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数据结构
# Convert MNIS h5 transformer model to ggml format
#
# Load the (state_dict) saved model using PyTorch
# Iterate over all variables and write them to a binary file.
#
# For each variable, write the following:
# - Number of dimensions (int)
# - Name length (int)
# - Dimensions (int[n_dims])
# - Name (char[name_length])
# - Data (float[n_dims])
#
# At the start of the ggml file we write the model parameters
- 这个简单的版本没有Name的部分，导出的数据最终如下
| ggml-model-f32.bin | 注释 |
|---|---|
| 0x67676d6c | magic |
| 2 | len(fc1.weight.shape) |
| 784 | fc1.weight.shape = (500, 784) |
| 500 | fc1.weight.shape = (500, 784) |
| data | fc1.weight |
| 1 | len(fc1.bias.shape) |
| 500 | fc1.bias.shape = (500, ) |
| data | fc1.bias |
| 2 | len(fc2.weight.shape) |
| 500 | fc1.weight.shape = (10, 500) |
| 10 | fc1.weight.shape =(10, 500) |
| data | fc2.weight |
| 1 | len(fc2.bias.shape) |
| 10 | fc2.bias.shape =(10,) |
| data | fc1.bias |
代码注释
import sys
import struct
import json
import numpy as np
import reimport torch
import torch.nn as nn
import torchvision.datasets as dsets
import torchvision.transforms as transforms
from torch.autograd import Variable# 检查是否提供了正确数量的命令行参数
if len(sys.argv) != 2:print("Usage: convert-h5-to-ggml.py model\n")sys.exit(1)# 获取输入h5模型和输出ggml模型的文件路径
state_dict_file = sys.argv[1]
fname_out = "models/mnist/ggml-model-f32.bin"# 加载PyTorch保存的state_dict模型
state_dict = torch.load(state_dict_file, map_location=torch.device('cpu'))# 以写入模式打开输出二进制文件
fout = open(fname_out, "wb")# 在文件中写入魔术数字'ggml'，以十六进制格式作为文件标识符
# 使用 Python 的 struct 模块将整数 0x67676d6c 打包为二进制数据的操作。在这里，"i" 表示使用整数格式进行打包。
fout.write(struct.pack("i", 0x67676d6c)) # magic: ggml in hex # 迭代state_dict中的所有变量
for name in state_dict.keys():# 从变量中提取数据并将其转换为NumPy数组data = state_dict[name].squeeze().numpy()print("Processing variable: " + name + " with shape: ", data.shape) n_dims = len(data.shape);# 将变量的维度数量写入二进制文件fout.write(struct.pack("i", n_dims))# 将数据转换为float32并将维度写入二进制文件data = data.astype(np.float32)for i in range(n_dims):fout.write(struct.pack("i", data.shape[n_dims - 1 - i]))# 将数据写入二进制文件data.tofile(fout)# 关闭二进制文件
fout.close()print("Done. Output file: " + fname_out)
print("")
tofile()
- NumPy提供的存数组内容的文件操作函数。读取使用fromfile。
struct.pack
- 将字节解释为打包的二进制数据。
输出
$:~/ggml/ggml/examples/mnist$ python3 ./convert-h5-to-ggml.py
./models/mnist/mnist_model.state_dictOrderedDict([('fc1.weight', tensor([[ 0.0130, 0.0034, -0.0287, ..., -0.0268, -0.0352, -0.0056],[-0.0134, 0.0077, -0.0028, ..., 0.0356, 0.0143, -0.0107],[-0.0329, 0.0154, -0.0167, ..., 0.0155, 0.0127, -0.0309],...,[-0.0216, -0.0302, 0.0085, ..., 0.0301, 0.0073, 0.0153],[ 0.0289, 0.0181, 0.0326, ..., 0.0107, -0.0314, -0.0349],[ 0.0273, 0.0127, 0.0105, ..., 0.0090, -0.0007, 0.0190]])), ('fc1.bias', tensor([ 1.9317e-01, -7.4255e-02, 8.3417e-02, 1.1681e-01, 7.5499e-03,8.7627e-02, -7.9260e-03, 6.8504e-02, 2.2217e-02, 9.7918e-02,1.5195e-01, 8.3765e-02, 1.4237e-02, 1.0847e-02, 9.6959e-02,-1.2500e-01, 4.2406e-02, -2.4611e-02, 5.9198e-03, 8.9767e-02,..., 1.3460e-03, 2.9106e-02, -4.0620e-02, 9.7568e-02, 8.5670e-02])), ('fc2.weight', tensor([[-0.0197, -0.0814, -0.3992, ..., 0.2697, 0.0386, -0.5380],[-0.4174, 0.0572, -0.1331, ..., -0.2564, -0.3926, -0.0514],...,[-0.2988, -0.1119, 0.0517, ..., 0.3296, 0.0800, 0.0651]])), ('fc2.bias', tensor([-0.1008, -0.1179, -0.0558, -0.0626, 0.0385, -0.0222, 0.0188, -0.1296,0.1507, 0.0033]))])
Processing variable: fc1.weight with shape: (500, 784)
Processing variable: fc1.bias with shape: (500,)
Processing variable: fc2.weight with shape: (10, 500)
Processing variable: fc2.bias with shape: (10,)
Done. Output file: models/mnist/ggml-model-f32.bin
————————————————
// 原文链接：https://blog.csdn.net/ResumeProject/article/details/131571641
