llama-265m / modeling_llama_moe_hf.py

fix state_dict loading in MoE model

3240d88 verified 3 months ago

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	from __future__ import annotations
	import math
	import warnings
	from dataclasses import dataclass
	from typing import Optional, Tuple

	import torch
	import torch.utils.checkpoint
	import torch.nn as nn
	import torch.nn.functional as F
	from torch.distributions.normal import Normal
	from transformers.modeling_outputs import (
	CausalLMOutputWithPast,
	)
	from transformers.modeling_utils import PreTrainedModel
	from transformers.activations import ACT2FN
	from transformers.utils import ModelOutput, logging

	from .configuration_llama_moe import LlamaMoEConfig

	logger = logging.get_logger(__name__)

	_CONFIG_FOR_DOC = "LlamaMoEConfig"


	@dataclass
	class CalculatorOutput(ModelOutput):
	hidden_states: Optional[torch.FloatTensor] = None
	num_dropped_tokens: Optional[int] = None


	@dataclass
	class BaseMoEModelOutputWithPast(ModelOutput):
	"""
	Args:
	num_dropped_tokens: layer idx to the number of dropped tokens
	"""

	last_hidden_state: torch.FloatTensor = None
	past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
	hidden_states: Optional[Tuple[torch.FloatTensor]] = None
	attentions: Optional[Tuple[torch.FloatTensor]] = None
	balance_loss: Optional[float] = None
	num_dropped_tokens: Optional[Tuple[torch.Tensor]] = None
	gate_load: Optional[Tuple[list]] = None
	gate_importance: Optional[Tuple[list]] = None



	@dataclass
	class MoECausalLMOutputWithPast(CausalLMOutputWithPast):
	pass
	#balance_loss: Optional[float] = None
	#num_dropped_tokens: Optional[Tuple[int]] = None
	#gate_load: Optional[Tuple[ list[torch.Tensor]]] = None
	#gate_importance: Optional[Tuple[ list[torch.Tensor]]] = None



	@dataclass
	class MoEMlpOutput(ModelOutput):
	hidden_states: Optional[torch.FloatTensor] = None
	balance_loss: Optional[torch.FloatTensor] = None
	num_dropped_tokens: Optional[int] = None
	gate_load: Optional[list] = None
	gate_importance: Optional[list] = None


	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 LlamaRMSNorm(nn.Module):
	def __init__(self, hidden_size, eps=1e-6):
	"""
	LlamaRMSNorm is equivalent to T5LayerNorm
	"""
	super().__init__()
	self.weight = nn.Parameter(torch.ones(hidden_size))
	self.variance_epsilon = eps

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


	class LlamaRotaryEmbedding(torch.nn.Module):
	def __init__(self, dim, max_position_embeddings=2048, base=10000, 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).float().to(device) / self.dim))
	self.register_buffer("inv_freq", inv_freq)

	# 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, 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.einsum("i,j->ij", t, self.inv_freq)
	# Different from paper, but it uses a different permutation in order to obtain the same calculation
	emb = torch.cat((freqs, freqs), dim=-1)
	self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
	self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)

	def forward(self, x, 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),
	)


	class LlamaLinearScalingRotaryEmbedding(LlamaRotaryEmbedding):
	"""LlamaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""

	def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
	self.scaling_factor = scaling_factor
	super().__init__(dim, max_position_embeddings, base, device)

	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)
	t = t / self.scaling_factor

	freqs = torch.einsum("i,j->ij", t, self.inv_freq)
	# Different from paper, but it uses a different permutation in order to obtain the same calculation
	emb = torch.cat((freqs, freqs), dim=-1)
	self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
	self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)


	class LlamaDynamicNTKScalingRotaryEmbedding(LlamaRotaryEmbedding):
	"""LlamaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""

	def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
	self.scaling_factor = scaling_factor
	super().__init__(dim, max_position_embeddings, base, device)

	def _set_cos_sin_cache(self, seq_len, device, dtype):
	self.max_seq_len_cached = seq_len

	if seq_len > self.max_position_embeddings:
	base = self.base * (
	(self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
	) ** (self.dim / (self.dim - 2))
	inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
	self.register_buffer("inv_freq", inv_freq)

	t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)

	freqs = torch.einsum("i,j->ij", t, self.inv_freq)
	# Different from paper, but it uses a different permutation in order to obtain the same calculation
	emb = torch.cat((freqs, freqs), dim=-1)
	self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
	self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)


	def 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)


	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) # [bs, 1, seq_len, dim]
	sin = sin[position_ids].unsqueeze(1) # [bs, 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 LlamaMLP(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.pretraining_tp = config.pretraining_tp
	self.hidden_size = config.hidden_size
	self.intermediate_size = config.intermediate_size
	self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
	self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
	self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
	self.act_fn = ACT2FN[config.hidden_act]

	def forward(self, x):
	if self.pretraining_tp > 1:
	slice = self.intermediate_size // self.pretraining_tp
	gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
	up_proj_slices = self.up_proj.weight.split(slice, dim=0)
	down_proj_slices = self.down_proj.weight.split(slice, dim=1)

	gate_proj = torch.cat([F.linear(x, gate_proj_slices[i]) for i in range(self.pretraining_tp)], dim=-1)
	up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.pretraining_tp)], dim=-1)

	intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)
	down_proj = [F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.pretraining_tp)]
	down_proj = sum(down_proj)
	else:
	down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))

	return down_proj


	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 LlamaAttention(nn.Module):
	"""Multi-headed attention from 'Attention Is All You Need' paper"""

	def __init__(self, config: LlamaMoEConfig):
	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.pretraining_tp = config.pretraining_tp
	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.num_heads * self.head_dim, self.hidden_size, bias=False)
	self._init_rope()

	def _init_rope(self):
	if self.config.rope_scaling is None:
	self.rotary_emb = LlamaRotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings)
	else:
	scaling_type = self.config.rope_scaling["type"]
	scaling_factor = self.config.rope_scaling["factor"]
	if scaling_type == "linear":
	self.rotary_emb = LlamaLinearScalingRotaryEmbedding(
	self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor
	)
	elif scaling_type == "dynamic":
	self.rotary_emb = LlamaDynamicNTKScalingRotaryEmbedding(
	self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor
	)
	else:
	raise ValueError(f"Unknown RoPE scaling type {scaling_type}")

	def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
	return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()

	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,
	) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
	bsz, q_len, _ = hidden_states.size()

	if self.pretraining_tp > 1:
	key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.pretraining_tp
	query_slices = self.q_proj.weight.split((self.num_heads * self.head_dim) // self.pretraining_tp, dim=0)
	key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
	value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)

	query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.pretraining_tp)]
	query_states = torch.cat(query_states, dim=-1)

	key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.pretraining_tp)]
	key_states = torch.cat(key_states, dim=-1)

	value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.pretraining_tp)]
	value_states = torch.cat(value_states, dim=-1)

	else:
	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)) / 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()}"
	)

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

	if self.pretraining_tp > 1:
	attn_output = attn_output.split(self.hidden_size // self.pretraining_tp, dim=2)
	o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.pretraining_tp, dim=1)
	attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.pretraining_tp)])
	else:
	attn_output = self.o_proj(attn_output)

	if not output_attentions:
	attn_weights = None

	return attn_output, attn_weights, past_key_value


	class TopKBalancedNoisyGate(nn.Module):
	def __init__(
	self,
	input_size,
	num_experts,
	num_selects,
	gate_network="mlp",
	use_softmax=True,
	use_balance=True,
	balance_loss_weight=1e-2,
	add_noise=True,
	noise_epsilon=1e-2,
	):
	super(TopKBalancedNoisyGate, self).__init__()
	assert num_selects <= num_experts
	self.input_size = input_size
	self.num_experts = num_experts
	self.num_selects = num_selects

	self.gate_network_type = gate_network
	self.gate_network = self.get_gate_network(gate_network, input_size, num_experts)

	self.use_softmax = use_softmax
	self.softmax = nn.Softmax(1)

	self.use_balance = use_balance
	self.balance_loss_weight = balance_loss_weight

	# add_noise
	self.add_noise = add_noise
	self.noise_epsilon = noise_epsilon
	self.warned = False
	if self.add_noise:
	self.weight_noise = nn.Linear(input_size, num_experts, bias=False)
	self.weight_noise.weight.data = torch.zeros(
	(num_experts, input_size),
	requires_grad=True,
	device=self.weight_noise.weight.data.device,
	dtype=self.weight_noise.weight.data.dtype,
	)
	self.mean = 0.0
	self.std = 1.0
	self.normal = Normal(self.mean, self.std)
	self.softplus = nn.Softplus()

	self.reset_parameters()

	def get_gate_network(self, gate_type, input_size, num_experts):
	gate_type = gate_type.lower()

	if gate_type == "linear":
	gate_network = nn.Linear(input_size, num_experts, bias=False)
	nn.init.zeros_(gate_network.weight)
	elif gate_type == "mlp":
	gate_network = torch.nn.Sequential(
	torch.nn.Linear(input_size, num_experts, bias=False),
	torch.nn.Tanh(),
	torch.nn.Linear(num_experts, num_experts, bias=False),
	)
	else:
	raise ValueError(f'Unexpected gate_type: {gate_type}.')

	return gate_network

	def reset_gate_network(self):
	if "gate_network_type" not in vars(self):
	raise KeyError(f"{type(self)} does not have a gate network.")
	else:
	self.gate_network = self.get_gate_network(
	self.gate_network_type, self.input_size, self.num_experts
	)

	def reset_parameters(self):
	if self.add_noise:
	nn.init.zeros_(self.weight_noise.weight)
	# nn.init.zeros_(self.weight_noise)

	def cv_squared(self, x, eps=1e-10):
	"""The squared coefficient of variation of a sample.
	Useful as a loss to encourage a positive distribution to be more uniform.
	Epsilons added for numerical stability.
	Returns 0 for an empty Tensor.
	Args:
	x: a `Tensor`.
	Returns:
	a `Scalar`.s
	"""
	if x.shape[0] == 1:
	return torch.tensor(0.0, device=x.device)
	return x.float().var() / (x.float().mean() ** 2 + eps)

	def forward(self, x):
	logits_gate = self.gate_network(x)
	if self.training and self.add_noise:
	noise_mm = self.weight_noise(x)
	noise_control = self.softplus(noise_mm) + self.noise_epsilon
	logits_noise = torch.randn_like(logits_gate) * noise_control
	logits = logits_gate + logits_noise
	else:
	logits = logits_gate

	top_logits, top_indices = logits.topk(min(self.num_selects + 1, self.num_experts), dim=1) # 选择并排序前k+1个权重
	top_k_logits = top_logits[:, :self.num_selects]
	top_k_indices = top_indices[:, :self.num_selects]
	top_k_scores = self.softmax(top_k_logits.to(torch.float32)) if self.use_softmax else top_k_logits
	top_k_scores = top_k_scores.to(logits.dtype)

	zeros = torch.zeros_like(logits, requires_grad=True, device=logits.device)
	scores_filtered = zeros.scatter(dim=1, index=top_k_indices, src=top_k_scores) # shape(batch_size, num_experts)
	importance = scores_filtered.sum(0) # shape(num_experts)

	if self.training:
	if self.add_noise and self.num_selects != self.num_experts:
	batch_size = top_logits.size(0)
	m = top_logits.size(1)
	top_values_flat = top_logits.flatten()
	threshold_positions_if_in = torch.arange(batch_size, device=x.device) * m + self.num_selects
	threshold_if_in = torch.unsqueeze(torch.gather(top_values_flat, 0, threshold_positions_if_in), 1)
	is_in = torch.gt(logits_noise, threshold_if_in)
	threshold_positions_if_out = threshold_positions_if_in - 1
	threshold_if_out = torch.unsqueeze(torch.gather(top_values_flat, 0, threshold_positions_if_out), 1)
	# is each value currently in the top k.
	prob_if_in = self.normal.cdf((logits_gate - threshold_if_in) / noise_control)
	prob_if_out = self.normal.cdf((logits_gate - threshold_if_out) / noise_control)
	prob = torch.where(is_in, prob_if_in, prob_if_out)
	load = prob.sum(0)
	else:
	load = (scores_filtered > 0).sum(0)
	if not self.add_noise and not self.warned:
	warnings.warn('Gradient-trackable implementation for load calculation is only available when "add_noise=True". '
	'Training without noise will block the gradient from "load" path and lead to inconsistency in optimization objectives.')
	self.warned = True
	else:
	load = (scores_filtered > 0).sum(0)

	if self.use_balance:
	balance_loss = self.cv_squared(importance) + self.cv_squared(load)
	balance_loss *= self.balance_loss_weight
	else:
	balance_loss = torch.tensor(-100.0, device=x.device)

	return {
	"topK_indices": top_k_indices,
	"topK_scores": top_k_scores,
	"balance_loss": balance_loss,
	"load": load,
	"importance": importance,
	}


	class LinearGLUExperts(nn.Module):
	"""
	Modified from transformers.models.llama.modeling_llama.LlamaMLP
	"""

	__constants__ = [
	"bias",
	"in_features",
	"hidden_features",
	"out_features",
	"hidden_act",
	"num_experts",
	"size_experts",
	]

	def __init__(
	self,
	in_features,
	hidden_features,
	out_features,
	hidden_act,
	num_experts,
	size_experts=None,
	bias=True,
	device=None,
	dtype=None,
	):
	factory_kwargs = {"device": device, "dtype": dtype}
	super(LinearGLUExperts, self).__init__()
	self.in_features = in_features
	self.hidden_features = hidden_features
	self.out_features = out_features
	self.hidden_act = hidden_act
	self.num_experts = num_experts

	if size_experts is None:
	# all experts share the same number of hidden neurons
	assert hidden_features % num_experts == 0
	size_per_expert = hidden_features // num_experts
	size_experts = [size_per_expert for _ in range(num_experts)]
	else:
	# use specified expert sizes
	assert (
	len(size_experts) == num_experts
	and sum(size_experts) == hidden_features
	)
	self.size_experts = size_experts

	self.act_fn = ACT2FN[hidden_act]

	self.weight_gate = nn.ParameterList()
	self.weight_up = nn.ParameterList()
	self.weight_down = nn.ParameterList()

	for i in range(num_experts):
	# this matrix will be transposed when performing linear forwarding
	this_expert_weight_gate = nn.Parameter(
	torch.empty((size_experts[i], in_features), **factory_kwargs)
	)
	# this matrix will be transposed when performing linear forwarding
	this_expert_weight_up = nn.Parameter(
	torch.empty((size_experts[i], in_features), **factory_kwargs)
	)
	# this matrix will be transposed when performing linear forwarding
	this_expert_weight_down = nn.Parameter(
	torch.empty((out_features, size_experts[i]), **factory_kwargs)
	)
	self.weight_gate.append(this_expert_weight_gate)
	self.weight_up.append(this_expert_weight_up)
	self.weight_down.append(this_expert_weight_down)

	if bias:
	self.bias_gate = nn.ParameterList()
	self.bias_up = nn.ParameterList()
	self.bias_down = nn.ParameterList()

	for i in range(num_experts):
	this_expert_bias_gate = nn.Parameter(
	torch.empty((size_experts[i],), **factory_kwargs)
	)
	this_expert_bias_up = nn.Parameter(
	torch.empty((size_experts[i],), **factory_kwargs)
	)
	this_expert_bias_down = nn.Parameter(
	torch.empty((out_features,), **factory_kwargs)
	)
	self.bias_gate.append(this_expert_bias_gate)
	self.bias_up.append(this_expert_bias_up)
	self.bias_down.append(this_expert_bias_down)
	else:
	self.register_parameter("bias_gate", None)
	self.register_parameter("bias_up", None)
	self.register_parameter("bias_down", None)

	self.reset_parameters()

	def reset_parameters(self):
	for i in range(self.num_experts):
	nn.init.kaiming_uniform_(self.weight_gate[i], a=math.sqrt(5))
	nn.init.kaiming_uniform_(self.weight_up[i], a=math.sqrt(5))
	nn.init.kaiming_uniform_(self.weight_down[i], a=math.sqrt(5))
	if self.bias_gate is not None:
	fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight_gate[i])
	bound = 1 / math.sqrt(fan_in)
	nn.init.uniform_(self.bias_gate[i], -bound, bound)
	if self.bias_up is not None:
	fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight_up[i])
	bound = 1 / math.sqrt(fan_in)
	nn.init.uniform_(self.bias_up[i], -bound, bound)
	if self.bias_down is not None:
	fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight_down[i])
	bound = 1 / math.sqrt(fan_in)
	nn.init.uniform_(self.bias_down[i], -bound, bound)

