remove triton dependency of DC-AE and fix bugs;

diffusion/model/dc_ae/efficientvit/models/efficientvit/dc_ae.py

@@ -48,7 +48,7 @@ class EncoderConfig:
     width_list: tuple[int, ...] = (128, 256, 512, 512, 1024, 1024)
     depth_list: tuple[int, ...] = (2, 2, 2, 2, 2, 2)
     block_type: Any = "ResBlock"
-    norm: str = "trms2d"
+    norm: str = "rms2d"
     act: str = "silu"
     downsample_block_type: str = "ConvPixelUnshuffle"
     downsample_match_channel: bool = True
@@ -67,12 +67,12 @@ class DecoderConfig:
     width_list: tuple[int, ...] = (128, 256, 512, 512, 1024, 1024)
     depth_list: tuple[int, ...] = (2, 2, 2, 2, 2, 2)
     block_type: Any = "ResBlock"
-    norm: Any = "trms2d"
+    norm: Any = "rms2d"
     act: Any = "silu"
     upsample_block_type: str = "ConvPixelShuffle"
     upsample_match_channel: bool = True
     upsample_shortcut: str = "duplicating"
-    out_norm: str = "trms2d"
+    out_norm: str = "rms2d"
     out_act: str = "relu"
 
 
@@ -470,7 +470,7 @@ def dc_ae_f32c32(name: str, pretrained_path: str) -> DCAEConfig:
             "decoder.block_type=[ResBlock,ResBlock,ResBlock,EViTS5_GLU,EViTS5_GLU,EViTS5_GLU] "
             "decoder.width_list=[128,256,512,512,1024,1024] decoder.depth_list=[3,3,3,3,3,3] "
             "decoder.upsample_block_type=InterpolateConv "
-            "decoder.norm=trms2d decoder.act=silu "
+            "decoder.norm=rms2d decoder.act=silu "
             "scaling_factor=0.41407"
         )
     else:

diffusion/model/dc_ae/efficientvit/models/nn/norm.py

@@ -40,12 +40,38 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
         return TritonRMSNorm2dFunc.apply(x, self.weight, self.bias, self.eps)
 
 
+class RMSNorm2d(nn.Module):
+    def __init__(
+        self, num_features: int, eps: float = 1e-5, elementwise_affine: bool = True, bias: bool = True
+    ) -> None:
+        super().__init__()
+        self.num_features = num_features
+        self.eps = eps
+        self.elementwise_affine = elementwise_affine
+        if self.elementwise_affine:
+            self.weight = torch.nn.parameter.Parameter(torch.empty(self.num_features))
+            if bias:
+                self.bias = torch.nn.parameter.Parameter(torch.empty(self.num_features))
+            else:
+                self.register_parameter("bias", None)
+        else:
+            self.register_parameter("weight", None)
+            self.register_parameter("bias", None)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = (x / torch.sqrt(torch.square(x.float()).mean(dim=1, keepdim=True) + self.eps)).to(x.dtype)
+        if self.elementwise_affine:
+            x = x * self.weight.view(1, -1, 1, 1) + self.bias.view(1, -1, 1, 1)
+        return x
+
+
 # register normalization function here
 REGISTERED_NORM_DICT: dict[str, type] = {
     "bn2d": nn.BatchNorm2d,
     "ln": nn.LayerNorm,
     "ln2d": LayerNorm2d,
     "trms2d": TritonRMSNorm2d,
+    "rms2d": RMSNorm2d,
 }