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Respect the dtype the op was initialized in for non quant mixed op. (#11282)
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comfyanonymous 2025-12-11 20:32:27 -08:00 committed by GitHub
parent 982876d59a
commit 5495589db3
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1 changed files with 8 additions and 3 deletions
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comfy/ops.py
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@ -497,8 +497,10 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
) -> None: ) -> None:
super().__init__() super().__init__()
self.factory_kwargs = {"device": device, "dtype": MixedPrecisionOps._compute_dtype} if dtype is None:
# self.factory_kwargs = {"device": device, "dtype": dtype} dtype = MixedPrecisionOps._compute_dtype
self.factory_kwargs = {"device": device, "dtype": dtype}
self.in_features = in_features self.in_features = in_features
self.out_features = out_features self.out_features = out_features
@ -530,7 +532,10 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
layer_conf = json.loads(layer_conf.numpy().tobytes()) layer_conf = json.loads(layer_conf.numpy().tobytes())
if layer_conf is None: if layer_conf is None:
self.weight = torch.nn.Parameter(weight.to(device=device, dtype=MixedPrecisionOps._compute_dtype), requires_grad=False) dtype = self.factory_kwargs["dtype"]
self.weight = torch.nn.Parameter(weight.to(device=device, dtype=dtype), requires_grad=False)
if dtype != MixedPrecisionOps._compute_dtype:
self.comfy_cast_weights = True
else: else:
self.quant_format = layer_conf.get("format", None) self.quant_format = layer_conf.get("format", None)
if not self._full_precision_mm: if not self._full_precision_mm: