dictalm-rab-7b / modeling_megatron_gpt.py

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	# coding=utf-8
	# Copyright 2022 EleutherAI The HuggingFace Inc. team. All rights reserved.
	#
	# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
	# and OPT implementation in this library. It has been modified from its
	# original forms to accommodate minor architectural differences compared
	# to GPT-NeoX and OPT used by the Nemo Framework
	#
	# 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 MegatronGPT model."""

	from dataclasses import dataclass
	import math
	from typing import Optional, Tuple, Union

	import torch
	import torch.utils.checkpoint
	from torch import nn
	from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

	from transformers.activations import ACT2FN
	from transformers.file_utils import (
	add_start_docstrings,
	add_start_docstrings_to_model_forward,
	replace_return_docstrings,
	)
	from transformers.modeling_outputs import (
	BaseModelOutputWithPast,
	CausalLMOutputWithPast,
	QuestionAnsweringModelOutput,
	SequenceClassifierOutputWithPast,
	TokenClassifierOutput,
	)
	from transformers.modeling_utils import PreTrainedModel
	from transformers.utils import logging
	# try to load using a relative path, but if it fails try loading it directly
	from .configuration_megatron_gpt import MegatronGPTConfig

	try:
	from flash_attn.bert_padding import unpad_input, pad_input
	from flash_attn import flash_attn_varlen_func as flash_attn_func
	HAS_FLASH = True
	except:
	try:
	from flash_attn.flash_attn_interface import flash_attn_unpadded_func as flash_attn_func
	HAS_FLASH = True
	except:
	HAS_FLASH = False


	def get_activation(act):
	if act in ["gelu", "geglu", "fast-geglu"]:
	act = 'gelu'
	elif act in ["reglu", "fast-reglu"]:
	act = 'relu'
	elif act in ["swiglu", "fast-swiglu"]:
	act = 'silu'
	return ACT2FN[act]

	logger = logging.get_logger(__name__)

	_CONFIG_FOR_DOC = "MegatronGPTConfig"

	@dataclass
	class CausalLMOutputWithPastAndEncoding(CausalLMOutputWithPast):
	encoding_states: Optional[torch.FloatTensor] = None

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

	config_class = MegatronGPTConfig
	base_model_prefix = "megatron_gpt"
	supports_gradient_checkpointing = True
	_no_split_modules = ["MegatronGPTLayer"]
	_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):
	module.bias.data.zero_()
	module.weight.data.fill_(1.0)

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


	class MegatronGPTAttention(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.config = config
	self.self_attention_relative_position_bias = config.self_attention_relative_position_bias
	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
	self.rotary_ndims = int(self.head_size * config.rotary_pct)
	self._init_bias(config.max_position_embeddings)

	self.register_buffer("masked_bias", torch.tensor(-1e9), persistent=False)
	self._init_rope()

	self.norm_factor_float = math.sqrt(self.head_size if config.normalize_attention_scores else 1.0)
	self.register_buffer(
	"norm_factor",
	torch.tensor(self.norm_factor_float, dtype=torch.float32).to(torch.get_default_dtype()),
	persistent=False,
	)
	self.query_key_value = nn.Linear(config.hidden_size, 3 * config.hidden_size, bias=config.bias)
	self.dense = nn.Linear(config.hidden_size, config.hidden_size, bias=config.bias)
	self.attention_dropout = nn.Dropout(config.attention_dropout)

	def _init_bias(self, max_positions, device=None):
	self.register_buffer(
	"bias",
	torch.tril(torch.ones((max_positions, max_positions), dtype=torch.bool)).view(
	1, 1, max_positions, max_positions
	),
	persistent=False,
	)
	if device is not None:
	self.bias = self.bias.to(device)

