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convert.py : advanced option (ggerganov#2753)
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* Allow convert.py to convert to q8_0

Fix issue with bounded_parallel_map and greedy consuming iterator

Display elapsed time during conversion

* Add --concurrency option

Minor improvements to help text

Clean up bounded_parallel_map function a bit

* Massive speed improvement thanks to Cebtenzzre

* Refactor types
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@KerfuffleV2 @akawrykow
KerfuffleV2 authored and akawrykow committed Aug 29, 2023
1 parent ca268b2 commit 499351d
Showing 1 changed file with 133 additions and 73 deletions.
206 changes: 133 additions & 73 deletions convert.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,6 +3,7 @@
import gguf
import argparse
import concurrent.futures
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
import copy
import enum
import faulthandler
Expand All @@ -17,13 +18,14 @@
import signal
import struct
import sys
import time
import zipfile
import numpy as np

from abc import ABCMeta, abstractmethod
from dataclasses import dataclass
from pathlib import Path
from typing import (IO, TYPE_CHECKING, Any, Callable, Dict, Iterable, List, Literal, Optional, Sequence, Tuple, TypeVar, Union)
from typing import (IO, TYPE_CHECKING, Any, Callable, Dict, Generator, Iterable, List, Literal, Optional, Sequence, Set, Tuple, TypeVar, Union)
from sentencepiece import SentencePieceProcessor # type: ignore

if TYPE_CHECKING:
Expand All @@ -37,30 +39,70 @@
ARCH=gguf.MODEL_ARCH.LLAMA
NAMES=gguf.MODEL_TENSOR_NAMES[ARCH]

DEFAULT_CONCURRENCY = 8
#
# data types
#

@dataclass(frozen=True)
class UnquantizedDataType:
class DataType:
name: str
dtype: 'np.dtype[Any]'
valid_conversions: List[str]

DT_F16 = UnquantizedDataType('F16')
DT_F32 = UnquantizedDataType('F32')
DT_I32 = UnquantizedDataType('I32')
DT_BF16 = UnquantizedDataType('BF16')
def elements_to_bytes(self, n_elements: int) -> int:
return n_elements * self.dtype.itemsize

DataType = Union[UnquantizedDataType]
@dataclass(frozen=True)
class UnquantizedDataType(DataType):
pass

DATA_TYPE_TO_NUMPY: Dict[DataType, 'np.dtype[Any]'] = {
DT_BF16: np.dtype(np.uint16),
DT_F16: np.dtype(np.float16),
DT_F32: np.dtype(np.float32),
DT_I32: np.dtype(np.int32),
}
DT_F16 = UnquantizedDataType('F16', dtype = np.dtype(np.float16), valid_conversions = ['F32', 'Q8_0'])
DT_F32 = UnquantizedDataType('F32', dtype = np.dtype(np.float32), valid_conversions = ['F16', 'Q8_0'])
DT_I32 = UnquantizedDataType('I32', dtype = np.dtype(np.int16), valid_conversions = [])
DT_BF16 = UnquantizedDataType('BF16', dtype = np.dtype(np.uint16), valid_conversions = ['F32', 'F16', 'Q8_0'])

@dataclass(frozen=True)
class QuantizedDataType(DataType):
block_size: int
quantized_dtype: 'np.dtype[Any]'
ggml_type: gguf.GGMLQuantizationType

NUMPY_TYPE_TO_DATA_TYPE: Dict['np.dtype[Any]', DataType] = \
{dtype: data_type for (data_type, dtype) in DATA_TYPE_TO_NUMPY.items()}
def quantize(self, arr: NDArray) -> NDArray:
raise NotImplementedError(f'Quantization for {self.name} not implemented')

def elements_to_bytes(self, n_elements: int) -> int:
assert n_elements % self.block_size == 0, f'Invalid number of elements {n_elements} for {self.name} with block size {self.block_size}'
return self.quantized_dtype.itemsize * (n_elements // self.block_size)

@dataclass(frozen=True)
class Q8_0QuantizedDataType(QuantizedDataType):
# Mini Q8_0 quantization in Python!
def quantize(self, arr: NDArray) -> NDArray:
assert arr.size % self.block_size == 0 and arr.size != 0, f'Bad array size {arr.size}'
assert arr.dtype == np.float32, f'Bad array type {arr.dtype}'
n_blocks = arr.size // self.block_size
blocks = arr.reshape((n_blocks, self.block_size))
# Much faster implementation of block quantization contributed by @Cebtenzzre
def quantize_blocks_q8_0(blocks: NDArray) -> Iterable[Tuple[Any, Any]]:
d = abs(blocks).max(axis = 1) / np.float32(127)
with np.errstate(divide = 'ignore'):
qs = (blocks / d[:, None]).round()
qs[d == 0] = 0
yield from zip(d, qs)
return np.fromiter(quantize_blocks_q8_0(blocks), count = n_blocks, dtype = self.quantized_dtype)

DT_Q8_0 = Q8_0QuantizedDataType('Q8_0',
dtype = np.dtype(np.float32), valid_conversions = [],
ggml_type = gguf.GGMLQuantizationType.Q8_0, block_size = 32,
quantized_dtype = np.dtype([('d', '<f2'), ('qs', 'i1', (32,))]))

# Quantized types skipped here because they may also map to np.float32
NUMPY_TYPE_TO_DATA_TYPE: Dict['np.dtype[Any]', DataType] = {}
for dt in (DT_BF16, DT_F16, DT_F32, DT_I32):
if dt.dtype in NUMPY_TYPE_TO_DATA_TYPE:
raise ValueError(f'Invalid duplicate data type {dt}')
NUMPY_TYPE_TO_DATA_TYPE[dt.dtype] = dt

SAFETENSORS_DATA_TYPES: Dict[str, DataType] = {
'BF16': DT_BF16,
Expand All @@ -73,20 +115,22 @@ class UnquantizedDataType:
# TODO: rename to LLAMAFileType
# TODO: move to `gguf.py`
class GGMLFileType(enum.IntEnum):
AllF32 = 0
MostlyF16 = 1 # except 1d tensors
AllF32 = 0
MostlyF16 = 1 # except 1d tensors
MostlyQ8_0 = 7 # except 1d tensors

def type_for_tensor(self, name: str, tensor: 'LazyTensor') -> DataType:
if len(tensor.shape) == 1:
# 1D tensors are always F32.
return DT_F32
elif self == GGMLFileType.AllF32:
return DT_F32
elif self == GGMLFileType.MostlyF16:
return DT_F16
else:
dt = GGML_FILE_TYPE_TO_DATA_TYPE.get(self)
if dt is None:
raise ValueError(self)
# 1D tensors are always F32.
return dt if len(tensor.shape) > 1 else DT_F32

GGML_FILE_TYPE_TO_DATA_TYPE: Dict[GGMLFileType, DataType] = {
GGMLFileType.AllF32 : DT_F32,
GGMLFileType.MostlyF16 : DT_F16,
GGMLFileType.MostlyQ8_0: DT_Q8_0,
}

#
# hparams loading
Expand Down Expand Up @@ -415,7 +459,7 @@ def __init__(self, ndarray: NDArray) -> None:
self.data_type = NUMPY_TYPE_TO_DATA_TYPE[ndarray.dtype]

def astype(self, data_type: DataType) -> Tensor:
dtype = DATA_TYPE_TO_NUMPY[data_type]
dtype = data_type.dtype
if self.data_type == DT_BF16:
self.ndarray = bf16_to_fp32(self.ndarray)
return UnquantizedTensor(self.ndarray.astype(dtype))
Expand Down Expand Up @@ -454,22 +498,6 @@ def load_unquantized(lazy_tensor: 'LazyTensor', expected_dtype: Any = None, conv
GGMLCompatibleTensor = Union[UnquantizedTensor]


class DeferredPermutedTensor(Tensor):
def __init__(self, base: Tensor, n_head: int, n_head_kv: int) -> None:
self.base = base
self.n_head = n_head
self.data_type = self.base.data_type

def astype(self, data_type: DataType) -> Tensor:
return self.base.astype(data_type).permute(self.n_head, self.n_head_kv)

def to_ggml(self) -> GGMLCompatibleTensor:
return self.base.to_ggml().permute(self.n_head, self.n_head_kv)

def permute(self, n_head: int, n_head_kv: int) -> Tensor:
raise Exception("shouldn't permute twice")


@dataclass
class LazyTensor:
_load: Callable[[], Tensor]
Expand All @@ -479,7 +507,9 @@ class LazyTensor:

def load(self) -> Tensor:
ret = self._load()
assert ret.data_type == self.data_type, (self.data_type, ret.data_type, self.description)
# Should be okay if it maps to the same numpy type?
assert ret.data_type == self.data_type or (self.data_type.dtype == ret.data_type.dtype), \
(self.data_type, ret.data_type, self.description)
return ret

def astype(self, data_type: DataType) -> 'LazyTensor':
Expand All @@ -490,8 +520,8 @@ def load() -> Tensor:
return LazyTensor(load, self.shape, data_type, f'convert({data_type}) {self.description}')

def validate_conversion_to(self, data_type: DataType) -> None:
if data_type == self.data_type:
return
if data_type != self.data_type and data_type.name not in self.data_type.valid_conversions:
raise ValueError(f'Cannot validate conversion from {self.data_type} to {data_type}.')


LazyModel = Dict[str, LazyTensor]
Expand Down Expand Up @@ -617,9 +647,7 @@ def persistent_load(self, pid: Any) -> Any:
info = self.zip_file.getinfo(filename)

def load(offset: int, elm_count: int) -> NDArray:
dtype = DATA_TYPE_TO_NUMPY.get(data_type)
if dtype is None:
raise Exception("tensor stored in unsupported format")
dtype = data_type.dtype
fp = self.zip_file.open(info)
fp.seek(offset * dtype.itemsize)
size = elm_count * dtype.itemsize
Expand Down Expand Up @@ -683,7 +711,7 @@ def lazy_load_safetensors_file(fp: IO[bytes], path: Path) -> ModelPlus:

def convert(info: Dict[str, Any]) -> LazyTensor:
data_type = SAFETENSORS_DATA_TYPES[info['dtype']]
numpy_dtype = DATA_TYPE_TO_NUMPY[data_type]
numpy_dtype = data_type.dtype
shape: List[int] = info['shape']
begin, end = info['data_offsets']
assert 0 <= begin <= end <= len(byte_buf)
Expand Down Expand Up @@ -723,23 +751,35 @@ def lazy_load_file(path: Path) -> ModelPlus:
In = TypeVar('In')
Out = TypeVar('Out')

def bounded_parallel_map(func: Callable[[In], Out], iterable: Iterable[In], concurrency: int) -> Iterable[Out]:
def bounded_parallel_map(func: Callable[[In], Out], iterable: Iterable[In], concurrency: int, max_workers: Optional[int] = None, factory: Callable = ThreadPoolExecutor) -> Iterable[Out]:
'''Parallel map, but with backpressure. If the caller doesn't call `next`
fast enough, this will stop calling `func` at some point rather than
letting results pile up in memory. Specifically, there is a max of one
output value buffered per thread.'''
with concurrent.futures.ThreadPoolExecutor() as executor:
if concurrency < 2:
yield from map(func, iterable)
# Not reached.
iterable = iter(iterable)
with factory(max_workers = max_workers) as executor:
futures: List[concurrent.futures.Future[Out]] = []
items_rev = list(iterable)[::-1]
for i in range(min(concurrency, len(items_rev))):
futures.append(executor.submit(func, items_rev.pop()))
done = False
for _ in range(concurrency):
try:
futures.append(executor.submit(func, next(iterable)))
except StopIteration:
done = True
break

while futures:
result = futures.pop(0).result()
if items_rev:
futures.append(executor.submit(func, items_rev.pop()))
while not done and len(futures) < concurrency:
try:
futures.append(executor.submit(func, next(iterable)))
except StopIteration:
done = True
break
yield result


def check_vocab_size(params: Params, vocab: Vocab) -> None:
if params.n_vocab != vocab.vocab_size:
assert isinstance(vocab, BpeVocab) or isinstance(vocab, SentencePieceVocab)
Expand Down Expand Up @@ -804,12 +844,11 @@ def add_meta_vocab(self, vocab: Vocab) -> None:
self.gguf.add_token_types(toktypes)

def add_tensor_info(self, name: str, tensor: LazyTensor) -> None:
n_elements = 1
for dim in tensor.shape:
n_elements *= dim
data_type = DATA_TYPE_TO_NUMPY[tensor.data_type]
data_nbytes = n_elements * data_type.itemsize
self.gguf.add_tensor_info(name, tensor.shape, data_type, data_nbytes)
n_elements = int(np.prod(tensor.shape))
raw_dtype = getattr(tensor.data_type, 'ggml_type', None)
data_type = getattr(tensor.data_type, 'quantized_type', None) or tensor.data_type.dtype
data_nbytes = tensor.data_type.elements_to_bytes(n_elements)
self.gguf.add_tensor_info(name, tensor.shape, data_type, data_nbytes, raw_dtype = raw_dtype)

def write_meta(self) -> None:
self.gguf.write_header_to_file()
Expand All @@ -835,7 +874,20 @@ def write_vocab_only(fname_out: Path, params: Params, vocab: Vocab) -> None:
of.close()

@staticmethod
def write_all(fname_out: Path, params: Params, model: LazyModel, vocab: Vocab) -> None:
def do_item(item: Tuple[str, LazyTensor]) -> Tuple[DataType, NDArray]:
name, lazy_tensor = item
tensor = lazy_tensor.load().to_ggml()
return (lazy_tensor.data_type, tensor.ndarray)

@staticmethod
def maybe_do_quantize(item: Tuple[DataType, NDArray]) -> NDArray:
dt, arr = item
if not isinstance(dt, QuantizedDataType):
return arr
return dt.quantize(arr)

@staticmethod
def write_all(fname_out: Path, ftype: GGMLFileType, params: Params, model: LazyModel, vocab: Vocab, concurrency: int = DEFAULT_CONCURRENCY) -> None:
check_vocab_size(params, vocab)

of = OutputFile(fname_out)
Expand All @@ -851,16 +903,19 @@ def write_all(fname_out: Path, params: Params, model: LazyModel, vocab: Vocab) -
of.write_meta()
of.write_tensor_info()

def do_item(item: Tuple[str, LazyTensor]) -> NDArray:
name, lazy_tensor = item
return lazy_tensor.load().to_ggml().ndarray

# tensor data
ndarrays = bounded_parallel_map(do_item, model.items(), concurrency=8)
ndarrays_inner = bounded_parallel_map(OutputFile.do_item, model.items(), concurrency = concurrency)
if ftype == GGMLFileType.MostlyQ8_0:
ndarrays = bounded_parallel_map(OutputFile.maybe_do_quantize, ndarrays_inner, concurrency = concurrency, max_workers = concurrency, factory = ProcessPoolExecutor)
else:
ndarrays = map(OutputFile.maybe_do_quantize, ndarrays_inner)

start = time.time()
for i, ((name, lazy_tensor), ndarray) in enumerate(zip(model.items(), ndarrays)):
elapsed = time.time() - start
size = ' x '.join(f"{dim:6d}" for dim in lazy_tensor.shape)
padi = len(str(len(model)))
print(f"[{i+1:{padi}d}/{len(model)}] Writing tensor {name:38s} | size {size:16} | type {lazy_tensor.data_type}")
print(f"[{i+1:{padi}d}/{len(model)}] Writing tensor {name:38s} | size {size:16} | type {lazy_tensor.data_type.name:4} | T+{int(elapsed):4}")
of.gguf.write_tensor_data(ndarray)

of.close()
Expand All @@ -872,6 +927,8 @@ def pick_output_type(model: LazyModel, output_type_str: Optional[str]) -> GGMLFi
return GGMLFileType.AllF32
if output_type_str == "f16" or (output_type_str is None and wq_type in (DT_F16, DT_BF16)):
return GGMLFileType.MostlyF16
if output_type_str == "q8_0":
return GGMLFileType.MostlyQ8_0

name_to_type = {name: lazy_tensor.data_type for (name, lazy_tensor) in model.items()}

Expand Down Expand Up @@ -918,7 +975,7 @@ def convert_model_names(model: LazyModel, params: Params) -> LazyModel:
print(f"skipping tensor {name_new}")
continue
else:
print(f"{name:48s} -> {name_new:40s} | {lazy_tensor.data_type} | {lazy_tensor.shape}")
print(f"{name:48s} -> {name_new:40s} | {lazy_tensor.data_type.name:6s} | {lazy_tensor.shape}")
out[name_new] = lazy_tensor

return out
Expand Down Expand Up @@ -1023,6 +1080,7 @@ def default_outfile(model_paths: List[Path], file_type: GGMLFileType) -> Path:
namestr = {
GGMLFileType.AllF32: "f32",
GGMLFileType.MostlyF16: "f16",
GGMLFileType.MostlyQ8_0:"q8_0",
}[file_type]
ret = model_paths[0].parent / f"ggml-model-{namestr}.gguf"
if ret in model_paths:
Expand All @@ -1046,12 +1104,13 @@ def main(args_in: Optional[List[str]] = None) -> None:
parser.add_argument("--dump", action="store_true", help="don't convert, just show what's in the model")
parser.add_argument("--dump-single", action="store_true", help="don't convert, just show what's in a single model file")
parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab")
parser.add_argument("--outtype", choices=["f32", "f16"], help="output format (default: based on input)")
parser.add_argument("--outtype", choices=["f32", "f16", "q8_0"], help="output format - note: q8_0 may be very slow (default: f16 or f32 based on input)")
parser.add_argument("--vocab-dir", type=Path, help="directory containing tokenizer.model, if separate from model file")
parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input")
parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.pth, *.pt, *.bin)")
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)

if args.dump_single:
Expand All @@ -1073,6 +1132,7 @@ def main(args_in: Optional[List[str]] = None) -> None:
params.ftype = {
"f32": GGMLFileType.AllF32,
"f16": GGMLFileType.MostlyF16,
"q8_0": GGMLFileType.MostlyQ8_0,
}[args.outtype]

print(f"params = {params}")
Expand Down Expand Up @@ -1104,7 +1164,7 @@ def main(args_in: Optional[List[str]] = None) -> None:
params.ftype = ftype
print(f"Writing {outfile}, format {ftype}")

OutputFile.write_all(outfile, params, model, vocab)
OutputFile.write_all(outfile, ftype, params, model, vocab, concurrency = args.concurrency)
print(f"Wrote {outfile}")


Expand Down

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