grok-1 / modeling_grok1.py

update config & modeling

11f282f 2 months ago

No virus

36.2 kB

	from typing import List, Optional, Tuple, Union

	import torch
	import torch.nn as nn
	import torch.nn.functional as F
	from transformers.modeling_utils import PreTrainedModel
	from transformers.utils import logging

	try:
	from transformers.modeling_attn_mask_utils import \
	_prepare_4d_causal_attention_mask

	HAS_MASK_UTILS = True
	except ImportError:
	HAS_MASK_UTILS = False

	from .configuration_grok1 import Grok1Config
	from .modeling_grok1_outputs import (MoeCausalLMOutputWithPast,
	MoeModelOutputWithPast)

	logger = logging.get_logger(__name__)


	# copied from https://github.com/huggingface/transformers/blob/v4.36.1/src/transformers/models/mixtral/modeling_mixtral.py
	def load_balancing_loss_func(
	gate_logits: torch.Tensor, num_experts: torch.Tensor = None, top_k=2
	) -> float:
	r"""
	Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch.

	See Switch Transformer (https://arxiv.org/abs/2101.03961) for more details. This function implements the loss
	function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between
	experts is too unbalanced.

	Args:
	gate_logits (Union[`torch.Tensor`, Tuple[torch.Tensor]):
	Logits from the `gate`, should be a tuple of tensors. Shape: [batch_size, seqeunce_length, num_experts].
	num_experts (`int`, optional):
	Number of experts

	Returns:
	The auxiliary loss.
	"""
	if gate_logits is None:
	return 0

	if isinstance(gate_logits, tuple):
	# cat along the layers?
	compute_device = gate_logits[0].device
	gate_logits = torch.cat(
	[gate.to(compute_device) for gate in gate_logits], dim=0
	)

	routing_weights, selected_experts = torch.topk(gate_logits, top_k, dim=-1)
	routing_weights = routing_weights.softmax(dim=-1)

	# cast the expert indices to int64, otherwise one-hot encoding will fail
	if selected_experts.dtype != torch.int64:
	selected_experts = selected_experts.to(torch.int64)

	if len(selected_experts.shape) == 2:
	selected_experts = selected_experts.unsqueeze(2)

	expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts)

	# For a given token, determine if it was routed to a given expert.
	expert_mask = torch.max(expert_mask, axis=-2).values

	# cast to float32 otherwise mean will fail
	expert_mask = expert_mask.to(torch.float32)
	tokens_per_group_and_expert = torch.mean(expert_mask, axis=-2)

	router_prob_per_group_and_expert = torch.mean(routing_weights, axis=-1)
	return torch.mean(
	tokens_per_group_and_expert * router_prob_per_group_and_expert.unsqueeze(-1)
	) * (num_experts**2)


	# Copied from transformers.models.llama.modeling_llama.repeat_kv
	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 RMSNorm(nn.Module):
	def __init__(
	self,
	hidden_size: int,
	eps: float = 1e-5,
	create_scale: bool = True,
	) -> None:
	super().__init__()
	self.variance_epsilon = eps
	if create_scale:
	self.scale = nn.Parameter(torch.zeros(hidden_size))
	else:
	self.scale = 1.0

	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)
	hidden_states = self.scale * hidden_states
	return hidden_states.to(input_dtype)


	class RotaryEmbedding(nn.Module):
	def __init__(
	self, dim: int, max_position_embeddings: int = 2048, base: int = 10000
	) -> None:
	super().__init__()
	assert dim % 2 == 0
	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() / self.dim)
	)
	self.register_buffer("inv_freq", inv_freq, persistent=False)

	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, device, 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().to(dtype), persistent=False)
	self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)

	def forward(self, x, seq_len=None):
	# 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),
	self.sin_cached[:seq_len].to(dtype=x.dtype),
	)


	# Copied from transformers.models.llama.modeling_llama.rotate_half
	def rotate_half(x):
	"""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)


	# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
	def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
	"""Applies Rotary Position Embedding to the query and key tensors.

	Args:
	q (`torch.Tensor`): The query tensor.
	k (`torch.Tensor`): The key tensor.
	cos (`torch.Tensor`): The cosine part of the rotary embedding.
	sin (`torch.Tensor`): The sine part of the rotary embedding.
	position_ids (`torch.Tensor`):
	The position indices of the tokens corresponding to the query and key tensors. For example, this can be
	used to pass offsetted position ids when working with a KV-cache.
	unsqueeze_dim (`int`, optional, defaults to 1):
	The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
	sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
	that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
	k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
	cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
	the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
	Returns:
	`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
	"""
	cos = cos[position_ids].unsqueeze(unsqueeze_dim)
	sin = sin[position_ids].unsqueeze(unsqueeze_dim)
	q_embed = (q * cos) + (rotate_half(q) * sin)
	k_embed = (k * cos) + (rotate_half(k) * sin)
	return q_embed, k_embed


	class MultiHeadAttention(nn.Module):
	def __init__(
	self,
	hidden_size: int,
	num_heads: int,
	num_key_value_heads: Optional[int] = None,
	max_position_embeddings: int = 2048,
	attn_output_multiplier: float = 1.0,
	max_attn_val: float = 30.0,
	):
	super().__init__()
	self.hidden_size = hidden_size
	self.num_heads = num_heads
	self.head_dim = hidden_size // num_heads
	if num_key_value_heads is None:
	num_key_value_heads = num_heads
	self.num_key_value_heads = num_key_value_heads
	self.num_key_value_groups = self.num_heads // self.num_key_value_heads
	self.attn_output_multiplier = attn_output_multiplier
	self.max_attn_val = max_attn_val

	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(hidden_size, self.num_heads * self.head_dim, bias=False)
	self.k_proj = nn.Linear(
	hidden_size, self.num_key_value_heads * self.head_dim, bias=False
	)
	self.v_proj = nn.Linear(
	hidden_size, self.num_key_value_heads * self.head_dim, bias=False
	)
	self.o_proj = nn.Linear(self.num_heads * self.head_dim, hidden_size, bias=False)

	self.rotary_emb = RotaryEmbedding(
	self.head_dim,
	max_position_embeddings=max_position_embeddings,
	)

	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: bool = False,
	use_cache: bool = False,
	**kwargs,
	) -> 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)

	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_states, key_states, cos, sin, position_ids
	)

	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)).to(
	torch.float
	)
	attn_weights = attn_weights * self.attn_output_multiplier
	attn_weights = self.max_attn_val * F.tanh(attn_weights / self.max_attn_val)

	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

	attn_weights = F.softmax(attn_weights, dim=-1).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()}"
	)

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

	attn_output = self.o_proj(attn_output)

	if not output_attentions:
	attn_weights = None

	return attn_output, attn_weights, past_key_value


	class MoeMLP(nn.Module):
	def __init__(
	self,
	hidden_dim: int,
	ffn_dim: int,
	) -> None:
	super().__init__()
	self.linear_v = nn.Linear(hidden_dim, ffn_dim, bias=False)
	self.linear_1 = nn.Linear(ffn_dim, hidden_dim, bias=False)
	self.linear = nn.Linear(hidden_dim, ffn_dim, bias=False)
	self.act_fn = nn.GELU()

	def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
	current_hidden_states = self.act_fn(self.linear(hidden_states)) * self.linear_v(
	hidden_states
	)
	current_hidden_states = self.linear_1(current_hidden_states)
	return current_hidden_states


	class MoeBlock(nn.Module):
	def __init__(
	self,
	hidden_dim: int,
	ffn_dim: int,
	num_experts: int,
	top_k: int,
	) -> None:
	super().__init__()
	self.num_experts = num_experts
	self.top_k = top_k
	self.gate = nn.Linear(hidden_dim, num_experts, bias=False)
	self.experts = nn.ModuleList(
	[MoeMLP(hidden_dim, ffn_dim) for _ in range(num_experts)]
	)

	def forward(self, hidden_states: torch.Tensor) -> Tuple[torch.Tensor]:
	batch_size, sequence_length, hidden_dim = hidden_states.shape
	hidden_states = hidden_states.view(-1, hidden_dim)
	# router_logits: (batch * sequence_length, n_experts)
	router_logits = self.gate(hidden_states)

	routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
	routing_weights, selected_experts = torch.topk(
	routing_weights, self.top_k, dim=-1
	)
	# we cast back to the input dtype
	routing_weights = routing_weights.to(hidden_states.dtype)

	final_hidden_states = torch.zeros(
	(batch_size * sequence_length, hidden_dim),
	dtype=hidden_states.dtype,
	device=hidden_states.device,
	)
	# One hot encode the selected experts to create an expert mask
	# this will be used to easily index which expert is going to be sollicitated
	expert_mask = torch.nn.functional.one_hot(
	selected_experts, num_classes=self.num_experts
	).permute(2, 1, 0)

	# Loop over all available experts in the model and perform the computation on each expert
	for expert_idx in range(self.num_experts):
	expert_layer = self.experts[expert_idx]
	idx, top_x = torch.where(expert_mask[expert_idx])

	if top_x.shape[0] == 0:
	continue

	# in torch it is faster to index using lists than torch tensors
	top_x_list = top_x.tolist()
	idx_list = idx.tolist()

	# Index the correct hidden states and compute the expert hidden state for
	# the current expert. We need to make sure to multiply the output hidden
	# states by `routing_weights` on the corresponding tokens (top-1 and top-2)
	current_state = hidden_states[None, top_x_list].reshape(-1, hidden_dim)
	current_hidden_states = (
	expert_layer(current_state)
	* routing_weights[top_x_list, idx_list, None]
	)

	# However `index_add_` only support torch tensors for indexing so we'll use
	# the `top_x` tensor here.
	final_hidden_states.index_add_(
	0, top_x, current_hidden_states.to(hidden_states.dtype)
	)
	final_hidden_states = final_hidden_states.reshape(
	batch_size, sequence_length, hidden_dim
	)
	return final_hidden_states, router_logits


	class DecoderLayer(nn.Module):
	def __init__(
	self,
	hidden_size: int,
	intermediate_size: int,
	num_heads: int,
	num_key_value_heads: int,
	num_experts: int,
	top_k: int,
	max_position_embeddings: int = 2048,
	attn_output_multiplier: float = 1.0,
	max_attn_val: float = 30.0,
	rms_norm_eps: float = 1e-5,
	) -> None:
	super().__init__()
	self.attn = MultiHeadAttention(
	hidden_size,
	num_heads,
	num_key_value_heads,
	max_position_embeddings=max_position_embeddings,
	attn_output_multiplier=attn_output_multiplier,
	max_attn_val=max_attn_val,
	)
	self.moe_block = MoeBlock(hidden_size, intermediate_size, num_experts, top_k)
	self.pre_attn_norm = RMSNorm(hidden_size, eps=rms_norm_eps)
	self.post_attn_norm = RMSNorm(hidden_size, eps=rms_norm_eps)
	self.pre_moe_norm = RMSNorm(hidden_size, eps=rms_norm_eps)
	self.post_moe_norm = RMSNorm(hidden_size, eps=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,
	output_router_logits: Optional[bool] = False,
	use_cache: Optional[bool] = False,
	**kwargs,
	) -> Tuple[
	torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
	]:
	residual = hidden_states
	hidden_states = self.pre_attn_norm(hidden_states)
	hidden_states, attention_weights, present_key_value = self.attn(
	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 = self.post_attn_norm(hidden_states)
	hidden_states = residual + hidden_states

	residual = hidden_states
	hidden_states = self.pre_moe_norm(hidden_states)
	hidden_states, router_logits = self.moe_block(hidden_states)
	hidden_states = self.post_moe_norm(hidden_states)
	hidden_states = residual + hidden_states

	outputs = (hidden_states,)
	if output_attentions:
	outputs += (attention_weights,)
	if use_cache:
	outputs += (present_key_value,)
	if output_router_logits:
	outputs += (router_logits,)
	return outputs


	class Grok1PretrainedModel(PreTrainedModel):
	config_class = Grok1Config
	base_model_prefix = "model"
	supports_gradient_checkpointing = True
	_no_split_modules = ["DecoderLayer"]
	_skip_keys_device_placement = "past_key_values"
	_supports_flash_attn_2 = False
	_supports_cache_class = False

	def _init_weights(self, module) -> None:
	if isinstance(module, nn.Linear):
	module.weight.data.zero_()
	if module.bias is not None:
	module.bias.data.zero_()
	elif isinstance(module, nn.Embedding):
	module.weight.data.zero_()


