mirror of
https://github.com/ggml-org/llama.cpp.git
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ee52225067
* 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.
110 lines
3.5 KiB
Python
110 lines
3.5 KiB
Python
from __future__ import annotations
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from typing import Callable
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from numpy.typing import DTypeLike
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from .constants import GGML_QUANT_SIZES, GGMLQuantizationType
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from .lazy import LazyNumpyTensor
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import numpy as np
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# same as ggml_compute_fp32_to_bf16 in ggml-impl.h
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def __compute_fp32_to_bf16(n: np.ndarray) -> np.ndarray:
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n = n.astype(np.float32, copy=False).view(np.int32)
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# force nan to quiet
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n = np.where((n & 0x7fffffff) > 0x7f800000, (n & 0xffff0000) | (64 << 16), n)
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# flush subnormals to zero
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n = np.where((n & 0x7f800000) == 0, n & 0x80000000, n)
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# round to nearest even
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n = (n + (0x7fff + ((n >> 16) & 1))) >> 16
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return n.astype(np.int16)
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# This is faster than np.vectorize and np.apply_along_axis because it works on more than one row at a time
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def __apply_over_grouped_rows(func: Callable[[np.ndarray], np.ndarray], arr: np.ndarray, otype: DTypeLike, oshape: tuple[int, ...]) -> np.ndarray:
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rows = arr.reshape((-1, arr.shape[-1]))
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osize = 1
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for dim in oshape:
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osize *= dim
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out = np.empty(shape=osize, dtype=otype)
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# compute over groups of 16 rows (arbitrary, but seems good for performance)
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n_groups = rows.shape[0] // 16
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np.concatenate([func(group).ravel() for group in np.array_split(rows, n_groups)], axis=0, out=out)
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return out.reshape(oshape)
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def __quantize_bf16_array(n: np.ndarray) -> np.ndarray:
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return __apply_over_grouped_rows(__compute_fp32_to_bf16, arr=n, otype=np.int16, oshape=n.shape)
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__quantize_bf16_lazy = LazyNumpyTensor._wrap_fn(__quantize_bf16_array, meta_noop=np.int16)
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def quantize_bf16(n: np.ndarray):
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if type(n) is LazyNumpyTensor:
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return __quantize_bf16_lazy(n)
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else:
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return __quantize_bf16_array(n)
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__q8_block_size, __q8_type_size = GGML_QUANT_SIZES[GGMLQuantizationType.Q8_0]
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def can_quantize_to_q8_0(n: np.ndarray) -> bool:
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return n.shape[-1] % __q8_block_size == 0
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# round away from zero
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# ref: https://stackoverflow.com/a/59143326/22827863
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def np_roundf(n: np.ndarray) -> np.ndarray:
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a = abs(n)
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floored = np.floor(a)
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b = floored + np.floor(2 * (a - floored))
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return np.sign(n) * b
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def __quantize_q8_0_shape_change(s: tuple[int, ...]) -> tuple[int, ...]:
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return (*s[:-1], s[-1] // __q8_block_size * __q8_type_size)
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# Implementation of Q8_0 with bit-exact same results as reference implementation in ggml-quants.c
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def __quantize_q8_0_rows(n: np.ndarray) -> np.ndarray:
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shape = n.shape
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assert shape[-1] % __q8_block_size == 0
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n_blocks = n.size // __q8_block_size
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blocks = n.reshape((n_blocks, __q8_block_size)).astype(np.float32, copy=False)
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d = abs(blocks).max(axis=1, keepdims=True) / 127
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with np.errstate(divide="ignore"):
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id = np.where(d == 0, 0, 1 / d)
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qs = np_roundf(blocks * id)
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# (n_blocks, 2)
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d = d.astype(np.float16).view(np.uint8)
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# (n_blocks, block_size)
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qs = qs.astype(np.int8).view(np.uint8)
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assert d.shape[1] + qs.shape[1] == __q8_type_size
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return np.concatenate([d, qs], axis=1).reshape(__quantize_q8_0_shape_change(shape))
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def __quantize_q8_0_array(n: np.ndarray) -> np.ndarray:
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return __apply_over_grouped_rows(__quantize_q8_0_rows, arr=n, otype=np.uint8, oshape=__quantize_q8_0_shape_change(n.shape))
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__quantize_q8_0_lazy = LazyNumpyTensor._wrap_fn(
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__quantize_q8_0_array,
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meta_noop=(np.uint8, __quantize_q8_0_shape_change),
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)
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def quantize_q8_0(data: np.ndarray):
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if type(data) is LazyNumpyTensor:
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return __quantize_q8_0_lazy(data)
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else:
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return __quantize_q8_0_array(data)
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