slim-sa-ner / modeling_stablelm_epoch.py

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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 Epoch 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_epoch import StableLMEpochConfig


	logger = logging.get_logger(__name__)


	# Copied from transformers.models.bart.modeling_bart._make_causal_mask
	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)


	# Copied from transformers.models.bart.modeling_bart._expand_mask
	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 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=torch.float32)

	# Don't do einsum, it converts fp32 to fp16 under AMP
	# freqs = torch.einsum("i,j->ij", t, self.inv_freq)
	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 MLP(nn.Module):
	def __init__(self, config: StableLMEpochConfig):
	super().__init__()
	self.config = config
	self.hidden_size = config.hidden_size
	self.intermediate_size = config.intermediate_size
	self.gate_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
	self.up_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
	self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
	self.act_fn = nn.SiLU()

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


	def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
	"""
	This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
	num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
	"""
	batch, num_key_value_heads, slen, head_dim = hidden_states.shape
	if n_rep == 1:
	return hidden_states
	hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
	return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)


	class Attention(nn.Module):
	def __init__(self, config: StableLMEpochConfig):
	super().__init__()
	self.config = config
	self.hidden_size = config.hidden_size
	self.num_heads = config.num_attention_heads
	self.head_dim = self.hidden_size // self.num_heads
	self.num_key_value_heads = config.num_key_value_heads
	self.num_key_value_groups = self.num_heads // self.num_key_value_heads
	self.max_position_embeddings = config.max_position_embeddings

	if (self.head_dim * self.num_heads) != self.hidden_size:
	raise ValueError(
	f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
	f" and `num_heads`: {self.num_heads})."
	)
	self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
	self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
	self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
	self.o_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.rope_pct)
	self.rotary_emb = RotaryEmbedding(
	self.rotary_ndims,
	max_position_embeddings=self.config.max_position_embeddings,
	base=self.config.rope_theta,
	)

	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]]]:
	bsz, q_len, _ = hidden_states.size()

	query_states = self.q_proj(hidden_states)
	key_states = self.k_proj(hidden_states)
	value_states = self.v_proj(hidden_states)

	query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
	key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
	value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)

	query_rot = query_states[..., : self.rotary_ndims]
	query_pass = query_states[..., self.rotary_ndims :]
	key_rot = key_states[..., : self.rotary_ndims]
	key_pass = key_states[..., self.rotary_ndims :]

	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)
	query_states, key_states = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, position_ids)

	# [batch_size, num_heads, seq_len, head_dim]
	query_states = torch.cat((query_states, query_pass), dim=-1)
	key_states = torch.cat((key_states, key_pass), dim=-1)

	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

	# Repeat k/v heads if n_kv_heads < n_heads
	key_states = repeat_kv(key_states, self.num_key_value_groups)
	value_states = repeat_kv(value_states, self.num_key_value_groups)

	attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)

	if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
	raise ValueError(
	f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
	f" {attn_weights.size()}"
	)

	if attention_mask is not None:
	if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
	raise ValueError(
	f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
	)
	attn_weights = attn_weights + attention_mask

	# Upcast attention to fp32
	attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
	attn_output = torch.matmul(attn_weights, value_states)

	if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
	raise ValueError(
	f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
	f" {attn_output.size()}"
	)

	# Merge heads
	attn_output = attn_output.transpose(1, 2).contiguous()
	attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)

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

	if not output_attentions:
	attn_weights = None

	return attn_output, attn_weights, past_key_value


	class DecoderLayer(nn.Module):
	def __init__(self, config: StableLMEpochConfig):
	super().__init__()
	self.self_attn = Attention(config)
	self.mlp = MLP(config)
	self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps)
	self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps)

	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

	hidden_states = self.input_layernorm(hidden_states)

	# Self Attention
	hidden_states, 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,
	)
	hidden_states = residual + hidden_states

	# Fully Connected
	residual = hidden_states
	hidden_states = self.post_attention_layernorm(hidden_states)
	hidden_states = self.mlp(hidden_states)
	hidden_states = residual + hidden_states

	outputs = (hidden_states,)

	if output_attentions:
	outputs += (self_attn_weights,)

	if use_cache:
	outputs += (present_key_value,)

	return outputs


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

	config_class = StableLMEpochConfig
	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, StableLMEpochModel):
	module.gradient_checkpointing = value


	class StableLMEpochModel(StableLMEpochPreTrainedModel):
	def __init__(self, config: StableLMEpochConfig):
	super().__init__(config)
	self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, config.pad_token_id)
	self.layers = nn.ModuleList([DecoderLayer(config) for _ in range(config.num_hidden_layers)])
	self.norm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps)

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

	def get_input_embeddings(self):
	return self.embed_tokens

	def set_input_embeddings(self, value: nn.Module):
	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: 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,
	past_key_values: Optional[Tuple[Tuple[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

	# 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
	elif inputs_embeds is not None:
	batch_size, seq_length, _ = inputs_embeds.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 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_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:
	logger.warning(
	"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
	)
	use_cache = False

	# Decoder layers
	all_hidden_states = () if output_hidden_states else None
	all_self_attns = () if output_attentions else None
	next_decoder_cache = () if use_cache else None

	for idx, decoder_layer in enumerate(self.layers):
	if output_hidden_states:
	all_hidden_states += (hidden_states,)

	past_key_value = (
	past_key_values[idx] if past_key_values is not None else None
	)

	if self.gradient_checkpointing and self.training:

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

	return custom_forward

	layer_outputs = torch.utils.checkpoint.checkpoint(
	create_custom_forward(decoder_layer),
	hidden_states,
	attention_mask,
	position_ids,
	)
	else:
	layer_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 = layer_outputs[0]

	if use_cache:
	next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)

	if output_attentions:
	all_self_attns += (layer_outputs[1],)

	hidden_states = self.norm(hidden_states)

	# Add hidden states from the last decoder layer
	if output_hidden_states:
	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 StableLMEpochForCausalLM(StableLMEpochPreTrainedModel):
	_tied_weights_keys = ["lm_head.weight"]

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

	self.model = StableLMEpochModel(config)
	self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

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

	def get_input_embeddings(self):
	return self.model.embed_tokens

	def set_input_embeddings(self, value):
	self.model.embed_tokens = value

	def get_output_embeddings(self):
	return self.lm_head

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

	def get_decoder(self):
	return self.transformer

	def set_decoder(self, decoder):
	self.transformer = decoder

	def forward(
	self,
	input_ids: Optional[torch.LongTensor] = None,
	attention_mask: Optional[torch.FloatTensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	past_key_values: Optional[Tuple[Tuple[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]:
	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,
	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).float()

	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 = 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,
	past_key_values: Optional[torch.Tensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	inputs_embeds: Optional[torch.Tensor] = None,
	**kwargs,
	):
	# Trim 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,
	"use_cache": kwargs.get("use_cache"),
	"position_ids": position_ids,
	}
	)
	return model_inputs

	@staticmethod
	def _reorder_cache(past_key_values, beam_idx):
	reordered_past = ()
	for layer_past in past_key_values:
	reordered_past += (
	tuple(
	past_state.index_select(0, beam_idx.to(past_state.device))
	for past_state in layer_past
	),
	)
	return reordered_past


	StableLMEpochConfig.register_for_auto_class()
	StableLMEpochForCausalLM.register_for_auto_class("AutoModelForCausalLM")