mirror of
https://github.com/ggerganov/llama.cpp.git
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gguf-py : Numpy dequantization for grid-based i-quants
This commit is contained in:
Francis Couture-Harpin
parent
5a9edda7ca
commit
73bc9350cd
@ -1,6 +1,7 @@
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from __future__ import annotations
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from abc import ABC, abstractmethod
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from typing import Any, Callable, Sequence
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from math import log2, ceil
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from numpy.typing import DTypeLike
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@ -79,6 +80,11 @@ class __Quant(ABC):
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block_size: int
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type_size: int
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grid: np.ndarray[Any, np.dtype[np.float32]] | None = None
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grid_shape: tuple[int, int] = (0, 0)
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grid_map: tuple[int | float, ...] = ()
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grid_hex: bytes | None = None
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def __init__(self):
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return TypeError("Quant conversion classes can't have instances")
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@ -96,6 +102,27 @@ class __Quant(ABC):
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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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def init_grid(cls):
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if cls.grid is not None or cls.grid_hex is None:
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return
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bits_per_elem = ceil(log2(len(cls.grid_map)))
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assert bits_per_elem != 0, cls.qtype.name
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elems_per_byte = 8 // bits_per_elem
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grid = np.frombuffer(cls.grid_hex, dtype=np.uint8)
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# decode hexadecimal chars from grid
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grid = grid.reshape((-1, 2))
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grid = (np.where(grid > 0x40, grid + 9, grid) & 0x0F) << np.array([4, 0], dtype=np.uint8).reshape((1, 2))
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grid = grid[..., 0] | grid[..., 1]
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# unpack the grid values
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grid = grid.reshape((-1, 1)) >> np.array([i for i in range(0, 8, 8 // elems_per_byte)], dtype=np.uint8).reshape((1, elems_per_byte))
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grid = (grid & ((1 << bits_per_elem) - 1)).reshape((-1, 1))
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grid_map = np.array(cls.grid_map, dtype=np.float32).reshape((1, -1))
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grid = np.take_along_axis(grid_map, grid, axis=-1)
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cls.grid = grid.reshape((1, 1, *cls.grid_shape))
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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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@ -142,6 +169,7 @@ class __Quant(ABC):
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@classmethod
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def __dequantize_array(cls, array: np.ndarray) -> np.ndarray:
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cls.init_grid()
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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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@ -546,9 +574,571 @@ class Q6_K(__Quant, qtype=GGMLQuantizationType.Q6_K):
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return (d * q).reshape((n_blocks, QK_K))
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class IQ4_NL(__Quant, qtype=GGMLQuantizationType.IQ4_NL):
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QK4_NL = 32
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class IQ2_XXS(__Quant, qtype=GGMLQuantizationType.IQ2_XXS):
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ksigns: bytes = (
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b"\x00\x81\x82\x03\x84\x05\x06\x87\x88\x09\x0a\x8b\x0c\x8d\x8e\x0f"
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b"\x90\x11\x12\x93\x14\x95\x96\x17\x18\x99\x9a\x1b\x9c\x1d\x1e\x9f"
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b"\xa0\x21\x22\xa3\x24\xa5\xa6\x27\x28\xa9\xaa\x2b\xac\x2d\x2e\xaf"
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b"\x30\xb1\xb2\x33\xb4\x35\x36\xb7\xb8\x39\x3a\xbb\x3c\xbd\xbe\x3f"
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b"\xc0\x41\x42\xc3\x44\xc5\xc6\x47\x48\xc9\xca\x4b\xcc\x4d\x4e\xcf"
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b"\x50\xd1\xd2\x53\xd4\x55\x56\xd7\xd8\x59\x5a\xdb\x5c\xdd\xde\x5f"
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b"\x60\xe1\xe2\x63\xe4\x65\x66\xe7\xe8\x69\x6a\xeb\x6c\xed\xee\x6f"
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b"\xf0\x71\x72\xf3\x74\xf5\xf6\x77\x78\xf9\xfa\x7b\xfc\x7d\x7e\xff"
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)
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# iq2xxs_grid, but with each byte of the original packed in 2 bits,
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# by mapping 0x08 to 0, 0x19 to 1, and 0x2b to 2.
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grid_shape = (256, 8)
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grid_map = (0x08, 0x19, 0x2b)
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grid_hex = (
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b"00000200050008000a00110014002000220028002a0041004400500058006100"
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b"6400800082008a00a20001010401100115014001840198010002020222028202"
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b"010404041004210424044004420448046004810484049004a404000502050805"
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b"200546056905800591050906100640068406a406000805080808140828084108"
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b"440850085208880804094009020a140a01100410101021104010601084109010"
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b"951000110811201150115a118011241245120014081420142514491480141815"
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b"6215001616160118041810184018811800190519a019511a002002200a204420"
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b"6120802082202921482100220222012404241024402456240025412564259026"
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b"082820289428442a014004401040184021402440404048405640604081408440"
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b"9040004120416141804185410142104248425642684200440844204480449944"
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b"124524450046014804481048404845480049584961498249454a904a00500850"
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b"1150195020508050885004514251a4519152905492540a550156545600581158"
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b"195864584059085a046010604060686000615561186260620064056410651265"
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b"84654268008002800a8041808280048118814081118201840484108415844084"
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b"608400854685948509864086608602880489118a0490109024904090a1901691"
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b"8091459200942294449451958198209902a050a085a009a100a218a450a804a9"
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)
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@classmethod
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def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
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n_blocks = blocks.shape[0]
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d, qs = np.hsplit(blocks, [2])
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d = d.view(np.float16).astype(np.float32)
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qs = qs.view(np.uint32).reshape(n_blocks, -1, 2)
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db = d * (np.float32(0.5) + (qs[..., 1] >> 28).astype(np.float32)) * np.float32(0.25)
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db = db.reshape((n_blocks, -1, 1, 1))
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# get the sign indices and unpack the bits
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signs = qs[..., 1].reshape((n_blocks, -1, 1)) >> np.array([0, 7, 14, 21], dtype=np.uint32).reshape((1, 1, 4))
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ksigns = np.frombuffer(cls.ksigns, dtype=np.uint8).reshape((1, 1, 1, 128))
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signs = (signs & np.uint32(0x7F)).reshape((n_blocks, -1, 4, 1))
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signs = np.take_along_axis(ksigns, signs, axis=-1)
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signs = signs.reshape((n_blocks, -1, 4, 1)) >> np.array([i for i in range(8)], dtype=np.uint8).reshape((1, 1, 1, 8))
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signs = signs & np.uint8(0x01)
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signs = np.where(signs == 0, np.float32(1), np.float32(-1))
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signs = signs.reshape((n_blocks, -1, 4, 8))
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assert cls.grid is not None
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grid = np.take_along_axis(cls.grid, qs[..., 0].copy().view(np.uint8).reshape((n_blocks, -1, 1, 1)), axis=-2)
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grid = grid.reshape((n_blocks, -1, 4, 8))
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return (db * grid * signs).reshape((n_blocks, -1))
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class IQ2_XS(__Quant, qtype=GGMLQuantizationType.IQ2_XS):
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# iq2xs_grid, but with each byte of the original packed in 2 bits,
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# by mapping 0x08 to 0, 0x19 to 1, and 0x2b to 2.
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grid_shape = (512, 8)
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grid_map = (0x08, 0x19, 0x2b)
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grid_hex = (
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b"00000200050008000a0011001400160019002000220025002800410044004600"
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b"49005000520055005800610064008000820085008800910094009900a0000101"
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b"04010601090110011201150118011a0121012401400142014501480151015401"
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b"6001680181018401900100020202050208021102140220024102440250025502"
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b"80028a0201040404060409041004120415041804210424044004420445044804"
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b"5104540456046004810484049004000502050505080511051405200541054405"
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b"500561058005010604061006260640064206840600080208050808080a081108"
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b"14082008250841084408500858088008a008aa08010904091009400981098909"
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b"000a200a280a960aa00a01100410061009101010121015101810211024104010"
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b"4210451048105110541060106a10811084109010001102110511081111111411"
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b"2011411144115011801194119611011204120612101240126012001402140514"
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b"0814111414142014411444144914501464148014011504151015401500161416"
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b"49160118041810181218401854188618001905196619511aa91a002002200520"
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b"08200a201120142020204120442050208020a020012104211021402148216521"
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b"002222228022a82201240424102429244024002541255225992501261a26a626"
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b"002808280a28202855288828a22868299029082a202a822a882a8a2a01400440"
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b"0640094010401240154018402140244040404240454048404a40514054406040"
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b"6540814084409040004102410541084111411441204141414441504180418541"
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b"a241014204421042124229424042004402440544084411441444194420444144"
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b"4444504480449444014504451045244540459a4500460a464446504601480448"
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b"1048404845485448624800491149444950496949044a00500250055008501150"
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b"145020502850415044505050805001510451105115514051425100524452aa52"
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b"0154045410542154405460548154a154005508558055885521566856a1560058"
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b"14584158505899581a5940594259855a0160046010604060546062608660a960"
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b"006124624a62926200641664106540654565a46501686a682569066a546a626a"
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b"00800280058008801180148020802a8041804480508080808280a880aa800181"
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b"0481068110814081518159810082208280828282a082a8820184048410841284"
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b"158440846084898400854485a58518866a860088088825885a8880888288a888"
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b"0689228a808a888a968aa88a0190049010904090569084900091229164915692"
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b"89920094059444945094589429959095929541965198a6984999159a609a00a0"
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b"02a008a00aa020a02aa0a0a051a159a1a6a100a202a208a22aa280a2a0a240a4"
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b"95a465a698a60aa820a822a828a8a0a8a8a804a984a986a928aa2aaa91aaaaaa"
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)
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@classmethod
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def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
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n_blocks = blocks.shape[0]
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d, rest = np.hsplit(blocks, [2])
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qs, scales = np.hsplit(rest, [2 * QK_K // 8])
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d = d.view(np.float16).astype(np.float32)
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qs = qs.view(np.uint16)
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scales = scales.reshape((n_blocks, -1, 1)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2))
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scales = (scales & 0x0F).reshape((n_blocks, -1))
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db = d * (np.float32(0.5) + scales) * np.float32(0.25)
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db = db.reshape((n_blocks, -1, 1, 1))
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# get the sign indices and unpack the bits
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signs = np.frombuffer(IQ2_XXS.ksigns, dtype=np.uint8).reshape(1, 1, 128)
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signs = np.take_along_axis(signs, (qs >> 9).reshape((n_blocks, -1, 1)), axis=-1)
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signs = signs.reshape((n_blocks, -1, 1)) >> np.array([i for i in range(8)], dtype=np.uint8).reshape((1, 1, 8))
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signs = signs & np.uint8(0x01)
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signs = np.where(signs == 0, np.float32(1), np.float32(-1))
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signs = signs.reshape((n_blocks, -1, 2, 8))
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assert cls.grid is not None
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grid = np.take_along_axis(cls.grid, (qs & np.uint16(511)).reshape((n_blocks, -1, 1, 1)), axis=-2)
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grid = grid.reshape((n_blocks, -1, 2, 8))
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return (db * grid * signs).reshape((n_blocks, -1))
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class IQ2_S(__Quant, qtype=GGMLQuantizationType.IQ2_S):
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# iq2s_grid, but with each byte of the original packed in 2 bits,
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# by mapping 0x08 to 0, 0x19 to 1, and 0x2b to 2.