权重读取
// load the model's weights from a file
bool mnist_model_load(const std::string & fname, mnist_model & model) {printf("%s: loading model from '%s'\n", __func__, fname.c_str());auto fin = std::ifstream(fname, std::ios::binary);// std::ifstream用于读文件操作if (!fin) {fprintf(stderr, "%s: failed to open '%s'\n", __func__, fname.c_str());return false;}// verify magic{uint32_t magic;// 32位的无符号整型数 uint32_t i = 0x67676d6c;fin.read((char *) &magic, sizeof(magic));if (magic != GGML_FILE_MAGIC) {fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname.c_str());return false;}}auto & ctx = model.ctx;size_t ctx_size = 0;// compute ctx_size use mnist_hparams{const auto & hparams = model.hparams;const int n_input = hparams.n_input;const int n_hidden = hparams.n_hidden;const int n_classes = hparams.n_classes;ctx_size += n_input * n_hidden * ggml_type_sizef(GGML_TYPE_F32); // fc1 weightctx_size += n_hidden * ggml_type_sizef(GGML_TYPE_F32); // fc1 biasctx_size += n_hidden * n_classes * ggml_type_sizef(GGML_TYPE_F32); // fc2 weightctx_size += n_classes * ggml_type_sizef(GGML_TYPE_F32); // fc2 biasprintf("%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0));}// create the ggml context{struct ggml_init_params params = {/*.mem_size =*/ ctx_size + 1024*1024,/*.mem_buffer =*/ NULL,/*.no_alloc =*/ false,};model.ctx = ggml_init(params);if (!model.ctx) {fprintf(stderr, "%s: ggml_init() failed\n", __func__);return false;}}// Read FC1 layer 1{// Read dimensions and keep in a signed int// 读取sizeof(n_dims)个字节的数据，并将其存储到n_dims指向的内存空间中。`reinterpret_cast<char *>` 是一个类型转换操作符，它将 `&n_dims` 的地址强制转换为 `char *` 类型的指针，这样可以将 `int32_t` 类型的数据按字节读取。int32_t n_dims; fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));{int32_t ne_weight[2] = { 1, 1 };for (int i = 0; i < n_dims; ++i) {fin.read(reinterpret_cast<char *>(&ne_weight[i]), sizeof(ne_weight[i]));}// FC1 dimensions taken from file, eg. 768x500model.hparams.n_input = ne_weight[0];model.hparams.n_hidden = ne_weight[1];model.fc1_weight = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, model.hparams.n_input, model.hparams.n_hidden);fin.read(reinterpret_cast<char *>(model.fc1_weight->data), ggml_nbytes(model.fc1_weight));ggml_set_name(model.fc1_weight, "fc1_weight");}{int32_t ne_bias[2] = { 1, 1 };for (int i = 0; i < n_dims; ++i) {fin.read(reinterpret_cast<char *>(&ne_bias[i]), sizeof(ne_bias[i]));}model.fc1_bias = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, model.hparams.n_hidden);fin.read(reinterpret_cast<char *>(model.fc1_bias->data), ggml_nbytes(model.fc1_bias));ggml_set_name(model.fc1_bias, "fc1_bias");// just for testing purposes, set some parameters to non-zeromodel.fc1_bias->op_params[0] = 0xdeadbeef;}}// Read FC2 layer 2{// Read dimensionsint32_t n_dims;fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));{int32_t ne_weight[2] = { 1, 1 };for (int i = 0; i < n_dims; ++i) {fin.read(reinterpret_cast<char *>(&ne_weight[i]), sizeof(ne_weight[i]));}// FC1 dimensions taken from file, eg. 10x500model.hparams.n_classes = ne_weight[1];model.fc2_weight = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, model.hparams.n_hidden, model.hparams.n_classes);fin.read(reinterpret_cast<char *>(model.fc2_weight->data), ggml_nbytes(model.fc2_weight));ggml_set_name(model.fc2_weight, "fc2_weight");}{int32_t ne_bias[2] = { 1, 1 };for (int i = 0; i < n_dims; ++i) {fin.read(reinterpret_cast<char *>(&ne_bias[i]), sizeof(ne_bias[i]));}model.fc2_bias = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, model.hparams.n_classes);fin.read(reinterpret_cast<char *>(model.fc2_bias->data), ggml_nbytes(model.fc2_bias));ggml_set_name(model.fc2_bias, "fc2_bias");}}fin.close();return true;
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