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	# coding=utf-8
	# Copyright 2022 The Fairseq Authors 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 OPT model."""

	from typing import List, Optional, Tuple, Union

	import torch

	import torch.nn.functional as F
	import torch.utils.checkpoint
	from torch import nn
	from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

	from transformers.activations import ACT2FN
	from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
	from transformers.modeling_outputs import (
	BaseModelOutputWithPast,
	CausalLMOutputWithPast,
	QuestionAnsweringModelOutput,
	SequenceClassifierOutputWithPast,
	)

	from transformers.modeling_utils import PreTrainedModel
	from transformers.utils import (
	add_code_sample_docstrings,
	add_start_docstrings,
	add_start_docstrings_to_model_forward,

	is_flash_attn_2_available,
	is_flash_attn_greater_or_equal_2_10,
	logging,
	replace_return_docstrings,

	)
	from .configuration_opt import OPTConfig



	if is_flash_attn_2_available():
	from flash_attn import flash_attn_func, flash_attn_varlen_func
	from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa


	logger = logging.get_logger(__name__)

	_CHECKPOINT_FOR_DOC = "facebook/opt-350m"
	_CONFIG_FOR_DOC = "OPTConfig"

	# Base model docstring
	_EXPECTED_OUTPUT_SHAPE = [1, 8, 1024]

	# SequenceClassification docstring
	_CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION = "ArthurZ/opt-350m-dummy-sc"
	_SEQ_CLASS_EXPECTED_LOSS = 1.71
	_SEQ_CLASS_EXPECTED_OUTPUT = "'LABEL_0'"


	# Copied from transformers.models.llama.modeling_llama._get_unpad_data
	def _get_unpad_data(attention_mask):
	seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
	indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
	max_seqlen_in_batch = seqlens_in_batch.max().item()
	cu_seqlens = F.pad(torch.cumsum(
	seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
	return (
	indices,
	cu_seqlens,
	max_seqlen_in_batch,
	)


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

	def __init__(self, num_embeddings: int, embedding_dim: int):
	# OPT is set up so that if padding_idx is specified then offset the embedding ids by 2
	# and adjust num_embeddings appropriately. Other models don't have this hack
	self.offset = 2
	super().__init__(num_embeddings + self.offset, embedding_dim)

	def forward(self, attention_mask: torch.LongTensor, past_key_values_length: int = 0):
	"""`input_ids_shape` is expected to be [bsz x seqlen]."""
	attention_mask = attention_mask.long()

	# create positions depending on attention_mask
	positions = (torch.cumsum(attention_mask, dim=1).type_as(
	attention_mask) * attention_mask).long() - 1

	# cut positions if `past_key_values_length` is > 0
	positions = positions[:, past_key_values_length:]

	return super().forward(positions + self.offset)


	def softmax_n_shifted_zeros(input: torch.Tensor, n: int, dim=-1) -> torch.Tensor:
	"""
	$\text(softmax)_n(x_i) = exp(x_i) / (n + \sum_j exp(x_j))$
	Note: softmax_n, with fixed input, is _not_ shift-symmetric when n != 0
	"""
	# compute the maxes along the last dimension
	input_maxes = input.max(dim=dim, keepdim=True).values
	# shift the input to prevent overflow (and underflow in the denominator)
	shifted_inputs = torch.subtract(input, input_maxes)
	# compute the numerator and softmax_0 denominator using the shifted input
	numerator = torch.exp(shifted_inputs)
	original_denominator = numerator.sum(dim=dim, keepdim=True)
	# we need to shift the zeros in the same way we shifted the inputs
	shifted_zeros = torch.multiply(input_maxes, -1)
	# and then add this contribution to the denominator
	denominator = torch.add(original_denominator,
	torch.multiply(torch.exp(shifted_zeros), n))
	return torch.divide(numerator, denominator)


	def softmax_1(input: torch.Tensor, dim=-1, dtype=torch.float32) -> torch.Tensor:
	"""
	$\text(softmax)_n(x_i) = exp(x_i) / (1 + \sum_j exp(x_j))$
	"""
	output = softmax_n_shifted_zeros(input, 1, dim=dim)
	return output if dtype is None else output.type(dtype=dtype)

	def clipped_softmax(data, dim=1, eta=1.1, gamma=-0.1, **kw):
	sm_out = torch.nn.functional.softmax(data, dim=dim, **kw)
	stretched_out = sm_out * (eta - gamma) + gamma
	return torch.clip(stretched_out, 0, 1)


	def clipped_softmax1(data, dim=1, eta=1.1, gamma=-0.1, **kw):
	sm_out = softmax_1(data, dim=dim, **kw)
	stretched_out = sm_out * (eta - gamma) + gamma
	return torch.clip(stretched_out, 0, 1)

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

	def __init__(
	self,
	config: OPTConfig,
	is_decoder: bool = False,
	**kwargs,
	):
	super().__init__()
	self.config = config
	self.embed_dim = config.hidden_size
	self.num_heads = config.num_attention_heads
	self.dropout = config.attention_dropout
	self.enable_bias = config.enable_bias

	self.head_dim = self.embed_dim // self.num_heads
	self.is_causal = True
	self.softmax_fn = clipped_softmax1
	if (self.head_dim * self.num_heads) != self.embed_dim:
	raise ValueError(
	f"embed_dim must be divisible by num_heads (got `embed_dim`: {
	self.embed_dim}"
	f" and `num_heads`: {self.num_heads})."
	)
	self.scaling = self.head_dim**-0.5
	self.is_decoder = is_decoder

	self.k_proj = nn.Linear(
	self.embed_dim, self.embed_dim, bias=self.enable_bias)
	self.v_proj = nn.Linear(
	self.embed_dim, self.embed_dim, bias=self.enable_bias)
	self.q_proj = nn.Linear(
	self.embed_dim, self.embed_dim, bias=self.enable_bias)
	self.out_proj = nn.Linear(
	self.embed_dim, self.embed_dim, bias=self.enable_bias)

	def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
	return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()

	def forward(
	self,
	hidden_states: torch.Tensor,
	key_value_states: Optional[torch.Tensor] = None,
	past_key_value: Optional[Tuple[torch.Tensor]] = None,
	attention_mask: Optional[torch.Tensor] = None,
	layer_head_mask: Optional[torch.Tensor] = None,
	output_attentions: bool = False,
	) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
	"""Input shape: Batch x Time x Channel"""

	# if key_value_states are provided this layer is used as a cross-attention layer
	# for the decoder
	is_cross_attention = key_value_states is not None

	bsz, tgt_len, _ = hidden_states.size()

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

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

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

	src_len = key_states.size(1)
	attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))

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

	if attention_mask is not None:
	if attention_mask.size() != (bsz, 1, tgt_len, src_len):
	raise ValueError(
	f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {
	attention_mask.size()}"
	)
	attn_weights = attn_weights.view(
	bsz, self.num_heads, tgt_len, src_len) + attention_mask
	attn_weights = torch.max(
	attn_weights, torch.tensor(torch.finfo(
	attn_weights.dtype).min, device=attn_weights.device)
	)
	attn_weights = attn_weights.view(
	bsz * self.num_heads, tgt_len, src_len)

	# upcast to fp32 if the weights are in fp16. Please see https://github.com/huggingface/transformers/pull/17437
	if attn_weights.dtype == torch.float16:
	attn_weights = self.softmax_fn(
	attn_weights, dim=-1, dtype=torch.float32).to(torch.float16)
	else:
	attn_weights = self.softmax_fn(attn_weights, dim=-1)

	if layer_head_mask is not None:
	if layer_head_mask.size() != (self.num_heads,):
	raise ValueError(
	f"Head mask for a single layer should be of size {
	(self.num_heads,)}, but is"
	f" {layer_head_mask.size()}"
	)
	attn_weights = layer_head_mask.view(
	1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
	attn_weights = attn_weights.view(
	bsz * self.num_heads, tgt_len, src_len)

	if output_attentions:
	# this operation is a bit awkward, but it's required to
	# make sure that attn_weights keeps its gradient.
	# In order to do so, attn_weights have to be reshaped
	# twice and have to be reused in the following
	attn_weights_reshaped = attn_weights.view(
	bsz, self.num_heads, tgt_len, src_len)
	attn_weights = attn_weights_reshaped.view(
	bsz * self.num_heads, tgt_len, src_len)
	else:
	attn_weights_reshaped = None

	attn_probs = nn.functional.dropout(
	attn_weights, p=self.dropout, training=self.training)

	attn_output = torch.bmm(attn_probs, value_states)

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

	attn_output = attn_output.view(
	bsz, self.num_heads, tgt_len, self.head_dim)
	attn_output = attn_output.transpose(1, 2)

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

	attn_output = self.out_proj(attn_output)

	return attn_output, attn_weights_reshaped, past_key_value



	class OptFlashAttention2(OPTAttention):
	"""
	OPT flash attention module. This module inherits from `OPTAttention` as the weights of the module stays untouched.
	The only required change would be on the forward pass where it needs to correctly call the public API of flash
	attention and deal with padding tokens in case the input contains any of them.
	"""

	# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
	def __init__(self, args, *kwargs):
	super().__init__(args, *kwargs)

	# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
	# flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
	# Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
	self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()

	def forward(
	self,
	hidden_states: torch.Tensor,
	key_value_states: Optional[torch.Tensor] = None,
	past_key_value: Optional[Tuple[torch.Tensor]] = None,
	attention_mask: Optional[torch.Tensor] = None,
	layer_head_mask: Optional[torch.Tensor] = None,
	output_attentions: bool = False,
	) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
	"""Input shape: Batch x Time x Channel"""

	# if key_value_states are provided this layer is used as a cross-attention layer
	# for the decoder
	is_cross_attention = key_value_states is not None

	bsz, _, _ = hidden_states.size()

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

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

	query_length = query_states.shape[1]
	tgt_len = key_states.shape[-2]

	# Flash attention requires the input to have the shape
	# batch_size x seq_length x head_dim x hidden_dim
	query_states = query_states.view(
	bsz, query_length, self.num_heads, self.head_dim)
	key_states = key_states.transpose(1, 2).view(
	bsz, tgt_len, self.num_heads, self.head_dim)
	value_states = value_states.transpose(1, 2).view(
	bsz, tgt_len, self.num_heads, self.head_dim)

	attn_dropout = self.dropout if self.training else 0.0

	# In PEFT, usually we cast the layer norms in float32 for training stability reasons
	# therefore the input hidden states gets silently casted in float32. Hence, we need
	# cast them back in float16 just to be sure everything works as expected.
	input_dtype = query_states.dtype
	if input_dtype == torch.float32:
	if torch.is_autocast_enabled():
	target_dtype = torch.get_autocast_gpu_dtype()
	# Handle the case where the model is quantized
	elif hasattr(self.config, "_pre_quantization_dtype"):
	target_dtype = self.config._pre_quantization_dtype
	else:
	target_dtype = self.q_proj.weight.dtype

	logger.warning_once(
	f"The input hidden states seems to be silently casted in float32, this might be related to"
	f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
	f" {target_dtype}."
	)

	query_states = query_states.to(target_dtype)
	key_states = key_states.to(target_dtype)
	value_states = value_states.to(target_dtype)

	attn_output = self._flash_attention_forward(
	query_states, key_states, value_states, attention_mask, query_length, dropout=attn_dropout
	)

	attn_weights_reshaped = attn_output.reshape(
	bsz, query_length, self.num_heads * self.head_dim)
	attn_output = self.out_proj(attn_weights_reshaped)

	if not output_attentions:
	attn_weights_reshaped = None

	return attn_output, attn_weights_reshaped, past_key_value

	# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._flash_attention_forward
	def _flash_attention_forward(
	self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
	):
	"""
	Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
	first unpad the input, then computes the attention scores and pad the final attention scores.

	Args:
	query_states (`torch.Tensor`):
	Input query states to be passed to Flash Attention API
	key_states (`torch.Tensor`):
	Input key states to be passed to Flash Attention API
	value_states (`torch.Tensor`):
	Input value states to be passed to Flash Attention API
	attention_mask (`torch.Tensor`):
	The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
	position of padding tokens and 1 for the position of non-padding tokens.
	dropout (`float`):
	Attention dropout
	softmax_scale (`float`, optional):
	The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
	"""
	if not self._flash_attn_uses_top_left_mask:
	causal = self.is_causal
	else:
	# TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
	causal = self.is_causal and query_length != 1

	# Contains at least one padding token in the sequence
	if attention_mask is not None:
	batch_size = query_states.shape[0]
	query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
	query_states, key_states, value_states, attention_mask, query_length
	)

	cu_seqlens_q, cu_seqlens_k = cu_seq_lens
	max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens

	attn_output_unpad = flash_attn_varlen_func(
	query_states,
	key_states,
	value_states,
	cu_seqlens_q=cu_seqlens_q,
	cu_seqlens_k=cu_seqlens_k,
	max_seqlen_q=max_seqlen_in_batch_q,
	max_seqlen_k=max_seqlen_in_batch_k,
	dropout_p=dropout,
	softmax_scale=softmax_scale,
	causal=causal,
	)

	attn_output = pad_input(
	attn_output_unpad, indices_q, batch_size, query_length)
	else:
	attn_output = flash_attn_func(
	query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
	)

	return attn_output

	# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._upad_input
	def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
	indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(
	attention_mask)
	batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape

	key_layer = index_first_axis(
	key_layer.reshape(batch_size * kv_seq_len,
	num_key_value_heads, head_dim), indices_k
	)
	value_layer = index_first_axis(
	value_layer.reshape(batch_size * kv_seq_len,
	num_key_value_heads, head_dim), indices_k
	)
	if query_length == kv_seq_len:
	query_layer = index_first_axis(
	query_layer.reshape(batch_size * kv_seq_len,
	self.num_heads, head_dim), indices_k
	)
	cu_seqlens_q = cu_seqlens_k
	max_seqlen_in_batch_q = max_seqlen_in_batch_k
	indices_q = indices_k
	elif query_length == 1:
	max_seqlen_in_batch_q = 1
	cu_seqlens_q = torch.arange(
	batch_size + 1, dtype=torch.int32, device=query_layer.device
	) # There is a memcpy here, that is very bad.
	indices_q = cu_seqlens_q[:-1]
	query_layer = query_layer.squeeze(1)
	else:
	# The -q_len: slice assumes left padding.
	attention_mask = attention_mask[:, -query_length:]
	query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(
	query_layer, attention_mask)

	return (
	query_layer,
	key_layer,
	value_layer,
	indices_q,
	(cu_seqlens_q, cu_seqlens_k),
	(max_seqlen_in_batch_q, max_seqlen_in_batch_k),
	)


	OPT_ATTENTION_CLASSES = {
	"eager": OPTAttention,
	"flash_attention_2": OptFlashAttention2,
	}


	class OPTDecoderLayer(nn.Module):
	def __init__(self, config: OPTConfig):
	super().__init__()
	self.embed_dim = config.hidden_size

	self.self_attn = OPTAttention(
	config=config, is_decoder=True)

	self.do_layer_norm_before = config.do_layer_norm_before
	self.dropout = config.dropout
	self.activation_fn = ACT2FN[config.activation_function]

	self.self_attn_layer_norm = nn.LayerNorm(
	self.embed_dim, elementwise_affine=config.layer_norm_elementwise_affine
	)
	self.fc1 = nn.Linear(self.embed_dim, config.ffn_dim,
	bias=config.enable_bias)
	self.fc2 = nn.Linear(config.ffn_dim, self.embed_dim,
	bias=config.enable_bias)
	self.final_layer_norm = nn.LayerNorm(
	self.embed_dim, elementwise_affine=config.layer_norm_elementwise_affine)

	def forward(
	self,
	hidden_states: torch.Tensor,
	attention_mask: Optional[torch.Tensor] = None,
	layer_head_mask: Optional[torch.Tensor] = None,
	past_key_value: Optional[Tuple[torch.Tensor]] = None,
	output_attentions: Optional[bool] = False,
	use_cache: Optional[bool] = False,
	) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
	"""
	Args:
	hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
	attention_mask (`torch.FloatTensor`, optional): attention mask of size
	`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
	layer_head_mask (`torch.FloatTensor`, optional): mask for attention heads in a given layer of size
	`(encoder_attention_heads,)`.
	output_attentions (`bool`, optional):
	Whether or not to return the attentions tensors of all attention layers. See `attentions` under
	returned tensors for more detail.
	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`).
	past_key_value (`Tuple(torch.FloatTensor)`, optional): cached past key and value projection states
	"""

	residual = hidden_states

	# 125m, 1.7B, ..., 175B applies layer norm BEFORE attention
	if self.do_layer_norm_before:
	hidden_states = self.self_attn_layer_norm(hidden_states)

	# Self Attention
	hidden_states, self_attn_weights, present_key_value = self.self_attn(
	hidden_states=hidden_states,
	past_key_value=past_key_value,
	attention_mask=attention_mask,
	layer_head_mask=layer_head_mask,
	output_attentions=output_attentions,
	)
	hidden_states = nn.functional.dropout(
	hidden_states, p=self.dropout, training=self.training)
	hidden_states = residual + hidden_states

	# 350m applies layer norm AFTER attention
	if not self.do_layer_norm_before:
	hidden_states = self.self_attn_layer_norm(hidden_states)

	# Fully Connected
	hidden_states_shape = hidden_states.shape
	hidden_states = hidden_states.reshape(-1, hidden_states.size(-1))
	residual = hidden_states

	# 125m, 1.7B, ..., 175B applies layer norm BEFORE attention
	if self.do_layer_norm_before:
	hidden_states = self.final_layer_norm(hidden_states)

	hidden_states = self.fc1(hidden_states)
	hidden_states = self.activation_fn(hidden_states)

	hidden_states = self.fc2(hidden_states)
	hidden_states = nn.functional.dropout(
	hidden_states, p=self.dropout, training=self.training)

	hidden_states = (residual + hidden_states).view(hidden_states_shape)

	# 350m applies layer norm AFTER attention
	if not self.do_layer_norm_before:
	hidden_states = self.final_layer_norm(hidden_states)

	outputs = (hidden_states,)

	if output_attentions:
	outputs += (self_attn_weights,)

	if use_cache:
	outputs += (present_key_value,)

	return outputs


	OPT_START_DOCSTRING = r"""
	This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
	library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
	etc.)

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

	Parameters:
	config ([`OPTConfig`]):
	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.
	"""


	@add_start_docstrings(
	"The bare OPT Model outputting raw hidden-states without any specific head on top.",
	OPT_START_DOCSTRING,
	)
	class OPTPreTrainedModel(PreTrainedModel):
	config_class = OPTConfig
	base_model_prefix = "model"
	supports_gradient_checkpointing = True
	_no_split_modules = ["OPTDecoderLayer"]
	_supports_flash_attn_2 = True

	def _init_weights(self, module):
	std = self.config.init_std
	if isinstance(module, nn.Linear):
	module.weight.data.normal_(mean=0.0, std=std)
	if module.bias is not None:
	module.bias.data.zero_()
	elif isinstance(module, nn.Embedding):
	module.weight.data.normal_(mean=0.0, std=std)
	if module.padding_idx is not None:
	module.weight.data[module.padding_idx].zero_()


	OPT_INPUTS_DOCSTRING = r"""
	Args:
	input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
	Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
	it.

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

	[What are input IDs?](../glossary#input-ids)
	attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional):
	Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

	- 1 for tokens that are not masked,
	- 0 for tokens that are masked.

	[What are attention masks?](../glossary#attention-mask)

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

	If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
	`past_key_values`).

	If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
	and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
	information on the default strategy.
	head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, optional):
	Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:

	- 1 indicates the head is not masked,
	- 0 indicates the head is masked.

	past_key_values (`tuple(tuple(torch.FloatTensor))`, optional, returned when `use_cache=True` is passed or when `config.use_cache=True`):
	Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
	`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
	`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.

	Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
	blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.

	If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
	don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
	`decoder_input_ids` of shape `(batch_size, sequence_length)`.
	inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, optional):
	Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
	is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
	model's internal embedding lookup matrix.
	use_cache (`bool`, optional):
	If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
	`past_key_values`).
	output_attentions (`bool`, optional):
	Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
	tensors for more detail.
	output_hidden_states (`bool`, optional):
	Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
	more detail.
	return_dict (`bool`, optional):
	Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
	"""


	class OPTDecoder(OPTPreTrainedModel):
	"""
	Transformer decoder consisting of config.num_hidden_layers layers. Each layer is a [`OPTDecoderLayer`]

	Args:
	config: OPTConfig
	"""

	def __init__(self, config: OPTConfig):
	super().__init__(config)
	self.dropout = config.dropout
	self.layerdrop = config.layerdrop
	self.padding_idx = config.pad_token_id
	self.max_target_positions = config.max_position_embeddings
	self.vocab_size = config.vocab_size

	self.embed_tokens = nn.Embedding(
	config.vocab_size, config.word_embed_proj_dim, self.padding_idx)
	self.embed_positions = OPTLearnedPositionalEmbedding(
	config.max_position_embeddings, config.hidden_size)

	if config.word_embed_proj_dim != config.hidden_size:
	self.project_out = nn.Linear(
	config.hidden_size, config.word_embed_proj_dim, bias=False)
	else:
	self.project_out = None

	if config.word_embed_proj_dim != config.hidden_size:
	self.project_in = nn.Linear(
	config.word_embed_proj_dim, config.hidden_size, bias=False)
	else:
	self.project_in = None

	# Note that the only purpose of `config._remove_final_layer_norm` is to keep backward compatibility
	# with checkpoints that have been fine-tuned before transformers v4.20.1
	# see https://github.com/facebookresearch/metaseq/pull/164
	if config.do_layer_norm_before and not config._remove_final_layer_norm:
	self.final_layer_norm = nn.LayerNorm(
	config.hidden_size, elementwise_affine=config.layer_norm_elementwise_affine
	)
	else:
	self.final_layer_norm = None

	self.layers = nn.ModuleList(
	[OPTDecoderLayer(config) for _ in range(config.num_hidden_layers)])
	self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"

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

	def get_input_embeddings(self):
	return self.embed_tokens

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

	def forward(
	self,
	input_ids: torch.LongTensor = None,
	attention_mask: Optional[torch.Tensor] = None,
	head_mask: Optional[torch.Tensor] = None,
	past_key_values: Optional[List[torch.FloatTensor]] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	use_cache: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, BaseModelOutputWithPast]:
	r"""
	Args:
	input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
	Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
	provide it.

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

	[What are input IDs?](../glossary#input-ids)
	attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional):
	Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

	- 1 for tokens that are not masked,
	- 0 for tokens that are masked.

	[What are attention masks?](../glossary#attention-mask)
	head_mask (`torch.Tensor` of shape `(num_hidden_layers, num_attention_heads)`, optional):
	Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:

	- 1 indicates the head is not masked,
	- 0 indicates the head is masked.

	past_key_values (`tuple(tuple(torch.FloatTensor))`, optional, returned when `use_cache=True` is passed or when `config.use_cache=True`):
	Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
	shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of

	Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
	cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.

	If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
	that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
	all `decoder_input_ids` of shape `(batch_size, sequence_length)`.

	inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, optional):
	Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
	This is useful if you want more control over how to convert `input_ids` indices into associated vectors
	than the model's internal embedding lookup matrix.
	output_attentions (`bool`, optional):
	Whether or not to return the attentions tensors of all attention layers. See `attentions` under
	returned tensors for more detail.
	output_hidden_states (`bool`, optional):
	Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
	for more detail.
	return_dict (`bool`, optional):
	Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
	"""
	output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
	output_hidden_states = (
	output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
	)
	use_cache = use_cache if use_cache is not None else self.config.use_cache

	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	# retrieve input_ids and inputs_embeds
	if input_ids is not None and inputs_embeds is not None:
	raise ValueError(
	"You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
	elif input_ids is not None:
	input_shape = input_ids.size()
	input_ids = input_ids.view(-1, input_shape[-1])
	elif inputs_embeds is not None:
	input_shape = inputs_embeds.size()[:-1]
	else:
	raise ValueError(
	"You have to specify either decoder_input_ids or decoder_inputs_embeds")

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

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

	# embed positions
	if self._use_flash_attention_2:
	# 2d mask is passed through the layers
	causal_attention_mask = attention_mask if (
	attention_mask is not None and 0 in attention_mask) else None
	attention_mask = (
	torch.ones(batch_size, mask_seq_length,
	device=inputs_embeds.device)
	if attention_mask is None
	else attention_mask
	)
	else:
	# 4d mask is passed through the layers
	if attention_mask is None:
	attention_mask = torch.ones(
	batch_size, mask_seq_length, device=inputs_embeds.device)
	elif attention_mask.shape[1] != mask_seq_length:
	raise ValueError(
	f"The provided attention mask has length {
	attention_mask.shape[1]}, but its length should be "
	f"{mask_seq_length} (sum of the lengths of current and past inputs)"
	)
	causal_attention_mask = _prepare_4d_causal_attention_mask(
	attention_mask, input_shape, inputs_embeds, past_key_values_length
	)

	pos_embeds = self.embed_positions(
	attention_mask, past_key_values_length)

	if self.project_in is not None:
	inputs_embeds = self.project_in(inputs_embeds)

	hidden_states = inputs_embeds + pos_embeds

	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

	# decoder layers
	all_hidden_states = () if output_hidden_states else None
	all_self_attns = () if output_attentions else None
	next_decoder_cache = () if use_cache else None

