74 lines
2.1 KiB
Python
74 lines
2.1 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__ = ["bnb_mm_dequant"]
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# A : quant input shape : [row, col] shape : torch.int
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def bnb_mm_dequant(
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A: torch.Tensor,
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quant_state: tuple,
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row_stats: torch.Tensor,
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col_stats: torch.Tensor,
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bias: torch.Tensor = None,
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add_bias: bool = False,
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training: bool = False,
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) -> torch.Tensor:
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"""
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Args:
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A: (row, col) torch.int8
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quant_state: tuple
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row_stats: (row) torch.float
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col_stats: (col) torch.float
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bias: (col) torch.half
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add_bias: bool
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training: bool
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Returns:
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Tensor: (row, col) torch.half
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"""
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assert A.dtype == torch.int
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if bias is not None:
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add_bias = True
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print("bias.dtype:", bias.dtype)
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assert bias.dtype == torch.half
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else:
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bias = A
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out_shape = quant_state[0]
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if len(out_shape) == 3:
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out_shape = (out_shape[0] * out_shape[1], out_shape[2])
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out = torch.full(size=out_shape, fill_value=0, dtype=torch.half, device=A.device)
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new_row_stats = torch.full(
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size=(out_shape[0],), fill_value=0, dtype=torch.float, device=A.device
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)
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new_col_stats = torch.full(
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size=(out_shape[1],), fill_value=0, dtype=torch.float, device=A.device
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)
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assert (
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new_row_stats.shape[0] == row_stats.shape[0]
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), f"{new_row_stats.shape} vs {row_stats.shape}"
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assert (
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new_col_stats.shape[0] == col_stats.shape[0]
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), f"{new_col_stats.shape} vs {col_stats.shape}"
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numRows = out_shape[0]
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numCols = out_shape[1]
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ops.infer.bnb_mm_dequant(
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A,
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row_stats,
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col_stats,
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out,
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new_row_stats,
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new_col_stats,
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numRows,
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numCols,
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add_bias,
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bias,
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)
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return out
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