Optimizations to --fast and scaled fp8.

comfy/ops.py

@@ -250,6 +250,12 @@ def fp8_linear(self, input):
     if dtype not in [torch.float8_e4m3fn]:
         return None
 
+    tensor_2d = False
+    if len(input.shape) == 2:
+        tensor_2d = True
+        input = input.unsqueeze(1)
+
+
     if len(input.shape) == 3:
         w, bias = cast_bias_weight(self, input, dtype=dtype, bias_dtype=input.dtype)
         w = w.t()
@@ -272,7 +278,11 @@ def fp8_linear(self, input):
         if isinstance(o, tuple):
             o = o[0]
 
+        if tensor_2d:
+            return o.reshape(input.shape[0], -1)
+
         return o.reshape((-1, input.shape[1], self.weight.shape[0]))
+
     return None
 
 class fp8_ops(manual_cast):
@@ -316,7 +326,11 @@ def forward_comfy_cast_weights(self, input):
                         return out
 
                 weight, bias = cast_bias_weight(self, input)
-                return torch.nn.functional.linear(input, weight * self.scale_weight.to(device=weight.device, dtype=weight.dtype), bias)
+
+                if weight.numel() < input.numel(): #TODO: optimize
+                    return torch.nn.functional.linear(input, weight * self.scale_weight.to(device=weight.device, dtype=weight.dtype), bias)
+                else:
+                    return torch.nn.functional.linear(input * self.scale_weight.to(device=weight.device, dtype=weight.dtype), weight, bias)
 
             def convert_weight(self, weight, inplace=False, **kwargs):
                 if inplace: