From 5a9edda7ca36481caa491d5aee2acd14fe24c7f9 Mon Sep 17 00:00:00 2001
From: Francis Couture-Harpin <git@compilade.net>
Date: Thu, 8 Aug 2024 23:11:42 -0400
Subject: [PATCH 1/2] gguf-py : Numpy dequantization for most types
---
gguf-py/gguf/quants.py | 392 ++++++++++++++++++++++++++++++++++-
gguf-py/tests/test_quants.py | 206 ++++++++++++++++++
2 files changed, 595 insertions(+), 3 deletions(-)
create mode 100755 gguf-py/tests/test_quants.py
diff --git a/gguf-py/gguf/quants.py b/gguf-py/gguf/quants.py
index a443dd27e62b3..bdcad8349b4d4 100644
--- a/gguf-py/gguf/quants.py
+++ b/gguf-py/gguf/quants.py
@@ -4,7 +4,7 @@
from numpy.typing import DTypeLike
-from .constants import GGML_QUANT_SIZES, GGMLQuantizationType
+from .constants import GGML_QUANT_SIZES, GGMLQuantizationType, QK_K
from .lazy import LazyNumpyTensor
import numpy as np
@@ -64,8 +64,10 @@ def quantize(data: np.ndarray, qtype: GGMLQuantizationType) -> np.ndarray:
def dequantize(data: np.ndarray, qtype: GGMLQuantizationType) -> np.ndarray:
- if qtype == GGMLQuantizationType.F32 or qtype == GGMLQuantizationType.F16:
- return data.astype(np.float32, copy=False)
+ if qtype == GGMLQuantizationType.F32:
+ return data.view(np.float32)
+ elif qtype == GGMLQuantizationType.F16:
+ return data.view(np.float16).astype(np.float32)
elif (q := _type_traits.get(qtype)) is not None:
return q.dequantize(data)
else:
@@ -187,6 +189,166 @@ def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
return (blocks.view(np.int16).astype(np.int32) << 16).view(np.float32)
+class Q4_0(__Quant, qtype=GGMLQuantizationType.Q4_0):
+ @classmethod
+ def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
+ n_blocks = blocks.shape[0]
+
+ imax = abs(blocks).argmax(axis=-1, keepdims=True)
+ max = np.take_along_axis(blocks, imax, axis=-1)
+
+ d = max / -8
+ with np.errstate(divide="ignore"):
+ id = np.where(d == 0, 0, 1 / d)
+ # FIXME: Q4_0's reference rounding is cursed and depends on FMA
+ qs = np.trunc((np.float64(blocks) * np.float64(id)) + np.float64(8.5), dtype=np.float32).astype(np.uint8).clip(0, 15)
+
+ qs = qs.reshape((n_blocks, 2, cls.block_size // 2))
+ qs = qs[..., 0, :] | (qs[..., 1, :] << np.uint8(4))
+
+ d = d.astype(np.float16).view(np.uint8)
+
+ return np.concatenate([d, qs], axis=-1)
+
+ @classmethod
+ def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
+ n_blocks = blocks.shape[0]
+
+ d, qs = np.hsplit(blocks, [2])
+
+ d = d.view(np.float16).astype(np.float32)
+
+ 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)).astype(np.int8) - np.int8(8)
+
+ return (d * qs.astype(np.float32))
+
+
+class Q4_1(__Quant, qtype=GGMLQuantizationType.Q4_1):
+ @classmethod
+ def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
+ n_blocks = blocks.shape[0]
+
+ max = blocks.max(axis=-1, keepdims=True)
+ min = blocks.min(axis=-1, keepdims=True)
+
+ d = (max - min) / 15
+ with np.errstate(divide="ignore"):
+ id = np.where(d == 0, 0, 1 / d)
+ qs = np.trunc((blocks - min) * id + np.float32(0.5), dtype=np.float32).astype(np.uint8).clip(0, 15)
+
+ qs = qs.reshape((n_blocks, 2, cls.block_size // 2))
+ qs = qs[..., 0, :] | (qs[..., 1, :] << np.uint8(4))
+
+ d = d.astype(np.float16).view(np.uint8)
+ m = min.astype(np.float16).view(np.uint8)
+
+ return np.concatenate([d, m, qs], axis=-1)
+
+ @classmethod
+ def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
+ n_blocks = blocks.shape[0]
+
+ d, rest = np.hsplit(blocks, [2])
+ m, qs = np.hsplit(rest, [2])
+
+ d = d.view(np.float16).astype(np.float32)
+ m = m.view(np.float16).astype(np.float32)
+
+ 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)).astype(np.float32)
+
+ return (d * qs) + m
+
+
+class Q5_0(__Quant, qtype=GGMLQuantizationType.Q5_0):
+ @classmethod
+ def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
+ n_blocks = blocks.shape[0]
+
+ imax = abs(blocks).argmax(axis=-1, keepdims=True)
+ max = np.take_along_axis(blocks, imax, axis=-1)
+
+ d = max / -16
+ with np.errstate(divide="ignore"):
+ id = np.where(d == 0, 0, 1 / d)
+ # FIXME: Q5_0's reference rounding is cursed and depends on FMA
+ q = np.trunc((np.float64(blocks) * np.float64(id)) + np.float64(16.5), dtype=np.float32).astype(np.uint8).clip(0, 31)
+
+ qs = q.reshape((n_blocks, 2, cls.block_size // 2))
+ qs = (qs[..., 0, :] & np.uint8(0x0F)) | (qs[..., 1, :] << np.uint8(4))
+
+ qh = np.packbits(q.reshape((n_blocks, 1, 32)) >> np.uint8(4), axis=-1, bitorder="little").reshape(n_blocks, 4)
+
+ d = d.astype(np.float16).view(np.uint8)
+
+ return np.concatenate([d, qh, qs], axis=-1)
+
+ @classmethod
+ def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
+ n_blocks = blocks.shape[0]
+
+ d, rest = np.hsplit(blocks, [2])
+ qh, qs = np.hsplit(rest, [4])
+
+ d = d.view(np.float16).astype(np.float32)
+ qh = qh.view(np.uint32)
+
+ qh = qh.reshape((n_blocks, 1)) >> np.array([i for i in range(32)], dtype=np.uint32).reshape((1, 32))
+ ql = qs.reshape((n_blocks, -1, 1, cls.block_size // 2)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1))
+ qh = (qh & np.uint32(0x01)).astype(np.uint8)
+ ql = (ql & np.uint8(0x0F)).reshape((n_blocks, -1))
+
+ qs = (ql | (qh << np.uint8(4))).astype(np.int8) - np.int8(16)
+
+ return (d * qs.astype(np.float32))
+
+
+class Q5_1(__Quant, qtype=GGMLQuantizationType.Q5_1):
+ @classmethod
+ def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
+ n_blocks = blocks.shape[0]
+
+ max = blocks.max(axis=-1, keepdims=True)
+ min = blocks.min(axis=-1, keepdims=True)
+
+ d = (max - min) / 31
+ with np.errstate(divide="ignore"):
+ id = np.where(d == 0, 0, 1 / d)
+ q = np.trunc((blocks - min) * id + np.float32(0.5), dtype=np.float32).astype(np.uint8).clip(0, 31)
+
+ qs = q.reshape((n_blocks, 2, cls.block_size // 2))
+ qs = (qs[..., 0, :] & np.uint8(0x0F)) | (qs[..., 1, :] << np.uint8(4))
+
+ qh = np.packbits(q.reshape((n_blocks, 1, 32)) >> np.uint8(4), axis=-1, bitorder="little").reshape(n_blocks, 4)
+
+ d = d.astype(np.float16).view(np.uint8)
+ m = min.astype(np.float16).view(np.uint8)
+
+ return np.concatenate([d, m, qh, qs], axis=-1)
+
+ @classmethod
+ def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
+ n_blocks = blocks.shape[0]
+
+ d, rest = np.hsplit(blocks, [2])
+ m, rest = np.hsplit(rest, [2])
+ qh, qs = np.hsplit(rest, [4])
+
+ d = d.view(np.float16).astype(np.float32)
+ m = m.view(np.float16).astype(np.float32)
+ qh = qh.view(np.uint32)
+
+ qh = qh.reshape((n_blocks, 1)) >> np.array([i for i in range(32)], dtype=np.uint32).reshape((1, 32))
+ ql = qs.reshape((n_blocks, -1, 1, cls.block_size // 2)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1))
+ qh = (qh & np.uint32(0x01)).astype(np.uint8)
+ ql = (ql & np.uint8(0x0F)).reshape((n_blocks, -1))
+
+ qs = (ql | (qh << np.uint8(4))).astype(np.float32)
+
+ return (d * qs) + m
+
+
class Q8_0(__Quant, qtype=GGMLQuantizationType.Q8_0):
@classmethod
# Implementation of Q8_0 with bit-exact same results as reference implementation in ggml-quants.c
@@ -211,3 +373,227 @@ def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
x = x.view(np.int8).astype(np.float32)
return (x * d)
+
+
+class Q2_K(__Quant, qtype=GGMLQuantizationType.Q2_K):
+ @classmethod
+ def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
+ n_blocks = blocks.shape[0]
+
+ scales, rest = np.hsplit(blocks, [QK_K // 16])
+ qs, rest = np.hsplit(rest, [QK_K // 4])
+ d, dmin = np.hsplit(rest, [2])
+
+ d = d.view(np.float16).astype(np.float32)
+ dmin = dmin.view(np.float16).astype(np.float32)
+
+ # (n_blocks, 16, 1)
+ dl = (d * (scales & 0xF).astype(np.float32)).reshape((n_blocks, QK_K // 16, 1))
+ ml = (dmin * (scales >> 4).astype(np.float32)).reshape((n_blocks, QK_K // 16, 1))
+
+ shift = np.array([0, 2, 4, 6], dtype=np.uint8).reshape((1, 1, 4, 1))
+
+ qs = (qs.reshape((n_blocks, -1, 1, 32)) >> shift) & np.uint8(3)
+
+ qs = qs.reshape((n_blocks, QK_K // 16, 16)).astype(np.float32)
+
+ qs = dl * qs - ml
+
+ return qs.reshape((n_blocks, -1))
+
+
+class Q3_K(__Quant, qtype=GGMLQuantizationType.Q3_K):
+ @classmethod
+ def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
+ n_blocks = blocks.shape[0]
+
+ hmask, rest = np.hsplit(blocks, [QK_K // 8])
+ qs, rest = np.hsplit(rest, [QK_K // 4])
+ scales, d = np.hsplit(rest, [12])
+
+ d = d.view(np.float16).astype(np.float32)
+
+ # The scales are packed at 6-bit each in this pattern:
+ # 0: IIIIAAAA
+ # 1: JJJJBBBB
+ # 2: KKKKCCCC
+ # 3: LLLLDDDD
+ # 4: MMMMEEEE
+ # 5: NNNNFFFF
+ # 6: OOOOGGGG
+ # 7: PPPPHHHH
+ # 8: MMIIEEAA
+ # 9: NNJJFFBB
+ # 10: OOKKGGCC
+ # 11: PPLLHHDD
+ lscales, hscales = np.hsplit(scales, [8])
+ lscales = lscales.reshape((n_blocks, 1, 8)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 2, 1))
+ lscales = lscales.reshape((n_blocks, 16))
+ hscales = hscales.reshape((n_blocks, 1, 4)) >> np.array([0, 2, 4, 6], dtype=np.uint8).reshape((1, 4, 1))
+ hscales = hscales.reshape((n_blocks, 16))
+ scales = (lscales & np.uint8(0x0F)) | ((hscales & np.uint8(0x03)) << np.uint8(4))
+ scales = (scales.astype(np.int8) - np.int8(32)).astype(np.float32)
+
+ dl = (d * scales).reshape((n_blocks, 16, 1))
+
+ ql = qs.reshape((n_blocks, -1, 1, 32)) >> np.array([0, 2, 4, 6], dtype=np.uint8).reshape((1, 1, 4, 1))
+ qh = hmask.reshape(n_blocks, -1, 1, 32) >> np.array([i for i in range(8)], dtype=np.uint8).reshape((1, 1, 8, 1))
+ ql = ql.reshape((n_blocks, 16, QK_K // 16)) & np.uint8(3)
+ qh = (qh.reshape((n_blocks, 16, QK_K // 16)) & np.uint8(1))
+ qh = qh ^ np.uint8(1) # strangely, the offset is zero when the bitmask is 1
+ q = (ql.astype(np.int8) - (qh << np.uint8(2)).astype(np.int8)).astype(np.float32)
+
+ return (dl * q).reshape((n_blocks, QK_K))
+
+
+class Q4_K(__Quant, qtype=GGMLQuantizationType.Q4_K):
+ K_SCALE_SIZE = 12
+
+ @staticmethod
+ def get_scale_min(scales: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
+ n_blocks = scales.shape[0]
+ scales = scales.view(np.uint8)
+ ### Unpacking the following: ###
+ # 0 EEAAAAAA
+ # 1 FFBBBBBB
+ # 2 GGCCCCCC
+ # 3 HHDDDDDD
+ # 4 eeaaaaaa
+ # 5 ffbbbbbb
+ # 6 ggcccccc
+ # 7 hhdddddd
+ # 8 eeeeEEEE
+ # 9 ffffFFFF
+ # 10 ggggGGGG
+ # 11 hhhhHHHH
+ scales = scales.reshape((n_blocks, 3, 4))
+ d, m, m_d = np.split(scales, 3, axis=-2)
+
+ sc = np.concatenate([d & 0x3F, (m_d & 0x0F) | ((d >> 2) & 0x30)], axis=-1)
+ min = np.concatenate([m & 0x3F, (m_d >> 4) | ((m >> 2) & 0x30)], axis=-1)
+
+ return (sc.reshape((n_blocks, 8)), min.reshape((n_blocks, 8)))
+
+ @classmethod
+ def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
+ n_blocks = blocks.shape[0]
+
+ d, rest = np.hsplit(blocks, [2])
+ dmin, rest = np.hsplit(rest, [2])
+ scales, qs = np.hsplit(rest, [cls.K_SCALE_SIZE])
+
+ d = d.view(np.float16).astype(np.float32)
+ dmin = dmin.view(np.float16).astype(np.float32)
+
+ sc, m = Q4_K.get_scale_min(scales)
+
+ d = (d * sc.astype(np.float32)).reshape((n_blocks, -1, 1))
+ dm = (dmin * m.astype(np.float32)).reshape((n_blocks, -1, 1))
+
+ qs = qs.reshape((n_blocks, -1, 1, 32)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1))
+ qs = (qs & np.uint8(0x0F)).reshape((n_blocks, -1, 32)).astype(np.float32)
+
+ return (d * qs - dm).reshape((n_blocks, QK_K))
+
+
+class Q5_K(__Quant, qtype=GGMLQuantizationType.Q5_K):
+ @classmethod
+ def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
+ n_blocks = blocks.shape[0]
+
+ d, rest = np.hsplit(blocks, [2])
+ dmin, rest = np.hsplit(rest, [2])
+ scales, rest = np.hsplit(rest, [Q4_K.K_SCALE_SIZE])
+ qh, qs = np.hsplit(rest, [QK_K // 8])
+
+ d = d.view(np.float16).astype(np.float32)
+ dmin = dmin.view(np.float16).astype(np.float32)
