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# Torch-native normalization for the MiniMax H3 visual VAE.
import math
import os
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
from .conv import SpatialParallelConv3d
from .parallel import all_reduce, get_parallel_state
def _validate_activation(activation):
valid_activations = {"identity", "silu", "relu"}
if activation not in valid_activations:
raise ValueError(
f"Unsupported activation: {activation}. Supported: {valid_activations}"
)
def _apply_activation(x, activation):
_validate_activation(activation)
if activation == "identity":
return x
if activation == "silu":
return F.silu(x)
return F.relu(x)
def _merge_time_to_batch(x):
batch, channels, depth, height, width = x.shape
return (
x.permute(0, 2, 1, 3, 4)
.contiguous()
.view(batch * depth, channels, 1, height, width)
)
def _split_time_from_batch(x, batch):
batch_depth, channels, _, height, width = x.shape
depth = batch_depth // batch
return (
x.view(batch, depth, channels, height, width)
.permute(0, 2, 1, 3, 4)
.contiguous()
)
def fused_group_norm(x, num_groups, weight, bias, eps=1e-5, activation="silu"):
out = F.group_norm(x, num_groups, weight=weight, bias=bias, eps=eps)
return _apply_activation(out, activation)
def fused_spatial_norm(
f,
num_groups,
norm_weight,
norm_bias,
dynamic_scale,
dynamic_bias,
eps=1e-5,
activation="silu",
):
norm_f = F.group_norm(
f,
num_groups,
weight=norm_weight,
bias=norm_bias,
eps=eps,
)
out = norm_f * dynamic_scale + dynamic_bias
return _apply_activation(out, activation)
class DummyAffine(torch.nn.Module):
def __init__(self, num_channels, affine=True):
super().__init__()
if affine:
self.weight = torch.nn.Parameter(torch.ones(num_channels))
self.bias = torch.nn.Parameter(torch.zeros(num_channels))
else:
self.register_parameter("weight", None)
self.register_parameter("bias", None)
def forward(self, input):
if self.weight is None:
return input
shape = [1, -1] + [1] * (input.dim() - 2)
return input * self.weight.view(*shape) + self.bias.view(*shape)
class FusedGroupNorm3D(torch.nn.Module):
"""Compatibility wrapper implemented with native PyTorch ops."""
def __init__(
self,
num_groups,
num_channels,
eps=1e-5,
affine=True,
activation="silu",
cond_channels=None,
use_t_isolated_gn=False,
padding_mode="zeros",
padding_mode_t=None,
causal=True,
):
super().__init__()
_validate_activation(activation)
self.num_groups = num_groups
self.num_channels = num_channels
self.eps = eps
self.affine = affine
self.activation = activation
self.use_t_isolated_gn = use_t_isolated_gn
if cond_channels is not None:
self.use_spatial_affine = True
self.norm_layer = DummyAffine(num_channels, affine=affine)
self.conv_y = SpatialParallelConv3d(
cond_channels,
num_channels,
kernel_size=1,
padding_mode=padding_mode,
padding_mode_t=padding_mode_t,
causal=causal,
)
self.conv_b = SpatialParallelConv3d(
cond_channels,
num_channels,
kernel_size=1,
padding_mode=padding_mode,
padding_mode_t=padding_mode_t,
causal=causal,
)
else:
self.use_spatial_affine = False
if self.affine:
self.weight = torch.nn.Parameter(torch.ones(num_channels))
self.bias = torch.nn.Parameter(torch.zeros(num_channels))
else:
self.register_parameter("weight", None)
self.register_parameter("bias", None)
def forward(self, f, cond=None):
need_reshape = self.use_t_isolated_gn and f.dim() == 5
batch = f.shape[0] if need_reshape else None
f_size = f.shape[-3:]
if need_reshape:
f = _merge_time_to_batch(f)
if self.use_spatial_affine:
scale = self.conv_y(cond)
bias = self.conv_b(cond)
if math.prod(scale.shape[-3:]) * math.prod(bias.shape[-3:]) > 1:
scale = F.interpolate(scale, size=f_size, mode="nearest")
bias = F.interpolate(bias, size=f_size, mode="nearest")
if need_reshape:
scale = _merge_time_to_batch(scale)
bias = _merge_time_to_batch(bias)
out = fused_spatial_norm(
f,
self.num_groups,
self.norm_layer.weight,
self.norm_layer.bias,
scale,
bias,
self.eps,
self.activation,
)
else:
if cond is not None:
raise NotImplementedError("Dynamic affine is not defined")
weight = self.weight if self.affine else None
bias = self.bias if self.affine else None
out = fused_group_norm(
f, self.num_groups, weight, bias, self.eps, self.activation
)
if need_reshape:
out = _split_time_from_batch(out, batch)
return out
class SpatialParallelGroupNorm(nn.GroupNorm):
def __init__(
self,
*args,
**kwargs,
):
super().__init__(*args, **kwargs)
self.spatial_parallel = False
def _compute_stats(self, input):
batch, channels = input.shape[0], input.shape[1]
spatial_dims = input.shape[2:]
spatial_size = math.prod(spatial_dims)
groups = self.num_groups
x = input.reshape(batch, groups, channels // groups, -1).to(torch.float32)
local_sum = x.sum(dim=(2, 3))
local_square_sum = (x * x).sum(dim=(2, 3))
local_n = (channels // groups) * spatial_size
local_n_tensor = torch.full_like(local_sum, float(local_n))
stats = torch.stack([local_sum, local_square_sum, local_n_tensor], dim=0)
local_process_group = get_parallel_state()["local_process_group"]
stats = all_reduce(stats, dist.ReduceOp.SUM, local_process_group)
total_sum = stats[0]
total_square_sum = stats[1]
total_n = stats[2]
mean = total_sum / total_n
var = (total_square_sum / total_n) - mean**2
return mean, var
def forward(self, input):
if not self.spatial_parallel:
return nn.GroupNorm.forward(self, input)
batch, channels = input.shape[0], input.shape[1]
orig_shape = input.shape
mean, var = self._compute_stats(input)
x = input.reshape(batch, self.num_groups, channels // self.num_groups, -1)
mean = mean.unsqueeze(-1).unsqueeze(-1)
var = var.unsqueeze(-1).unsqueeze(-1)
x = (x - mean) / torch.sqrt(var + self.eps)
x = x.reshape(orig_shape)
if self.affine:
shape = [1, -1] + [1] * (len(orig_shape) - 2)
x *= self.weight.view(*shape)
x += self.bias.view(*shape)
return x
class TemporalIsolatedSpatialParallelGroupNorm(SpatialParallelGroupNorm):
def forward(self, input):
if input.dim() == 5:
batch = input.shape[0]
input = _merge_time_to_batch(input)
output = super().forward(input)
return _split_time_from_batch(output, batch)
return super().forward(input)
class SpatialNorm3D(nn.Module):
def __init__(
self,
f_channels,
zq_channels,
padding_mode="zeros",
padding_mode_t=None,
causal=True,
use_t_isolated_gn=False,
):
super().__init__()
norm_cls = (
TemporalIsolatedSpatialParallelGroupNorm
if use_t_isolated_gn
else SpatialParallelGroupNorm
)
self.norm_layer = norm_cls(
num_groups=32, num_channels=f_channels, eps=1e-6, affine=True
)
self.conv_y = SpatialParallelConv3d(
zq_channels,
f_channels,
kernel_size=1,
padding_mode=padding_mode,
padding_mode_t=padding_mode_t,
causal=causal,
)
self.conv_b = SpatialParallelConv3d(
zq_channels,
f_channels,
kernel_size=1,
padding_mode=padding_mode,
padding_mode_t=padding_mode_t,
causal=causal,
)
def forward(self, f, zq):
f_size = f.shape[-3:]
norm_f = self.norm_layer(f)
scale = self.conv_y(zq)
bias = self.conv_b(zq)
if math.prod(scale.shape[-3:]) * math.prod(bias.shape[-3:]) > 1:
scale = F.interpolate(scale, size=f_size, mode="nearest")
bias = F.interpolate(bias, size=f_size, mode="nearest")
return norm_f * scale + bias
def get_spatial_norm_3d(
num_channels,
cond_channels,
*,
padding_mode="zeros",
padding_mode_t=None,
causal=True,
use_t_isolated_gn=False,
):
if os.environ.get("MINIMAX_H3_USE_FUSED_NORM", "false").lower() == "true":
return FusedGroupNorm3D(
num_groups=32,
num_channels=num_channels,
eps=1e-6,
affine=True,
cond_channels=cond_channels,
use_t_isolated_gn=use_t_isolated_gn,
padding_mode=padding_mode,
padding_mode_t=padding_mode_t,
causal=causal,
)
return SpatialNorm3D(
num_channels,
cond_channels,
padding_mode=padding_mode,
padding_mode_t=padding_mode_t,
causal=causal,
use_t_isolated_gn=use_t_isolated_gn,
)
def get_group_norm_3d(num_channels, use_t_isolated_gn=False):
if os.environ.get("MINIMAX_H3_USE_FUSED_NORM", "false").lower() == "true":
return FusedGroupNorm3D(
num_groups=32,
num_channels=num_channels,
eps=1e-6,
affine=True,
use_t_isolated_gn=use_t_isolated_gn,
)
norm_cls = (
TemporalIsolatedSpatialParallelGroupNorm
if use_t_isolated_gn
else SpatialParallelGroupNorm
)
return norm_cls(num_groups=32, num_channels=num_channels, eps=1e-6, affine=True)
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