OpenELM-3B / modeling_openelm.py

add OpenELM-3B

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	#
	# For licensing see accompanying LICENSE file.
	# Copyright (C) 2024 Apple Inc. All Rights Reserved.
	#

	from typing import List, Optional, Tuple, Union

	import torch
	import torch.utils.checkpoint
	from torch import Tensor, nn
	from torch.nn import CrossEntropyLoss
	from torch.nn import functional as F
	from transformers import PreTrainedModel
	from transformers.activations import ACT2FN
	from transformers.cache_utils import Cache, DynamicCache, StaticCache
	from transformers.modeling_outputs import (
	BaseModelOutputWithPast,
	CausalLMOutputWithPast,
	)
	from transformers.utils import logging

	logger = logging.get_logger(__name__)

	# this import has to be relative, otherwise, when setting trust_remote_code=True
	# huggingface transformers won't be able to load the module correctly
	from .configuration_openelm import OpenELMConfig, make_divisible


	class OpenELMRMSNorm(nn.Module):
	def __init__(self, num_features: int, eps: float = 1e-6):
	"""
	Initialize the OpenELMRMSNorm normalization layer.

	Args:
	dim (int): The dimension of the input tensor.
	eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6.

	Attributes:
	eps (float): A small value added to the denominator for numerical stability.
	weight (nn.Parameter): Learnable scaling parameter.

	"""
	super().__init__()
	self.eps = eps
	self.weight = nn.Parameter(torch.ones(num_features))
	self.num_features = num_features

	def _norm(self, x: Tensor) -> Tensor:
	"""
	Apply the OpenELMRMSNorm normalization to the input tensor.

	Args:
	x (torch.Tensor): The input tensor.

	Returns:
	torch.Tensor: The normalized tensor.

	"""
	return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)

	def forward(self, x: Tensor) -> Tensor:
	"""
	Forward pass through the OpenELMRMSNorm layer.

	Args:
	x (torch.Tensor): The input tensor.

	Returns:
	torch.Tensor: The output tensor after applying OpenELMRMSNorm.

	"""
	output = self._norm(x.float()).type_as(x)
	return output * self.weight

	def extra_repr(self) -> str:
	return (
	super().extra_repr() + f"num_features={self.num_features}, eps={self.eps}"
	)


	class OpenELMPreTrainedModel(PreTrainedModel):
	config_class = OpenELMConfig
	base_model_prefix = "transformer"
	supports_gradient_checkpointing = True
	_no_split_modules = ["OpenELMDecoderLayer"]
	_skip_keys_device_placement = "past_key_values"

	def __init__(self, inputs, *kwargs) -> None:
	super().__init__(inputs, *kwargs)

	def _init_weights(self, module: nn.Module) -> None:
	"""Initialize the weights."""
	if isinstance(module, nn.Linear):
	# Slightly different from the TF version which uses truncated_normal for initialization
	# cf https://github.com/pytorch/pytorch/pull/5617
	module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
	if module.bias is not None:
	module.bias.data.zero_()
	elif isinstance(module, nn.Embedding):
	module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
	if module.padding_idx is not None:
	module.weight.data[module.padding_idx].zero_()
	elif isinstance(module, OpenELMRMSNorm):
	module.weight.data.fill_(1.0)


	def _rotate_half(x: Tensor) -> Tensor:
	x1, x2 = x.chunk(2, dim=-1)
	return torch.cat((-x2, x1), dim=-1)


	def _apply_rotary_pos_emb(x: Tensor, pos_sin: Tensor, pos_cos: Tensor) -> Tensor:
	return (x * pos_cos) + (_rotate_half(x) * pos_sin)


	class OpenELMRotaryEmbedding(torch.nn.Module):
	"""
	The rotary position embeddings (aka RoPE) from `RoFormer <https://arxiv.org/abs/2104.09864>`_.

	RoPE encodes the position information of tokens using a rotation matrix, and is able to capture
	explicit relative positional dependencies.

	Args:
	model_dim: The dimensionality of the model's hidden state.
	max_seq_length: Maximum sequence length.
	freq_constant: A constant used for computing frequencies.
	"""

	def __init__(
	self, model_dim: int, max_seq_length: int, freq_constant: int = 10000
	) -> None:
	inv_freq = 1.0 / (
	freq_constant
	** (torch.arange(0, model_dim, 2, dtype=torch.float32) / model_dim)
	)
	super().__init__()

	self.model_dim = model_dim
	self.freq_constant = freq_constant
	self.max_seq_length = max_seq_length

	self.register_buffer("inv_freq", inv_freq, persistent=False)
	self._cached_cos = None
	self._cached_sin = None
	self._cached_seq_length = max_seq_length
	self._compute_sin_cos_embeddings(max_seq_length)

	def extra_repr(self) -> str:
	return f"\tmodel_dim={self.model_dim}, max_seq_length={self.max_seq_length}, freq_constant={self.freq_constant}"

	def _compute_sin_cos_embeddings(
	self,
	key_len: int,
	key_device: torch.device = torch.device("cpu"),
	key_dtype: torch.dtype = torch.float32,
	) -> None:
	"""
	Compute sine and cos embeddings.

	Args:
	key_len: Number of tokens in the key embeddings in the transformer model.
	device: Device where the key embeddings are stored.
	key_dtype: Data type of the key embeddings.

	Returns:
	None

	...note:
	We recalculate the sine and cosine embeddings if any of the following conditions are met:
	1. The number of tokens in key embeddings are greater than the cached sequence length.
	2. Sine and cosine caches are empty.
	3. The device and data type of sine and cosine embeddings does not match with the key embeddings.
	"""
	if (
	key_len > self._cached_seq_length
	or self._cached_cos is None
	or (self._cached_cos is not None and self._cached_cos.device != key_device)
	or (self._cached_cos is not None and self._cached_cos.dtype != key_dtype)
	or self._cached_sin is None
	or (self._cached_sin is not None and self._cached_sin.device != key_device)
	or (self._cached_sin is not None and self._cached_sin.dtype != key_dtype)
	):
	self._cached_seq_length = max(key_len, self._cached_seq_length)

	# The shape of 'pos_index' is [number of key tokens]
	pos_index = torch.arange(
	self._cached_seq_length,
	dtype=torch.float32,
	device=self.inv_freq.device,
	)
	# The shape of 'pos_index_theta' is [number of key tokens, model dimension]
	pos_index_theta = torch.einsum("i,j->ij", pos_index, self.inv_freq)
	# The shape of 'emb' is [number of key tokens, model dimension]
	emb = torch.cat((pos_index_theta, pos_index_theta), dim=-1)

	# the shape of cos and sin embeddings is [number of key tokens, model_dim]
	cos_emb = emb.cos().to(dtype=key_dtype, device=key_device)
	sin_emb = emb.sin().to(dtype=key_dtype, device=key_device)

	# the shape of cached cos and sin embeddings is [1, 1, number of key tokens, model_dim]
	self._cached_cos = cos_emb[None, None, :, :]
	self._cached_sin = sin_emb[None, None, :, :]

	def forward(
	self,
	query: torch.Tensor,
	key: torch.Tensor,
	) -> Tuple[torch.Tensor, torch.Tensor]:
	"""
	The forward function of RoPE embeddings.

	Args:
	query: Query embeddings in the transformer model. The shape of query embeddings is
	[Batch, number of query heads, number of query tokens, model dimension].
	key: Key embeddings in the transformer model. The shape of key embeddings is
	[Batch, number of key heads, number of key tokens, model dimension].

	Returns:
	A tuple containing the query and key embeddings with positional information. The shape of the returned query
	and key embeddings is the same as the input query and key embeddings respectively.