	def _init_rope(self):
	if self.config.rope_scaling is None:
	self.rotary_emb = MegatronGPTRotaryEmbedding(
	self.rotary_ndims, self.config.max_position_embeddings, base=self.config.rotary_emb_base
	)
	else:
	scaling_type = self.config.rope_scaling["type"]
	scaling_factor = self.config.rope_scaling["factor"]
	if scaling_type == "linear":
	self.rotary_emb = MegatronGPTLinearScalingRotaryEmbedding(
	self.rotary_ndims,
	self.config.max_position_embeddings,
	base=self.config.rotary_emb_base,
	scaling_factor=scaling_factor,
	)
	elif scaling_type == "dynamic":
	self.rotary_emb = MegatronGPTDynamicNTKScalingRotaryEmbedding(
	self.rotary_ndims,
	self.config.max_position_embeddings,
	base=self.config.rotary_emb_base,
	scaling_factor=scaling_factor,
	)
	else:
	raise ValueError(f"Unknown RoPE scaling type {scaling_type}")

	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)
	seq_len = key.shape[-2]
	if has_layer_past:
	seq_len = seq_len + layer_past[0].shape[-2]
	cos, sin = self.rotary_emb(value, seq_len=seq_len)
	query, key = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, position_ids)
	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
	if not HAS_FLASH or output_attentions or head_mask is not None or not self.config.use_flash_attention:
	attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask)
	else:
	attn_output = self._flash_attn(query, key, value, attention_mask)

	# Reshape outputs
	attn_output = self._merge_heads(attn_output, 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

	@classmethod
	def _split_heads(cls, tensor, num_attention_heads, attn_head_size):
	"""
	Splits hidden dim into attn_head_size and num_attention_heads
	"""
	# tensor: [bs, seq_len, hidden_size]
	new_shape = tensor.size()[:-1] + (num_attention_heads, attn_head_size)
	# -> [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, num_attention_heads, attn_head_size):
	"""
	Merges attn_head_size dim and num_attn_heads dim into hidden dim
	"""
	# 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.size(0), tensor.size(1), num_attention_heads * attn_head_size)
	# -> [bs, seq_len, hidden_size]
	return tensor

	def _flash_attn(self, query, key, value, attention_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_seq_length, attn_head_size = query.size()

	# transpose_for_scores_flash returns b s h d
	query_layer = query.transpose(1, 2).half()
	key_layer = key.transpose(1, 2).half()
	value_layer = value.transpose(1, 2).half()

	# fix the mask
	attention_mask = (attention_mask == 0).int().squeeze(1).squeeze(1)
	query_layer, query_indicies, cu_seqlens_q, max_seqlen_q = unpad_input(query_layer, attention_mask[:, -query_seq_length:])
	key_layer, _, cu_seqlens_k, max_seqlen_k = unpad_input(key_layer, attention_mask)
	value_layer, _, _, _ = unpad_input(value_layer, attention_mask)

	# returns [batch * seq, nheads, headdim]
	context_layer = flash_attn_func(query_layer, key_layer, value_layer,
	cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k,
	dropout_p=self.config.attention_dropout, softmax_scale=1 / self.norm_factor_float, causal=self.self_attention_relative_position_bias if max_seqlen_q > 1 else False)

	# fix the shape to be [bs, num_attention_heads, seq_len, attn_head_size]
	context_layer = pad_input(context_layer, query_indicies, batch_size, query_seq_length)
	context_layer = context_layer.view(batch_size, query_seq_length, num_attention_heads, attn_head_size) \
	.transpose(1, 2)

	return context_layer.to(value.dtype)

	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)

	# dynamically increase the causal mask with the key length, if needed.
	if key_length > self.bias.shape[-1]:
	self._init_bias(key_length, device=key.device)
	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=0.0,
	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)

	if self.self_attention_relative_position_bias:
	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).to(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

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

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

	attn_weights = self.attention_dropout(attn_weights)

	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 MegatronGPTRotaryEmbedding(torch.nn.Module):
	def __init__(self, dim, max_position_embeddings, base=10000, device=None):
	super().__init__()

