mirror of
https://github.com/LostRuins/koboldcpp.git
synced 2025-09-11 17:44:38 +00:00
Merge commit 'ccf58aa3ec' into concedo_experimental
# Conflicts: # .gitignore # Makefile # README-sycl.md # ggml-cuda.cu
This commit is contained in:
Concedo
commit
c348223dff
14 changed files with 4472 additions and 3161 deletions
|
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@ -21,6 +21,7 @@ else()
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add_subdirectory(embedding)
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add_subdirectory(finetune)
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add_subdirectory(gritlm)
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add_subdirectory(gguf-split)
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add_subdirectory(infill)
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add_subdirectory(llama-bench)
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add_subdirectory(llava)
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5
examples/gguf-split/CMakeLists.txt
Normal file
5
examples/gguf-split/CMakeLists.txt
Normal file
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@ -0,0 +1,5 @@
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set(TARGET gguf-split)
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add_executable(${TARGET} gguf-split.cpp)
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install(TARGETS ${TARGET} RUNTIME)
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target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
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target_compile_features(${TARGET} PRIVATE cxx_std_11)
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9
examples/gguf-split/README.md
Normal file
9
examples/gguf-split/README.md
Normal file
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@ -0,0 +1,9 @@
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## GGUF split Example
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CLI to split / merge GGUF files.
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**Command line options:**
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- `--split`: split GGUF to multiple GGUF, default operation.
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- `--split-max-tensors`: maximum tensors in each split: default(128)
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- `--merge`: merge multiple GGUF to a single GGUF.
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489
examples/gguf-split/gguf-split.cpp
Normal file
489
examples/gguf-split/gguf-split.cpp
Normal file
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@ -0,0 +1,489 @@
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#include "llama.h"
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#include "ggml.h"
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#include "common.h"
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#include <algorithm>
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#include <cmath>
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#include <cstdint>
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#include <cstdlib>
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#include <fstream>
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#include <ios>
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#include <string>
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#include <vector>
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#include <stdio.h>
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#include <fcntl.h>
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#include <string.h>
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enum split_operation : uint8_t {
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SPLIT_OP_SPLIT,
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SPLIT_OP_MERGE,
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};
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static const char * const LLM_KV_GENERAL_SPLIT_I_SPLIT = "general.split";
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static const char * const LLM_KV_GENERAL_SPLIT_N_SPLIT = "general.split_count";
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static const int SPLIT_FILENAME_MAX = 256;
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static const char * const SPLIT_FILENAME_FORMAT = "%s-%05d-of-%05d.gguf";
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struct split_params {
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split_operation operation = SPLIT_OP_SPLIT;
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int n_split_tensors = 128;
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std::string input;
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std::string output;
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};
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static void split_print_usage(const char * executable) {
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const split_params default_params;
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printf("\n");
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printf("usage: %s [options] GGUF_IN GGUF_OUT\n", executable);
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printf("\n");
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printf("Apply a GGUF operation on IN to OUT.");
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printf("\n");
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printf("options:\n");
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printf(" -h, --help show this help message and exit\n");
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printf(" --version show version and build info\n");
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printf(" --split split GGUF to multiple GGUF (default)\n");
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printf(" --split-max-tensors max tensors in each split: default(%d)\n", default_params.n_split_tensors);
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printf(" --merge merge multiple GGUF to a single GGUF\n");
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printf("\n");
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}
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static bool split_params_parse_ex(int argc, const char ** argv, split_params & params) {
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std::string arg;
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const std::string arg_prefix = "--";
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bool invalid_param = false;
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int arg_idx = 1;
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for (; arg_idx < argc && strncmp(argv[arg_idx], "--", 2) == 0; arg_idx++) {
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arg = argv[arg_idx];
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if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
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std::replace(arg.begin(), arg.end(), '_', '-');
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}
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bool arg_found = false;
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if (arg == "-h" || arg == "--help") {
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split_print_usage(argv[0]);
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exit(0);
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}
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if (arg == "--version") {
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fprintf(stderr, "version: %d (%s)\n", LLAMA_BUILD_NUMBER, LLAMA_COMMIT);
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fprintf(stderr, "built with %s for %s\n", LLAMA_COMPILER, LLAMA_BUILD_TARGET);
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exit(0);
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}
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if (arg == "--merge") {
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arg_found = true;
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params.operation = SPLIT_OP_MERGE;
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}
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if (arg == "--split") {
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arg_found = true;
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params.operation = SPLIT_OP_SPLIT;
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}
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if (arg == "--split-max-tensors") {
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if (++arg_idx >= argc) {
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invalid_param = true;
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break;
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}
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arg_found = true;
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params.n_split_tensors = atoi(argv[arg_idx]);
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}
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if (!arg_found) {
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throw std::invalid_argument("error: unknown argument: " + arg);
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}
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}
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if (invalid_param) {
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throw std::invalid_argument("error: invalid parameter for argument: " + arg);
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}
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if (argc - arg_idx < 2) {
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printf("%s: bad arguments\n", argv[0]);
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split_print_usage(argv[0]);
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return false;
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}
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params.input = argv[arg_idx++];
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params.output = argv[arg_idx++];
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return true;
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}
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static bool split_params_parse(int argc, const char ** argv, split_params & params) {
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bool result = true;
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try {
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if (!split_params_parse_ex(argc, argv, params)) {
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split_print_usage(argv[0]);
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exit(1);
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}
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}
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catch (const std::invalid_argument & ex) {
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fprintf(stderr, "%s\n", ex.what());
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split_print_usage(argv[0]);
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exit(1);
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}
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return result;
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}
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static void zeros(std::ofstream & file, size_t n) {
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char zero = 0;
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for (size_t i = 0; i < n; ++i) {
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file.write(&zero, 1);
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}
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}
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static std::string split_file_name(const std::string & path, int i_split, int n_split) {
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char f_split[SPLIT_FILENAME_MAX] = {0};
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snprintf(f_split, sizeof(f_split), SPLIT_FILENAME_FORMAT, path.c_str(), i_split + 1, n_split);
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return std::string(f_split);
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}
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struct split_strategy {
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const split_params params;
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std::ifstream & f_input;
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struct gguf_context * ctx_gguf;
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struct ggml_context * ctx_meta = NULL;
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const int n_tensors;
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const int n_split;
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int i_split = 0;
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int i_tensor = 0;
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std::vector<uint8_t> read_data;
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struct gguf_context * ctx_out;
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std::ofstream fout;
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split_strategy(const split_params & params,
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std::ifstream & f_input,
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struct gguf_context * ctx_gguf,
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struct ggml_context * ctx_meta) :
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params(params),
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f_input(f_input),
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ctx_gguf(ctx_gguf),
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ctx_meta(ctx_meta),
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n_tensors(gguf_get_n_tensors(ctx_gguf)),
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n_split(std::ceil(1. * n_tensors / params.n_split_tensors)) {
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}
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bool should_split() const {
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return i_tensor < n_tensors && i_tensor % params.n_split_tensors == 0;
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}
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void split_start() {
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ctx_out = gguf_init_empty();
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// Save all metadata in first split only
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if (i_split == 0) {
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gguf_set_kv(ctx_out, ctx_gguf);
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}
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gguf_set_val_u8(ctx_out, LLM_KV_GENERAL_SPLIT_I_SPLIT, i_split);
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gguf_set_val_u8(ctx_out, LLM_KV_GENERAL_SPLIT_N_SPLIT, n_split);
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// populate the original tensors, so we get an initial metadata
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for (int i = i_split * params.n_split_tensors; i < n_tensors && i < (i_split + 1) * params.n_split_tensors; ++i) {
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struct ggml_tensor * meta = ggml_get_tensor(ctx_meta, gguf_get_tensor_name(ctx_gguf, i));
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gguf_add_tensor(ctx_out, meta);
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}
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auto split_name = split_file_name(params.output, i_split, n_split);
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fprintf(stderr, "%s: %s ...", __func__, split_name.c_str());
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fout = std::ofstream(split_name, std::ios::binary);
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fout.exceptions(std::ofstream::failbit); // fail fast on write errors
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auto meta_size = gguf_get_meta_size(ctx_out);
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// placeholder for the meta data
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::zeros(fout, meta_size);
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i_split++;
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}
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void next_tensor() {
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const char * t_name = gguf_get_tensor_name(ctx_gguf, i_tensor);
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struct ggml_tensor * t = ggml_get_tensor(ctx_meta, t_name);
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auto n_bytes = ggml_nbytes(t);
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if (read_data.size() < n_bytes) {
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read_data.resize(n_bytes);
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}
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auto offset = gguf_get_data_offset(ctx_gguf) + gguf_get_tensor_offset(ctx_gguf, i_tensor);
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f_input.seekg(offset);
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f_input.read((char *)read_data.data(), n_bytes);
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t->data = read_data.data();
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// write tensor data + padding
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fout.write((const char *)t->data, n_bytes);
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zeros(fout, GGML_PAD(n_bytes, GGUF_DEFAULT_ALIGNMENT) - n_bytes);
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i_tensor++;
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}
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void split_end() {
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// go back to beginning of file and write the updated metadata
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fout.seekp(0);
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std::vector<uint8_t> data(gguf_get_meta_size(ctx_out));
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gguf_get_meta_data(ctx_out, data.data());
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fout.write((const char *)data.data(), data.size());
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fout.close();
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gguf_free(ctx_out);
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fprintf(stderr, "\033[3Ddone\n");
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}
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};
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static void gguf_split(const split_params & split_params) {
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struct ggml_context * ctx_meta = NULL;
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struct gguf_init_params params = {
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/*.no_alloc = */ true,
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/*.ctx = */ &ctx_meta,
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};
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std::ifstream f_input(split_params.input.c_str(), std::ios::binary);
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if (!f_input.is_open()) {
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fprintf(stderr, "%s: failed to open input GGUF from %s\n", __func__, split_params.input.c_str());
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exit(1);
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}
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auto * ctx_gguf = gguf_init_from_file(split_params.input.c_str(), params);
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if (!ctx_gguf) {
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fprintf(stderr, "%s: failed to load input GGUF from %s\n", __func__, split_params.input.c_str());
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exit(1);
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}
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split_strategy strategy(split_params, f_input, ctx_gguf, ctx_meta);
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fprintf(stderr, "%s: %s -> %s (%d tensors per file)\n",
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__func__, split_params.input.c_str(),
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split_file_name(split_params.output, strategy.i_split, strategy.n_split).c_str(),
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split_params.n_split_tensors);
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strategy.split_start();
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|
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while (strategy.i_tensor < strategy.n_tensors) {
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strategy.next_tensor();
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if (strategy.should_split()) {
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strategy.split_end();
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strategy.split_start();
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}
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}
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strategy.split_end();
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gguf_free(ctx_gguf);
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f_input.close();
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fprintf(stderr, "%s: %d gguf split written with a total of %d tensors.\n",
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__func__, strategy.n_split, strategy.n_tensors);
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}
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static void gguf_merge(const split_params & split_params) {
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fprintf(stderr, "%s: %s -> %s\n",
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__func__, split_params.input.c_str(),
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split_params.output.c_str());
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int n_split = 1;
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int total_tensors = 0;
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auto * ctx_out = gguf_init_empty();
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std::ofstream fout(split_params.output.c_str(), std::ios::binary);
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fout.exceptions(std::ofstream::failbit); // fail fast on write errors
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|
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std::vector<uint8_t> read_data;
|
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std::vector<ggml_context *> ctx_metas;
|
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std::vector<gguf_context *> ctx_ggufs;
|
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|
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std::string split_prefix;
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|
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// First pass to find KV and tensors metadata
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for (int i_split = 0; i_split < n_split; i_split++) {
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struct ggml_context * ctx_meta = NULL;
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struct gguf_init_params params = {
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/*.no_alloc = */ true,
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/*.ctx = */ &ctx_meta,
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};
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auto split_name = split_params.input;
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if (i_split > 0) {
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split_name = split_file_name(split_prefix, i_split, n_split);
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}
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fprintf(stderr, "%s: reading metadata %s ...", __func__, split_name.c_str());
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auto * ctx_gguf = gguf_init_from_file(split_name.c_str(), params);
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if (!ctx_gguf) {
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fprintf(stderr, "\n%s: failed to load input GGUF from %s\n", __func__, split_params.input.c_str());
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exit(1);
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}
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ctx_ggufs.push_back(ctx_gguf);
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ctx_metas.push_back(ctx_meta);
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|
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if (i_split == 0) {
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auto key_n_split = gguf_find_key(ctx_gguf, LLM_KV_GENERAL_SPLIT_N_SPLIT);
|
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if (key_n_split < 0) {
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fprintf(stderr,
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"\n%s: input file does not contain %s metadata\n",
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__func__,
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LLM_KV_GENERAL_SPLIT_N_SPLIT);
|
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gguf_free(ctx_gguf);
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gguf_free(ctx_out);
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fout.close();
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exit(1);
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}
|
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|
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n_split = gguf_get_val_u8(ctx_gguf, key_n_split);
|
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if (n_split < 1) {
|
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fprintf(stderr,
|
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"\n%s: input file does not contain a valid split count %d\n",
|
||||
__func__,
|
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n_split);
|
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gguf_free(ctx_gguf);
|
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gguf_free(ctx_out);
|
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fout.close();
|
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exit(1);
|
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}
|
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|
||||
// Do not trigger merge if we try to merge again the output
|
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gguf_set_val_u8(ctx_out, LLM_KV_GENERAL_SPLIT_N_SPLIT, 0);
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|
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// Set metadata from the first split
|
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gguf_set_kv(ctx_out, ctx_gguf);
|
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}
|
||||
|
||||
// Verify the file naming
|
||||
{
|
||||
int i_split_file = 0;
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||||
int n_split_file = 0;
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const char * i_split_format = "-00000-of-00000.gguf";
|
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|
||||
if (split_name.size() < strlen(i_split_format)) {
|
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fprintf(stderr, "\n%s: unexpected input file name: %s\n", __func__, split_params.input.c_str());
|
||||
for (auto * _ctx_gguf : ctx_ggufs) {
|
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gguf_free(_ctx_gguf);
|
||||
}
|
||||
gguf_free(ctx_out);
|
||||
fout.close();
|
||||
exit(1);
|
||||
}
|
||||
|
||||
split_prefix = split_name.substr(0, split_name.size() - strlen(i_split_format));
|
||||
|
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const char * split_name_c_str = split_name.c_str();
|
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int n_part = sscanf(&split_name_c_str[0] + split_prefix.size(), "-%d-of-%d", &i_split_file, &n_split_file);
|
||||
|
||||
if (n_part != 2 || i_split_file - 1 != i_split || n_split_file != n_split) {
|
||||
fprintf(stderr, "\n%s: unexpected input file name: %s"
|
||||
" i_split=%d i_split_file=%d"
|
||||
" n_split=%d n_split_file=%d\n", __func__,
|
||||
split_params.input.c_str(),
|
||||
i_split, i_split_file,
|
||||
n_split, n_split_file);
|
||||
for (auto * _ctx_gguf : ctx_ggufs) {
|
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gguf_free(_ctx_gguf);
|
||||
}
|
||||
gguf_free(ctx_out);
|
||||
fout.close();
|
||||
exit(1);
|
||||
}
|
||||
}
|
||||
|
||||
auto n_tensors = gguf_get_n_tensors(ctx_gguf);
|
||||
for (int i_tensor = 0; i_tensor < n_tensors; i_tensor++) {
|
||||
const char * t_name = gguf_get_tensor_name(ctx_gguf, i_tensor);
|
||||
struct ggml_tensor * t = ggml_get_tensor(ctx_meta, t_name);
|
||||
gguf_add_tensor(ctx_out, t);
|
||||
}
|
||||
total_tensors += n_tensors;
|
||||
|
||||
fprintf(stderr, "\033[3Ddone\n");
|
||||
}
|
||||
|
||||
// placeholder for the meta data
|
||||
{
|
||||
auto meta_size = gguf_get_meta_size(ctx_out);
|
||||
::zeros(fout, meta_size);
|
||||
}
|
||||
|
||||
// Write tensors data
|
||||
for (int i_split = 0; i_split < n_split; i_split++) {
|
||||
auto split_name = split_file_name(split_prefix, i_split, n_split);
|
||||
std::ifstream f_input(split_name.c_str(), std::ios::binary);
|
||||
if (!f_input.is_open()) {
|
||||
fprintf(stderr, "%s: failed to open input GGUF from %s\n", __func__, split_name.c_str());
|
||||
for (auto * _ctx_gguf : ctx_ggufs) {
|
||||
gguf_free(_ctx_gguf);
|
||||
}
|
||||
gguf_free(ctx_out);
|
||||
fout.close();
|
||||
exit(1);
|
||||
}
|
||||
fprintf(stderr, "%s: writing tensors %s ...", __func__, split_name.c_str());
|
||||
|
||||
auto * ctx_gguf = ctx_ggufs[i_split];
|
||||
auto * ctx_meta = ctx_metas[i_split];
|
||||
|
||||
auto n_tensors = gguf_get_n_tensors(ctx_gguf);
|
||||
for (int i_tensor = 0; i_tensor < n_tensors; i_tensor++) {
|
||||
const char * t_name = gguf_get_tensor_name(ctx_gguf, i_tensor);
|
||||
struct ggml_tensor * t = ggml_get_tensor(ctx_meta, t_name);
|
||||
|
||||
auto n_bytes = ggml_nbytes(t);
|
||||
|
||||
if (read_data.size() < n_bytes) {
|
||||
read_data.resize(n_bytes);
|
||||
}
|
||||
|
||||
auto offset = gguf_get_data_offset(ctx_gguf) + gguf_get_tensor_offset(ctx_gguf, i_tensor);
|
||||
f_input.seekg(offset);
|
||||
f_input.read((char *)read_data.data(), n_bytes);
|
||||
|
||||
// write tensor data + padding
|
||||
fout.write((const char *)read_data.data(), n_bytes);
|
||||
zeros(fout, GGML_PAD(n_bytes, GGUF_DEFAULT_ALIGNMENT) - n_bytes);
|
||||
}
|
||||
|
||||
gguf_free(ctx_gguf);
|
||||
ggml_free(ctx_meta);
|
||||
f_input.close();
|
||||
fprintf(stderr, "\033[3Ddone\n");
|
||||
}
|
||||
|
||||
{
|
||||
// go back to beginning of file and write the updated metadata
|
||||
fout.seekp(0);
|
||||
std::vector<uint8_t> data(gguf_get_meta_size(ctx_out));
|
||||
gguf_get_meta_data(ctx_out, data.data());
|
||||
fout.write((const char *)data.data(), data.size());
|
||||
|
||||
fout.close();
|
||||
gguf_free(ctx_out);
|
||||
}
|
||||
|
||||
fprintf(stderr, "%s: %s merged from %d split with %d tensors.\n",
|
||||
__func__, split_params.output.c_str(), n_split, total_tensors);
|
||||
}
|
||||
|
||||
int main(int argc, const char ** argv) {
|
||||
if (argc < 3) {
|
||||
split_print_usage(argv[0]);
|
||||
}
|
||||
|
||||
split_params params;
|
||||
split_params_parse(argc, argv, params);
|
||||
|
||||
switch (params.operation) {
|
||||
case SPLIT_OP_SPLIT: gguf_split(params);
|
||||
break;
|
||||
case SPLIT_OP_MERGE: gguf_merge(params);
|
||||
break;
|
||||
default:split_print_usage(argv[0]);
|
||||
exit(1);
|
||||
}
|
||||
|
||||
return 0;
|
||||
}
|
||||
File diff suppressed because it is too large
Load diff
File diff suppressed because it is too large
Load diff
|
|
@ -5,15 +5,14 @@ import sys
|
|||
import time
|
||||
import traceback
|
||||
from contextlib import closing
|
||||
|
||||
import psutil
|
||||
from subprocess import TimeoutExpired
|
||||
|
||||
|
||||
def before_scenario(context, scenario):
|
||||
context.debug = 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON'
|
||||
if context.debug:
|
||||
print("DEBUG=ON\n")
|
||||
print(f"\x1b[33;42mStarting new scenario: {scenario.name}!\x1b[0m\n")
|
||||
print("DEBUG=ON")
|
||||
print(f"\x1b[33;42mStarting new scenario: {scenario.name}!\x1b[0m")
|
||||
port = 8080
|
||||
if 'PORT' in os.environ:
|
||||
port = int(os.environ['PORT'])
|
||||
|
|
@ -27,60 +26,40 @@ def after_scenario(context, scenario):
|
|||
return
|
||||
if scenario.status == "failed":
|
||||
if 'GITHUB_ACTIONS' in os.environ:
|
||||
print(f"\x1b[33;101mSCENARIO FAILED: {scenario.name} server logs:\x1b[0m\n\n")
|
||||
print(f"\x1b[33;101mSCENARIO FAILED: {scenario.name} server logs:\x1b[0m\n")
|
||||
if os.path.isfile('llama.log'):
|
||||
with closing(open('llama.log', 'r')) as f:
|
||||
for line in f:
|
||||
print(line)
|
||||
if not is_server_listening(context.server_fqdn, context.server_port):
|
||||
print("\x1b[33;101mERROR: Server stopped listening\x1b[0m\n")
|
||||
print("\x1b[33;101mERROR: Server stopped listening\x1b[0m")
|
||||
|
||||
if not pid_exists(context.server_process.pid):
|
||||
if context.server_process.poll() is not None:
|
||||
assert False, f"Server not running pid={context.server_process.pid} ..."
|
||||
|
||||
server_graceful_shutdown(context)
|
||||
server_graceful_shutdown(context) # SIGINT
|
||||
|
||||
# Wait few for socket to free up
|
||||
time.sleep(0.05)
|
||||
try:
|
||||
context.server_process.wait(0.5)
|
||||
except TimeoutExpired:
|
||||
print(f"server still alive after 500ms, force-killing pid={context.server_process.pid} ...")
|
||||
context.server_process.kill() # SIGKILL
|
||||
context.server_process.wait()
|
||||
|
||||
attempts = 0
|
||||
while pid_exists(context.server_process.pid) or is_server_listening(context.server_fqdn, context.server_port):
|
||||
server_kill(context)
|
||||
while is_server_listening(context.server_fqdn, context.server_port):
|
||||
time.sleep(0.1)
|
||||
attempts += 1
|
||||
if attempts > 5:
|
||||
server_kill_hard(context)
|
||||
except:
|
||||
exc = sys.exception()
|
||||
print("error in after scenario: \n")
|
||||
print(exc)
|
||||
print("*** print_tb: \n")
|
||||
traceback.print_tb(exc.__traceback__, file=sys.stdout)
|
||||
except Exception:
|
||||
print("ignoring error in after_scenario:")
|
||||
traceback.print_exc(file=sys.stdout)
|
||||
|
||||
|
||||
def server_graceful_shutdown(context):
|
||||
print(f"shutting down server pid={context.server_process.pid} ...\n")
|
||||
print(f"shutting down server pid={context.server_process.pid} ...")
