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#!/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),
)
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",
"tq1_0", "tq2_0",
"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)
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")
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
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)
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}")
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, quick: bool = False):
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
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)
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:
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 random f16 data as {qtype.name} does not match ❌")
else:
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, args.quick)
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