mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-10-31 08:51:55 +00:00
433 lines
14 KiB
C++
433 lines
14 KiB
C++
#include "ggml.h"
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#include "gguf-util.h"
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#include "gguf-llama.h"
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#include <cstdio>
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#include <cinttypes>
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#include <string>
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#include <sstream>
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#include <fstream>
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#include <vector>
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/*
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template<typename T>
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static std::string to_string(const T & val) {
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std::stringstream ss;
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ss << val;
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return ss.str();
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}
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*/
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void gguf_ex_write_str(std::ofstream & fout, const std::string & val) {
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const int32_t n = val.size();
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fout.write((const char *) &n, sizeof(n));
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fout.write(val.c_str(), n);
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}
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void gguf_ex_write_i32(std::ofstream & fout, int32_t val) {
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fout.write((const char *) &val, sizeof(val));
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}
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void gguf_ex_write_u64(std::ofstream & fout, size_t val) {
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fout.write((const char *) &val, sizeof(val));
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}
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template<typename T>
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void gguf_ex_write_val(std::ofstream & fout, const std::string & key, enum gguf_type type, const T & val) {
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gguf_ex_write_str(fout, key);
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fout.write((const char *) &type, sizeof(type));
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fout.write((const char *) &val, sizeof(val));
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fprintf(stdout, "%s: write param: %s = %s\n", __func__, key.c_str(), to_string(val).c_str());
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}
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template<>
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void gguf_ex_write_val<std::string>(std::ofstream & fout, const std::string & key, enum gguf_type type, const std::string & val) {
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gguf_ex_write_str(fout, key);
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fout.write((const char *) &type, sizeof(type));
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const int32_t n = val.size();
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fout.write((const char *) &n, sizeof(n));
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fout.write(val.c_str(), n);
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fprintf(stdout, "%s: write param: %s = %s\n", __func__, key.c_str(), val.c_str());
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}
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template<typename T>
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void gguf_ex_write_arr(std::ofstream & fout, const std::string & key, enum gguf_type type, const std::vector<T> & val) {
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gguf_ex_write_str(fout, key);
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{
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const enum gguf_type tarr = GGUF_TYPE_ARRAY;
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fout.write((const char *) &tarr, sizeof(tarr));
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}
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const int32_t n = val.size();
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fout.write((const char *) &type, sizeof(type));
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fout.write((const char *) &n, sizeof(n));
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fout.write((const char *) val.data(), n * sizeof(T));
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fprintf(stdout, "%s: write param: %s = [", __func__, key.c_str());
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for (int i = 0; i < n; ++i) {
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fprintf(stdout, "%s", to_string(val[i]).c_str());
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if (i < n - 1) {
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fprintf(stdout, ", ");
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}
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}
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fprintf(stdout, "]\n");
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}
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template<>
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void gguf_ex_write_arr<std::string>(std::ofstream & fout, const std::string & key, enum gguf_type type, const std::vector<std::string> & val) {
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gguf_ex_write_str(fout, key);
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{
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const enum gguf_type tarr = GGUF_TYPE_ARRAY;
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fout.write((const char *) &tarr, sizeof(tarr));
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}
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const int32_t n = val.size();
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fout.write((const char *) &type, sizeof(type));
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fout.write((const char *) &n, sizeof(n));
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for (int i = 0; i < n; ++i) {
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const int32_t nstr = val[i].size();
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fout.write((const char *) &nstr, sizeof(nstr));
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fout.write(val[i].c_str(), nstr);
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}
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fprintf(stdout, "%s: write param: %s = [", __func__, key.c_str());
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for (int i = 0; i < n; ++i) {
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fprintf(stdout, "%s", val[i].c_str());
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if (i < n - 1) {
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fprintf(stdout, ", ");
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}
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}
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fprintf(stdout, "]\n");
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}
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bool gguf_ex_write(const std::string & fname) {
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std::ofstream fout(fname.c_str(), std::ios::binary);
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{
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const int32_t magic = GGUF_MAGIC;
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fout.write((const char *) &magic, sizeof(magic));
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}
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{
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const int32_t version = GGUF_VERSION;
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fout.write((const char *) &version, sizeof(version));
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}
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// NOTE: these have to match the output below!
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const int n_tensors = 10;
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const int n_kv = 12;
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fout.write((const char*) &n_tensors, sizeof(n_tensors));
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fout.write((const char*) &n_kv, sizeof(n_kv));
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fprintf(stdout, "%s: write header\n", __func__);
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// kv data
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{
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gguf_ex_write_val< uint8_t>(fout, "some.parameter.uint8", GGUF_TYPE_UINT8, 0x12);
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gguf_ex_write_val< int8_t>(fout, "some.parameter.int8", GGUF_TYPE_INT8, -0x13);
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gguf_ex_write_val<uint16_t>(fout, "some.parameter.uint16", GGUF_TYPE_UINT16, 0x1234);
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gguf_ex_write_val< int16_t>(fout, "some.parameter.int16", GGUF_TYPE_INT16, -0x1235);
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gguf_ex_write_val<uint32_t>(fout, "some.parameter.uint32", GGUF_TYPE_UINT32, 0x12345678);
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gguf_ex_write_val< int32_t>(fout, "some.parameter.int32", GGUF_TYPE_INT32, -0x12345679);
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gguf_ex_write_val<float> (fout, "some.parameter.float32", GGUF_TYPE_FLOAT32, 0.123456789f);
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gguf_ex_write_val<bool> (fout, "some.parameter.bool", GGUF_TYPE_BOOL, true);
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gguf_ex_write_val<std::string>(fout, "some.parameter.string", GGUF_TYPE_STRING, "hello world");
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gguf_ex_write_arr<int16_t> (fout, "some.parameter.arr.i16", GGUF_TYPE_INT16, { 1, 2, 3, 4, });
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gguf_ex_write_arr<float> (fout, "some.parameter.arr.f32", GGUF_TYPE_FLOAT32, { 3.145f, 2.718f, 1.414f, });
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gguf_ex_write_arr<std::string>(fout, "some.parameter.arr.str", GGUF_TYPE_STRING, { "hello", "world", "!" });
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}
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uint64_t offset_tensor = 0;
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struct ggml_init_params params = {
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/*.mem_size =*/ 128ull*1024ull*1024ull,
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/*.mem_buffer =*/ NULL,
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/*.no_alloc =*/ false,
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};
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struct ggml_context * ctx_data = ggml_init(params);
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// tensor infos
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for (int i = 0; i < n_tensors; ++i) {
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const std::string name = "tensor_" + to_string(i);
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int64_t ne[GGML_MAX_DIMS] = { 1 };
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int32_t n_dims = rand() % GGML_MAX_DIMS + 1;
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for (int j = 0; j < n_dims; ++j) {
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ne[j] = rand() % 10 + 1;
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}
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struct ggml_tensor * cur = ggml_new_tensor(ctx_data, GGML_TYPE_F32, n_dims, ne);
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ggml_set_name(cur, name.c_str());
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{
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float * data = (float *) cur->data;
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for (int j = 0; j < ggml_nelements(cur); ++j) {
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data[j] = 100 + i;
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}
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}
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fprintf(stdout, "%s: tensor: %s, %d dims, ne = [", __func__, name.c_str(), n_dims);
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for (int j = 0; j < 4; ++j) {
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fprintf(stdout, "%s%3d", j == 0 ? "" : ", ", (int) cur->ne[j]);
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}
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fprintf(stdout, "], offset_tensor = %6" PRIu64 "\n", offset_tensor);
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gguf_ex_write_str(fout, name);
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gguf_ex_write_i32(fout, n_dims);
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for (int j = 0; j < n_dims; ++j) {
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gguf_ex_write_i32(fout, cur->ne[j]);
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}
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gguf_ex_write_i32(fout, cur->type);
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gguf_ex_write_u64(fout, offset_tensor);
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offset_tensor += GGML_PAD(ggml_nbytes(cur), GGUF_DEFAULT_ALIGNMENT);
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}
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const uint64_t offset_data = GGML_PAD((uint64_t) fout.tellp(), GGUF_DEFAULT_ALIGNMENT);
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fprintf(stdout, "%s: data offset = %" PRIu64 "\n", __func__, offset_data);
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{
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const size_t pad = offset_data - fout.tellp();
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for (size_t j = 0; j < pad; ++j) {
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fout.put(0);
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}
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}
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for (int i = 0; i < n_tensors; ++i) {
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fprintf(stdout, "%s: writing tensor %d data\n", __func__, i);
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const std::string name = "tensor_" + to_string(i);
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struct ggml_tensor * cur = ggml_get_tensor(ctx_data, name.c_str());
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fout.write((const char *) cur->data, ggml_nbytes(cur));
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{
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const size_t pad = GGML_PAD(ggml_nbytes(cur), GGUF_DEFAULT_ALIGNMENT) - ggml_nbytes(cur);
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for (size_t j = 0; j < pad; ++j) {
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fout.put(0);
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}
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}
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}
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fout.close();
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fprintf(stdout, "%s: wrote file '%s;\n", __func__, fname.c_str());
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ggml_free(ctx_data);
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return true;
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}
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// just read tensor info
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bool gguf_ex_read_0(const std::string & fname) {
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struct gguf_init_params params = {
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/*.no_alloc = */ false,
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/*.ctx = */ NULL,
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};
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struct gguf_context * ctx = gguf_init_from_file(fname.c_str(), params);
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fprintf(stdout, "%s: version: %d\n", __func__, gguf_get_version(ctx));
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fprintf(stdout, "%s: alignment: %zu\n", __func__, gguf_get_alignment(ctx));
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fprintf(stdout, "%s: data offset: %zu\n", __func__, gguf_get_data_offset(ctx));
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// kv
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{
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const int n_kv = gguf_get_n_kv(ctx);
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fprintf(stdout, "%s: n_kv: %d\n", __func__, n_kv);
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for (int i = 0; i < n_kv; ++i) {
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const char * key = gguf_get_key(ctx, i);
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fprintf(stdout, "%s: kv[%d]: key = %s\n", __func__, i, key);
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}
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}
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// find kv string
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{
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const char * findkey = "some.parameter.string";
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const int keyidx = gguf_find_key(ctx, findkey);
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if (keyidx == -1) {
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fprintf(stdout, "%s: find key: %s not found.\n", __func__, findkey);
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} else {
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const char * key_value = gguf_get_val_str(ctx, keyidx);
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fprintf(stdout, "%s: find key: %s found, kv[%d] value = %s\n", __func__, findkey, keyidx, key_value);
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}
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}
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// tensor info
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{
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const int n_tensors = gguf_get_n_tensors(ctx);
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fprintf(stdout, "%s: n_tensors: %d\n", __func__, n_tensors);
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for (int i = 0; i < n_tensors; ++i) {
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const char * name = gguf_get_tensor_name (ctx, i);
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const size_t offset = gguf_get_tensor_offset(ctx, i);
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fprintf(stdout, "%s: tensor[%d]: name = %s, offset = %zu\n", __func__, i, name, offset);
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}
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}
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gguf_free(ctx);
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return true;
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}
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// read and create ggml_context containing the tensors and their data
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bool gguf_ex_read_1(const std::string & fname) {
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struct ggml_context * ctx_data = NULL;
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struct gguf_init_params params = {
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/*.no_alloc = */ false,
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/*.ctx = */ &ctx_data,
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};
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struct gguf_context * ctx = gguf_init_from_file(fname.c_str(), params);
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fprintf(stdout, "%s: version: %d\n", __func__, gguf_get_version(ctx));
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fprintf(stdout, "%s: alignment: %zu\n", __func__, gguf_get_alignment(ctx));
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fprintf(stdout, "%s: data offset: %zu\n", __func__, gguf_get_data_offset(ctx));
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// kv
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{
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const int n_kv = gguf_get_n_kv(ctx);
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fprintf(stdout, "%s: n_kv: %d\n", __func__, n_kv);
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for (int i = 0; i < n_kv; ++i) {
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const char * key = gguf_get_key(ctx, i);
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fprintf(stdout, "%s: kv[%d]: key = %s\n", __func__, i, key);
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}
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}
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// tensor info
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{
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const int n_tensors = gguf_get_n_tensors(ctx);
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fprintf(stdout, "%s: n_tensors: %d\n", __func__, n_tensors);
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for (int i = 0; i < n_tensors; ++i) {
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const char * name = gguf_get_tensor_name (ctx, i);
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const size_t offset = gguf_get_tensor_offset(ctx, i);
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fprintf(stdout, "%s: tensor[%d]: name = %s, offset = %zu\n", __func__, i, name, offset);
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}
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}
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// data
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{
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const int n_tensors = gguf_get_n_tensors(ctx);
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for (int i = 0; i < n_tensors; ++i) {
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fprintf(stdout, "%s: reading tensor %d data\n", __func__, i);
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const char * name = gguf_get_tensor_name(ctx, i);
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struct ggml_tensor * cur = ggml_get_tensor(ctx_data, name);
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fprintf(stdout, "%s: tensor[%d]: n_dims = %d, name = %s, data = %p\n",
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__func__, i, cur->n_dims, cur->name, cur->data);
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// check data
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{
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const float * data = (const float *) cur->data;
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for (int j = 0; j < ggml_nelements(cur); ++j) {
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if (data[j] != 100 + i) {
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fprintf(stderr, "%s: tensor[%d]: data[%d] = %f\n", __func__, i, j, data[j]);
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return false;
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}
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}
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}
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}
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}
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fprintf(stdout, "%s: ctx_data size: %zu\n", __func__, ggml_get_mem_size(ctx_data));
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ggml_free(ctx_data);
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gguf_free(ctx);
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return true;
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}
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// read just the tensor info and mmap the data in user code
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bool gguf_ex_read_2(const std::string & fname) {
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struct ggml_context * ctx_data = NULL;
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struct gguf_init_params params = {
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/*.no_alloc = */ true,
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/*.ctx = */ &ctx_data,
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};
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struct gguf_context * ctx = gguf_init_from_file(fname.c_str(), params);
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struct gguf_file file(fname.c_str(), "rb");
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gguf_mmap data_mmap(&file, 0, false);
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const int n_tensors = gguf_get_n_tensors(ctx);
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for (int i = 0; i < n_tensors; ++i) {
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const char * name = gguf_get_tensor_name(ctx, i);
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const size_t offset = gguf_get_data_offset(ctx) + gguf_get_tensor_offset(ctx, i);
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struct ggml_tensor * cur = ggml_get_tensor(ctx_data, name);
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cur->data = static_cast<char *>(data_mmap.addr) + offset;
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// print first 10 elements
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const float * data = (const float *) cur->data;
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printf("%s data[:10] : ", name);
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for (int j = 0; j < 10; ++j) {
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printf("%f ", data[j]);
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}
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printf("\n\n");
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}
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fprintf(stdout, "%s: ctx_data size: %zu\n", __func__, ggml_get_mem_size(ctx_data));
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ggml_free(ctx_data);
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gguf_free(ctx);
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return true;
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}
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int main(int argc, char ** argv) {
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if (argc < 3) {
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fprintf(stdout, "usage: %s data.gguf r|w\n", argv[0]);
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return -1;
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}
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const std::string fname(argv[1]);
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const std::string mode (argv[2]);
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GGML_ASSERT((mode == "r" || mode == "w" || mode == "q") && "mode must be r, w or q");
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if (mode == "w") {
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GGML_ASSERT(gguf_ex_write(fname) && "failed to write gguf file");
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} else if (mode == "r") {
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GGML_ASSERT(gguf_ex_read_0(fname) && "failed to read gguf file");
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//GGML_ASSERT(gguf_ex_read_1(fname) && "failed to read gguf file");
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GGML_ASSERT(gguf_ex_read_2(fname) && "failed to read gguf file");
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} else if (mode == "q") {
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llama_model_quantize_params params = llama_model_quantize_default_params();
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llama_model_quantize(fname.c_str(), "quant.gguf", ¶ms);
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}
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return 0;
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}
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