|
||||
if os.name == 'nt':
|
||||
os.kill(context.server_process.pid, signal.CTRL_C_EVENT)
|
||||
interrupt = signal.CTRL_C_EVENT
|
||||
else:
|
||||
os.kill(context.server_process.pid, signal.SIGINT)
|
||||
|
||||
|
||||
def server_kill(context):
|
||||
print(f"killing server pid={context.server_process.pid} ...\n")
|
||||
context.server_process.kill()
|
||||
|
||||
|
||||
def server_kill_hard(context):
|
||||
pid = context.server_process.pid
|
||||
path = context.server_path
|
||||
|
||||
print(f"Server dangling exits, hard killing force {pid}={path}...\n")
|
||||
try:
|
||||
psutil.Process(pid).kill()
|
||||
except psutil.NoSuchProcess:
|
||||
return False
|
||||
return True
|
||||
interrupt = signal.SIGINT
|
||||
context.server_process.send_signal(interrupt)
|
||||
|
||||
|
||||
def is_server_listening(server_fqdn, server_port):
|
||||
|
|
@ -88,14 +67,5 @@ def is_server_listening(server_fqdn, server_port):
|
|||
result = sock.connect_ex((server_fqdn, server_port))
|
||||
_is_server_listening = result == 0
|
||||
if _is_server_listening:
|
||||
print(f"server is listening on {server_fqdn}:{server_port}...\n")
|
||||
print(f"server is listening on {server_fqdn}:{server_port}...")
|
||||
return _is_server_listening
|
||||
|
||||
|
||||
def pid_exists(pid):
|
||||
try:
|
||||
psutil.Process(pid)
|
||||
except psutil.NoSuchProcess:
|
||||
return False
|
||||
return True
|
||||
|
||||
|
|
|
|||
|
|
@ -35,9 +35,9 @@ Feature: llama.cpp server
|
|||
And metric llamacpp:tokens_predicted is <n_predicted>
|
||||
|
||||
Examples: Prompts
|
||||
| prompt | n_predict | re_content | n_prompt | n_predicted | truncated |
|
||||
| I believe the meaning of life is | 8 | (read\|going)+ | 18 | 8 | not |
|
||||
| Write a joke about AI from a very long prompt which will not be truncated | 256 | (princesses\|everyone\|kids)+ | 46 | 64 | not |
|
||||
| prompt | n_predict | re_content | n_prompt | n_predicted | truncated |
|
||||
| I believe the meaning of life is | 8 | (read\|going)+ | 18 | 8 | not |
|
||||
| Write a joke about AI from a very long prompt which will not be truncated | 256 | (princesses\|everyone\|kids\|Anna\|forest)+ | 46 | 64 | not |
|
||||
|
||||
Scenario: Completion prompt truncated
|
||||
Given a prompt:
|
||||
|
|
@ -48,7 +48,7 @@ Feature: llama.cpp server
|
|||
Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
|
||||
"""
|
||||
And a completion request with no api error
|
||||
Then 64 tokens are predicted matching fun|Annaks|popcorns|pictry
|
||||
Then 64 tokens are predicted matching fun|Annaks|popcorns|pictry|bowl
|
||||
And the completion is truncated
|
||||
And 109 prompt tokens are processed
|
||||
|
||||
|
|
@ -65,9 +65,9 @@ Feature: llama.cpp server
|
|||
And the completion is <truncated> truncated
|
||||
|
||||
Examples: Prompts
|
||||
| model | system_prompt | user_prompt | max_tokens | re_content | n_prompt | n_predicted | enable_streaming | truncated |
|
||||
| llama-2 | Book | What is the best book | 8 | (Here\|what)+ | 77 | 8 | disabled | not |
|
||||
| codellama70b | You are a coding assistant. | Write the fibonacci function in c++. | 128 | (thanks\|happy\|bird)+ | -1 | 64 | enabled | |
|
||||
| model | system_prompt | user_prompt | max_tokens | re_content | n_prompt | n_predicted | enable_streaming | truncated |
|
||||
| llama-2 | Book | What is the best book | 8 | (Here\|what)+ | 77 | 8 | disabled | not |
|
||||
| codellama70b | You are a coding assistant. | Write the fibonacci function in c++. | 128 | (thanks\|happy\|bird\|Annabyear)+ | -1 | 64 | enabled | |
|
||||
|
||||
|
||||
Scenario: Tokenize / Detokenize
|
||||
|
|
|
|||
|
|
@ -24,12 +24,16 @@ from prometheus_client import parser
|
|||
def step_server_config(context, server_fqdn, server_port):
|
||||
context.server_fqdn = server_fqdn
|
||||
context.server_port = int(server_port)
|
||||
context.n_gpu_layer = None
|
||||
if 'PORT' in os.environ:
|
||||
context.server_port = int(os.environ['PORT'])
|
||||
print(f"$PORT set, overriding server port with to {context.server_port}")
|
||||
if 'FQDN' in os.environ:
|
||||
context.server_fqdn = os.environ['FQDN']
|
||||
print(f"$FQDN set, overriding server fqdn with to {context.server_fqdn}")
|
||||
if 'N_GPU_LAYERS' in os.environ:
|
||||
context.n_gpu_layer = int(os.environ['N_GPU_LAYERS'])
|
||||
print(f"$N_GPU_LAYERS set, overriding n_gpu_layer with to {context.n_gpu_layer}")
|
||||
|
||||
context.base_url = f'http://{context.server_fqdn}:{context.server_port}'
|
||||
|
||||
|
|
@ -41,7 +45,6 @@ def step_server_config(context, server_fqdn, server_port):
|
|||
context.n_ctx = None
|
||||
context.n_ga = None
|
||||
context.n_ga_w = None
|
||||
context.n_gpu_layer = None
|
||||
context.n_predict = None
|
||||
context.n_prompts = 0
|
||||
context.n_server_predict = None
|
||||
|
|
@ -66,7 +69,7 @@ def step_server_config(context, server_fqdn, server_port):
|
|||
def step_download_hf_model(context, hf_file, hf_repo):
|
||||
context.model_file = hf_hub_download(repo_id=hf_repo, filename=hf_file)
|
||||
if context.debug:
|
||||
print(f"model file: {context.model_file}\n")
|
||||
print(f"model file: {context.model_file}")
|
||||
|
||||
|
||||
@step('a model file {model_file}')
|
||||
|
|
@ -137,9 +140,12 @@ def step_start_server(context):
|
|||
if 'GITHUB_ACTIONS' in os.environ:
|
||||
max_attempts *= 2
|
||||
|
||||
addrs = socket.getaddrinfo(context.server_fqdn, context.server_port, type=socket.SOCK_STREAM)
|
||||
family, typ, proto, _, sockaddr = addrs[0]
|
||||
|
||||
while True:
|
||||
with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock:
|
||||
result = sock.connect_ex((context.server_fqdn, context.server_port))
|
||||
with closing(socket.socket(family, typ, proto)) as sock:
|
||||
result = sock.connect_ex(sockaddr)
|
||||
if result == 0:
|
||||
print("\x1b[33;46mserver started!\x1b[0m")
|
||||
return
|
||||
|
|
@ -209,7 +215,7 @@ async def step_request_completion(context, api_error):
|
|||
user_api_key=context.user_api_key)
|
||||
context.tasks_result.append(completion)
|
||||
if context.debug:
|
||||
print(f"Completion response: {completion}\n")
|
||||
print(f"Completion response: {completion}")
|
||||
if expect_api_error:
|
||||
assert completion == 401, f"completion must be an 401 status code: {completion}"
|
||||
|
||||
|
|
@ -354,7 +360,7 @@ def step_prompt_passkey(context, passkey, i_pos):
|
|||
prompt += context.prompt_junk_suffix
|
||||
if context.debug:
|
||||
passkey_highlight = "\x1b[33m" + passkey + "\x1b[0m"
|
||||
print(f"Passkey challenge:\n```{prompt.replace(passkey, passkey_highlight)}```\n")
|
||||
print(f"Passkey challenge:\n```{prompt.replace(passkey, passkey_highlight)}```")
|
||||
context.prompts.append(context.prompt_prefix + prompt + context.prompt_suffix)
|
||||
context.n_prompts = len(context.prompts)
|
||||
|
||||
|
|
@ -363,7 +369,7 @@ def step_prompt_passkey(context, passkey, i_pos):
|
|||
@async_run_until_complete
|
||||
async def step_oai_chat_completions(context, api_error):
|
||||
if context.debug:
|
||||
print(f"Submitting OAI compatible completions request...\n")
|
||||
print(f"Submitting OAI compatible completions request...")
|
||||
expect_api_error = api_error == 'raised'
|
||||
completion = await oai_chat_completions(context.prompts.pop(),
|
||||
context.system_prompt,
|
||||
|
|
@ -508,12 +514,12 @@ async def step_all_embeddings_are_the_same(context):
|
|||
embedding1 = np.array(embeddings[i])
|
||||
embedding2 = np.array(embeddings[j])
|
||||
if context.debug:
|
||||
print(f"embedding1: {embedding1[-8:]}\n")
|
||||
print(f"embedding2: {embedding2[-8:]}\n")
|
||||
print(f"embedding1: {embedding1[-8:]}")
|
||||
print(f"embedding2: {embedding2[-8:]}")
|
||||
similarity = np.dot(embedding1, embedding2) / (np.linalg.norm(embedding1) * np.linalg.norm(embedding2))
|
||||
msg = f"Similarity between {i} and {j}: {similarity:.10f}"
|
||||
if context.debug:
|
||||
print(f"{msg}\n")
|
||||
print(f"{msg}")
|
||||
assert np.isclose(similarity, 1.0, rtol=1e-05, atol=1e-08, equal_nan=False), msg
|
||||
|
||||
|
||||
|
|
@ -630,7 +636,7 @@ async def step_prometheus_metrics_exported(context):
|
|||
metrics_raw = await metrics_response.text()
|
||||
metric_exported = False
|
||||
if context.debug:
|
||||
print(f"/metrics answer:\n{metrics_raw}\n")
|
||||
print(f"/metrics answer:\n{metrics_raw}")
|
||||
context.metrics = {}
|
||||
for metric in parser.text_string_to_metric_families(metrics_raw):
|
||||
match metric.name:
|
||||
|
|
@ -932,7 +938,7 @@ def assert_n_tokens_predicted(completion_response, expected_predicted_n=None, re
|
|||
last_match = end
|
||||
highlighted += content[last_match:]
|
||||
if 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON':
|
||||
print(f"Checking completion response: {highlighted}\n")
|
||||
print(f"Checking completion response: {highlighted}")
|
||||
assert last_match > 0, f'/{re_content}/ must match ```{highlighted}```'
|
||||
if expected_predicted_n and expected_predicted_n > 0:
|
||||
assert n_predicted == expected_predicted_n, (f'invalid number of tokens predicted:'
|
||||
|
|
@ -942,7 +948,7 @@ def assert_n_tokens_predicted(completion_response, expected_predicted_n=None, re
|
|||
async def gather_tasks_results(context):
|
||||
n_tasks = len(context.concurrent_tasks)
|
||||
if context.debug:
|
||||
print(f"Waiting for all {n_tasks} tasks results...\n")
|
||||
print(f"Waiting for all {n_tasks} tasks results...")
|
||||
for task_no in range(n_tasks):
|
||||
context.tasks_result.append(await context.concurrent_tasks.pop())
|
||||
n_completions = len(context.tasks_result)
|
||||
|
|
@ -959,7 +965,7 @@ async def wait_for_health_status(context,
|
|||
slots_processing=None,
|
||||
expected_slots=None):
|
||||
if context.debug:
|
||||
print(f"Starting checking for health for expected_health_status={expected_health_status}\n")
|
||||
print(f"Starting checking for health for expected_health_status={expected_health_status}")
|
||||
interval = 0.5
|
||||
counter = 0
|
||||
if 'GITHUB_ACTIONS' in os.environ:
|
||||
|
|
@ -1048,8 +1054,6 @@ def start_server_background(context):
|
|||
if 'LLAMA_SERVER_BIN_PATH' in os.environ:
|
||||
context.server_path = os.environ['LLAMA_SERVER_BIN_PATH']
|
||||
server_listen_addr = context.server_fqdn
|
||||
if os.name == 'nt':
|
||||
server_listen_addr = '0.0.0.0'
|
||||
server_args = [
|
||||
'--host', server_listen_addr,
|
||||
'--port', context.server_port,
|
||||
|
|
@ -1088,7 +1092,7 @@ def start_server_background(context):
|
|||
server_args.append('--verbose')
|
||||
if 'SERVER_LOG_FORMAT_JSON' not in os.environ:
|
||||
server_args.extend(['--log-format', "text"])
|
||||
print(f"starting server with: {context.server_path} {server_args}\n")
|
||||
print(f"starting server with: {context.server_path} {server_args}")
|
||||
flags = 0
|
||||
if 'nt' == os.name:
|
||||
flags |= subprocess.DETACHED_PROCESS
|
||||
|
|
|
|||
|
|
@ -3,5 +3,4 @@ behave~=1.2.6
|
|||
huggingface_hub~=0.20.3
|
||||
numpy~=1.24.4
|
||||
openai~=0.25.0
|
||||
psutil~=5.9.8
|
||||
prometheus-client~=0.20.0
|
||||
|
|
|
|||
|
|
@ -371,6 +371,7 @@ static json oaicompat_completion_params_parse(
|
|||
llama_params["repeat_last_n"] = json_value(body, "repeat_last_n", default_sparams.penalty_last_n);
|
||||
llama_params["ignore_eos"] = json_value(body, "ignore_eos", false);
|
||||
llama_params["tfs_z"] = json_value(body, "tfs_z", default_sparams.tfs_z);
|
||||
llama_params["n_keep"] = json_value(body, "n_keep", 0);
|
||||
|
||||
if (body.count("grammar") != 0) {
|
||||
llama_params["grammar"] = json_value(body, "grammar", json::object());
|
||||
|
|
|
|||
|
|
@ -6,8 +6,6 @@ set INPUT2="Building a website can be done in 10 simple steps:\nStep 1:"
|
|||
@call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force
|
||||
|
||||
|
||||
set GGML_SYCL_DEVICE=0
|
||||
rem set GGML_SYCL_DEBUG=1
|
||||
.\build\bin\main.exe -m models\llama-2-7b.Q4_0.gguf -p %INPUT2% -n 400 -e -ngl 33 -s 0
|
||||
|
||||
|
||||
|
|
|
|||
3526
ggml-cuda.cu
3526
ggml-cuda.cu
File diff suppressed because it is too large
Load diff
|
|
@ -13,7 +13,7 @@
|
|||
extern "C" {
|
||||
#endif
|
||||
|
||||
#define GGML_SYCL_MAX_DEVICES 16
|
||||
#define GGML_SYCL_MAX_DEVICES 48
|
||||
#define GGML_SYCL_NAME "SYCL"
|
||||
|
||||
GGML_API void ggml_init_sycl(void);
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue