moss-moon-003-sft-int4 / modeling_moss.py

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	""" PyTorch Moss model."""

	from typing import Optional, Tuple, Union

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

	from transformers.activations import ACT2FN
	from transformers.modeling_utils import PreTrainedModel
	from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
	from transformers.utils import (
	add_code_sample_docstrings,
	add_start_docstrings,
	add_start_docstrings_to_model_forward,
	logging
	)

	from .configuration_moss import MossConfig


	logger = logging.get_logger(__name__)

	_CHECKPOINT_FOR_DOC = "fnlp/moss-moon-003-base"
	_CONFIG_FOR_DOC = "MossConfig"


	MOSS_PRETRAINED_MODEL_ARCHIVE_LIST = [
	"fnlp/moss-moon-003-base",
	"fnlp/moss-moon-003-sft",
	"fnlp/moss-moon-003-sft-plugin",
	]


	# Copied from transformers.models.gptj.modeling_gptj.create_sinusoidal_positions
	def create_sinusoidal_positions(num_pos: int, dim: int) -> torch.Tensor:
	inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2) / dim))
	sinusoid_inp = torch.einsum("i , j -> i j", torch.arange(num_pos, dtype=torch.float), inv_freq).float()
	return torch.cat((torch.sin(sinusoid_inp), torch.cos(sinusoid_inp)), dim=1)


	# Copied from transformers.models.gptj.modeling_gptj.rotate_every_two
	def rotate_every_two(x: torch.Tensor) -> torch.Tensor:
	x1 = x[:, :, :, ::2]
	x2 = x[:, :, :, 1::2]
	x = torch.stack((-x2, x1), dim=-1)
	return x.flatten(-2) # in einsum notation: rearrange(x, '... d j -> ... (d j)')


	# Copied from transformers.models.gptj.modeling_gptj.apply_rotary_pos_emb
	def apply_rotary_pos_emb(tensor: torch.Tensor, sin: torch.Tensor, cos: torch.Tensor) -> torch.Tensor:
	sin = torch.repeat_interleave(sin[:, :, None, :], 2, 3)
	cos = torch.repeat_interleave(cos[:, :, None, :], 2, 3)
	return (tensor * cos) + (rotate_every_two(tensor) * sin)


	class MossAttention(nn.Module):
	def __init__(self, config):
	super().__init__()

	max_positions = config.max_position_embeddings
	self.register_buffer(
	"causal_mask",
	torch.tril(torch.ones((max_positions, max_positions), dtype=torch.bool)).view(
	1, 1, max_positions, max_positions
	),
	)

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

	self.embed_dim = config.hidden_size
	self.num_attention_heads = config.num_attention_heads
	self.head_dim = self.embed_dim // self.num_attention_heads
	if self.head_dim * self.num_attention_heads != self.embed_dim:
	raise ValueError(
	f"embed_dim must be divisible by num_attention_heads (got `embed_dim`: {self.embed_dim} and"
	f" `num_attention_heads`: {self.num_attention_heads})."
	)
	self.scale_attn = torch.sqrt(torch.tensor(self.head_dim, dtype=torch.float32)).to(torch.get_default_dtype())
	self.qkv_proj = nn.Linear(self.embed_dim, self.embed_dim * 3, bias=False)

	self.out_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False)
	self.rotary_dim = config.rotary_dim
	pos_embd_dim = self.rotary_dim or self.embed_dim
	self.embed_positions = create_sinusoidal_positions(max_positions, pos_embd_dim)

	def _split_heads(self, x, n_head, dim_head, mp_num):
	reshaped = x.reshape(x.shape[:-1] + (n_head // mp_num, dim_head))
	reshaped = reshaped.reshape(x.shape[:-2] + (-1,) + reshaped.shape[-1:])
	return reshaped

	def _merge_heads(self, tensor, num_attention_heads, attn_head_size):
	"""
	Merges attn_head_size dim and num_attn_heads dim into n_ctx
	"""
	if len(tensor.shape) == 5:
	tensor = tensor.permute(0, 1, 3, 2, 4).contiguous()
	elif len(tensor.shape) == 4:
	tensor = tensor.permute(0, 2, 1, 3).contiguous()
	else:
	raise ValueError(f"Input tensor rank should be one of [4, 5], but is: {len(tensor.shape)}")
	new_shape = tensor.size()[:-2] + (num_attention_heads * attn_head_size,)
	return tensor.view(new_shape)

	def _attn(
	self,
	query,
	key,
	value,
	attention_mask=None,
	head_mask=None,
	):
	# compute causal mask from causal mask buffer
	query_length, key_length = query.size(-2), key.size(-2)
	causal_mask = self.causal_mask[:, :, key_length - query_length : key_length, :key_length]

	# Keep the attention weights computation in fp32 to avoid overflow issues
	query = query.to(torch.float32)
	key = key.to(torch.float32)

	attn_weights = torch.matmul(query, key.transpose(-1, -2))

	attn_weights = attn_weights / self.scale_attn
	mask_value = torch.finfo(attn_weights.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_weights.dtype).to(attn_weights.device)
	attn_weights = torch.where(causal_mask, attn_weights, mask_value)

