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Don't try fp8 matrix mult in quantized ops if not supported by hardware. (#10874)

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comfyanonymous 2025-11-24 23:55:49 -08:00 committed by GitHub
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commit acfaa5c4a1
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1 changed files with 3 additions and 2 deletions
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comfy/ops.py
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@ -646,11 +646,12 @@ def mixed_precision_ops(layer_quant_config={}, compute_dtype=torch.bfloat16, ful
return MixedPrecisionOps
def pick_operations(weight_dtype, compute_dtype, load_device=None, disable_fast_fp8=False, fp8_optimizations=False, scaled_fp8=None, model_config=None):
fp8_compute = comfy.model_management.supports_fp8_compute(load_device) # TODO: if we support more ops this needs to be more granular
if model_config and hasattr(model_config, 'layer_quant_config') and model_config.layer_quant_config:
logging.info(f"Using mixed precision operations: {len(model_config.layer_quant_config)} quantized layers")
return mixed_precision_ops(model_config.layer_quant_config, compute_dtype)
return mixed_precision_ops(model_config.layer_quant_config, compute_dtype, full_precision_mm=not fp8_compute)
fp8_compute = comfy.model_management.supports_fp8_compute(load_device)
if scaled_fp8 is not None:
return scaled_fp8_ops(fp8_matrix_mult=fp8_compute and fp8_optimizations, scale_input=fp8_optimizations, override_dtype=scaled_fp8)