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

	for idx, decoder_layer in enumerate(self.layers):
	# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
	if output_hidden_states:
	all_hidden_states += (hidden_states,)

	if self.training:
	dropout_probability = torch.rand([])
	if dropout_probability < self.layerdrop:
	continue

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

	if self.gradient_checkpointing and self.training:
	layer_outputs = self._gradient_checkpointing_func(
	decoder_layer.__call__,
	hidden_states,
	causal_attention_mask,
	head_mask[idx] if head_mask is not None else None,
	None,
	output_attentions,
	use_cache,
	)
	else:
	layer_outputs = decoder_layer(
	hidden_states,
	attention_mask=causal_attention_mask,
	layer_head_mask=(
	head_mask[idx] if head_mask is not None else None),
	past_key_value=past_key_value,
	output_attentions=output_attentions,
	use_cache=use_cache,
	)

	hidden_states = layer_outputs[0]

	if use_cache:
	next_decoder_cache += (
	layer_outputs[2 if output_attentions else 1],)

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

	if self.final_layer_norm is not None:
	hidden_states = self.final_layer_norm(hidden_states)

	if self.project_out is not None:
	hidden_states = self.project_out(hidden_states)

	# add hidden states from the last decoder layer
	if output_hidden_states:
	all_hidden_states += (hidden_states,)

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


	@add_start_docstrings(
	"The bare OPT Model outputting raw hidden-states without any specific head on top.",
	OPT_START_DOCSTRING,
	)
	class OPTModel(OPTPreTrainedModel):
	def __init__(self, config: OPTConfig):
	super().__init__(config)
	self.decoder = OPTDecoder(config)
	# Initialize weights and apply final processing
	self.post_init()

	def get_input_embeddings(self):
	return self.decoder.embed_tokens

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

	def get_decoder(self):
	return self.decoder

	@add_start_docstrings_to_model_forward(OPT_INPUTS_DOCSTRING)
	@add_code_sample_docstrings(
	checkpoint=_CHECKPOINT_FOR_DOC,
	output_type=BaseModelOutputWithPast,
	config_class=_CONFIG_FOR_DOC,
	expected_output=_EXPECTED_OUTPUT_SHAPE,
	)
	def forward(
	self,
	input_ids: torch.LongTensor = None,
	attention_mask: Optional[torch.Tensor] = None,
	head_mask: Optional[torch.Tensor] = None,
	past_key_values: Optional[List[torch.FloatTensor]] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	use_cache: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, 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
	)
	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

	# decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn)
	decoder_outputs = self.decoder(
	input_ids=input_ids,
	attention_mask=attention_mask,
	head_mask=head_mask,
	past_key_values=past_key_values,
	inputs_embeds=inputs_embeds,
	use_cache=use_cache,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	if not return_dict:
	return decoder_outputs

	return BaseModelOutputWithPast(
	last_hidden_state=decoder_outputs.last_hidden_state,
	past_key_values=decoder_outputs.past_key_values,
	hidden_states=decoder_outputs.hidden_states,
	attentions=decoder_outputs.attentions,
	)


	class OPTForCausalLM(OPTPreTrainedModel):
	_tied_weights_keys = ["lm_head.weight"]

	def __init__(self, config):
	super().__init__(config)
	self.model = OPTModel(config)

	# the lm_head weight is automatically tied to the embed tokens weight
	self.lm_head = nn.Linear(
	config.word_embed_proj_dim, config.vocab_size, bias=False)

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

	def get_input_embeddings(self):
	return self.model.decoder.embed_tokens

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

	def get_output_embeddings(self):
	return self.lm_head

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

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

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

	@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
	def forward(
	self,
	input_ids: torch.LongTensor = None,
	attention_mask: Optional[torch.Tensor] = None,
	head_mask: Optional[torch.Tensor] = None,
	past_key_values: Optional[List[torch.FloatTensor]] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	labels: Optional[torch.LongTensor] = None,
	use_cache: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, CausalLMOutputWithPast]:
	r"""
	Args:
	input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
	Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
	provide it.

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

	[What are input IDs?](../glossary#input-ids)
	attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional):
	Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

	- 1 for tokens that are not masked,
	- 0 for tokens that are masked.

	[What are attention masks?](../glossary#attention-mask)
	head_mask (`torch.Tensor` of shape `(num_hidden_layers, num_attention_heads)`, optional):
	Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:

	- 1 indicates the head is not masked,
	- 0 indicates the head is masked.

	past_key_values (`tuple(tuple(torch.FloatTensor))`, optional, returned when `use_cache=True` is passed or when `config.use_cache=True`):
	Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
	shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
	shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional
	tensors are only required when the model is used as a decoder in a Sequence to Sequence model.

	Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
	cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.

	If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
	that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
	all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
	inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, optional):
	Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
	This is useful if you want more control over how to convert `input_ids` indices into associated vectors
	than the model's internal embedding lookup matrix.
	labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional):
	Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
	config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
	(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
	use_cache (`bool`, optional):
	If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
	(see `past_key_values`).
	output_attentions (`bool`, optional):
	Whether or not to return the attentions tensors of all attention layers. See `attentions` under
	returned tensors for more detail.
	output_hidden_states (`bool`, optional):
	Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
	for more detail.
	return_dict (`bool`, optional):
	Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.

