webassembly003 GGML Tensor Library part-1

news/2025/3/19 20:32:57/

GGML

ggml的函数

  • 可以看到官方示例项目仅依赖于#include "ggml/ggml.h"， #include "common.h"，可以阅读ggml.h获取ggml的使用帮助
函数解释注释
ggml_tensor多维张量按行主顺序存储。ggml_tensor结构包含每个维度中元素数（“ne”）和字节数（“nb”，又称步幅）的字段。这允许在存储器中存储不连续的张量，这对于诸如换位和置换之类的操作是有用的。所有张量运算都必须考虑步长，而不是假设张量在内存中是连续的。 int64_t ne[GGML_MAX_DIMS]; // number of elements size_t nb[GGML_MAX_DIMS]; // stride in bytesnb[0] = sizeof(type) nb[1] = nb[0] * ne[0] + padding nb[i] = nb[i-1] * ne[i-1]
ggml_context使用ggml_init_params 初始化ggml context(例如 mem_size,mem_buffer,mem_buffer_owned)
ggml_init_params
ggml_type_sizef
ggml_init
ggml_new_tensor
ggml_new_tensor_1dstruct ggml_tensor * input = ggml_new_tensor_1d(ctx , GGML_TYPE_F32, 28*28);
ggml_new_tensor_2d二维张量
ggml_new_tensor_3d
ggml_new_tensor_4d
ggml_nbytes返回读取的大小值
#define GGML_PAD(x, n) (((x) + (n) - 1) & ~((n) - 1))x按照n进行向上取整后的值，将x与n-1相加，然后再与~(n-1)进行按位与操作。
ggml_set_name
enum ggml_op所有已经实现和未实现的算子
ggml_mul_matmul opggml_tensor * temp = ggml_mul_mat(ctx0, model.fc1_weight, input) ;
ggml_addadd op
ggml_add_inplace
ggml_soft_maxsoftmax op
ggml_normnorm op
ggml_cpycopy op
ggml_permutepermute op
ggml_flash_attnattention op
ggml_relurelu op
ggml_build_forward_expand构建计算图ggml_cgraph
ggml_graph_compute_with_ctx运行计算图(最初的版本是没有这个函数的),而是ggml_graph_compute
ggml_graph_dump_dotggml_graph_print
ggml_graph_export导出计算图供以后使用,示例 “mnist-cpu”
ggml_get_data_f32从tensor中获取数值
ggml_set_f32设置值，当前项目没有用到，大多使用直接赋值 fin.read(reinterpret_cast<char *>(model.fc1_weight->data), ggml_nbytes(model.fc1_weight));
ggml_time_init初始化GGML的时间测量
本项目没有用到的函数
ggml_set_paramggml_set_param(ctx, x); // 反向传播时将x设置为变量 The ggml_set_param() function marks a tensor as an input variable. This is used by the automatic differentiation and optimization algorithms.
ggml_graph_reset训练时的梯度归零
ggml_get_f32_1dfloat (*ggml_get_f32_1d) (const struct ggml_tensor * tensor, int i)读取1d数据的index处的值，对应的也有set方法ggml_set_f32_1d
未暴露的，但在机器学习中比较重要的函数
ggml_opt_adamresult = ggml_opt_adam(ctx, opt, opt->params, f, gf, gb);
ggml_opt_lbfgsresult = ggml_opt_lbfgs(ctx, opt, opt->params, f, gf, gb);

ggml的使用

  • 通过下面的例子可以看出使用ggml进行推理主要包括以下几个步骤：
  • 上下文环境创建=>
  • tensors数据初始化=>
  • 构建计算图=>
  • 设置tensor值=>
  • 前向推理=>
  • 输出值，释放上下文<=>

权重的读取与转换

  • https://github.com/ggerganov/ggml/tree/master/examples/mnist

  • git clone --recursive https://github.com/ggerganov/ggml.git

$:~/ggml/ggml/examples/mnist$ tree
.
├── CMakeLists.txt
├── convert-h5-to-ggml.py
├── main.cpp
├── main-cpu.cpp
├── main-mtl.cpp
├── main-mtl.h
├── main-mtl.m
├── models
│   └── mnist
│       ├── mnist_model.state_dict
│       └── t10k-images.idx3-ubyte
├── README.md
└── web└── index.html
$:~/ggml/ggml/examples/mnist$ conda activate trt2
$:~/ggml/ggml/examples/mnist$ 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
$:~/ggml/ggml/examples/mnist$ tree
.
├── CMakeLists.txt
├── convert-h5-to-ggml.py
├── main.cpp
├── main-cpu.cpp
├── main-mtl.cpp
├── main-mtl.h
├── main-mtl.m
├── models
│   └── mnist
│       ├── ggml-model-f32.bin
│       ├── mnist_model.state_dict
│       └── t10k-images.idx3-ubyte
├── README.md
└── web└── index.html3 directories, 12 files

