use nn.Sequential to remove python control flow from autoencoder up/downsampling

flux/modules/autoencoder.py

@@ -105,6 +105,16 @@ def forward(self, x: Tensor):
         x = self.conv(x)
         return x
 
+class DownBlock(nn.Module):
+    def __init__(self, block: list, downsample: nn.Module) -> None:
+        super().__init__()
+        # we're doing this instead of a flat nn.Sequential to preserve the keys "block" "downsample"
+        self.block = nn.Sequential(*block)
+        self.downsample = downsample
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.downsample(self.block(x))
+
 
 class Encoder(nn.Module):
     def __init__(
@@ -128,23 +138,25 @@ def __init__(
         curr_res = resolution
         in_ch_mult = (1,) + tuple(ch_mult)
         self.in_ch_mult = in_ch_mult
-        self.down = nn.ModuleList()
+        down_layers = []
         block_in = self.ch
+        # ideally, this would all append to a single flat nn.Sequential
+        # we cannot do this due to the existing state dict keys
         for i_level in range(self.num_resolutions):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
             block_in = ch * in_ch_mult[i_level]
             block_out = ch * ch_mult[i_level]
+            block_layers = []
             for _ in range(self.num_res_blocks):
-                block.append(ResnetBlock(in_channels=block_in, out_channels=block_out))
-                block_in = block_out
-            down = nn.Module()
-            down.block = block
-            down.attn = attn
+                block_layers.append(ResnetBlock(in_channels=block_in, out_channels=block_out))
+                block_in = block_out # ?
+            # originally this provided for attn layers, but those are never actually created
             if i_level != self.num_resolutions - 1:
-                down.downsample = Downsample(block_in)
+                downsample = Downsample(block_in)
                 curr_res = curr_res // 2
-            self.down.append(down)
+            else:
+                downsample = nn.Identity()
+            down_layers.append(DownBlock(block_layers, downsample))
+        self.down = nn.Sequential(*down_layers)
 
         # middle
         self.mid = nn.Module()
@@ -158,18 +170,10 @@ def __init__(
 
     def forward(self, x: Tensor) -> Tensor:
         # downsampling
-        hs = [self.conv_in(x)]
-        for i_level in range(self.num_resolutions):
-            for i_block in range(self.num_res_blocks):
-                h = self.down[i_level].block[i_block](hs[-1])
-                if len(self.down[i_level].attn) > 0:
-                    h = self.down[i_level].attn[i_block](h)
-                hs.append(h)
-            if i_level != self.num_resolutions - 1:
-                hs.append(self.down[i_level].downsample(hs[-1]))
+        h = self.conv_in(h)
+        h = self.down(h)
 
         # middle
-        h = hs[-1]
         h = self.mid.block_1(h)
         h = self.mid.attn_1(h)
         h = self.mid.block_2(h)
@@ -179,6 +183,15 @@ def forward(self, x: Tensor) -> Tensor:
         h = self.conv_out(h)
         return h
 
+class UpBlock(nn.Module):
+    def __init__(self, block: list, upsample: nn.Module) -> None:
+        super().__init__()
+        self.block = nn.Sequential(*block)
+        self.upsample = upsample
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.upsample(self.block(x))
+
 
 class Decoder(nn.Module):
     def __init__(
@@ -214,26 +227,37 @@ def __init__(
         self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in)
 
         # upsampling
-        self.up = nn.ModuleList()
+        up_blocks = []
+        # 3, 2, 1, 0, descending order
         for i_level in reversed(range(self.num_resolutions)):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
+            level_blocks = []
             block_out = ch * ch_mult[i_level]
             for _ in range(self.num_res_blocks + 1):
-                block.append(ResnetBlock(in_channels=block_in, out_channels=block_out))
+                level_blocks.append(ResnetBlock(in_channels=block_in, out_channels=block_out))
                 block_in = block_out
-            up = nn.Module()
-            up.block = block
-            up.attn = attn
             if i_level != 0:
-                up.upsample = Upsample(block_in)
+                upsample = Upsample(block_in)
                 curr_res = curr_res * 2
-            self.up.insert(0, up)  # prepend to get consistent order
+            else:
+                upsample = nn.Identity()
+            # 0, 1, 2, 3, ascending order
+            up_blocks.insert(0, UpBlock(level_blocks, upsample)) # prepend to get consistent order
+        self.up = nn.Sequential(*up_blocks)
 
         # end
         self.norm_out = nn.GroupNorm(num_groups=32, num_channels=block_in, eps=1e-6, affine=True)
         self.conv_out = nn.Conv2d(block_in, out_ch, kernel_size=3, stride=1, padding=1)
 
+    # this is a hack to get something like property but only evaluate it once
+    # we're doing it like this so that up_descending isn't in the state_dict keys
+    # without adding anything conditional to the main flow
+    def __getattr__(self, name):
+        if name == "up_descending":
+            self.up_descending = nn.Sequential(*reversed(self.up))
+            Decoder.__getattr__ = nn.Module.__getattr__
+            return self.up_descending
+        return super().__getattr__(name)
+
     def forward(self, z: Tensor) -> Tensor:
         # z to block_in
         h = self.conv_in(z)
@@ -244,13 +268,7 @@ def forward(self, z: Tensor) -> Tensor:
         h = self.mid.block_2(h)
 
         # upsampling
-        for i_level in reversed(range(self.num_resolutions)):
-            for i_block in range(self.num_res_blocks + 1):
-                h = self.up[i_level].block[i_block](h)
-                if len(self.up[i_level].attn) > 0:
-                    h = self.up[i_level].attn[i_block](h)
-            if i_level != 0:
-                h = self.up[i_level].upsample(h)
+        h = self.up_descending(h)
 
         # end
         h = self.norm_out(h)