madlad400-8b-lm / decoderonlyt5_modeling.py

25d8903 6 months ago

No virus

34.3 kB

	import copy
	import math
	from typing import Optional, Tuple, Union

	import torch
	from torch import nn
	from torch.nn import CrossEntropyLoss
	from transformers.models.t5 import modeling_t5
	from transformers.modeling_outputs import CausalLMOutputWithPast
	from transformers.utils import (
	add_start_docstrings_to_model_forward,
	logging,
	replace_return_docstrings,
	)

	from .decoderonlyt5_config import DecoderOnlyT5Config


	logger = logging.get_logger(__name__)
	_CONFIG_FOR_DOC = "DecoderOnlyT5Config"


	class DecoderOnlyT5LayerNorm(nn.Module):
	def __init__(self, hidden_size, eps=1e-6, use_scale=True, center_scale_at_zero=False):
	"""
	Construct a layernorm module in the T5 style No bias and no subtraction of mean.
	"""
	super().__init__()
	if use_scale:
	self.weight = nn.Parameter(torch.ones(hidden_size))
	else:
	assert not center_scale_at_zero
	self.weight = None
	self.center_scale_at_zero = center_scale_at_zero
	self.variance_epsilon = eps

	def forward(self, hidden_states):
	# https://github.com/google/flaxformer/blob/ea17eb012a1d340ddff017b7a534c2162aaec34c/flaxformer/components/layer_norm.py#L30

	# layer norm should always be calculated in float32
	mean2 = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
	hidden_states = hidden_states * torch.rsqrt(mean2 + self.variance_epsilon)

	# convert into float16 if necessary
	if self.weight is None:
	return hidden_states
	if self.weight.dtype == torch.float16:
	hidden_states = hidden_states.to(torch.float16)
	if self.center_scale_at_zero:
	return (self.weight + 1.0) * hidden_states
	else:
	return self.weight * hidden_states



	class DecoderOnlyT5LayerFF(modeling_t5.T5LayerFF):
	def __init__(self, config: DecoderOnlyT5Config):
	super(modeling_t5.T5LayerFF, self).__init__()
	if config.is_gated_act:
	self.DenseReluDense = modeling_t5.T5DenseGatedActDense(config)
	else:
	self.DenseReluDense = modeling_t5.T5DenseActDense(config)

	if not config.parallel_layers:
	self.layer_norm = modeling_t5.DecoderOnlyT5LayerNorm(
	config.d_model, eps=config.layer_norm_epsilon
	)
	else:
	self.layer_norm = nn.Identity()
	self.dropout = nn.Dropout(config.dropout_rate)


	# LlamaRotaryEmbedding
	class DecoderOnlyT5RotaryEmbedding(nn.Module):
	def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
	super().__init__()

	self.dim = dim
	self.max_position_embeddings = max_position_embeddings
	self.base = base
	inv_freq = 1.0 / (
	self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)
	)
	self.register_buffer("inv_freq", inv_freq, persistent=False)

	# Build here to make `torch.jit.trace` work.
	self._set_cos_sin_cache(
	seq_len=max_position_embeddings,
	device=self.inv_freq.device,
	dtype=torch.get_default_dtype(),
	)

	def _set_cos_sin_cache(self, seq_len, device, dtype):
	self.max_seq_len_cached = seq_len
	t = torch.arange(
	self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
	)

	freqs = torch.einsum("i,j->ij", t, self.inv_freq)
	# Different from paper, but it uses a different permutation in order to obtain the same calculation
	emb = torch.cat((freqs, freqs), dim=-1)
	self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
	self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)

	def forward(self, x, seq_len=None):
	# x: [bs, num_attention_heads, seq_len, head_size]
	if seq_len > self.max_seq_len_cached:
	self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)

	return (
	self.cos_cached[:seq_len].to(dtype=x.dtype),
	self.sin_cached[:seq_len].to(dtype=x.dtype),
	)


	def rotate_half(x):
	"""Rotates half the hidden dims of the input."""
	x1 = x[..., : x.shape[-1] // 2]
	x2 = x[..., x.shape[-1] // 2 :]
	return torch.cat((-x2, x1), dim=-1)


	def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
	"""Applies Rotary Position Embedding to the query and key tensors.

	Args:
	q (`torch.Tensor`): The query tensor.
	k (`torch.Tensor`): The key tensor.
	cos (`torch.Tensor`): The cosine part of the rotary embedding.
	sin (`torch.Tensor`): The sine part of the rotary embedding.
	position_ids (`torch.Tensor`):
	The position indices of the tokens corresponding to the query and key tensors. For example, this can be
	used to pass offsetted position ids when working with a KV-cache.
	unsqueeze_dim (`int`, optional, defaults to 1):
	The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
	sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
	that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
	k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
	cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
	the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
	Returns:
	`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
	"""
	cos = cos[position_ids].unsqueeze(unsqueeze_dim)
	sin = sin[position_ids].unsqueeze(unsqueeze_dim)
	q_embed = (q * cos) + (rotate_half(q) * sin)
	k_embed = (k * cos) + (rotate_half(k) * sin)
	return q_embed, k_embed


	# https://github.com/huggingface/transformers/blob/7ee995fd9c692761c4601ddbffa2ac2ec9f27b0b/src/transformers/models/llama/modeling_llama.py#L263
	def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
	"""
	This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
	num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
	"""
	batch, num_key_value_heads, slen, head_dim = hidden_states.shape
	if n_rep == 1:
	return hidden_states
	hidden_states = hidden_states[:, :, None, :, :].expand(
	batch, num_key_value_heads, n_rep, slen, head_dim
	)
	return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)


	class DecoderOnlyT5Attention(modeling_t5.T5Attention):
	"""
	Supports both multi-head and multi-query attention.
	https://arxiv.org/abs/1911.02150
	https://github.com/google/flaxformer/blob/ea17eb012a1d340ddff017b7a534c2162aaec34c/flaxformer/components/attention/dense_attention.py#L292
	"""

	def __init__(self, config: DecoderOnlyT5Config, has_relative_attention_bias=False):
	super(modeling_t5.T5Attention, self).__init__()
	self.is_decoder = config.is_decoder
	assert not has_relative_attention_bias
	assert config.use_rotary_embedding
	self.d_model = config.d_model
	self.head_dim = config.d_kv
	self.num_heads = config.num_heads
	self.num_key_value_heads = 1 if config.multi_query_attention else self.n_heads
	self.num_key_value_groups = self.num_heads // self.num_key_value_heads
	self.attention_dropout = config.dropout_rate
	self.inner_dim = self.num_heads * self.head_dim
	self.kv_inner_dim = self.num_key_value_heads * self.head_dim
	self.rotary_emb = DecoderOnlyT5RotaryEmbedding(
	self.head_dim,
	max_position_embeddings=config.relative_attention_max_distance,
	base=config.rotary_embedding_max_timescale,
	)

	# Mesh TensorFlow initialization to avoid scaling before softmax
	self.q = nn.Linear(self.d_model, self.inner_dim, bias=False)
	self.k = nn.Linear(self.d_model, self.kv_inner_dim, bias=False)
	self.v = nn.Linear(self.d_model, self.kv_inner_dim, bias=False)
	self.o = nn.Linear(self.inner_dim, self.d_model, bias=False)

	self.pruned_heads = set()
	self.gradient_checkpointing = False

	def forward(
	self,
	hidden_states: torch.Tensor,
	key_value_states=None,
	position_bias=None,
	mask: Optional[torch.Tensor] = None,
	layer_head_mask=None,
	position_ids: Optional[torch.LongTensor] = None,
	past_key_value: Optional[Tuple[torch.Tensor]] = None,
	output_attentions: bool = False,
	use_cache: bool = False,
	) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
	assert key_value_states is None
	assert position_bias is None
	assert layer_head_mask is None

	bsz, q_len, _ = hidden_states.size()

	query_states = self.q(hidden_states)
	key_states = self.k(hidden_states)
	value_states = self.v(hidden_states)

	query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
	key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
	value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)

	kv_seq_len = key_states.shape[-2]
	if past_key_value is not None:
	kv_seq_len += past_key_value[0].shape[-2]
	cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
	query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)

	if past_key_value is not None:
	# reuse k, v, self_attention
	key_states = torch.cat([past_key_value[0], key_states], dim=2)
	value_states = torch.cat([past_key_value[1], value_states], dim=2)

	past_key_value = (key_states, value_states) if use_cache else None

	key_states = repeat_kv(key_states, self.num_key_value_groups)
	value_states = repeat_kv(value_states, self.num_key_value_groups)

	attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)

	if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
	raise ValueError(
	f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
	f" {attn_weights.size()}"
	)

	if mask is not None:
	if mask.size() != (bsz, 1, q_len, kv_seq_len):
	raise ValueError(
	f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {mask.size()}"
	)
	attn_weights = attn_weights + mask

	# upcast attention to fp32
	attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
	attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout)
	attn_output = torch.matmul(attn_weights, value_states)

	if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
	raise ValueError(
	f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
	f" {attn_output.size()}"
	)

	attn_output = attn_output.transpose(1, 2).contiguous()
	attn_output = attn_output.reshape(bsz, q_len, self.inner_dim)
	attn_output = self.o(attn_output)

	present_key_value_state = (
	(key_states, value_states) if (self.is_decoder and use_cache) else None
	)
	outputs = (attn_output,) + (present_key_value_state,) + (position_bias,)

	if output_attentions:
	outputs = outputs + (attn_weights,)
	return outputs


	class DecoderOnlyT5LayerSelfAttention(modeling_t5.T5LayerSelfAttention):
	def __init__(self, config, has_relative_attention_bias=False):
	super(modeling_t5.T5LayerSelfAttention, self).__init__()
	self.SelfAttention = DecoderOnlyT5Attention(
	config, has_relative_attention_bias=has_relative_attention_bias
	)
	self.layer_norm = DecoderOnlyT5LayerNorm(
	config.d_model,
	eps=config.layer_norm_epsilon,
	use_scale=True,
	center_scale_at_zero=True,
	)
	self.dropout = nn.Dropout(config.dropout_rate)
	self.parallel_layers = config.parallel_layers

	def forward(
	self,
	hidden_states,
	attention_mask=None,
	position_bias=None,
	position_ids=None,
	layer_head_mask=None,
	past_key_value=None,
	use_cache=False,
	output_attentions=False,
	):
	if not self.parallel_layers:
	x = self.layer_norm(hidden_states)
	else:
	x = hidden_states
	attention_output = self.SelfAttention(
	x,
	mask=attention_mask,
	position_bias=position_bias,
	position_ids=position_ids,
	layer_head_mask=layer_head_mask,
	past_key_value=past_key_value,
	use_cache=use_cache,
	output_attentions=output_attentions,
	)
	if not self.parallel_layers:
	# When parallel_layers is True, the residual connection is applied
	# in the decoder block instead of here.
	hidden_states = hidden_states + self.dropout(attention_output[0])
	else:
	hidden_states = attention_output[0]
	outputs = (hidden_states,) + attention_output[
	1:
	] # add attentions if we output them
	return outputs


	class DecoderOnlyT5Block(modeling_t5.T5Block):
	def __init__(self, config, has_relative_attention_bias=False):
	super(modeling_t5.T5Block, self).__init__()
	self.is_decoder = config.is_decoder
	self.is_decoder_only = config.is_decoder_only
	self.layer = nn.ModuleList()
	self.layer.append(
	DecoderOnlyT5LayerSelfAttention(
	config, has_relative_attention_bias=has_relative_attention_bias
	)
	)
	if self.is_decoder:
	if config.is_decoder_only:
	self.layer.append(nn.Identity())
	else:
	self.layer.append(modeling_t5.T5LayerCrossAttention(config))
	self.parallel_layers = config.parallel_layers
	self.layer.append(DecoderOnlyT5LayerFF(config))

	def forward(
	self,
	hidden_states,
	attention_mask=None,
	position_bias=None,
	position_ids=None,
	encoder_hidden_states=None,
	layer_head_mask=None,
	past_key_value=None,
	use_cache=False,
	output_attentions=False,
	encoder_attention_mask=None,
	encoder_decoder_position_bias=None,
	cross_attn_layer_head_mask=None,
	return_dict=True,
	):
	assert encoder_attention_mask is None
	assert encoder_decoder_position_bias is None
	assert cross_attn_layer_head_mask is None
	if past_key_value is not None:
	expected_num_past_key_values = 2 if encoder_hidden_states is None else 4

	if len(past_key_value) != expected_num_past_key_values:
	raise ValueError(
	f"There should be {expected_num_past_key_values} past states. "
	f"{'2 (past / key) for cross attention. ' if expected_num_past_key_values == 4 else ''}"
	f"Got {len(past_key_value)} past key / value states"
	)
	self_attn_past_key_value = past_key_value[:2]
	else:
	self_attn_past_key_value = None

	ff_layer = self.layer[-1]
	if self.parallel_layers:
	# https://github.com/google/flaxformer/blob/ea17eb012a1d340ddff017b7a534c2162aaec34c/flaxformer/architectures/t5/t5_architecture.py#L563-L568
	x = self.layer[0].layer_norm(hidden_states)
	ff_output = ff_layer(x)
	else:
	x = hidden_states

	self_attention_outputs = self.layer[0](
	x,
	attention_mask=attention_mask,
	position_bias=position_bias,
	position_ids=position_ids,
	layer_head_mask=layer_head_mask,
	past_key_value=self_attn_past_key_value,
	use_cache=use_cache,
	output_attentions=output_attentions,
	)
	x, present_key_value_state = self_attention_outputs[:2]
	attention_outputs = self_attention_outputs[
	2:
	] # Keep self-attention outputs and relative position weights

	# clamp inf values to enable fp16 training
	if x.dtype == torch.float16:
	clamp_value = torch.where(
	torch.isinf(x).any(),
	torch.finfo(x.dtype).max - 1000,
	torch.finfo(x.dtype).max,
	)
	x = torch.clamp(x, min=-clamp_value, max=clamp_value)

	do_cross_attention = (
	self.is_decoder
	and not self.is_decoder_only
	and encoder_hidden_states is not None
	)
	assert not do_cross_attention

	if self.parallel_layers:
	# https://github.com/google/flaxformer/blob/ea17eb012a1d340ddff017b7a534c2162aaec34c/flaxformer/architectures/t5/t5_architecture.py#L534-L578
	x = x + ff_output
	x = 2*-0.5
	hidden_states = hidden_states + self.layer[0].dropout(x)
	else:
	hidden_states = ff_layer(x)

	# clamp inf values to enable fp16 training
	if hidden_states.dtype == torch.float16:
	clamp_value = torch.where(
	torch.isinf(hidden_states).any(),
	torch.finfo(hidden_states.dtype).max - 1000,
	torch.finfo(hidden_states.dtype).max,
	)
	hidden_states = torch.clamp(
	hidden_states, min=-clamp_value, max=clamp_value
	)

	outputs = (hidden_states,)

	if use_cache:
	outputs = outputs + (present_key_value_state,) + attention_outputs
	else:
	outputs = outputs + attention_outputs

	return outputs # hidden-states, present_key_value_states, (self-attention position bias), (self-attention weights), (cross-attention position bias), (cross-attention weights)


	class DecoderOnlyT5Stack(modeling_t5.T5Stack):
	def __init__(self, config, embed_tokens=None):
	super(modeling_t5.T5Stack, self).__init__(config)

	self.embed_tokens = embed_tokens
	self.is_decoder = config.is_decoder

	self.block = nn.ModuleList(
	[
	DecoderOnlyT5Block(
	config,
	has_relative_attention_bias=(
	config.has_relative_attention_bias and bool(i == 0)
	),
	)
	for i in range(config.num_layers)
	]
	)
	self.final_layer_norm = DecoderOnlyT5LayerNorm(
	config.d_model,
	eps=config.layer_norm_epsilon,
	use_scale=False,
	center_scale_at_zero=False,
	)
	self.dropout = nn.Dropout(config.dropout_rate)

	# Initialize weights and apply final processing
	self.post_init()
	# Model parallel
	self.model_parallel = False
	self.device_map = None
	self.gradient_checkpointing = False

	def forward(
	self,
	input_ids=None,
	position_ids=None,
	attention_mask=None,
	encoder_hidden_states=None,
	encoder_attention_mask=None,
	inputs_embeds=None,
	head_mask=None,
	cross_attn_head_mask=None,
	past_key_values=None,
	use_cache=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	):
	# Model parallel
	if self.model_parallel:
	torch.cuda.set_device(self.first_device)
	self.embed_tokens = self.embed_tokens.to(self.first_device)
	use_cache = use_cache if use_cache is not None else self.config.use_cache
	output_attentions = (
	output_attentions
	if output_attentions is not None
	else self.config.output_attentions
	)
	output_hidden_states = (
	output_hidden_states
	if output_hidden_states is not None
	else self.config.output_hidden_states
	)
	return_dict = (
	return_dict if return_dict is not None else self.config.use_return_dict
	)

	if input_ids is not None and inputs_embeds is not None:
	err_msg_prefix = "decoder_" if self.is_decoder else ""
	raise ValueError(
	f"You cannot specify both {err_msg_prefix}input_ids and {err_msg_prefix}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:
	err_msg_prefix = "decoder_" if self.is_decoder else ""
	raise ValueError(
	f"You have to specify either {err_msg_prefix}input_ids or {err_msg_prefix}inputs_embeds"
	)

	if position_ids is None:
	seq_length = input_ids.shape[1]
	past_key_values_length = (
	0 if past_key_values is None else past_key_values[0][0].shape[2]
	)
	device = input_ids.device if input_ids is not None else inputs_embeds.device
	position_ids = torch.arange(
	past_key_values_length,
	seq_length + past_key_values_length,
	dtype=torch.long,
	device=device,
	).unsqueeze(0)

	if inputs_embeds is None:
	if self.embed_tokens is None:
	raise ValueError(
	"You have to initialize the model with valid token embeddings"
	)
	inputs_embeds = self.embed_tokens(input_ids)

	batch_size, seq_length = input_shape

	# required mask seq length can be calculated via length of past
	mask_seq_length = (
	past_key_values[0][0].shape[2] + seq_length
	if past_key_values is not None
	else seq_length
	)

	if use_cache is True:
	if not self.is_decoder:
	raise ValueError(
	f"`use_cache` can only be set to `True` if {self} is used as a decoder"
	)

	if attention_mask is None:
	attention_mask = torch.ones(
	batch_size, mask_seq_length, device=inputs_embeds.device
	)

	# initialize past_key_values with `None` if past does not exist
	if past_key_values is None:
	past_key_values = [None] * len(self.block)

	# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
	# ourselves in which case we just need to make it broadcastable to all heads.
	extended_attention_mask = self.get_extended_attention_mask(
	attention_mask, input_shape
	)

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

	# Prepare head mask if needed
	head_mask = self.get_head_mask(head_mask, self.config.num_layers)
	cross_attn_head_mask = self.get_head_mask(
	cross_attn_head_mask, self.config.num_layers
	)
	present_key_value_states = () if use_cache else None
	all_hidden_states = () if output_hidden_states else None
	all_attentions = () if output_attentions else None
	all_cross_attentions = () if (output_attentions and self.is_decoder) else None
	position_bias = None

	hidden_states = self.dropout(inputs_embeds)

	for i, (layer_module, past_key_value) in enumerate(
	zip(self.block, past_key_values)
	):
	layer_head_mask = head_mask[i]
	cross_attn_layer_head_mask = cross_attn_head_mask[i]
	# Model parallel
	if self.model_parallel:
	torch.cuda.set_device(hidden_states.device)
	# Ensure that attention_mask is always on the same device as hidden_states
	if attention_mask is not None:
	attention_mask = attention_mask.to(hidden_states.device)
	if position_bias is not None:
	position_bias = position_bias.to(hidden_states.device)
	if layer_head_mask is not None:
	layer_head_mask = layer_head_mask.to(hidden_states.device)

	if output_hidden_states:
	all_hidden_states = all_hidden_states + (hidden_states,)

	if self.gradient_checkpointing and self.training:
	layer_outputs = self._gradient_checkpointing_func(
	layer_module.forward,
	hidden_states,
	extended_attention_mask,
	position_bias,
	None,
	None,
	None,
	layer_head_mask,
	cross_attn_layer_head_mask,
	None, # past_key_value is always None with gradient checkpointing
	use_cache,
	output_attentions,
	)
	else:
	layer_outputs = layer_module(
	hidden_states,
	attention_mask=extended_attention_mask,
	position_bias=position_bias,
	position_ids=position_ids,
	encoder_hidden_states=None,
	encoder_attention_mask=None,
	encoder_decoder_position_bias=None,
	layer_head_mask=layer_head_mask,
	cross_attn_layer_head_mask=cross_attn_layer_head_mask,
	past_key_value=past_key_value,
	use_cache=use_cache,
	output_attentions=output_attentions,
	)

	# layer_outputs is a tuple with:
	# hidden-states, key-value-states, (self-attention position bias), (self-attention weights), (cross-attention position bias), (cross-attention weights)
	if use_cache is False:
	layer_outputs = layer_outputs[:1] + (None,) + layer_outputs[1:]

	hidden_states, present_key_value_state = layer_outputs[:2]

	# We share the position biases between the layers - the first layer store them
	# layer_outputs = hidden-states, key-value-states (self-attention position bias), (self-attention weights),
	# (cross-attention position bias), (cross-attention weights)
	position_bias = layer_outputs[2]
	# append next layer key value states
	if use_cache:
	present_key_value_states = present_key_value_states + (
	present_key_value_state,
	)

	if output_attentions:
	all_attentions = all_attentions + (layer_outputs[3],)
	if self.is_decoder:
	all_cross_attentions = all_cross_attentions + (layer_outputs[5],)

