japanese-stablelm-instruct-alpha-7b-v2 / modeling_japanese_stablelm_alpha.py

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	# coding=utf-8
	# Copyright 2023 Stability and The HuggingFace Inc. team. All rights reserved.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	""" PyTorch JapaneseStableLMAlpha model. """
	from typing import Optional, Tuple, Union

	import torch
	import torch.utils.checkpoint
	from torch import nn
	from torch.nn import CrossEntropyLoss
	from transformers.modeling_outputs import (
	BaseModelOutputWithPast,
	CausalLMOutputWithPast,
	)
	from transformers.modeling_utils import PreTrainedModel
	from transformers.utils import logging
	from .configuration_japanese_stablelm_alpha import JapaneseStableLMAlphaConfig


	logger = logging.get_logger(__name__)


	class JapaneseStableLMAlphaPreTrainedModel(PreTrainedModel):
	"""
	An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
	models.
	"""

	config_class = JapaneseStableLMAlphaConfig
	base_model_prefix = "transformer"
	supports_gradient_checkpointing = True
	_no_split_modules = ["DecoderLayer"]
	_skip_keys_device_placement = "past_key_values"

	def _init_weights(self, module):
	"""Initialize the weights"""
	if isinstance(module, nn.Linear):
	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):
	if module.bias is not None:
	module.bias.data.zero_()
	if module.weight is not None:
	module.weight.data.fill_(1.0)

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


	class JapaneseStableLMAlphaModel(JapaneseStableLMAlphaPreTrainedModel):
	def __init__(self, config):
	super().__init__(config)
	self.config = config

	self.embed_in = nn.Embedding(config.vocab_size, config.hidden_size)
	self.layers = nn.ModuleList([DecoderLayer(config) for _ in range(config.num_hidden_layers)])
	self.final_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)

	self.gradient_checkpointing = False

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

	def get_input_embeddings(self):
	return self.embed_in

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

	def forward(
	self,
	input_ids: Optional[torch.LongTensor] = None,
	attention_mask: Optional[torch.FloatTensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	head_mask: Optional[torch.FloatTensor] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	past_key_values: Optional[Tuple[Tuple[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, BaseModelOutputWithPast]:
	r"""
	past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
	Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up 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)`.
	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 = 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
	use_cache = use_cache if use_cache is not None else self.config.use_cache

	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()
	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")

	batch_size, seq_length = input_shape

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

	if position_ids is None:
	device = input_ids.device if input_ids is not None else inputs_embeds.device
	position_ids = torch.arange(past_length, seq_length + past_length, dtype=torch.long, device=device)
	position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
	else:
	position_ids = position_ids.view(-1, seq_length).long()

	# Attention mask.
	if attention_mask is not None:
	assert batch_size > 0, "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

	# Prepare head mask if needed
	# 1.0 in head_mask indicate we keep the head
	# attention_probs has shape bsz x n_heads x N x N
	# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
	# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
	head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)

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

	hidden_states = inputs_embeds

	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

	presents = () if use_cache else None
	all_attentions = () if output_attentions else None
	all_hidden_states = () if output_hidden_states else None
	for i, (layer, layer_past) in enumerate(zip(self.layers, past_key_values)):
	if output_hidden_states:
	all_hidden_states = all_hidden_states + (hidden_states,)

	if self.gradient_checkpointing and self.training:

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

	return custom_forward

	outputs = torch.utils.checkpoint.checkpoint(
	create_custom_forward(layer),
	hidden_states,
	attention_mask,
	position_ids,
	head_mask[i],
	)
	else:
	outputs = layer(
	hidden_states,
	attention_mask=attention_mask,
	position_ids=position_ids,
	head_mask=head_mask[i],
	layer_past=layer_past,
	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_attentions = all_attentions + (outputs[2 if use_cache else 1],)

	hidden_states = self.final_layer_norm(hidden_states)
	# 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_attentions] if v is not None)