	Returns:

	Example:

	```python
	>>> from transformers import AutoTokenizer, OPTForCausalLM

	>>> model = OPTForCausalLM.from_pretrained("facebook/opt-350m")
	>>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")

	>>> prompt = "Hey, are you conscious? Can you talk to me?"
	>>> inputs = tokenizer(prompt, return_tensors="pt")

	>>> # Generate
	>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
	>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
	"Hey, are you conscious? Can you talk to me?\nI'm not conscious. I'm just a little bit of a weirdo."
	```"""

	output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
	output_hidden_states = (
	output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
	)
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
	outputs = self.model.decoder(
	input_ids=input_ids,
	attention_mask=attention_mask,
	head_mask=head_mask,
	past_key_values=past_key_values,
	inputs_embeds=inputs_embeds,
	use_cache=use_cache,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	logits = self.lm_head(outputs[0]).contiguous()

	loss = None
	if labels is not None:
	# move labels to correct device to enable model parallelism
	labels = labels.to(logits.device)
	# Shift so that tokens < n predict n
	shift_logits = logits[..., :-1, :].contiguous()
	shift_labels = labels[..., 1:].contiguous()
	# Flatten the tokens
	loss_fct = CrossEntropyLoss()
	loss = loss_fct(
	shift_logits.view(-1, self.config.vocab_size), shift_labels.view(-1))

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

	return CausalLMOutputWithPast(
	loss=loss,
	logits=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
	):
	if past_key_values is not None:
	past_length = past_key_values[0][0].shape[2]

	# Some generation methods already pass only the last input ID
	if input_ids.shape[1] > past_length:
	remove_prefix_length = past_length
	else:
	# Default to old behavior: keep only final ID
	remove_prefix_length = input_ids.shape[1] - 1

	input_ids = input_ids[:, remove_prefix_length:]

	# 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(
	{
	"past_key_values": past_key_values,
	"use_cache": kwargs.get("use_cache"),
	"attention_mask": attention_mask,
	}
	)
	return model_inputs

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


	@add_start_docstrings(
	"""
	The OPT Model transformer with a sequence classification head on top (linear layer).

	[`OPTForSequenceClassification`] uses the last token in order to do the classification, as other causal models
	(e.g. GPT-2) do.

	Since it does classification on the last token, it requires to know the position of the last token. If a
	`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
	no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
	padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
	each row of the batch).
	""",
	OPT_START_DOCSTRING,
	)
	class OPTForSequenceClassification(OPTPreTrainedModel):
	def __init__(self, config: OPTConfig):
	super().__init__(config)
	self.num_labels = config.num_labels
	self.model = OPTModel(config)
	self.score = nn.Linear(config.word_embed_proj_dim,
	self.num_labels, bias=False)

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

	@add_start_docstrings_to_model_forward(OPT_INPUTS_DOCSTRING)
	@add_code_sample_docstrings(
	checkpoint=_CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION,
	output_type=SequenceClassifierOutputWithPast,
	config_class=_CONFIG_FOR_DOC,
	expected_output=_SEQ_CLASS_EXPECTED_OUTPUT,
	expected_loss=_SEQ_CLASS_EXPECTED_LOSS,
	)
	def forward(
	self,
	input_ids: Optional[torch.LongTensor] = None,
	attention_mask: Optional[torch.FloatTensor] = None,
	head_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, SequenceClassifierOutputWithPast]:
	r"""
	labels (`torch.LongTensor` of shape `(batch_size,)`, optional):
	Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
	config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
	`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
	"""
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	transformer_outputs = self.model(
	input_ids,
	past_key_values=past_key_values,
	attention_mask=attention_mask,
	head_mask=head_mask,
	inputs_embeds=inputs_embeds,
	use_cache=use_cache,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)
	hidden_states = transformer_outputs[0]
	logits = self.score(hidden_states)

	if input_ids is not None:
	batch_size, sequence_length = input_ids.shape[:2]
	else:
	batch_size, sequence_length = inputs_embeds.shape[:2]

	if self.config.pad_token_id is None:
	sequence_lengths = -1
	else:
	if input_ids is not None:
	# if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
	sequence_lengths = torch.eq(
	input_ids, self.config.pad_token_id).int().argmax(-1) - 1
	sequence_lengths = sequence_lengths % input_ids.shape[-1]
	sequence_lengths = sequence_lengths.to(logits.device)
	else:
	sequence_lengths = -1
	logger.warning(
	f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
	"unexpected if using padding tokens in conjunction with `inputs_embeds.`"
	)

	pooled_logits = logits[torch.arange(
	batch_size, device=logits.device), sequence_lengths]

	loss = None
	if labels is not None:
	if self.config.problem_type is None:
	if self.num_labels == 1:
	self.config.problem_type = "regression"
	elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
	self.config.problem_type = "single_label_classification"
	else:
	self.config.problem_type = "multi_label_classification"