	...note:
	The RoPE embedding computation is done in full-precision. After the computation, input query and key tensors
	are casted to original input datatype.
	"""
	dim = key.shape[-1]
	key_len = key.shape[2]
	query_len = query.shape[2]

	assert dim == self.model_dim
	assert key.device == query.device
	assert key.dtype == query.dtype

	# In the context of self-attention, the lengths of keys and queries are equal.
	# However, in generation tasks, such as predicting the next token in a sequence, the lengths of keys and queries
	# can differ. For instance, when employing key-value (KV) caching for sequence prediction, the keys
	# represent embeddings of previous tokens and the current token, while the query corresponds
	# to the embedding of the current token only.
	assert (
	key_len >= query_len
	), "Number of keys has to be greater than or equal to number of queries."

	query_float = query.float()
	key_float = key.float()

	self._compute_sin_cos_embeddings(
	key_len, key_device=key_float.device, key_dtype=key_float.dtype
	)
	query_float = _apply_rotary_pos_emb(
	x=query_float,
	pos_sin=self._cached_sin[..., key_len - query_len : key_len, :],
	pos_cos=self._cached_cos[..., key_len - query_len : key_len, :],
	)
	key_float = _apply_rotary_pos_emb(
	x=key_float,
	pos_sin=self._cached_sin[..., :key_len, :],
	pos_cos=self._cached_cos[..., :key_len, :],
	)

	return query_float.type_as(query), key_float.type_as(key)


	class OpenELMMultiHeadCausalAttention(nn.Module):
	def __init__(self, config: OpenELMConfig, layer_idx: int) -> None:
	super().__init__()
	self.layer_idx = layer_idx
	head_dim = config.head_dim
	q_heads = config.num_query_heads[layer_idx]
	k_heads = config.num_kv_heads[layer_idx]
	v_heads = config.num_kv_heads[layer_idx]

	self.qkv_proj = nn.Linear(
	in_features=config.model_dim,
	out_features=(q_heads + k_heads + v_heads) * head_dim,
	bias=False,
	)

	self.pos_embedding = OpenELMRotaryEmbedding(
	model_dim=config.head_dim,
	max_seq_length=config.rope_max_length,
	freq_constant=config.rope_freq_constant,
	)

	if config.normalize_qk_projections:
	self.q_norm = OpenELMRMSNorm(
	num_features=config.head_dim,
	)
	self.k_norm = OpenELMRMSNorm(
	num_features=config.head_dim,
	)
	else:
	self.q_norm = None
	self.k_norm = None

	self.out_proj = nn.Linear(
	in_features=q_heads * head_dim,
	out_features=config.model_dim,
	bias=False,
	)

	self.head_dim = config.head_dim
	self.num_q_heads = q_heads
	self.num_k_heads = k_heads
	self.num_v_heads = v_heads
	self.transformer_dim = config.model_dim
	self.num_groups = self.num_q_heads // self.num_k_heads

	def extra_repr(self) -> str:
	return (
	super().extra_repr()
	+ f"query_heads={self.num_q_heads}, key_heads={self.num_k_heads}, value_heads={self.num_v_heads}"
	)

	def forward(
	self,
	hidden_states: torch.Tensor,
	attention_mask: Optional[torch.Tensor] = None,
	past_key_value: Optional[Cache] = None,
	output_attentions: bool = False,
	use_cache: bool = False,
	cache_position: Optional[torch.LongTensor] = None,
	) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
	"""
	Forward pass of multi-head self-attention.

	Args:
	hidden_states: Input tensor of the shape [batch size, sequence length, model dimension].
	past_key_value: Tensor storing the cached keys and values.
	output_attentions: output attention weights.
	use_cache: Specifies whether to use kv-cache for generation.
	cache_position: used for updating the kv-cache.

	Returns:
	The output of the same shape as the input, optionally with a tensor containing cached keys and values.
	"""

	# scaled_dot_product_attention does not return attention weights, set output_attentions to False
	output_attentions = False
	batch_size, seq_length, d_model = hidden_states.size()

	# [B, S, d] --> [B, S, (q_h + k_h + v_h) * h]
	qkv = self.qkv_proj(hidden_states)
	# [B, S, (q_h + k_h + v_h) * h] --> [B, S, (q_h + k_h + v_h), h]
	qkv = qkv.reshape(
	batch_size,
	seq_length,
	self.num_q_heads + self.num_k_heads + self.num_v_heads,
	self.head_dim,
	)
	# [B, S, (q_h + k_h + v_h), h] --> [B, (q_h + k_h + v_h), S, h]
	qkv = qkv.transpose(1, 2)
	# [B, (q_h + k_h + v_h), S, h] --> [B, q_h, S h], [B, k_h, S, h], [B, v_h, S, h]
	queries, keys, values = qkv.split(
	[self.num_q_heads, self.num_k_heads, self.num_v_heads], dim=1
	)

	if self.q_norm is not None:
	queries = self.q_norm(queries)

	if self.k_norm is not None:
	keys = self.k_norm(keys)

	past_key_value = getattr(self, "past_key_value", past_key_value)

	if past_key_value is not None:
	# sin and cos are specific to RoPE models; position_ids needed for the static cache
	# cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
	cache_kwargs = {"cache_position": cache_position}
	keys, values = past_key_value.update(
	keys, values, self.layer_idx, cache_kwargs
	)

	# Add positional embedding
	queries, keys = self.pos_embedding(queries, keys)

	if self.num_groups != 1:
	# GQA
	# [B, k_h, S, h] --> [B, q_h, S, h]
	keys = keys.repeat_interleave(self.num_groups, dim=1)
	# [B, v_h, S, h] --> [B, q_h, S, h]
	values = values.repeat_interleave(self.num_groups, dim=1)

	causal_mask = attention_mask
	if attention_mask is not None and cache_position is not None:
	causal_mask = causal_mask[:, :, cache_position, : keys.shape[-2]]

	attn_output = F.scaled_dot_product_attention(
	queries,
	keys,
	values,
	attn_mask=causal_mask,
	dropout_p=0,
	)

	attn_output = attn_output.transpose(1, 2).contiguous()
	attn_output = attn_output.reshape(
	batch_size, seq_length, self.num_q_heads * self.head_dim
	)
	attn_output = self.out_proj(attn_output)
	if not output_attentions:
	attn_weights = None
	return attn_output, attn_weights, past_key_value


	class OpenELMFeedForwardNetwork(nn.Module):
	def __init__(self, config: OpenELMConfig, layer_idx: int) -> None:
	super().__init__()
	ffn_multiplier = config.ffn_multipliers[layer_idx]
	intermediate_dim = int(
	make_divisible(
	ffn_multiplier * config.model_dim,
	divisor=config.ffn_dim_divisor,
	)
	)
	if config.ffn_with_glu:
	# FFN with Gated linear unit, as described in https://arxiv.org/abs/2002.05202v1.
	self.proj_1 = nn.Linear(
	in_features=config.model_dim,
	out_features=2 * intermediate_dim,
	bias=False,
	)
	self.proj_2 = nn.Linear(
	in_features=intermediate_dim,
	out_features=config.model_dim,
	bias=False,
	)
	self.ffn_with_glu = True
	else:
	# Standard FFN, as described in https://arxiv.org/abs/1706.03762
	self.proj_1 = nn.Linear(
	in_features=config.model_dim,
	out_features=intermediate_dim,
	bias=False,
	)
	self.proj_2 = nn.Linear(
	in_features=intermediate_dim,
	out_features=config.model_dim,
	bias=False,
	)
	self.ffn_with_glu = False

	self.act = ACT2FN[config.activation_fn_name]

	def extra_repr(self) -> str:
	return super().extra_repr() + f"(ffn_with_glu) : {self.ffn_with_glu}"

	def forward(self, x: Tensor) -> Tensor:
	"""Forward function of FFN layer.

	Args:
	x: Input tensor of the shape [batch size, sequence length, model dimension].

	Returns:
	A tensor of the same shape as the input.
	"""
	if self.ffn_with_glu:
	y_12 = self.proj_1(x)
	y_1, y_2 = y_12.chunk(2, dim=-1)
	y = self.act(y_1) * y_2
	return self.proj_2(y)
	else:
	return self.proj_2(self.act(self.proj_1(x)))


	class OpenELMDecoderLayer(nn.Module):
	def __init__(self, config: OpenELMConfig, layer_idx: int) -> None:
	super().__init__()
	self.attn = OpenELMMultiHeadCausalAttention(config=config, layer_idx=layer_idx)
	self.ffn = OpenELMFeedForwardNetwork(config=config, layer_idx=layer_idx)
	self.ffn_norm = OpenELMRMSNorm(
	num_features=config.model_dim,
	)
	self.attn_norm = OpenELMRMSNorm(
	num_features=config.model_dim,
	)

	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,
	cache_position: Optional[torch.LongTensor] = None,
	**kwargs,
	) -> Tuple[
	torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
	]:
	"""
	Args:
	hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
	attention_mask (`torch.FloatTensor`, optional):
	attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
	query_sequence_length, key_sequence_length)` if default attention is used.
	output_attentions (`bool`, optional):
	Whether or not to return the attentions tensors of all attention layers. See `attentions` under
	returned tensors for more detail.
	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`).
	past_key_value (`Tuple(torch.FloatTensor)`, optional): cached past key and value projection states
	"""
	residual = hidden_states
	hidden_states = self.attn_norm(hidden_states)

	# Self Attention
	hidden_states, self_attn_weights, present_key_value = self.attn(
	hidden_states=hidden_states,
	attention_mask=attention_mask,
	past_key_value=past_key_value,
	output_attentions=output_attentions,
	use_cache=use_cache,
	cache_position=cache_position,
	**kwargs,
	)
	hidden_states = residual + hidden_states

	# Fully Connected
	residual = hidden_states
	hidden_states = self.ffn_norm(hidden_states)
	hidden_states = self.ffn(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 OpenELMModel(OpenELMPreTrainedModel):
	config_class = OpenELMConfig

	def __init__(self, config: OpenELMConfig):
	super().__init__(config)
	self.config = config

	self.token_embeddings = nn.Embedding(
	embedding_dim=config.model_dim,
	num_embeddings=config.vocab_size,
	)

	self.layers = nn.ModuleList(
	OpenELMDecoderLayer(config=config, layer_idx=layer_idx)
	for layer_idx in range(config.num_transformer_layers)
	)
	self.norm = OpenELMRMSNorm(num_features=config.model_dim)
	if config.share_input_output_layers:
	self.classifier = None
	else:
	self.classifier = nn.Linear(
	in_features=config.model_dim,
	out_features=config.vocab_size,
	bias=False,
	)
	self.num_transformer_layers = config.num_transformer_layers
	self.gradient_checkpointing = False

	# Register a causal mask to separate causal and padding mask creation. Merging happens in the attention class.
	# NOTE: This is not friendly with TorchScript, ONNX, ExportedProgram serialization for very large `max_context_length`.
	causal_mask = torch.full(
	(config.max_context_length, config.max_context_length),
	fill_value=True,
	dtype=torch.bool,
	)
	self.register_buffer(
	"causal_mask", torch.triu(causal_mask, diagonal=1), persistent=False
	)

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

	def get_input_embeddings(self):
	return self.token_embeddings

	def set_input_embeddings(self, new_embeddings: torch.Tensor):
	self.token_embeddings = new_embeddings

	def reset_parameters(self, config: OpenELMConfig) -> None:
	"""Initialize the layers in Language Model

	The initialization scheme is followed, following `OPT <https://arxiv.org/pdf/2205.01068.pdf>`_.

	Args:
	use_megatron_std: Use standard deviation as described in Megatron-LM.

	Returns:
	None
	"""
	for module in self.modules():
	if isinstance(module, nn.Linear):
	std = module.in_features**-0.5
	torch.nn.init.normal_(module.weight, mean=0.0, std=std)
	if module.bias is not None:
	torch.nn.init.zeros_(module.bias)
	elif isinstance(module, nn.Embedding):
	std = module.embedding_dim**-0.5
	torch.nn.init.normal_(module.weight, mean=0.0, std=std)
	elif isinstance(module, OpenELMRMSNorm):
	if module.weight is not None:
	torch.nn.init.ones_(module.weight)
	if hasattr(module, "bias") and module.bias is not None:
	torch.nn.init.zeros_(module.bias)

	model_dim = config.model_dim
	n_layers = config.num_transformer_layers
	std = (model_dim*-0.5) ((2 * n_layers) ** -0.5)
	for param_name, param in self.named_parameters():
	if param_name.endswith("out_proj.weight") or param_name.endswith(
	"ffn.proj_2.weight"
	):
	torch.nn.init.normal_(param, mean=0.0, std=std)

	def forward(
	self,
	input_ids: torch.LongTensor = None,
	attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.LongTensor] = 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,
	cache_position: Optional[torch.LongTensor] = 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 None) ^ (inputs_embeds is not None):
	raise ValueError(
	"You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
	)

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

	if inputs_embeds is None:
	inputs_embeds = self.token_embeddings(input_ids)

	past_seen_tokens = 0
	if use_cache: # kept for BC (cache positions)
	if not isinstance(past_key_values, StaticCache):
	past_key_values = DynamicCache.from_legacy_cache(past_key_values)
	past_seen_tokens = past_key_values.get_seq_length()

	if cache_position is None:
	cache_position = torch.arange(
	past_seen_tokens,
	past_seen_tokens + inputs_embeds.shape[1],
	device=inputs_embeds.device,
	)

	if position_ids is None:
	position_ids = cache_position.unsqueeze(0)

	causal_mask = self._update_causal_mask(attention_mask, inputs_embeds)

	# embed positions
	hidden_states = inputs_embeds

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

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

	if self.gradient_checkpointing and self.training:
	layer_outputs = self._gradient_checkpointing_func(
	decoder_layer.__call__,
	hidden_states,
	causal_mask,
	position_ids,
	past_key_values,
	output_attentions,
	use_cache,
	cache_position,
	)
	else:
	layer_outputs = decoder_layer(
	hidden_states,
	attention_mask=causal_mask,
	position_ids=position_ids,
	past_key_value=past_key_values,
	output_attentions=output_attentions,
	use_cache=use_cache,
	cache_position=cache_position,
	)

	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 = None
	if use_cache:
	next_cache = (
	next_decoder_cache.to_legacy_cache()
	if isinstance(next_decoder_cache, Cache)
	else next_decoder_cache
	)
	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,
	)

	def _update_causal_mask(self, attention_mask, input_tensor):
	if self.config._attn_implementation == "flash_attention_2":
	if attention_mask is not None and 0.0 in attention_mask:
	return attention_mask
	return None

	batch_size, seq_length = input_tensor.shape[:2]
	dtype = input_tensor.dtype
	device = input_tensor.device

	# support going beyond cached `max_position_embedding`
	if seq_length > self.causal_mask.shape[-1]:
	causal_mask = torch.full(
	(2 * self.causal_mask.shape[-1], 2 * self.causal_mask.shape[-1]),
	fill_value=1,
	)
	self.register_buffer(
	"causal_mask", torch.triu(causal_mask, diagonal=1), persistent=False
	)

	# We use the current dtype to avoid any overflows
	min_dtype = torch.finfo(dtype).min
	causal_mask = (
	self.causal_mask[None, None, :, :].repeat(batch_size, 1, 1, 1).to(dtype)
	* min_dtype
	)

	causal_mask = causal_mask.to(dtype=dtype, device=device)
	if attention_mask is not None and attention_mask.dim() == 2:
	mask_length = attention_mask.shape[-1]
	padding_mask = causal_mask[..., :mask_length].eq(0.0) * attention_mask[
	:, None, None, :
	].eq(0.0)
	causal_mask[..., :mask_length] = causal_mask[..., :mask_length].masked_fill(
	padding_mask, min_dtype
	)

	if self.config._attn_implementation == "sdpa" and attention_mask is not None:
	# For dynamo, rather use a check on fullgraph=True once this is possible (https://github.com/pytorch/pytorch/pull/120400).
	is_tracing = (
	torch.jit.is_tracing()
	or isinstance(input_tensor, torch.fx.Proxy)
	or (hasattr(torch, "_dynamo") and torch._dynamo.is_compiling())
	)
	if not is_tracing and torch.any(attention_mask != 1):
	# Attend to all tokens in masked rows from the causal_mask, for example the relevant first rows when
	# using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
	# Details: https://github.com/pytorch/pytorch/issues/110213
	causal_mask = causal_mask.mul(
	~torch.all(causal_mask == min_dtype, dim=-1, keepdim=True)
	).to(dtype)

	return causal_mask


	class OpenELMForCausalLM(OpenELMPreTrainedModel):
	_tied_weights_keys = ["lm_head.weight"]

	def __init__(self, config: OpenELMConfig):
	super().__init__(config)
	self.transformer = OpenELMModel(config)
	self.vocab_size = config.vocab_size
	if config.share_input_output_layers:
	self.lm_head = None
	else:
	self.lm_head = nn.Linear(config.model_dim, config.vocab_size, bias=False)

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

	def get_input_embeddings(self):
	return self.transformer.token_embeddings

	def set_input_embeddings(self, value):
	self.transformer.token_embeddings = value

	def get_output_embeddings(self):
	return self.lm_head

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

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

	def get_decoder(self):
	return self.transformer

	def forward(
	self,
	input_ids: torch.LongTensor = None,
	attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.LongTensor] = 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,
	cache_position: Optional[torch.LongTensor] = 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.transformer(
	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,
	cache_position=cache_position,
	)

	hidden_states = outputs[0]
	if self.lm_head is None:
	# shared
	logits = F.linear(
	hidden_states, weight=self.transformer.token_embeddings.weight
	)
	else:
	logits = self.lm_head(hidden_states)
	logits = logits[:, : self.config.vocab_size]
	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=None,
	attention_mask=None,
	inputs_embeds=None,
	**kwargs,
	):
	past_length = 0
	if past_key_values is not None:
	if isinstance(past_key_values, Cache):
	cache_length = past_key_values.get_seq_length()
	past_length = past_key_values.seen_tokens
	max_cache_length = past_key_values.get_max_length()
	else:
	cache_length = past_length = past_key_values[0][0].shape[2]
	max_cache_length = None

	# Keep only the unprocessed tokens:
	# 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
	# some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
	# input)
	if (
	attention_mask is not None
	and attention_mask.shape[1] > input_ids.shape[1]
	):
	input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
	# 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
	# input_ids based on the past_length.
	elif past_length < input_ids.shape[1]:
	input_ids = input_ids[:, past_length:]
	# 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.

	# If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
	if (
	max_cache_length is not None
	and attention_mask is not None
	and cache_length + input_ids.shape[1] > max_cache_length
	):
	attention_mask = attention_mask[:, -max_cache_length:]

	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[:, -input_ids.shape[1] :]

	if self.generation_config.cache_implementation == "static":
	# generation with static cache
	cache_position = kwargs.get("cache_position", None)
	if cache_position is None:
	past_length = 0
	else:
	past_length = cache_position[-1] + 1
	input_ids = input_ids[:, past_length:]
	position_ids = position_ids[:, past_length:]

	# we should only keep a `cache_position` in generate, and do +=1.
	# same goes for position ids. Could also help with continued generation.
	cache_position = torch.arange(
	past_length,
	past_length + position_ids.shape[-1],
	device=position_ids.device,
	)

	# 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:
	# The `contiguous()` here is necessary to have a static stride during decoding. torchdynamo otherwise
	# recompiles graphs as the stride of the inputs is a guard. Ref: https://github.com/huggingface/transformers/pull/29114
	# We could use `next_tokens` directly instead.
	model_inputs = {"input_ids": input_ids.contiguous()}

	model_inputs.update(
	{
	"position_ids": position_ids.contiguous(),
	"cache_position": cache_position,
	"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, 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