diff --git a/comfy/ops.py b/comfy/ops.py index 77ad1d527..966561b9e 100644 --- a/comfy/ops.py +++ b/comfy/ops.py @@ -1063,9 +1063,17 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec if self.quant_format is None: raise ValueError(f"Unknown quantization format for layer {layer_name}") + if self.quant_format not in QUANT_ALGOS: + raise ValueError( + f"Quantization format '{self.quant_format}' for layer {layer_name} " + f"is not available in this build (supported: {sorted(QUANT_ALGOS.keys())}). " + "Update comfy_kitchen to enable it." + ) qconfig = QUANT_ALGOS[self.quant_format] self.layout_type = qconfig["comfy_tensor_layout"] - layout_cls = get_layout_class(self.layout_type) + self._layout_cls = get_layout_class(self.layout_type) + self._layout_quantizes_input = getattr(self._layout_cls, "QUANTIZES_INPUT", True) + layout_cls = self._layout_cls # Load format-specific parameters if self.quant_format in ["float8_e4m3fn", "float8_e5m2"]: @@ -1109,6 +1117,42 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec orig_dtype=MixedPrecisionOps._compute_dtype, orig_shape=(self.out_features, self.in_features), ) + elif self.quant_format == "svdquant_w4a4": + # SVDQuant W4A4: per-group weight scales + low-rank correction + # (proj_down, proj_up) + activation smoothing (smooth_factor) + wscales = self._load_scale_param(state_dict, prefix, "weight_scale", device, manually_loaded_keys) + proj_down = self._load_scale_param(state_dict, prefix, "proj_down", device, manually_loaded_keys) + proj_up = self._load_scale_param(state_dict, prefix, "proj_up", device, manually_loaded_keys) + smooth_factor = self._load_scale_param(state_dict, prefix, "smooth_factor", device, manually_loaded_keys) + act_unsigned = bool(layer_conf.get("act_unsigned", False)) + + if any(t is None for t in (wscales, proj_down, proj_up, smooth_factor)): + raise ValueError(f"Missing SVDQuant W4A4 parameters for layer {layer_name}") + + params = layout_cls.Params( + scale=wscales, + orig_dtype=MixedPrecisionOps._compute_dtype, + orig_shape=(self.out_features, self.in_features), + proj_down=proj_down, + proj_up=proj_up, + smooth_factor=smooth_factor, + act_unsigned=act_unsigned, + ) + elif self.quant_format == "awq_w4a16": + # AWQ W4A16: int4 weight, fp16/bf16 activation. Used by + # Qwen-Image-Edit modulation linears so they stay packed + # instead of being dequantized to bf16 at load time. + wscales = self._load_scale_param(state_dict, prefix, "weight_scale", device, manually_loaded_keys) + wzeros = self._load_scale_param(state_dict, prefix, "weight_zero", device, manually_loaded_keys) + if wscales is None or wzeros is None: + raise ValueError(f"Missing AWQ W4A16 parameters for layer {layer_name}") + params = layout_cls.Params( + scale=wscales, + zeros=wzeros, + group_size=int(layer_conf.get("group_size", qconfig.get("group_size", 64))), + orig_dtype=MixedPrecisionOps._compute_dtype, + orig_shape=(self.out_features, self.in_features), + ) else: raise ValueError(f"Unsupported quantization format: {self.quant_format}") @@ -1158,6 +1202,8 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec quant_conf = {"format": self.quant_format} if self._full_precision_mm_config: quant_conf["full_precision_matrix_mult"] = True + if bool(getattr(getattr(self.weight, "_params", None), "act_unsigned", False)): + quant_conf["act_unsigned"] = True sd["{}comfy_quant".format(prefix)] = torch.tensor(list(json.dumps(quant_conf).encode('utf-8')), dtype=torch.uint8) input_scale = getattr(self, 'input_scale', None) @@ -1215,18 +1261,18 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec # Inference path (unchanged) if _use_quantized: + if getattr(self, "_layout_quantizes_input", True): + # Reshape 3D tensors to 2D for quantization (needed for NVFP4 and others) + input_reshaped = input.reshape(-1, input_shape[2]) if input.ndim == 3 else input - # Reshape 3D tensors to 2D for quantization (needed for NVFP4 and others) - input_reshaped = input.reshape(-1, input_shape[2]) if input.ndim == 3 else input - - # Fall back to non-quantized for non-2D tensors - if input_reshaped.ndim == 2: - reshaped_3d = input.ndim == 3 - # dtype is now implicit in the layout class - scale = getattr(self, 'input_scale', None) - if scale is not None: - scale = comfy.model_management.cast_to_device(scale, input.device, None) - input = QuantizedTensor.from_float(input_reshaped, self.layout_type, scale=scale) + # Fall back to non-quantized for non-2D tensors + if input_reshaped.ndim == 2: + reshaped_3d = input.ndim == 3 + # dtype is now