LLaMA-MoE-v1-3_0B-2_16 / modeling_llama_moe_hf.py
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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):
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
]
# (bsz*seq_len*num_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,
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()