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

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

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

	freqs = torch.einsum("i,j->ij", t, self.inv_freq)
	# Different from paper, but it uses a different permutation in order to obtain the same calculation
	emb = torch.cat((freqs, freqs), dim=-1)
	self.cos_cached = emb.cos()[None, None, :, :]
	self.sin_cached = emb.sin()[None, None, :, :]

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


	class MegatronGPTLinearScalingRotaryEmbedding(MegatronGPTRotaryEmbedding):
	"""MegatronGPTRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""

	def __init__(self, dim, max_position_embeddings, base=10000, device=None, scaling_factor=1.0):
	self.scaling_factor = scaling_factor
	super().__init__(dim, max_position_embeddings, base, device)

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

	freqs = torch.einsum("i,j->ij", t, self.inv_freq)
	# Different from paper, but it uses a different permutation in order to obtain the same calculation
	emb = torch.cat((freqs, freqs), dim=-1)
	self.cos_cached = emb.cos()[None, None, :, :]
	self.sin_cached = emb.sin()[None, None, :, :]


	class MegatronGPTDynamicNTKScalingRotaryEmbedding(MegatronGPTRotaryEmbedding):
	"""MegatronGPTRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""

	def __init__(self, dim, max_position_embeddings, base=10000, device=None, scaling_factor=1.0):
	self.scaling_factor = scaling_factor
	super().__init__(dim, max_position_embeddings, base, device)

	def _set_cos_sin_cache(self, seq_len, device):
	self.max_seq_len_cached = seq_len

	if seq_len > self.max_position_embeddings:
	base = self.base * (
	(self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
	) ** (self.dim / (self.dim - 2))
	inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
	self.register_buffer("inv_freq", inv_freq)

	t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)

	freqs = torch.einsum("i,j->ij", t, self.inv_freq)
	# Different from paper, but it uses a different permutation in order to obtain the same calculation
	emb = torch.cat((freqs, freqs), dim=-1)
	self.cos_cached = emb.cos()[None, None, :, :]
	self.sin_cached = emb.sin()[None, None, :, :]


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


	def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
	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)
	k_embed = (k * cos) + (rotate_half(k) * sin)
	return q_embed, k_embed


	class MegatronGPTMLP(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.fast_glu_activation = config.hidden_act in ['fast-geglu', 'fast-swiglu', 'fast-reglu']
	self.glu_activation_family = self.fast_glu_activation or config.hidden_act in ['geglu','reglu','swiglu']

	self.dense_h_to_4h = nn.Linear(config.hidden_size, config.intermediate_size * (2 if self.fast_glu_activation else 1), bias=config.bias)
	if config.hidden_act in ['geglu', 'reglu', 'swiglu']:
	self.dense_h_to_4h_2 = nn.Linear(config.hidden_size, config.intermediate_size, bias=config.bias)

	self.dense_4h_to_h = nn.Linear(config.intermediate_size, config.hidden_size, bias=config.bias)
	self.act = get_activation(config.hidden_act)

	def forward(self, hidden_states):
	intermediate_states = self.dense_h_to_4h(hidden_states)
	if self.glu_activation_family:
	if self.fast_glu_activation:
	intermediate_states, intermediate_states_2 = torch.chunk(intermediate_states, 2, dim=-1)
	else:
	intermediate_states_2 = self.dense_h_to_4h_2(hidden_states)

	hidden_states = self.act(intermediate_states) * intermediate_states_2
	else:
	hidden_states = self.act(intermediate_states)
	hidden_states = self.dense_4h_to_h(hidden_states)
	return hidden_states


	class MegatronGPTLayer(nn.Module):
	def __init__(self, config, layer_idx):
	super().__init__()
	self.input_layernorm = MegatronGPTLayerNorm(config.normalization, config.hidden_size, eps=config.layer_norm_eps)
	self.post_attention_layernorm = MegatronGPTLayerNorm(config.normalization, config.hidden_size, eps=config.layer_norm_eps)
	self.post_attention_dropout = nn.Dropout(config.hidden_dropout)
	self.post_mlp_dropout = nn.Dropout(config.hidden_dropout)
	self.self_attention = MegatronGPTAttention(config)
	self.mlp = MegatronGPTMLP(config)
	self.layer_idx = layer_idx

	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.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)
	attn_output = self.post_attention_dropout(attn_output)

	# pseudocode:
	# x = x + attn(ln1(x))
	# x = x + mlp(ln2(x))
	attn_output = attn_output + hidden_states
	mlp_output = self.mlp(self.post_attention_layernorm(attn_output))
	mlp_output = self.post_mlp_dropout(mlp_output)
	hidden_states = mlp_output + attn_output

	outputs = attention_layer_outputs[1:]
	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 MegatronGPTLayerNorm(torch.nn.LayerNorm):
	def __init__(self, normalization, normalized_shape, eps=1e-05, elementwise_affine=True, device=None, dtype=None):
	normalization = normalization.lower()
	assert normalization in ['layernorm', 'layernorm1p', 'rmsnorm']
	if normalization == 'rmsnorm':
	torch.nn.Module.__init__(self)
	self.weight = nn.Parameter(torch.ones(normalized_shape))
	self.variance_epsilon = eps
	else:
	super().__init__(
	normalized_shape=normalized_shape,
	eps=eps,
	elementwise_affine=elementwise_affine,
	device=device,
	dtype=dtype,
	)
	self.normalization = normalization

	def forward(self, x):
	if self.normalization == 'rmsnorm':
	input_dtype = x.dtype
	x = x.to(torch.float32)
	variance = x.pow(2).mean(-1, keepdim=True)
	x = x * torch.rsqrt(variance + self.variance_epsilon)
	return self.weight * x.to(input_dtype)
	else:
	weight_bias = 1 if self.normalization == 'layernorm1p' else 0
	return torch.nn.functional.layer_norm(
	x, self.normalized_shape, self.weight + weight_bias, self.bias, self.eps
	)



	MEGATRON_GPT_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 ([`~MegatronGPTConfig`]): 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.
	"""

	MEGATRON_GPT_INPUTS_DOCSTRING = r"""
	Args:
	input_ids (`torch.LongTensor` of shape `({0})`):
	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 `({0})`, 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.

	inputs_embeds (`torch.FloatTensor` of shape `({0}, 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 MegatronGPT Model transformer outputting raw hidden-states without any specific head on top.",
	MEGATRON_GPT_START_DOCSTRING,
	)
	class MegatronGPTModel(MegatronGPTPreTrainedModel):
	def __init__(self, config):
	super().__init__(config)
	self.config = config

	self.embed_in = nn.Embedding(config.vocab_size, config.hidden_size)
	self.emb_dropout = nn.Dropout(config.hidden_dropout)
	self.layers = nn.ModuleList([MegatronGPTLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)])
	self.final_layernorm = MegatronGPTLayerNorm(config.normalization, 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

	@add_start_docstrings_to_model_forward(MEGATRON_GPT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
	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 = self.emb_dropout(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_layernorm(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,
	)


	@add_start_docstrings(
	"""MegatronGPT Model with a `language modeling` head on top for CLM fine-tuning.""", MEGATRON_GPT_START_DOCSTRING
	)
	class MegatronGPTForCausalLM(MegatronGPTPreTrainedModel):
	_tied_weights_keys = ["embed_out.weight"]

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

	self.megatron_gpt = MegatronGPTModel(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

	@add_start_docstrings_to_model_forward(MEGATRON_GPT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
	@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, CausalLMOutputWithPastAndEncoding]:
	r"""
	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 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)`.
	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]`.
	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`).

	Returns:
	"""
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	outputs = self.megatron_gpt(
	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 CausalLMOutputWithPastAndEncoding(
	loss=lm_loss,
	logits=lm_logits,
	past_key_values=outputs.past_key_values,
	hidden_states=outputs.hidden_states,
	attentions=outputs.attentions,
	encoding_states=hidden_states
	)

	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


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

	[`MegatronGPTForSequenceClassification`] uses the last token in order to do the classification, as other causal models
	(e.g. GPT-1) 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).
	""",
	MEGATRON_GPT_START_DOCSTRING,
	)
	class MegatronGPTForSequenceClassification(MegatronGPTPreTrainedModel):
	def __init__(self, config):
	super().__init__(config)
	self.num_labels = config.num_labels
	self.megatron_gpt = MegatronGPTModel(config)
	self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)

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

	@add_start_docstrings_to_model_forward(MEGATRON_GPT_INPUTS_DOCSTRING)
	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[torch.Tensor], 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

	outputs = self.megatron_gpt(
	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]
	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 and batch_size != 1:
	raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
	if self.config.pad_token_id is None:
	sequence_lengths = -1
	else:
	if input_ids is not None:
	sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).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:
	labels = labels.to(logits.device)
	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,) + outputs[1:]
	return ((loss,) + output) if loss is not None else output

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


	class MegatronGPTForTokenClassification(MegatronGPTPreTrainedModel):
	def __init__(self, config):
	super().__init__(config)
	self.num_labels = config.num_labels

	self.megatron_gpt = MegatronGPTModel(config)
	self.dropout = nn.Dropout(config.classifier_dropout)
	self.classifier = nn.Linear(config.hidden_size, config.num_labels)

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

	@add_start_docstrings_to_model_forward(MEGATRON_GPT_INPUTS_DOCSTRING)
	def forward(
	self,
	input_ids: Optional[torch.LongTensor] = None,
	past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
	attention_mask: Optional[torch.FloatTensor] = None,
	token_type_ids: Optional[torch.LongTensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	head_mask: Optional[torch.FloatTensor] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	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, TokenClassifierOutput]:
	r"""
	labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, 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

	outputs = self.megatron_gpt(
	input_ids,
	past_key_values=past_key_values,
	attention_mask=attention_mask,
	position_ids=position_ids,
	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 = outputs[0]
	hidden_states = self.dropout(hidden_states)
	logits = self.classifier(hidden_states)

	loss = None
	if labels is not None:
	labels = labels.to(logits.device)
	loss_fct = CrossEntropyLoss()
	loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))

	if not return_dict:
	output = (logits,) + outputs[2:]
	return ((loss,) + output) if loss is not None else output

	return TokenClassifierOutput(
	loss=loss,
	logits=logits,
	hidden_states=outputs.hidden_states,
	attentions=outputs.attentions,
	)


	@add_start_docstrings(
	"""
	The Megatron-GPT Model transformer with a span classification head on top for extractive question-answering tasks like
	SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`).
	""",
	MEGATRON_GPT_START_DOCSTRING,
	)
	class MegatronGPTForQuestionAnswering(MegatronGPTPreTrainedModel):
	def __init__(self, config):
	super().__init__(config)
	self.num_labels = config.num_labels
	self.megatron_gpt = MegatronGPTModel(config)
	self.qa_outputs = nn.Linear(config.hidden_size, 2)

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

	@add_start_docstrings_to_model_forward(MEGATRON_GPT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
	def forward(
	self,
	input_ids: Optional[torch.LongTensor] = None,
	attention_mask: Optional[torch.FloatTensor] = None,
	token_type_ids: Optional[torch.LongTensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	head_mask: Optional[torch.FloatTensor] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	start_positions: Optional[torch.LongTensor] = None,
	end_positions: Optional[torch.LongTensor] = 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.
	"""
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	outputs = self.megatron_gpt(
	input_ids,
	attention_mask=attention_mask,
	position_ids=position_ids,
	head_mask=head_mask,
	inputs_embeds=inputs_embeds,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	sequence_output = outputs[0]

	logits = self.qa_outputs(sequence_output)
	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).to(start_logits.device)
	if len(end_positions.size()) > 1:
	end_positions = end_positions.squeeze(-1).to(end_logits.device)
	# 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)
	end_positions = end_positions.clamp(0, ignored_index)

	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) + 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=outputs.hidden_states,
	attentions=outputs.attentions,
	)