	# 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.
	"""
	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):
	"""
	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
	)


	class Grok1Model(Grok1PretrainedModel):
	def __init__(self, config: Grok1Config, **kwargs) -> None:
	super().__init__(config)
	self.padding_idx = config.pad_token_id
	self.vocab_size = config.vocab_size
	self.embedding_multiplier_scale = config.embedding_multiplier_scale

	self.embed_tokens = nn.Embedding(
	config.vocab_size, config.hidden_size, self.padding_idx
	)
	self.layers = nn.ModuleList(
	[
	DecoderLayer(
	hidden_size=config.hidden_size,
	intermediate_size=config.intermediate_size,
	num_heads=config.num_attention_heads,
	num_key_value_heads=config.num_key_value_heads,
	num_experts=config.num_experts,
	top_k=config.num_experts_per_tok,
	max_position_embeddings=config.max_position_embeddings,
	attn_output_multiplier=config.attn_output_multiplier,
	max_attn_val=config.max_attn_value,
	rms_norm_eps=config.rms_norm_eps,
	)
	for layer_idx in range(config.num_hidden_layers)
	]
	)
	self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
	self.gradient_checkpointing = False
	self.post_init()

	def get_input_embeddings(self):
	return self.embed_tokens

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

	# Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
	def _prepare_decoder_attention_mask(
	self, attention_mask, input_shape, inputs_embeds, past_key_values_length
	):
	# create causal mask
	# [bsz, seq_len] -> [bsz, 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:
	# [bsz, seq_len] -> [bsz, 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: 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,
	output_router_logits: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, MoeModelOutputWithPast]:
	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 input_ids and inputs_embeds at the same time"
	)
	elif input_ids is not None:
	batch_size, seq_length = input_ids.shape[:2]
	elif inputs_embeds is not None:
	batch_size, seq_length = inputs_embeds.shape[:2]
	else:
	raise ValueError("You have to specify either input_ids or 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)

	if inputs_embeds is None:
	inputs_embeds = self.embed_tokens(input_ids)
	inputs_embeds = inputs_embeds * self.embedding_multiplier_scale

	if HAS_MASK_UTILS:
	# 4d mask is passed through the layers
	attention_mask = _prepare_4d_causal_attention_mask(
	attention_mask,
	(batch_size, seq_length),
	inputs_embeds,
	past_key_values_length,
	)
	else:
	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,
	)

	# embed positions
	hidden_states = inputs_embeds

	if self.gradient_checkpointing and self.training:
	if use_cache:
	logger.warning_once(
	"`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
	all_router_logits = () if output_router_logits 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],)

	if output_router_logits:
	all_router_logits += (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,
	all_router_logits,
	]
	if v is not None
	)
	return MoeModelOutputWithPast(
	last_hidden_state=hidden_states,
	past_key_values=next_cache,
	hidden_states=all_hidden_states,
	attentions=all_self_attns,
	router_logits=all_router_logits,
	)


	class Grok1ModelForCausalLM(Grok1PretrainedModel):
	_tied_weights_keys = ["lm_head.weight"]

	def __init__(self, config: Grok1Config, **kwargs):
	super().__init__(config)
	self.model = Grok1Model(config)
	self.vocab_size = config.vocab_size
	self.output_multiplier_scale = config.output_multiplier_scale
	self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
	self.router_aux_loss_coef = config.router_aux_loss_coef
	self.num_experts = config.num_experts
	self.num_experts_per_tok = config.num_experts_per_tok
	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):
	self.lm_head = new_embeddings

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

	def get_decoder(self):
	return self.model

	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,
	output_router_logits: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, MoeCausalLMOutputWithPast]:
	output_attentions = (
	output_attentions
	if output_attentions is not None
	else self.config.output_attentions
	)
	output_router_logits = (
	output_router_logits
	if output_router_logits is not None
	else self.config.output_router_logits
	)

	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,
	output_router_logits=output_router_logits,
	return_dict=return_dict,
	)

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

	aux_loss = None
	if output_router_logits:
	aux_loss = load_balancing_loss_func(
	outputs.router_logits if return_dict else outputs[-1],
	self.num_experts,
	self.num_experts_per_tok,
	)
	if labels is not None:
	loss += self.router_aux_loss_coef * aux_loss

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

	return MoeCausalLMOutputWithPast(
	loss=loss,
	aux_loss=aux_loss,
	logits=logits,
	past_key_values=outputs.past_key_values,
	hidden_states=outputs.hidden_states,
	attentions=outputs.attentions,
	router_logits=outputs.router_logits,
	)

	def prepare_inputs_for_generation(
	self,
	input_ids,
	past_key_values=None,
	attention_mask=None,
	inputs_embeds=None,
	**kwargs,
	):
	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 = {"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