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Zero
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# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
# except for the third-party components listed below.
# Hunyuan 3D does not impose any additional limitations beyond what is outlined
# in the repsective licenses of these third-party components.
# Users must comply with all terms and conditions of original licenses of these third-party
# components and must ensure that the usage of the third party components adheres to
# all relevant laws and regulations.
# For avoidance of doubts, Hunyuan 3D means the large language models and
# their software and algorithms, including trained model weights, parameters (including
# optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
# fine-tuning enabling code and other elements of the foregoing made publicly available
# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
import torch
import torch.nn as nn
import math
import torch.nn.functional as F
from diffusers.models.attention import FeedForward
class AddAuxiliaryLoss(torch.autograd.Function):
"""
The trick function of adding auxiliary (aux) loss,
which includes the gradient of the aux loss during backpropagation.
"""
@staticmethod
def forward(ctx, x, loss):
assert loss.numel() == 1
ctx.dtype = loss.dtype
ctx.required_aux_loss = loss.requires_grad
return x
@staticmethod
def backward(ctx, grad_output):
grad_loss = None
if ctx.required_aux_loss:
grad_loss = torch.ones(1, dtype=ctx.dtype, device=grad_output.device)
return grad_output, grad_loss
class MoEGate(nn.Module):
def __init__(
self, embed_dim, num_experts=16, num_experts_per_tok=2, aux_loss_alpha=0.01
):
super().__init__()
self.top_k = num_experts_per_tok
self.n_routed_experts = num_experts
self.scoring_func = "softmax"
self.alpha = aux_loss_alpha
self.seq_aux = False
# topk selection algorithm
self.norm_topk_prob = False
self.gating_dim = embed_dim
self.weight = nn.Parameter(
torch.empty((self.n_routed_experts, self.gating_dim))
)
self.reset_parameters()
def reset_parameters(self) -> None:
import torch.nn.init as init
init.kaiming_uniform_(self.weight, a=math.sqrt(5))
def forward(self, hidden_states):
bsz, seq_len, h = hidden_states.shape
# print(bsz, seq_len, h)
### compute gating score
hidden_states = hidden_states.view(-1, h)
logits = F.linear(hidden_states, self.weight, None)
if self.scoring_func == "softmax":
scores = logits.softmax(dim=-1)
else:
raise NotImplementedError(
f"insupportable scoring function for MoE gating: {self.scoring_func}"
)
### select top-k experts
topk_weight, topk_idx = torch.topk(scores, k=self.top_k, dim=-1, sorted=False)
### norm gate to sum 1
if self.top_k > 1 and self.norm_topk_prob:
denominator = topk_weight.sum(dim=-1, keepdim=True) + 1e-20
topk_weight = topk_weight / denominator
### expert-level computation auxiliary loss
if self.training and self.alpha > 0.0:
scores_for_aux = scores
aux_topk = self.top_k
# always compute aux loss based on the naive greedy topk method
topk_idx_for_aux_loss = topk_idx.view(bsz, -1)
if self.seq_aux:
scores_for_seq_aux = scores_for_aux.view(bsz, seq_len, -1)
ce = torch.zeros(
bsz, self.n_routed_experts, device=hidden_states.device
)
ce.scatter_add_(
1,
topk_idx_for_aux_loss,
torch.ones(bsz, seq_len * aux_topk, device=hidden_states.device),
).div_(seq_len * aux_topk / self.n_routed_experts)
aux_loss = (ce * scores_for_seq_aux.mean(dim=1)).sum(dim=1).mean()
aux_loss = aux_loss * self.alpha
else:
mask_ce = F.one_hot(
topk_idx_for_aux_loss.view(-1), num_classes=self.n_routed_experts
)
ce = mask_ce.float().mean(0)
Pi = scores_for_aux.mean(0)
fi = ce * self.n_routed_experts
aux_loss = (Pi * fi).sum() * self.alpha
else:
aux_loss = None
return topk_idx, topk_weight, aux_loss
class MoEBlock(nn.Module):
def __init__(
self,
dim,
num_experts=8,
moe_top_k=2,
activation_fn="gelu",
dropout=0.0,
final_dropout=False,
ff_inner_dim=None,
ff_bias=True,
):
super().__init__()
self.moe_top_k = moe_top_k
self.experts = nn.ModuleList([
FeedForward(
dim,
dropout=dropout,
activation_fn=activation_fn,
final_dropout=final_dropout,
inner_dim=ff_inner_dim,
bias=ff_bias,
)
for i in range(num_experts)
])
self.gate = MoEGate(
embed_dim=dim, num_experts=num_experts, num_experts_per_tok=moe_top_k
)
self.shared_experts = FeedForward(
dim,
dropout=dropout,
activation_fn=activation_fn,
final_dropout=final_dropout,
inner_dim=ff_inner_dim,
bias=ff_bias,
)
def initialize_weight(self):
pass
def forward(self, hidden_states):
identity = hidden_states
orig_shape = hidden_states.shape
topk_idx, topk_weight, aux_loss = self.gate(hidden_states)
hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
flat_topk_idx = topk_idx.view(-1)
if self.training:
hidden_states = hidden_states.repeat_interleave(self.moe_top_k, dim=0)
y = torch.empty_like(hidden_states, dtype=hidden_states.dtype)
for i, expert in enumerate(self.experts):
tmp = expert(hidden_states[flat_topk_idx == i])
y[flat_topk_idx == i] = tmp.to(hidden_states.dtype)
y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1)
y = y.view(*orig_shape)
y = AddAuxiliaryLoss.apply(y, aux_loss)
else:
y = self.moe_infer(
hidden_states, flat_topk_idx, topk_weight.view(-1, 1)
).view(*orig_shape)
y = y + self.shared_experts(identity)
return y
@torch.no_grad()
def moe_infer(self, x, flat_expert_indices, flat_expert_weights):
expert_cache = torch.zeros_like(x)
idxs = flat_expert_indices.argsort()
tokens_per_expert = flat_expert_indices.bincount().cpu().numpy().cumsum(0)
token_idxs = idxs // self.moe_top_k
for i, end_idx in enumerate(tokens_per_expert):
start_idx = 0 if i == 0 else tokens_per_expert[i - 1]
if start_idx == end_idx:
continue
expert = self.experts[i]
exp_token_idx = token_idxs[start_idx:end_idx]
expert_tokens = x[exp_token_idx]
expert_out = expert(expert_tokens)
expert_out.mul_(flat_expert_weights[idxs[start_idx:end_idx]])
# for fp16 and other dtype
expert_cache = expert_cache.to(expert_out.dtype)
expert_cache.scatter_reduce_(
0,
exp_token_idx.view(-1, 1).repeat(1, x.shape[-1]),
expert_out,
reduce="sum",
)
return expert_cache
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