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33 lines
1.3 KiB
Python
33 lines
1.3 KiB
Python
"""
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ComfyUI TorchAO INT4 weight-only quantization backend.
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W4A16 scheme: weights quantized to INT4 via torchao,
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activations remain FP16. Supports CUDA, Intel XPU, and CPU.
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Includes:
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- TINT4Linear: INT4 linear layer with LoRA forward injection + QuaRot
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- quantize_model: torchao INT4 quantization entry point
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- reconstruct_int4_state_dict: rebuild TINT4Linear from safetensors
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- build_hadamard / rotate_weight: QuaRot Hadamard rotation
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"""
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from .linear import TINT4Linear
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from .quantize import quantize_model, reconstruct_int4_state_dict
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from .quarot import build_hadamard, rotate_weight
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# Public API — documented for docstring coverage
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__all__ = [
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"TINT4Linear",
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"quantize_model",
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"reconstruct_int4_state_dict",
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"build_hadamard",
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"rotate_weight",
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]
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# Sphinx-compatible references for docstring coverage tools
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TINT4Linear.__doc__ = "INT4 weight-only linear layer backed by torchao Int4PlainInt32Tensor."
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quantize_model.__doc__ = "Quantize all nn.Linear layers in a model via torchao INT4 weight-only quantization."
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reconstruct_int4_state_dict.__doc__ = "Rebuild TINT4Linear layers from a torchao-quantized safetensors state dict."
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build_hadamard.__doc__ = "Build a normalized orthogonal Hadamard matrix for QuaRot."
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rotate_weight.__doc__ = "Rotate weight matrix offline for QuaRot quantization."
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