stablelm-base-alpha-3b-v2 / modeling_stablelm_alpha.py

refactor: clean nn package access

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	# coding=utf-8
	# Copyright 2023 Stability AI, EleutherAI, and The HuggingFace Inc. team. All rights reserved.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	#
	# This code is based off the following work:
	# https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py
	# https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox/modeling_gpt_neox.py
	""" PyTorch StableLM-Alpha model. """
	from typing import Optional, Tuple, Union
	import math

	import torch
	import torch.utils.checkpoint
	from torch import nn
	from torch.nn import CrossEntropyLoss
	from transformers.modeling_outputs import (
	BaseModelOutputWithPast,
	CausalLMOutputWithPast,
	)
	from transformers.modeling_utils import PreTrainedModel
	from transformers.utils import logging

	from .configuration_stablelm_alpha import StableLMAlphaConfig


	logger = logging.get_logger(__name__)


	def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
	"""Expands attention_mask from `[batch_size, seq_len]` to `[batch_size, 1, tgt_seq_len, src_seq_len]`."""
	batch_size, src_len = mask.size()
	tgt_len = tgt_len if tgt_len is not None else src_len

	expanded_mask = mask[:, None, None, :].expand(batch_size, 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)


	class LayerNorm(nn.LayerNorm):
	def __init__(self, normalized_shape: torch.Size, bias: bool = True, **kwargs):
	r"""
	bias (`bool`, default = True): whether to use the bias term.
	"""
	super().__init__(normalized_shape, **kwargs)
	if not bias:
	self.bias = None


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

	self.norm = LayerNorm(config.hidden_size, eps=config.norm_eps)
	self.attention = Attention(config)
	self.mlp = MLP(config)

	def forward(
	self,
	hidden_states: Optional[torch.FloatTensor],
	attention_mask: Optional[torch.FloatTensor] = 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,
	) -> Union[Tuple[torch.Tensor], Optional[Tuple[torch.Tensor, Tuple[torch.FloatTensor, ...]]]]:
	residual = hidden_states

	# Pre-Norm
	hidden_states = self.norm(hidden_states)

	# Self-Attention
	attn_output, attn_weights, present_key_value = self.attention(
	hidden_states=hidden_states,
	attention_mask=attention_mask,
	position_ids=position_ids,
	past_key_value=past_key_value,
	use_cache=use_cache,
	output_attentions=output_attentions,
	)

	# Feed-forward
	mlp_output = self.mlp(hidden_states)

	hidden_states = residual + attn_output + mlp_output

	outputs = (hidden_states,)
	if output_attentions:
	outputs += (attn_weights,)
	if use_cache:
	outputs += (present_key_value,)
	return outputs # hidden_states, (optional: attn_weights), (optional: present_key_value)


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

	hidden_size = config.hidden_size
	multiple_of = 256
	ff_dim = int(8 * hidden_size / 3)
	intermediate_size = multiple_of * ((ff_dim + multiple_of - 1) // multiple_of)

	self.gate_proj = nn.Linear(hidden_size, 2 * intermediate_size, bias=False)
	self.out_proj = nn.Linear(intermediate_size, hidden_size, bias=False)
	self.act = nn.SiLU()

	def forward(self, x: torch.Tensor) -> torch.Tensor:
	ff, ff_gate = self.gate_proj(x).chunk(2, dim=-1)
	return self.out_proj(ff * self.act(ff_gate))


	class RotaryEmbedding(nn.Module):
	def __init__(
	self,
	dim: int,
	max_position_embeddings: int,
	base: int = 10_000,
	device: Optional[torch.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, device=device, dtype=torch.float32) / 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: torch.device, dtype: torch.dtype):
	self.max_seq_len_cached = seq_len
	t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
	freqs = torch.outer(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: Optional[int] = None):
	# x: [batch_size, num_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=torch.get_default_dtype())
	return (
	self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
	self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
	)


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


	def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
	# 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) # [batch_size, 1, seq_len, dim]
	sin = sin[position_ids].unsqueeze(1) # [batch_size, 1, seq_len, dim]
	q_embed = (q * cos) + (rotate_half(q) * sin)
	k_embed = (k * cos) + (rotate_half(k) * sin)
	return q_embed, k_embed