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grid_shape = (1024, 8)
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grid_map = (0x08, 0x19, 0x2b)
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grid_hex = (
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b"00000200050008000a0011001400160019002000220025002800410044004600"
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b"490050005200550058006100640066006900800082008500880091009400a000"
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b"a500aa0001010401060109011001120115011801210124014001420145014801"
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b"510154015601590160016501680181018401900192019501a101a40100020202"
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b"050208021102140220022a02410244024602490250025502800285028a029402"
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b"a202010404040604090410041204150418042104240426042904400442044504"
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b"48044a0451045404560459046004620465048104840486048904900495049804"
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b"a104a40400050205050508050a05110514051605190520052505280541054405"
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b"46054905500552055505580561056405800582058505880591059405a0050106"
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b"0406060609061006150640064506480651065406600681068406900600080208"
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b"050808081108140816081908200825082a084108440846084908500852085508"
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b"580861086408800885089408aa08010904091009120915091809210940094509"
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b"480951095409600981099009000a110a140a220a280a2a0a500a990a01100410"
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b"0610091010101210151018102110241026104010421045104810511054105610"
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b"59106010621065106810811084108610901095109810a110a410001102110511"
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b"08110a1111111411161119112011221125112811411144114611491150115211"
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b"5511581161116411801182118511881191119411011204120912101215122112"
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b"2412401245125112541281128412901200140214051408141114141416141914"
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b"2014251428144114441446144914501452145514581461146414801482148514"
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b"881491149414a014011504150615091510151215151518152115241540154215"
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b"4515481551155415601581158415901500160516081611161416201641164416"
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b"50168016aa160118041806180918101815181818211840184218451848185118"
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b"541860188118841800190219051908191119141920194119441950196919a219"
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b"041a101a401a561a00200220052008201120142016201920202025202a204120"
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b"4420502052205520642080208a209420aa200121042110211221152121214021"
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b"4221452151215421602181218421902100220a22222228222a22442250228822"
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b"8a22a82201240424062409241024152418242124242440244224452448245124"
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b"5424602481248424902400250525082511251425202541254425502566258025"
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b"0126042610264026592600280528112814284128442850288a28aa2801290429"
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b"102995290a2a222a642a882a8a2a014004400640094010401240154018401a40"
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b"21402440264040404240454048404a4051405440564059406040624065408140"
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b"8440904095409840a140a4400041024105410841114114411641194120412241"
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b"2541414144414641494150415241554158416141644180418241854188419141"
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b"9441a04101420442104212421542184224424042454248425142544260428142"
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b"844200440244054408440a441144144416441944204422442544284441444444"
|
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b"46444944504452445544584461446444804482448544884491449444a0440145"
|
||||
b"0445064509451045124515451845214524454045424545454845514554456045"
|
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b"6a4581458445904500460246054608461146144620464146444650468046a546"
|
||||
b"0148044809481048124815481848214824484048424845484848514854486048"
|
||||
b"84489048004902490549084911491449204941494449504980499649014a044a"
|
||||
b"104a404a00500250055008501150145016501950205022502550285041504450"
|
||||
b"4650495050505250555058506150645080508250855088509150945001510451"
|
||||
b"0651095110511251155118512151245140514251455148515151545160518151"
|
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b"8451905100520552085211521452205241524452505269528052015404540654"
|
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b"0954105412541554185421542454405442544554485451545454605481548454"
|
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b"9054005502550555085511551455205541554455505580550156045610562656"
|
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b"405600580258055808581158145820584158445850585a588058015904591059"
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b"4059005a195a855aa85a01600460066010601260156018602160246040604560"
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b"4860516054606060846090600061026105610861116114612061416144615061"
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b"806199610462106240625662a162006405640864116414642064416444645064"
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b"806401650465106540654a656865926500669466016804681068656898680069"
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b"2a69426aa16a0080028005800880118014801980208025804180448050805280"
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b"5580588061808080858091809480018104810981108112811581188121812481"
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b"408142814581488151815481818184819081a981008205820a82118214824182"
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b"4482508201840484068409841084128415841884218440844284458448845184"
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b"5484608481848484908400850285058508851185148520854185448550858085"
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b"8a85018604861086298640860088058811881488418844885088a28801890489"
|
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b"40896589228a588a5a8a828aa28a019004900990109012901590189024904090"
|
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b"4290459048905190549060908190849090900091059111911491419144915091"
|
||||
b"5a910192049210924092a6920094029405940894119414942094419444945094"
|
||||
b"8094969401950495109540959895a19500964696649601980498109826984098"
|
||||
b"a998009949995299909a00a005a00aa014a022a02aa041a044a050a0a2a0aaa0"
|
||||
b"40a165a102a20aa222a228a22aa282a288a28aa2a8a201a404a410a440a489a4"
|
||||
b"a4a400a519a551a60aa828a8a2a854a986a908aa0aaa20aa22aa28aa88aaaaaa"
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
|
||||
n_blocks = blocks.shape[0]
|
||||
|
||||
d, rest = np.hsplit(blocks, [2])
|
||||
qs, rest = np.hsplit(rest, [QK_K // 8])
|
||||
signs, rest = np.hsplit(rest, [QK_K // 8])
|
||||
qh, scales = np.hsplit(rest, [QK_K // 32])
|
||||
|
||||
d = d.view(np.float16).astype(np.float32)
|
||||
|
||||
scales = scales.reshape((n_blocks, -1, 1)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2))
|
||||
scales = (scales & 0x0F).reshape((n_blocks, -1))
|
||||
db = d * (np.float32(0.5) + scales) * np.float32(0.25)
|
||||
db = db.reshape((n_blocks, -1, 1, 1))
|
||||
|
||||
# unpack the sign bits
|
||||
signs = signs.reshape((n_blocks, -1, 1)) >> np.array([i for i in range(8)], dtype=np.uint8).reshape((1, 1, 8))
|
||||
signs = signs & np.uint8(0x01)
|
||||
signs = np.where(signs == 0, np.float32(1), np.float32(-1))
|
||||
signs = signs.reshape((n_blocks, -1, 2, 8))
|
||||
|
||||
qh = qh.reshape((n_blocks, -1, 1)) >> np.array([0, 2, 4, 6], dtype=np.uint8).reshape((1, 1, 4))
|
||||
qs = qs.astype(np.uint16) | ((qh & 0x03).astype(np.uint16) << 8).reshape((n_blocks, -1))
|
||||
|
||||
assert cls.grid is not None
|
||||
grid = np.take_along_axis(cls.grid, qs.reshape((n_blocks, -1, 1, 1)), axis=-2)
|
||||
grid = grid.reshape((n_blocks, -1, 2, 8))
|
||||
|
||||
return (db * grid * signs).reshape((n_blocks, -1))
|
||||
|
||||
|
||||
class IQ3_XXS(__Quant, qtype=GGMLQuantizationType.IQ3_XXS):
|
||||
grid_shape = (256, 4)
|
||||
grid_map = (0x04, 0x0c, 0x14, 0x1c, 0x24, 0x2c, 0x34, 0x3e)
|
||||
grid_hex = (
|
||||
b"0000020004001100130017002000220031004200730075000101030110011201"
|
||||
b"2101250130013201410154017001000202020402110220022202310233023702"
|
||||
b"5102570275020103070310031203250370031304370444045704730475040105"
|
||||
b"0705320552053506640610071407160743076107011003101010121021102310"
|
||||
b"3010321034104710501000110211111120112211011203121012121221123012"
|
||||
b"7212001302132013311346136613011405145014201524154615711505162217"
|
||||
b"4017002002201120132020202220262031204220012103210521102112212121"
|
||||
b"3021632167217021002202221122172220222222372240225522012310231423"
|
||||
b"7023742335245324032527254125742501270327162745270130103012302130"
|
||||
b"2330503065307230003102312031313144314631013203321032253252327232"
|
||||
b"1133333330344734723400350635223555351436363663363337603704401740"
|
||||
b"3540374053405740744120423742404260426642074345430444514464442545"
|
||||
b"4345704505471047124730471250415070500051065126515551145232527252"
|
||||
b"0253535310542354275472540255315550562457425724604460466064602161"
|
||||
b"6161176264623063366344640565526533660367216703700570077010703270"
|
||||
b"5270267140711272457252720073157333736073217441740075027524753076"
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
|
||||
n_blocks = blocks.shape[0]
|
||||
|
||||
d, rest = np.hsplit(blocks, [2])
|
||||
qs, scales = np.hsplit(rest, [QK_K // 4])
|
||||
|
||||
d = d.view(np.float16).astype(np.float32)
|
||||
scales = scales.view(np.uint32)
|
||||
|
||||
db = d * (np.float32(0.5) + (scales >> 28).astype(np.float32)) * np.float32(0.5)
|
||||
db = db.reshape((n_blocks, -1, 1, 1))
|
||||
|
||||
# get the sign indices and unpack the bits
|
||||
signs = scales.reshape((n_blocks, -1, 1)) >> np.array([0, 7, 14, 21], dtype=np.uint32).reshape((1, 1, 4))
|
||||
ksigns = np.frombuffer(IQ2_XXS.ksigns, dtype=np.uint8).reshape((1, 1, 1, 128))
|
||||
signs = (signs & np.uint32(0x7F)).reshape((n_blocks, -1, 4, 1))
|
||||
signs = np.take_along_axis(ksigns, signs, axis=-1)
|
||||
signs = signs.reshape((n_blocks, -1, 4, 1)) >> np.array([i for i in range(8)], dtype=np.uint8).reshape((1, 1, 1, 8))
|
||||
signs = signs & np.uint8(0x01)
|
||||
signs = np.where(signs == 0, np.float32(1), np.float32(-1))
|
||||
signs = signs.reshape((n_blocks, -1, 4, 8))
|
||||
|
||||
assert cls.grid is not None
|
||||
grid = np.take_along_axis(cls.grid, qs.reshape((n_blocks, -1, 1, 1)), axis=-2)
|
||||
grid = grid.reshape((n_blocks, -1, 4, 8))
|
||||
|
||||
return (db * grid * signs).reshape((n_blocks, -1))
|
||||
|
||||
|
||||
class IQ3_S(__Quant, qtype=GGMLQuantizationType.IQ3_S):
|
||||
grid_shape = (512, 4)
|
||||
grid_map = (0x01, 0x03, 0x05, 0x07, 0x09, 0x0b, 0x0d, 0x0f)
|
||||
grid_hex = (
|
||||
b"0000010002000500070010001100120014001600200021002500330040004200"
|
||||
b"4500470051005300600062007100740077000001010102010401100111011501"
|
||||
b"2001230127013101350144016101650172010002010205020702100213021602"
|
||||
b"2102250230023402420245024702510253027002730203031103150320032203"
|
||||
b"3103330336034403500352036703710375030004130417042104240432044004"
|
||||
b"4304510470040205040520052205260533054105450547056605730506061106"
|
||||
b"1306310652067106000702070407200722072607330750075407001001100210"
|
||||
b"0410101011101310151017102010221031103410361054105610611072100011"
|
||||
b"0111031106111011141121113011331141115011521170117611001212121512"
|
||||
b"1712201224123212401243125512601272120113041307131013131321132713"
|
||||
b"3013341341136213701303140514121414143114331442144614501454140115"
|
||||
b"1015131521153015321551152016241627164416461601170317101712172117"
|
||||
b"3517411762177017002001200320052007201020122014201620212023202720"
|
||||
b"3020322041204320452050205220672070207320752000210221102113211721"
|
||||
b"2221252131213421422151210122042207222122232230223722412253225722"
|
||||
b"7122742200230223052311232223242331233323422350236623012407242024"
|
||||
b"2324322435244124722475240425112522253725402553257025002602260726"
|
||||
b"2126552661260527112726273027432750270230113013301530173022303130"
|
||||
b"3330353042304430473051306330713001310331053114312131233140316031"
|
||||
