lordcoder-v0-13-2B / modeling_lordcoder_v0.py

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	"""LoRDCoder model class, based on GPT model.

	License: Apache-2.0
	"""
	import math
	from typing import List, 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.modeling_outputs import (
	BaseModelOutputWithPastAndCrossAttentions,
	CausalLMOutputWithCrossAttentions,
	SequenceClassifierOutputWithPast,
	TokenClassifierOutput,
	)
	from transformers.modeling_utils import PreTrainedModel
	from .configuration_lordcoder_v0 import LoRDCoderConfig


	# Fused kernels
	# Use separate functions for each case because conditionals prevent kernel fusion.
	@torch.jit.script
	def upcast_masked_softmax(
	x: torch.Tensor, mask: torch.Tensor, mask_value: torch.Tensor, scale: float, softmax_dtype: torch.dtype
	):
	input_dtype = x.dtype
	x = x.to(softmax_dtype) * scale
	x = torch.where(mask, x, mask_value)
	x = torch.nn.functional.softmax(x, dim=-1).to(input_dtype)
	return x


	@torch.jit.script
	def upcast_softmax(x: torch.Tensor, scale: float, softmax_dtype: torch.dtype):
	input_dtype = x.dtype
	x = x.to(softmax_dtype) * scale
	x = torch.nn.functional.softmax(x, dim=-1).to(input_dtype)
	return x


	@torch.jit.script
	def masked_softmax(x: torch.Tensor, mask: torch.Tensor, mask_value: torch.Tensor):
	x = torch.where(mask, x, mask_value)
	x = torch.nn.functional.softmax(x, dim=-1)
	return x


	class LoRDCoderAttention(nn.Module):
	def __init__(self, config, is_cross_attention=False, layer_idx=None):
	super().__init__()
	self.mask_value = None

	self.multi_query = config.multi_query
	self.embed_dim = config.hidden_size
	self.num_heads = config.num_attention_heads
	self.head_dim = self.embed_dim // self.num_heads
	self.kv_heads = 1 if self.multi_query else self.num_heads
	self.kv_dim = self.kv_heads * self.head_dim
	self.split_size = self.embed_dim
	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} and `num_heads`:"
	f" {self.num_heads})."
	)

	self.scale_attn_weights = config.scale_attn_weights
	self.is_cross_attention = is_cross_attention

	self.layer_idx = layer_idx
	self.attention_softmax_in_fp32 = config.attention_softmax_in_fp32
	self.scale_attention_softmax_in_fp32 = (
	config.scale_attention_softmax_in_fp32 and config.attention_softmax_in_fp32
	)

	if self.is_cross_attention:
	raise NotImplementedError("Cross Attention not supported.")
	if self.multi_query:
	raise NotImplementedError("Multi-Query Attention not supported for cross_attention")

	self.c_attn = nn.Linear(self.embed_dim, 2 * self.embed_dim)
	self.q_attn = nn.Linear(self.embed_dim, self.embed_dim)
	else:
	self.c_attn = nn.Linear(self.embed_dim, self.embed_dim + 2 * self.kv_dim)

	self.c_proj = nn.Linear(self.embed_dim, self.embed_dim)

	self.attn_dropout = nn.Dropout(config.attn_pdrop)
	self.resid_dropout = nn.Dropout(config.resid_pdrop)

	def _get_mask_value(self, device, dtype):
	# torch.where expects a tensor. We use a cache to avoid recreating it every time.
	if self.mask_value is None or self.mask_value.dtype != dtype or self.mask_value.device != device:
	self.mask_value = torch.full([], torch.finfo(dtype).min, dtype=dtype, device=device)
	return self.mask_value

	def _attn(self, query, key, value, attention_mask=None, head_mask=None):
	dtype = query.dtype
	softmax_dtype = torch.float32 if self.attention_softmax_in_fp32 else dtype
	upcast = dtype != softmax_dtype

	unscale = self.layer_idx + 1 if self.scale_attention_softmax_in_fp32 and upcast else 1
	scale_factor = unscale**-1
	if self.scale_attn_weights:
	scale_factor /= self.head_dim**0.5

	# MQA models: (batch_size, query_length, num_heads * head_dim)
	# MHA models: (batch_size, num_heads, query_length, head_dim)
	query_shape = query.shape
	batch_size = query_shape[0]
	key_length = key.size(-1)
	if self.multi_query:
	# (batch_size, query_length, num_heads, head_dim) x (batch_size, head_dim, key_length)
	# -> (batch_size, query_length, num_heads, key_length)
	query_length = query_shape[1]
	attn_shape = (batch_size, query_length, self.num_heads, key_length)
	attn_view = (batch_size, query_length * self.num_heads, key_length)
	# No copy needed for MQA 2, or when layer_past is provided.
	query = query.reshape(batch_size, query_length * self.num_heads, self.head_dim)
	else:
	# (batch_size, num_heads, query_length, head_dim) x (batch_size, num_heads, head_dim, key_length)
	# -> (batch_size, num_heads, query_length, key_length)
	query_length = query_shape[2]
	attn_shape = (batch_size, self.num_heads, query_length, key_length)
	attn_view = (batch_size * self.num_heads, query_length, key_length)
	# Always copies
	query = query.reshape(batch_size * self.num_heads, query_length, self.head_dim)
	# No copy when layer_past is provided.
	key = key.reshape(batch_size * self.num_heads, self.head_dim, key_length)

	attn_weights = torch.empty(attn_view, device=query.device, dtype=query.dtype)
	if query.device.type == "cpu":
	# This is needed because of a bug in pytorch https://github.com/pytorch/pytorch/issues/80588.
	# The bug was fixed in https://github.com/pytorch/pytorch/pull/96086,
	# but the fix has not been released as of pytorch version 2.0.0.
	attn_weights = torch.zeros_like(attn_weights)
	beta = 1
	else:
	beta = 0
	attn_weights = torch.baddbmm(attn_weights, query, key, beta=beta, alpha=scale_factor).view(attn_shape)

	if upcast:
	# Use a fused kernel to prevent a large overhead from casting and scaling.
	# Sub-optimal when the key length is not a multiple of 8.
	if attention_mask is None:
	attn_weights = upcast_softmax(attn_weights, unscale, softmax_dtype)
	else:
	mask_value = self._get_mask_value(attn_weights.device, softmax_dtype)
	# print(attn_weights.device, attention_mask.device, mask_value.device, unscale.device, softmax_dtype)
	attn_weights = upcast_masked_softmax(attn_weights, attention_mask, mask_value, unscale, softmax_dtype)
	else:
	if attention_mask is not None:
	mask_value = self._get_mask_value(attn_weights.device, softmax_dtype)

	# The fused kernel is very slow when the key length is not a multiple of 8, so we skip fusion.
	attn_weights = torch.where(attention_mask, attn_weights, mask_value)

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

	attn_weights = self.attn_dropout(attn_weights)

	# Mask heads if we want to
	if head_mask is not None:
	if self.multi_query:
	head_mask = head_mask.transpose(1, 2)
	attn_weights = attn_weights * head_mask

	if self.multi_query:
	attn_output = torch.bmm(attn_weights.view(attn_view), value).view(query_shape)
	else:
	attn_output = torch.matmul(attn_weights, value)

	return attn_output, attn_weights

	def forward(
	self,
	hidden_states: torch.Tensor,
	layer_past: Optional[torch.Tensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	head_mask: Optional[torch.Tensor] = None,
	encoder_hidden_states: Optional[torch.Tensor] = None,
	encoder_attention_mask: Optional[torch.Tensor] = None,
	use_cache: Optional[bool] = False,
	output_attentions: Optional[bool] = False,
	) -> Union[
	Tuple[torch.Tensor, Optional[torch.Tensor]],
	Tuple[torch.Tensor, Optional[torch.Tensor], Tuple[torch.Tensor, ...]],
	]:
	if encoder_hidden_states is not None:
	if not hasattr(self, "q_attn") or not self.is_cross_attention:
	raise ValueError(
	"If class is used as cross attention, the weights `q_attn` have to be defined. "
	"Please make sure to instantiate class with `LoRDCoderAttention(..., is_cross_attention=True)`."
	)

	query = self.q_attn(hidden_states)
	key_value = self.c_attn(encoder_hidden_states)
	attention_mask = encoder_attention_mask
	elif self.multi_query:
	query, key_value = self.c_attn(hidden_states).split((self.embed_dim, 2 * self.kv_dim), dim=2)
	else:
	# Note: We split as (self.num_heads, 3, self.head_dim) instead of (3, self.num_heads, self.head_dim),
	# i.e., the memory layout is not the same as GPT2.
	# This makes the concatenation with past_key_value more efficient.
	query, key_value = (
	self.c_attn(hidden_states)
	.view(hidden_states.shape[:2], self.num_heads, 3 self.head_dim)
	.transpose(1, 2)
	.split((self.head_dim, 2 * self.head_dim), dim=3)
	)

	if layer_past is not None:
	key_value = torch.cat((layer_past, key_value), dim=-2)
	present = key_value if use_cache else None

	key, value = key_value.split((self.head_dim, self.head_dim), dim=-1)

	attn_output, attn_weights = self._attn(query, key.transpose(-1, -2), value, attention_mask, head_mask)

	if not self.multi_query:
	attn_output = attn_output.transpose(1, 2).reshape(hidden_states.shape)
	attn_output = self.c_proj(attn_output)
	attn_output = self.resid_dropout(attn_output)

	outputs = (attn_output, present)
	if output_attentions:
	if self.multi_query:
	# Transpose to return weights in the usual format (batch_size, num_heads, query_length, key_length)
	attn_weights = attn_weights.transpose(1, 2)
	outputs += (attn_weights,)

	return outputs # a, present, (attentions)


	class LoRDCoderMLP(nn.Module):
	def __init__(self, intermediate_size, config):
	super().__init__()
	embed_dim = config.hidden_size
	self.gate_dim = config.gate_dim

	self.c_fc = torch.nn.Linear(in_features=embed_dim, out_features=intermediate_size, bias=True)
	self.c_gate = torch.nn.Linear(in_features=intermediate_size, out_features=self.gate_dim, bias=True)
	self.c_proj = torch.nn.Linear(in_features=self.gate_dim, out_features=embed_dim, bias=True)

	self.act = ACT2FN[config.activation_function]
	self.dropout = nn.Dropout(config.resid_pdrop)

	def forward(self, hidden_states: Optional[Tuple[torch.FloatTensor]]) -> torch.FloatTensor:
	hidden_states = self.c_fc(hidden_states)
	hidden_states = self.c_gate(self.act(hidden_states))
	hidden_states = self.c_proj(hidden_states)
	hidden_states = self.dropout(hidden_states)
	return hidden_states


	class LoRDCoderBlock(nn.Module):
	def __init__(self, config, layer_idx=None):
	super().__init__()
	hidden_size = config.hidden_size
	self.inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size

	self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
	self.attn = LoRDCoderAttention(config, layer_idx=layer_idx)
	self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)

	if config.add_cross_attention:
	if config.multi_query:
	raise NotImplementedError("Cross-attention not implemented for MQA")
	self.crossattention = LoRDCoderAttention(config, is_cross_attention=True, layer_idx=layer_idx)
	self.ln_cross_attn = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)

	self.mlp = LoRDCoderMLP(self.inner_dim, config)

	def forward(
	self,
	hidden_states: Optional[Tuple[torch.Tensor]],
	layer_past: Optional[torch.Tensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	head_mask: Optional[torch.Tensor] = None,
	encoder_hidden_states: Optional[torch.Tensor] = None,
	encoder_attention_mask: Optional[torch.Tensor] = None,
	use_cache: Optional[bool] = False,
	output_attentions: Optional[bool] = False,
	) -> Union[
	Tuple[torch.Tensor], Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor, torch.Tensor, torch.Tensor]
	]:
	residual = hidden_states
	hidden_states = self.ln_1(hidden_states)
	attn_outputs = self.attn(
	hidden_states,
	layer_past=layer_past,
	attention_mask=attention_mask,
	head_mask=head_mask,
	use_cache=use_cache,
	output_attentions=output_attentions,
	)
	attn_output = attn_outputs[0] # output_attn: a, present, (attentions)
	outputs = attn_outputs[1:]
	# residual connection
	hidden_states = attn_output + residual

	if encoder_hidden_states is not None:
	# add one self-attention block for cross-attention
	if not hasattr(self, "crossattention"):
	raise ValueError(
	f"If `encoder_hidden_states` are passed, {self} has to be instantiated with "
	"cross-attention layers by setting `config.add_cross_attention=True`"
	)
	residual = hidden_states
	hidden_states = self.ln_cross_attn(hidden_states)
	cross_attn_outputs = self.crossattention(
	hidden_states,
	attention_mask=attention_mask,
	head_mask=head_mask,
	encoder_hidden_states=encoder_hidden_states,
	encoder_attention_mask=encoder_attention_mask,
	output_attentions=output_attentions,
	)
	attn_output = cross_attn_outputs[0]
	# residual connection
	hidden_states = residual + attn_output
	outputs = outputs + cross_attn_outputs[2:] # add cross attentions if we output attention weights

	residual = hidden_states
	hidden_states = self.ln_2(hidden_states)
	feed_forward_hidden_states = self.mlp(hidden_states)
	# residual connection
	hidden_states = residual + feed_forward_hidden_states

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

	return outputs # hidden_states, present, (attentions, cross_attentions)


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

	config_class = LoRDCoderConfig
	base_model_prefix = "transformer"
	supports_gradient_checkpointing = True
	_no_split_modules = ["LoRDCoderBlock"]
	_skip_keys_device_placement = "past_key_values"

	def __init__(self, inputs, *kwargs):
	super().__init__(inputs, *kwargs)

	def _init_weights(self, module):
	"""Initialize the weights."""
	if isinstance(module, LoRDCoderMLP):
	# Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme:
	# > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale
	# > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers.
	# > -- GPT-2 :: https://openai.com/blog/better-language-models/
	#
	# Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py
	module.c_proj.weight.data.normal_(
	mean=0.0, std=(self.config.initializer_range / math.sqrt(2 * self.config.n_layer))
	)
	module.c_proj._is_hf_initialized = True
	elif isinstance(module, LoRDCoderAttention):
	# Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme:
	# > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale
	# > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers.
	# > -- GPT-2 :: https://openai.com/blog/better-language-models/
	#
	# Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py
	module.c_proj.weight.data.normal_(
	mean=0.0, std=(self.config.initializer_range / math.sqrt(2 * self.config.n_layer))
	)
	module.c_proj.weight.data.normal_(
	mean=0.0, std=(self.config.initializer_range / math.sqrt(2 * self.config.n_layer))
	)
	module.c_proj._is_hf_initialized = True
	elif isinstance(module, nn.Linear):
	# Slightly different from the TF version which uses truncated_normal for initialization
	# cf https://github.com/pytorch/pytorch/pull/5617
	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, LoRDCoderModel):
	module.gradient_checkpointing = value


	class LoRDCoderModel(LoRDCoderPreTrainedModel):
	def __init__(self, config):
	super().__init__(config)

	self.multi_query = config.multi_query
	self.embed_dim = config.hidden_size

	self.wte = nn.Embedding(config.vocab_size, self.embed_dim)
	self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim)

	self.drop = nn.Dropout(config.embd_pdrop)
	self.h = nn.ModuleList([LoRDCoderBlock(config, layer_idx=i) for i in range(config.num_hidden_layers)])
	self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)

	max_positions = config.max_position_embeddings
	self.register_buffer(
	"bias", torch.tril(torch.ones((max_positions, max_positions), dtype=torch.bool)), persistent=False
	)

	self.gradient_checkpointing = False

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

	def get_input_embeddings(self):
	return self.wte

	def set_input_embeddings(self, new_embeddings):
	self.wte = new_embeddings

	def warn_if_padding_and_no_attention_mask(self, input_ids, attention_mask):
	"""
	Shows a one-time warning if the input_ids appear to contain padding and no attention mask was given.
	"""

	if (attention_mask is not None) or (self.config.pad_token_id is None):
	return

	# Check only the first and last input IDs to reduce overhead.
	if self.config.pad_token_id in input_ids[:, [-1, 0]]:
	warn_string = (
	"We strongly recommend passing in an `attention_mask` since your input_ids may be padded. See "
	"https://huggingface.co/docs/transformers/troubleshooting"
	"#incorrect-output-when-padding-tokens-arent-masked."
	)

	# If the pad token is equal to either BOS, EOS, or SEP, we do not know whether the user should use an
	# attention_mask or not. In this case, we should still show a warning because this is a rare case.
	if (
	(self.config.bos_token_id is not None and self.config.bos_token_id == self.config.pad_token_id)
	or (self.config.eos_token_id is not None and self.config.eos_token_id == self.config.pad_token_id)
	or (self.config.sep_token_id is not None and self.config.sep_token_id == self.config.pad_token_id)
	):
	warn_string += (
	f"\nYou may ignore this warning if your `pad_token_id` ({self.config.pad_token_id}) is identical "
	f"to the `bos_token_id` ({self.config.bos_token_id}), `eos_token_id` ({self.config.eos_token_id}), "
	f"or the `sep_token_id` ({self.config.sep_token_id}), and your input is not padded."
	)

	print("Warning:", warn_string)

	def forward(
	self,
	input_ids: Optional[torch.Tensor] = None,
	past_key_values: Optional[List[torch.Tensor]] = None,
	attention_mask: Optional[torch.Tensor] = None,
	token_type_ids: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.Tensor] = None,
	head_mask: Optional[torch.Tensor] = None,
	inputs_embeds: Optional[torch.Tensor] = None,
	encoder_hidden_states: Optional[torch.Tensor] = None,
	encoder_attention_mask: Optional[torch.Tensor] = None,
	use_cache: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]:
	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

	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:
	self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
	input_shape = input_ids.size()
	input_ids = input_ids.view(-1, input_shape[-1])
	batch_size = input_ids.shape[0]
	elif inputs_embeds is not None:
	input_shape = inputs_embeds.size()[:-1]
	batch_size = inputs_embeds.shape[0]
	else:
	raise ValueError("You have to specify either input_ids or inputs_embeds")

	if batch_size <= 0:
	raise ValueError("batch_size has to be defined and > 0")

	device = input_ids.device if input_ids is not None else inputs_embeds.device

	if token_type_ids is not None:
	token_type_ids = token_type_ids.view(-1, input_shape[-1])
	if position_ids is not None:
	position_ids = position_ids.view(-1, input_shape[-1])

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

	if attention_mask is not None and len(attention_mask.shape) == 2 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_length > 0:
	position_ids = position_ids[:, past_length : input_shape[-1] + past_length :]
	elif position_ids is None:
	position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device)
	position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])

	# Self-attention mask.
	query_length = input_shape[-1]
	key_length = past_length + query_length
	self_attention_mask = self.bias[None, key_length - query_length : key_length, :key_length]

	if attention_mask is not None:
	self_attention_mask = self_attention_mask * attention_mask.view(batch_size, 1, -1).to(
	dtype=torch.bool, device=self_attention_mask.device
	)

	# MQA models: (batch_size, query_length, n_heads, key_length)
	# MHA models: (batch_size, n_heads, query_length, key_length)
	attention_mask = self_attention_mask.unsqueeze(2 if self.multi_query else 1)

	# If a 2D or 3D attention mask is provided for the cross-attention
	# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
	if (
	self.config.add_cross_attention
	and encoder_hidden_states is not None
	and encoder_attention_mask is not None
	):
	if encoder_attention_mask.dim() == 2:
	encoder_attention_mask.unsqueeze(1)
	assert encoder_attention_mask.dim() == 3
	encoder_attention_mask = encoder_attention_mask.bool().unsqueeze(2 if self.multi_query else 1)
	else:
	encoder_attention_mask = None

	# 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
	# head_mask has shape n_layer x batch x n_heads x N x N
	head_mask = self.get_head_mask(head_mask, self.config.n_layer)

	if inputs_embeds is None:
	inputs_embeds = self.wte(input_ids)
	position_embeds = self.wpe(position_ids)
	hidden_states = inputs_embeds + position_embeds

	if token_type_ids is not None:
	token_type_embeds = self.wte(token_type_ids)
	hidden_states = hidden_states + token_type_embeds

	hidden_states = self.drop(hidden_states)

	output_shape = input_shape + (hidden_states.size(-1),)

	presents = [] if use_cache else None
	all_self_attentions = () if output_attentions else None
	all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
	all_hidden_states = () if output_hidden_states else None
	for i, (block, layer_past) in enumerate(zip(self.h, 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 past_key_value
	return module(*inputs, use_cache, output_attentions)

	return custom_forward

	outputs = torch.utils.checkpoint.checkpoint(
	create_custom_forward(block),
	hidden_states,
	None,
	attention_mask,
	head_mask[i],
	encoder_hidden_states,
	encoder_attention_mask,
	)
	else:
	outputs = block(
	hidden_states,
	layer_past=layer_past,
	attention_mask=attention_mask,
	head_mask=head_mask[i],
	encoder_hidden_states=encoder_hidden_states,
	encoder_attention_mask=encoder_attention_mask,
	use_cache=use_cache,
	output_attentions=output_attentions,
	)

	hidden_states = outputs[0]
	if use_cache:
	presents.append(outputs[1])

	if output_attentions:
	all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
	if self.config.add_cross_attention:
	all_cross_attentions = all_cross_attentions + (outputs[3 if use_cache else 2],)

	hidden_states = self.ln_f(hidden_states)

	hidden_states = hidden_states.view(output_shape)
	# 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_self_attentions, all_cross_attentions]
	if v is not None
	)

	return BaseModelOutputWithPastAndCrossAttentions(
	last_hidden_state=hidden_states,
	past_key_values=presents,
	hidden_states=all_hidden_states,
	attentions=all_self_attentions,
	cross_attentions=all_cross_attentions,
	)


	class LoRDCoderForCausalLM(LoRDCoderPreTrainedModel):

	def __init__(self, config):
	super().__init__(config)
	self.transformer = LoRDCoderModel(config)
	self.lm_head = lambda x: torch.matmul(x, self.transformer.wte.weight.T)

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

	def get_output_embeddings(self):
	return self.lm_head

	def set_output_embeddings(self, new_embeddings):
	raise NotImplementedError("Cannot resize the embeddings of LoRDCoderForCausalLM.")

	def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs):
	token_type_ids = kwargs.get("token_type_ids", None)
	# only last token for inputs_ids if past is defined in kwargs
	if past_key_values:
	input_ids = input_ids[:, -1].unsqueeze(-1)
	if token_type_ids is not None:
	token_type_ids = token_type_ids[:, -1].unsqueeze(-1)

	attention_mask = kwargs.get("attention_mask", None)
	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)
	else:
	position_ids = None

	# 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"),
	"position_ids": position_ids,
	"attention_mask": attention_mask,
	"token_type_ids": token_type_ids,
	}
	)
	return model_inputs

	def forward(
	self,
	input_ids: Optional[torch.Tensor] = None,
	past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
	attention_mask: Optional[torch.Tensor] = None,
	token_type_ids: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.Tensor] = None,
	head_mask: Optional[torch.Tensor] = None,
	inputs_embeds: Optional[torch.Tensor] = None,
	encoder_hidden_states: Optional[torch.Tensor] = None,
	encoder_attention_mask: Optional[torch.Tensor] = None,
	labels: Optional[torch.Tensor] = None,
	use_cache: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, CausalLMOutputWithCrossAttentions]:
	r"""
	labels (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional):
	Labels for language modeling. Note that the labels are shifted inside the model, i.e. you can set
	`labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
	are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
	"""
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	transformer_outputs = self.transformer(
	input_ids,
	past_key_values=past_key_values,
	attention_mask=attention_mask,
	token_type_ids=token_type_ids,
	position_ids=position_ids,
	head_mask=head_mask,
	inputs_embeds=inputs_embeds,
	encoder_hidden_states=encoder_hidden_states,
	encoder_attention_mask=encoder_attention_mask,
	use_cache=use_cache,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)
	hidden_states = transformer_outputs[0]

	lm_logits = self.lm_head(hidden_states)

	loss = None
	if labels is not None:
	# Shift so that tokens < n predict n
	shift_logits = lm_logits[..., :-1, :].contiguous()
	shift_labels = labels[..., 1:].contiguous().to(shift_logits.device)
	# Flatten the tokens
	loss_fct = CrossEntropyLoss()
	loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))

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

	return CausalLMOutputWithCrossAttentions(
	loss=loss,
	logits=lm_logits,
	past_key_values=transformer_outputs.past_key_values,
	hidden_states=transformer_outputs.hidden_states,
	attentions=transformer_outputs.attentions,
	cross_attentions=transformer_outputs.cross_attentions,
	)

	@staticmethod
	def _reorder_cache(
	past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor
	) -> Tuple[Tuple[torch.Tensor]]:
	"""
	This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or
	[`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
	beam_idx at every generation step.
	"""
	return tuple(layer_past.index_select(0, beam_idx.to(layer_past.device)) for layer_past in past_key_values)