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

	self.config = config
	self.hidden_size = config.hidden_size
	self.num_heads = config.num_heads
	self.head_dim = self.hidden_size // self.num_heads
	self.max_position_embeddings = config.max_position_embeddings
	if self.hidden_size % self.num_heads != 0:
	raise ValueError(
	"`hidden_size` is not divisble by the number of attention heads! Make sure to update them"
	)

	self.qkv_proj = nn.Linear(self.hidden_size, 3 * self.hidden_size, bias=False)
	self.out_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
	self._init_rope()

	def _init_rope(self):
	self.rotary_ndims = int(self.head_dim * self.config.rotary_pct)
	self.rotary_emb = RotaryEmbedding(
	self.rotary_ndims,
	max_position_embeddings=self.config.max_position_embeddings,
	base=self.config.rotary_emb_base,
	)

	def forward(
	self,
	hidden_states: torch.FloatTensor,
	attention_mask: torch.FloatTensor,
	position_ids: torch.LongTensor,
	past_key_value: Optional[Tuple[torch.Tensor]] = None,
	output_attentions: Optional[bool] = False,
	use_cache: Optional[bool] = False,
	) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
	has_past_key_value = past_key_value is not None

	# Compute QKV
	# [batch_size, seq_len, (num_heads * 3 * head_dim)]
	qkv = self.qkv_proj(hidden_states)

	# [batch_size, seq_len, num_heads, 3 * head_dim]
	new_qkv_shape = qkv.size()[:-1] + (self.num_heads, 3 * self.head_dim)
	qkv = qkv.view(*new_qkv_shape)

	# 3 * [batch_size, num_heads, seq_len, head_dim]
	query = qkv[..., : self.head_dim].permute(0, 2, 1, 3)
	key = qkv[..., self.head_dim:(2 * self.head_dim)].permute(0, 2, 1, 3)
	value = qkv[..., (2 * self.head_dim):].permute(0, 2, 1, 3)

	# Compute rotary embeddings on rotary_ndims
	# [batch_size, num_heads, seq_len, 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)
	kv_seq_len = key.shape[-2]
	if has_past_key_value:
	kv_seq_len += past_key_value[0].shape[-2]

	# Add rotary embeddings to query and key
	cos, sin = self.rotary_emb(value, seq_len=kv_seq_len)
	query, key = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, position_ids)

	# Concatenate rotary embeddings with pass-through query and key
	# [batch_size, num_heads, seq_len, head_dim]
	query = torch.cat((query, query_pass), dim=-1)
	key = torch.cat((key, key_pass), dim=-1)

	# Reuse past key-value states
	if has_past_key_value:
	key = torch.cat((past_key_value[0], key), dim=2)
	value = torch.cat((past_key_value[1], value), dim=2)
	present_key_value = (key, value) if use_cache else None

	# [batch_size, num_heads, seq_len, head_dim]
	query = query.transpose(1, 2).contiguous()
	key = key.transpose(1, 2).contiguous()
	value = value.transpose(1, 2).contiguous()

	# Compute attention
	softmax_scale = 1 / math.sqrt(self.head_dim)
	attn_scores = torch.einsum('bthd,bshd->bhts', query, key * softmax_scale)
	# Apply the attention mask
	if attention_mask is not None:
	attn_scores = attn_scores + attention_mask
	attn_weights = nn.functional.softmax(attn_scores, dim=-1, dtype=torch.float32).to(query.dtype)
	attn_output = torch.einsum('bhts,bshd->bthd', attn_weights, value)

	# Merge heads
	attn_output = attn_output.reshape(attn_output.shape[0], attn_output.shape[1], -1)

	# Final linear projection
	attn_output = self.out_proj(attn_output)

	if not output_attentions:
	attn_weights = None

	return attn_output, attn_weights, present_key_value


	def attention_mask_func(attention_scores: torch.Tensor, ltor_mask: torch.Tensor):
	attention_scores.masked_fill_(~ltor_mask, torch.finfo(attention_scores.dtype).min)
	return attention_scores


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

	config_class = StableLMAlphaConfig
	base_model_prefix = "transformer"
	supports_gradient_checkpointing = True
	_no_split_modules = ["DecoderLayer"]
	_skip_keys_device_placement = "past_key_values"

	def _init_weights(self, module: nn.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: nn.Module, value=False):
	if isinstance(module, StableLMAlphaModel):
	module.gradient_checkpointing = value


	def _make_causal_mask(
	input_ids_shape: torch.Size,
	dtype: torch.dtype,
	device: torch.device,
	past_key_values_length: int = 0
	):
	"""Make causal mask used for bi-directional self-attention."""
	batch_size, tgt_len = input_ids_shape
	mask = torch.full((tgt_len, tgt_len), torch.finfo(torch.float16).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(batch_size, 1, tgt_len, tgt_len + past_key_values_length)


	class StableLMAlphaModel(StableLMAlphaPreTrainedModel):
	def __init__(self, config: StableLMAlphaConfig):
	super().__init__(config)
	self.config = config

	self.embed = nn.Embedding(config.vocab_size, config.hidden_size)
	self.layers = nn.ModuleList([DecoderLayer(config) for _ in range(config.num_hidden_layers)])
	self.final_norm = LayerNorm(config.hidden_size, eps=config.norm_eps)

