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offline-weight-repacker.py
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offline-weight-repacker.py
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import argparse
import torch
import numpy as np
from typing import List
from collections import OrderedDict
def qweight_unpack(qweight):
assert qweight.dtype == torch.int32
n = qweight.shape[0]
k = qweight.shape[1] * 8
unpacked_qweight = torch.zeros((n, k), dtype=torch.int32, device=qweight.device)
mask = 0x0000000F
for kk in range(k):
ele_offset = kk // 8
bit_offset = (kk % 8) * 4
unpacked_qweight[:, kk] = (qweight[:, ele_offset] >> bit_offset) & mask
return unpacked_qweight
def packing_v2_from_unpacked(unpacked_qweight, interleave, kstride):
# unpacked_qweight: [N, K]
N = unpacked_qweight.shape[0]
K = unpacked_qweight.shape[1]
Packed_Kernel = unpacked_qweight.cpu().numpy().reshape(N, K // 32, 32)
# np.arange(32).reshape(4, 4, 2).transpose(1, 0, 2) => [0, 1, 8, 9, 16, 17, 24, 25, ...]
Packed_Kernel = Packed_Kernel.reshape(N, K // 32, 4, 4, 2).transpose(0, 1, 3, 2, 4)
Packed_Kernel = Packed_Kernel.reshape(N, K // 32, 32)
# reorder each 8 weights for fast dequantization
# [0, 1, 2, 3, 4, 5, 6, 7] => [0, 2, 4, 6, 1, 3, 5, 7]
Packed_Kernel = Packed_Kernel.reshape(N, K // 32, 4, 8)
Packed_Kernel = Packed_Kernel.reshape(N, K // 32, 4, 4, 2).transpose(0, 1, 2, 4, 3)
Packed_Kernel = Packed_Kernel.reshape(N, K)
# interleaving every four rows
Packed_Kernel = Packed_Kernel.reshape(
N // interleave, interleave, K // kstride, kstride
)
# N // 4, K // 64, 4, 64
Packed_Kernel = Packed_Kernel.transpose(0, 2, 1, 3)
Packed_Kernel = Packed_Kernel.reshape(
N // interleave, K // kstride, kstride, interleave
)
# Packing -> (N // 4, K // 64, 64)
Packed_Kernel = (
Packed_Kernel[..., 0]
| (Packed_Kernel[..., 1] << 4)
| (Packed_Kernel[..., 2] << 8)
| (Packed_Kernel[..., 3] << 12)
)
# reshape to (N // 4, K), FP16 format
Packed_Kernel = Packed_Kernel.reshape(N // interleave, K)
qweight_v2 = (
torch.tensor(Packed_Kernel.astype("int16"))
.to(unpacked_qweight.device)
.contiguous()
)
return qweight_v2
def multiply_scale_qzero_negative(scales, qzeros, zp_shift=-8):
pack_size = 8
k_groups = scales.shape[1]
scaled_zeros = torch.zeros_like(scales)
for group_idx in range(k_groups):
zero_idx = group_idx // pack_size
zero_offset = group_idx % pack_size
zero = qzeros[:, zero_idx] >> (4 * zero_offset) & 0x0000000F
scaled_zeros[:, group_idx] = scales[:, group_idx] * zero.to(scales.dtype)
return -(scaled_zeros + (zp_shift * scales))
def qweight_pack_v1_to_v2(qweight, interleave, kstride):
unpacked_qweight = qweight_unpack(qweight)
qweight_v2 = packing_v2_from_unpacked(unpacked_qweight, interleave, kstride)
return qweight_v2
def ckpt_check():
parser = argparse.ArgumentParser()
parser.add_argument("--input1", type=str, default="./vicuna-7b-w4-g128-awq-v2-1.pt")
parser.add_argument("--input2", type=str, default="./vicuna-7b-w4-g128-awq-v2-2.pt")
args = parser.parse_args()
model_dict1 = torch.load(args.input1)
model_dict2 = torch.load(args.input2)
keys = model_dict1.keys()
for key in keys:
param = model_dict1[key]
assert type(param) == torch.Tensor
if (
"qweight" in key
or "scales" in key
or "qzeros" in key
or "scaled_zeros" in key
):
print("=" * 50)
print(key)
# print(model_dict1[key])
# print(model_dict2[key])
diff = torch.max(torch.abs(model_dict2[key] - model_dict1[key]))
print(diff)
assert diff < 1e-6
print("=" * 50)
def offline_repacker():
parser = argparse.ArgumentParser()
parser.add_argument("--input", type=str, default="./vicuna-7b-w4-g128-awq.pt")
parser.add_argument("--output", type=str, default="./vicuna-7b-w4-g128-awq-v2.pt")
args = parser.parse_args()
model_dict = torch.load(args.input)
model_dict_v2 = OrderedDict()
keys = model_dict.keys()
for key in keys:
param = model_dict[key]
assert type(param) == torch.Tensor
if "qweight" in key:
print("repacking:", key)
qweight = param
qweight_v2 = qweight_pack_v1_to_v2(qweight, interleave=4, kstride=64)
model_dict_v2[key] = qweight_v2
elif "scales" in key:
print("repacking:", key)
scales = param
# print(scales.shape)
scales_v2 = scales.transpose(1, 0).contiguous()
model_dict_v2[key] = scales_v2
# deal with corresponding zero points
zeros_key = key.replace("scales", "qzeros")
print("repacking:", zeros_key)
zeros_key_v2 = key.replace("scales", "scaled_zeros")
qzeros = model_dict[zeros_key]
scaled_zeros_v2 = multiply_scale_qzero_negative(scales, qzeros, zp_shift=0)
# K // G, N
scaled_zeros_v2 = scaled_zeros_v2.transpose(1, 0).contiguous()
model_dict_v2[zeros_key_v2] = scaled_zeros_v2
elif "qzeros" in key:
pass
else:
print("copying:", key)
model_dict_v2[key] = param
torch.save(model_dict_v2, args.output)
if __name__ == "__main__":
offline_repacker()
# ckpt_check()