ggml进行推理

//  https://github1s.com/ggerganov/ggml/blob/HEAD/examples/mnist/main.cpp#L1-L329
#include "ggml/ggml.h"#include "common.h"#include <cmath>
#include <cstdio>
#include <cstring>
#include <ctime>
#include <fstream>
#include <string>
#include <vector>
#include <algorithm>#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif

模型的状态和超参数

  • 定义默认超参数结构体 mnist_hparams，包括输入维度、隐藏层维度和类别数。定义 mnist_model 结构体，用于存储模型的状态和超参数。
// default hparams
struct mnist_hparams {int32_t n_input   = 784;int32_t n_hidden  = 500;int32_t n_classes = 10;
};struct mnist_model {mnist_hparams hparams;struct ggml_tensor * fc1_weight;struct ggml_tensor * fc1_bias;struct ggml_tensor * fc2_weight;struct ggml_tensor * fc2_bias;struct ggml_context * ctx;
};

读取权重 mnist_model_load

  • mnist_model_load 函数，用于加载模型文件。函数首先检查文件是否存在，然后读取模型文件的超参数，创建 ggml_context 对象，并从文件中加载模型的权重和偏置。
  • 调用过程：
  • ./bin/mnist ./models/mnist/ggml-model-f32.bin …/examples/mnist/models/mnist/t10k-images.idx3-ubyte
  • mnist_model_load(argv[1], model)，model是一个未初始化的mnist_model 结构体，后续使用.bin文件进行初始化。
// 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;
}

构建模型的前向传递计算图 mnist_eval

  • 定义 mnist_eval 函数，用于构建模型的前向传递计算图 评估模型，返回预测结果（0-9的数字）。
// evaluate the model
//
//   - model:     the model
//   - n_threads: number of threads to use
//   - digit:     784 pixel values
//
// returns 0 - 9 prediction
int mnist_eval(const mnist_model & model,const int n_threads,std::vector<float> digit,const char * fname_cgraph) {const auto & hparams = model.hparams;static size_t buf_size = hparams.n_input * sizeof(float) * 4;static void * buf = malloc(buf_size);struct ggml_init_params params = {/*.mem_size   =*/ buf_size,/*.mem_buffer =*/ buf,/*.no_alloc   =*/ false,};struct ggml_context * ctx0 = ggml_init(params);struct ggml_cgraph gf = {};struct ggml_tensor * input = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, hparams.n_input);memcpy(input->data, digit.data(), ggml_nbytes(input));ggml_set_name(input, "input");// fc1 MLP = Ax + bggml_tensor * fc1 = ggml_add(ctx0, ggml_mul_mat(ctx0, model.fc1_weight, input),                model.fc1_bias);ggml_tensor * fc2 = ggml_add(ctx0, ggml_mul_mat(ctx0, model.fc2_weight, ggml_relu(ctx0, fc1)), model.fc2_bias);// soft maxggml_tensor * probs = ggml_soft_max(ctx0, fc2);ggml_set_name(probs, "probs");// build / export / run the computation graphggml_build_forward_expand(&gf, probs);ggml_graph_compute_with_ctx(ctx0, &gf, n_threads);//ggml_graph_print   (&gf);ggml_graph_dump_dot(&gf, NULL, "mnist.dot");if (fname_cgraph) {// export the compute graph for later use// see the "mnist-cpu" exampleggml_graph_export(&gf, "mnist.ggml");fprintf(stderr, "%s: exported compute graph to '%s'\n", __func__, fname_cgraph);}const float * probs_data = ggml_get_data_f32(probs);const int prediction = std::max_element(probs_data, probs_data + 10) - probs_data;ggml_free(ctx0);return prediction;
}

wasm_eval用于调用WebAssembly版本的神经网络模型评估函数，wasm_random_digit用于从测试数据集中随机读取一个数字。

#ifdef __cplusplus  //如果编译器是C++编译器
extern "C" {
#endifint wasm_eval(uint8_t * digitPtr) {mnist_model model;if (!mnist_model_load("models/mnist/ggml-model-f32.bin", model)) {fprintf(stderr, "error loading model\n");return -1;}std::vector<float> digit(digitPtr, digitPtr + 784);int result = mnist_eval(model, 1, digit, nullptr);ggml_free(model.ctx);return result;
}int wasm_random_digit(char * digitPtr) {auto fin = std::ifstream("models/mnist/t10k-images.idx3-ubyte", std::ios::binary);if (!fin) {fprintf(stderr, "failed to open digits file\n");return 0;}srand(time(NULL));// Seek to a random digit: 16-byte header + 28*28 * (random 0 - 10000)fin.seekg(16 + 784 * (rand() % 10000));fin.read(digitPtr, 784);return 1;
}#ifdef __cplusplus
}
#endif

main

int main(int argc, char ** argv) {srand(time(NULL));ggml_time_init();if (argc != 3) {fprintf(stderr, "Usage: %s models/mnist/ggml-model-f32.bin models/mnist/t10k-images.idx3-ubyte\n", argv[0]);exit(0);}uint8_t buf[784];mnist_model model;std::vector<float> digit;// load the model{const int64_t t_start_us = ggml_time_us();if (!mnist_model_load(argv[1], model)) {fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, "models/ggml-model-f32.bin");return 1;}const int64_t t_load_us = ggml_time_us() - t_start_us;fprintf(stdout, "%s: loaded model in %8.2f ms\n", __func__, t_load_us / 1000.0f);}// read a random digit from the test set{std::ifstream fin(argv[2], std::ios::binary);if (!fin) {fprintf(stderr, "%s: failed to open '%s'\n", __func__, argv[2]);return 1;}// seek to a random digit: 16-byte header + 28*28 * (random 0 - 10000)fin.seekg(16 + 784 * (rand() % 10000));fin.read((char *) &buf, sizeof(buf));}// render the digit in ASCII{digit.resize(sizeof(buf));for (int row = 0; row < 28; row++) {for (int col = 0; col < 28; col++) {fprintf(stderr, "%c ", (float)buf[row*28 + col] > 230 ? '*' : '_');digit[row*28 + col] = ((float)buf[row*28 + col]);}fprintf(stderr, "\n");}fprintf(stderr, "\n");}const int prediction = mnist_eval(model, 1, digit, "mnist.ggml");fprintf(stdout, "%s: predicted digit is %d\n", __func__, prediction);ggml_free(model.ctx);return 0;
}

运行

$:~/ggml/ggml$ mkdir build && cd build
$:~/ggml/ggml/build$ cmake ..
-- The C compiler identification is GNU 9.5.0
-- The CXX compiler identification is GNU 9.5.0
-- Detecting C compiler ABI info
-- Detecting C compiler ABI info - done
-- Check for working C compiler: /usr/bin/cc - skipped
-- Detecting C compile features
-- Detecting C compile features - done
-- Detecting CXX compiler ABI info
-- Detecting CXX compiler ABI info - done
-- Check for working CXX compiler: /usr/bin/c++ - skipped
-- Detecting CXX compile features
-- Detecting CXX compile features - done
-- Found Git: /usr/bin/git (found version "2.34.1") 
-- Looking for pthread.h
-- Looking for pthread.h - found
-- Performing Test CMAKE_HAVE_LIBC_PTHREAD
-- Performing Test CMAKE_HAVE_LIBC_PTHREAD - Success
-- Found Threads: TRUE  
-- CMAKE_SYSTEM_PROCESSOR: x86_64
-- x86 detected
-- Linux detected
-- x86 detected
-- Linux detected
-- Configuring done
-- Generating done
-- Build files have been written to: /home/pdd/ggml/ggml/build
(trt2) pdd@pdd-Dell-G15-5511:~/ggml/ggml/build$ make -j4 mnist
[ 16%] Building CXX object examples/CMakeFiles/common.dir/common.cpp.o
[ 33%] Building C object src/CMakeFiles/ggml.dir/ggml.c.o
[ 50%] Linking C static library libggml.a
[ 50%] Built target ggml
[ 66%] Linking CXX static library libcommon.a
[ 66%] Built target common
[ 83%] Building CXX object examples/mnist/CMakeFiles/mnist.dir/main.cpp.o
[100%] Linking CXX executable ../../bin/mnist
[100%] Built target mnist

$:~/ggml/ggml/build/bin$ ls -ahl
总用量 352K
drwxrwxr-x 2 pdd pdd 4.0K Aug 15 12:17 .
drwxrwxr-x 7 pdd pdd 4.0K Aug 15 12:20 ..
-rwxrwxr-x 1 pdd pdd 341K Aug 15 12:17 mnist