	# Model Parallel: If it's the last layer for that device, put things on the next device
	if self.model_parallel:
	for k, v in self.device_map.items():
	if i == v[-1] and "cuda:" + str(k) != self.last_device:
	hidden_states = hidden_states.to("cuda:" + str(k + 1))

	hidden_states = self.final_layer_norm(hidden_states)
	hidden_states = self.dropout(hidden_states)

	# Add last layer
	if output_hidden_states:
	all_hidden_states = all_hidden_states + (hidden_states,)

	if not return_dict:
	return tuple(
	v
	for v in [
	hidden_states,
	present_key_value_states,
	all_hidden_states,
	all_attentions,
	all_cross_attentions,
	]
	if v is not None
	)
	return modeling_t5.BaseModelOutputWithPastAndCrossAttentions(
	last_hidden_state=hidden_states,
	past_key_values=present_key_value_states,
	hidden_states=all_hidden_states,
	attentions=all_attentions,
	cross_attentions=all_cross_attentions,
	)


	class DecoderOnlyT5Model(modeling_t5.T5ForConditionalGeneration):
	def __init__(self, config: DecoderOnlyT5Config):
	super(modeling_t5.T5ForConditionalGeneration, self).__init__(config)
	self.model_dim = config.d_model

	self.shared = nn.Embedding(config.vocab_size, config.d_model)
	assert (
	self.config.num_layers == 0
	), "Decoder only model cannot have encoder layers"
	self.encoder = None

	decoder_config = copy.deepcopy(config)
	decoder_config.is_decoder = True
	decoder_config.is_encoder_decoder = False
	decoder_config.num_layers = config.num_decoder_layers
	self.decoder = DecoderOnlyT5Stack(decoder_config, self.shared)

	self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)

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

	# Model parallel
	self.model_parallel = False
	self.device_map = None

	def _tie_weights(self):
	if not self.config.tie_word_embeddings:
	return
	if self.decoder:
	self._tie_or_clone_weights(self.decoder.embed_tokens, self.shared)

	@add_start_docstrings_to_model_forward(modeling_t5.T5_INPUTS_DOCSTRING)
	@replace_return_docstrings(
	output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
	)
	def forward(
	self,
	input_ids: Optional[torch.LongTensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	attention_mask: Optional[torch.FloatTensor] = None,
	past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = 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, CausalLMOutputWithPast]:
	r"""
	labels (`torch.LongTensor` of shape `(batch_size,)`, optional):
	Labels for computing the sequence classification/regression loss. Indices should be in `[-100, 0, ...,
	config.vocab_size - 1]`. All labels set to `-100` are ignored (masked), the loss is only computed for
	labels in `[0, ..., config.vocab_size]`

	Returns:

	Examples:

	```"""
	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 self.model_parallel:
	torch.cuda.set_device(self.decoder.first_device)

	# Set device for model parallelism
	if self.model_parallel:
	torch.cuda.set_device(self.decoder.first_device)
	if input_ids is not None:
	input_ids = input_ids.to(self.decoder.first_device)
	if attention_mask is not None:
	attention_mask = attention_mask.to(self.decoder.first_device)

	# Decode
	outputs = self.decoder(
	input_ids=input_ids,
	position_ids=position_ids,
	attention_mask=attention_mask,
	inputs_embeds=inputs_embeds,
	past_key_values=past_key_values,
	encoder_hidden_states=None,
	encoder_attention_mask=None,
	head_mask=None,
	cross_attn_head_mask=None,
	use_cache=use_cache,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	sequence_output = outputs[0]

	# Set device for model parallelism
	if self.model_parallel:
	torch.cuda.set_device(self.decoder.first_device)
	self.lm_head = self.lm_head.to(self.decoder.first_device)
	sequence_output = sequence_output.to(self.lm_head.weight.device)

	if self.config.tie_word_embeddings:
	# Rescale output before projecting on vocab
	# See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586
	sequence_output = sequence_output * (self.model_dim**-0.5)

	lm_logits = self.lm_head(sequence_output)

	loss = None
	if labels is not None:
	loss_fct = CrossEntropyLoss(ignore_index=-100)
	# move labels to correct device to enable PP
	labels = labels.to(lm_logits.device)
	loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1))
	# TODO(thom): Add z_loss https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L666

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

	return CausalLMOutputWithPast(
	loss=loss,
	logits=lm_logits,
	past_key_values=outputs.past_key_values,
	hidden_states=outputs.hidden_states,
	attentions=outputs.attentions,
	)