56 lines
1.5 KiB
Python
56 lines
1.5 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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from torch.autograd.function import Function, FunctionCtx
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__all__ = [
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"bnb_dequant",
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"ref_bnb_dequant",
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]
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def ref_bnb_dequant(
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qA: 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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dequant_type: int = 0,
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):
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A = torch.empty(qA.shape, dtype = SA.dtype, device = SA.device)
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if dequant_type == 0:
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for i in range(qA.size(0)):
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A[i:] = qA[i:] * (SA[i].to(torch.float) / scale).to(SA.dtype)
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else:
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for i in range(qA.size(1)):
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A[:,i] = qA[:,i] * (SA[i].to(torch.float) / scale).to(SA.dtype)
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return A
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def bnb_dequant(
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qA: 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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dequant_type: int = 0,
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) -> torch.Tensor:
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"""
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Args:
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qA: (row, col) torch.int8
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dequant 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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dequnt_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.half
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dequant output
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"""
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return ops.infer.bnb_dequant(qA, SA, scale, dequant_type)
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