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gguf : support big endian platform (#3552)

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* check whether platform is 390x if yes->do not import immintrin.h

* support s390x big endian

* support --bigendian option for s390x
1. verified with baichuan7b-chat with float 16 on s390x
2. verified with baichuan7b-chat
3. verified with chinese-alpaca-2-13b-f16

* update format based on editor-config checker result

* Update convert-baichuan-hf-to-gguf.py

* 1. check in ggml.c if endianess is not match
2. update GGUF version
3. change get_pack_prefix to property
4. update information log

* always use "GGUF" as beginng of GGUF file

* Compare "GGUF" with file header char by char
1.  Set GGUF_MAGIC to "GGUF" string instead of int value
2. Compare "GGUF" char by char to ensure its byte order
3. Move bytes swap code from convert.py to gguf.py write_tensor_data

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
Qin Yue Chen 2023-10-20 06:19:40 -05:00 committed by GitHub
parent a0edf73bda
commit 8cf19d60dc
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GPG Key ID: 4AEE18F83AFDEB23
9 changed files with 84 additions and 49 deletions
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8
convert-baichuan-hf-to-gguf.py
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@ -76,6 +76,7 @@ def parse_args() -> argparse.Namespace:
"ftype", type=int, choices=[0, 1], default=1, nargs='?',
help="output format - use 0 for float32, 1 for float16",
)
parser.add_argument("--bigendian", action="store_true", help="model is executed on big endian machine")
return parser.parse_args()
args = parse_args()
@ -86,6 +87,11 @@ if not dir_model.is_dir():
print(f'Error: {args.model} is not a directory', file = sys.stderr)
sys.exit(1)
endianess = gguf.GGUFEndian.LITTLE
if args.bigendian:
endianess = gguf.GGUFEndian.BIG
endianess_str = "Big Endian" if args.bigendian else "Little Endian"
print(f"gguf: Conversion Endianess {endianess}")
# possible tensor data types
# ftype == 0 -> float32
# ftype == 1 -> float16
@ -113,7 +119,7 @@ if hparams["architectures"][0] != "BaichuanForCausalLM":
num_parts = count_model_parts(dir_model)
print(f"num_parts:{num_parts}\n")
ARCH=gguf.MODEL_ARCH.BAICHUAN
gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH])
gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH], endianess=endianess)
print("gguf: get model metadata")

20
convert.py
View File

@ -803,8 +803,8 @@ def check_vocab_size(params: Params, vocab: Vocab) -> None:
class OutputFile:
def __init__(self, fname_out: Path) -> None:
self.gguf = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH])
def __init__(self, fname_out: Path, endianess:gguf.GGUFEndian=gguf.GGUFEndian.LITTLE) -> None:
self.gguf = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH], endianess=endianess)
def add_meta_arch(self, params: Params) -> None:
name = "LLaMA"
@ -875,10 +875,10 @@ class OutputFile:
self.gguf.close()
@staticmethod
def write_vocab_only(fname_out: Path, params: Params, vocab: Vocab, svocab: gguf.SpecialVocab) -> None:
def write_vocab_only(fname_out: Path, params: Params, vocab: Vocab, svocab: gguf.SpecialVocab, endianess:gguf.GGUFEndian=gguf.GGUFEndian.LITTLE) -> None:
check_vocab_size(params, vocab)
of = OutputFile(fname_out)
of = OutputFile(fname_out, endianess=endianess)
# meta data
of.add_meta_arch(params)
@ -903,10 +903,10 @@ class OutputFile:
return dt.quantize(arr)
@staticmethod
def write_all(fname_out: Path, ftype: GGMLFileType, params: Params, model: LazyModel, vocab: Vocab, svocab: gguf.SpecialVocab, concurrency: int = DEFAULT_CONCURRENCY) -> None:
def write_all(fname_out: Path, ftype: GGMLFileType, params: Params, model: LazyModel, vocab: Vocab, svocab: gguf.SpecialVocab, concurrency: int = DEFAULT_CONCURRENCY, endianess=gguf.GGUFEndian.LITTLE) -> None:
check_vocab_size(params, vocab)
of = OutputFile(fname_out)
of = OutputFile(fname_out, endianess=endianess)
# meta data
of.add_meta_arch(params)
@ -1123,8 +1123,9 @@ def main(args_in: list[str] | None = None) -> None:
parser.add_argument("--vocabtype", choices=["spm", "bpe"], help="vocab format (default: spm)", default="spm")
parser.add_argument("--ctx", type=int, help="model training context (default: based on input)")
parser.add_argument("--concurrency", type=int, help=f"concurrency used for conversion (default: {DEFAULT_CONCURRENCY})", default = DEFAULT_CONCURRENCY)
args = parser.parse_args(args_in)
parser.add_argument("--bigendian", action="store_true", help="model is executed on big endian machine")
args = parser.parse_args(args_in)
if args.dump_single:
model_plus = lazy_load_file(args.model)
do_dump_model(model_plus)
@ -1138,6 +1139,9 @@ def main(args_in: list[str] | None = None) -> None:
if args.dump:
do_dump_model(model_plus)
return
endianess = gguf.GGUFEndian.LITTLE
if args.bigendian:
endianess = gguf.GGUFEndian.BIG
params = Params.load(model_plus)
if params.n_ctx == -1:
@ -1185,7 +1189,7 @@ def main(args_in: list[str] | None = None) -> None:
params.ftype = ftype
print(f"Writing {outfile}, format {ftype}")
OutputFile.write_all(outfile, ftype, params, model, vocab, special_vocab, concurrency = args.concurrency)
OutputFile.write_all(outfile, ftype, params, model, vocab, special_vocab, concurrency = args.concurrency, endianess=endianess)
print(f"Wrote {outfile}")

2
examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp
View File

@ -536,7 +536,7 @@ static bool is_ggml_file(const char * filename) {
if (file.size < 4) {
return false;
}
uint32_t magic = file.read_u32();
std::string magic = file.read_string(4);
return magic == GGUF_MAGIC;
}

15
ggml.c
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@ -20845,7 +20845,7 @@ struct gguf_kv {
};
struct gguf_header {
uint32_t magic;
char magic[4];
uint32_t version;
uint64_t n_tensors; // GGUFv2
uint64_t n_kv; // GGUFv2
@ -20915,7 +20915,7 @@ static bool gguf_fread_str_v1(FILE * file, struct gguf_str * p, size_t * offset)
struct gguf_context * gguf_init_empty(void) {
struct gguf_context * ctx = GGML_ALIGNED_MALLOC(sizeof(struct gguf_context));
ctx->header.magic = GGUF_MAGIC;
memcpy(ctx->header.magic, GGUF_MAGIC, sizeof(ctx->header.magic));
ctx->header.version = GGUF_VERSION;
ctx->header.n_tensors = 0;
ctx->header.n_kv = 0;
@ -20941,18 +20941,20 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
// offset from start of file
size_t offset = 0;
uint32_t magic = 0;
char magic[4];
// check the magic before making allocations
{
gguf_fread_el(file, &magic, sizeof(magic), &offset);
if (magic != GGUF_MAGIC) {
fprintf(stderr, "%s: invalid magic number %08x\n", __func__, magic);
for (uint32_t i = 0; i < sizeof(magic); i++) {
if (magic[i] != GGUF_MAGIC[i]) {
fprintf(stderr, "%s: invalid magic characters %s.\n", __func__, magic);
fclose(file);
return NULL;
}
}
}
bool ok = true;
@ -20960,7 +20962,8 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
// read the header
{
ctx->header.magic = magic;
strncpy(ctx->header.magic, magic, 4);
ctx->kv = NULL;
ctx->infos = NULL;

5
ggml.h
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@ -231,8 +231,9 @@
#define GGML_EXIT_SUCCESS 0
#define GGML_EXIT_ABORTED 1
#define GGUF_MAGIC 0x46554747 // "GGUF"
#define GGUF_VERSION 2
#define GGUF_MAGIC "GGUF"
#define GGUF_VERSION 3
#define GGUF_DEFAULT_ALIGNMENT 32

73
gguf-py/gguf/gguf.py
View File

@ -19,9 +19,10 @@ import numpy as np
#
GGUF_MAGIC = 0x46554747
GGUF_VERSION = 2
GGUF_VERSION = 3
GGUF_DEFAULT_ALIGNMENT = 32
# general
KEY_GENERAL_ARCHITECTURE = "general.architecture"
KEY_GENERAL_QUANTIZATION_VERSION = "general.quantization_version"
@ -597,6 +598,10 @@ class GGMLQuantizationType(IntEnum):
Q6_K = 14
Q8_K = 15
class GGUFEndian(IntEnum):
LITTLE = 0
BIG = 1
class GGUFValueType(IntEnum):
UINT8 = 0
@ -644,18 +649,41 @@ class GGUFWriter:
temp_file: tempfile.SpooledTemporaryFile[bytes] | None = None
tensors: list[tuple[np.ndarray[Any, Any], int]]
def __init__(self, path: os.PathLike[str] | str, arch: str, use_temp_file = True):
@property
def pack_prefix(self):
if self.endianess==GGUFEndian.LITTLE:
return "<"
else:
return ">"
def __init__(self, path: os.PathLike[str] | str, arch: str, use_temp_file = True, endianess=GGUFEndian.LITTLE):
self.fout = open(path, "wb")
self.arch = arch
self.endianess = endianess
self._simple_value_packing = {
GGUFValueType.UINT8: f"{self.pack_prefix}B",
GGUFValueType.INT8: f"{self.pack_prefix}b",
GGUFValueType.UINT16: f"{self.pack_prefix}H",
GGUFValueType.INT16: f"{self.pack_prefix}h",
GGUFValueType.UINT32: f"{self.pack_prefix}I",
GGUFValueType.INT32: f"{self.pack_prefix}i",
GGUFValueType.FLOAT32: f"{self.pack_prefix}f",
GGUFValueType.UINT64: f"{self.pack_prefix}Q",
GGUFValueType.INT64: f"{self.pack_prefix}q",
GGUFValueType.FLOAT64: f"{self.pack_prefix}d",
GGUFValueType.BOOL: "?" ,
}
self.add_architecture()
self.use_temp_file = use_temp_file
self.tensors = []
endianess_str = "Big Endian" if self.endianess == GGUFEndian.BIG else "Little Endian"
print(f"This gguf file is for {endianess_str} only")
def write_header_to_file(self):
self.fout.write(struct.pack("<I", GGUF_MAGIC))
self.fout.write(struct.pack("<I", GGUF_VERSION))
self.fout.write(struct.pack("<Q", self.ti_data_count))
self.fout.write(struct.pack("<Q", self.kv_data_count))
self.fout.write(struct.pack(f"{self.pack_prefix}I", GGUF_VERSION))
self.fout.write(struct.pack(f"{self.pack_prefix}Q", self.ti_data_count))
self.fout.write(struct.pack(f"{self.pack_prefix}Q", self.kv_data_count))
self.flush()
# print("tensors " + str(self.ti_data_count) + " kv " + str(self.kv_data_count))
@ -727,25 +755,12 @@ class GGUFWriter:
self.add_key(key)
self.add_val(val, GGUFValueType.ARRAY)
_simple_value_packing = {
GGUFValueType.UINT8: "<B",
GGUFValueType.INT8: "<b",
GGUFValueType.UINT16: "<H",
GGUFValueType.INT16: "<h",
GGUFValueType.UINT32: "<I",
GGUFValueType.INT32: "<i",
GGUFValueType.FLOAT32: "<f",
GGUFValueType.UINT64: "<Q",
GGUFValueType.INT64: "<q",
GGUFValueType.FLOAT64: "<d",
GGUFValueType.BOOL: "?" ,
}
def add_val(self, val: Any, vtype: GGUFValueType | None = None, add_vtype: bool = True):
if vtype is None:
vtype = GGUFValueType.get_type(val)
if add_vtype:
self.kv_data += struct.pack("<I", vtype)
self.kv_data += struct.pack(f"{self.pack_prefix}I", vtype)
self.kv_data_count += 1
pack_fmt = self._simple_value_packing.get(vtype)
@ -753,14 +768,14 @@ class GGUFWriter:
self.kv_data += struct.pack(pack_fmt, val)
elif vtype == GGUFValueType.STRING:
encoded_val = val.encode("utf8") if isinstance(val, str) else val
self.kv_data += struct.pack("<Q", len(encoded_val))
self.kv_data += struct.pack(f"{self.pack_prefix}Q", len(encoded_val))
self.kv_data += encoded_val
elif vtype == GGUFValueType.ARRAY and isinstance(val, Sequence) and len(val) > 0:
ltype = GGUFValueType.get_type(val[0])
if not all(GGUFValueType.get_type(i) is ltype for i in val[1:]):
raise ValueError("All items in a GGUF array should be of the same type")
self.kv_data += struct.pack("<I", ltype)
self.kv_data += struct.pack("<Q", len(val))
self.kv_data += struct.pack(f"{self.pack_prefix}I", ltype)
self.kv_data += struct.pack(f"{self.pack_prefix}Q", len(val))
for item in val:
self.add_val(item, add_vtype=False)
else:
@ -774,22 +789,24 @@ class GGUFWriter:
assert raw_dtype is not None or tensor_dtype in (np.float32, np.float16), "Only F32 and F16 tensors are supported for now"
encoded_name = name.encode("utf8")
self.ti_data += struct.pack("<Q", len(encoded_name))
self.ti_data += struct.pack(f"{self.pack_prefix}Q", len(encoded_name))
self.ti_data += encoded_name
n_dims = len(tensor_shape)
self.ti_data += struct.pack("<I", n_dims)
self.ti_data += struct.pack(f"{self.pack_prefix}I", n_dims)
for i in range(n_dims):
self.ti_data += struct.pack("<Q", tensor_shape[n_dims - 1 - i])
self.ti_data += struct.pack(f"{self.pack_prefix}Q", tensor_shape[n_dims - 1 - i])
if raw_dtype is None:
dtype = GGMLQuantizationType.F32 if tensor_dtype == np.float32 else GGMLQuantizationType.F16
else:
dtype = raw_dtype
self.ti_data += struct.pack("<I", dtype)
self.ti_data += struct.pack("<Q", self.offset_tensor)
self.ti_data += struct.pack(f"{self.pack_prefix}I", dtype)
self.ti_data += struct.pack(f"{self.pack_prefix}Q", self.offset_tensor)
self.offset_tensor += GGUFWriter.ggml_pad(tensor_nbytes, self.data_alignment)
self.ti_data_count += 1
def add_tensor(self, name: str, tensor: np.ndarray[Any, Any], raw_shape: Sequence[int] | None = None, raw_dtype: GGMLQuantizationType | None = None):
if self.endianess == GGUFEndian.BIG:
tensor.byteswap(inplace=True)
if self.use_temp_file and self.temp_file is None:
fp = tempfile.SpooledTemporaryFile(mode="w+b", max_size=256*1024*1024)
fp.seek(0)
@ -815,6 +832,8 @@ class GGUFWriter:
fp.write(bytes([0] * pad))
def write_tensor_data(self, tensor: np.ndarray[Any, Any]):
if self.endianess==GGUFEndian.BIG:
tensor.byteswap(inplace=True)
self.write_padding(self.fout, self.fout.tell())
tensor.tofile(self.fout)
self.write_padding(self.fout, tensor.nbytes)

2
gguf-py/pyproject.toml
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@ -1,6 +1,6 @@
[tool.poetry]
name = "gguf"
version = "0.4.4"
version = "0.4.5"
description = "Write ML models in GGUF for GGML"
authors = ["GGML <ggml@ggml.ai>"]
packages = [

2
k_quants.c
View File

@ -46,7 +46,7 @@ inline static int32_t vaddvq_s32(int32x4_t v) {
#if defined(_MSC_VER) || defined(__MINGW32__)
#include <intrin.h>
#else
#if !defined(__riscv)
#if !defined(__riscv) && !defined(__s390__)
#include <immintrin.h>
#endif
#endif

2
tests/test-double-float.cpp
View File

@ -4,7 +4,9 @@
#undef NDEBUG
#include <cassert>
#if !defined(__riscv) && !defined(__s390__)
#include <immintrin.h>
#endif
#include <cmath>
#include <cstdint>
#include <cstring>