	def forward(self, input, i):
	gate = self.act_fn(
	F.linear(
	input,
	self.weight_gate[i],
	self.bias_gate[i] if self.bias_gate is not None else None,
	)
	)
	up = F.linear(
	input,
	self.weight_up[i],
	self.bias_up[i] if self.bias_up is not None else None,
	)
	down = F.linear(
	gate * up,
	self.weight_down[i],
	self.bias_down[i] if self.bias_down is not None else None,
	)
	return down

	def extra_repr(self):
	return (
	"in_features={}, hidden_features={}, out_features={}, hidden_act={},"
	" num_experts={}, size_experts={}, bias={}".format(
	self.in_features,
	self.hidden_features,
	self.out_features,
	self.hidden_act,
	self.num_experts,
	self.size_experts,
	self.bias_gate is not None,
	)
	)


	class UniversalCalculator(nn.Module):
	def __init__(
	self,
	experts: LinearGLUExperts,
	multiply_gate_scores=True,
	score_scale_factor=1.0,
	add_weight_norm: bool = False,
	):
	super(UniversalCalculator, self).__init__()
	self.experts = experts
	# TODO (zhutong): use vmap to boost the training efficiency
	# self.experts_vmap = torch.vmap(self.experts)
	self.multiply_gate_scores = multiply_gate_scores
	self.score_scale_factor = score_scale_factor
	self.num_experts = experts.num_experts
	self.mlp_norm = None
	if multiply_gate_scores and add_weight_norm:
	raise NotImplementedError

	def reset_experts(self):
	self.experts.reset_parameters()

	def forward(
	self, x, topK_indices, topK_scores, expert_batch_size=None, **kwargs
	) -> CalculatorOutput:
	batch_size = topK_indices.size(0) # topK_indices: (bsz*seq_len, num_selects)
	num_selects = topK_indices.size(1)
	topK_indices = topK_indices.flatten() # shape(batch_size*num_selects)
	topK_scores = topK_scores.flatten() # shape(batch_size*num_selects)
	batch_indices = torch.arange(
	batch_size, device=topK_scores.device
	).repeat_interleave(num_selects)

	_, index_sorted_topK_indices = topK_indices.sort(0)

	sorted_topK_scores = topK_scores.index_select(0, index_sorted_topK_indices)
	sorted_batch_indices = batch_indices.index_select(0, index_sorted_topK_indices)

	if expert_batch_size is None:
	expert_batch_size = topK_indices.bincount(
	minlength=self.num_experts
	).tolist()

	sorted_x = x.index_select(0, sorted_batch_indices)
	split_x = torch.split(sorted_x, expert_batch_size, dim=0)

	expert_outputs = [
	self.experts(split_x[i], i)
	for i in range(self.num_experts)
	if split_x[i].shape[0] > 0
	]

	# (bszseq_lennum_selects, hidden_size)
	cat_expert_outputs = torch.cat(expert_outputs, 0)
	output_dim = cat_expert_outputs.size(1)
	if self.multiply_gate_scores:
	if self.mlp_norm is None:
	cat_expert_outputs = torch.mul(
	cat_expert_outputs,
	sorted_topK_scores.reshape(-1, 1) * self.score_scale_factor,
	)
	# cat_expert_outputs = torch.mul(cat_expert_outputs, sorted_topK_scores.reshape(-1, 1) * 1.0)
	else:
	cat_expert_outputs = torch.mul(
	cat_expert_outputs, sorted_topK_scores.reshape(-1, 1)
	)
	cat_expert_outputs = self.mlp_norm(cat_expert_outputs)

	zeros = torch.zeros(
	(batch_size, output_dim),
	device=cat_expert_outputs.device,
	dtype=cat_expert_outputs.dtype,
	)
	y = zeros.index_add(0, sorted_batch_indices, cat_expert_outputs)

	return CalculatorOutput(hidden_states=y, num_dropped_tokens=torch.tensor(-1.0))


	class BaseMoELayer(nn.Module):
	def __init__(self):
	super(BaseMoELayer, self).__init__()

	self.gate: TopKBalancedNoisyGate
	self.calculator: UniversalCalculator

	def _create_gate(self, **kwargs):
	self.gate_type = kwargs.get("gate_type", "TopKBalancedNoisyGate")

	if self.gate_type == "TopKBalancedNoisyGate": # noisy gate
	self.gate = TopKBalancedNoisyGate(
	self.input_size,
	self.num_experts,
	self.num_selects,
	gate_network=kwargs.get("gate_network", "mlp"),
	use_softmax=kwargs.get("gate_use_softmax", True),
	use_balance=kwargs.get("gate_use_balance", True),
	balance_loss_weight=kwargs.get("gate_balance_loss_weight", 1e-2),
	add_noise=kwargs.get("gate_add_noise", True),
	noise_epsilon=kwargs.get("gate_noise_epsilon", 1e-2),
	)
	else:
	raise NotImplementedError

	def _create_calculator(self, experts, **kwargs):
	self.calculator_type = kwargs.get("calculator_type", "UniversalCalculator")

	if self.calculator_type == "UniversalCalculator": # top K calculator
	self.calculator = UniversalCalculator(
	experts,
	multiply_gate_scores=kwargs.get("multiply_gate_scores", True),
	score_scale_factor=kwargs.get("score_scale_factor", 1.0),
	add_weight_norm=kwargs.get("add_weight_norm", False),
	)
	else:
	raise NotImplementedError