	return BaseModelOutputWithPast(
	last_hidden_state=hidden_states,
	past_key_values=presents,
	hidden_states=all_hidden_states,
	attentions=all_attentions,
	)


	class DecoderLayer(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.use_parallel_residual = config.use_parallel_residual
	self.input_layernorm = nn.LayerNorm(
	config.hidden_size,
	eps=config.layer_norm_eps,
	elementwise_affine=False,
	)
	self.post_attention_layernorm = nn.LayerNorm(
	config.hidden_size,
	eps=config.layer_norm_eps
	)
	self.attention = Attention(config)
	self.mlp = MLP(config)

	def forward(
	self,
	hidden_states: Optional[torch.FloatTensor],
	attention_mask: Optional[torch.FloatTensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	head_mask: Optional[torch.FloatTensor] = None,
	use_cache: Optional[bool] = False,
	layer_past: Optional[Tuple[torch.Tensor]] = None,
	output_attentions: Optional[bool] = False,
	):
	attention_layer_outputs = self.attention(
	self.input_layernorm(hidden_states),
	attention_mask=attention_mask,
	position_ids=position_ids,
	layer_past=layer_past,
	head_mask=head_mask,
	use_cache=use_cache,
	output_attentions=output_attentions,
	)
	attn_output = attention_layer_outputs[0] # output_attn: attn_output, present, (attn_weights)
	outputs = attention_layer_outputs[1:]

	mlp_output = self.mlp(self.post_attention_layernorm(hidden_states))
	hidden_states = hidden_states + mlp_output + attn_output

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

	return outputs


	class MLP(nn.Module):
	def __init__(self, config: JapaneseStableLMAlphaConfig):
	super().__init__()
	hidden_size = config.hidden_size
	multiple_of = 256
	ff_dim = int(8 * hidden_size / 3)
	intermediate_size = multiple_of * ((ff_dim + multiple_of - 1) // multiple_of)

	self.packed_input_proj = torch.nn.Linear(hidden_size, 2 * intermediate_size, bias=False)
	self.out_proj = nn.Linear(intermediate_size, hidden_size, bias=False)
	self.act = nn.SiLU()

	def forward(self, x: torch.Tensor) -> torch.Tensor:
	ff, ff_gate = self.packed_input_proj(x).chunk(2, dim=-1)
	return self.out_proj(ff * self.act(ff_gate))


	class RotaryEmbedding(torch.nn.Module):
	"""Based on Tri Dao's XPos: https://github.com/HazyResearch/flash-attention/blob/main/flash_attn/layers/rotary.py"""
	def __init__(
	self,
	dim: int,
	max_position_embeddings: int,
	base: int = 10_000,
	scale_base: int = 512,
	device: str = None
	):
	super().__init__()
	self.dim = dim
	self.seq_len_cached = max_position_embeddings

	# Set up `inv_freq` term
	inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, device=device, dtype=torch.float32) / dim))
	self.register_buffer("inv_freq", inv_freq)

	# Set up `scale` term
	self.scale_base = scale_base
	scale = (
	(torch.arange(0, dim, 2, device=device, dtype=torch.float32) + 0.4 * dim) / (1.4 * dim)
	if scale_base is not None else None
	)
	self.register_buffer("scale", scale)

	# Seet up `cos..` and `sin...` cache terms
	t = torch.arange(self.seq_len_cached, device=device, dtype=torch.float32)
	freqs = torch.outer(t, self.inv_freq)
	# freqs = torch.cat((freqs, freqs), dim=-1)
	seq_range = torch.arange(self.seq_len_cached, dtype=self.scale.dtype, device=self.scale.device)
	power = (seq_range - self.seq_len_cached // 2) / self.scale_base
	scale_cached = self.scale.to(device=power.device) ** power.unsqueeze(-1)
	# scale_cached = torch.cat((scale_cached, scale_cached), dim=-1)
	self.register_buffer("cos_cached", torch.cos(freqs) * scale_cached, persistent=False)
	self.register_buffer("sin_cached", torch.sin(freqs) * scale_cached, persistent=False)
	self.register_buffer("cos_k_cached", torch.cos(freqs) / scale_cached, persistent=False)
	self.register_buffer("sin_k_cached", torch.sin(freqs) / scale_cached, persistent=False)

	def forward(self, x, seq_len=None):
	if seq_len > self.seq_len_cached:
	self.seq_len_cached = seq_len
	t = torch.arange(seq_len, device=x.device, dtype=torch.float32)
	freqs = torch.outer(t, self.inv_freq)
	freqs = torch.cat((freqs, freqs), dim=-1)
	seq_range = torch.arange(self.seq_len_cached, dtype=self.scale.dtype, device=self.scale.device)
	power = (seq_range - self.seq_len_cached // 2) / self.scale_base
	scale_cached = self.scale.to(device=power.device) ** power.unsqueeze(-1)
	scale_cached = torch.cat((scale_cached, scale_cached), dim=-1)
	self.register_buffer("cos_cached", torch.cos(freqs) * scale_cached, persistent=False)
	self.register_buffer("sin_cached", torch.sin(freqs) * scale_cached, persistent=False)
	self.register_buffer("cos_k_cached", torch.cos(freqs) / scale_cached, persistent=False)
	self.register_buffer("sin_k_cached", torch.sin(freqs) / scale_cached, persistent=False)
	return (
	self.cos_cached[:seq_len, ...],
	self.sin_cached[:seq_len, ...],
	self.cos_k_cached[:seq_len, ...],
	self.sin_k_cached[:seq_len, ...],
	)


	def rotate_half(x):
	x1, x2 = x.chunk(2, dim=-1)
	return torch.cat((-x2, x1), dim=-1)


	def apply_rotary_pos_emb(q, k, cos, sin, position_ids, cos_k=None, sin_k=None):
	"""
	q, k: [bs, num_heads, seq_len, rot_dim]
	cos, sin: [seq_len, rot_dim / 2]
	position_ids: [bs, seq_len]
	"""
	# print(f"q: {q.shape}, k: {k.shape}, cos: {cos.shape}, sin: {sin.shape}, position_ids: {position_ids.shape}")
	import einops
	cos = einops.repeat(cos, 's r -> s (2 r)')
	sin = einops.repeat(sin, 's r -> s (2 r)')
	cos_k = einops.repeat(cos_k, 's r -> s (2 r)')
	sin_k = einops.repeat(sin_k, 's r -> s (2 r)')
	cos = cos[position_ids].unsqueeze(1) # [bs, 1, seq_len, rot_dim]
	sin = sin[position_ids].unsqueeze(1) # [bs, 1, seq_len, rot_dim]
	cos_k = cos_k[position_ids].unsqueeze(1) # [bs, 1, seq_len, rot_dim]
	sin_k = sin_k[position_ids].unsqueeze(1) # [bs, 1, seq_len, rot_dim]

	q_embed = (q * cos) + (rotate_half(q) * sin)
	k_embed = (k * cos_k) + (rotate_half(k) * sin_k)
	return q_embed, k_embed


	class Attention(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.num_attention_heads = config.num_attention_heads
	self.hidden_size = config.hidden_size
	if self.hidden_size % self.num_attention_heads != 0:
	raise ValueError(
	"The hidden size is not divisble by the number of attention heads! Make sure to update them"
	)
	self.head_size = self.hidden_size // self.num_attention_heads

	max_positions = config.max_position_embeddings
	self.register_buffer(
	"bias",
	torch.tril(torch.ones((max_positions, max_positions), dtype=torch.bool)).view(
	1, 1, max_positions, max_positions
	),
	persistent=False,
	)
	self.register_buffer("masked_bias", torch.tensor(-1e9), persistent=False)

	self.rotary_ndims = int(self.head_size * config.rotary_pct)
	self.rotary_emb = RotaryEmbedding(
	self.rotary_ndims,
	max_position_embeddings=config.max_position_embeddings,
	base=config.rotary_emb_base,
	scale_base=config.rotary_scale_base,
	)

	self.register_buffer(
	"norm_factor",
	torch.sqrt(torch.tensor(self.head_size, dtype=torch.float32)).to(torch.get_default_dtype()),
	persistent=False,
	)

	self.query_key_value = nn.Linear(self.hidden_size, 3 * self.hidden_size, bias=False)
	self.dense = nn.Linear(self.hidden_size, self.hidden_size, bias=False)

	def forward(
	self,
	hidden_states: torch.FloatTensor,
	attention_mask: torch.FloatTensor,
	position_ids: torch.LongTensor,
	head_mask: Optional[torch.FloatTensor] = None,
	layer_past: Optional[Tuple[torch.Tensor]] = None,
	use_cache: Optional[bool] = False,
	output_attentions: Optional[bool] = False,
	):
	has_layer_past = layer_past is not None

	# Compute QKV
	# Attention heads [batch, seq_len, hidden_size]
	# --> [batch, seq_len, (np * 3 * head_size)]
	qkv = self.query_key_value(hidden_states)

	# [batch, seq_len, (num_heads * 3 * head_size)]
	# --> [batch, seq_len, num_heads, 3 * head_size]
	new_qkv_shape = qkv.size()[:-1] + (self.num_attention_heads, 3 * self.head_size)
	qkv = qkv.view(*new_qkv_shape)

	# [batch, seq_len, num_attention_heads, 3 * head_size] --> 3 [batch, num_attention_heads, seq_len, head_size]
	query = qkv[..., : self.head_size].permute(0, 2, 1, 3)
	key = qkv[..., self.head_size : 2 * self.head_size].permute(0, 2, 1, 3)
	value = qkv[..., 2 * self.head_size :].permute(0, 2, 1, 3)

	# Compute rotary embeddings on rotary_ndims
	query_rot = query[..., : self.rotary_ndims]
	query_pass = query[..., self.rotary_ndims :]
	key_rot = key[..., : self.rotary_ndims]
	key_pass = key[..., self.rotary_ndims :]

	# Compute token offset for rotary embeddings (when decoding)
	kv_seq_len = key.shape[-2]
	if has_layer_past:
	kv_seq_len += layer_past[0].shape[-2]

	# Add rotary embeddings to query and key
	# TODO: Check if using xpos
	cos, sin, cos_k, sin_k = self.rotary_emb(value, seq_len=kv_seq_len)
	query, key = apply_rotary_pos_emb(
	query_rot, key_rot, cos, sin, position_ids, cos_k=cos_k, sin_k=sin_k)

	query = torch.cat((query, query_pass), dim=-1)
	key = torch.cat((key, key_pass), dim=-1)

	# Cache QKV values
	if has_layer_past:
	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)
	present = (key, value) if use_cache else None

	# Compute attention
	attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask)

	# Merge attn_head_size dim and num_attn_heads dim into hidden dim
	# [bs, seq_len, num_attention_heads, attn_head_size]
	attn_output = attn_output.permute(0, 2, 1, 3).contiguous()
	attn_output = attn_output.view(attn_output.size(0), attn_output.size(1), self.num_attention_heads * self.head_size)

	attn_output = self.dense(attn_output)

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

	return outputs

	def _attn(self, query, key, value, attention_mask=None, head_mask=None):
	# q, k, v: [bs, num_attention_heads, seq_len, attn_head_size]
	# compute causal mask from causal mask buffer

	batch_size, num_attention_heads, query_length, attn_head_size = query.size()
	key_length = key.size(-2)

	causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length]

	query = query.view(batch_size * num_attention_heads, query_length, attn_head_size)
	key = key.view(batch_size * num_attention_heads, key_length, attn_head_size)
	attn_scores = torch.zeros(
	batch_size * num_attention_heads,
	query_length,
	key_length,
	dtype=query.dtype,
	device=key.device,
	)
	attn_scores = torch.baddbmm(
	attn_scores,
	query,
	key.transpose(1, 2),
	beta=1.0,
	alpha=(torch.tensor(1.0, dtype=self.norm_factor.dtype, device=self.norm_factor.device) / self.norm_factor),
	)
	attn_scores = attn_scores.view(batch_size, num_attention_heads, query_length, key_length)

	mask_value = torch.finfo(attn_scores.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_scores.dtype, device=attn_scores.device)
	attn_scores = torch.where(causal_mask, attn_scores, mask_value)

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

	# NOTE: Upcast to float32
	attn_weights = nn.functional.softmax(attn_scores, dim=-1, dtype=torch.float32).type_as(value)

	# 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 attention_mask_func(attention_scores, ltor_mask):
	attention_scores.masked_fill_(~ltor_mask, torch.finfo(attention_scores.dtype).min)
	return attention_scores


	class JapaneseStableLMAlphaForCausalLM(JapaneseStableLMAlphaPreTrainedModel):
	_tied_weights_keys = ["embed_out.weight"]

	def __init__(self, config):
	super().__init__(config)

	self.transformer = JapaneseStableLMAlphaModel(config)
	self.embed_out = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

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

	def get_output_embeddings(self):
	return self.embed_out

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

	def forward(
	self,
	input_ids: Optional[torch.LongTensor] = None,
	attention_mask: Optional[torch.FloatTensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	head_mask: Optional[torch.FloatTensor] = None,
	past_key_values: Optional[Tuple[Tuple[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"""
	Example:

	```python
	>>> import torch
	>>> from transformers import LlamaTokenizer, JapaneseStableLMAlphaForCausalLM, JapaneseStableLMAlphaConfig

	>>> tokenizer = LlamaTokenizer.from_pretrained("novelai/nerdstash-tokenizer-v1")
	>>> config = JapaneseStableLMAlphaConfig.from_pretrained("stabilityai/stablelm-ja-base-alpha-7b")
	>>> config.is_decoder = True
	>>> model = JapaneseStableLMAlphaForCausalLM.from_pretrained("stabilityai/stablelm-ja-base-alpha-7b", config=config, trust_remote_code=True)

	>>> inputs = tokenizer("日本語の美しいところは、", return_tensors="pt")
	>>> outputs = model(**inputs)

	>>> prediction_logits = outputs.logits
	```"""
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	outputs = self.transformer(
	input_ids,
	attention_mask=attention_mask,
	position_ids=position_ids,
	head_mask=head_mask,
	inputs_embeds=inputs_embeds,
	past_key_values=past_key_values,
	use_cache=use_cache,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	hidden_states = outputs[0]
	lm_logits = self.embed_out(hidden_states)

	lm_loss = None
	if labels is not None:
	# move labels to correct device to enable model parallelism
	labels = labels.to(lm_logits.device)
	# we are doing next-token prediction; shift prediction scores and input ids by one
	shift_logits = lm_logits[:, :-1, :].contiguous()
	labels = labels[:, 1:].contiguous()
	loss_fct = CrossEntropyLoss()
	lm_loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), labels.view(-1))

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

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

	def prepare_inputs_for_generation(
	self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
	):
	input_shape = input_ids.shape

	# cut decoder_input_ids if past is used
	if past_key_values and past_key_values[0] is not None:
	input_ids = input_ids[:, -1:]

	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)

	# 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_shape)

	# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
	if inputs_embeds is not None and past_key_values is None:
	model_inputs = {"inputs_embeds": inputs_embeds}
	else:
	model_inputs = {"input_ids": input_ids}

	model_inputs.update(
	{
	"attention_mask": attention_mask,
	"past_key_values": past_key_values,
	"position_ids": position_ids,
	}
	)

	return model_inputs

	def _reorder_cache(self, past_key_values, beam_idx):
	reordered_past = ()
	for layer_past in past_key_values:
	reordered_past += (
	tuple(past_state.index_select(0, beam_idx) for past_state in layer_past[:2]) + layer_past[2:],
	)
	return reordered_past