GeoV-9b / modeling_geov.py

model code

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	# coding=utf-8
	# Copyright 2022 EleutherAI The HuggingFace Inc. team. All rights reserved.
	# Modifications Copyright 2023 Better Planet Investments and labml.ai 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 GeoV model."""
	import math
	from typing import Optional, Tuple, Union

	import torch
	import torch.utils.checkpoint
	from torch import nn
	from torch.nn import CrossEntropyLoss

	from transformers.file_utils import (
	add_code_sample_docstrings,
	add_start_docstrings,
	add_start_docstrings_to_model_forward,
	replace_return_docstrings,
	)
	from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
	from transformers.modeling_utils import PreTrainedModel
	from transformers.utils import logging
	from .configuration_geov import GeoVConfig

	logger = logging.get_logger(__name__)

	_CHECKPOINT_FOR_DOC = "GeoV/GeoV-9b"
	_REAL_CHECKPOINT_FOR_DOC = "GeoV/GeoV-9b"
	_CONFIG_FOR_DOC = "GeoVConfig"

	GEOV_PRETRAINED_MODEL_ARCHIVE_LIST = [
	"GeoV/GeoV-9b",
	# See all GeoV models at https://huggingface.co/models?filter=geov
	]


	class RotaryEmbedding(torch.nn.Module):
	def __init__(self, dim, base=10000):
	super().__init__()
	inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))
	self.register_buffer("inv_freq", inv_freq)

	self.max_seq_len_cached = -1

	def forward(self, x, seq_len=None):
	# x: [bs, num_attention_heads, seq_len, head_size]
	# This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case.
	if seq_len > self.max_seq_len_cached:
	self.max_seq_len_cached = seq_len
	t = torch.arange(self.max_seq_len_cached, device=x.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).to(x.device)
	self.cos_cached = emb.cos()[None, None, :, :].to(x.dtype)
	self.sin_cached = emb.sin()[None, None, :, :].to(x.dtype)
	return self.cos_cached.to(x.device), self.sin_cached.to(x.device)


	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, cos, sin, position_ids):
	"""Apply positional embeddings"""
	gather_indices = position_ids[:, None, :, None] # [bs, 1, seq_len, 1]
	gather_indices = gather_indices.repeat(1, cos.shape[1], 1, cos.shape[3])
	cos = torch.gather(cos.repeat(gather_indices.shape[0], 1, 1, 1), 2, gather_indices)
	sin = torch.gather(sin.repeat(gather_indices.shape[0], 1, 1, 1), 2, gather_indices)
	q_embed = (q * cos) + (rotate_half(q) * sin)
	return q_embed


	def apply_rotary_pos_emb_reverse(q, cos, sin, position_ids):
	"""Apply positional embeddings in reverse"""
	gather_indices = position_ids[:, None, :, None] # [bs, 1, seq_len, 1]
	gather_indices = gather_indices.repeat(1, cos.shape[1], 1, cos.shape[3])
	cos = torch.gather(cos.repeat(gather_indices.shape[0], 1, 1, 1), 2, gather_indices)
	sin = torch.gather(sin.repeat(gather_indices.shape[0], 1, 1, 1), 2, gather_indices)
	q_embed = (q * cos) - (rotate_half(q) * sin)
	return q_embed


	class GeoVAttention(nn.Module):
	"""
	Attention module
	"""

	def __init__(self, config):
	super().__init__()
	self.num_attention_heads = config.num_attention_heads
	self.hidden_size = config.hidden_size
	self.head_size = self.hidden_size // self.num_attention_heads
	max_positions = config.max_position_embeddings
	self.register_buffer("causal_mask", torch.tril(torch.ones((max_positions, max_positions), dtype=torch.bool)))
	self.rotary_emb = RotaryEmbedding(self.head_size, base=config.rotary_emb_base)
	self.qkv = nn.Linear(config.hidden_size, 3 * config.hidden_size)
	self.dense = nn.Linear(config.hidden_size, config.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.qkv(hidden_states)
	query, key, value = torch.tensor_split(qkv, 3, dim=-1)

	# 'b l (h q) -> b h l q'
	query = self._split_heads(query, self.num_attention_heads)
	key = self._split_heads(key, self.num_attention_heads)
	value = self._split_heads(value, self.num_attention_heads)

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

	cos, sin = self.rotary_emb(query, seq_len=seq_len)
	query = apply_rotary_pos_emb(query, cos, sin, position_ids)
	key = apply_rotary_pos_emb(key, cos, sin, position_ids)
	value = apply_rotary_pos_emb(value, cos, sin, position_ids)

	# 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)

	attn_output = apply_rotary_pos_emb_reverse(attn_output, cos, sin, position_ids)

	# Reshape outputs
	attn_output = self._merge_heads(attn_output)
	attn_output = self.dense(attn_output)

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

	return outputs

	@classmethod
	def _split_heads(cls, tensor, num_attention_heads):
	"""
	Splits hidden dim into num_attention_heads
	"""
	# tensor: [bs, seq_len, hidden_size]
	new_shape = tensor.shape[:-1] + (num_attention_heads, tensor.shape[-1] // num_attention_heads)
	# -> [bs, seq_len, num_attention_heads, attn_head_size]
	tensor = tensor.view(new_shape)
	# -> [bs, num_attention_heads, seq_len, attn_head_size]
	tensor = tensor.permute(0, 2, 1, 3)
	return tensor

	@classmethod
	def _merge_heads(cls, tensor):
	"""
	Merges heads
	"""
	# tensor [bs, num_attention_heads, seq_len, attn_head_size]
	tensor = tensor.permute(0, 2, 1, 3).contiguous()
	# -> [bs, seq_len, num_attention_heads, attn_head_size]
	tensor = tensor.view(tensor.shape[:2], tensor.shape[2] tensor.shape[3])
	# -> [bs, seq_len, hidden_size]
	return tensor

	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.shape
	key_length = key.shape[-2]

	causal_mask = self.causal_mask[None, None, key_length - query_length: key_length, :key_length]

	attn_scores = torch.einsum("bhid,bhjd->bhij", query, key) / math.sqrt(attn_head_size)

	attn_scores.masked_fill_(causal_mask == 0, torch.finfo(attn_scores.dtype).min)

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

	attn_weights = nn.functional.softmax(attn_scores, dim=-1)

	# 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


	class GeoVMLP(nn.Module):
	"""Position wise Feed-forward network"""

	def __init__(self, config: "GeoVConfig"):
	super().__init__()
	self.dense_h_to_4h = nn.Linear(config.hidden_size, config.intermediate_size)
	self.dense_2h_to_h = nn.Linear(
	config.intermediate_size // 2, config.hidden_size, bias=config.use_extra_biases_ffn
	)
	self.act = nn.GELU()

	def forward(self, hidden_states):
	hidden_states = self.dense_h_to_4h(hidden_states)
	# Gated GELU
	gate, pass_through = torch.tensor_split(hidden_states, 2, dim=-1)
	gate = self.act(gate)
	hidden_states = gate * pass_through

	hidden_states = self.dense_2h_to_h(hidden_states)
	return hidden_states


	class GeoVLayer(nn.Module):
	"""GeoV transformer layer"""

	def __init__(self, config: "GeoVConfig"):
	super().__init__()
	self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
	self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
	self.attention = GeoVAttention(config)
	self.mlp = GeoVMLP(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:]

	attn_output = attn_output + hidden_states
	mlp_output = self.mlp(self.post_attention_layernorm(attn_output))
	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 GeoVPreTrainedModel(PreTrainedModel):
	"""
	An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
	models.
	"""

	config_class = GeoVConfig
	base_model_prefix = "geov"
	supports_gradient_checkpointing = True
	_no_split_modules = ["GeoVLayer"]

	def _init_weights(self, module):
	pass

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


	GEOV_START_DOCSTRING = r"""
	This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
	it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
	behavior.

	Parameters:
	config ([`~GeoVConfig`]): 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.
	"""

	GEOV_INPUTS_DOCSTRING = r"""
	Args:
	input_ids (`torch.LongTensor` of shape `(batch_size, seq_len)`):
	Indices of input sequence tokens in the vocabulary.

	Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
	[`PreTrainedTokenizer.__call__`] for details.

	[What are input IDs?](../glossary#input-ids)
	attention_mask (`torch.FloatTensor` of shape `(batch_size, seq_len)`, 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)
	position_ids (`torch.LongTensor` of shape `({0})`, optional):
	Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
	config.n_positions - 1]`.

	[What are position IDs?](../glossary#position-ids)
	head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, optional):
	Mask to nullify selected heads of the self-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]]` of length `n_layers`, with each tuple having 2 tensors of shape `(batch_size, n_heads, seq_len - 1, head_size)`, optional):
	Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.

	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`).
	inputs_embeds (`torch.FloatTensor` of shape `(batch_size, seq_len, 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 [`~file_utils.ModelOutput`] instead of a plain tuple.
	"""


	@add_start_docstrings(
	"The bare GeoV Model transformer outputting raw hidden-states without any specific head on top.",
	GEOV_START_DOCSTRING,
	)
	class GeoVModel(GeoVPreTrainedModel):
	def __init__(self, config: "GeoVConfig"):
	super().__init__(config)
	self.config = config

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

	self.embed_in.to(torch.bfloat16)
	self.layers.to(torch.bfloat16)

	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

	@add_start_docstrings_to_model_forward(GEOV_INPUTS_DOCSTRING)
	@add_code_sample_docstrings(
	checkpoint=_CHECKPOINT_FOR_DOC,
	real_checkpoint=_REAL_CHECKPOINT_FOR_DOC,
	output_type=BaseModelOutputWithPast,
	config_class=_CONFIG_FOR_DOC,
	)
	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]:
	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],)

	# Cast the hidden state to final layer norm data type (this is the modification from GPTNeoX)
	hidden_states = self.final_layer_norm(hidden_states.to(self.final_layer_norm.weight.dtype))
	# 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,
	)


	@add_start_docstrings(
	"""GeoV Model with a `language modeling` head on top for CLM fine-tuning.""", GEOV_START_DOCSTRING
	)
	class GeoVForCausalLM(GeoVPreTrainedModel):
	_keys_to_ignore_on_load_missing = [r"causal_mask", r"inv_freq"]

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

	self.geov = GeoVModel(config)
	self.embed_out = nn.Linear(config.hidden_size, config.vocab_size)

	# 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

	@add_start_docstrings_to_model_forward(GEOV_INPUTS_DOCSTRING)
	@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
	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"""
	labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional):
	Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
	`[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are
	ignored (masked), the loss is only computed for the tokens with labels n `[0, ..., config.vocab_size]`.

	Returns:

	Example:

	```python
	>>> from transformers import AutoTokenizer, GeoVForCausalLM, GeoVConfig
	>>> import torch

	>>> tokenizer = AutoTokenizer.from_pretrained("GeoV/GeoV-9b")
	>>> model = GeoVForCausalLM.from_pretrained("GeoV/GeoV-9b")

	>>> inputs = tokenizer("Hello, my dog is cute", 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.geov(
	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:
	# 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, **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)

	return {
	"input_ids": input_ids,
	"attention_mask": attention_mask,
	"position_ids": position_ids,
	"past_key_values": past_key_values,
	}

	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