+
+ sc, m = Q4_K.get_scale_min(scales)
+
+ d = (d * sc.astype(np.float32)).reshape((n_blocks, -1, 1))
+ dm = (dmin * m.astype(np.float32)).reshape((n_blocks, -1, 1))
+
+ ql = qs.reshape((n_blocks, -1, 1, 32)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1))
+ qh = qh.reshape((n_blocks, -1, 1, 32)) >> np.array([i for i in range(8)], dtype=np.uint8).reshape((1, 1, 8, 1))
+ ql = (ql & np.uint8(0x0F)).reshape((n_blocks, -1, 32))
+ qh = (qh & np.uint8(0x01)).reshape((n_blocks, -1, 32))
+ q = (ql | (qh << np.uint8(4))).astype(np.float32)
+
+ return (d * q - dm).reshape((n_blocks, QK_K))
+
+
+class Q6_K(__Quant, qtype=GGMLQuantizationType.Q6_K):
+ @classmethod
+ def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
+ n_blocks = blocks.shape[0]
+
+ ql, rest = np.hsplit(blocks, [QK_K // 2])
+ qh, rest = np.hsplit(rest, [QK_K // 4])
+ scales, d = np.hsplit(rest, [QK_K // 16])
+
+ scales = scales.view(np.int8).astype(np.float32)
+ d = d.view(np.float16).astype(np.float32)
+ d = (d * scales).reshape((n_blocks, QK_K // 16, 1))
+
+ ql = ql.reshape((n_blocks, -1, 1, 64)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1))
+ ql = (ql & np.uint8(0x0F)).reshape((n_blocks, -1, 32))
+ qh = qh.reshape((n_blocks, -1, 1, 32)) >> np.array([0, 2, 4, 6], dtype=np.uint8).reshape((1, 1, 4, 1))
+ qh = (qh & np.uint8(0x03)).reshape((n_blocks, -1, 32))
+ q = (ql | (qh << np.uint8(4))).astype(np.int8) - np.int8(32)
+ q = q.reshape((n_blocks, QK_K // 16, -1)).astype(np.float32)
+
+ return (d * q).reshape((n_blocks, QK_K))
+
+
+class IQ4_NL(__Quant, qtype=GGMLQuantizationType.IQ4_NL):
+ QK4_NL = 32
+
+ kvalues = (-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113)
+
+ @classmethod
+ def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
+ n_blocks = blocks.shape[0]
+
+ d, qs = np.hsplit(blocks, [2])
+
+ 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 & np.uint8(0x0F)).reshape((n_blocks, -1, 1))
+
+ kvalues = np.array(cls.kvalues, dtype=np.int8).reshape(1, 1, 16)
+ qs = np.take_along_axis(kvalues, qs, axis=-1).astype(np.float32).reshape((n_blocks, -1))
+
+ return (d * qs)
+
+
+class IQ4_XS(__Quant, qtype=GGMLQuantizationType.IQ4_XS):
+
+ @classmethod
+ def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
+ n_blocks = blocks.shape[0]
+
+ d, rest = np.hsplit(blocks, [2])
+ scales_h, rest = np.hsplit(rest, [2])
+ scales_l, qs = np.hsplit(rest, [QK_K // 64])
+
+ d = d.view(np.float16).astype(np.float32)
+ scales_h = scales_h.view(np.uint16)
+
+ scales_l = scales_l.reshape((n_blocks, -1, 1)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2))
+ scales_h = scales_h.reshape((n_blocks, 1, -1)) >> np.array([2 * i for i in range(QK_K // 32)], dtype=np.uint16).reshape((1, -1, 1))
+ scales_l = scales_l.reshape((n_blocks, -1)) & np.uint8(0x0F)
+ scales_h = scales_h.reshape((n_blocks, -1)).astype(np.uint8) & np.uint8(0x03)
+
+ scales = (scales_l | (scales_h << np.uint8(4))).astype(np.int8) - np.int8(32)
+ dl = (d * scales.astype(np.float32)).reshape((n_blocks, -1, 1))
+
+ qs = qs.reshape((n_blocks, -1, 1, 16)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1))
+ qs = qs.reshape((n_blocks, -1, 32, 1)) & np.uint8(0x0F)
+
+ kvalues = np.array(IQ4_NL.kvalues, dtype=np.int8).reshape((1, 1, 1, -1))
+ qs = np.take_along_axis(kvalues, qs, axis=-1).astype(np.float32).reshape((n_blocks, -1, 32))
+
+ return (dl * qs).reshape((n_blocks, -1))
diff --git a/gguf-py/tests/test_quants.py b/gguf-py/tests/test_quants.py
new file mode 100755
index 0000000000000..9e7a9ae682072
--- /dev/null
+++ b/gguf-py/tests/test_quants.py
@@ -0,0 +1,206 @@
+#!/usr/bin/env python3
+
+# Test gguf.quants so that it exactly matches the C implementation of the (de)quantization
+
+# NOTE: this is kind of a mess, but at least it worked for initially testing the Python implementations.
+
+from __future__ import annotations
+
+import argparse
+from math import prod
+import os
+import sys
+from pathlib import Path
+import ctypes
+import logging
+import numpy as np
+
+# Necessary to load the local gguf package
+if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent / 'gguf-py').exists():
+ sys.path.insert(0, str(Path(__file__).parent.parent))
+
+import gguf
+from gguf.constants import GGMLQuantizationType
+
+
+logger = logging.getLogger("test-quants")
+
+
+c_float_p = ctypes.POINTER(ctypes.c_float)
+
+
+class ggml_init_params(ctypes.Structure):
+ _fields_ = [
+ ("mem_size", ctypes.c_size_t),
+ ("mem_buffer", ctypes.c_void_p),
+ ("no_alloc", ctypes.c_bool),
+ ]
+
+
+class GGMLQuants:
+ libggml: ctypes.CDLL
+
+ def __init__(self, libggml: Path):
+ self.libggml = ctypes.CDLL(str(libggml))
+ self.libggml.ggml_quantize_chunk.restype = ctypes.c_size_t
+ # enum ggml_type type,
+ # const float * src,
+ # void * dst,
+ # int64_t start,
+ # int64_t nrows,
+ # int64_t n_per_row,
+ # const float * imatrix) {
+ self.libggml.ggml_quantize_chunk.argtypes = (
+ ctypes.c_int,
+ ctypes.POINTER(ctypes.c_float),
+ ctypes.c_void_p,
+ ctypes.c_int64,
+ ctypes.c_int64,
+ ctypes.c_int64,
+ ctypes.POINTER(ctypes.c_float),
+ )
+
+ for t in (
+ "q4_0", "q4_1", "q5_0", "q5_1", "q8_0",
+ "q2_K", "q3_K", "q4_K", "q5_K", "q6_K",
+ "iq4_nl", "iq4_xs",
+ ):
+ dequant_func: ctypes._NamedFuncPointer = getattr(self.libggml, "dequantize_row_" + t)
+ dequant_func.restype = None
+ dequant_func.argtypes = (ctypes.c_void_p, ctypes.POINTER(ctypes.c_float), ctypes.c_int64)
+
+ self.libggml.ggml_fp16_to_fp32_row.restype = None
+ self.libggml.ggml_fp16_to_fp32_row.argtypes = (ctypes.POINTER(ctypes.c_uint16), ctypes.POINTER(ctypes.c_float), ctypes.c_int64)
+ self.libggml.ggml_bf16_to_fp32_row.restype = None
+ self.libggml.ggml_bf16_to_fp32_row.argtypes = (ctypes.POINTER(ctypes.c_uint16), ctypes.POINTER(ctypes.c_float), ctypes.c_int64)
+
+ self.libggml.ggml_init.argtypes = (ggml_init_params,)
+
+ self.libggml.ggml_init(ggml_init_params(1 * 1024 * 1024, 0, False))
+
+ def dequantize(self, tensor: np.ndarray, qtype: GGMLQuantizationType) -> np.ndarray:
+ result = np.zeros(gguf.quant_shape_from_byte_shape(tensor.shape, qtype), dtype=np.float32, order="C")
+ if qtype == GGMLQuantizationType.F32:
+ # no-op
+ result = tensor.view(np.float32)
+ elif qtype == GGMLQuantizationType.F16:
+ self.libggml.ggml_fp16_to_fp32_row(tensor.ctypes.data_as(ctypes.POINTER(ctypes.c_uint16)), result.ctypes.data_as(c_float_p), result.size)
+ elif qtype == GGMLQuantizationType.BF16:
+ self.libggml.ggml_bf16_to_fp32_row(tensor.ctypes.data_as(ctypes.POINTER(ctypes.c_uint16)), result.ctypes.data_as(c_float_p), result.size)
+ else:
+ lw_qname = qtype.name.lower()
+ if lw_qname[-1] == "k":
+ lw_qname = lw_qname[:-1] + "K"
+ dequant_func: ctypes._NamedFuncPointer = getattr(self.libggml, "dequantize_row_" + lw_qname)
+ dequant_func(tensor.ctypes.data_as(ctypes.c_void_p), result.ctypes.data_as(c_float_p), result.size)
+ return result
+
+ 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))
+ assert result.size == result_size
+ return result
+
+
+def compare_tensors(t1: np.ndarray, t2: np.ndarray, qtype: GGMLQuantizationType) -> bool:
+ same = np.array_equal(t1, t2)
+ if same:
+ return True
+ else:
+ block_size, type_size = gguf.GGML_QUANT_SIZES[qtype]
+ if t1.dtype == np.float32:
+ t1 = t1.reshape((-1, block_size))
+ t2 = t2.reshape((-1, block_size))
+ else:
+ t1 = t1.reshape((-1, type_size))
+ 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)
+ 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}")
+ logger.debug(f"Sample bad block ({diff_bits[bad_block_id]} differing bits):\n{t1[bad_block_id]}\nReference:\n{t2[bad_block_id]}")
+
+ sum_diff_bits = np.sum(diff_bits)
+ logger.debug(f"{sum_diff_bits} bits differ ({100 * sum_diff_bits/(x.size * 8):.6f}%)")
+ return False
+
+
+def do_test(libggml_path: Path):
+ ggml_quants = GGMLQuants(libggml_path)
+
+ np.set_printoptions(precision=None, threshold=(4 * 256) + 1, formatter={"int": lambda n: "0x%02X" % n})
+
+ r = np.random.randn(8, 1024, 1024).astype(np.float32, copy=False)
+
+ for qtype in (GGMLQuantizationType.F16, *gguf.quants._type_traits.keys()):
+ has_dequantize = False
+ has_quantize = False
+
+ try:
+ gguf.dequantize(np.zeros((gguf.GGML_QUANT_SIZES[qtype][1]), dtype=np.uint8), qtype)
+ has_dequantize = True
+ except (NotImplementedError, AssertionError) as e:
+ if isinstance(e, AssertionError):
+ logger.error(f"Error with {qtype.name}: {e}")
+ raise e
+ try:
+ gguf.quantize(np.zeros((gguf.GGML_QUANT_SIZES[qtype][0]), dtype=np.float32), qtype)
+ has_quantize = True
+ except (NotImplementedError, AssertionError) as e:
+ if isinstance(e, AssertionError):
+ logger.error(f"Error with {qtype.name}: {e}")
+ raise e
+
+ if not has_dequantize and not has_quantize:
+ continue
+
+ logger.info(f"Testing {qtype.name}")
+
+ rc = r.copy(order="C")
+
+ pyq = 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)
+
+ if has_quantize:
+ assert pyq is not None
+ if qtype == GGMLQuantizationType.F16:
+ pyq = pyq.view(np.uint8)
+ quant_equal = compare_tensors(pyq, ggq, qtype)
+
+ if not quant_equal:
+ logger.error(f"Quantization to {qtype.name} does not match ❌")
+ else:
+ 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)
+
+ 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 ✅")
+
+
+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")
+
+ args = parser.parse_args()
+
+ logging.basicConfig(level=logging.DEBUG)
+
+ do_test(args.libggml)
From 73bc9350cd614adfdd4cdbcdac31c838d10af84a Mon Sep 17 00:00:00 2001
From: Francis Couture-Harpin <git@compilade.net>
Date: Fri, 9 Aug 2024 23:47:31 -0400
Subject: [PATCH 2/2] gguf-py : Numpy dequantization for grid-based i-quants
---
gguf-py/gguf/quants.py | 597 ++++++++++++++++++++++++++++++++++-
gguf-py/tests/test_quants.py | 59 +++-
2 files changed, 638 insertions(+), 18 deletions(-)
diff --git a/gguf-py/gguf/quants.py b/gguf-py/gguf/quants.py
index bdcad8349b4d4..ff589b85245e5 100644
--- a/gguf-py/gguf/quants.py
+++ b/gguf-py/gguf/quants.py
@@ -1,6 +1,7 @@
from __future__ import annotations
from abc import ABC, abstractmethod
from typing import Any, Callable, Sequence
+from math import log2, ceil
from numpy.typing import DTypeLike
@@ -79,6 +80,11 @@ class __Quant(ABC):
block_size: int
type_size: int
+ grid: np.ndarray[Any, np.dtype[np.float32]] | None = None
+ grid_shape: tuple[int, int] = (0, 0)
+ grid_map: tuple[int | float, ...] = ()
+ grid_hex: bytes | None = None
+
def __init__(self):
return TypeError("Quant conversion classes can't have instances")
@@ -96,6 +102,27 @@ def __init_subclass__(cls, qtype: GGMLQuantizationType) -> None:
assert qtype not in _type_traits
_type_traits[qtype] = cls
+ @classmethod
+ def init_grid(cls):
+ if cls.grid is not None or cls.grid_hex is None:
+ return
+
+ bits_per_elem = ceil(log2(len(cls.grid_map)))
+ assert bits_per_elem != 0, cls.qtype.name
+ elems_per_byte = 8 // bits_per_elem
+
+ grid = np.frombuffer(cls.grid_hex, dtype=np.uint8)
+ # decode hexadecimal chars from grid
+ grid = grid.reshape((-1, 2))
+ grid = (np.where(grid > 0x40, grid + 9, grid) & 0x0F) << np.array([4, 0], dtype=np.uint8).reshape((1, 2))
+ grid = grid[..., 0] | grid[..., 1]
+ # unpack the grid values
+ 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))
+ grid = (grid & ((1 << bits_per_elem) - 1)).reshape((-1, 1))
+ grid_map = np.array(cls.grid_map, dtype=np.float32).reshape((1, -1))
+ grid = np.take_along_axis(grid_map, grid, axis=-1)
+ cls.grid = grid.reshape((1, 1, *cls.grid_shape))
+
@classmethod
@abstractmethod
def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
@@ -142,6 +169,7 @@ def __quantize_array(cls, array: np.ndarray) -> np.ndarray:
@classmethod
def __dequantize_array(cls, array: np.ndarray) -> np.ndarray:
+ cls.init_grid()
return _apply_over_grouped_rows(cls.dequantize_rows, arr=array, otype=np.float32, oshape=cls.__shape_from_bytes(array.shape))
@classmethod
@@ -546,9 +574,571 @@ def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
return (d * q).reshape((n_blocks, QK_K))
-class IQ4_NL(__Quant, qtype=GGMLQuantizationType.IQ4_NL):
- QK4_NL = 32
+class IQ2_XXS(__Quant, qtype=GGMLQuantizationType.IQ2_XXS):
+ ksigns: bytes = (
+ b"\x00\x81\x82\x03\x84\x05\x06\x87\x88\x09\x0a\x8b\x0c\x8d\x8e\x0f"
+ b"\x90\x11\x12\x93\x14\x95\x96\x17\x18\x99\x9a\x1b\x9c\x1d\x1e\x9f"
+ b"\xa0\x21\x22\xa3\x24\xa5\xa6\x27\x28\xa9\xaa\x2b\xac\x2d\x2e\xaf"
+ b"\x30\xb1\xb2\x33\xb4\x35\x36\xb7\xb8\x39\x3a\xbb\x3c\xbd\xbe\x3f"
+ b"\xc0\x41\x42\xc3\x44\xc5\xc6\x47\x48\xc9\xca\x4b\xcc\x4d\x4e\xcf"
+ b"\x50\xd1\xd2\x53\xd4\x55\x56\xd7\xd8\x59\x5a\xdb\x5c\xdd\xde\x5f"
+ b"\x60\xe1\xe2\x63\xe4\x65\x66\xe7\xe8\x69\x6a\xeb\x6c\xed\xee\x6f"
+ b"\xf0\x71\x72\xf3\x74\xf5\xf6\x77\x78\xf9\xfa\x7b\xfc\x7d\x7e\xff"
+ )
+
+ # iq2xxs_grid, but with each byte of the original packed in 2 bits,
+ # by mapping 0x08 to 0, 0x19 to 1, and 0x2b to 2.
+ grid_shape = (256, 8)
+ grid_map = (0x08, 0x19, 0x2b)
+ grid_hex = (
+ b"00000200050008000a00110014002000220028002a0041004400500058006100"
+ b"6400800082008a00a20001010401100115014001840198010002020222028202"
+ b"010404041004210424044004420448046004810484049004a404000502050805"
+ b"200546056905800591050906100640068406a406000805080808140828084108"
+ b"440850085208880804094009020a140a01100410101021104010601084109010"
+ b"951000110811201150115a118011241245120014081420142514491480141815"
+ b"6215001616160118041810184018811800190519a019511a002002200a204420"
+ b"6120802082202921482100220222012404241024402456240025412564259026"
+ b"082820289428442a014004401040184021402440404048405640604081408440"
+ b"9040004120416141804185410142104248425642684200440844204480449944"
+ b"124524450046014804481048404845480049584961498249454a904a00500850"
+ b"1150195020508050885004514251a4519152905492540a550156545600581158"
+ b"195864584059085a046010604060686000615561186260620064056410651265"
+ b"84654268008002800a8041808280048118814081118201840484108415844084"
+ b"608400854685948509864086608602880489118a0490109024904090a1901691"
+ b"8091459200942294449451958198209902a050a085a009a100a218a450a804a9"
+ )
+
+ @classmethod
+ def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
+ n_blocks = blocks.shape[0]
+
+ d, qs = np.hsplit(blocks, [2])
+
+ d = d.view(np.float16).astype(np.float32)
+
+ qs = qs.view(np.uint32).reshape(n_blocks, -1, 2)
+
+ db = d * (np.float32(0.5) + (qs[..., 1] >> 28).astype(np.float32)) * np.float32(0.25)
+ db = db.reshape((n_blocks, -1, 1, 1))
+
+ # get the sign indices and unpack the bits
+ signs = qs[..., 1].reshape((n_blocks, -1, 1)) >> np.array([0, 7, 14, 21], dtype=np.uint32).reshape((1, 1, 4))
+ ksigns = np.frombuffer(cls.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[..., 0].copy().view(np.uint8).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 IQ2_XS(__Quant, qtype=GGMLQuantizationType.IQ2_XS):
+ # iq2xs_grid, but with each byte of the original packed in 2 bits,
+ # by mapping 0x08 to 0, 0x19 to 1, and 0x2b to 2.
+ grid_shape = (512, 8)
+ grid_map = (0x08, 0x19, 0x2b)
+ grid_hex = (
+ b"00000200050008000a0011001400160019002000220025002800410044004600"
+ b"49005000520055005800610064008000820085008800910094009900a0000101"
+ b"04010601090110011201150118011a0121012401400142014501480151015401"
+ b"6001680181018401900100020202050208021102140220024102440250025502"
+ b"80028a0201040404060409041004120415041804210424044004420445044804"
+ b"5104540456046004810484049004000502050505080511051405200541054405"
+ b"500561058005010604061006260640064206840600080208050808080a081108"
+ b"14082008250841084408500858088008a008aa08010904091009400981098909"
+ b"000a200a280a960aa00a01100410061009101010121015101810211024104010"
+ b"4210451048105110541060106a10811084109010001102110511081111111411"
+ b"2011411144115011801194119611011204120612101240126012001402140514"
+ b"0814111414142014411444144914501464148014011504151015401500161416"
+ b"49160118041810181218401854188618001905196619511aa91a002002200520"
+ b"08200a201120142020204120442050208020a020012104211021402148216521"
+ b"002222228022a82201240424102429244024002541255225992501261a26a626"
+ b"002808280a28202855288828a22868299029082a202a822a882a8a2a01400440"
+ b"0640094010401240154018402140244040404240454048404a40514054406040"
+ b"6540814084409040004102410541084111411441204141414441504180418541"
+ b"a241014204421042124229424042004402440544084411441444194420444144"
+ b"4444504480449444014504451045244540459a4500460a464446504601480448"
+ b"1048404845485448624800491149444950496949044a00500250055008501150"
+ b"145020502850415044505050805001510451105115514051425100524452aa52"
+ b"0154045410542154405460548154a154005508558055885521566856a1560058"
+ b"14584158505899581a5940594259855a0160046010604060546062608660a960"
+ b"006124624a62926200641664106540654565a46501686a682569066a546a626a"
+ b"00800280058008801180148020802a8041804480508080808280a880aa800181"
+ b"0481068110814081518159810082208280828282a082a8820184048410841284"
+ b"158440846084898400854485a58518866a860088088825885a8880888288a888"
+ b"0689228a808a888a968aa88a0190049010904090569084900091229164915692"
+ b"89920094059444945094589429959095929541965198a6984999159a609a00a0"
+ b"02a008a00aa020a02aa0a0a051a159a1a6a100a202a208a22aa280a2a0a240a4"
+ b"95a465a698a60aa820a822a828a8a0a8a8a804a984a986a928aa2aaa91aaaaaa"
+ )
+
+ @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, [2 * QK_K // 8])
+
+ d = d.view(np.float16).astype(np.float32)
+ qs = qs.view(np.uint16)
+
+ 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))
+
+ # get the sign indices and unpack the bits
+ signs = np.frombuffer(IQ2_XXS.ksigns, dtype=np.uint8).reshape(1, 1, 128)
+ signs = np.take_along_axis(signs, (qs >> 9).reshape((n_blocks, -1, 1)), axis=-1)
+ 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))
+
+ assert cls.grid is not None
+ grid = np.take_along_axis(cls.grid, (qs & np.uint16(511)).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 IQ2_S(__Quant, qtype=GGMLQuantizationType.IQ2_S):
+ # iq2s_grid, but with each byte of the original packed in 2 bits,
+ # by mapping 0x08 to 0, 0x19 to 1, and 0x2b to 2.
+ grid_shape = (1024, 8)
+ grid_map = (0x08, 0x19, 0x2b)
+ grid_hex = (
+ b"00000200050008000a0011001400160019002000220025002800410044004600"
+ b"490050005200550058006100640066006900800082008500880091009400a000"
+ b"a500aa0001010401060109011001120115011801210124014001420145014801"
+ b"510154015601590160016501680181018401900192019501a101a40100020202"
+ b"050208021102140220022a02410244024602490250025502800285028a029402"
+ b"a202010404040604090410041204150418042104240426042904400442044504"
+ b"48044a0451045404560459046004620465048104840486048904900495049804"
+ b"a104a40400050205050508050a05110514051605190520052505280541054405"
+ b"46054905500552055505580561056405800582058505880591059405a0050106"
+ b"0406060609061006150640064506480651065406600681068406900600080208"
+ b"050808081108140816081908200825082a084108440846084908500852085508"
+ b"580861086408800885089408aa08010904091009120915091809210940094509"
+ b"480951095409600981099009000a110a140a220a280a2a0a500a990a01100410"
+ b"0610091010101210151018102110241026104010421045104810511054105610"
+ b"59106010621065106810811084108610901095109810a110a410001102110511"
+ b"08110a1111111411161119112011221125112811411144114611491150115211"
+ b"5511581161116411801182118511881191119411011204120912101215122112"
+ b"2412401245125112541281128412901200140214051408141114141416141914"
+ b"2014251428144114441446144914501452145514581461146414801482148514"
+ b"881491149414a014011504150615091510151215151518152115241540154215"
+ b"4515481551155415601581158415901500160516081611161416201641164416"
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+ b"4650495050505250555058506150645080508250855088509150945001510451"
+ b"0651095110511251155118512151245140514251455148515151545160518151"
+ b"8451905100520552085211521452205241524452505269528052015404540654"
+ b"0954105412541554185421542454405442544554485451545454605481548454"
+ b"9054005502550555085511551455205541554455505580550156045610562656"
+ b"405600580258055808581158145820584158445850585a588058015904591059"
+ b"4059005a195a855aa85a01600460066010601260156018602160246040604560"
+ b"4860516054606060846090600061026105610861116114612061416144615061"
+ b"806199610462106240625662a162006405640864116414642064416444645064"
+ b"806401650465106540654a656865926500669466016804681068656898680069"
+ b"2a69426aa16a0080028005800880118014801980208025804180448050805280"
+ b"5580588061808080858091809480018104810981108112811581188121812481"
+ b"408142814581488151815481818184819081a981008205820a82118214824182"
+ b"4482508201840484068409841084128415841884218440844284458448845184"
+ b"5484608481848484908400850285058508851185148520854185448550858085"
+ b"8a85018604861086298640860088058811881488418844885088a28801890489"
+ b"40896589228a588a5a8a828aa28a019004900990109012901590189024904090"
+ 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"
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+ b"45285128542865288028822888288a28a028a228a828aa280929112914291929"
+ b"2529462949295229552961296429662969298529902996299929a429a529002a"
+ b"022a082a0a2a202a222a282a2a2a452a512a562a592a652a802a822a882a8a2a"
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+ 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 @@ def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
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 @@ def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
class IQ4_XS(__Quant, qtype=GGMLQuantizationType.IQ4_XS):
-
@classmethod
def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
n_blocks = blocks.shape[0]
diff --git a/gguf-py/tests/test_quants.py b/gguf-py/tests/test_quants.py
index 9e7a9ae682072..8b7a85c2c36d7 100755
--- a/gguf-py/tests/test_quants.py
+++ b/gguf-py/tests/test_quants.py
@@ -60,9 +60,13 @@ def __init__(self, libggml: Path):
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 @@ def dequantize(self, tensor: np.ndarray, qtype: GGMLQuantizationType) -> np.ndar
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)