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torch compile fixes.
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comfyanonymous 2025-12-04 22:17:13 -05:00
parent 295a0170d6
commit b8afb60ee8
1 changed files with 3 additions and 7 deletions
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10
comfy/quant_ops.py
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@ -252,12 +252,6 @@ def _create_transformed_qtensor(qt, transform_fn):
def _handle_device_transfer(qt, target_device, target_dtype=None, target_layout=None, op_name="to"): def _handle_device_transfer(qt, target_device, target_dtype=None, target_layout=None, op_name="to"):
if target_dtype is not None and target_dtype != qt.dtype:
logging.warning(
f"QuantizedTensor: dtype conversion requested to {target_dtype}, "
f"but not supported for quantized tensors. Ignoring dtype."
)
if target_layout is not None and target_layout != torch.strided: if target_layout is not None and target_layout != torch.strided:
logging.warning( logging.warning(
f"QuantizedTensor: layout change requested to {target_layout}, " f"QuantizedTensor: layout change requested to {target_layout}, "
@ -277,6 +271,8 @@ def _handle_device_transfer(qt, target_device, target_dtype=None, target_layout=
logging.debug(f"QuantizedTensor.{op_name}: Moving from {current_device} to {target_device}") logging.debug(f"QuantizedTensor.{op_name}: Moving from {current_device} to {target_device}")
new_q_data = qt._qdata.to(device=target_device) new_q_data = qt._qdata.to(device=target_device)
new_params = _move_layout_params_to_device(qt._layout_params, target_device) new_params = _move_layout_params_to_device(qt._layout_params, target_device)
if target_dtype is not None:
new_params["orig_dtype"] = target_dtype
new_qt = QuantizedTensor(new_q_data, qt._layout_type, new_params) new_qt = QuantizedTensor(new_q_data, qt._layout_type, new_params)
logging.debug(f"QuantizedTensor.{op_name}: Created new tensor on {target_device}") logging.debug(f"QuantizedTensor.{op_name}: Created new tensor on {target_device}")
return new_qt return new_qt
@ -400,7 +396,7 @@ class TensorCoreFP8Layout(QuantizedLayout):
def quantize(cls, tensor, scale=None, dtype=torch.float8_e4m3fn, stochastic_rounding=0, inplace_ops=False): def quantize(cls, tensor, scale=None, dtype=torch.float8_e4m3fn, stochastic_rounding=0, inplace_ops=False):
orig_dtype = tensor.dtype orig_dtype = tensor.dtype
if scale == "recalculate": if isinstance(scale, str) and scale == "recalculate":
scale = torch.amax(tensor.abs()) / torch.finfo(dtype).max scale = torch.amax(tensor.abs()) / torch.finfo(dtype).max
if scale is not None: if scale is not None: