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# 3D causal CNN encoder for the MiniMax H3 visual VAE (inference-only bundle).
import os
import torch.nn as nn
import torch.nn.functional as F
from .attention import maybe_checkpoint
from .conv import SpatialParallelConv3d
from .norm import get_spatial_norm_3d
from .parallel import get_parallel_state, exchange_strides
from .norm import get_group_norm_3d
# ============================================================================
# 3D CNN Components
# ============================================================================
def norm_silu(x, norm, cond=None):
if cond is None:
return F.silu(norm(x))
else:
return F.silu(norm(x, cond))
class Downsample3D(nn.Module):
def __init__(
self,
in_channels,
out_channels,
time_stride=1,
space_stride=2,
padding_mode="zeros",
padding_mode_t=None,
causal=True,
):
super().__init__()
self.time_stride = time_stride
self.space_stride = space_stride
assert time_stride in [1, 2]
assert space_stride in [1, 2, 3]
self.conv = SpatialParallelConv3d(
in_channels,
out_channels,
kernel_size=3,
padding=(1, 0, 0),
stride=(time_stride, space_stride, space_stride),
padding_mode=padding_mode,
padding_mode_t=padding_mode_t,
causal=causal,
)
self.causal = self.conv.causal
self.pad_mode = self.conv.pad_mode
def forward(self, x):
if self.space_stride == 2:
if getattr(self.conv, "spatial_parallel", False):
state = get_parallel_state()
x = exchange_strides(
x,
self.pad_mode,
state["sp_rank"],
state["sp_size"],
state["sp_process_group"],
self.conv.chunk_dim,
)
else:
pad = (0, 1, 0, 1, 0, 0)
x = F.pad(x, pad, mode=self.pad_mode)
return self.conv(x)
class ResnetBlock3D(nn.Module):
def __init__(
self,
in_channels,
out_channels=None,
zq_ch=None,
padding_mode="zeros",
padding_mode_t=None,
causal=True,
use_t_isolated_gn=False,
):
super().__init__()
self.in_channels = in_channels
out_channels = in_channels if out_channels is None else out_channels
self.out_channels = out_channels
self.use_fused_norm = (
os.environ.get("MINIMAX_H3_USE_FUSED_NORM", "false").lower() == "true"
)
if zq_ch is None:
self.norm1 = get_group_norm_3d(in_channels, use_t_isolated_gn=use_t_isolated_gn)
self.norm2 = get_group_norm_3d(out_channels, use_t_isolated_gn=use_t_isolated_gn)
else:
self.norm1 = get_spatial_norm_3d(
in_channels,
zq_ch,
padding_mode=padding_mode,
padding_mode_t=padding_mode_t,
causal=causal,
use_t_isolated_gn=use_t_isolated_gn,
)
self.norm2 = get_spatial_norm_3d(
out_channels,
zq_ch,
padding_mode=padding_mode,
padding_mode_t=padding_mode_t,
causal=causal,
use_t_isolated_gn=use_t_isolated_gn,
)
self.conv1 = SpatialParallelConv3d(
in_channels,
out_channels,
kernel_size=3,
padding=1,
padding_mode=padding_mode,
padding_mode_t=padding_mode_t,
causal=causal,
)
self.conv2 = SpatialParallelConv3d(
out_channels,
out_channels,
kernel_size=3,
padding=1,
padding_mode=padding_mode,
padding_mode_t=padding_mode_t,
causal=causal,
)
if self.in_channels != self.out_channels:
self.nin_shortcut = SpatialParallelConv3d(
in_channels,
out_channels,
kernel_size=1,
padding_mode=padding_mode,
padding_mode_t=padding_mode_t,
causal=causal,
)
def forward(self, x, zq=None):
h = x
if self.use_fused_norm:
h = self.norm1(h, zq)
else:
h = norm_silu(h, self.norm1, zq)
h = self.conv1(h)
if self.use_fused_norm:
h = self.norm2(h, zq)
else:
h = norm_silu(h, self.norm2, zq)
h = self.conv2(h)
if self.in_channels != self.out_channels:
x = self.nin_shortcut(x)
return x + h
class EncoderFCN3D(nn.Module):
def __init__(
self,
ch,
ch_mult,
space_down,
time_down,
num_res_blocks,
in_channels,
z_channels,
double_z=False,
zq_ch=None,
padding_mode="zeros",
padding_mode_t=None,
causal=True,
use_t_isolated_gn=False,
):
super().__init__()
self.ch = ch
self.num_levels = len(ch_mult)
if isinstance(num_res_blocks, int):
self.num_res_blocks = [num_res_blocks] * self.num_levels
else:
self.num_res_blocks = num_res_blocks
self.space_down_factors = space_down
self.time_down_factors = time_down
self.in_channels = in_channels
self.use_fused_norm = (
os.environ.get("MINIMAX_H3_USE_FUSED_NORM", "false").lower() == "true"
)
block_mid = [ch * ch_mult[i] for i in range(self.num_levels)]
block_in = [block_mid[0]] + block_mid[:-1]
block_out = block_mid
conv_kwargs = dict(
padding_mode=padding_mode,
padding_mode_t=padding_mode_t,
causal=causal,
)
self.conv_in = SpatialParallelConv3d(
in_channels, block_in[0], kernel_size=3, padding=1, **conv_kwargs
)
self.down = nn.ModuleList()
for i_level in range(self.num_levels):
down = nn.Module()
down.block = nn.ModuleList()
for i in range(self.num_res_blocks[i_level]):
down.block.append(
ResnetBlock3D(
in_channels=block_in[i_level] if i == 0 else block_mid[i_level],
out_channels=block_mid[i_level],
zq_ch=zq_ch,
use_t_isolated_gn=use_t_isolated_gn,
**conv_kwargs,
)
)
if space_down[i_level] * time_down[i_level] > 1:
down.downsample = Downsample3D(
block_mid[i_level],
block_out[i_level],
time_stride=time_down[i_level],
space_stride=space_down[i_level],
**conv_kwargs,
)
else:
if block_out[i_level] != block_mid[i_level]:
down.downsample = SpatialParallelConv3d(
block_mid[i_level],
block_out[i_level],
kernel_size=1,
**conv_kwargs,
)
self.down.append(down)
if zq_ch is None:
self.norm_out = get_group_norm_3d(
block_out[-1], use_t_isolated_gn=use_t_isolated_gn
)
else:
self.norm_out = get_spatial_norm_3d(
block_out[-1],
zq_ch,
use_t_isolated_gn=use_t_isolated_gn,
**conv_kwargs,
)
self.conv_out = SpatialParallelConv3d(
block_out[-1],
2 * z_channels if double_z else z_channels,
kernel_size=3,
padding=1,
**conv_kwargs,
)
self.gradient_checkpointing = False
def _set_gradient_checkpointing(self, module, value=False):
if hasattr(module, "gradient_checkpointing"):
module.gradient_checkpointing = value
def forward(self, x, zq=None):
h = self.conv_in(x)
for i_level in range(self.num_levels):
for i_block in range(self.num_res_blocks[i_level]):
h = maybe_checkpoint(self, self.down[i_level].block[i_block], h, zq)
if hasattr(self.down[i_level], "downsample"):
h = self.down[i_level].downsample(h)
if self.use_fused_norm:
h = self.norm_out(h, zq)
else:
h = norm_silu(h, self.norm_out, zq)
h = self.conv_out(h)
return h
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