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kvcache-ai-ktransformers/ktransformers/util/custom_gguf.py
Atream b443c7dfa2
Merge pull request #657 from kvcache-ai/feat-absorb-for-long-prefill 
Feat absorb for long prefill
2025-02-25 16:53:21 +08:00

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#!/usr/bin/env python
# coding=utf-8
'''
Description :
Author : Azure-Tang, Boxin Zhang, chenht2022
Date : 2024-07-26 08:48:54
Version : 1.0.0
LastEditors : kkk1nak0
LastEditTime : 2024-08-14 08:20:45
Adapted from https://github.com/99991/pygguf/blob/main/gguf.py
Copyright (c) 2023-2024 The ggml authors
Copyright (c) 2024 Thomas Germer
Copyright (c) 2024 by KVCache.AI, All Rights Reserved.
'''
# copied from llama.cpp/gguf-py/gguf/constants.py to satisfy dependence of gguf
# GGUF specification
# https://github.com/ggerganov/ggml/blob/master/docs/gguf.md
import struct
import warnings
import numpy as np
import re
import numpy.typing as npt
from typing import Sequence
import os
from enum import IntEnum
import torch
import KTransformersOps
from .custom_loader import SafeTensorLoader
import ctypes
class GGMLQuantizationType(IntEnum):
F32 = 0
F16 = 1
Q4_0 = 2
Q4_1 = 3
Q5_0 = 6
Q5_1 = 7
Q8_0 = 8
Q8_1 = 9
Q2_K = 10
Q3_K = 11
Q4_K = 12
Q5_K = 13
Q6_K = 14
Q8_K = 15
IQ2_XXS = 16
IQ2_XS = 17
IQ3_XXS = 18
IQ1_S = 19
IQ4_NL = 20
IQ3_S = 21
IQ2_S = 22
IQ4_XS = 23
I8 = 24
I16 = 25
I32 = 26
I64 = 27
F64 = 28
IQ1_M = 29
BF16 = 30
QK_K = 256
GGML_QUANT_SIZES: dict[GGMLQuantizationType, tuple[int, int]] = {
GGMLQuantizationType.F32: (1, 4),
GGMLQuantizationType.F16: (1, 2),
GGMLQuantizationType.Q4_0: (32, 2 + 16),
GGMLQuantizationType.Q4_1: (32, 2 + 2 + 16),
GGMLQuantizationType.Q5_0: (32, 2 + 4 + 16),
GGMLQuantizationType.Q5_1: (32, 2 + 2 + 4 + 16),
GGMLQuantizationType.Q8_0: (32, 2 + 32),
GGMLQuantizationType.Q8_1: (32, 4 + 4 + 32),
GGMLQuantizationType.Q2_K: (256, 2 + 2 + QK_K // 16 + QK_K // 4),
GGMLQuantizationType.Q3_K: (256, 2 + QK_K // 4 + QK_K // 8 + 12),
GGMLQuantizationType.Q4_K: (256, 2 + 2 + QK_K // 2 + 12),
GGMLQuantizationType.Q5_K: (256, 2 + 2 + QK_K // 2 + QK_K // 8 + 12),
GGMLQuantizationType.Q6_K: (256, 2 + QK_K // 2 + QK_K // 4 + QK_K // 16),
GGMLQuantizationType.Q8_K: (256, 4 + QK_K + QK_K // 8),
GGMLQuantizationType.IQ2_XXS: (256, 2 + QK_K // 4),
GGMLQuantizationType.IQ2_XS: (256, 2 + QK_K // 4 + QK_K // 32),
GGMLQuantizationType.IQ3_XXS: (256, 2 + QK_K // 4 + QK_K // 8),
GGMLQuantizationType.IQ1_S: (256, 2 + QK_K // 8 + QK_K // 16),
GGMLQuantizationType.IQ4_NL: (32, 2 + 16),
GGMLQuantizationType.IQ3_S: (256, 2 + QK_K // 4 + QK_K // 8 + QK_K // 32 + 4),
GGMLQuantizationType.IQ2_S: (256, 2 + QK_K // 4 + QK_K // 16),
GGMLQuantizationType.IQ4_XS: (256, 2 + 2 + QK_K // 2 + QK_K // 64),
GGMLQuantizationType.I8: (1, 1),
GGMLQuantizationType.I16: (1, 2),
GGMLQuantizationType.I32: (1, 4),
GGMLQuantizationType.I64: (1, 8),
GGMLQuantizationType.F64: (1, 8),
GGMLQuantizationType.IQ1_M: (256, QK_K // 8 + QK_K // 16 + QK_K // 32),
GGMLQuantizationType.BF16: (1, 2),
}
# copied from llama.cpp/gguf-py/gguf/quants.py to avoid dependence of gguf
def quant_shape_to_byte_shape(shape: Sequence[int], quant_type: GGMLQuantizationType):
block_size, type_size = GGML_QUANT_SIZES[quant_type]
if shape[-1] % block_size != 0:
raise ValueError(f"Quantized tensor row size ({shape[-1]}) is not a multiple of {quant_type.name} block size ({block_size})")
return (*shape[:-1], shape[-1] // block_size * type_size)
GGML_TYPES = {
"F32": 0,
"F16": 1,
"Q4_0": 2,
"Q5_0": 6,
"Q8_0": 8,
"Q2_K": 10,
"Q3_K": 11,
"Q4_K": 12,
"Q5_K": 13,
"Q6_K": 14,
"IQ4_XS": 23,
"BF16": 30,
}
GGML_NAMES = {ggml_type: name for name, ggml_type in GGML_TYPES.items()}
GGML_BLOCK_SIZES = {
"F32": 4,
"F16": 2,
"BF16": 2,
"Q4_0": 2 + 16,
"Q5_0": 2 + 4 + 16,
"Q8_0": 2 + 32,
"Q2_K": 256 // 16 + 256 // 4 + 2 + 2,
"Q3_K": 256 // 8 + 256 // 4 + 12 + 2,
"Q4_K": 2 + 2 + 12 + 256 // 2,
"Q5_K": 2 + 2 + 12 + 256 // 8 + 256 // 2,
"Q6_K": 256 // 2 + 256 // 4 + 256 // 16 + 2,
"IQ4_XS": 2 + 2 + 256 // 2 + 256 // 64,
"FP8": 1,
}
GGML_ELEMENTS_PER_BLOCK = {
"F32": 1,
"F16": 1,
"BF16": 1,
"Q4_0": 32,
"Q5_0": 32,
"Q8_0": 32,
"Q2_K": 256,
"Q3_K": 256,
"Q4_K": 256,
"Q5_K": 256,
"Q6_K": 256,
"IQ4_XS": 256,
"FP8": 1,
}
DATA_TYPES = {
"uint8": 0,
"int8": 1,
"uint16": 2,
"int16": 3,
"uint32": 4,
"int32": 5,
"float32": 6,
"bool": 7,
"string": 8,
"array": 9,
"uint64": 10,
"int64": 11,
"float64": 12,
"FP8": 13,
}
class GGUFLoader:
tensor_info: dict
gguf_path: str
tensor_file_map: dict # {tensor_name: tensor_file_path}
gguf_file_meta: dict
safetensor_loader: SafeTensorLoader
def __init__(self, gguf_path: str):
# Check dir exist
if not os.path.exists(gguf_path):
raise FileNotFoundError(f"GGUF dir not found: {gguf_path}")
if os.path.isfile(gguf_path):
gguf_path = os.path.dirname(gguf_path)
self.safetensor_loader = None
self.tensor_info = {}
self.gguf_path = gguf_path
self.tensor_file_map = {}
self.file_data_map = {}
self.gguf_file_meta = {}
self.tensor_device_map = {}
# I know this is ugly, but I don't want to change the original code too much
# TODO: merge gguf load and other loads.
safetensor_loader = SafeTensorLoader(gguf_path)
if safetensor_loader.tensor_file_map:
self.safetensor_loader = safetensor_loader
return
# Walk through all the .gguf files in the directory
found_gguf = False
for root, dirs, files in os.walk(gguf_path):
for file in files:
if file.endswith(".gguf"):
found_gguf = True
file_name = os.path.join(root, file)
with open(file_name, "rb") as f:
self.load_gguf(f)
if file_name not in self.file_data_map:
self.file_data_map[file_name] = np.memmap(file_name, mode = 'r')
if not found_gguf:
raise FileNotFoundError(f"Cannot find any .gguf files in: {gguf_path}")
def load_gguf(self, f):
f.seek(0)
assert f.read(4) == b'GGUF'
values = struct.unpack("<IQQ", f.read(4+8+8))
version, n_tensors, n_kv = values
if version != 3:
warnings.warn(f"Version {version} has never been tested, might not work")
info = {}
for _ in range(n_kv):
name = read_value(f, DATA_TYPES["string"])
data_type = struct.unpack("<I", f.read(4))[0]
info[name] = read_value(f, data_type)
tensor_info = {}
for _ in range(n_tensors):
name = read_value(f, DATA_TYPES["string"])
shape_len = read_value(f, DATA_TYPES["uint32"])
shape = [read_value(f, DATA_TYPES["uint64"]) for _ in range(shape_len)]
ggml_type = read_value(f, DATA_TYPES["uint32"])
bad_offset = read_value(f, DATA_TYPES["uint64"])
n_elems = int(np.prod(shape))
block_size, type_size = GGML_QUANT_SIZES[ggml_type]
n_bytes = n_elems * type_size // block_size
np_dims = tuple(reversed(shape))
item_type: npt.DTypeLike
if ggml_type == GGMLQuantizationType.F16:
item_count = n_elems
item_type = np.float16
elif ggml_type == GGMLQuantizationType.F32:
item_count = n_elems
item_type = np.float32
elif ggml_type == GGMLQuantizationType.F64:
item_count = n_elems
item_type = np.float64
elif ggml_type == GGMLQuantizationType.I8:
item_count = n_elems
item_type = np.int8
elif ggml_type == GGMLQuantizationType.I16:
item_count = n_elems
item_type = np.int16
elif ggml_type == GGMLQuantizationType.I32:
item_count = n_elems
item_type = np.int32
elif ggml_type == GGMLQuantizationType.I64:
item_count = n_elems
item_type = np.int64
else:
item_count = n_bytes
item_type = np.uint8
np_dims = quant_shape_to_byte_shape(np_dims, ggml_type)
tensor_info[name] = {
"ggml_type": ggml_type,
"shape": shape,
"bad_offset": bad_offset,
"item_type": item_type,
"item_count": item_count,
"np_dims": np_dims
}
start = f.tell()
# Alignment is 32 by default.
# https://github.com/ggerganov/ggml/blob/e1daebbf9d38d510ba456c4d50b4500a73ac2b14/docs/gguf.md?plain=1#L253
alignment = info.get("general.alignment", 32)
# Inconveniently, the offset defined in gguf files is relative to the
# end of the header and is unaligned.
# We need to compute the absolute file offset ourselves instead.
for t in tensor_info.values():
offset = start + t["bad_offset"]
offset += (alignment - offset % alignment) % alignment
t["offset"] = offset
for name in tensor_info:
self.tensor_file_map[name] = f.name
self.tensor_info.update(tensor_info)
self.gguf_file_meta.update(info)
def get_mmap_tensor(self, name):
t = self.tensor_info[name]
mmap_data = self.file_data_map[ self.tensor_file_map[name] ]
offset = t["offset"]
item_type = t["item_type"]
item_count = t["item_count"]
itemsize = int(np.empty([], dtype = item_type).itemsize)
return mmap_data[offset : offset + itemsize * item_count]
def get_undequanted_tensor_and_ggml_type(self, name):
t = self.tensor_info[name]
data = self.get_mmap_tensor(name)
ggml_type = t["ggml_type"]
data = torch.from_numpy(data)
return data, ggml_type
def load_expert_tensor(self, name, data, expert_id, elements_per_expert, device = "cuda", target_dtype = torch.get_default_dtype())->torch.Tensor:
t = self.tensor_info[name]
if device.lower() == "cpu":
print(f"loading expert {expert_id} of {name} with CPU")
shape = t["shape"]
ggml_type = t["ggml_type"]
if ggml_type not in GGML_NAMES:
raise NotImplementedError(f"ggml_type {ggml_type} not implemented")
ggml_name = GGML_NAMES[ggml_type]
# TODO: experts may fused in quant block, split it
assert elements_per_expert % GGML_ELEMENTS_PER_BLOCK[ggml_name] == 0, "experts may fused in quant block, please use CPU dequant"
blocks_per_experts = elements_per_expert // GGML_ELEMENTS_PER_BLOCK[ggml_name]
block_size = GGML_BLOCK_SIZES[ggml_name]
offset = expert_id * block_size * blocks_per_experts
data = data[offset: offset + block_size * blocks_per_experts]
if "cuda" in device.lower():
values = GGML_DEQUANTIZE_GPU[ggml_name](data, device, target_dtype)
else:
values = GGML_DEQUANTIZE[ggml_name](data)
values = torch.from_numpy(values.copy())
if ggml_name == "BF16":
values = values.view(torch.bfloat16)
values = values.view(shape[-2::-1])
return values
def load_gguf_tensor(self, name: str, device:str = "cpu", target_dtype = None)->torch.Tensor:
t = self.tensor_info[name]
if device.lower() == "cpu":
print(f"loading {name} with CPU")
if target_dtype == None:
target_dtype = torch.get_default_dtype()
shape = t["shape"]
ggml_type = t["ggml_type"]
if ggml_type not in GGML_NAMES:
raise NotImplementedError(f"ggml_type {ggml_type} not implemented")
ggml_name = GGML_NAMES[ggml_type]
data = self.get_mmap_tensor(name)
block_size = GGML_BLOCK_SIZES[ggml_name]
elements_per_block = GGML_ELEMENTS_PER_BLOCK[ggml_name]
num_elements = int(np.prod(shape))
num_blocks = num_elements // elements_per_block
blocks_per_iter = 16384
if num_blocks > blocks_per_iter: # dequant large tensor
values = torch.empty((num_blocks, elements_per_block), dtype=target_dtype, device=device)
for i in range( (num_blocks + blocks_per_iter - 1) // blocks_per_iter):
blocks_begin = i * blocks_per_iter
blocks_end = min(blocks_begin + blocks_per_iter, num_blocks)
if "cuda" in device.lower():
cur_values = GGML_DEQUANTIZE_GPU[ggml_name](data[blocks_begin*block_size : blocks_end*block_size], device, target_dtype)
else:
cur_values = GGML_DEQUANTIZE[ggml_name](data[blocks_begin*block_size : blocks_end*block_size])
cur_values = torch.from_numpy(cur_values.copy())
cur_values = cur_values.view(-1, elements_per_block)
if ggml_name == "BF16":
cur_values = cur_values.view(torch.bfloat16)
values[blocks_begin : blocks_end] = cur_values
else:
if "cuda" in device.lower():
values = GGML_DEQUANTIZE_GPU[ggml_name](data, device)
else:
values = GGML_DEQUANTIZE[ggml_name](data)
values = torch.from_numpy(values)
if ggml_name == "BF16":
values = values.view(torch.bfloat16)
values = values.view(shape[::-1])
if "attn_q" in name and self.gguf_file_meta['general.architecture'] in ["llama"]:
n_head = self.gguf_file_meta['llama.attention.head_count']
values = (values.reshape(n_head, values.shape[0] // n_head // 2, 2, *values.shape[1:])
.swapaxes(1, 2)
.reshape(values.shape))
elif "attn_k" in name and self.gguf_file_meta['general.architecture'] in ["llama"]:
n_head = self.gguf_file_meta['llama.attention.head_count_kv']
values = (values.reshape(n_head, values.shape[0] // n_head // 2, 2, *values.shape[1:])
.swapaxes(1, 2)
.reshape(values.shape))
return values
def read_value(f, data_type):
if data_type == DATA_TYPES["string"]:
length = struct.unpack("<Q", f.read(8))[0]
return f.read(length).decode("utf-8")
elif data_type == DATA_TYPES["bool"]:
return bool(struct.unpack("<?", f.read(1))[0])
elif data_type == DATA_TYPES["uint8"]:
return struct.unpack("<B", f.read(1))[0]
elif data_type == DATA_TYPES["int8"]:
return struct.unpack("<b", f.read(1))[0]
elif data_type == DATA_TYPES["uint16"]:
return struct.unpack("<H", f.read(2))[0]
elif data_type == DATA_TYPES["int16"]:
return struct.unpack("<h", f.read(2))[0]
elif data_type == DATA_TYPES["uint32"]:
return struct.unpack("<I", f.read(4))[0]
elif data_type == DATA_TYPES["int32"]:
return struct.unpack("<i", f.read(4))[0]
elif data_type == DATA_TYPES["float32"]:
return struct.unpack("<f", f.read(4))[0]
elif data_type == DATA_TYPES["uint64"]:
return struct.unpack("<Q", f.read(8))[0]
elif data_type == DATA_TYPES["int64"]:
return struct.unpack("<q", f.read(8))[0]
elif data_type == DATA_TYPES["float64"]:
return struct.unpack("<d", f.read(8))[0]
elif data_type == DATA_TYPES["array"]:
elem_type, count = struct.unpack("<IQ", f.read(4 + 8))
return [read_value(f, elem_type) for _ in range(count)]
elif data_type == DATA_TYPES["FP8"]:
return struct.unpack("<B", f.read(1))[0]
else:
raise NotImplementedError(f"Data type {data_type} not implemented")
def dequantize_q2_k(data):
# C implementation
# https://github.com/ggerganov/ggml/blob/fca1caafea7de9fbd7efc733b9818f9cf2da3050/src/ggml-quants.c#L1547
# C struct definition
# https://github.com/ggerganov/ggml/blob/fca1caafea7de9fbd7efc733b9818f9cf2da3050/src/ggml-quants.h#L74
block_size = GGML_BLOCK_SIZES["Q2_K"]
num_blocks = len(data) // block_size
data_f16 = np.frombuffer(data, dtype=np.float16).reshape(num_blocks, block_size // 2)
data_u8 = np.frombuffer(data, dtype=np.uint8).reshape(num_blocks, block_size)
dmin = data_f16[:, -1].reshape(num_blocks, 1, 1).astype(np.float32)
d = data_f16[:, -2].reshape(num_blocks, 1, 1).astype(np.float32)
scales = data_u8[:, :16].reshape(num_blocks, 16, 1)
qs = data_u8[:, 16:80].reshape(num_blocks, 64)
tmp = np.stack([
qs[:, 00:16] >> 0,
qs[:, 16:32] >> 0,
qs[:, 00:16] >> 2,
qs[:, 16:32] >> 2,
qs[:, 00:16] >> 4,
qs[:, 16:32] >> 4,
qs[:, 00:16] >> 6,
qs[:, 16:32] >> 6,
qs[:, 32:48] >> 0,
qs[:, 48:64] >> 0,
qs[:, 32:48] >> 2,
qs[:, 48:64] >> 2,
qs[:, 32:48] >> 4,
qs[:, 48:64] >> 4,
qs[:, 32:48] >> 6,
qs[:, 48:64] >> 6,
], axis=1)
return d * (scales & 15) * (tmp & 3) - dmin * (scales >> 4)
def dequantize_q2_k_gpu(data, device:str ="cuda", target_dtype = torch.get_default_dtype()):
block_size = GGML_BLOCK_SIZES["Q2_K"]
ele_per_blk = GGML_ELEMENTS_PER_BLOCK["Q2_K"]
data = np.frombuffer(data, dtype=data.dtype)
device = torch.device(device)
# TODO: this and from_numpy in other functions will cause a warning saying that numpy is not writable,
# the best way to fix this is transfer ptr to KTransformersOps instead of Tensor.
c_pointer = ctypes.addressof(ctypes.cast(data.ctypes.data, ctypes.POINTER(ctypes.c_int8)).contents)
return KTransformersOps.dequantize_q2_k(c_pointer, data.size, block_size, ele_per_blk, device, target_dtype)
def dequantize_q3_k(data):
# C implementation
# https://github.com/ggerganov/ggml/blob/fca1caafea7de9fbd7efc733b9818f9cf2da3050/src/ggml-quants.c#L1723C32-L1723C42
# C struct definition
# https://github.com/ggerganov/ggml/blob/fca1caafea7de9fbd7efc733b9818f9cf2da3050/src/ggml-quants.h#L95
block_size = GGML_BLOCK_SIZES["Q3_K"]
num_blocks = len(data) // block_size
data_f16 = np.frombuffer(data, dtype=np.float16).reshape(num_blocks, block_size // 2)
data_u8 = np.frombuffer(data, dtype=np.uint8).reshape(num_blocks, block_size)
d = data_f16[:, -1].reshape(num_blocks, 1, 1).astype(np.float32)
bits = np.unpackbits(data_u8[:, :32].reshape(num_blocks, 32, 1), axis=-1, bitorder="little")
bits = 4 ^ (bits << 2)
qs = data_u8[:, 32:32 + 64].astype(np.int16)
a, b, c = data_u8[:, 96: 96 + 12].reshape(num_blocks, 3, 4).transpose(1, 0, 2)
scales = np.zeros((num_blocks, 4, 4), dtype=np.uint8)
scales[:, 0] = (a & 15) | ((c & 3) << 4)
scales[:, 1] = (b & 15) | (((c >> 2) & 3) << 4)
scales[:, 2] = (a >> 4) | (((c >> 4) & 3) << 4)
scales[:, 3] = (b >> 4) | ((c >> 6) << 4)
scales = scales.reshape(num_blocks, 16, 1).astype(np.int16)
return d * (scales - 32) * np.stack([
(((qs[:, 00:16] >> 0) & 3) - bits[:, :16, 0]),
(((qs[:, 16:32] >> 0) & 3) - bits[:, 16:, 0]),
(((qs[:, 00:16] >> 2) & 3) - bits[:, :16, 1]),
(((qs[:, 16:32] >> 2) & 3) - bits[:, 16:, 1]),
(((qs[:, 00:16] >> 4) & 3) - bits[:, :16, 2]),
(((qs[:, 16:32] >> 4) & 3) - bits[:, 16:, 2]),
(((qs[:, 00:16] >> 6) & 3) - bits[:, :16, 3]),
(((qs[:, 16:32] >> 6) & 3) - bits[:, 16:, 3]),
(((qs[:, 32:48] >> 0) & 3) - bits[:, :16, 4]),
(((qs[:, 48:64] >> 0) & 3) - bits[:, 16:, 4]),
(((qs[:, 32:48] >> 2) & 3) - bits[:, :16, 5]),
(((qs[:, 48:64] >> 2) & 3) - bits[:, 16:, 5]),
(((qs[:, 32:48] >> 4) & 3) - bits[:, :16, 6]),
(((qs[:, 48:64] >> 4) & 3) - bits[:, 16:, 6]),
(((qs[:, 32:48] >> 6) & 3) - bits[:, :16, 7]),
(((qs[:, 48:64] >> 6) & 3) - bits[:, 16:, 7])
], axis=1)
def dequantize_q3_k_gpu(data, device:str ="cuda", target_dtype = torch.get_default_dtype()):
block_size = GGML_BLOCK_SIZES["Q3_K"]
ele_per_blk = GGML_ELEMENTS_PER_BLOCK["Q3_K"]
data = np.frombuffer(data, dtype=data.dtype)
device = torch.device(device)
# TODO: this and from_numpy in other functions will cause a warning saying that numpy is not writable,
# the best way to fix this is transfer ptr to KTransformersOps instead of Tensor.
c_pointer = ctypes.addressof(ctypes.cast(data.ctypes.data, ctypes.POINTER(ctypes.c_int8)).contents)
return KTransformersOps.dequantize_q3_k(c_pointer, data.size, block_size, ele_per_blk, device, target_dtype)
def dequantize_q4_k(data):
# C implementation
# https://github.com/ggerganov/ggml/blob/fca1caafea7de9fbd7efc733b9818f9cf2da3050/src/ggml-quants.c#L1929
# C struct definition
# https://github.com/ggerganov/ggml/blob/fca1caafea7de9fbd7efc733b9818f9cf2da3050/src/ggml-quants.h#L116
block_size = GGML_BLOCK_SIZES["Q4_K"]
num_blocks = len(data) // block_size
data_f16 = np.frombuffer(data, dtype=np.float16).reshape(num_blocks, block_size // 2)
data_u8 = np.frombuffer(data, dtype=np.uint8).reshape(num_blocks, block_size)
# Casting to float32 because float16 is very slow on CPU
scale_factors = data_f16[:, 0].reshape(num_blocks, 1, 1).astype(np.float32)
scale_offsets = data_f16[:, 1].reshape(num_blocks, 1, 1).astype(np.float32)
qs1 = data_u8[:, 4:16].reshape(num_blocks, 12, 1)
qs2 = data_u8[:, 16:].reshape(num_blocks, 4, 32)
# Dequantize scales and offsets (6 bits and 4 + 2 bits)
factors = scale_factors * np.concatenate([qs1[:, 0:4] & 0b111111, (qs1[:, 8:] & 15) | ((qs1[:, 0:4] >> 6) << 4)], axis=1)
offsets = scale_offsets * np.concatenate([qs1[:, 4:8] & 0b111111, (qs1[:, 8:] >> 4) | ((qs1[:, 4:8] >> 6) << 4)], axis=1)
# Interleave low and high quantized bits
qs2 = np.stack([qs2 & 0xf, qs2 >> 4], axis=2).reshape(num_blocks, 8, 32)
# Dequantize final weights using scales and offsets
return factors * qs2 - offsets
def dequantize_q4_k_gpu(data, device:str ="cuda", target_dtype = torch.get_default_dtype()):
block_size = GGML_BLOCK_SIZES["Q4_K"]
ele_per_blk = GGML_ELEMENTS_PER_BLOCK["Q4_K"]
data = np.frombuffer(data, dtype=data.dtype)
device = torch.device(device)
# TODO: this and from_numpy in other functions will cause a warning saying that numpy is not writable,
# the best way to fix this is transfer ptr to KTransformersOps instead of Tensor.
c_pointer = ctypes.addressof(ctypes.cast(data.ctypes.data, ctypes.POINTER(ctypes.c_int8)).contents)
return KTransformersOps.dequantize_q4_k(c_pointer, data.size, block_size, ele_per_blk, device, target_dtype)
def dequantize_q5_k(data):
# C implementation
# https://github.com/ggerganov/ggml/blob/fca1caafea7de9fbd7efc733b9818f9cf2da3050/src/ggml-quants.c#L2129
# C struct definition
# https://github.com/ggerganov/ggml/blob/fca1caafea7de9fbd7efc733b9818f9cf2da3050/src/ggml-quants.h#L138
block_size = GGML_BLOCK_SIZES["Q5_K"]
num_blocks = len(data) // block_size
data_f16 = np.frombuffer(data, dtype=np.float16).reshape(num_blocks, block_size // 2)
data_u8 = np.frombuffer(data, dtype=np.uint8).reshape(num_blocks, block_size)
d = data_f16[:, 0].reshape(num_blocks, 1).astype(np.float32)
dmin = data_f16[:, 1].reshape(num_blocks, 1).astype(np.float32)
scales = data_u8[:, 4:16].reshape(num_blocks, 12, 1)
qh = data_u8[:, 16: 16 + 32].reshape(num_blocks, 32, 1)
qs = data_u8[:, 48: 48 + 128].reshape(num_blocks, 4, 32)
bits = np.unpackbits(qh, axis=-1, bitorder="little")
qs_hi_4 = qs >> 4
qs_lo_4 = qs & 15
scales_lo_6 = scales[:, :8] & 63
scales_hi_6 = scales[:, :8] >> 6
scales_lo_4 = scales[:, 8:] & 15
scales_hi_4 = scales[:, 8:] >> 4
m1 = dmin * scales_lo_6[:, 4]
m2 = dmin * scales_lo_6[:, 5]
m3 = dmin * scales_lo_6[:, 6]
m4 = dmin * scales_lo_6[:, 7]
m5 = dmin * (scales_hi_4[:, 0] | (scales_hi_6[:, 4] << 4))
m6 = dmin * (scales_hi_4[:, 1] | (scales_hi_6[:, 5] << 4))
m7 = dmin * (scales_hi_4[:, 2] | (scales_hi_6[:, 6] << 4))
m8 = dmin * (scales_hi_4[:, 3] | (scales_hi_6[:, 7] << 4))
d1 = d * scales_lo_6[:, 0]
d2 = d * scales_lo_6[:, 1]
d3 = d * scales_lo_6[:, 2]
d4 = d * scales_lo_6[:, 3]
d5 = d * (scales_lo_4[:, 0] | (scales_hi_6[:, 0] << 4))
d6 = d * (scales_lo_4[:, 1] | (scales_hi_6[:, 1] << 4))
d7 = d * (scales_lo_4[:, 2] | (scales_hi_6[:, 2] << 4))
d8 = d * (scales_lo_4[:, 3] | (scales_hi_6[:, 3] << 4))
return np.concatenate([
d1 * (qs_lo_4[:, 0] + (bits[:, :, 0] << 4)) - m1,
d2 * (qs_hi_4[:, 0] + (bits[:, :, 1] << 4)) - m2,
d3 * (qs_lo_4[:, 1] + (bits[:, :, 2] << 4)) - m3,
d4 * (qs_hi_4[:, 1] + (bits[:, :, 3] << 4)) - m4,
d5 * (qs_lo_4[:, 2] + (bits[:, :, 4] << 4)) - m5,
d6 * (qs_hi_4[:, 2] + (bits[:, :, 5] << 4)) - m6,
d7 * (qs_lo_4[:, 3] + (bits[:, :, 6] << 4)) - m7,
d8 * (qs_hi_4[:, 3] + (bits[:, :, 7] << 4)) - m8,
], axis=1)
def dequantize_q5_k_gpu(data, device:str ="cuda", target_dtype = torch.get_default_dtype()):
block_size = GGML_BLOCK_SIZES["Q5_K"]
ele_per_blk = GGML_ELEMENTS_PER_BLOCK["Q5_K"]
data = np.frombuffer(data, dtype=data.dtype)
device = torch.device(device)
# TODO: this and from_numpy in other functions will cause a warning saying that numpy is not writable,
# the best way to fix this is transfer ptr to KTransformersOps instead of Tensor.
c_pointer = ctypes.addressof(ctypes.cast(data.ctypes.data, ctypes.POINTER(ctypes.c_int8)).contents)
return KTransformersOps.dequantize_q5_k(c_pointer, data.size, block_size, ele_per_blk, device, target_dtype)
def dequantize_q6_k(data):
# C implementation
# https://github.com/ggerganov/ggml/blob/fca1caafea7de9fbd7efc733b9818f9cf2da3050/src/ggml-quants.c#L2275
# C struct definition
# https://github.com/ggerganov/ggml/blob/fca1caafea7de9fbd7efc733b9818f9cf2da3050/src/ggml-quants.h#L152
block_size = GGML_BLOCK_SIZES["Q6_K"]
num_blocks = len(data) // block_size
data_f16 = np.frombuffer(data, dtype=np.float16).reshape(num_blocks, block_size // 2)
data_u8 = np.frombuffer(data, dtype=np.uint8).reshape(num_blocks, block_size)
data_i8 = np.frombuffer(data, dtype=np.int8).reshape(num_blocks, block_size)
scales = data_f16[:, -1].reshape(num_blocks, 1).astype(np.float32)
# TODO use uint8 and cast later?
ql = data_u8[:, :128].astype(np.int16)
qh = data_u8[:, 128:192].astype(np.int16)
sc = data_i8[:, 192:208, np.newaxis].astype(np.float32)
# Unpack bits, subtraction requires signed data type
q1 = (ql[:, :32 ] & 0xF) | (((qh[:, :32] >> 0) & 3) << 4) - 32
q2 = (ql[:, 32:64 ] & 0xF) | (((qh[:, :32] >> 2) & 3) << 4) - 32
q3 = (ql[:, :32 ] >> 4) | (((qh[:, :32] >> 4) & 3) << 4) - 32
q4 = (ql[:, 32:64 ] >> 4) | (((qh[:, :32] >> 6) & 3) << 4) - 32
q5 = (ql[:, 64:96 ] & 0xF) | (((qh[:, 32:] >> 0) & 3) << 4) - 32
q6 = (ql[:, 96:128] & 0xF) | (((qh[:, 32:] >> 2) & 3) << 4) - 32
q7 = (ql[:, 64:96 ] >> 4) | (((qh[:, 32:] >> 4) & 3) << 4) - 32
q8 = (ql[:, 96:128] >> 4) | (((qh[:, 32:] >> 6) & 3) << 4) - 32
# Dequantize
return scales * np.concatenate([
sc[:, 0] * q1[:, :16],
sc[:, 1] * q1[:, 16:],
sc[:, 2] * q2[:, :16],
sc[:, 3] * q2[:, 16:],
sc[:, 4] * q3[:, :16],
sc[:, 5] * q3[:, 16:],
sc[:, 6] * q4[:, :16],
sc[:, 7] * q4[:, 16:],
sc[:, 8] * q5[:, :16],
sc[:, 9] * q5[:, 16:],
sc[:, 10] * q6[:, :16],
sc[:, 11] * q6[:, 16:],
sc[:, 12] * q7[:, :16],
sc[:, 13] * q7[:, 16:],
sc[:, 14] * q8[:, :16],
sc[:, 15] * q8[:, 16:],
], axis=1)
# @torch.jit.script
def dequantize_q6_k_gpu(data: np.ndarray, device:str = "cuda", target_dtype = torch.get_default_dtype()):
block_size = GGML_BLOCK_SIZES["Q6_K"]
ele_per_blk = GGML_ELEMENTS_PER_BLOCK["Q6_K"]
device = torch.device(device)
num_blocks = len(data) // block_size
data = np.frombuffer(data, dtype=data.dtype)
c_pointer = ctypes.addressof(ctypes.cast(data.ctypes.data, ctypes.POINTER(ctypes.c_int8)).contents)
return KTransformersOps.dequantize_q6_k(c_pointer, data.size, block_size, ele_per_blk, device, target_dtype)
kvalues_iq4nl = np.array([-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113], dtype=np.int8)
def dequantize_iq4_xs(data):
# C implementation
# https://github.com/ggerganov/ggml/blob/21d3a308fcb7f31cb9beceaeebad4fb622f3c337/src/ggml-quants.c#L3568
# C struct definition
# https://github.com/ggerganov/ggml/blob/21d3a308fcb7f31cb9beceaeebad4fb622f3c337/src/ggml-common.h#L393
block_size = GGML_BLOCK_SIZES["IQ4_XS"]
num_blocks = len(data) // block_size
d = np.frombuffer(data, dtype=np.float16)[0::block_size//2].astype(np.float32).reshape(num_blocks, 1)
scales_h = np.frombuffer(data, dtype=np.uint16)[1::block_size//2].reshape(num_blocks, 1)
data_u8 = np.frombuffer(data, dtype=np.uint8).reshape(num_blocks, block_size)[:, 4:]
scales_l = data_u8[:, :4].reshape(num_blocks, 4)
qs = data_u8[:, 4:].reshape(num_blocks, block_size - 8)
ls = np.zeros((num_blocks, QK_K // 32), dtype=np.int8)
for ib in range(QK_K // 32):
ls[:, ib] = ((scales_l[:, ib // 2] >> 4 * (ib % 2)) & 0xf) | (((scales_h[:, 0] >> 2 * ib) & 3) << 4)
dl = (d * (ls - 32)).reshape(num_blocks, -1, 1)
qs_lo_4 = qs[:, :QK_K // 2].reshape(num_blocks, -1, 16) & 0xf
qs_hi_4 = qs[:, :QK_K // 2].reshape(num_blocks, -1, 16) >> 4
y = np.zeros((num_blocks, QK_K), dtype=np.float32)
for ib in range(QK_K // 32):
y[:, ib*32:(ib*32)+16] = dl[:, ib] * kvalues_iq4nl[qs_lo_4[:, ib]]
y[:, (ib*32)+16:(ib*32)+32] = dl[:, ib] * kvalues_iq4nl[qs_hi_4[:, ib]]
return y.flatten()
def dequantize_iq4_xs_gpu(data: np.ndarray, device:str = "cuda", target_dtype = torch.get_default_dtype()):
block_size = GGML_BLOCK_SIZES["IQ4_XS"]
ele_per_blk = GGML_ELEMENTS_PER_BLOCK["IQ4_XS"]
device = torch.device(device)
num_blocks = len(data) // block_size
data = np.frombuffer(data, dtype=data.dtype)
c_pointer = ctypes.addressof(ctypes.cast(data.ctypes.data, ctypes.POINTER(ctypes.c_int8)).contents)
return KTransformersOps.dequantize_iq4_xs(c_pointer, data.size, block_size, ele_per_blk, device, target_dtype)
def dequantize_q4_0(data):
# C implementation
# https://github.com/ggerganov/ggml/blob/a3c0188a4b5d3dec052ff87c9f773baa53631d70/src/ggml-quants.c#L1515
# C struct definition
# https://github.com/ggerganov/ggml/blob/a3c0188a4b5d3dec052ff87c9f773baa53631d70/src/ggml-common.h#L141
num_blocks = len(data) // GGML_BLOCK_SIZES["Q4_0"]
scales = np.frombuffer(data, dtype=np.float16).reshape(num_blocks, 1 + 8)[:, :1].astype(np.float32)
qs = np.frombuffer(data, dtype=np.uint8).reshape(num_blocks, 2 + 16)[:, 2:]
return np.concatenate([
scales * ((qs & 0xf).astype(np.int8) - 8),
scales * ((qs >> 4).astype(np.int8) - 8),
], axis=1)
def dequantize_q4_0_gpu(data, device:str = "cuda", target_dtype = torch.get_default_dtype()):
raise NotImplementedError()
def dequantize_q5_0(data):
# C implementation
# https://github.com/ggerganov/ggml/blob/a3c0188a4b5d3dec052ff87c9f773baa53631d70/src/ggml-quants.c#L1556
# C struct definition
# https://github.com/ggerganov/ggml/blob/a3c0188a4b5d3dec052ff87c9f773baa53631d70/src/ggml-common.h#L161
num_blocks = len(data) // GGML_BLOCK_SIZES["Q5_0"]
scales = np.frombuffer(data, dtype=np.float16).reshape(num_blocks, 1 + 2 + 8)[:, :1].astype(np.float32)
qh = np.frombuffer(data, dtype=np.uint8).reshape(num_blocks, 2 + 4 + 16)[:, 2:2 + 4]
qs = np.frombuffer(data, dtype=np.uint8).reshape(num_blocks, 2 + 4 + 16)[:, 2 + 4:]
bits = np.unpackbits(qh, axis=-1, bitorder="little")
x0 = ((qs & 0xf).astype(np.int8) | (bits[:, :16] << 4)) - 16
x1 = ((qs >> 4).astype(np.int8) | (bits[:, 16:] << 4)) - 16
return np.concatenate([
scales * x0,
scales * x1,
], axis=1)
def dequantize_q5_0_gpu(data, device:str = "cuda", target_dtype = torch.get_default_dtype()):
raise NotImplementedError()
def dequantize_q8_0(data):
# C struct definition
# https://github.com/ggerganov/ggml/blob/fca1caafea7de9fbd7efc733b9818f9cf2da3050/src/ggml-quants.h#L43
num_blocks = len(data) // GGML_BLOCK_SIZES["Q8_0"]
scales = np.frombuffer(data, dtype=np.float16).reshape(num_blocks, 1 + 16)[:, :1].astype(np.float32)
qs = np.frombuffer(data, dtype=np.int8).reshape(num_blocks, 2 + 32)[:, 2:]
return scales * qs
def dequantize_q8_0_gpu(data, device:str = "cuda", target_dtype = torch.get_default_dtype()):
# C struct definition
# https://github.com/ggerganov/ggml/blob/fca1caafea7de9fbd7efc733b9818f9cf2da3050/src/ggml-quants.h#L43
block_size = GGML_BLOCK_SIZES["Q8_0"]
ele_per_blk = GGML_ELEMENTS_PER_BLOCK["Q8_0"]
device = torch.device(device)
data = np.frombuffer(data, dtype=data.dtype)
c_pointer = ctypes.addressof(ctypes.cast(data.ctypes.data, ctypes.POINTER(ctypes.c_int8)).contents)
return KTransformersOps.dequantize_q8_0(c_pointer, data.size, block_size, ele_per_blk, device, target_dtype)
def dequantize_f32(data):
return np.frombuffer(data, dtype=np.float32)
def dequantize_f32_gpu(data, device, target_dtype = torch.get_default_dtype()):
data = np.frombuffer(data, dtype=np.float32)
res = torch.from_numpy(data.copy())
res_gpu = torch.empty_like(res, device=device, dtype=target_dtype)
res_gpu.copy_(res)
return res_gpu
def dequantize_f16(data):
return np.frombuffer(data, dtype=np.float16)
def dequantize_f16_gpu(data, device, target_dtype = torch.get_default_dtype()):
data = np.frombuffer(data, dtype=np.float16)
res = torch.from_numpy(data.copy())
res_gpu = torch.empty_like(res, device=device, dtype=target_dtype)
res_gpu.copy_(res)
return res_gpu
def dequantize_bf16_gpu(data, device, target_dtype = torch.get_default_dtype()):
data = np.frombuffer(data, dtype=np.float16)
res = torch.from_numpy(data.copy())
res_gpu = torch.empty_like(res, device=device)
res_gpu.copy_(res)
return res_gpu
GGML_DEQUANTIZE = {
"F32": dequantize_f32,
"F16": dequantize_f16,
"BF16": dequantize_f16,
"Q4_0": dequantize_q4_0,
"Q5_0": dequantize_q5_0,
"Q8_0": dequantize_q8_0,
"Q2_K": dequantize_q2_k,
"Q3_K": dequantize_q3_k,
"Q4_K": dequantize_q4_k,
"Q5_K": dequantize_q5_k,
"Q6_K": dequantize_q6_k,
"IQ4_XS": dequantize_iq4_xs,
}
GGML_DEQUANTIZE_GPU = {
"F32": dequantize_f32_gpu,
"F16": dequantize_f16_gpu,
"BF16": dequantize_bf16_gpu,
"Q4_0": dequantize_q4_0_gpu,
"Q5_0": dequantize_q5_0_gpu,
"Q8_0": dequantize_q8_0_gpu,
"Q2_K": dequantize_q2_k_gpu,
"Q3_K": dequantize_q3_k_gpu,
"Q4_K": dequantize_q4_k_gpu,
"Q5_K": dequantize_q5_k_gpu,
"Q6_K": dequantize_q6_k_gpu,
"IQ4_XS": dequantize_iq4_xs_gpu,
}
def translate_name_to_gguf_mixtral(name):
replacement_template = {
"w1.weight": "ffn_gate",
"w2.weight": "ffn_down",
"w3.weight": "ffn_up"
}
pattern = re.compile(r"model.layers\.(\d+)\.block_sparse_moe\.experts\.(\d+)\.(w\d\.weight)")
def replace_match(match):
blk_id = match.group(1)
expert_id = match.group(2)
weight_type = match.group(3)
if weight_type in replacement_template:
return f"blk.{blk_id}.{replacement_template[weight_type]}.{expert_id}.weight"
else:
return match.group(0)
new_name = re.sub(pattern, replace_match, name)
return new_name
def translate_name_to_gguf(name):
name = translate_name_to_gguf_mixtral(name)
name = name.replace("lm_head.", "output.")
name = name.replace("model.embed_tokens.", "token_embd.")
name = name.replace("model.norm.", "output_norm.")
name = name.replace("model.layers.", "blk.")
name = name.replace(".input_layernorm", ".attn_norm")
name = name.replace(".mlp.down_proj", ".ffn_down")
name = name.replace(".mlp.gate_proj", ".ffn_gate")
name = name.replace(".mlp.up_proj", ".ffn_up")
name = name.replace(".post_attention_layernorm", ".ffn_norm")
name = name.replace(".self_attn.q_proj", ".attn_q")
name = name.replace(".self_attn.k_proj", ".attn_k")
name = name.replace(".self_attn.v_proj", ".attn_v")
name = name.replace(".self_attn.o_proj", ".attn_output")
name = name.replace(".self_attn.qkv_proj", ".attn_qkv")
name = name.replace(".self_attn.kv_a_proj_with_mqa", ".attn_kv_a_mqa")
name = name.replace(".self_attn.kv_a_layernorm", ".attn_kv_a_norm")
name = name.replace(".self_attn.kv_b_proj", ".attn_kv_b")
name = name.replace(".self_attn.q_a_proj", ".attn_q_a")
name = name.replace(".self_attn.q_a_layernorm", ".attn_q_a_norm")
name = name.replace(".self_attn.q_b_proj", ".attn_q_b")
name = name.replace(".shared_expert.", ".shared_experts.")
name = name.replace(".shared_expert_", ".shared_experts_")
name = name.replace(".gate_up_proj.", ".up_proj")
name = name.replace(".mlp.shared_experts.down_proj", ".ffn_down_shexp")
name = name.replace(".mlp.gate", ".ffn_gate_inp")
name = name.replace(".mlp.shared_experts.gate_proj", ".ffn_gate_shexp")
name = name.replace(".mlp.shared_experts.up_proj", ".ffn_up_shexp")
name = name.replace(".mlp.shared_experts_gate", ".ffn_gate_inp_shexp")
name = name.replace(".mlp.experts", "")
name = name.replace(".mlp.experts.ffn_down_exps", ".ffn_down_exps")
name = name.replace(".mlp.experts.ffn_gate_exps", ".ffn_gate_exps")
name = name.replace(".mlp.experts.ffn_up_exps", ".ffn_up_exps")
name = name.replace(".block_sparse_moe.gate.", ".ffn_gate_inp.")
name = name.replace(".block_sparse_moe.experts", "")
return name
if __name__ == '__main__':
gguf_path = '/mnt/data/model/DeepSeek-Coder-V2-GGUF-WJH'
loader = GGUFLoader(gguf_path)
loader.load_gguf_tensor('token_embd.weight')
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