b"7231763100321232203232323432503201331033143321332333273330334133"
|
||||
b"4333473355337333033411341634223431345234603464340135103512352535"
|
||||
b"3235443556357335163641360137033720372237353700400440124020402440"
|
||||
b"2740324041405040704002410741114113412241304135414341514155410142"
|
||||
b"0342104215422142334240425742624270420443114313432043224331433543"
|
||||
b"0044024424443744404471440545074521456245134634466046104715473047"
|
||||
b"4347514702501050145022504050445047505250665074500151035105511251"
|
||||
b"2151325172510052115223523052365253520253075310532753445351536553"
|
||||
b"7353015404542054325446541255265551555355425602570457225711601360"
|
||||
b"1560316033606060006120612761646112623462426255626262706200631463"
|
||||
b"2163406325644364626400650365346560650566406611671367007004700770"
|
||||
b"2070227036704070547062700271117124714371457101720472107216722172"
|
||||
b"3072517202733273357353730174057413742074507422754275027631760077"
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
|
||||
n_blocks = blocks.shape[0]
|
||||
|
||||
d, rest = np.hsplit(blocks, [2])
|
||||
qs, rest = np.hsplit(rest, [QK_K // 4])
|
||||
qh, rest = np.hsplit(rest, [QK_K // 32])
|
||||
signs, scales = np.hsplit(rest, [QK_K // 8])
|
||||
|
||||
d = d.view(np.float16).astype(np.float32)
|
||||
|
||||
scales = scales.reshape((n_blocks, -1, 1)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2))
|
||||
scales = (scales & 0x0F).reshape((n_blocks, -1))
|
||||
db = d * (1 + 2 * scales)
|
||||
db = db.reshape((n_blocks, -1, 1, 1))
|
||||
|
||||
# unpack the sign bits
|
||||
signs = signs.reshape((n_blocks, -1, 1)) >> np.array([i for i in range(8)], dtype=np.uint8).reshape((1, 1, 8))
|
||||
signs = signs & np.uint8(0x01)
|
||||
signs = np.where(signs == 0, np.float32(1), np.float32(-1))
|
||||
signs = signs.reshape((n_blocks, -1, 4, 8))
|
||||
|
||||
qh = qh.reshape((n_blocks, -1, 1)) >> np.array([i for i in range(8)], dtype=np.uint8)
|
||||
qh = (qh & 0x01).astype(np.uint16).reshape((n_blocks, -1))
|
||||
qs = qs.astype(np.uint16) | (qh << 8)
|
||||
|
||||
assert cls.grid is not None
|
||||
grid = np.take_along_axis(cls.grid, qs.reshape((n_blocks, -1, 1, 1)), axis=-2)
|
||||
grid = grid.reshape((n_blocks, -1, 4, 8))
|
||||
|
||||
return (db * grid * signs).reshape((n_blocks, -1))
|
||||
|
||||
|
||||
class IQ1_S(__Quant, qtype=GGMLQuantizationType.IQ1_S):
|
||||
# iq1s_grid, with each byte packed into 2 bits
|
||||
# -1, 0, 1 <=> 0, 1, 2
|
||||
grid_shape = (2048, 8)
|
||||
grid_map = (-1, 0, 1)
|
||||
grid_hex = (
|
||||
b"00000200050008000a00110015002000220028002a0045005100540056006500"
|
||||
b"8000820088008a009500a000a200a800aa000401050111011401160119011a01"
|
||||
b"2501410146014901520155015a0161016401660168018501910194019601a501"
|
||||
b"0002020208020a0215022002220228022a024502510259026402690280028202"
|
||||
b"88028a02910295029902a002a202a802aa021104140416042504410449045504"
|
||||
b"5a046404650491049904a5040105040505050605150518051a05290540054505"
|
||||
b"4a0550055105540555055605590560056205650568056a058105910595059805"
|
||||
b"9a05a105a405a505a605a9051406190641064406500652065506580660066106"
|
||||
b"6606690685069106940699060008020808080a0815082008220828082a084508"
|
||||
b"5108560865088008820888088a089508a008a208a808aa080509110914091909"
|
||||
b"2409250941095009510955096109640969099109940996099909a509000a020a"
|
||||
b"080a0a0a150a200a220a280a2a0a450a510a590a610a650a800a820a850a880a"
|
||||
b"8a0a950aa00aa20aa80aaa0a1010111014101910241025104110441050105510"
|
||||
b"58106110641065106910911094109610a110a510011104110611091110111211"
|
||||
b"1511181121112411291145114a11501151115211541155115611591160116511"
|
||||
b"841192119511a111a41111121412161225124012461249125212551258125a12"
|
||||
b"641266128512911294129612a512011406140914141415141814191421142614"
|
||||
b"41144514461448144a1451145414551456145914621465146814841489149014"
|
||||
b"94149514981499149a14a114a414a514a914021505150a151115141515151615"
|
||||
b"191520152215251528152a154115441545154615511552155415551556155915"
|
||||
b"5a1561156415651566156915801582158415851588158a159015911594159515"
|
||||
b"961599159a15a015a215a51501160416051606161516161618161a1621162616"
|
||||
b"401642164416451648164a165116551656165816591661166416651668166916"
|
||||
b"6a1686168a1692169516a416a916111816182518411844184618491850185518"
|
||||
b"58185a1860186118641866186918851891189418a5181019121915191a192119"
|
||||
b"25194219441945194819511954195519561959195a19601965196a1989199119"
|
||||
b"921995199819a119a619a919091a161a241a261a441a461a491a501a521a551a"
|
||||
b"581a611a661a691a851a911a961a9a1a0020022008200a201520202022202520"
|
||||
b"28202a20452051205920612065208020822088208a209520a020a220a520a820"
|
||||
b"aa2005211121142119212521422144214921552158215a216121642165216621"
|
||||
b"8521902196219921a521012208220a22112215222022222228222a2245225122"
|
||||
b"562259226522812288228a2291229522a022a222a822aa220524142416241924"
|
||||
b"252444244524462449245224552458245a2466248524912494249924a124a524"
|
||||
b"0925152521252925402545254825512554255525592562256525682589259025"
|
||||
b"9425952598259a25a125a425a625a92505261026122619262526412649265526"
|
||||
b"6026612669268426862690269a260028022808280a2815282028222828282a28"
|
||||
b"45285128542865288028822888288a28a028a228a828aa280929112914291929"
|
||||
b"2529462949295229552961296429662969298529902996299929a429a529002a"
|
||||
b"022a082a0a2a202a222a282a2a2a452a512a562a592a652a802a822a882a8a2a"
|
||||
b"952aa02aa22aa82aaa2a054011401640254049405240554058405a4061406440"
|
||||
b"664094409940a140a6400041014104410641094112411541164118411a412141"
|
||||
b"26412941454148414a41514154415541564159415a41654168416a4181418441"
|
||||
b"8641904192419541a041a141a241054211421442164225424142524255425a42"
|
||||
b"6442694289429442a5420144154419442944454448444a445144544455445644"
|
||||
b"61446244654468446a44814486448944904492449544a044a144a94401450245"
|
||||
b"05450a4511451445154516451945204525452a45414544454545464549455045"
|
||||
b"5145544555455645584559456145644565456645694582458445854588459145"
|
||||
b"94459545964599459a45a545a845aa450146054609461446154618461a462146"
|
||||
b"2446294640464246454648465046514652465546564659466246654668468146"
|
||||
b"85468a4694469546a146a446a6460548114815481a4825484248494850485548"
|
||||
b"5848614864486648694885489148944896489948a5480149054906490a491049"
|
||||
b"144915491849214924492649404945494a495149524954495549564959496049"
|
||||
b"6249654966496a49864989499249954996499849a149a449a649a949164a444a"
|
||||
b"464a494a554a584a5a4a644a694a944aa54a0150045005500650095012501550"
|
||||