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

	attn_weights = nn.Softmax(dim=-1)(attn_weights)
	attn_weights = attn_weights.to(value.dtype)
	attn_weights = self.attn_dropout(attn_weights)

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

	attn_output = torch.matmul(attn_weights, value)

	return attn_output, attn_weights

	def forward(
	self,
	hidden_states: Optional[torch.FloatTensor],
	layer_past: Optional[Tuple[torch.Tensor]] = None,
	attention_mask: Optional[torch.FloatTensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	head_mask: Optional[torch.FloatTensor] = None,
	use_cache: Optional[bool] = False,
	output_attentions: Optional[bool] = False,
	) -> Union[
	Tuple[torch.Tensor, Tuple[torch.Tensor]],
	Optional[Tuple[torch.Tensor, Tuple[torch.Tensor], Tuple[torch.Tensor, ...]]],
	]:
	qkv = self.qkv_proj(hidden_states)
	# TODO(enijkamp): factor out number of logical TPU-v4 cores or make forward pass agnostic
	mp_num = 4
	qkv_split = qkv.reshape(qkv.shape[:-1] + (mp_num, -1))

	local_dim = self.head_dim * self.num_attention_heads // mp_num
	query, value, key = torch.split(qkv_split, local_dim, dim=-1)
	query = self._split_heads(query, self.num_attention_heads, self.head_dim, mp_num=mp_num)
	key = self._split_heads(key, self.num_attention_heads, self.head_dim, mp_num=mp_num)

	value = self._split_heads(value, self.num_attention_heads, self.head_dim, mp_num=mp_num)
	value = value.permute(0, 2, 1, 3)

	embed_positions = self.embed_positions
	if embed_positions.device != position_ids.device:
	embed_positions = embed_positions.to(position_ids.device)
	self.embed_positions = embed_positions

	sincos = embed_positions[position_ids]
	sin, cos = torch.split(sincos, sincos.shape[-1] // 2, dim=-1)

	if self.rotary_dim is not None:
	k_rot = key[:, :, :, : self.rotary_dim]
	k_pass = key[:, :, :, self.rotary_dim :]

	q_rot = query[:, :, :, : self.rotary_dim]
	q_pass = query[:, :, :, self.rotary_dim :]

	k_rot = apply_rotary_pos_emb(k_rot, sin, cos)
	q_rot = apply_rotary_pos_emb(q_rot, sin, cos)

	key = torch.cat([k_rot, k_pass], dim=-1)
	query = torch.cat([q_rot, q_pass], dim=-1)
	else:
	key = apply_rotary_pos_emb(key, sin, cos)
	query = apply_rotary_pos_emb(query, sin, cos)

	key = key.permute(0, 2, 1, 3)
	query = query.permute(0, 2, 1, 3)

	if layer_past is not None:
	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)

	if use_cache is True:
	present = (key, value)
	else:
	present = None

	# compute self-attention: V x Softmax(QK^T)
	attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask)

	attn_output = self._merge_heads(attn_output, self.num_attention_heads, self.head_dim)
	attn_output = self.out_proj(attn_output)
	attn_output = self.resid_dropout(attn_output)

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

	return outputs # a, present, (attentions)


	# Copied from transformers.models.gptj.modeling_gptj.GPTJMLP with GPTJ->Moss
	class MossMLP(nn.Module):
	def __init__(self, intermediate_size, config): # in MLP: intermediate_size= 4 * embed_dim
	super().__init__()
	embed_dim = config.n_embd

	self.fc_in = nn.Linear(embed_dim, intermediate_size)
	self.fc_out = nn.Linear(intermediate_size, embed_dim)

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

	def forward(self, hidden_states: Optional[torch.FloatTensor]) -> torch.FloatTensor:
	hidden_states = self.fc_in(hidden_states)
	hidden_states = self.act(hidden_states)
	hidden_states = self.fc_out(hidden_states)
	hidden_states = self.dropout(hidden_states)
	return hidden_states


	# Copied from transformers.models.gptj.modeling_gptj.GPTJBlock with GPTJ->Moss
	class MossBlock(nn.Module):
	def __init__(self, config):
	super().__init__()
	inner_dim = config.n_inner if config.n_inner is not None else 4 * config.n_embd
	self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
	self.attn = MossAttention(config)
	self.mlp = MossMLP(inner_dim, config)

	def forward(
	self,
	hidden_states: Optional[torch.FloatTensor],
	layer_past: Optional[Tuple[torch.Tensor]] = None,
	attention_mask: Optional[torch.FloatTensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	head_mask: Optional[torch.FloatTensor] = None,
	use_cache: Optional[bool] = False,
	output_attentions: Optional[bool] = False,
	) -> Union[Tuple[torch.Tensor], Optional[Tuple[torch.Tensor, Tuple[torch.FloatTensor, ...]]]]:
	residual = hidden_states
	hidden_states = self.ln_1(hidden_states)
	attn_outputs = self.attn(
	hidden_states=hidden_states,
	layer_past=layer_past,
	attention_mask=attention_mask,
	position_ids=position_ids,
	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:]

	feed_forward_hidden_states = self.mlp(hidden_states)
	hidden_states = attn_output + feed_forward_hidden_states + residual

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

	return outputs # hidden_states, present, (attentions)


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

	config_class = MossConfig
	base_model_prefix = "transformer"
	supports_gradient_checkpointing = True
	_no_split_modules = ["MossBlock"]

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

	def _init_weights(self, module):
	"""Initialize the weights."""
	if isinstance(module, (nn.Linear,)):
	# Slightly different from Mesh Transformer JAX 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, MossModel):
	module.gradient_checkpointing = value


	MOSS_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 ([`MossConfig`]): 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.
	"""

	MOSS_INPUTS_DOCSTRING = r"""
	Args:
	input_ids (`torch.LongTensor` of shape `({0})`):
	Indices of input sequence tokens in the vocabulary.

	Indices can be obtained using [`AutoProcenizer`]. 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)
	token_type_ids (`torch.LongTensor` of shape `({0})`, optional):
	Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
	1]`:

	- 0 corresponds to a sentence A token,
	- 1 corresponds to a sentence B token.

	[What are token type IDs?](../glossary#token-type-ids)
	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_attention_heads,)` or `(n_layer, num_attention_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_dim)`, 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.
	"""


	@add_start_docstrings(
	"The bare Moss Model transformer outputting raw hidden-states without any specific head on top.",
	MOSS_START_DOCSTRING,
	)
	class MossModel(MossPreTrainedModel):
	def __init__(self, config):
	super().__init__(config)

	self.embed_dim = config.n_embd
	self.vocab_size = config.vocab_size
	self.wte = nn.Embedding(config.vocab_size, self.embed_dim)
	self.drop = nn.Dropout(config.embd_pdrop)
	self.h = nn.ModuleList([MossBlock(config) for _ in range(config.n_layer)])
	self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
	self.rotary_dim = min(config.rotary_dim, config.n_ctx // config.num_attention_heads)

	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

	@add_start_docstrings_to_model_forward(MOSS_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
	@add_code_sample_docstrings(
	checkpoint=_CHECKPOINT_FOR_DOC,
	output_type=BaseModelOutputWithPast,
	config_class=_CONFIG_FOR_DOC,
	)
	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,
	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

	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()
	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")

	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]).long()

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

	if 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])

	# Attention mask.
	if attention_mask is not None:
	if batch_size <= 0:
	raise ValueError("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 num_attention_heads x N x N
	# head_mask has shape n_layer x batch x num_attention_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)

	hidden_states = inputs_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),)

	if self.gradient_checkpointing and self.training:
	if use_cache:
	logger.warning_once(
	"`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting "
	"`use_cache=False`..."
	)
	use_cache = False

	presents = () if use_cache else None
	all_self_attentions = () if output_attentions 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,
	position_ids,
	head_mask[i],
	)
	else:
	outputs = block(
	hidden_states=hidden_states,
	layer_past=layer_past,
	attention_mask=attention_mask,
	position_ids=position_ids,
	head_mask=head_mask[i],
	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_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)

	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] if v is not None)

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


	@add_start_docstrings(
	"""
	The Moss Model transformer with a language modeling head on top.
	""",
	MOSS_START_DOCSTRING,
	)
	class MossForCausalLM(MossPreTrainedModel):
	_keys_to_ignore_on_load_missing = [r"h\.\d+\.attn\.causal_mask"]

	def __init__(self, config):
	super().__init__(config)
	self.transformer = MossModel(config)
	self.lm_head = nn.Linear(config.n_embd, config.vocab_size)

	# 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):
	self.lm_head = new_embeddings

	def prepare_inputs_for_generation(self, input_ids, past_key_values=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)

	return {
	"input_ids": input_ids,
	"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,
	}

	@add_start_docstrings_to_model_forward(MOSS_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
	@add_code_sample_docstrings(
	checkpoint=_CHECKPOINT_FOR_DOC,
	output_type=CausalLMOutputWithPast,
	config_class=_CONFIG_FOR_DOC,
	)
	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, CausalLMOutputWithPast]:
	r"""
	labels (`torch.LongTensor` 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,
	use_cache=use_cache,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)
	hidden_states = transformer_outputs[0]

	# make sure sampling in fp16 works correctly and
	# compute loss in fp32 to match with mesh-tf version
	# https://github.com/EleutherAI/gpt-neo/blob/89ce74164da2fb16179106f54e2269b5da8db333/models/gpt2/gpt2.py#L179
	lm_logits = self.lm_head(hidden_states).to(torch.float32)

	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()
	# Flatten the tokens
	loss_fct = CrossEntropyLoss()
	loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))

	loss = loss.to(hidden_states.dtype)

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

	return CausalLMOutputWithPast(
	loss=loss,
	logits=lm_logits,
	past_key_values=transformer_outputs.past_key_values,
	hidden_states=transformer_outputs.hidden_states,
	attentions=transformer_outputs.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(
	tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past)
	for layer_past in past_key_values
	)