Make --fast work on pytorch nightly.

comfy/ops.py

@@ -254,16 +254,33 @@ def fp8_linear(self, input):
  non_blocking = comfy.model_management.device_supports_non_blocking(input.device)
  w = cast_to(self.weight, device=input.device, non_blocking=non_blocking).t()
 
+ scale_weight = self.scale_weight
+ scale_input = self.scale_input
+ if scale_weight is None:
+ scale_weight = torch.ones((1), device=input.device, dtype=torch.float32)
+ if scale_input is None:
+ scale_input = scale_weight
+ if scale_input is None:
+ scale_input = torch.ones((1), device=input.device, dtype=torch.float32)
+
  if self.bias is not None:
- o, _ = torch._scaled_mm(inn, w, out_dtype=input.dtype, bias=cast_to_input(self.bias, input, non_blocking=non_blocking))
+ o = torch._scaled_mm(inn, w, out_dtype=input.dtype, bias=cast_to_input(self.bias, input, non_blocking=non_blocking), scale_a=scale_input, scale_b=scale_weight)
  else:
- o, _ = torch._scaled_mm(inn, w, out_dtype=input.dtype)
+ o = torch._scaled_mm(inn, w, out_dtype=input.dtype, scale_a=scale_input, scale_b=scale_weight)
+
+ if isinstance(o, tuple):
+ o = o[0]
 
  return o.reshape((-1, input.shape[1], self.weight.shape[0]))
  return None
 
 class fp8_ops(manual_cast):
  class Linear(manual_cast.Linear):
+ def reset_parameters(self):
+ self.scale_weight = None
+ self.scale_input = None
+ return None
+
  def forward_comfy_cast_weights(self, input):
  out = fp8_linear(self, input)
  if out is not None: