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from __future__ import annotations
import argparse
from math import prod
from pathlib import Path
import ctypes
import logging
import numpy as np
import gguf
from gguf.constants import GGMLQuantizationType
logger = logging.getLogger(__name__)
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), winmode=0)
# self.libggml = ctypes.WinDLL(str(libggml), winmode=0)
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 create_sample(ggml_quants: GGMLQuants, hidden_size, qtype: GGMLQuantizationType) -> np.ndarray:
gguf_writer = gguf.GGUFWriter(f"Quant_{qtype.name}_{hidden_size}.gguf", "llama")
# Create a sample tensor
for size in [768, 1024, 2048, 5120, 18944]:
tensor = np.random.randn(size, hidden_size).astype(np.float32)
shape_str = "x".join(map(str, tensor.shape))
gguf_writer.add_tensor(f"tensor_{qtype.name}_{shape_str}", ggml_quants.quantize(tensor, qtype), raw_dtype=qtype)
gguf_writer.write_header_to_file()
gguf_writer.write_kv_data_to_file()
gguf_writer.write_tensors_to_file()
gguf_writer.close()
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Test Python (de)quantization against the reference C implementation")
parser.add_argument("--libggml", type=Path, default="libggml.so", help="The path to libggml.so")
parser.add_argument("--hidden_size", type=int, default=256, help="The hidden size of the sample tensor")
parser.add_argument("--seed", type=int, default=0, help="The hidden size of the sample tensor")
np.random.seed(0)
args = parser.parse_args()
logging.basicConfig(level=logging.DEBUG)
ggml_quants = GGMLQuants(args.libggml)
qtypes = [
GGMLQuantizationType.IQ1_M,
GGMLQuantizationType.IQ1_S,
GGMLQuantizationType.IQ2_S,
GGMLQuantizationType.IQ2_XS,
GGMLQuantizationType.IQ2_XXS,
GGMLQuantizationType.IQ3_S,
GGMLQuantizationType.IQ3_XXS,
GGMLQuantizationType.IQ4_NL,
GGMLQuantizationType.IQ4_XS,
GGMLQuantizationType.Q2_K,
GGMLQuantizationType.Q3_K,
GGMLQuantizationType.Q4_K,
GGMLQuantizationType.Q5_K,
GGMLQuantizationType.Q6_K,
GGMLQuantizationType.Q4_0,
GGMLQuantizationType.Q5_0,
GGMLQuantizationType.Q8_0,
]
for qtype in qtypes:
create_sample(ggml_quants, args.hidden_size, qtype)
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