use_cache=use_cache,
	output_attentions=output_attentions,
	)
	attn_output = attn_outputs[0] # output_attn: a, present, (attentions)
	outputs = attn_outputs[1:]
	feed_forward_hidden_states = self.mlp(hidden_states)

	if encoder_hidden_states is not None and self.add_cross_attention_by_layer:
	# add one self-attention block for cross-attention
	if not hasattr(self, "crossattention"):
	raise ValueError(
	f"If `encoder_hidden_states` are passed, {self} has to be instantiated with "
	"cross-attention layers by setting `config.add_cross_attention=True`"
	)
	# residual = hidden_states
	# hidden_states = self.ln_cross_attn(residual)
	cross_attn_outputs = self.crossattention(
	hidden_states,
	attention_mask=attention_mask,
	head_mask=head_mask,
	encoder_hidden_states=encoder_hidden_states,
	encoder_attention_mask=encoder_attention_mask,
	output_attentions=output_attentions,
	)
	xattn_output = cross_attn_outputs[0]
	attn_output = attn_output + xattn_output
	outputs = outputs + cross_attn_outputs[2:] # add cross attentions if we output attention weights

	hidden_states = attn_output + feed_forward_hidden_states + residual

	if use_cache:
	outputs = (hidden_states,) + outputs
	else:
	outputs = (hidden_states,) + outputs[1:]

	return outputs # hidden_states, present, (attentions)


	# Adapted from transformers.models.codegen.modeling_codegen.CodeGenPreTrainedModel
	class CodeT5pPreTrainedModel(PreTrainedModel):
	"""
	An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
	models.
	"""
	config_class = CodeT5pModuleConfig
	base_model_prefix = "transformer"
	supports_gradient_checkpointing = True
	_no_split_modules = ["CodeT5pBlock"]

	def __init__(self, inputs, *kwargs):
	super().__init__(inputs, *kwargs)

	def _init_weights(self, module):
	"""Initialize the weights."""
	if isinstance(module, (nn.Linear,)):
	# Slightly different from Mesh Transformer JAX which uses truncated_normal for initialization
	# cf https://github.com/pytorch/pytorch/pull/5617
	module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
	if module.bias is not None:
	module.bias.data.zero_()
	elif isinstance(module, nn.Embedding):
	module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
	if module.padding_idx is not None:
	module.weight.data[module.padding_idx].zero_()
	elif isinstance(module, nn.LayerNorm):
	module.bias.data.zero_()
	module.weight.data.fill_(1.0)

	def _set_gradient_checkpointing(self, module, value=False):
	if isinstance(module, CodeT5pModel):
	module.gradient_checkpointing = value


	# Adapted from transformers.models.codegen.modeling_codegen.CodeGenModel
	class CodeT5pModel(CodeT5pPreTrainedModel):
	def __init__(self, config):
	super().__init__(config)

	self.embed_dim = config.n_embd
	self.vocab_size = config.vocab_size
	self.wte = nn.Embedding(config.vocab_size, self.embed_dim)
	self.drop = nn.Dropout(config.embd_pdrop)
	self.h = nn.ModuleList([CodeT5pBlock(config, idx) for idx in range(config.n_layer)])
	self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
	self.rotary_dim = min(config.rotary_dim, config.n_ctx // config.num_attention_heads)

	self.gradient_checkpointing = False

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

	def get_input_embeddings(self):
	return self.wte

	def set_input_embeddings(self, new_embeddings):
	self.wte = new_embeddings

	def forward(
	self,
	input_ids: Optional[torch.LongTensor] = None,
	past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
	attention_mask: Optional[torch.FloatTensor] = None,
	token_type_ids: Optional[torch.LongTensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	head_mask: Optional[torch.FloatTensor] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	encoder_hidden_states: Optional[torch.Tensor] = None,
	encoder_attention_mask: 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]:
	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 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])
	batch_size = input_ids.shape[0]
	elif inputs_embeds is not None:
	input_shape = inputs_embeds.size()[:-1]
	batch_size = inputs_embeds.shape[0]
	else:
	raise ValueError("You have to specify either input_ids or inputs_embeds")

	device = input_ids.device if input_ids is not None else inputs_embeds.device

	if token_type_ids is not None:
	token_type_ids = token_type_ids.view(-1, input_shape[-1])

	if position_ids is not None:
	position_ids = position_ids.view(-1, input_shape[-1])

	if past_key_values is None:
	past_length = 0
	past_key_values = tuple([None] * len(self.h))
	else:
	past_length = past_key_values[0][0].size(-2)

	if position_ids is None:
	position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device)
	position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])

	# Attention mask.
	if attention_mask is not None:
	if batch_size <= 0:
	raise ValueError("batch_size has to be defined and > 0")
	attention_mask = attention_mask.view(batch_size, -1)
	# We create a 3D attention mask from a 2D tensor mask.
	# Sizes are [batch_size, 1, 1, to_seq_length]
	# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
	# this attention mask is more simple than the triangular masking of causal attention
	# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
	attention_mask = attention_mask[:, None, None, :]

	# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
	# masked positions, this operation will create a tensor which is 0.0 for
	# positions we want to attend and the dtype's smallest value for masked positions.
	# Since we are adding it to the raw scores before the softmax, this is
	# effectively the same as removing these entirely.
	attention_mask = attention_mask.to(dtype=self.dtype) # fp16 compatibility
	attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min

	# If a 2D or 3D attention mask is provided for the cross-attention
	# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
	if self.config.add_cross_attention and encoder_hidden_states is not None:
	encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
	encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
	if encoder_attention_mask is None:
	encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
	encoder_attention_mask = self.invert_attention_mask(encoder_attention_mask)
	else:
	encoder_attention_mask = None

	# Prepare head mask if needed
	# 1.0 in head_mask indicate we keep the head
	# attention_probs has shape bsz x num_attention_heads x N x N
	# head_mask has shape n_layer x batch x num_attention_heads x N x N
	head_mask = self.get_head_mask(head_mask, self.config.n_layer)

	if inputs_embeds is None:
	inputs_embeds = self.wte(input_ids)

	hidden_states = inputs_embeds

	if token_type_ids is not None:
	token_type_embeds = self.wte(token_type_ids)
	hidden_states = hidden_states + token_type_embeds

	hidden_states = self.drop(hidden_states)

	output_shape = input_shape + (hidden_states.size(-1),)

	presents = () if use_cache else None
	all_self_attentions = () if output_attentions else None
	all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
	all_hidden_states = () if output_hidden_states else None
	for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
	if output_hidden_states:
	all_hidden_states = all_hidden_states + (hidden_states,)

	if self.gradient_checkpointing and self.training:
	if use_cache:
	logger.warning(
	"`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting "
	"`use_cache=False`..."
	)
	use_cache = False

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

	return custom_forward

	outputs = torch.utils.checkpoint.checkpoint(
	create_custom_forward(block),
	hidden_states,
	None,
	attention_mask,
	head_mask[i],
	encoder_hidden_states,
	encoder_attention_mask,
	)
	else:
	outputs = block(
	hidden_states,
	layer_past=layer_past,
	attention_mask=attention_mask,
	head_mask=head_mask[i],
	encoder_hidden_states=encoder_hidden_states,
	encoder_attention_mask=encoder_attention_mask,
	use_cache=use_cache,
	output_attentions=output_attentions,
	)

	hidden_states = outputs[0]
	if use_cache is True:
	presents = presents + (outputs[1],)

	if output_attentions:
	all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
	if self.config.add_cross_attention and self.add_cross_attention_by_layer:
	all_cross_attentions = all_cross_attentions + (outputs[3 if use_cache else 2],)

	hidden_states = self.ln_f(hidden_states)

	hidden_states = hidden_states.view(output_shape)
	# Add last hidden state
	if output_hidden_states:
	all_hidden_states = all_hidden_states + (hidden_states,)

	if not return_dict:
	return tuple(
	v for v in [hidden_states, presents, all_hidden_states, all_self_attentions, all_cross_attentions] if
	v is not None)

	return BaseModelOutputWithPastAndCrossAttentions(
	last_hidden_state=hidden_states,
	past_key_values=presents,
	hidden_states=all_hidden_states,
	attentions=all_self_attentions,
	cross_attentions=all_cross_attentions,
	)


	# Adapted from transformers.models.codegen.modeling_codegen.CodeGenForCausalLM
	class CodeT5pForCausalLM(CodeT5pPreTrainedModel):
	_keys_to_ignore_on_load_missing = [r"h\.\d+\.attn\.causal_mask"]

	def __init__(self, config):
	super().__init__(config)
	self.transformer = CodeT5pModel(config)
	self.lm_head = nn.Linear(config.n_embd, config.vocab_size)

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

	def get_output_embeddings(self):
	return self.lm_head

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

	def prepare_inputs_for_generation(self, input_ids, past_key_values=None, **kwargs):
	token_type_ids = kwargs.get("token_type_ids", None)
	# only last token for inputs_ids if past is defined in kwargs
	if past_key_values:
	input_ids = input_ids[:, -1].unsqueeze(-1)
	if token_type_ids is not None:
	token_type_ids = token_type_ids[:, -1].unsqueeze(-1)

	attention_mask = kwargs.get("attention_mask", None)
	position_ids = kwargs.get("position_ids", None)

	if attention_mask is not None and position_ids is None:
	# create position_ids on the fly for batch generation
	position_ids = attention_mask.long().cumsum(-1) - 1
	position_ids.masked_fill_(attention_mask == 0, 1)
	if past_key_values:
	position_ids = position_ids[:, -1].unsqueeze(-1)
	else:
	position_ids = None
	return {
	"input_ids": input_ids,
	"past_key_values": past_key_values,
	"use_cache": kwargs.get("use_cache"),
	"position_ids": position_ids,
	"attention_mask": attention_mask,
	"token_type_ids": token_type_ids,
	}

	def forward(
	self,
	input_ids: Optional[torch.LongTensor] = None,
	past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
	attention_mask: Optional[torch.FloatTensor] = None,
	token_type_ids: Optional[torch.LongTensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	head_mask: Optional[torch.FloatTensor] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	encoder_hidden_states: Optional[torch.Tensor] = None,
	encoder_attention_mask: 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"""
	labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional):
	Labels for language modeling. Note that the labels are shifted inside the model, i.e. you can set
	`labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
	are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
	"""
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	transformer_outputs = self.transformer(
	input_ids,
	past_key_values=past_key_values,
	attention_mask=attention_mask,
	token_type_ids=token_type_ids,
	position_ids=position_ids,
	head_mask=head_mask,
	inputs_embeds=inputs_embeds,
	encoder_hidden_states=encoder_hidden_states,
	encoder_attention_mask=encoder_attention_mask,
	use_cache=use_cache,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)
	hidden_states = transformer_outputs[0]

	# make sure sampling in fp16 works correctly and
	# compute loss in fp32 to match with mesh-tf version
	# https://github.com/EleutherAI/gpt-neo/blob/89ce74164da2fb16179106f54e2269b5da8db333/models/gpt2/gpt2.py#L179
	lm_logits = self.lm_head(hidden_states).to(torch.float32)

	loss = None
	if labels is not None:
	# Shift so that tokens < n predict n
	shift_logits = lm_logits[..., :-1, :].contiguous()
	shift_labels = labels[..., 1:].contiguous()
	# Flatten the tokens
	loss_fct = CrossEntropyLoss()
	loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))

	loss = loss.to(hidden_states.dtype)

	if not return_dict:
	output = (lm_logits,) + transformer_outputs[1:]
	return ((loss,) + output) if loss is not None else output

	return CausalLMOutputWithCrossAttentions(
	loss=loss,
	logits=lm_logits,
	past_key_values=transformer_outputs.past_key_values,
	hidden_states=transformer_outputs.hidden_states,
	attentions=transformer_outputs.attentions,
	cross_attentions=transformer_outputs.cross_attentions,
	)

	@staticmethod
	def _reorder_cache(
	past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor
	) -> Tuple[Tuple[torch.Tensor]]:
	"""
	This function is used to re-order the `past_key_values` cache if [`~PretrainedModel.beam_search`] or
	[`~PretrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
	beam_idx at every generation step.
	"""
	return tuple(
	tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past)
	for layer_past in past_key_values
	)


	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()
	if decoder_start_token_id is None:
	raise ValueError("Make sure to set the decoder_start_token_id attribute of the model's configuration.")
	shifted_input_ids[:, 0] = decoder_start_token_id

	if pad_token_id is None:
	raise ValueError("Make sure to set the pad_token_id attribute of the model's configuration.")
	# 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


	# Adapted from transformers.models.encoder_decoder.modeling_encoder_decoder.EncoderDecoderModel
	class CodeT5pEncoderDecoderModel(PreTrainedModel):
	config_class = CodeT5pConfig
	_no_split_modules = ["CodeT5pBlock"]
	def __init__(
	self,
	config: Optional[PretrainedConfig] = None,
	encoder: Optional[PreTrainedModel] = None,
	decoder: Optional[PreTrainedModel] = None,
	):
	if config is None and (encoder is None or decoder is None):
	raise ValueError("Either a configuration or an encoder and a decoder has to be provided.")
	if config is None:
	config = CodeT5pConfig.from_encoder_decoder_configs(encoder.config, decoder.config)
	else:
	if not isinstance(config, self.config_class):
	raise ValueError(f"Config: {config} has to be of type {self.config_class}")

	if config.decoder.cross_attention_hidden_size is not None:
	if config.decoder.cross_attention_hidden_size != config.encoder.hidden_size:
	raise ValueError(
	"If `cross_attention_hidden_size` is specified in the decoder's configuration, it has to be equal"
	f" to the encoder's `hidden_size`. Got {config.decoder.cross_attention_hidden_size} for"
	f" `config.decoder.cross_attention_hidden_size` and {config.encoder.hidden_size} for"
	" `config.encoder.hidden_size`."
	)

	# initialize with config
	super().__init__(config)

	if encoder is None:
	encoder = CodeT5pModel(config.encoder)

	if decoder is None:
	decoder = CodeT5pForCausalLM(config.decoder)

	self.encoder = encoder
	self.decoder = decoder

	if self.encoder.config.to_dict() != self.config.encoder.to_dict():
	logger.warning(
	f"Config of the encoder: {self.encoder.__class__} is overwritten by shared encoder config:"
	f" {self.config.encoder}"
	)
	if self.decoder.config.to_dict() != self.config.decoder.to_dict():
	logger.warning(
	f"Config of the decoder: {self.decoder.__class__} is overwritten by shared decoder config:"
	f" {self.config.decoder}"
	)

	# make sure that the individual model's config refers to the shared config
	# so that the updates to the config will be synced
	self.encoder.config = self.config.encoder
	self.decoder.config = self.config.decoder

	# encoder outputs might need to be projected to different dimension for decoder
	if (
	self.encoder.config.hidden_size != self.decoder.config.hidden_size
	and self.decoder.config.cross_attention_hidden_size is None
	):
	self.enc_to_dec_proj = nn.Linear(self.encoder.config.hidden_size, self.decoder.config.hidden_size)

	if self.encoder.get_output_embeddings() is not None:
	raise ValueError(
	f"The encoder {self.encoder} should not have a LM Head. Please use a model without LM Head"
	)
	# tie encoder, decoder weights if config set accordingly
	self.tie_weights()

	def tie_weights(self):
	# tie encoder & decoder if needed
	if self.config.tie_encoder_decoder:
	# tie encoder and decoder base model
	decoder_base_model_prefix = self.decoder.base_model_prefix
	self._tie_encoder_decoder_weights(
	self.encoder, self.decoder._modules[decoder_base_model_prefix], self.decoder.base_model_prefix
	)

	def get_encoder(self):
	return self.encoder

	def get_decoder(self):
	return self.decoder

	def get_input_embeddings(self):
	return self.encoder.get_input_embeddings()

	def get_output_embeddings(self):
	return self.decoder.get_output_embeddings()

	def set_output_embeddings(self, new_embeddings):
	return self.decoder.set_output_embeddings(new_embeddings)

	@classmethod
	def from_pretrained(cls, args, *kwargs):
	# At the moment fast initialization is not supported for composite models
	if kwargs.get("_fast_init", False):
	logger.warning(
	"Fast initialization is currently not supported for EncoderDecoderModel. "
	"Falling back to slow initialization..."
	)
	kwargs["_fast_init"] = False
	return super().from_pretrained(args, *kwargs)

	def forward(
	self,
	input_ids: Optional[torch.LongTensor] = None,
	attention_mask: Optional[torch.FloatTensor] = None,
	decoder_input_ids: Optional[torch.LongTensor] = None,
	decoder_attention_mask: Optional[torch.BoolTensor] = None,
	encoder_outputs: Optional[Tuple[torch.FloatTensor]] = None,
	past_key_values: Tuple[Tuple[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,
	**kwargs,
	) -> Union[Tuple, Seq2SeqLMOutput]:
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	kwargs_encoder = {argument: value for argument, value in kwargs.items() if not argument.startswith("decoder_")}

	kwargs_decoder = {
	argument[len("decoder_"):]: value for argument, value in kwargs.items() if argument.startswith("decoder_")
	}

	if encoder_outputs is None:
	encoder_outputs = self.encoder(
	input_ids=input_ids,
	attention_mask=attention_mask,
	inputs_embeds=inputs_embeds,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	**kwargs_encoder,
	)
	elif isinstance(encoder_outputs, tuple):
	encoder_outputs = BaseModelOutput(*encoder_outputs)

	encoder_hidden_states = encoder_outputs[0]

	# optionally project encoder_hidden_states
	if (
	self.encoder.config.hidden_size != self.decoder.config.hidden_size
	and self.decoder.config.cross_attention_hidden_size is None
	):
	encoder_hidden_states = self.enc_to_dec_proj(encoder_hidden_states)

	if (labels is not None) and (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
	)

	# Decode
	decoder_outputs = self.decoder(
	input_ids=decoder_input_ids,
	attention_mask=decoder_attention_mask,
	encoder_hidden_states=encoder_hidden_states,
	encoder_attention_mask=attention_mask,
	inputs_embeds=decoder_inputs_embeds,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	use_cache=use_cache,
	past_key_values=past_key_values,
	return_dict=return_dict,
	**kwargs_decoder,
	)

	# Compute loss independent from decoder (as some shift the logits inside them)
	loss = None
	if labels is not None:
	# warnings.warn(DEPRECATION_WARNING, FutureWarning)
	logits = decoder_outputs.logits if return_dict else decoder_outputs[0]
	loss_fct = CrossEntropyLoss()
	loss = loss_fct(logits.reshape(-1, self.decoder.config.vocab_size), labels.view(-1))

	if not return_dict:
	if loss is not None:
	return (loss,) + decoder_outputs + encoder_outputs
	else:
	return decoder_outputs + encoder_outputs

	return Seq2SeqLMOutput(
	loss=loss,
	logits=decoder_outputs.logits,
	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,
	)

	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)

	def prepare_inputs_for_generation(
	self, input_ids, past=None, attention_mask=None, use_cache=None, encoder_outputs=None, **kwargs
	):
	decoder_inputs = self.decoder.prepare_inputs_for_generation(input_ids, past=past)
	decoder_attention_mask = decoder_inputs["attention_mask"] if "attention_mask" in decoder_inputs else None
	input_dict = {
	"attention_mask": attention_mask,
	"decoder_attention_mask": decoder_attention_mask,
	"decoder_input_ids": decoder_inputs["input_ids"],
	"encoder_outputs": encoder_outputs,
	"past_key_values": decoder_inputs["past_key_values"],
	"use_cache": use_cache,
	}
	return input_dict

	def resize_token_embeddings(self, args, *kwargs):
	raise NotImplementedError(
	"Resizing the embedding layers via the EncoderDecoderModel directly is not supported. Please use the"
	" respective methods of the wrapped objects (model.encoder.resize_token_embeddings(...) or"
	" model.decoder.resize_token_embeddings(...))"
	)

	def _reorder_cache(self, past, beam_idx):
	# apply decoder cache reordering here
	return self.decoder._reorder_cache(past, beam_idx)