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convert-hf : support direct Q8_0 conversion (#7234)

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* convert-hf : support q8_0 conversion

* convert-hf : add missing ftype

This was messing with the checksums otherwise.

* convert-hf : add missing ftype to Baichuan and Xverse

I didn't notice these on my first pass.
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compilade 2024-05-13 14:10:51 -04:00 committed by GitHub
parent 614d3b914e
commit ee52225067
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5 changed files with 169 additions and 58 deletions
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72
convert-hf-to-gguf.py
View File

@ -240,23 +240,6 @@ class Model:
return False
def write_tensors(self):
# same as ggml_compute_fp32_to_bf16 in ggml-impl.h
def np_fp32_to_bf16(n: np.ndarray):
# force nan to quiet
n = np.where((n & 0x7fffffff) > 0x7f800000, (n & 0xffff0000) | (64 << 16), n)
# flush subnormals to zero
n = np.where((n & 0x7f800000) == 0, n & 0x80000000, n)
# round to nearest even
n = (n + (0x7fff + ((n >> 16) & 1))) >> 16
return n.astype(np.int16)
# Doing this row-wise is much, much faster than element-wise, hence the signature
v_fp32_to_bf16 = np.vectorize(np_fp32_to_bf16, otypes=[np.int16], signature="(n)->(n)")
if self.lazy:
# TODO: find a way to implicitly wrap np.vectorize functions
# NOTE: the type is changed to reflect otypes passed to np.vectorize above
v_fp32_to_bf16 = gguf.LazyNumpyTensor._wrap_fn(v_fp32_to_bf16, meta_noop=np.int16)
max_name_len = max(len(s) for _, s in self.tensor_map.mapping.values()) + len(".weight,")
for name, data_torch in self.get_tensors():
@ -309,27 +292,31 @@ class Model:
))
if self.ftype != gguf.LlamaFileType.ALL_F32 and extra_f16 and not extra_f32:
if self.ftype == gguf.LlamaFileType.MOSTLY_F16:
if self.ftype == gguf.LlamaFileType.MOSTLY_BF16:
data = gguf.quantize_bf16(data)
assert data.dtype == np.int16
data_qtype = gguf.GGMLQuantizationType.BF16
elif self.ftype == gguf.LlamaFileType.MOSTLY_Q8_0 and gguf.can_quantize_to_q8_0(data):
data = gguf.quantize_q8_0(data)
assert data.dtype == np.uint8
data_qtype = gguf.GGMLQuantizationType.Q8_0
else: # default to float16 for quantized tensors
if data_dtype != np.float16:
data = data.astype(np.float16)
data_qtype = gguf.GGMLQuantizationType.F16
elif self.ftype == gguf.LlamaFileType.MOSTLY_BF16:
if data_dtype != np.float32:
data = data.astype(np.float32)
data = v_fp32_to_bf16(data.view(np.int32))
assert data.dtype == np.int16
data_qtype = gguf.GGMLQuantizationType.BF16
else: # by default, convert to float32
if data_qtype is None: # by default, convert to float32
if data_dtype != np.float32:
data = data.astype(np.float32)
data_qtype = gguf.GGMLQuantizationType.F32
assert data_qtype is not None
block_size, type_size = gguf.GGML_QUANT_SIZES[data_qtype]
# reverse shape to make it similar to the internal ggml dimension order
shape_str = f"{{{', '.join(str(n) for n in reversed(data.shape))}}}"
shape_str = f"""{{{', '.join(str(n) for n in reversed(
(*data.shape[:-1], data.shape[-1] * data.dtype.itemsize // type_size * block_size))
)}}}"""
# n_dims is implicit in the shape
logger.info(f"{f'%-{max_name_len}s' % f'{new_name},'} {old_dtype} --> {data_qtype.name}, shape = {shape_str}")
@ -859,6 +846,7 @@ class BaichuanModel(Model):
self.gguf_writer.add_head_count(head_count)
self.gguf_writer.add_head_count_kv(head_count_kv)
self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
self.gguf_writer.add_file_type(self.ftype)
if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]:
if self.hparams["rope_scaling"].get("type") == "linear":
@ -981,6 +969,7 @@ class XverseModel(Model):
self.gguf_writer.add_head_count(head_count)
self.gguf_writer.add_head_count_kv(head_count_kv)
self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
self.gguf_writer.add_file_type(self.ftype)
if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]:
if self.hparams["rope_scaling"].get("type") == "linear":
@ -1215,6 +1204,7 @@ class StableLMModel(Model):
self.gguf_writer.add_head_count_kv(hparams["num_key_value_heads"])
self.gguf_writer.add_parallel_residual(hparams["use_parallel_residual"] if "use_parallel_residual" in hparams else True)
self.gguf_writer.add_layer_norm_eps(self.find_hparam(["layer_norm_eps", "norm_eps"]))
self.gguf_writer.add_file_type(self.ftype)
_q_norms: list[dict[str, Tensor]] | None = None
_k_norms: list[dict[str, Tensor]] | None = None
@ -1591,6 +1581,7 @@ class QwenModel(Model):
self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
self.gguf_writer.add_head_count(self.hparams["num_attention_heads"])
self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"])
self.gguf_writer.add_file_type(self.ftype)
@Model.register("Qwen2ForCausalLM")
@ -1828,6 +1819,7 @@ class PlamoModel(Model):
self.gguf_writer.add_head_count(hparams["num_attention_heads"])
self.gguf_writer.add_head_count_kv(5) # hparams["num_key_value_heads"]) is wrong
self.gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"])
self.gguf_writer.add_file_type(self.ftype)
def shuffle_attn_q_weight(self, data_torch):
assert data_torch.size() == (5120, 5120)
@ -2007,6 +1999,7 @@ in chat mode so that the conversation can end normally.")
self.gguf_writer.add_head_count(self.hparams["num_attention_heads"])
self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"])
self.gguf_writer.add_file_type(self.ftype)
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
num_heads = self.hparams["num_attention_heads"]
@ -2415,25 +2408,15 @@ class LazyTorchTensor(gguf.LazyBase):
def numpy(self) -> gguf.LazyNumpyTensor:
dtype = self._dtype_map[self.dtype]
return gguf.LazyNumpyTensor(
meta=np.lib.stride_tricks.as_strided(np.zeros(1, dtype), self.shape, (0 for _ in self.shape)),
meta=gguf.LazyNumpyTensor.meta_with_dtype_and_shape(dtype, self.shape),
lazy=self._lazy,
args=(self,),
func=(lambda s: s[0].numpy())
)
@classmethod
def eager_to_meta(cls, t: Tensor) -> Tensor:
if t.is_meta:
return t
return t.detach().to("meta")
@classmethod
def meta_with_dtype(cls, m: Tensor, dtype: torch.dtype) -> Tensor:
m = m.detach()
if not m.is_meta:
m = m.to("meta")
m.dtype = dtype
return m
def meta_with_dtype_and_shape(cls, dtype: torch.dtype, shape: torch.Size) -> Tensor:
return torch.empty(size=shape, dtype=dtype, device="meta")
@classmethod
def __torch_function__(cls, func, types, args=(), kwargs=None):
@ -2464,8 +2447,8 @@ def parse_args() -> argparse.Namespace:
help="path to write to; default: based on input. {ftype} will be replaced by the outtype.",
)
parser.add_argument(
"--outtype", type=str, choices=["f32", "f16", "bf16", "auto"], default="f16",
help="output format - use f32 for float32, f16 for float16, bf16 for bfloat16, auto for the highest-fidelity 16-bit float type depending on the first loaded tensor type",
"--outtype", type=str, choices=["f32", "f16", "bf16", "q8_0", "auto"], default="f16",
help="output format - use f32 for float32, f16 for float16, bf16 for bfloat16, q8_0 for Q8_0, auto for the highest-fidelity 16-bit float type depending on the first loaded tensor type",
)
parser.add_argument(
"--bigendian", action="store_true",
@ -2523,6 +2506,7 @@ def main() -> None:
"f32": gguf.LlamaFileType.ALL_F32,
"f16": gguf.LlamaFileType.MOSTLY_F16,
"bf16": gguf.LlamaFileType.MOSTLY_BF16,
"q8_0": gguf.LlamaFileType.MOSTLY_Q8_0,
"auto": gguf.LlamaFileType.GUESSED,
}

1
gguf-py/gguf/__init__.py
View File

@ -2,5 +2,6 @@ from .constants import *
from .lazy import *
from .gguf_reader import *
from .gguf_writer import *
from .quants import *
from .tensor_mapping import *
from .vocab import *

16
gguf-py/gguf/gguf_writer.py
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@ -13,6 +13,7 @@ from string import ascii_letters, digits
import numpy as np
from .constants import (
GGML_QUANT_SIZES,
GGUF_DEFAULT_ALIGNMENT,
GGUF_MAGIC,
GGUF_VERSION,
@ -195,7 +196,7 @@ class GGUFWriter:
return ((x + n - 1) // n) * n
def add_tensor_info(
self, name: str, tensor_shape: Sequence[int], tensor_dtype: np.dtype[np.float16] | np.dtype[np.float32],
self, name: str, tensor_shape: Sequence[int], tensor_dtype: np.dtype,
tensor_nbytes: int, raw_dtype: GGMLQuantizationType | None = None,
) -> None:
if self.state is not WriterState.EMPTY:
@ -208,10 +209,6 @@ class GGUFWriter:
encoded_name = name.encode("utf-8")
self.ti_data += self._pack("Q", len(encoded_name))
self.ti_data += encoded_name
n_dims = len(tensor_shape)
self.ti_data += self._pack("I", n_dims)
for i in range(n_dims):
self.ti_data += self._pack("Q", tensor_shape[n_dims - 1 - i])
if raw_dtype is None:
if tensor_dtype == np.float16:
dtype = GGMLQuantizationType.F16
@ -231,6 +228,15 @@ class GGUFWriter:
raise ValueError("Only F16, F32, F64, I8, I16, I32, I64 tensors are supported for now")
else:
dtype = raw_dtype
if tensor_dtype == np.uint8:
block_size, type_size = GGML_QUANT_SIZES[raw_dtype]
if tensor_shape[-1] % type_size != 0:
raise ValueError(f"Quantized tensor row size ({tensor_shape[-1]}) is not a multiple of {dtype.name} type size ({type_size})")
tensor_shape = tuple(tensor_shape[:-1]) + (tensor_shape[-1] // type_size * block_size,)
n_dims = len(tensor_shape)
self.ti_data += self._pack("I", n_dims)
for i in range(n_dims):
self.ti_data += self._pack("Q", tensor_shape[n_dims - 1 - i])
self.ti_data += self._pack("I", dtype)
self.ti_data += self._pack("Q", self.offset_tensor)
self.offset_tensor += GGUFWriter.ggml_pad(tensor_nbytes, self.data_alignment)

29
gguf-py/gguf/lazy.py
View File

@ -6,6 +6,7 @@ from typing import Any, Callable
from collections import deque
import numpy as np
from numpy._typing import _Shape
from numpy.typing import DTypeLike
@ -110,7 +111,7 @@ class LazyBase(ABC, metaclass=LazyMeta):
return o
@classmethod
def _wrap_fn(cls, fn: Callable, *, use_self: LazyBase | None = None, meta_noop: bool | DTypeLike = False) -> Callable[[Any], Any]:
def _wrap_fn(cls, fn: Callable, *, use_self: LazyBase | None = None, meta_noop: bool | DTypeLike | tuple[DTypeLike, Callable[[tuple[int, ...]], tuple[int, ...]]] = False) -> Callable[[Any], Any]:
def wrapped_fn(*args, **kwargs):
if kwargs is None:
kwargs = {}
@ -130,9 +131,14 @@ class LazyBase(ABC, metaclass=LazyMeta):
res = args[0]
assert isinstance(res, cls)
res = res._meta
# allow operations to override the dtype
# allow operations to override the dtype and shape
if meta_noop is not True:
res = cls.meta_with_dtype(res, meta_noop)
if isinstance(meta_noop, tuple):
dtype, shape = meta_noop
assert callable(shape)
res = cls.meta_with_dtype_and_shape(dtype, shape(res.shape))
else:
res = cls.meta_with_dtype_and_shape(meta_noop, res.shape)
if isinstance(res, cls._tensor_type):
def collect_replace(t: LazyBase):
@ -168,7 +174,12 @@ class LazyBase(ABC, metaclass=LazyMeta):
while _t._data is None:
lt = _t._lazy.popleft()
if lt._data is not None:
raise ValueError(f"{lt} did not belong in the lazy queue")
# Lazy tensor did not belong in the lazy queue.
# Weirdly only happens with Bloom models...
# likely because tensors aren't unique in the queue.
# The final output is still the same as in eager mode,
# so it's safe to ignore this.
continue
assert lt._func is not None
lt._args = cls._recurse_apply(lt._args, already_eager_to_eager)
lt._data = lt._func(lt._args)
@ -183,12 +194,12 @@ class LazyBase(ABC, metaclass=LazyMeta):
@classmethod
def eager_to_meta(cls, t: Any) -> Any:
return cls.meta_with_dtype(t, t.dtype)
return cls.meta_with_dtype_and_shape(t.dtype, t.shape)
# must be overridden, meta tensor init is backend-specific
@classmethod
@abstractmethod
def meta_with_dtype(cls, m: Any, dtype: Any) -> Any: pass
def meta_with_dtype_and_shape(cls, dtype: Any, shape: Any) -> Any: pass
@classmethod
def from_eager(cls, t: Any) -> Any:
@ -205,15 +216,15 @@ class LazyNumpyTensor(LazyBase):
_tensor_type = np.ndarray
@classmethod
def meta_with_dtype(cls, m: np.ndarray[Any, Any], dtype: DTypeLike) -> np.ndarray[Any, Any]:
def meta_with_dtype_and_shape(cls, dtype: DTypeLike, shape: _Shape) -> np.ndarray[Any, Any]:
# The initial idea was to use np.nan as the fill value,
# but non-float types like np.int16 can't use that.
# So zero it is.
cheat = np.zeros(1, dtype)
return np.lib.stride_tricks.as_strided(cheat, m.shape, (0 for _ in m.shape))
return np.lib.stride_tricks.as_strided(cheat, shape, (0 for _ in shape))
def astype(self, dtype, *args, **kwargs):
meta = type(self).meta_with_dtype(self._meta, dtype)
meta = type(self).meta_with_dtype_and_shape(dtype, self._meta.shape)
full_args = (self, dtype,) + args
# very important to pass the shared _lazy deque, or else there's an infinite loop somewhere.
return type(self)(meta=meta, args=full_args, lazy=self._lazy, func=(lambda a: a[0].astype(*a[1:], **kwargs)))

109
gguf-py/gguf/quants.py Normal file
View File

@ -0,0 +1,109 @@
from __future__ import annotations
from typing import Callable
from numpy.typing import DTypeLike
from .constants import GGML_QUANT_SIZES, GGMLQuantizationType
from .lazy import LazyNumpyTensor
import numpy as np
# same as ggml_compute_fp32_to_bf16 in ggml-impl.h
def __compute_fp32_to_bf16(n: np.ndarray) -> np.ndarray:
n = n.astype(np.float32, copy=False).view(np.int32)
# force nan to quiet
n = np.where((n & 0x7fffffff) > 0x7f800000, (n & 0xffff0000) | (64 << 16), n)
# flush subnormals to zero
n = np.where((n & 0x7f800000) == 0, n & 0x80000000, n)
# round to nearest even
n = (n + (0x7fff + ((n >> 16) & 1))) >> 16
return n.astype(np.int16)
# This is faster than np.vectorize and np.apply_along_axis because it works on more than one row at a time
def __apply_over_grouped_rows(func: Callable[[np.ndarray], np.ndarray], arr: np.ndarray, otype: DTypeLike, oshape: tuple[int, ...]) -> np.ndarray:
rows = arr.reshape((-1, arr.shape[-1]))
osize = 1
for dim in oshape:
osize *= dim
out = np.empty(shape=osize, dtype=otype)
# compute over groups of 16 rows (arbitrary, but seems good for performance)
n_groups = rows.shape[0] // 16
np.concatenate([func(group).ravel() for group in np.array_split(rows, n_groups)], axis=0, out=out)
return out.reshape(oshape)
def __quantize_bf16_array(n: np.ndarray) -> np.ndarray:
return __apply_over_grouped_rows(__compute_fp32_to_bf16, arr=n, otype=np.int16, oshape=n.shape)
__quantize_bf16_lazy = LazyNumpyTensor._wrap_fn(__quantize_bf16_array, meta_noop=np.int16)
def quantize_bf16(n: np.ndarray):
if type(n) is LazyNumpyTensor:
return __quantize_bf16_lazy(n)
else:
return __quantize_bf16_array(n)
__q8_block_size, __q8_type_size = GGML_QUANT_SIZES[GGMLQuantizationType.Q8_0]
def can_quantize_to_q8_0(n: np.ndarray) -> bool:
return n.shape[-1] % __q8_block_size == 0
# round away from zero
# ref: https://stackoverflow.com/a/59143326/22827863
def np_roundf(n: np.ndarray) -> np.ndarray:
a = abs(n)
floored = np.floor(a)
b = floored + np.floor(2 * (a - floored))
return np.sign(n) * b
def __quantize_q8_0_shape_change(s: tuple[int, ...]) -> tuple[int, ...]:
return (*s[:-1], s[-1] // __q8_block_size * __q8_type_size)
# Implementation of Q8_0 with bit-exact same results as reference implementation in ggml-quants.c
def __quantize_q8_0_rows(n: np.ndarray) -> np.ndarray:
shape = n.shape
assert shape[-1] % __q8_block_size == 0
n_blocks = n.size // __q8_block_size
blocks = n.reshape((n_blocks, __q8_block_size)).astype(np.float32, copy=False)
d = abs(blocks).max(axis=1, keepdims=True) / 127
with np.errstate(divide="ignore"):
id = np.where(d == 0, 0, 1 / d)
qs = np_roundf(blocks * id)
# (n_blocks, 2)
d = d.astype(np.float16).view(np.uint8)
# (n_blocks, block_size)
qs = qs.astype(np.int8).view(np.uint8)
assert d.shape[1] + qs.shape[1] == __q8_type_size
return np.concatenate([d, qs], axis=1).reshape(__quantize_q8_0_shape_change(shape))
def __quantize_q8_0_array(n: np.ndarray) -> np.ndarray:
return __apply_over_grouped_rows(__quantize_q8_0_rows, arr=n, otype=np.uint8, oshape=__quantize_q8_0_shape_change(n.shape))
__quantize_q8_0_lazy = LazyNumpyTensor._wrap_fn(
__quantize_q8_0_array,
meta_noop=(np.uint8, __quantize_q8_0_shape_change),
)
def quantize_q8_0(data: np.ndarray):
if type(data) is LazyNumpyTensor:
return __quantize_q8_0_lazy(data)
else:
return __quantize_q8_0_array(data)