561 lines
20 KiB
Python
561 lines
20 KiB
Python
# Copyright (c) 2024 Black Forest Labs.
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# Copyright (c) 2025 Bytedance Ltd. and/or its affiliates.
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# SPDX-License-Identifier: Apache-2.0
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#
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# This file has been modified by ByteDance Ltd. and/or its affiliates. on 2025-05-20.
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#
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# Original file was released under Apache-2.0, with the full license text
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# available at https://github.com/black-forest-labs/flux/blob/main/LICENSE.
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#
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# This modified file is released under the same license.
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from dataclasses import dataclass
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import torch
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from einops import rearrange
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from torch import Tensor, nn
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from vllm.logger import init_logger
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from vllm_omni.diffusion.distributed.autoencoders.distributed_vae_executor import (
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DistributedOperator,
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DistributedVaeMixin,
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GridSpec,
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TileTask,
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)
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logger = init_logger(__name__)
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@dataclass
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class AutoEncoderParams:
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resolution: int
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in_channels: int
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downsample: int
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ch: int
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out_ch: int
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ch_mult: list[int]
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num_res_blocks: int
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z_channels: int
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scale_factor: float
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shift_factor: float
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def swish(x: Tensor) -> Tensor:
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return x * torch.sigmoid(x)
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class AttnBlock(nn.Module):
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def __init__(self, in_channels: int):
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super().__init__()
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self.in_channels = in_channels
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self.norm = nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
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self.q = nn.Conv2d(in_channels, in_channels, kernel_size=1)
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self.k = nn.Conv2d(in_channels, in_channels, kernel_size=1)
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self.v = nn.Conv2d(in_channels, in_channels, kernel_size=1)
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self.proj_out = nn.Conv2d(in_channels, in_channels, kernel_size=1)
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def attention(self, h_: Tensor) -> Tensor:
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h_ = self.norm(h_)
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q = self.q(h_)
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k = self.k(h_)
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v = self.v(h_)
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b, c, h, w = q.shape
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q = rearrange(q, "b c h w -> b 1 (h w) c").contiguous()
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k = rearrange(k, "b c h w -> b 1 (h w) c").contiguous()
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v = rearrange(v, "b c h w -> b 1 (h w) c").contiguous()
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h_ = nn.functional.scaled_dot_product_attention(q, k, v)
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return rearrange(h_, "b 1 (h w) c -> b c h w", h=h, w=w, c=c, b=b)
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def forward(self, x: Tensor) -> Tensor:
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return x + self.proj_out(self.attention(x))
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class ResnetBlock(nn.Module):
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def __init__(self, in_channels: int, out_channels: int):
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super().__init__()
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self.in_channels = in_channels
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out_channels = in_channels if out_channels is None else out_channels
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self.out_channels = out_channels
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self.norm1 = nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
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self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
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self.norm2 = nn.GroupNorm(num_groups=32, num_channels=out_channels, eps=1e-6, affine=True)
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self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
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if self.in_channels != self.out_channels:
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self.nin_shortcut = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
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def forward(self, x):
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h = x
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h = self.norm1(h)
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h = swish(h)
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h = self.conv1(h)
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h = self.norm2(h)
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h = swish(h)
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h = self.conv2(h)
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if self.in_channels != self.out_channels:
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x = self.nin_shortcut(x)
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return x + h
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class Downsample(nn.Module):
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def __init__(self, in_channels: int):
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super().__init__()
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# no asymmetric padding in torch conv, must do it ourselves
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self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0)
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def forward(self, x: Tensor):
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pad = (0, 1, 0, 1)
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x = nn.functional.pad(x, pad, mode="constant", value=0)
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x = self.conv(x)
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return x
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class Upsample(nn.Module):
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def __init__(self, in_channels: int):
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super().__init__()
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self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
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def forward(self, x: Tensor):
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x = nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
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x = self.conv(x)
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return x
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class Encoder(nn.Module):
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def __init__(
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self,
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resolution: int,
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in_channels: int,
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ch: int,
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ch_mult: list[int],
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num_res_blocks: int,
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z_channels: int,
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):
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super().__init__()
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self.ch = ch
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self.num_resolutions = len(ch_mult)
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self.num_res_blocks = num_res_blocks
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self.resolution = resolution
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self.in_channels = in_channels
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# downsampling
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self.conv_in = nn.Conv2d(in_channels, self.ch, kernel_size=3, stride=1, padding=1)
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curr_res = resolution
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in_ch_mult = (1,) + tuple(ch_mult)
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self.in_ch_mult = in_ch_mult
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self.down = nn.ModuleList()
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block_in = self.ch
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for i_level in range(self.num_resolutions):
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block = nn.ModuleList()
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attn = nn.ModuleList()
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block_in = ch * in_ch_mult[i_level]
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block_out = ch * ch_mult[i_level]
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for _ in range(self.num_res_blocks):
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block.append(ResnetBlock(in_channels=block_in, out_channels=block_out))
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block_in = block_out
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down = nn.Module()
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down.block = block
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down.attn = attn
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if i_level != self.num_resolutions - 1:
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down.downsample = Downsample(block_in)
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curr_res = curr_res // 2
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self.down.append(down)
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# middle
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self.mid = nn.Module()
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self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in)
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self.mid.attn_1 = AttnBlock(block_in)
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self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in)
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# end
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self.norm_out = nn.GroupNorm(num_groups=32, num_channels=block_in, eps=1e-6, affine=True)
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self.conv_out = nn.Conv2d(block_in, 2 * z_channels, kernel_size=3, stride=1, padding=1)
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def forward(self, x: Tensor) -> Tensor:
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# downsampling
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hs = [self.conv_in(x)]
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for i_level in range(self.num_resolutions):
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for i_block in range(self.num_res_blocks):
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h = self.down[i_level].block[i_block](hs[-1])
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if len(self.down[i_level].attn) > 0:
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h = self.down[i_level].attn[i_block](h)
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hs.append(h)
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if i_level != self.num_resolutions - 1:
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hs.append(self.down[i_level].downsample(hs[-1]))
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# middle
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h = hs[-1]
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h = self.mid.block_1(h)
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h = self.mid.attn_1(h)
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h = self.mid.block_2(h)
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# end
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h = self.norm_out(h)
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h = swish(h)
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h = self.conv_out(h)
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return h
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class Decoder(nn.Module):
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def __init__(
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self,
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ch: int,
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out_ch: int,
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ch_mult: list[int],
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num_res_blocks: int,
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in_channels: int,
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resolution: int,
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z_channels: int,
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):
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super().__init__()
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self.ch = ch
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self.num_resolutions = len(ch_mult)
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self.num_res_blocks = num_res_blocks
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self.resolution = resolution
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self.in_channels = in_channels
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self.ffactor = 2 ** (self.num_resolutions - 1)
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# compute in_ch_mult, block_in and curr_res at lowest res
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block_in = ch * ch_mult[self.num_resolutions - 1]
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curr_res = resolution // 2 ** (self.num_resolutions - 1)
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self.z_shape = (1, z_channels, curr_res, curr_res)
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# z to block_in
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self.conv_in = nn.Conv2d(z_channels, block_in, kernel_size=3, stride=1, padding=1)
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# middle
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self.mid = nn.Module()
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self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in)
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self.mid.attn_1 = AttnBlock(block_in)
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self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in)
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# upsampling
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self.up = nn.ModuleList()
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for i_level in reversed(range(self.num_resolutions)):
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block = nn.ModuleList()
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attn = nn.ModuleList()
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block_out = ch * ch_mult[i_level]
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for _ in range(self.num_res_blocks + 1):
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block.append(ResnetBlock(in_channels=block_in, out_channels=block_out))
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block_in = block_out
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up = nn.Module()
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up.block = block
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up.attn = attn
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if i_level != 0:
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up.upsample = Upsample(block_in)
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curr_res = curr_res * 2
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self.up.insert(0, up) # prepend to get consistent order
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# end
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self.norm_out = nn.GroupNorm(num_groups=32, num_channels=block_in, eps=1e-6, affine=True)
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self.conv_out = nn.Conv2d(block_in, out_ch, kernel_size=3, stride=1, padding=1)
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def forward(self, z: Tensor) -> Tensor:
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# z to block_in
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h = self.conv_in(z)
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# middle
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h = self.mid.block_1(h)
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h = self.mid.attn_1(h)
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h = self.mid.block_2(h)
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# upsampling
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for i_level in reversed(range(self.num_resolutions)):
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for i_block in range(self.num_res_blocks + 1):
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h = self.up[i_level].block[i_block](h)
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if len(self.up[i_level].attn) > 0:
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h = self.up[i_level].attn[i_block](h)
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if i_level != 0:
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h = self.up[i_level].upsample(h)
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# end
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h = self.norm_out(h)
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h = swish(h)
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h = self.conv_out(h)
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return h
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class DiagonalGaussian(nn.Module):
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def __init__(self, sample: bool = True, chunk_dim: int = 1):
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super().__init__()
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self.sample = sample
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self.chunk_dim = chunk_dim
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def forward(self, z: Tensor) -> Tensor:
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mean, logvar = torch.chunk(z, 2, dim=self.chunk_dim)
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if self.sample:
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std = torch.exp(0.5 * logvar)
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return mean + std * torch.randn_like(mean)
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else:
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return mean
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class AutoEncoder(nn.Module):
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def __init__(self, params: AutoEncoderParams):
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super().__init__()
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self.encoder = Encoder(
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resolution=params.resolution,
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in_channels=params.in_channels,
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ch=params.ch,
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ch_mult=params.ch_mult,
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num_res_blocks=params.num_res_blocks,
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z_channels=params.z_channels,
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)
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self.decoder = Decoder(
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resolution=params.resolution,
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in_channels=params.in_channels,
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ch=params.ch,
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out_ch=params.out_ch,
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ch_mult=params.ch_mult,
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num_res_blocks=params.num_res_blocks,
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z_channels=params.z_channels,
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)
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self.reg = DiagonalGaussian()
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self.scale_factor = params.scale_factor
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self.shift_factor = params.shift_factor
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def encode(self, x: Tensor) -> Tensor:
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z = self.reg(self.encoder(x))
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z = self.scale_factor * (z - self.shift_factor)
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return z
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def decode(self, z: Tensor) -> Tensor:
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z = z / self.scale_factor + self.shift_factor
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return self.decoder(z)
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def forward(self, x: Tensor) -> Tensor:
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return self.decode(self.encode(x))
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class DistributedAutoEncoder(AutoEncoder, DistributedVaeMixin):
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def __init__(self, params: AutoEncoderParams):
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super().__init__(params)
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self.init_distributed()
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self.use_tiling = False
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self.tile_sample_min_height = 512
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self.tile_sample_min_width = 512
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self.tile_sample_stride_height = 448
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self.tile_sample_stride_width = 448
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self.spatial_compression_ratio = params.downsample
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def blend_v(self, above: Tensor, current: Tensor, blend_extent: int) -> Tensor:
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blend_extent = min(above.shape[-2], current.shape[-2], blend_extent)
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if blend_extent <= 0:
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return current
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for y in range(blend_extent):
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alpha = y / blend_extent
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current[:, :, y, :] = above[:, :, -blend_extent + y, :] * (1 - alpha) + current[:, :, y, :] * alpha
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return current
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def blend_h(self, left: Tensor, current: Tensor, blend_extent: int) -> Tensor:
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blend_extent = min(left.shape[-1], current.shape[-1], blend_extent)
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if blend_extent <= 0:
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return current
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for x in range(blend_extent):
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alpha = x / blend_extent
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current[:, :, :, x] = left[:, :, :, -blend_extent + x] * (1 - alpha) + current[:, :, :, x] * alpha
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return current
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def decode_tile_split(self, z: Tensor) -> tuple[list[TileTask], GridSpec]:
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_, _, latent_height, latent_width = z.shape
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sample_height = latent_height * self.spatial_compression_ratio
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sample_width = latent_width * self.spatial_compression_ratio
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tile_latent_min_height = self.tile_sample_min_height // self.spatial_compression_ratio
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tile_latent_min_width = self.tile_sample_min_width // self.spatial_compression_ratio
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tile_latent_stride_height = self.tile_sample_stride_height // self.spatial_compression_ratio
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tile_latent_stride_width = self.tile_sample_stride_width // self.spatial_compression_ratio
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blend_height = self.tile_sample_min_height - self.tile_sample_stride_height
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blend_width = self.tile_sample_min_width - self.tile_sample_stride_width
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tiletask_list = []
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for i in range(0, latent_height, tile_latent_stride_height):
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for j in range(0, latent_width, tile_latent_stride_width):
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tile = z[
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:,
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:,
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i : i + tile_latent_min_height,
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j : j + tile_latent_min_width,
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]
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tiletask_list.append(
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TileTask(
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tile_id=len(tiletask_list),
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grid_coord=(i // tile_latent_stride_height, j // tile_latent_stride_width),
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tensor=tile,
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workload=tile.shape[-2] * tile.shape[-1],
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)
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)
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grid_spec = GridSpec(
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split_dims=(2, 3),
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grid_shape=(tiletask_list[-1].grid_coord[0] + 1, tiletask_list[-1].grid_coord[1] + 1),
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tile_spec={
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"sample_height": sample_height,
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"sample_width": sample_width,
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"blend_height": blend_height,
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"blend_width": blend_width,
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},
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output_dtype=z.dtype,
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)
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return tiletask_list, grid_spec
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def decode_tile_exec(self, task: TileTask) -> Tensor:
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tile = task.tensor
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tile = tile / self.scale_factor + self.shift_factor
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return self.decoder(tile)
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def decode_tile_merge(self, coord_tensor_map: dict[tuple[int, ...], Tensor], grid_spec: GridSpec) -> Tensor:
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grid_h, grid_w = grid_spec.grid_shape
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result_rows = []
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for i in range(grid_h):
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result_row = []
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for j in range(grid_w):
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tile = coord_tensor_map[(i, j)]
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if i > 0:
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tile = self.blend_v(coord_tensor_map[(i - 1, j)], tile, grid_spec.tile_spec["blend_height"])
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if j > 0:
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tile = self.blend_h(coord_tensor_map[(i, j - 1)], tile, grid_spec.tile_spec["blend_width"])
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result_row.append(
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tile[
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:,
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:,
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: self.tile_sample_stride_height,
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: self.tile_sample_stride_width,
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]
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)
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result_rows.append(torch.cat(result_row, dim=-1))
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dec = torch.cat(result_rows, dim=-2)[
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:,
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:,
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: grid_spec.tile_spec["sample_height"],
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: grid_spec.tile_spec["sample_width"],
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]
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return dec
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def decode(self, z: Tensor) -> Tensor:
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if not self.is_distributed_enabled():
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return super().decode(z)
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logger.debug("Bagel VAE decode running with distributed executor")
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return self.distributed_executor.execute(
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z,
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DistributedOperator(
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split=self.decode_tile_split,
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exec=self.decode_tile_exec,
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merge=self.decode_tile_merge,
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),
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broadcast_result=True,
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)
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def encode_tile_split(self, x: Tensor) -> tuple[list[TileTask], GridSpec]:
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_, _, height, width = x.shape
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latent_height = height // self.spatial_compression_ratio
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latent_width = width // self.spatial_compression_ratio
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tile_latent_min_height = self.tile_sample_min_height // self.spatial_compression_ratio
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tile_latent_min_width = self.tile_sample_min_width // self.spatial_compression_ratio
|
|
|
|
tile_latent_stride_height = self.tile_sample_stride_height // self.spatial_compression_ratio
|
|
tile_latent_stride_width = self.tile_sample_stride_width // self.spatial_compression_ratio
|
|
|
|
blend_height = tile_latent_min_height - tile_latent_stride_height
|
|
blend_width = tile_latent_min_width - tile_latent_stride_width
|
|
|
|
tiletask_list = []
|
|
for i in range(0, height, self.tile_sample_stride_height):
|
|
for j in range(0, width, self.tile_sample_stride_width):
|
|
tile = x[
|
|
:,
|
|
:,
|
|
i : i + self.tile_sample_min_height,
|
|
j : j + self.tile_sample_min_width,
|
|
]
|
|
tiletask_list.append(
|
|
TileTask(
|
|
tile_id=len(tiletask_list),
|
|
grid_coord=(i // self.tile_sample_stride_height, j // self.tile_sample_stride_width),
|
|
tensor=tile,
|
|
workload=tile.shape[-2] * tile.shape[-1],
|
|
)
|
|
)
|
|
grid_spec = GridSpec(
|
|
split_dims=(2, 3),
|
|
grid_shape=(tiletask_list[-1].grid_coord[0] + 1, tiletask_list[-1].grid_coord[1] + 1),
|
|
tile_spec={
|
|
"latent_height": latent_height,
|
|
"latent_width": latent_width,
|
|
"blend_height": blend_height,
|
|
"blend_width": blend_width,
|
|
"tile_latent_stride_height": tile_latent_stride_height,
|
|
"tile_latent_stride_width": tile_latent_stride_width,
|
|
},
|
|
output_dtype=next(self.parameters()).dtype,
|
|
)
|
|
return tiletask_list, grid_spec
|
|
|
|
def encode_tile_exec(self, task: TileTask) -> Tensor:
|
|
tile = task.tensor
|
|
z = self.reg(self.encoder(tile))
|
|
z = self.scale_factor * (z - self.shift_factor)
|
|
return z
|
|
|
|
def encode_tile_merge(self, coord_tensor_map: dict[tuple[int, ...], Tensor], grid_spec: GridSpec) -> Tensor:
|
|
grid_h, grid_w = grid_spec.grid_shape
|
|
result_rows = []
|
|
|
|
for i in range(grid_h):
|
|
result_row = []
|
|
for j in range(grid_w):
|
|
tile = coord_tensor_map[(i, j)]
|
|
if i > 0:
|
|
tile = self.blend_v(coord_tensor_map[(i - 1, j)], tile, grid_spec.tile_spec["blend_height"])
|
|
if j > 0:
|
|
tile = self.blend_h(coord_tensor_map[(i, j - 1)], tile, grid_spec.tile_spec["blend_width"])
|
|
|
|
result_row.append(
|
|
tile[
|
|
:,
|
|
:,
|
|
: grid_spec.tile_spec["tile_latent_stride_height"],
|
|
: grid_spec.tile_spec["tile_latent_stride_width"],
|
|
]
|
|
)
|
|
result_rows.append(torch.cat(result_row, dim=-1))
|
|
|
|
enc = torch.cat(result_rows, dim=-2)[
|
|
:,
|
|
:,
|
|
: grid_spec.tile_spec["latent_height"],
|
|
: grid_spec.tile_spec["latent_width"],
|
|
]
|
|
return enc
|
|
|
|
def encode(self, x: Tensor) -> Tensor:
|
|
if not self.is_distributed_enabled():
|
|
return super().encode(x)
|
|
logger.debug("Bagel VAE encode running with distributed executor")
|
|
return self.distributed_executor.execute(
|
|
x,
|
|
DistributedOperator(
|
|
split=self.encode_tile_split,
|
|
exec=self.encode_tile_exec,
|
|
merge=self.encode_tile_merge,
|
|
),
|
|
broadcast_result=True,
|
|
)
|