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revert: remove unrelated quant_ops.py changes
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@ -3,6 +3,22 @@ import logging
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from comfy.cli_args import args
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from comfy.cli_args import args
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def _rocm_kitchen_arch_supported():
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"""comfy-kitchen's INT8 Triton kernels compile tl.dot to matrix-core instructions.
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RDNA3/3.5/4 (gfx11xx/gfx12xx) have WMMA and CDNA (gfx9xx) has MFMA; RDNA1/RDNA2
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(gfx10xx) have neither, so the INT8 path hangs the GPU there. Gates the automatic
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ROCm default so those cards stay on the eager fallback (an explicit
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--enable-triton-backend still forces it on any arch)."""
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try:
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arch = torch.cuda.get_device_properties(torch.cuda.current_device()).gcnArchName.split(":")[0]
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except Exception:
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return False
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if arch.startswith(("gfx11", "gfx12")):
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return True
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return arch in ("gfx908", "gfx90a", "gfx940", "gfx941", "gfx942", "gfx950")
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try:
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try:
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import comfy_kitchen as ck
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import comfy_kitchen as ck
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from comfy_kitchen.tensor import (
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from comfy_kitchen.tensor import (
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@ -26,9 +42,13 @@ try:
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logging.warning("WARNING: You need pytorch with cu130 or higher to use optimized CUDA operations.")
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logging.warning("WARNING: You need pytorch with cu130 or higher to use optimized CUDA operations.")
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# On ROCm/AMD the CUDA backend is unavailable, so Triton is the only accelerated
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# On ROCm/AMD the CUDA backend is unavailable, so Triton is the only accelerated
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# comfy-kitchen backend. Enable it by default there, but only on Triton >= 3.7:
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# comfy-kitchen backend. Enable it by default there, but only on Triton >= 3.7 AND a
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# matrix-core GPU (RDNA3+ WMMA gfx11xx/gfx12xx, CDNA MFMA gfx9xx). RDNA1/RDNA2
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# (gfx10xx) have no WMMA -> the INT8 tl.dot path hangs the GPU, so they stay eager.
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# older Triton lacks libdevice.rint on the HIP backend and hard-crashes the INT8 path.
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# older Triton lacks libdevice.rint on the HIP backend and hard-crashes the INT8 path.
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if args.enable_triton_backend or torch.version.hip is not None:
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if args.disable_triton_backend:
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ck.registry.disable("triton")
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elif args.enable_triton_backend: # or (torch.version.hip is not None and _rocm_kitchen_arch_supported()):
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try:
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try:
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import triton
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import triton
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triton_version = tuple(int(v) for v in triton.__version__.split(".")[:2])
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triton_version = tuple(int(v) for v in triton.__version__.split(".")[:2])
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