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import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
from .modeling_promoe_common import (
Attention,
FinalLayer,
LabelEmbedder,
Mlp,
MoeMLP_DiffMoE as MoeMLP,
PatchEmbed,
TimestepEmbedder,
get_2d_sincos_pos_embed,
modulate,
)
class SparseMoEBlock(nn.Module):
def __init__(
self,
experts,
hidden_dim,
num_experts,
n_shared_experts=0,
capacity=2,
mlp_ratio=4.0,
use_diff_expert=False,
):
super().__init__()
self.gate_weight = nn.Parameter(torch.empty((num_experts, hidden_dim)))
nn.init.normal_(self.gate_weight, std=0.006)
self.experts = nn.ModuleList(experts)
self.capacity = capacity
self.num_experts = num_experts
self.n_shared_experts = n_shared_experts
self.use_diff_expert = use_diff_expert
if use_diff_expert:
self.diff_expert = MoeMLP(hidden_size=hidden_dim, intermediate_size=int(hidden_dim * mlp_ratio))
self.capacity_predictor = nn.Sequential(
nn.Linear(hidden_dim, hidden_dim, bias=True),
nn.SiLU(),
nn.Linear(hidden_dim, self.num_experts, bias=True),
)
if self.n_shared_experts > 0:
mlp_hidden_dim = int(hidden_dim * mlp_ratio * 2)
approx_gelu = lambda: nn.GELU(approximate="tanh")
self.shared_experts = Mlp(in_features=hidden_dim, hidden_features=mlp_hidden_dim, act_layer=approx_gelu, drop=0)
self.register_buffer("expert_threshold", torch.tensor([0.0] * num_experts))
self.register_buffer("ema_decay", torch.tensor([0.95]))
def forward(self, x):
if self.training:
return self.forward_train(x)
return self.forward_eval(x)
def update_threshold(self, capacity_pred):
if not self.training:
return
capacity_pred = torch.sigmoid(capacity_pred)
seq_len = capacity_pred.size(0)
topk = int((seq_len / self.num_experts) * self.capacity)
threshold = self.expert_threshold
ema_decay = self.ema_decay
for i in range(self.num_experts):
scores, _ = torch.topk(capacity_pred[:, i], k=topk, dim=-1, sorted=True)
quantile = scores[-1].detach()
threshold[i] = threshold[i] * ema_decay + (1 - ema_decay) * quantile
if dist.is_available() and dist.is_initialized():
dist.all_reduce(threshold, op=dist.ReduceOp.SUM)
threshold /= dist.get_world_size()
self.expert_threshold = threshold
def forward_train(self, x):
bsz, seq_len, hidden_dim = x.shape
identity = x
x = x.view(-1, hidden_dim)
total_tokens = x.shape[0]
capacity_pred = self.capacity_predictor(x.detach())
k = int((total_tokens / self.num_experts) * self.capacity)
logits = F.linear(x, self.gate_weight, None)
scores = logits.softmax(dim=-1).permute(1, 0)
gating, index = torch.topk(scores, k=k, dim=-1, sorted=False)
mask = torch.zeros((self.num_experts, total_tokens), dtype=x.dtype, device=x.device)
mask.scatter_(1, index, 1.0)
expert_inputs = x[index]
expert_outputs = torch.stack([expert(expert_inputs[i]) for i, expert in enumerate(self.experts)])
gated_outputs = gating.unsqueeze(-1) * expert_outputs
y = torch.zeros((total_tokens * self.num_experts, hidden_dim), dtype=x.dtype, device=x.device)
offset = torch.arange(0, self.num_experts, device=x.device).unsqueeze(1) * total_tokens
flat_index = (index + offset.long()).view(-1)
y = torch.scatter(y, 0, flat_index.unsqueeze(1).expand(-1, hidden_dim), gated_outputs.view(-1, hidden_dim))
y = y.view(self.num_experts, total_tokens, hidden_dim).sum(dim=0, keepdim=False)
self.update_threshold(capacity_pred)
x_out = y.view(bsz, seq_len, hidden_dim)
ones = mask.permute(1, 0).view(bsz, seq_len, self.num_experts)
capacity_pred = capacity_pred.view(bsz, seq_len, self.num_experts)
if self.n_shared_experts > 0:
x_out = x_out + self.shared_experts(identity)
if self.use_diff_expert:
x_out = x_out - self.diff_expert(identity)
return x_out, ones, capacity_pred
def forward_eval(self, x):
bsz, seq_len, hidden_dim = x.shape
identity = x
x = x.view(-1, hidden_dim)
total_tokens = x.shape[0]
capacity_pred = torch.sigmoid(self.capacity_predictor(x.detach()))
threshold = self.expert_threshold
logits = F.linear(x, self.gate_weight, None)
scores = logits.softmax(dim=-1).permute(-1, -2)
y = torch.zeros_like(x, dtype=x.dtype)
for i, expert in enumerate(self.experts):
k_fixed = torch.where(capacity_pred[:, i] > threshold[i], 1, 0).sum()
gating, index = torch.topk(scores[i], k=k_fixed, dim=-1, sorted=False)
y[index, :] += gating.unsqueeze(-1) * expert(x[index, :])
x_out = y.view(bsz, seq_len, hidden_dim)
if self.n_shared_experts > 0:
x_out = x_out + self.shared_experts(identity)
return x_out, None, None
class DiTBlock(nn.Module):
def __init__(
self,
hidden_size,
num_heads,
head_dim=None,
mlp_ratio=4.0,
use_swiglu=False,
MoE_config=None,
use_moe=False,
qk_norm=False,
**block_kwargs,
):
super().__init__()
self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
self.attn = Attention(hidden_size, num_heads=num_heads, head_dim=head_dim, qkv_bias=True, qk_norm=qk_norm, **block_kwargs)
self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
mlp_hidden_dim = int(hidden_size * mlp_ratio)
self.use_moe = use_moe
if use_moe:
if not use_swiglu:
approx_gelu = lambda: nn.GELU(approximate="tanh")
experts = [
Mlp(in_features=hidden_size, hidden_features=mlp_hidden_dim, act_layer=approx_gelu, drop=0)
for _ in range(MoE_config.num_experts)
]
else:
experts = [MoeMLP(hidden_size=hidden_size, intermediate_size=mlp_hidden_dim) for _ in range(MoE_config.num_experts)]
self.mlp = SparseMoEBlock(
experts=experts,
hidden_dim=hidden_size,
num_experts=MoE_config.num_experts,
capacity=MoE_config.capacity,
n_shared_experts=MoE_config.n_shared_experts,
mlp_ratio=4.0,
)
else:
if not use_swiglu:
approx_gelu = lambda: nn.GELU(approximate="tanh")
self.mlp = Mlp(in_features=hidden_size, hidden_features=mlp_hidden_dim, act_layer=approx_gelu, drop=0)
else:
self.mlp = MoeMLP(hidden_size=hidden_size, intermediate_size=mlp_hidden_dim)
self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 6 * hidden_size, bias=True))
def forward(self, x, c):
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(c).chunk(6, dim=1)
x = x + gate_msa.unsqueeze(1) * self.attn(modulate(self.norm1(x), shift_msa, scale_msa))
if self.use_moe:
x_mlp, ones, pred_c = self.mlp(modulate(self.norm2(x), shift_mlp, scale_mlp))
x = x + gate_mlp.unsqueeze(1) * x_mlp
return x, ones, pred_c
x = x + gate_mlp.unsqueeze(1) * self.mlp(modulate(self.norm2(x), shift_mlp, scale_mlp))
return x, None, None
class DiT(nn.Module):
def __init__(
self,
input_size=32,
patch_size=2,
in_channels=4,
hidden_size=1152,
depth=28,
num_heads=16,
mlp_ratio=4.0,
qk_norm=False,
class_dropout_prob=0.1,
num_classes=1000,
learn_sigma=True,
use_swiglu=False,
MoE_config=None,
head_dim=None,
CapacityPred_loss_weight=0.01,
):
super().__init__()
self.learn_sigma = learn_sigma
self.in_channels = in_channels
self.out_channels = in_channels * 2 if learn_sigma else in_channels
self.patch_size = patch_size
self.num_heads = num_heads
self.MoE_config = MoE_config
use_moe_flag = [i % 2 == 1 for i in range(depth)] if self.MoE_config.interleave else [True] * depth
self.CapacityPred_loss_weight = CapacityPred_loss_weight
self.x_embedder = PatchEmbed(input_size, patch_size, in_channels, hidden_size, bias=True)
self.t_embedder = TimestepEmbedder(hidden_size)
self.y_embedder = LabelEmbedder(num_classes, hidden_size, class_dropout_prob)
num_patches = self.x_embedder.num_patches
self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, hidden_size), requires_grad=False)
self.blocks = nn.ModuleList(
[
DiTBlock(
hidden_size,
num_heads,
head_dim=head_dim,
mlp_ratio=mlp_ratio,
qk_norm=qk_norm,
use_swiglu=use_swiglu,
MoE_config=MoE_config,
use_moe=use_moe_flag[i],
)
for i in range(depth)
]
)
self.final_layer = FinalLayer(hidden_size, patch_size, self.out_channels)
self.init_MoeMLP = MoE_config.init_MoeMLP
self.initialize_weights()
self.capacity_schedule = MoE_config.get("capacity_schedule", None)
if self.capacity_schedule:
self.training_iters = -1
def initialize_weights(self):
def _basic_init(module):
if isinstance(module, nn.Linear):
torch.nn.init.xavier_uniform_(module.weight)
if module.bias is not None:
nn.init.constant_(module.bias, 0)
self.apply(_basic_init)
pos_embed = get_2d_sincos_pos_embed(self.pos_embed.shape[-1], int(self.x_embedder.num_patches**0.5))
self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0))
w = self.x_embedder.proj.weight.data
nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
nn.init.constant_(self.x_embedder.proj.bias, 0)
nn.init.normal_(self.y_embedder.embedding_table.weight, std=0.02)
nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)
for block in self.blocks:
nn.init.constant_(block.adaLN_modulation[-1].weight, 0)
nn.init.constant_(block.adaLN_modulation[-1].bias, 0)
nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0)
nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0)
nn.init.constant_(self.final_layer.linear.weight, 0)
nn.init.constant_(self.final_layer.linear.bias, 0)
def unpatchify(self, x):
c = self.out_channels
p = self.x_embedder.patch_size[0]
h = w = int(x.shape[1] ** 0.5)
x = x.reshape(shape=(x.shape[0], h, w, p, p, c))
x = torch.einsum("nhwpqc->nchpwq", x)
return x.reshape(shape=(x.shape[0], c, h * p, h * p))
def forward(self, x, t, context, **kwargs):
y = context
if len(x.shape) != 4:
x = x.squeeze(2)
if self.training and self.capacity_schedule:
num_experts = self.MoE_config.num_experts
capacity = self.MoE_config.capacity
stage_i = self.MoE_config.capacity_schedule.capacity_schedule_stage_I_iters
stage_ii = self.MoE_config.capacity_schedule.capacity_schedule_stage_II_iters
if self.training_iters <= stage_i:
capacity = num_experts
elif self.training_iters <= stage_ii:
capacity = capacity + (num_experts - capacity) * (stage_ii - self.training_iters) / (stage_ii - stage_i)
for block in self.blocks:
if hasattr(block.mlp, "capacity"):
block.mlp.capacity = capacity
x = self.x_embedder(x) + self.pos_embed
t = self.t_embedder(t)
y = self.y_embedder(y, self.training)
c = t + y
ones_list, pred_c_list, layer_idx_list = [], [], []
for layer_idx, block in enumerate(self.blocks):
x, ones, pred_c = block(x, c)
if ones is not None:
ones_list.append(ones)
pred_c_list.append(pred_c)
layer_idx_list.append(layer_idx)
x = self.final_layer(x, c)
x = self.unpatchify(x)
return x, "Capacity_Pred", layer_idx_list, ones_list, pred_c_list, self.CapacityPred_loss_weight
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