	if self.config.problem_type == "regression":
	loss_fct = MSELoss()
	if self.num_labels == 1:
	loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
	else:
	loss = loss_fct(pooled_logits, labels)
	elif self.config.problem_type == "single_label_classification":
	loss_fct = CrossEntropyLoss()
	loss = loss_fct(
	pooled_logits.view(-1, self.num_labels), labels.view(-1))
	elif self.config.problem_type == "multi_label_classification":
	loss_fct = BCEWithLogitsLoss()
	loss = loss_fct(pooled_logits, labels)
	if not return_dict:
	output = (pooled_logits,) + transformer_outputs[1:]
	return ((loss,) + output) if loss is not None else output

	return SequenceClassifierOutputWithPast(
	loss=loss,
	logits=pooled_logits,
	past_key_values=transformer_outputs.past_key_values,
	hidden_states=transformer_outputs.hidden_states,
	attentions=transformer_outputs.attentions,
	)

	def get_input_embeddings(self):
	return self.model.decoder.embed_tokens

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


	@add_start_docstrings(
	"""
	The OPT Model transformer with a span classification head on top for extractive question-answering tasks like SQuAD
	(a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
	""",
	OPT_START_DOCSTRING,
	)
	class OPTForQuestionAnswering(OPTPreTrainedModel):
	def __init__(self, config: OPTConfig):
	super().__init__(config)
	self.model = OPTModel(config)
	self.qa_outputs = nn.Linear(config.word_embed_proj_dim, 2)

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

	@add_start_docstrings_to_model_forward(OPT_INPUTS_DOCSTRING)
	@replace_return_docstrings(output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC)
	def forward(
	self,
	input_ids: Optional[torch.LongTensor] = None,
	attention_mask: Optional[torch.FloatTensor] = None,
	head_mask: Optional[torch.FloatTensor] = None,
	past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	start_positions: Optional[torch.LongTensor] = None,
	end_positions: 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, QuestionAnsweringModelOutput]:
	r"""
	start_positions (`torch.LongTensor` of shape `(batch_size,)`, optional):
	Labels for position (index) of the start of the labelled span for computing the token classification loss.
	Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
	are not taken into account for computing the loss.
	end_positions (`torch.LongTensor` of shape `(batch_size,)`, optional):
	Labels for position (index) of the end of the labelled span for computing the token classification loss.
	Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
	are not taken into account for computing the loss.

	Returns:

	Example:

	```python
	>>> from transformers import AutoTokenizer, OPTForQuestionAnswering
	>>> import torch

	>>> torch.manual_seed(4) # doctest: +IGNORE_RESULT
	>>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")

	>>> # note: we are loading a OPTForQuestionAnswering from the hub here,
	>>> # so the head will be randomly initialized, hence the predictions will be random
	>>> model = OPTForQuestionAnswering.from_pretrained("facebook/opt-350m")

	>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"

	>>> inputs = tokenizer(question, text, return_tensors="pt")
	>>> with torch.no_grad():
	... outputs = model(**inputs)

	>>> answer_start_index = outputs.start_logits.argmax()
	>>> answer_end_index = outputs.end_logits.argmax()

	>>> answer_offset = len(tokenizer(question)[0])

	>>> predict_answer_tokens = inputs.input_ids[
	... 0, answer_offset + answer_start_index : answer_offset + answer_end_index + 1
	... ]
	>>> predicted = tokenizer.decode(predict_answer_tokens)
	>>> predicted
	' a nice puppet'
	```"""
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	transformer_outputs = self.model(
	input_ids,
	past_key_values=past_key_values,
	attention_mask=attention_mask,
	head_mask=head_mask,
	inputs_embeds=inputs_embeds,
	use_cache=use_cache,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)
	hidden_states = transformer_outputs[0]

	logits = self.qa_outputs(hidden_states)
	start_logits, end_logits = logits.split(1, dim=-1)
	start_logits = start_logits.squeeze(-1).contiguous()
	end_logits = end_logits.squeeze(-1).contiguous()

	total_loss = None
	if start_positions is not None and end_positions is not None:
	# If we are on multi-GPU, split add a dimension
	if len(start_positions.size()) > 1:
	start_positions = start_positions.squeeze(-1)
	if len(end_positions.size()) > 1:
	end_positions = end_positions.squeeze(-1)
	# sometimes the start/end positions are outside our model inputs, we ignore these terms
	ignored_index = start_logits.size(1)
	start_positions = start_positions.clamp(
	0, ignored_index).to(logits.device)
	end_positions = end_positions.clamp(
	0, ignored_index).to(logits.device)

	loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
	start_loss = loss_fct(start_logits, start_positions)
	end_loss = loss_fct(end_logits, end_positions)
	total_loss = (start_loss + end_loss) / 2

	if not return_dict:
	output = (start_logits, end_logits) + transformer_outputs[2:]
	return ((total_loss,) + output) if total_loss is not None else output

	return QuestionAnsweringModelOutput(
	loss=total_loss,
	start_logits=start_logits,
	end_logits=end_logits,
	hidden_states=transformer_outputs.hidden_states,
	attentions=transformer_outputs.attentions,
	)

	def get_input_embeddings(self):
	return self.model.decoder.embed_tokens

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