implicit in the layout class + scale = getattr(self, 'input_scale', None) + if scale is not None: + scale = comfy.model_management.cast_to_device(scale, input.device, None) + input = QuantizedTensor.from_float(input_reshaped, self.layout_type, scale=scale) output = self.forward_comfy_cast_weights(input, compute_dtype, want_requant=isinstance(input, QuantizedTensor)) diff --git a/comfy/quant_ops.py b/comfy/quant_ops.py index b90bcfd25..cae5f1180 100644 --- a/comfy/quant_ops.py +++ b/comfy/quant_ops.py @@ -20,8 +20,14 @@ try: else: cuda_version = tuple(map(int, str(torch.version.cuda).split('.'))) if cuda_version < (13,): - ck.registry.disable("cuda") - logging.warning("WARNING: You need pytorch with cu130 or higher to use optimized CUDA operations.") + # cu<13 lacks the block-scale FP4 cuBLASLt APIs but not the int4 + # MMA or fp8 paths. Kitchen's per-op FunctionConstraints already + # gate scaled_mm_nvfp4 behind HAS_CUBLASLT, so we keep the CUDA + # backend enabled for svdquant_w4a4 / fp8 / mxfp8 / rope. + logging.warning( + "cuda_version=%s < 13: NVFP4 cuBLAS path unavailable; " + "other kitchen CUDA ops (svdquant W4A4, fp8, mxfp8, rope) remain active.", + ".".join(map(str, cuda_version))) if args.enable_triton_backend: try: @@ -47,6 +53,12 @@ except ImportError as e: class _CKNvfp4Layout: pass + class _CKSVDQuantW4A4Layout: + pass + + class _CKAWQW4A16Layout: + pass + def register_layout_class(name, cls): pass @@ -65,6 +77,30 @@ if not _CK_MXFP8_AVAILABLE: class _CKMxfp8Layout: pass +_CK_SVDQUANT_W4A4_AVAILABLE = False +if _CK_AVAILABLE: + try: + from comfy_kitchen.tensor import TensorCoreSVDQuantW4A4Layout as _CKSVDQuantW4A4Layout + _CK_SVDQUANT_W4A4_AVAILABLE = True + except ImportError: + logging.info("comfy_kitchen does not expose SVDQuant W4A4 layout; int4 SVDQuant checkpoints will not be supported.") + +if not _CK_SVDQUANT_W4A4_AVAILABLE: + class _CKSVDQuantW4A4Layout: + pass + +_CK_AWQ_W4A16_AVAILABLE = False +if _CK_AVAILABLE: + try: + from comfy_kitchen.tensor import TensorCoreAWQW4A16Layout as _CKAWQW4A16Layout + _CK_AWQ_W4A16_AVAILABLE = True + except ImportError: + logging.info("comfy_kitchen does not expose AWQ W4A16 layout; int4 AWQ modulation checkpoints will not be supported.") + +if not _CK_AWQ_W4A16_AVAILABLE: + class _CKAWQW4A16Layout: + pass + import comfy.float # ============================================================================== @@ -172,6 +208,19 @@ class TensorCoreFP8E5M2Layout(_TensorCoreFP8LayoutBase): FP8_DTYPE = torch.float8_e5m2 +# SVDQuant W4A4 — pre-quantized offline (no runtime quantize), pass through the +# kitchen-registered layout class unchanged. Comfy-side extension reserved in +# case per-layer input scales or other Comfy-specific metadata are added later. +class TensorCoreSVDQuantW4A4Layout(_CKSVDQuantW4A4Layout): + pass + + +# AWQ W4A16 — pre-quantized offline modulation linears. Kitchen owns the +# tensor subclass dispatch and gemv implementation; ComfyUI only loads params. +class TensorCoreAWQW4A16Layout(_CKAWQW4A16Layout): + pass + + # Backward compatibility alias - default to E4M3 TensorCoreFP8Layout = TensorCoreFP8E4M3Layout @@ -186,6 +235,10 @@ register_layout_class("TensorCoreFP8E5M2Layout", TensorCoreFP8E5M2Layout) register_layout_class("TensorCoreNVFP4Layout", TensorCoreNVFP4Layout) if _CK_MXFP8_AVAILABLE: register_layout_class("TensorCoreMXFP8Layout", TensorCoreMXFP8Layout) +if _CK_SVDQUANT_W4A4_AVAILABLE: + register_layout_class("TensorCoreSVDQuantW4A4Layout", TensorCoreSVDQuantW4A4Layout) +if _CK_AWQ_W4A16_AVAILABLE: + register_layout_class("TensorCoreAWQW4A16Layout", TensorCoreAWQW4A16Layout) QUANT_ALGOS = { "float8_e4m3fn": { @@ -214,6 +267,22 @@ if _CK_MXFP8_AVAILABLE: "group_size": 32, } +if _CK_SVDQUANT_W4A4_AVAILABLE: + QUANT_ALGOS["svdquant_w4a4"] = { + "storage_t": torch.int8, + "parameters": {"weight_scale", "proj_down", "proj_up", "smooth_factor"}, + "comfy_tensor_layout": "TensorCoreSVDQuantW4A4Layout", + "group_size": 64, + } + +if _CK_AWQ_W4A16_AVAILABLE: + QUANT_ALGOS["awq_w4a16"] = { + "storage_t": torch.int8, + "parameters": {"weight_scale", "weight_zero"}, + "comfy_tensor_layout": "TensorCoreAWQW4A16Layout", + "group_size": 64, + } + # ============================================================================== # Re-exports for backward compatibility @@ -226,6 +295,8 @@ __all__ = [ "TensorCoreFP8E4M3Layout", "TensorCoreFP8E5M2Layout", "TensorCoreNVFP4Layout", + "TensorCoreSVDQuantW4A4Layout", + "TensorCoreAWQW4A16Layout", "QUANT_ALGOS", "register_layout_op", ]