$:~/ggml/ggml/build$ ./bin/mnist /home/pdd/ggml/ggml/examples/mnist/models/mnist/ggml-model-f32.bin /home/pdd/ggml/ggml/examples/mnist/models/mnist/t10k-images.idx3-ubyte
mnist_model_load: loading model from '/home/pdd/ggml/ggml/examples/mnist/models/mnist/ggml-model-f32.bin'
mnist_model_load: ggml ctx size =   1.52 MB
main: loaded model in     3.82 ms
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
_ _ _ _ _ _ _ _ _ * * * * * * * * * _ _ _ _ _ _ _ _ _ _ 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ * * * * * _ _ _ _ _ _ _ _ _ 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ * * * _ _ _ _ _ _ _ _ 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ * * * _ _ _ _ _ _ _ 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ * * * _ _ _ _ _ _ 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ * * * _ _ _ _ _ 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ * * * _ _ _ _ _ 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ * * * _ _ _ _ _ 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ * * * _ _ _ _ _ 
_ _ _ _ _ _ _ _ _ * * * * * * _ _ _ _ _ * * * _ _ _ _ _ 
_ _ _ _ _ _ _ * * * * * * * * * * * * * * * _ _ _ _ _ _ 
_ _ _ _ _ _ * * * _ _ _ _ * * * * * * * * * * _ _ _ _ _ 
_ _ _ _ _ * * _ _ _ _ _ _ _ _ _ * * * * * * * _ _ _ _ _ 
_ _ _ _ * * * _ _ _ _ _ _ _ _ _ * * * * _ _ _ _ _ _ _ _ 
_ _ _ _ * * * _ _ _ _ _ _ _ * * * * * _ _ _ _ _ _ _ _ _ 
_ _ _ _ * * * _ _ _ _ _ * * * * * _ _ _ _ _ _ _ _ _ _ _ 
_ _ _ _ _ _ * * * * * * * * * _ _ _ _ _ _ _ _ _ _ _ _ _ 
_ _ _ _ _ _ _ * * * * * * * _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ ggml_graph_dump_dot: dot -Tpng mnist.dot -o mnist.dot.png && open mnist.dot.pngmagic            67676d6c
version                 1
leafs                   5
nodes                   6
eval             6144TYPE   OP              NDIMS      NE0      NE1      NE2      NE3              NB0              NB1              NB2              NB3             DATA             NAME
f32    NONE                2 500 10 1 1                4             2000            20000            20000   0x7feee8650870                       fc2_weight
f32    NONE                2 784 500 1 1                4             3136          1568000          1568000   0x7feee84d1140                       fc1_weight
f32    NONE                1 784 1 1 1                4             3136             3136             3136   0x55cb404f7ec0                            input
f32    NONE                1 500 1 1 1                4             2000             2000             2000   0x7feee864ff70                         fc1_bias
f32    NONE                1 10 1 1 1                4               40               40               40   0x7feee86557c0                         fc2_biasARG    TYPE   OP              NDIMS      NE0      NE1      NE2      NE3              NB0              NB1              NB2              NB3   NTASKS             DATA             NAME
DST    f32    MUL_MAT             2 500 1 1 1                4             2000             2000             2000   0x55cb404f8c30                           node_0
SRC    f32    NONE                2 784 500 1 1                4             3136          1568000          1568000   0x7feee84d1140                       fc1_weight
SRC    f32    NONE                1 784 1 1 1                4             3136             3136             3136   0x55cb404f7ec0                            inputDST    f32    ADD                 2 500 1 1 1                4             2000             2000             2000   0x55cb404f9530                           node_1
SRC    f32    MUL_MAT             2 500 1 1 1                4             2000             2000             2000   0x55cb404f8c30                           node_0
SRC    f32    NONE                1 500 1 1 1                4             2000             2000             2000   0x7feee864ff70                         fc1_biasDST    f32    UNARY               2 500 1 1 1                4             2000             2000             2000   0x55cb404f9e30                           node_2
SRC    f32    ADD                 2 500 1 1 1                4             2000             2000             2000   0x55cb404f9530                           node_1DST    f32    MUL_MAT             2 10 1 1 1                4               40               40               40   0x55cb404fa730                           node_3
SRC    f32    NONE                2 500 10 1 1                4             2000            20000            20000   0x7feee8650870                       fc2_weight
SRC    f32    UNARY               2 500 1 1 1                4             2000             2000             2000   0x55cb404f9e30                           node_2DST    f32    ADD                 2 10 1 1 1                4               40               40               40   0x55cb404fa890                           node_4
SRC    f32    MUL_MAT             2 10 1 1 1                4               40               40               40   0x55cb404fa730                           node_3
SRC    f32    NONE                1 10 1 1 1                4               40               40               40   0x7feee86557c0                         fc2_biasDST    f32    SOFT_MAX            2 10 1 1 1                4               40               40               40   0x55cb404fa9f0                            probs
SRC    f32    ADD                 2 10 1 1 1                4               40               40               40   0x55cb404fa890                           node_4mnist_eval: exported compute graph to 'mnist.ggml'
main: predicted digit is 2

