59 lines
1.6 KiB
Python
59 lines
1.6 KiB
Python
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from typing import List, Union
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import ixformer._C as ops
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import torch
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__all__ = ["bnb_quant", "ref_bnb_quant"]
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# A : input shape : [row, col]
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# SA : scale vector
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# quant_type
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# 0 : every row shared a scale, SA shape : [row]
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# 1 : every col shared a scale, SA shape : [col]
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def ref_bnb_quant(
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A: torch.Tensor,
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SA: torch.Tensor,
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training: bool = False,
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scale: float = 127.0,
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quant_type: int = 0,
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):
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qA = torch.empty(A.shape, device = SA.device)
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if quant_type == 0:
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for i in range(A.size(0)):
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qA[i:] = torch.round(A[i:] * (scale / SA[i].to(torch.float)))
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else:
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for i in range(A.size(1)):
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qA[:,i] = torch.round(A[:,i] * (scale / SA[i].to(torch.float)))
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qA_clamped = torch.clamp(qA, min=-128, max=127)
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qA = qA_clamped.to(torch.int8)
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return qA
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def bnb_quant(
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A: torch.Tensor,
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SA: torch.Tensor,
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training: bool = False,
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scale: float = 127.0,
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quant_type: int = 0,
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) -> torch.Tensor:
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"""
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Args:
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A: (row, col) torch.half
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quant input
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SA: (row) or (col) torch.half
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scale vector
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training: bool
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scale: float
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qunt_type: int
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0 : every row shared a scale, SA shape : [row]
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1 : every col shared a scale, SA shape : [col]
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Returns:
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Tensor: (row, col) torch.int8
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quant output
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"""
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return ops.infer.bnb_quant(A, SA, scale, quant_type)
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