2024-05-13 18:10:51 +00:00
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from __future__ import annotations
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2024-08-08 17:33:09 +00:00
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from abc import ABC, abstractmethod
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from typing import Any, Callable, Sequence
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2024-05-13 18:10:51 +00:00
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from numpy.typing import DTypeLike
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from .constants import GGML_QUANT_SIZES, GGMLQuantizationType
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from .lazy import LazyNumpyTensor
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import numpy as np
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def quant_shape_to_byte_shape(shape: Sequence[int], quant_type: GGMLQuantizationType) -> tuple[int, ...]:
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block_size, type_size = GGML_QUANT_SIZES[quant_type]
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if shape[-1] % block_size != 0:
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raise ValueError(f"Quantized tensor row size ({shape[-1]}) is not a multiple of {quant_type.name} block size ({block_size})")
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return (*shape[:-1], shape[-1] // block_size * type_size)
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def quant_shape_from_byte_shape(shape: Sequence[int], quant_type: GGMLQuantizationType) -> tuple[int, ...]:
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block_size, type_size = GGML_QUANT_SIZES[quant_type]
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if shape[-1] % type_size != 0:
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raise ValueError(f"Quantized tensor bytes per row ({shape[-1]}) is not a multiple of {quant_type.name} type size ({type_size})")
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return (*shape[:-1], shape[-1] // type_size * block_size)
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# This is faster than np.vectorize and np.apply_along_axis because it works on more than one row at a time
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def _apply_over_grouped_rows(func: Callable[[np.ndarray], np.ndarray], arr: np.ndarray, otype: DTypeLike, oshape: tuple[int, ...]) -> np.ndarray:
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rows = arr.reshape((-1, arr.shape[-1]))
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osize = 1
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for dim in oshape:
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osize *= dim
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out = np.empty(shape=osize, dtype=otype)
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# compute over groups of 16 rows (arbitrary, but seems good for performance)
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n_groups = (rows.shape[0] // 16) or 1
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np.concatenate([func(group).ravel() for group in np.array_split(rows, n_groups)], axis=0, out=out)
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return out.reshape(oshape)
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# round away from zero
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# ref: https://stackoverflow.com/a/59143326/22827863
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def np_roundf(n: np.ndarray) -> np.ndarray:
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a = abs(n)
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floored = np.floor(a)
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b = floored + np.floor(2 * (a - floored))
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return np.sign(n) * b
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class QuantError(Exception): ...
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_type_traits: dict[GGMLQuantizationType, type[__Quant]] = {}
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def quantize(data: np.ndarray, qtype: GGMLQuantizationType) -> np.ndarray:
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if qtype == GGMLQuantizationType.F32:
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return data.astype(np.float32, copy=False)
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elif qtype == GGMLQuantizationType.F16:
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return data.astype(np.float16, copy=False)
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elif (q := _type_traits.get(qtype)) is not None:
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return q.quantize(data)
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else:
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raise NotImplementedError(f"Quantization for {qtype.name} is not yet implemented")
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def dequantize(data: np.ndarray, qtype: GGMLQuantizationType) -> np.ndarray:
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if qtype == GGMLQuantizationType.F32 or qtype == GGMLQuantizationType.F16:
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return data.astype(np.float32, copy=False)
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elif (q := _type_traits.get(qtype)) is not None:
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return q.dequantize(data)
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else:
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raise NotImplementedError(f"Dequantization for {qtype.name} is not yet implemented")
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class __Quant(ABC):
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qtype: GGMLQuantizationType
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block_size: int
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type_size: int
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def __init__(self):
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return TypeError("Quant conversion classes can't have instances")
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def __init_subclass__(cls, qtype: GGMLQuantizationType) -> None:
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cls.qtype = qtype
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cls.block_size, cls.type_size = GGML_QUANT_SIZES[qtype]
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cls.__quantize_lazy = LazyNumpyTensor._wrap_fn(
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cls.__quantize_array,
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meta_noop=(np.uint8, cls.__shape_to_bytes)
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)
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cls.__dequantize_lazy = LazyNumpyTensor._wrap_fn(
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cls.__dequantize_array,
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meta_noop=(np.float32, cls.__shape_from_bytes)
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)
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assert qtype not in _type_traits
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_type_traits[qtype] = cls
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@classmethod
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@abstractmethod
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def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
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raise NotImplementedError
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@classmethod
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@abstractmethod
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def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
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raise NotImplementedError
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@classmethod
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def quantize_rows(cls, rows: np.ndarray) -> np.ndarray:
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rows = rows.astype(np.float32, copy=False)
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shape = rows.shape
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n_blocks = rows.size // cls.block_size
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blocks = rows.reshape((n_blocks, cls.block_size))
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blocks = cls.quantize_blocks(blocks)
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assert blocks.dtype == np.uint8
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assert blocks.shape[-1] == cls.type_size
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return blocks.reshape(cls.__shape_to_bytes(shape))
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@classmethod
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def dequantize_rows(cls, rows: np.ndarray) -> np.ndarray:
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rows = rows.view(np.uint8)
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shape = rows.shape
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n_blocks = rows.size // cls.type_size
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blocks = rows.reshape((n_blocks, cls.type_size))
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blocks = cls.dequantize_blocks(blocks)
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assert blocks.dtype == np.float32
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assert blocks.shape[-1] == cls.block_size
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return blocks.reshape(cls.__shape_from_bytes(shape))
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@classmethod
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def __shape_to_bytes(cls, shape: Sequence[int]):
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return quant_shape_to_byte_shape(shape, cls.qtype)
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@classmethod
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def __shape_from_bytes(cls, shape: Sequence[int]):
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return quant_shape_from_byte_shape(shape, cls.qtype)
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@classmethod
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def __quantize_array(cls, array: np.ndarray) -> np.ndarray:
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return _apply_over_grouped_rows(cls.quantize_rows, arr=array, otype=np.uint8, oshape=cls.__shape_to_bytes(array.shape))
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@classmethod
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def __dequantize_array(cls, array: np.ndarray) -> np.ndarray:
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return _apply_over_grouped_rows(cls.dequantize_rows, arr=array, otype=np.float32, oshape=cls.__shape_from_bytes(array.shape))
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@classmethod
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def __quantize_lazy(cls, lazy_tensor: LazyNumpyTensor, /) -> Any:
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pass
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@classmethod
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def __dequantize_lazy(cls, lazy_tensor: LazyNumpyTensor, /) -> Any:
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pass
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@classmethod
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def can_quantize(cls, tensor: np.ndarray | LazyNumpyTensor) -> bool:
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return tensor.shape[-1] % cls.block_size == 0
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@classmethod
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def quantize(cls, tensor: np.ndarray | LazyNumpyTensor) -> np.ndarray:
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if not cls.can_quantize(tensor):
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raise QuantError(f"Can't quantize tensor with shape {tensor.shape} to {cls.qtype.name}")
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if isinstance(tensor, LazyNumpyTensor):
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return cls.__quantize_lazy(tensor)
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else:
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return cls.__quantize_array(tensor)
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@classmethod
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def dequantize(cls, tensor: np.ndarray | LazyNumpyTensor) -> np.ndarray:
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if isinstance(tensor, LazyNumpyTensor):
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return cls.__dequantize_lazy(tensor)
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else:
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return cls.__dequantize_array(tensor)
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class BF16(__Quant, qtype=GGMLQuantizationType.BF16):
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@classmethod
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# same as ggml_compute_fp32_to_bf16 in ggml-impl.h
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def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
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n = blocks.view(np.uint32)
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# force nan to quiet
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n = np.where((n & 0x7fffffff) > 0x7f800000, (n & np.uint32(0xffff0000)) | np.uint32(64 << 16), n)
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# round to nearest even
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n = (np.uint64(n) + (0x7fff + ((n >> 16) & 1))) >> 16
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return n.astype(np.uint16).view(np.uint8)
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@classmethod
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def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
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return (blocks.view(np.int16).astype(np.int32) << 16).view(np.float32)
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class Q8_0(__Quant, qtype=GGMLQuantizationType.Q8_0):
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@classmethod
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# Implementation of Q8_0 with bit-exact same results as reference implementation in ggml-quants.c
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def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
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d = abs(blocks).max(axis=1, keepdims=True) / 127
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with np.errstate(divide="ignore"):
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id = np.where(d == 0, 0, 1 / d)
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qs = np_roundf(blocks * id)
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# (n_blocks, 2)
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d = d.astype(np.float16).view(np.uint8)
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# (n_blocks, block_size)
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qs = qs.astype(np.int8).view(np.uint8)
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return np.concatenate([d, qs], axis=1)
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@classmethod
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def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
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d, x = np.split(blocks, [2], axis=1)
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d = d.view(np.float16).astype(np.float32)
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x = x.view(np.int8).astype(np.float32)
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return (x * d)
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