CG

  • Extract images from MNIST idx3 ubyte file format in Python

  • 2023.08.18今天发现ggml的引用文件变成两个了，这个库还在不断的更新中
    在这里插入图片描述


http://www.ppmy.cn/news/1065943.html

相关文章

触发JVM fatal error并配置相关JVM参数

触发JVM fatal error并配置相关JVM参数

1. 絮絮叨叨 工作中&#xff0c;Java服务因为fatal error&#xff08;致命错误&#xff0c;笔者称其为jvm crash&#xff09;&#xff0c;在服务运行日志中出现了致命错误的概要信息&#xff1a; # # A fatal error has been detected by the Java Runtime Environment: # # S…
阅读更多...
TCP 和 UDP 的区别、TCP 是如何保证可靠传输的？

TCP 和 UDP 的区别、TCP 是如何保证可靠传输的？

先来介绍一些osi七层模型 分为应用层、表示层、会话层、运输层、网络层、链路层、物理层。 应用层(数据)&#xff1a;确定进程之间通信的性质以及满足用户需要以及提供网络和用户应用&#xff0c;为应用程序提供服务&#xff0c;DNS&#xff0c;HTTP&#xff0c;HTTPS&#xf…
阅读更多...
ScheduleJS Crack,新的“信息列”水平滚动功能

ScheduleJS Crack,新的“信息列”水平滚动功能

ScheduleJS Crack,新的“信息列”水平滚动功能 增加了对Angular 16的支持 新的“信息列”水平滚动功能。 新的“信息列”固定功能。 添加了输入属性以处理组件模板中的偶数和奇数ScheduleRowPlainBackgroundColor以及CSS变量。 改进了“信息列”和角度甘特组件的类型。 Schedul…
阅读更多...
Dart PowerTCP Emulation for .NET Crack

Dart PowerTCP Emulation for .NET Crack

Dart PowerTCP Emulation for .NET Crack .NET CF上的PowerTCP Emulation为手持设备提供了高级的Internet通信组件。这些功能允许同步操作&#xff0c;这样可以消耗更少的资源&#xff0c;提供更大的灵活性&#xff0c;并生成易于维护的软件。带有.NET的PowerTCP仿真包括VT52、…
阅读更多...
软件测试自动化测试学习指南

软件测试自动化测试学习指南

软件测试自动化是现代软件开发过程中的重要环节&#xff0c;它能够提高测试效率、减少人工测试的工作量&#xff0c;并确保软件质量。要学习软件测试自动化&#xff0c;你可以按照以下步骤进行&#xff1a; 1、学习基本软件测试概念&#xff1a; 在开始学习自动化测试之前&am…
阅读更多...
BDCC - 闲聊数据仓库的架构

BDCC - 闲聊数据仓库的架构

文章目录 典型数据仓库架构图数据仓库ETL vs ELTETLELT区别联系 数据仓库分层&#xff08;1&#xff09;数据仓库ODS层&#xff08;2&#xff09;数据仓库CDM层DWD数据明细层DWS数据汇总层 &#xff08;3&#xff09;数据仓库ADS层 典型数据仓库架构图 按自下而上的顺序&#x…
阅读更多...
第一百二十七回 空安全

第一百二十七回 空安全

文章目录 概念介绍使用方法示例代码 我们在上一章回中介绍了级联操作符相关的内容&#xff0c;本章回中将介绍 空安全.闲话休提&#xff0c;让我们一起Talk Flutter吧。 概念介绍 在编译语言中都有空指针或者空对象(null)&#xff0c;它们会引起程序的异常。在移动开发中如果…
阅读更多...
测试.net开源音频库NAudio

测试.net开源音频库NAudio

微信公众号“dotNET跨平台”看到一篇文章《【.NET】使用NAudio实现录音功能》介绍基于NAudio实现录音功能&#xff08;参考文献1&#xff09;。NAudio是开源.net音频库&#xff0c;其支持播放多种格式的音频&#xff08;WAV、AIFF、MP3、WMA等&#xff09;、音频格式转换、录音…
阅读更多...
最新文章

Copyright @ 2022~2023