	def forward(self, x) -> MoEMlpOutput:
	original_shape = x.shape[:-1]
	x = x.reshape(-1, self.input_size)
	gate_outputs: dict = self.gate(x)
	calc_outs: CalculatorOutput = self.calculator(x, **gate_outputs)
	y = calc_outs.hidden_states
	y = y.reshape(original_shape + (self.output_size,))

	return MoEMlpOutput(
	hidden_states=y,
	balance_loss=gate_outputs.get("balance_loss"),
	num_dropped_tokens=calc_outs.num_dropped_tokens,
	gate_load=gate_outputs.get("load", torch.tensor(-1)),
	gate_importance=gate_outputs.get("importance", torch.tensor(-1)),
	)

	def set_num_selects(self, num_selects):
	if "num_selects" not in vars(self.gate):
	raise KeyError(f'{self.gate_type} does not have a key named "num_selects".')
	elif num_selects > self.gate.num_experts:
	raise ValueError(
	'The value of "num_selects" must satisfy "num_selects <= num_experts"!'
	)
	elif self.gate_type in ("SwitchBalancedGate",):
	raise ValueError(
	f"{self.gate_type} doesn't support manually setting num_selects."
	)
	else:
	self.num_selects = num_selects
	self.gate.num_selects = num_selects

	def set_gate_use_softmax(self, use_softmax):
	if "use_softmax" not in vars(self.gate):
	raise KeyError(f'{self.gate_type} does not have a key named "use_softmax".')
	else:
	self.gate.use_softmax = use_softmax

	def set_gate_use_balance(self, use_balance):
	if "use_balance" not in vars(self.gate):
	raise KeyError(f'{self.gate_type} does not have a key named "use_balance".')
	else:
	self.gate.use_balance = use_balance

	def set_gate_balance_loss_weight(self, balance_loss_weight):
	if "balance_loss_weight" not in vars(self.gate):
	raise KeyError(
	f'{self.gate_type} does not have a key named "balance_loss_weight".'
	)
	else:
	self.gate.balance_loss_weight = balance_loss_weight

	def set_gate_add_noise(self, add_noise):
	if "add_noise" not in vars(self.gate):
	raise KeyError(f'{self.gate_type} does not have a key named "add_noise".')
	else:
	self.gate.add_noise = add_noise

	def set_gate_noise_epsilon(self, noise_epsilon):
	if "noise_epsilon" not in vars(self.gate):
	raise KeyError(
	f'{self.gate_type} does not have a key named "noise_epsilon".'
	)
	else:
	self.gate.noise_epsilon = noise_epsilon

	def set_calculator_multiply_gate_scores(self, multiply_gate_scores):
	if "multiply_gate_scores" not in vars(self.calculator):
	raise KeyError(
	f'{self.gate_type} does not have a key named "multiply_gate_scores".'
	)
	else:
	self.calculator.multiply_gate_scores = multiply_gate_scores

	def set_calculator_score_scale_factor(self, score_scale_factor):
	if "score_scale_factor" not in vars(self.calculator):
	raise KeyError(
	f'{self.gate_type} does not have a key named "score_scale_factor".'
	)
	else:
	self.calculator.score_scale_factor = score_scale_factor

	def set_calculator_drop_tokens(self, drop_tokens):
	if "drop_tokens" not in vars(self.calculator):
	raise KeyError(f'{self.gate_type} does not have a key named "drop_tokens".')
	elif (
	drop_tokens
	and self.calculator.dropped_padding != "zero"
	and self.input_size != self.output_size
	):
	warnings.warn(
	'Setting "drop_tokens=True" without zero dropped padding when "input_size != output_size" will cause error!'
	)
	else:
	self.calculator.drop_tokens = drop_tokens

	def set_calculator_dropped_padding(self, dropped_padding):
	if "dropped_padding" not in vars(self.calculator):
	raise KeyError(
	f'{self.gate_type} does not have a key named "dropped_padding".'
	)
	elif dropped_padding not in self.calculator.available_dropped_padding_choices:
	raise ValueError(
	f"'dropped_padding' type not available! (available choices: {self.calculator.available_dropped_padding_choices})"
	)
	elif (
	self.calculator.drop_tokens
	and dropped_padding != "zero"
	and self.input_size != self.output_size
	):
	warnings.warn(
	f'Setting "dropped_padding={dropped_padding}" with "drop_tokens=True" when "input_size != output_size" will cause error!'
	)
	else:
	self.calculator.dropped_padding = dropped_padding

	def set_calculator_capacity_factor(self, capacity_factor):
	if "capacity_factor" not in vars(self.calculator):
	raise KeyError(
	f'{self.gate_type} does not have a key named "capacity_factor".'
	)
	else:
	self.calculator.capacity_factor = capacity_factor

	def reset_gate_network(self):
	self.gate.reset_gate_network()

	def reset_experts(self):
	self.calculator.reset_experts()


	class LinearGLUMoELayer(BaseMoELayer):
	def __init__(
	self,
	input_size,
	hidden_size,
	output_size,
	hidden_act,
	num_experts,
	num_selects,
	size_experts=None,
	bias=True,
	**kwargs,
	):
	super(LinearGLUMoELayer, self).__init__()
	assert num_selects <= num_experts
	self.input_size = input_size
	self.hidden_size = hidden_size
	self.output_size = output_size
	self.hidden_act = hidden_act
	self.num_experts = num_experts
	self.num_selects = num_selects
	self.size_experts = size_experts
	self.bias = bias

	experts = LinearGLUExperts(
	input_size,
	hidden_size,
	output_size,
	hidden_act,
	num_experts,
	size_experts=size_experts,
	bias=bias,
	)

	self._create_gate(**kwargs)
	self._create_calculator(experts, **kwargs)


	class LlamaMoEDecoderLayer(nn.Module):
	def __init__(self, config: LlamaMoEConfig, layer_index):
	super().__init__()

	self.hidden_size = config.hidden_size
	self.self_attn = LlamaAttention(config=config)
	self.mlp = LlamaMLP(config)
	self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
	self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

	gating_config = {
	# all gates
	"gate_type": config.gate_type,
	"gate_network": config.gate_network,
	"gate_use_softmax": config.gate_use_softmax,
	"gate_use_balance": config.gate_use_balance,
	"gate_balance_loss_weight": config.gate_balance_loss_weight,
	"gate_add_noise": config.gate_add_noise,
	# TopKBalancedNoisyGate
	"gate_noise_epsilon": config.gate_noise_epsilon,
	}
	calculator_config = {
	# all calculators
	"calculator_type": config.calculator_type,
	"multiply_gate_scores": config.multiply_gate_scores,
	"score_scale_factor": (
	config.score_scale_factor[layer_index]
	if isinstance(config.score_scale_factor, list)
	else config.score_scale_factor
	),
	"add_weight_norm": config.add_weight_norm,
	# SwitchDropTokenCalculator
	"drop_tokens": config.drop_tokens,
	"dropped_padding": config.dropped_padding,
	"capacity_factor": config.capacity_factor,
	}

	self.mlp = LinearGLUMoELayer(
	input_size=self.hidden_size,
	hidden_size=config.intermediate_size,
	output_size=self.hidden_size,
	hidden_act=config.hidden_act,
	num_experts=config.num_experts,
	num_selects=config.num_selects,
	size_experts=(
	config.size_experts[layer_index]
	if config.size_experts is not None
	else None
	),
	bias=False,
	**gating_config,
	**calculator_config,
	)

	def forward(
	self,
	hidden_states,
	attention_mask=None,
	position_ids=None,
	past_key_value=None,
	output_attentions=False,
	use_cache=False,
	) -> tuple:
	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)
	mlp_outs: MoEMlpOutput = self.mlp(hidden_states)
	hidden_states = residual + mlp_outs.hidden_states

	outputs = (
	hidden_states,
	mlp_outs.balance_loss,
	mlp_outs.num_dropped_tokens,
	mlp_outs.gate_load,
	mlp_outs.gate_importance,
	)
	if output_attentions:
	outputs += (self_attn_weights,)
	if use_cache:
	outputs += (present_key_value,)

	return outputs

	def set_moe_num_selects(self, num_selects):
	self.mlp.set_num_selects(num_selects)

	def set_moe_gate_use_softmax(self, use_softmax):
	self.mlp.set_gate_use_softmax(use_softmax)

	def set_moe_gate_use_balance(self, use_balance):
	self.mlp.set_gate_use_balance(use_balance)

	def set_moe_gate_balance_loss_weight(self, balance_loss_weight):
	self.mlp.set_gate_balance_loss_weight(balance_loss_weight)

	def set_moe_gate_add_noise(self, add_noise):
	self.mlp.set_gate_add_noise(add_noise)

	def set_moe_gate_noise_epsilon(self, noise_epsilon):
	self.mlp.set_gate_noise_epsilon(noise_epsilon)

	def set_moe_calculator_multiply_gate_scores(self, multiply_gate_scores):
	self.mlp.set_calculator_multiply_gate_scores(multiply_gate_scores)

	def set_moe_calculator_score_scale_factor(self, score_scale_factor):
	self.mlp.set_calculator_score_scale_factor(score_scale_factor)

	def set_moe_calculator_drop_tokens(self, drop_tokens):
	self.mlp.set_calculator_drop_tokens(drop_tokens)

	def set_moe_calculator_dropped_padding(self, dropped_padding):
	self.mlp.set_calculator_dropped_padding(dropped_padding)

	def set_moe_calculator_capacity_factor(self, capacity_factor):
	self.mlp.set_calculator_capacity_factor(capacity_factor)

	def reset_gate_network(self):
	self.mlp.reset_gate_network()

	def reset_experts(self):
	self.mlp.reset_experts()


	class LlamaMoEPreTrainedModel(PreTrainedModel):
	config_class = LlamaMoEConfig
	base_model_prefix = "model"
	supports_gradient_checkpointing = True
	_no_split_modules = ["LlamaMoEDecoderLayer"]
	_skip_keys_device_placement = "past_key_values"

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

	def _set_gradient_checkpointing(self, module, value=False):
	if isinstance(module, LlamaMoEModel):
	module.gradient_checkpointing = value


	class LlamaMoEModel(LlamaMoEPreTrainedModel):
	def __init__(self, config: LlamaMoEConfig):
	super().__init__(config)
	self.padding_idx = config.pad_token_id
	self.vocab_size = config.vocab_size

	self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
	self.layers = nn.ModuleList(
	[LlamaMoEDecoderLayer(config, i) for i in range(config.num_hidden_layers)]
	)
	self.norm = LlamaRMSNorm(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=None,
	attention_mask=None,
	position_ids=None,
	past_key_values=None,
	inputs_embeds=None,
	use_cache=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	):
	output_attentions = (
	output_attentions
	if output_attentions is not None
	else self.config.output_attentions
	)
	output_hidden_states = (
	output_hidden_states
	if output_hidden_states is not None
	else self.config.output_hidden_states
	)
	use_cache = use_cache if use_cache is not None else self.config.use_cache

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

	# retrieve input_ids and inputs_embeds
	if input_ids is not None and inputs_embeds is not None:
	raise ValueError(
	"You cannot specify both decoder_input_ids and decoder_inputs_embeds at"
	" the same time"
	)
	elif input_ids is not None:
	batch_size, seq_length = input_ids.shape
	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
	balance_loss = 0.0

	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
	next_decoder_cache = () if use_cache else None

	num_dropped_tokens = ()
	gate_load = ()
	gate_importance = ()
	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, output_attentions, None)

	return custom_forward

	layer_outputs: tuple = torch.utils.checkpoint.checkpoint(
	create_custom_forward(decoder_layer),
	hidden_states,
	attention_mask,
	position_ids,
	None,
	)
	else:
	layer_outputs: tuple = 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 layer_outputs[1] is not None:
	balance_loss += layer_outputs[1]

	if use_cache:
	next_decoder_cache += (layer_outputs[6 if output_attentions else 5],)

	if output_attentions:
	all_self_attns += (layer_outputs[5],)

	num_dropped_tokens += (layer_outputs[2],)
	gate_load += (layer_outputs[3],)
	gate_importance += (layer_outputs[4],)

	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 BaseMoEModelOutputWithPast(
	last_hidden_state=hidden_states,
	balance_loss=balance_loss,
	past_key_values=next_cache,
	hidden_states=all_hidden_states,
	attentions=all_self_attns,
	num_dropped_tokens=num_dropped_tokens,
	gate_load=gate_load,
	gate_importance=gate_importance,
	)

	def update_config(self):
	self.config.vocab_size = self.config.vocab_size
	self.config.max_position_embeddings = self.config.max_position_embeddings
	# ↓↓↓↓↓↓↓↓↓↓↓↓ changed here ↓↓↓↓↓↓↓↓↓↓↓↓ #
	self.config.hidden_size = self.layers[0].mlp.input_size
	self.config.intermediate_size = self.layers[0].mlp.hidden_size
	self.config.num_hidden_layers = len(self.layers)
	self.config.num_attention_heads = self.layers[0].self_attn.num_heads
	self.config.hidden_act = self.layers[0].mlp.hidden_act
	# ↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑ #
	self.config.initializer_range = self.config.initializer_range
	self.config.rms_norm_eps = self.config.rms_norm_eps
	self.config.pretraining_tp = self.config.pretraining_tp
	self.config.use_cache = self.config.use_cache
	self.config.rope_scaling = self.config.rope_scaling
	self.config._rope_scaling_validation()

	self.config.num_experts = self.layers[0].mlp.num_experts
	self.config.num_selects = self.layers[0].mlp.num_selects
	self.config.size_experts = [
	self.layers[i].mlp.calculator.experts.size_experts
	for i in range(self.config.num_hidden_layers)
	]

	self.config.gate_type = vars(self.layers[0].mlp).get(
	"gate_type", "TopKBalancedNoisyGate"
	)
	self.config.gate_network = vars(self.layers[0].mlp.gate).get(
	"gate_network_type", "mlp"
	)
	self.config.gate_use_softmax = vars(self.layers[0].mlp.gate).get(
	"use_softmax", True
	)
	self.config.gate_use_balance = vars(self.layers[0].mlp.gate).get(
	"use_balance", True
	)
	self.config.gate_balance_loss_weight = vars(self.layers[0].mlp.gate).get(
	"balance_loss_weight", 1e-2
	)
	self.config.gate_add_noise = vars(self.layers[0].mlp.gate).get(
	"add_noise", True
	)
	self.config.gate_noise_epsilon = vars(self.layers[0].mlp.gate).get(
	"noise_epsilon", 1e-2
	)

	self.config.calculator_type = vars(self.layers[0].mlp).get(
	"calculator_type", "UniversalCalculator"
	)
	self.config.multiply_gate_scores = vars(self.layers[0].mlp.calculator).get(
	"multiply_gate_scores", True
	)
	self.config.score_scale_factor = [
	vars(self.layers[i].mlp.calculator).get("score_scale_factor", 1.0)
	for i in range(self.config.num_hidden_layers)
	]
	self.config.drop_tokens = vars(self.layers[0].mlp.calculator).get(
	"drop_tokens", True
	)
	self.config.dropped_padding = vars(self.layers[0].mlp.calculator).get(
	"dropped_padding", "zero"
	)
	self.config.capacity_factor = vars(self.layers[0].mlp.calculator).get(
	"capacity_factor", 1.25
	)

	def set_moe_num_selects(self, num_selects):
	for idx, decoder_layer in enumerate(self.layers):
	decoder_layer.set_moe_num_selects(num_selects)

	def set_moe_gate_use_softmax(self, use_softmax):
	for idx, decoder_layer in enumerate(self.layers):
	decoder_layer.set_moe_gate_use_softmax(use_softmax)

	def set_moe_gate_use_balance(self, use_balance):
	for idx, decoder_layer in enumerate(self.layers):
	decoder_layer.set_moe_gate_use_balance(use_balance)

	def set_moe_gate_balance_loss_weight(self, balance_loss_weight):
	for idx, decoder_layer in enumerate(self.layers):
	decoder_layer.set_moe_gate_balance_loss_weight(balance_loss_weight)

	def set_moe_gate_add_noise(self, add_noise):
	for idx, decoder_layer in enumerate(self.layers):
	decoder_layer.set_moe_gate_add_noise(add_noise)

	def set_moe_gate_noise_epsilon(self, noise_epsilon):
	for idx, decoder_layer in enumerate(self.layers):
	decoder_layer.set_moe_gate_noise_epsilon(noise_epsilon)

	def set_moe_calculator_multiply_gate_scores(self, multiply_gate_scores):
	for idx, decoder_layer in enumerate(self.layers):
	decoder_layer.set_moe_calculator_multiply_gate_scores(multiply_gate_scores)

	def set_moe_calculator_score_scale_factor(
	self, score_scale_factor, layer_index=None
	):
	if layer_index is None:
	for idx, decoder_layer in enumerate(self.layers):
	decoder_layer.set_moe_calculator_score_scale_factor(score_scale_factor)
	else:
	self.layers[layer_index].set_moe_calculator_score_scale_factor(
	score_scale_factor
	)

	def set_moe_calculator_drop_tokens(self, drop_tokens):
	for idx, decoder_layer in enumerate(self.layers):
	decoder_layer.set_moe_calculator_drop_tokens(drop_tokens)

	def set_moe_calculator_dropped_padding(self, dropped_padding):
	for idx, decoder_layer in enumerate(self.layers):
	decoder_layer.set_moe_calculator_dropped_padding(dropped_padding)

	def set_moe_calculator_capacity_factor(self, capacity_factor):
	for idx, decoder_layer in enumerate(self.layers):
	decoder_layer.set_moe_calculator_capacity_factor(capacity_factor)

	def reset_gate_network(self):
	for idx, decoder_layer in enumerate(self.layers):
	decoder_layer.reset_gate_network()

	def reset_experts(self):
	for idx, decoder_layer in enumerate(self.layers):
	decoder_layer.reset_experts()


	class LlamaMoEForCausalLM(LlamaMoEPreTrainedModel):
	_tied_weights_keys = ["lm_head.weight"]

	def __init__(self, config):
	super().__init__(config)
	self.model = LlamaMoEModel(config)
	self.pretraining_tp = config.pretraining_tp
	self.vocab_size = config.vocab_size
	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):
	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=None,
	attention_mask=None,
	position_ids=None,
	past_key_values=None,
	inputs_embeds=None,
	labels=None,
	use_cache=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	**kwargs,
	):
	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: BaseMoEModelOutputWithPast = self.model(
	input_ids=input_ids,
	attention_mask=attention_mask,
	position_ids=position_ids,
	past_key_values=past_key_values,
	inputs_embeds=inputs_embeds,
	use_cache=use_cache,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	hidden_states = outputs.last_hidden_state
	logits = self.lm_head(hidden_states)

	loss = None
	if labels is not None:
	# Shift so that tokens < n predict n
	shift_logits = logits[..., :-1, :].contiguous()
	shift_labels = labels[..., 1:].contiguous()
	# Flatten the tokens
	loss_fct = nn.CrossEntropyLoss()
	shift_logits = shift_logits.view(-1, self.config.vocab_size)
	shift_labels = shift_labels.view(-1)
	# Enable model parallelism
	shift_labels = shift_labels.to(shift_logits.device)
	loss = loss_fct(shift_logits, shift_labels)
	if outputs.balance_loss is not None and outputs.balance_loss > 0:
	loss += outputs.balance_loss

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

	return MoECausalLMOutputWithPast(loss=loss,logits=logits)
	"""
	return MoECausalLMOutputWithPast(
	loss=loss,
	logits=logits,
	past_key_values=outputs.past_key_values,
	hidden_states=outputs.hidden_states,
	attentions=outputs.attentions,
	num_dropped_tokens=outputs.num_dropped_tokens,
	balance_loss=outputs.balance_loss,
	gate_load=outputs.gate_load,
	gate_importance=outputs.gate_importance,
	)
	"""

	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

	@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

	def update_config(self):
	self.model.update_config()

	def set_moe_num_selects(self, num_selects):
	self.model.set_moe_num_selects(num_selects)

	def set_moe_gate_use_softmax(self, use_softmax):
	self.model.set_moe_gate_use_softmax(use_softmax)

	def set_moe_gate_use_balance(self, use_balance):
	self.model.set_moe_gate_use_balance(use_balance)

	def set_moe_gate_balance_loss_weight(self, balance_loss_weight):
	self.model.set_moe_gate_balance_loss_weight(balance_loss_weight)

	def set_moe_gate_add_noise(self, add_noise):
	self.model.set_moe_gate_add_noise(add_noise)

	def set_moe_gate_noise_epsilon(self, noise_epsilon):
	self.model.set_moe_gate_noise_epsilon(noise_epsilon)

	def set_moe_calculator_multiply_gate_scores(self, multiply_gate_scores):
	self.model.set_moe_calculator_multiply_gate_scores(multiply_gate_scores)

	def set_moe_calculator_score_scale_factor(
	self, score_scale_factor, layer_index=None
	):
	self.model.set_moe_calculator_score_scale_factor(
	score_scale_factor, layer_index=layer_index
	)

	def set_moe_calculator_drop_tokens(self, drop_tokens):
	self.model.set_moe_calculator_drop_tokens(drop_tokens)

	def set_moe_calculator_dropped_padding(self, dropped_padding):
	self.model.set_moe_calculator_dropped_padding(dropped_padding)

	def set_moe_calculator_capacity_factor(self, capacity_factor):
	self.model.set_moe_calculator_capacity_factor(capacity_factor)

	def reset_gate_network(self):
	self.model.reset_gate_network()

	def reset_experts(self):
	self.model.reset_experts()