b"1a50215024502950405045504850515054505550565059506550685086508950"
|
||||
b"95509850a050a150a650a9500551085109510a51115114511551165118511951"
|
||||
b"20512551265128512a5141514451455146514951505151515251545155515651"
|
||||
b"585159515a51615164516551665169518251855191519451955196519951a051"
|
||||
b"a551aa5101520652125215521a5221522452425245524a525152545255525652"
|
||||
b"595262526552855290529252955299529a52a452045405541154145415541654"
|
||||
b"185419542154255428542a54415444544554465449544a545054515454545554"
|
||||
b"5654585459545a54615462546454655466546954805488548a54915494549554"
|
||||
b"96549954a154a454a554aa540155025504550555065509551055115512551455"
|
||||
b"1555165519551a55215524552555265529554055415542554455455546554855"
|
||||
b"4955505551555255545555555655585559555a55605561556455655566556855"
|
||||
b"69556a5581558455855589558a559055915594559555965598559955a155a455"
|
||||
b"a555a655a9550056015602560456065608560956115614561556185619562056"
|
||||
b"2156225624562556265628562956415645564656485649564a56505651565256"
|
||||
b"545655565656585659565a566156645665566956825685568656885689568a56"
|
||||
b"915695569a56a256a556a656a856a95604580558065809581058155818582158"
|
||||
b"2a58455848584a58515854585558565858585958605862586458655882588958"
|
||||
b"9058925895589858a158a9580159025905590a59115914591559165919592559"
|
||||
b"41594459455946594959505951595259545955595659585959595a5961596459"
|
||||
b"655966596959815985598959915994599559965998599959a559045a085a155a"
|
||||
b"1a5a205a255a265a295a455a485a495a515a555a565a585a595a625a655a685a"
|
||||
b"6a5a815a8a5a925a955a965a985a9a5aa15a0560146016601960256044605060"
|
||||
b"5560566058605a60616064606660696081609660a56001610461066109611261"
|
||||
b"15612161226126612961456149615161556156615961656166616a6184618a61"
|
||||
b"92619561a161a661a96111621662196240624162466255625662586260628562"
|
||||
b"91629662a56211641264156416641a6421642664296440644264456448644a64"
|
||||
b"516454645564566459645a646064626465648464856489649064926494649564"
|
||||
b"966498649a64a164a464a964056508650a651165156516651965446545654665"
|
||||
b"496550655165546555655665596561656465656566656965866589658a659165"
|
||||
b"9565966599659a65a265a565a665a86502660966156620662666286629664066"
|
||||
b"456648664a66516654665566566658665a666066656668668066826685668a66"
|
||||
b"9466966698669966a066a466a666aa661668196825684168526855685a686168"
|
||||
b"6968856891689868a66801690469106915692169246926692969406941694569"
|
||||
b"4669486951695469556956695969606965696a69826984698a699569a169a469"
|
||||
b"a569a969116a166a186a416a446a496a506a556a586a5a6a646a656a696a866a"
|
||||
b"946a986a9a6aa66a0080028008800a802080228028802a804580508051805480"
|
||||
b"5680598065808080828088808a809580a080a280a880aa800581118114811681"
|
||||
b"1981258141814481498150815281558156815881598164816681698185818981"
|
||||
b"948196819981a5810082028208820a8215822082228228822a82518254825982"
|
||||
b"65828082828288828a829582a082a282a882aa82148419844184448451845584"
|
||||
b"5a846184648469849484998401850985128515851a8526852985408541854585"
|
||||
b"4885518554855585568559855a856585668568856a8581858485868589859085"
|
||||
b"928595859885a68511861686198625864186448649864a865086558659865a86"
|
||||
b"618666866a86858691869a86a4860088028808880a8815882088228828882a88"
|
||||
b"41884588518854885988658869888088828888888a889588a088a288a888aa88"
|
||||
b"05890689118914891689258941894489468949895089528955895a8961896489"
|
||||
b"858996899989a589008a028a088a0a8a158a208a228a288a2a8a458a518a548a"
|
||||
b"568a808a828a888a8a8a958aa08aa28aa88aaa8a059011901690189019902590"
|
||||
b"419046904990559058905a9069906a9085909190949096909990a59001910491"
|
||||
b"069109911091159118911a912191249126912991409145915091519154915591"
|
||||
b"569159916291659184918691929195919891a191a491a691a991059211921492"
|
||||
b"19922592449246924992509252925592589266926992859294929692a9920194"
|
||||
b"04940694109415941894269440944a9451945494559456945894599460946194"
|
||||
b"62946594849486949294949495949894a194a9940095059508950a9510951195"
|
||||
b"14951595169519952195259529952a9541954495459546954995509551955295"
|
||||
b"549555955695589559955a956195649565956695699581958595889591959295"
|
||||
b"94959595969599959a95a095a295a595a895aa95019604961096159619962096"
|
||||
b"2696299645964896499651965296559656965996659668968296849689968a96"
|
||||
b"929694969596a496a696a9960598169819982598419846985098529855985698"
|
||||
b"5a98649865988598919896989998a59804990699099910991299159918991a99"
|
||||
b"209921992499269940994299459948994a995199549955995699599962996599"
|
||||
b"66996a99819984999099929995999a99a199a699059a159a259a449a469a499a"
|
||||
b"509a559a589a619a859a919a949a959a969a00a002a008a00aa015a020a022a0"
|
||||
b"28a02aa045a051a054a056a059a080a082a088a08aa095a0a0a0a2a0a8a0aaa0"
|
||||
b"05a109a111a114a116a119a11aa146a149a151a155a158a15aa161a164a185a1"
|
||||
b"90a192a196a199a102a208a20aa210a219a222a228a22aa245a251a256a259a2"
|
||||
b"65a280a282a288a28aa295a2a0a2a2a2a8a2aaa219a425a441a444a450a454a4"
|
||||
b"55a458a45aa461a465a466a468a469a485a406a509a510a512a515a518a526a5"
|
||||
b"29a542a545a551a554a555a556a559a565a56aa581a584a585a586a589a592a5"
|
||||
b"95a598a505a611a616a61aa621a625a644a646a64aa652a655a656a658a660a6"
|
||||
b"62a686a690a695a696a699a6a1a6a4a6a6a600a802a808a80aa820a822a828a8"
|
||||
b"2aa851a854a856a859a880a882a888a88aa895a8a0a8a2a8a8a8aaa805a914a9"
|
||||
b"19a921a925a941a950a955a95aa961a966a969a990a996a900aa02aa08aa0aaa"
|
||||
b"20aa22aa28aa2aaa51aa54aa56aa80aa82aa88aa8aaa95aaa0aaa2aaa8aaaaaa"
|
||||
)
|
||||
|
||||
delta = np.float32(0.125)
|
||||
|
||||
@classmethod
|
||||
def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
|
||||
n_blocks = blocks.shape[0]
|
||||
|
||||
d, rest = np.hsplit(blocks, [2])
|
||||
qs, qh = np.hsplit(rest, [QK_K // 8])
|
||||
|
||||
d = d.view(np.float16).astype(np.float32)
|
||||
qh = qh.view(np.uint16)
|
||||
|
||||
dl = d * (2 * ((qh >> 12) & 7) + 1)
|
||||
dl = dl.reshape((n_blocks, -1, 1, 1))
|
||||
delta = np.where((qh & np.uint16(0x8000)) == 0, cls.delta, -cls.delta)
|
||||
delta = delta.reshape((n_blocks, -1, 1, 1))
|
||||
|
||||
qh = qh.reshape((n_blocks, -1, 1)) >> np.array([0, 3, 6, 9], dtype=np.uint16).reshape((1, 1, 4))
|
||||
qs = qs.astype(np.uint16) | ((qh & 7) << 8).reshape((n_blocks, -1))
|
||||
|
||||
assert cls.grid is not None
|
||||
grid = np.take_along_axis(cls.grid, qs.reshape((n_blocks, -1, 1, 1)), axis=-2)
|
||||
grid = grid.reshape((n_blocks, -1, 4, 8))
|
||||
|
||||
return (dl * (grid + delta)).reshape((n_blocks, -1))
|
||||
|
||||
|
||||
class IQ1_M(__Quant, qtype=GGMLQuantizationType.IQ1_M):
|
||||
grid_shape = IQ1_S.grid_shape
|
||||
grid_map = IQ1_S.grid_map
|
||||
grid_hex = IQ1_S.grid_hex
|
||||
|
||||
delta = IQ1_S.delta
|
||||
|
||||
# Okay *this* type is weird. It's the only one which stores the f16 scales in multiple parts.
|
||||
@classmethod
|
||||
def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
|
||||
n_blocks = blocks.shape[0]
|
||||
|
||||
qs, rest = np.hsplit(blocks, [QK_K // 8])
|
||||
qh, scales = np.hsplit(rest, [QK_K // 16])
|
||||
|
||||
# The f16 scale is packed across multiple bytes
|
||||
scales = scales.view(np.uint16)
|
||||
d = (scales.reshape((n_blocks, 4)) & np.uint16(0xF000)) >> np.array([12, 8, 4, 0], dtype=np.uint16).reshape((1, 4))
|
||||
d = d[..., 0] | d[..., 1] | d[..., 2] | d[..., 3]
|
||||
d = d.view(np.float16).astype(np.float32).reshape((n_blocks, 1))
|
||||
|
||||
scales = scales.reshape(n_blocks, -1, 1) >> np.array([0, 3, 6, 9], dtype=np.uint16).reshape((1, 1, 4))
|
||||
scales = (scales & 0x07).reshape((n_blocks, -1))
|
||||
dl = d * (2 * scales + 1)
|
||||
dl = dl.reshape((n_blocks, -1, 2, 1, 1))
|
||||
|
||||
qh = qh.reshape((n_blocks, -1, 1)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2))
|
||||
qs = qs.astype(np.uint16) | ((qh & 0x07).astype(np.uint16) << 8).reshape((n_blocks, -1))
|
||||
|
||||
delta = np.where(qh & 0x08 == 0, cls.delta, -cls.delta)
|
||||
delta = delta.reshape((n_blocks, -1, 2, 2, 1))
|
||||
|
||||
assert cls.grid is not None
|
||||
grid = np.take_along_axis(cls.grid, qs.reshape((n_blocks, -1, 1, 1)), axis=-2)
|
||||
grid = grid.reshape((n_blocks, -1, 2, 2, 8))
|
||||
|
||||
return (dl * (grid + delta)).reshape((n_blocks, -1))
|
||||
|
||||
|
||||
class IQ4_NL(__Quant, qtype=GGMLQuantizationType.IQ4_NL):
|
||||
kvalues = (-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113)
|
||||
|
||||
@classmethod
|
||||
@ -559,7 +1149,7 @@ class IQ4_NL(__Quant, qtype=GGMLQuantizationType.IQ4_NL):
|
||||
|
||||
d = d.view(np.float16).astype(np.float32)
|
||||
|
||||
qs = qs.reshape((n_blocks, -1, 1, cls.QK4_NL // 2)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1))
|
||||
qs = qs.reshape((n_blocks, -1, 1, cls.block_size // 2)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1))
|
||||
|
||||
qs = (qs & np.uint8(0x0F)).reshape((n_blocks, -1, 1))
|
||||
|
||||
@ -570,7 +1160,6 @@ class IQ4_NL(__Quant, qtype=GGMLQuantizationType.IQ4_NL):
|
||||
|
||||
|
||||
class IQ4_XS(__Quant, qtype=GGMLQuantizationType.IQ4_XS):
|
||||
|
||||
@classmethod
|
||||
def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
|
||||
n_blocks = blocks.shape[0]
|
||||
|
||||
@ -60,9 +60,13 @@ class GGMLQuants:
|
||||
ctypes.POINTER(ctypes.c_float),
|
||||
)
|
||||
|
||||
self.libggml.ggml_quantize_requires_imatrix.restype = ctypes.c_bool
|
||||
self.libggml.ggml_quantize_requires_imatrix.argtypes = (ctypes.c_int,)
|
||||
|
||||
for t in (
|
||||
"q4_0", "q4_1", "q5_0", "q5_1", "q8_0",
|
||||
"q2_K", "q3_K", "q4_K", "q5_K", "q6_K",
|
||||
"iq2_xxs", "iq2_xs", "iq2_s", "iq3_xxs", "iq3_s", "iq1_s", "iq1_m",
|
||||
"iq4_nl", "iq4_xs",
|
||||
):
|
||||
dequant_func: ctypes._NamedFuncPointer = getattr(self.libggml, "dequantize_row_" + t)
|
||||
@ -97,7 +101,12 @@ class GGMLQuants:
|
||||
|
||||
def quantize(self, data: np.ndarray, qtype: GGMLQuantizationType) -> np.ndarray:
|
||||
result = np.zeros(gguf.quant_shape_to_byte_shape(data.shape, qtype), dtype=np.uint8, order="C")
|
||||
result_size = self.libggml.ggml_quantize_chunk(qtype.value, data.ctypes.data_as(c_float_p), result.ctypes.data_as(ctypes.c_void_p), 0, prod(data.shape[:-1]), data.shape[-1], ctypes.cast(0, c_float_p))
|
||||
if self.libggml.ggml_quantize_requires_imatrix(qtype.value):
|
||||
# TODO: is a column-wise sum of squares appropriate?
|
||||
qw = np.sum((data * data).reshape((-1, data.shape[-1])), axis=0).ctypes.data_as(c_float_p)
|
||||
else:
|
||||
qw = ctypes.cast(0, c_float_p)
|
||||
result_size = self.libggml.ggml_quantize_chunk(qtype.value, data.ctypes.data_as(c_float_p), result.ctypes.data_as(ctypes.c_void_p), 0, prod(data.shape[:-1]), data.shape[-1], qw)
|
||||
assert result.size == result_size
|
||||
return result
|
||||
|
||||
@ -116,8 +125,10 @@ def compare_tensors(t1: np.ndarray, t2: np.ndarray, qtype: GGMLQuantizationType)
|
||||
t2 = t2.reshape((-1, type_size))
|
||||
x = t1.view(np.uint8) ^ t2.view(np.uint8)
|
||||
diff_bits = np.count_nonzero(np.unpackbits(x, axis=-1), axis=-1)
|
||||
logger.debug(f"{diff_bits.shape=}")
|
||||
num_bad_blocks = np.count_nonzero(diff_bits, axis=0)
|
||||
if num_bad_blocks == 0 and t1.shape == t2.shape:
|
||||
logger.debug("Bits are equal, but arrays don't match, likely contains NANs")
|
||||
return True
|
||||
logger.debug(f"{num_bad_blocks} bad blocks ({100 * num_bad_blocks / x.shape[0]:.6f}%)")
|
||||
bad_block_id = np.argmax(diff_bits, axis=0)
|
||||
logger.debug(f"Worst block id: {bad_block_id}")
|
||||
@ -128,7 +139,7 @@ def compare_tensors(t1: np.ndarray, t2: np.ndarray, qtype: GGMLQuantizationType)
|
||||
return False
|
||||
|
||||
|
||||
def do_test(libggml_path: Path):
|
||||
def do_test(libggml_path: Path, quick: bool = False):
|
||||
ggml_quants = GGMLQuants(libggml_path)
|
||||
|
||||
np.set_printoptions(precision=None, threshold=(4 * 256) + 1, formatter={"int": lambda n: "0x%02X" % n})
|
||||
@ -162,16 +173,15 @@ def do_test(libggml_path: Path):
|
||||
rc = r.copy(order="C")
|
||||
|
||||
pyq = None
|
||||
ggq = None
|
||||
|
||||
if has_quantize:
|
||||
logger.debug(f"Quantizing to {qtype.name} with Python")
|
||||
pyq = gguf.quants.quantize(rc, qtype)
|
||||
|
||||
logger.debug(f"Quantizing to {qtype.name} with C")
|
||||
ggq = ggml_quants.quantize(rc, qtype)
|
||||
logger.debug(f"Quantizing to {qtype.name} with C")
|
||||
ggq = ggml_quants.quantize(rc, qtype)
|
||||
|
||||
if has_quantize:
|
||||
assert pyq is not None
|
||||
if qtype == GGMLQuantizationType.F16:
|
||||
pyq = pyq.view(np.uint8)
|
||||
quant_equal = compare_tensors(pyq, ggq, qtype)
|
||||
@ -182,25 +192,46 @@ def do_test(libggml_path: Path):
|
||||
logger.info(f"Quantization to {qtype.name} matches exactly ✅")
|
||||
|
||||
if has_dequantize:
|
||||
logger.debug(f"Dequantizing from {qtype.name} with Python")
|
||||
pydq = gguf.quants.dequantize(ggq, qtype)
|
||||
logger.debug(f"Dequantizing from {qtype.name} with C")
|
||||
ggdq = ggml_quants.dequantize(ggq, qtype)
|
||||
if ggq is None and not quick:
|
||||
logger.debug(f"Quantizing to {qtype.name} with C")
|
||||
ggq = ggml_quants.quantize(rc, qtype)
|
||||
|
||||
if ggq is not None:
|
||||
logger.debug(f"Dequantizing from {qtype.name} with Python")
|
||||
pydq = gguf.quants.dequantize(ggq, qtype)
|
||||
logger.debug(f"Dequantizing from {qtype.name} with C")
|
||||
ggdq = ggml_quants.dequantize(ggq, qtype)
|
||||
|
||||
dequant_equal = compare_tensors(pydq, ggdq, qtype)
|
||||
|
||||
if not dequant_equal:
|
||||
logger.error(f"Dequantization from {qtype.name} does not match ❌")
|
||||
else:
|
||||
logger.info(f"Dequantization from {qtype.name} matches exactly ✅")
|
||||
|
||||
rq_shape = gguf.quants.quant_shape_to_byte_shape((8, 1024, 1024 // 2), qtype)
|
||||
rq = np.random.random(rq_shape).astype(np.float16).view(np.uint8)
|
||||
|
||||
logger.debug(f"Dequantizing random f16 data as {qtype.name} with Python")
|
||||
pydq = gguf.quants.dequantize(rq, qtype)
|
||||
logger.debug(f"Dequantizing random f16 data as {qtype.name} with C")
|
||||
ggdq = ggml_quants.dequantize(rq, qtype)
|
||||
|
||||
dequant_equal = compare_tensors(pydq, ggdq, qtype)
|
||||
|
||||
if not dequant_equal:
|
||||
logger.error(f"Dequantization from {qtype.name} does not match ❌")
|
||||
logger.error(f"Dequantization from random f16 data as {qtype.name} does not match ❌")
|
||||
else:
|
||||
logger.info(f"Dequantization from {qtype.name} matches exactly ✅")
|
||||
logger.info(f"Dequantization from random f16 data as {qtype.name} matches exactly ✅")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser(description="Test Python (de)quantization against the reference C implementation")
|
||||
parser.add_argument("--libggml", type=Path, default=Path(__file__).parent.parent.parent / "build" / "ggml" / "src" / "libggml.so", help="The path to libggml.so")
|
||||
parser.add_argument("--quick", action="store_true", help="Don't quantize with C when it's not strictly necessary")
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
logging.basicConfig(level=logging.DEBUG)
|
||||
|
||||
do_test(args.libggml)
|
||||
do_test(args.libggml, args.quick)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user