plamo-13b-instruct-nc / modeling_plamo.py

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	from typing import Any, Dict, List, NamedTuple, Optional, Tuple, Union

	import numpy as np
	import torch
	from torch import nn
	from torch.nn import functional as F
	from transformers import PretrainedConfig, PreTrainedModel
	from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast


	class DecoderInput(NamedTuple):
	hidden_states: torch.Tensor
	position_ids: torch.Tensor
	attention_mask: Optional[torch.Tensor] = None
	past_key_values: Optional[List[torch.FloatTensor]] = None
	output_hidden_states: Optional[bool] = False
	output_attentions: Optional[bool] = False
	use_cache: Optional[bool] = False
	gradient_checkpointing: bool = False


	class DecoderOutput(NamedTuple):
	hidden_states: torch.Tensor
	all_hidden_states: Optional[Tuple[torch.Tensor, ...]]
	all_self_attns: Optional[Tuple[torch.Tensor, ...]]
	next_decoder_cache: Optional[Tuple[torch.Tensor, ...]]


	class PlamoConfig(PretrainedConfig): # type: ignore
	model_type: str = "plamo"

	def __init__(
	self,
	vocab_size: int = 32000,
	hidden_size: int = 4096,
	intermediate_size: int = 13312,
	num_hidden_layers: int = 32,
	num_attention_heads: int = 32,
	num_key_value_heads: Optional[int] = None,
	max_position_embeddings: int = 2048,
	initializer_range: float = 0.02,
	rms_norm_eps: float = 1e-6,
	use_cache: bool = True,
	tokenizer_class: str = "PlamoTokenizer",
	pad_token_id: Optional[int] = None,
	bos_token_id: int = 1,
	eos_token_id: int = 2,
	n_shared_head: int = 8,
	tie_word_embeddings: bool = False,
	**kwargs: Any,
	) -> None:
	self.vocab_size = vocab_size
	self.max_position_embeddings = max_position_embeddings
	self.hidden_size = hidden_size
	self.intermediate_size = intermediate_size
	self.num_hidden_layers = num_hidden_layers
	self.num_attention_heads = num_attention_heads

	# for backward compatibility
	if num_key_value_heads is None:
	num_key_value_heads = num_attention_heads

	self.num_key_value_heads = num_key_value_heads
	self.initializer_range = initializer_range
	self.rms_norm_eps = rms_norm_eps
	self.use_cache = use_cache

	self.n_shared_head = n_shared_head

	super().__init__(
	tokenizer_class=tokenizer_class,
	pad_token_id=pad_token_id,
	bos_token_id=bos_token_id,
	eos_token_id=eos_token_id,
	tie_word_embeddings=tie_word_embeddings,
	**kwargs,
	)


	# Copied from transformers.models.bart.modeling_bart._make_causal_mask
	def _make_causal_mask(
	input_ids_shape: Tuple[int, int], dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
	) -> torch.Tensor:
	"""
	Make causal mask used for bi-directional self-attention.
	"""
	bsz, tgt_len = input_ids_shape
	mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
	mask_cond = torch.arange(mask.size(-1), device=device)
	mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
	mask = mask.to(dtype)

	if past_key_values_length > 0:
	mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
	return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)


	# Copied from transformers.models.bart.modeling_bart._expand_mask
	def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None) -> torch.Tensor:
	"""
	Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
	"""
	bsz, src_len = mask.size()
	tgt_len = tgt_len if tgt_len is not None else src_len

	expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)

	inverted_mask = 1.0 - expanded_mask

	return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min) # type: ignore


	class RotaryEmbedding(torch.nn.Module):
	def __init__(
	self, dim: int, max_position_embeddings: int = 2048, base: int = 10000, device: Optional[torch.device] = None
	) -> 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, persistent=False)

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

	def _set_cos_sin_cache(self, seq_len: int, device: Any, dtype: Any) -> None:
	self.max_seq_len_cached = seq_len
	t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) # type: ignore

	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.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
	self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)

	def forward(self, x: torch.Tensor, seq_len: int) -> Tuple[torch.Tensor, torch.Tensor]:
	# 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, dtype=x.dtype)

	return (
	self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype), # type: ignore
	self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype), # type: ignore
	)


	def _rotate_half(x: torch.Tensor) -> torch.Tensor:
	"""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 _rotary_pos_emb(x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor, position_ids: torch.Tensor) -> torch.Tensor:
	# The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
	cos = cos.squeeze(1).squeeze(0) # [seq_len, dim]
	sin = sin.squeeze(1).squeeze(0) # [seq_len, dim]
	cos = cos[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
	sin = sin[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
	x_embed = (x * cos) + (_rotate_half(x) * sin)
	return x_embed


	class RMSNorm(nn.Module):
	def __init__(self, hidden_size: int, eps: float = 1e-6) -> None:
	super().__init__()
	self.weight = nn.Parameter(torch.ones(hidden_size))
	self.variance_epsilon = eps

	def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
	input_dtype = hidden_states.dtype
	hidden_states = hidden_states.to(torch.float32)
	variance = hidden_states.pow(2).mean(-1, keepdim=True)
	hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
	return self.weight * hidden_states.to(input_dtype)


	class Attention(torch.nn.Module):
	def __init__(self, config: PlamoConfig) -> None:
	super().__init__()
	self.config = config
	self.hidden_size = config.hidden_size
	head_dim = self.hidden_size // config.num_attention_heads
	self.max_position_embeddings = config.max_position_embeddings

	self.q_num_heads = config.num_attention_heads
	self.qk_dim = self.v_dim = head_dim
	self.k_num_heads = self.v_num_heads = int(np.ceil(self.q_num_heads / config.n_shared_head))

	self.q_proj = nn.Linear(self.hidden_size, self.q_num_heads * self.qk_dim, bias=False)
	self.k_proj = nn.Linear(self.hidden_size, self.k_num_heads * self.qk_dim, bias=False)
	self.v_proj = nn.Linear(self.hidden_size, self.v_num_heads * self.v_dim, bias=False)
	self.o_proj = nn.Linear(self.q_num_heads * self.v_dim, self.hidden_size, bias=False)
	self.rotary_emb = RotaryEmbedding(self.qk_dim, max_position_embeddings=self.max_position_embeddings)

	def forward(
	self,
	hidden_states: torch.Tensor,
	attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.Tensor] = None,
	past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
	output_attentions: bool = False,
	use_cache: bool = False,
	) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor, torch.Tensor]]]:
	bsz, q_len, _ = hidden_states.size()

	query_states = self.q_proj(hidden_states).view(bsz, q_len, self.q_num_heads, self.qk_dim).transpose(1, 2)
	key_states = self.k_proj(hidden_states).view(bsz, q_len, self.k_num_heads, self.qk_dim).transpose(1, 2)
	value_states = self.v_proj(hidden_states).view(bsz, q_len, self.v_num_heads, self.v_dim).transpose(1, 2)

	def _expand_kv(t: torch.Tensor, repeat: int, target: int) -> torch.Tensor:
	return t.repeat(1, repeat, 1, 1)[:, :target]

	# expand shared kv
	assert self.k_num_heads == self.v_num_heads
	key_states = _expand_kv(key_states, self.config.n_shared_head, self.q_num_heads)
	value_states = _expand_kv(value_states, self.config.n_shared_head, self.q_num_heads)

	kv_seq_len = key_states.shape[-2]
	if past_key_value is not None:
	kv_seq_len += past_key_value[0].shape[-2]
	cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
	assert position_ids is not None
	query_states = _rotary_pos_emb(query_states, cos, sin, position_ids)
	key_states = _rotary_pos_emb(key_states, cos, sin, position_ids)
	# [bsz, nh, t, hd]

	if past_key_value is not None:
	# reuse k, v, self_attention
	key_states = torch.cat([past_key_value[0], key_states], dim=2)
	value_states = torch.cat([past_key_value[1], value_states], dim=2)

	past_key_value = (key_states, value_states) if use_cache else None

	attn_output = F.scaled_dot_product_attention(query_states, key_states, value_states, attn_mask=attention_mask)
	attn_output = attn_output.transpose(1, 2)

	attn_output = attn_output.reshape(bsz, q_len, self.q_num_heads * self.v_dim)
	attn_output = self.o_proj(attn_output)

	if not output_attentions:
	attn_weights = None

	return attn_output, attn_weights, past_key_value


	class MLP(nn.Module):
	def __init__(self, config: PlamoConfig) -> None:
	super().__init__()
	self.config = config
	self.hidden_size = config.hidden_size
	self.intermediate_size = config.intermediate_size
	self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
	self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
	self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
	self.act_fn = torch.nn.functional.silu

	def forward(self, x: torch.Tensor) -> torch.Tensor:
	return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) # type: ignore


	class PlamoDecoderLayer(torch.nn.Module):
	def __init__(self, config: PlamoConfig) -> None:
	super().__init__()
	self.config = config
	self.hidden_size = config.hidden_size
	self.self_attn = Attention(config)
	self.mlp = MLP(config)
	self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)

	def forward(
	self,
	hidden_states: torch.Tensor,
	attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	past_key_value: Optional[Tuple[torch.Tensor]] = None,
	output_attentions: Optional[bool] = False,
	use_cache: Optional[bool] = False,
	) -> Tuple[Any, ...]:
	# from LlamaDecoder
	residual = hidden_states

	hidden_states = self.norm(hidden_states)

	# Self Attention
	hidden_states_sa, self_attn_weights, present_key_value = self.self_attn(
	hidden_states=hidden_states,
	attention_mask=attention_mask,
	position_ids=position_ids,
	past_key_value=past_key_value,
	output_attentions=output_attentions,
	use_cache=use_cache,
	)

	# Fully Connected
	hidden_states_mlp = self.mlp(hidden_states)

	# Residual
	hidden_states = residual + hidden_states_sa + hidden_states_mlp

	outputs: Any = (hidden_states,)

	if output_attentions:
	outputs += (self_attn_weights,)

	if use_cache:
	outputs += (present_key_value,)

	return outputs # type: ignore


	class PlamoDecoder(torch.nn.Module):
	def __init__(self, config: PlamoConfig) -> None:
	super().__init__()
	self.layers = torch.nn.ModuleList([PlamoDecoderLayer(config) for _ in range(config.num_hidden_layers)])

	def forward(self, x: DecoderInput) -> DecoderOutput:
	all_hidden_states: Optional[Tuple[torch.Tensor, ...]] = () if x.output_hidden_states else None
	all_self_attns: Optional[Tuple[torch.Tensor, ...]] = () if x.output_attentions else None
	next_decoder_cache: Optional[Tuple[torch.Tensor, ...]] = () if x.use_cache else None
	hidden_states = x.hidden_states

	for idx, decoder_layer in enumerate(self.layers):
	if x.output_hidden_states:
	assert all_hidden_states is not None
	all_hidden_states += (hidden_states,)

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

	if self.training and x.gradient_checkpointing:

	def create_custom_forward(module): # type: ignore
	def custom_forward(*inputs): # type: ignore
	# None for past_key_value
	return module(*inputs, x.output_attentions, None)

	return custom_forward

	layer_outputs = torch.utils.checkpoint.checkpoint(
	create_custom_forward(decoder_layer), # type: ignore
	hidden_states,
	x.attention_mask,
	x.position_ids,
	None,
	)
	else:
	layer_outputs = decoder_layer(
	hidden_states,
	attention_mask=x.attention_mask,
	position_ids=x.position_ids,
	past_key_value=past_key_value,
	output_attentions=x.output_attentions,
	use_cache=x.use_cache,
	)

	hidden_states = layer_outputs[0]

	if x.use_cache:
	cache = layer_outputs[2 if x.output_attentions else 1]
	assert cache is not None
	assert next_decoder_cache is not None
	next_decoder_cache += (cache,)

	if x.output_attentions:
	assert layer_outputs[1] is not None
	assert all_self_attns is not None
	all_self_attns += (layer_outputs[1],)
	return DecoderOutput(hidden_states, all_hidden_states, all_self_attns, next_decoder_cache)


	class PlamoPreTrainedModel(PreTrainedModel): # type: ignore
	config_class = PlamoConfig
	_no_split_modules: List[str]
	base_model_prefix = "model"
	supports_gradient_checkpointing = True
	_no_split_modules = ["PlamoDecoderLayer"]
	_skip_keys_device_placement = "past_key_values"
	_keys_to_ignore_on_load_unexpected = [r"decoder\.version"]

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

	def _set_gradient_checkpointing(self, module: torch.nn.Module, value: bool = False) -> None:
	module.gradient_checkpointing = value # type: ignore


	class PlamoModel(PlamoPreTrainedModel):
	def __init__(self, config: PlamoConfig):
	super().__init__(config)
	self.padding_idx = config.pad_token_id
	self.vocab_size = config.vocab_size

	self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
	self.layers = PlamoDecoder(config) # type: ignore
	self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)

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

	def get_input_embeddings(self) -> torch.nn.Embedding:
	return self.embed_tokens

	def set_input_embeddings(self, value: torch.nn.Embedding) -> None:
	self.embed_tokens = value

	# Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
	def _prepare_decoder_attention_mask(
	self,
	attention_mask: torch.Tensor,
	input_shape: Tuple[int, int],
	inputs_embeds: Optional[torch.FloatTensor],
	past_key_values_length: int,
	) -> Optional[torch.Tensor]:
	# create causal mask
	# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
	combined_attention_mask: Optional[torch.Tensor] = None
	if input_shape[-1] > 1:
	assert inputs_embeds is not None
	combined_attention_mask = _make_causal_mask(
	input_shape,
	inputs_embeds.dtype,
	device=inputs_embeds.device,
	past_key_values_length=past_key_values_length,
	)

	if attention_mask is not None:
	# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
	assert inputs_embeds is not None
	expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
	inputs_embeds.device
	)
	combined_attention_mask = (
	expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
	)

	return combined_attention_mask

	def forward(
	self,
	input_ids: Optional[torch.LongTensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.Tensor] = None,
	past_key_values: Optional[List[torch.FloatTensor]] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	use_cache: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, BaseModelOutputWithPast]:
	assert input_ids is not None
	output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
	output_hidden_states = (
	output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
	)
	use_cache = use_cache if use_cache is not None else self.config.use_cache

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

	# retrieve input_ids and inputs_embeds
	if input_ids is not None and inputs_embeds is not None:
	raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
	elif input_ids is not None:
	batch_size, seq_length = input_ids.shape
	else:
	raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")

	seq_length_with_past = seq_length
	past_key_values_length = 0

	if past_key_values is not None:
	past_key_values_length = past_key_values[0][0].shape[2]
	seq_length_with_past = seq_length_with_past + past_key_values_length

	if position_ids is None:
	device = input_ids.device
	position_ids = torch.arange(
	past_key_values_length, seq_length + past_key_values_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()

	if inputs_embeds is None:
	inputs_embeds = self.embed_tokens(input_ids)
	# embed positions
	if attention_mask is None:
	attention_mask = torch.ones(
	(batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
	)
	attention_mask = self._prepare_decoder_attention_mask(
	attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
	)

	hidden_states = inputs_embeds

	if self.gradient_checkpointing and self.training:
	if use_cache:
	use_cache = False

	# decoder layers
	out = self.layers(
	DecoderInput(
	hidden_states,
	position_ids,
	attention_mask,
	past_key_values,
	output_hidden_states,
	output_attentions,
	use_cache,
	self.gradient_checkpointing,
	)
	)
	assert isinstance(out, DecoderOutput)
	hidden_states = out.hidden_states
	all_hidden_states = out.all_hidden_states
	all_self_attns = out.all_self_attns
	next_decoder_cache = out.next_decoder_cache

	hidden_states = self.norm(hidden_states)

	# add hidden states from the last decoder layer
	if output_hidden_states:
	assert all_hidden_states is not None
	all_hidden_states += (hidden_states,)

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


	class PlamoForCausalLM(PlamoPreTrainedModel):
	def __init__(self, config: PretrainedConfig) -> None:
	super().__init__(config)
	self.model = PlamoModel(config)

	self.lm_head: torch.nn.Module = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

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

	def get_input_embeddings(self) -> torch.nn.Embedding:
	return self.model.embed_tokens

	def set_input_embeddings(self, value: torch.nn.Embedding) -> None:
	self.model.embed_tokens = value

	def get_output_embeddings(self) -> torch.nn.Module:
	return self.lm_head

	def set_output_embeddings(self, new_embeddings: torch.nn.Module) -> None:
	self.lm_head = new_embeddings

	def set_decoder(self, decoder: PlamoModel) -> None:
	self.model = decoder

	def get_decoder(self) -> PlamoModel:
	return self.model

	def forward( # type: ignore
	self,
	input_ids: Optional[torch.LongTensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.Tensor] = None,
	past_key_values: Optional[List[torch.FloatTensor]] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	labels: Optional[torch.LongTensor] = None,
	use_cache: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, CausalLMOutputWithPast]:
	r"""
	Args:
	labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional):
	Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
	config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
	(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.

	Returns:

	Example:

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

	>>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
	>>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)

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

	>>> # Generate
	>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
	>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
	"Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you."
	```"""
	assert input_ids is not None

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

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

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

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

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

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

	def prepare_inputs_for_generation(
	self,
	input_ids: torch.Tensor,
	past_key_values: Optional[List[torch.FloatTensor]] = None,
	attention_mask: Optional[torch.Tensor] = None,
	inputs_embeds: Optional[torch.Tensor] = None,
	**kwargs: Any,
	) -> Dict[str, Any]:
	if past_key_values:
	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 `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: Dict[str, Any] = {"inputs_embeds": inputs_embeds}
	else:
	model_inputs = {"input_ids": input_ids}

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

	@staticmethod
	def _reorder_cache(past_key_values: List[torch.FloatTensor], beam_idx: int) -> Tuple[Any, ...]:
	reordered_past: Tuple[Any, ...] = ()
	for layer_past in past_key_values:
	reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
	return reordered_past