	self.gradient_checkpointing = False
	self.post_init()

	def get_input_embeddings(self):
	return self.embed

	def set_input_embeddings(self, value: nn.Module):
	self.embed = 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: torch.Size,
	inputs_embeds: torch.Tensor,
	past_key_values_length: int,
	):
	# Create causal mask
	# [batch_size, seq_len] -> [batch_size, 1, tgt_seq_len, src_seq_len]
	combined_attention_mask = None
	if input_shape[-1] > 1:
	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:
	# [batch_size, seq_len] -> [batch_size, 1, tgt_seq_len, src_seq_len]
	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.FloatTensor] = None,
	position_ids: Optional[torch.LongTensor] = 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_key_values_length = 0
	past_key_values = tuple([None] * self.config.num_hidden_layers)
	seq_length_with_past = seq_length
	else:
	past_key_values_length = past_key_values[0][0].shape[2]
	seq_length_with_past = seq_length + past_key_values_length

	if position_ids is None:
	device = input_ids.device if input_ids is not None else inputs_embeds.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(input_ids)

	# Attention mask.
	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:
	logger.warning(
	"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
	)
	use_cache = False

	all_hidden_states = () if output_hidden_states else None
	all_attentions = () if output_attentions else None
	present_key_values = () if use_cache else None

	for _, (decoder_layer, past_key_value) 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 `use_cache`
	return module(*inputs, output_attentions, None)

	return custom_forward

	outputs = torch.utils.checkpoint.checkpoint(
	create_custom_forward(decoder_layer),
	hidden_states,
	attention_mask,
	position_ids,
	# `None` for `past_key_value`
	None,
	)
	else:
	outputs = decoder_layer(
	hidden_states,
	attention_mask=attention_mask,
	position_ids=position_ids,
	past_key_value=past_key_value,
	output_attentions=output_attentions,
	use_cache=use_cache,
	)

	hidden_states = outputs[0]

	if output_attentions:
	all_attentions = all_attentions + (outputs[1],)

	if use_cache:
	present_key_values += (outputs[2 if output_attentions else 1],)

	hidden_states = self.final_norm(hidden_states)

	# Add last hidden state
	if output_hidden_states:
	all_hidden_states += (hidden_states,)

	present_key_values = present_key_values if use_cache else None
	if not return_dict:
	return tuple(v for v in [hidden_states, present_key_values, all_hidden_states, all_attentions] if v is not None)

	return BaseModelOutputWithPast(
	last_hidden_state=hidden_states,
	past_key_values=present_key_values,
	hidden_states=all_hidden_states,
	attentions=all_attentions,
	)


	class StableLMAlphaForCausalLM(StableLMAlphaPreTrainedModel):
	_tied_weights_keys = ["lm_head.weight"]

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

	self.transformer = StableLMAlphaModel(config)
	self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

	self.post_init()

	def get_output_embeddings(self):
	return self.lm_head

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

	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,
	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, CausalLMOutputWithPast]:
	r"""
	Example:

	```python
	>>> from transformers import AutoTokenizer, StableLMAlphaForCausalLM, StableLMAlphaConfig
	>>> import torch

	>>> tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-base-alpha-3b-v2", trust_remote_code=True)
	>>> config = StableLMAlphaConfig.from_pretrained("stabilityai/stablelm-base-alpha-3b-v2")
	>>> config.is_decoder = True
	>>> model = StableLMAlphaForCausalLM.from_pretrained("stabilityai/stablelm-base-alpha-3b-v2", config=config)

	>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
	>>> outputs = model(**inputs)

	>>> logits = outputs.logits
	```
	"""
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	outputs = self.transformer(
	input_ids,
	attention_mask=attention_mask,
	position_ids=position_ids,
	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.lm_head(hidden_states)

	lm_loss = None
	if labels is not None:
	# move labels to correct device to enable model parallelism
	labels = labels.to(logits.device)
	# we are doing next-token prediction; shift prediction scores and input ids by one
	shift_logits = 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 = (logits,) + outputs[1:]
	return ((lm_loss,) + output) if lm_loss is not None else output

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

	def prepare_inputs_for_generation(
	self,
	input_ids,
	past_key_values: Optional[torch.Tensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	inputs_embeds: Optional[torch.Tensor] = None,
	**kwargs
	):
	# 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 `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: torch.Tensor, beam_idx: int):
	reordered_past = ()
	for past_key_value in past_key_values:
	reordered_past += (
	tuple(past_state.index_select(0, beam_idx) for past_state in past_key_value[:2]) + past_key_value[2:],
	)
	return reordered_past


	StableLMAlphaConfig.register_for_auto_class()
	StableLMAlphaForCausalLM.register_for_auto_class("AutoModelForCausalLM")