wangbo/ComfyUI
1
0
Fork 0
mirror of https://github.com/comfyanonymous/ComfyUI.git synced 2026-02-10 05:22:34 +08:00
Code Issues Actions 14 Packages Projects Releases Wiki Activity

add generic support for quantize tensor casting and proper scaling factor

Browse Source
This commit is contained in:
Sandro Cavallari 2025-11-25 01:23:05 +01:00
parent db730ee283
commit b95c05d095
1 changed files with 14 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

17
comfy/quant_ops.py
View File

@ -337,6 +337,16 @@ def generic_copy_(func, args, kwargs):
return qt_dest
return func(*args, **kwargs)
@register_generic_util(torch.ops.aten.to.dtype)
def generic_to_dtype(func, args, kwargs):
"""Handle .to(dtype) calls - dtype conversion only."""
src = args[0]
if isinstance(src, QuantizedTensor):
# For dtype-only conversion, just change the orig_dtype, no real cast is needed
target_dtype = args[1] if len(args) > 1 else kwargs.get('dtype')
src._layout_params["orig_dtype"] = target_dtype
return src
return func(*args, **kwargs)
@register_generic_util(torch.ops.aten._has_compatible_shallow_copy_type.default)
def generic_has_compatible_shallow_copy_type(func, args, kwargs):
@ -383,10 +393,11 @@ class TensorCoreFP8Layout(QuantizedLayout):
scale = torch.tensor(scale)
scale = scale.to(device=tensor.device, dtype=torch.float32)
tensor_scaled = tensor * (1.0 / scale).to(tensor.dtype)
tensor_fp32 = tensor.to(torch.float32)
tensor_scaled = tensor_fp32 * (1.0 / scale)
# TODO: uncomment this if it's actually needed because the clamp has a small performance penality'
# lp_amax = torch.finfo(dtype).max
# torch.clamp(tensor_scaled, min=-lp_amax, max=lp_amax, out=tensor_scaled)
lp_amax = torch.finfo(dtype).max
torch.clamp(tensor_scaled, min=-lp_amax, max=lp_amax, out=tensor_scaled)
qdata = tensor_scaled.to(dtype, memory_format=torch.contiguous_format)
layout_params = {