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Make --fast work on pytorch nightly.

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comfyanonymous 2024-08-21 14:01:41 -04:00
parent 5f50263088
commit 904bf58e7d
1 changed files with 19 additions and 2 deletions
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21
comfy/ops.py
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@ -254,16 +254,33 @@ def fp8_linear(self, input):
non_blocking = comfy.model_management.device_supports_non_blocking(input.device) non_blocking = comfy.model_management.device_supports_non_blocking(input.device)
w = cast_to(self.weight, device=input.device, non_blocking=non_blocking).t() 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: 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: 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 o.reshape((-1, input.shape[1], self.weight.shape[0]))
return None return None
class fp8_ops(manual_cast): class fp8_ops(manual_cast):
class Linear(manual_cast.Linear): 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): def forward_comfy_cast_weights(self, input):
out = fp8_linear(self, input) out = fp8_linear(self, input)
if out is not None: if out is not None: