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Merge branch 'master' into concedo_experimental

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# Conflicts:
#	.gitignore
#	CMakeLists.txt
#	Makefile
#	README.md
#	tests/test-grad0.cpp
This commit is contained in:
Concedo 2023-08-30 10:51:28 +08:00
commit f2c02dd06d
33 changed files with 3000 additions and 2837 deletions
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44
.devops/lamma-cpp-clblast.srpm.spec → .devops/llama-cpp-clblast.srpm.spec
View file

@ -13,12 +13,13 @@
# It is up to the user to install the correct vendor-specific support. # It is up to the user to install the correct vendor-specific support.
Name: llama.cpp-clblast Name: llama.cpp-clblast
Version: master Version: %( date "+%%Y%%m%%d" )
Release: 1%{?dist} Release: 1%{?dist}
Summary: OpenCL Inference of LLaMA model in pure C/C++ Summary: OpenCL Inference of LLaMA model in C/C++
License: MIT License: MIT
Source0: https://github.com/ggerganov/llama.cpp/archive/refs/heads/master.tar.gz Source0: https://github.com/ggerganov/llama.cpp/archive/refs/heads/master.tar.gz
BuildRequires: coreutils make gcc-c++ git mesa-libOpenCL-devel BuildRequires: coreutils make gcc-c++ git mesa-libOpenCL-devel clblast-devel
Requires: clblast
URL: https://github.com/ggerganov/llama.cpp URL: https://github.com/ggerganov/llama.cpp
%define debug_package %{nil} %define debug_package %{nil}
@ -35,18 +36,43 @@ make -j LLAMA_CLBLAST=1
%install %install
mkdir -p %{buildroot}%{_bindir}/ mkdir -p %{buildroot}%{_bindir}/
cp -p main %{buildroot}%{_bindir}/llamacppclblast cp -p main %{buildroot}%{_bindir}/llamaclblast
cp -p server %{buildroot}%{_bindir}/llamacppclblastserver cp -p server %{buildroot}%{_bindir}/llamaclblastserver
cp -p simple %{buildroot}%{_bindir}/llamacppclblastsimple cp -p simple %{buildroot}%{_bindir}/llamaclblastsimple
mkdir -p %{buildroot}/usr/lib/systemd/system
%{__cat} <<EOF > %{buildroot}/usr/lib/systemd/system/llamaclblast.service
[Unit]
Description=Llama.cpp server, CPU only (no GPU support in this build).
After=syslog.target network.target local-fs.target remote-fs.target nss-lookup.target
[Service]
Type=simple
EnvironmentFile=/etc/sysconfig/llama
ExecStart=/usr/bin/llamaclblastserver $LLAMA_ARGS
ExecReload=/bin/kill -s HUP $MAINPID
Restart=never
[Install]
WantedBy=default.target
EOF
mkdir -p %{buildroot}/etc/sysconfig
%{__cat} <<EOF > %{buildroot}/etc/sysconfig/llama
LLAMA_ARGS="-m /opt/llama2/ggml-model-f32.bin"
EOF
%clean %clean
rm -rf %{buildroot} rm -rf %{buildroot}
rm -rf %{_builddir}/* rm -rf %{_builddir}/*
%files %files
%{_bindir}/llamacppclblast %{_bindir}/llamaclblast
%{_bindir}/llamacppclblastserver %{_bindir}/llamaclblastserver
%{_bindir}/llamacppclblastsimple %{_bindir}/llamaclblastsimple
/usr/lib/systemd/system/llamaclblast.service
%config /etc/sysconfig/llama
%pre %pre

26
.devops/lamma-cpp-cublas.srpm.spec → .devops/llama-cpp-cublas.srpm.spec
View file

@ -13,7 +13,7 @@
# It is up to the user to install the correct vendor-specific support. # It is up to the user to install the correct vendor-specific support.
Name: llama.cpp-cublas Name: llama.cpp-cublas
Version: master Version: %( date "+%%Y%%m%%d" )
Release: 1%{?dist} Release: 1%{?dist}
Summary: CPU Inference of LLaMA model in pure C/C++ (no CUDA/OpenCL) Summary: CPU Inference of LLaMA model in pure C/C++ (no CUDA/OpenCL)
License: MIT License: MIT
@ -40,6 +40,28 @@ cp -p main %{buildroot}%{_bindir}/llamacppcublas
cp -p server %{buildroot}%{_bindir}/llamacppcublasserver cp -p server %{buildroot}%{_bindir}/llamacppcublasserver
cp -p simple %{buildroot}%{_bindir}/llamacppcublassimple cp -p simple %{buildroot}%{_bindir}/llamacppcublassimple
mkdir -p %{buildroot}/usr/lib/systemd/system
%{__cat} <<EOF > %{buildroot}/usr/lib/systemd/system/llamacublas.service
[Unit]
Description=Llama.cpp server, CPU only (no GPU support in this build).
After=syslog.target network.target local-fs.target remote-fs.target nss-lookup.target
[Service]
Type=simple
EnvironmentFile=/etc/sysconfig/llama
ExecStart=/usr/bin/llamacppcublasserver $LLAMA_ARGS
ExecReload=/bin/kill -s HUP $MAINPID
Restart=never
[Install]
WantedBy=default.target
EOF
mkdir -p %{buildroot}/etc/sysconfig
%{__cat} <<EOF > %{buildroot}/etc/sysconfig/llama
LLAMA_ARGS="-m /opt/llama2/ggml-model-f32.bin"
EOF
%clean %clean
rm -rf %{buildroot} rm -rf %{buildroot}
rm -rf %{_builddir}/* rm -rf %{_builddir}/*
@ -48,6 +70,8 @@ rm -rf %{_builddir}/*
%{_bindir}/llamacppcublas %{_bindir}/llamacppcublas
%{_bindir}/llamacppcublasserver %{_bindir}/llamacppcublasserver
%{_bindir}/llamacppcublassimple %{_bindir}/llamacppcublassimple
/usr/lib/systemd/system/llamacublas.service
%config /etc/sysconfig/llama
%pre %pre

45
.devops/llama-cpp.srpm.spec
View file

@ -6,6 +6,7 @@
# Notes for llama.cpp: # Notes for llama.cpp:
# 1. Tags are currently based on hash - which will not sort asciibetically. # 1. Tags are currently based on hash - which will not sort asciibetically.
# We need to declare standard versioning if people want to sort latest releases. # We need to declare standard versioning if people want to sort latest releases.
# In the meantime, YYYYMMDD format will be used.
# 2. Builds for CUDA/OpenCL support are separate, with different depenedencies. # 2. Builds for CUDA/OpenCL support are separate, with different depenedencies.
# 3. NVidia's developer repo must be enabled with nvcc, cublas, clblas, etc installed. # 3. NVidia's developer repo must be enabled with nvcc, cublas, clblas, etc installed.
# Example: https://developer.download.nvidia.com/compute/cuda/repos/fedora37/x86_64/cuda-fedora37.repo # Example: https://developer.download.nvidia.com/compute/cuda/repos/fedora37/x86_64/cuda-fedora37.repo
@ -13,12 +14,13 @@
# It is up to the user to install the correct vendor-specific support. # It is up to the user to install the correct vendor-specific support.
Name: llama.cpp Name: llama.cpp
Version: master Version: %( date "+%%Y%%m%%d" )
Release: 1%{?dist} Release: 1%{?dist}
Summary: CPU Inference of LLaMA model in pure C/C++ (no CUDA/OpenCL) Summary: CPU Inference of LLaMA model in pure C/C++ (no CUDA/OpenCL)
License: MIT License: MIT
Source0: https://github.com/ggerganov/llama.cpp/archive/refs/heads/master.tar.gz Source0: https://github.com/ggerganov/llama.cpp/archive/refs/heads/master.tar.gz
BuildRequires: coreutils make gcc-c++ git BuildRequires: coreutils make gcc-c++ git libstdc++-devel
Requires: libstdc++
URL: https://github.com/ggerganov/llama.cpp URL: https://github.com/ggerganov/llama.cpp
%define debug_package %{nil} %define debug_package %{nil}
@ -26,27 +28,52 @@ URL: https://github.com/ggerganov/llama.cpp
%description %description
CPU inference for Meta's Lllama2 models using default options. CPU inference for Meta's Lllama2 models using default options.
Models are not included in this package and must be downloaded separately.
%prep %prep
%autosetup %setup -n llama.cpp-master
%build %build
make -j make -j
%install %install
mkdir -p %{buildroot}%{_bindir}/ mkdir -p %{buildroot}%{_bindir}/
cp -p main %{buildroot}%{_bindir}/llamacpp cp -p main %{buildroot}%{_bindir}/llama
cp -p server %{buildroot}%{_bindir}/llamacppserver cp -p server %{buildroot}%{_bindir}/llamaserver
cp -p simple %{buildroot}%{_bindir}/llamacppsimple cp -p simple %{buildroot}%{_bindir}/llamasimple
mkdir -p %{buildroot}/usr/lib/systemd/system
%{__cat} <<EOF > %{buildroot}/usr/lib/systemd/system/llama.service
[Unit]
Description=Llama.cpp server, CPU only (no GPU support in this build).
After=syslog.target network.target local-fs.target remote-fs.target nss-lookup.target
[Service]
Type=simple
EnvironmentFile=/etc/sysconfig/llama
ExecStart=/usr/bin/llamaserver $LLAMA_ARGS
ExecReload=/bin/kill -s HUP $MAINPID
Restart=never
[Install]
WantedBy=default.target
EOF
mkdir -p %{buildroot}/etc/sysconfig
%{__cat} <<EOF > %{buildroot}/etc/sysconfig/llama
LLAMA_ARGS="-m /opt/llama2/ggml-model-f32.bin"
EOF
%clean %clean
rm -rf %{buildroot} rm -rf %{buildroot}
rm -rf %{_builddir}/* rm -rf %{_builddir}/*
%files %files
%{_bindir}/llamacpp %{_bindir}/llama
%{_bindir}/llamacppserver %{_bindir}/llamaserver
%{_bindir}/llamacppsimple %{_bindir}/llamasimple
/usr/lib/systemd/system/llama.service
%config /etc/sysconfig/llama
%pre %pre

332
common/common.cpp
View file

@ -1,15 +1,21 @@
#include "common.h" #include "common.h"
#include "build-info.h"
#include "llama.h"
#include <cassert>
#include <iostream>
#include <cstring>
#include <fstream>
#include <string>
#include <iterator>
#include <algorithm> #include <algorithm>
#include <sstream> #include <cassert>
#include <unordered_set> #include <cmath>
#include <cstring>
#include <ctime>
#include <fstream>
#include <iterator>
#include <iostream>
#include <regex> #include <regex>
#include <sstream>
#include <string>
#include <unordered_set>
#include <vector>
#include <cinttypes>
#if defined(__APPLE__) && defined(__MACH__) #if defined(__APPLE__) && defined(__MACH__)
#include <sys/types.h> #include <sys/types.h>
@ -19,11 +25,14 @@
#if defined(_WIN32) #if defined(_WIN32)
#define WIN32_LEAN_AND_MEAN #define WIN32_LEAN_AND_MEAN
#define NOMINMAX #define NOMINMAX
#include <codecvt>
#include <locale>
#include <windows.h> #include <windows.h>
#include <fcntl.h> #include <fcntl.h>
#include <io.h> #include <io.h>
#else #else
#include <sys/ioctl.h> #include <sys/ioctl.h>
#include <sys/stat.h>
#include <unistd.h> #include <unistd.h>
#endif #endif
@ -93,7 +102,6 @@ void process_escapes(std::string& input) {
bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
bool invalid_param = false; bool invalid_param = false;
bool escape_prompt = false;
std::string arg; std::string arg;
gpt_params default_params; gpt_params default_params;
const std::string arg_prefix = "--"; const std::string arg_prefix = "--";
@ -125,8 +133,8 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
break; break;
} }
params.prompt = argv[i]; params.prompt = argv[i];
} else if (arg == "-e") { } else if (arg == "-e" || arg == "--escape") {
escape_prompt = true; params.escape = true;
} else if (arg == "--prompt-cache") { } else if (arg == "--prompt-cache") {
if (++i >= argc) { if (++i >= argc) {
invalid_param = true; invalid_param = true;
@ -415,6 +423,16 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
break; break;
} }
params.antiprompt.push_back(argv[i]); params.antiprompt.push_back(argv[i]);
} else if (arg == "-ld" || arg == "--logdir") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.logdir = argv[i];
if (params.logdir.back() != DIRECTORY_SEPARATOR) {
params.logdir += DIRECTORY_SEPARATOR;
}
} else if (arg == "--perplexity") { } else if (arg == "--perplexity") {
params.perplexity = true; params.perplexity = true;
} else if (arg == "--ppl-stride") { } else if (arg == "--ppl-stride") {
@ -520,7 +538,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
exit(1); exit(1);
} }
if (escape_prompt) { if (params.escape) {
process_escapes(params.prompt); process_escapes(params.prompt);
process_escapes(params.input_prefix); process_escapes(params.input_prefix);
process_escapes(params.input_suffix); process_escapes(params.input_suffix);
@ -546,7 +564,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
fprintf(stdout, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads); fprintf(stdout, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads);
fprintf(stdout, " -p PROMPT, --prompt PROMPT\n"); fprintf(stdout, " -p PROMPT, --prompt PROMPT\n");
fprintf(stdout, " prompt to start generation with (default: empty)\n"); fprintf(stdout, " prompt to start generation with (default: empty)\n");
fprintf(stdout, " -e process prompt escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\)\n"); fprintf(stdout, " -e, --escape process prompt escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\)\n");
fprintf(stdout, " --prompt-cache FNAME file to cache prompt state for faster startup (default: none)\n"); fprintf(stdout, " --prompt-cache FNAME file to cache prompt state for faster startup (default: none)\n");
fprintf(stdout, " --prompt-cache-all if specified, saves user input and generations to cache as well.\n"); fprintf(stdout, " --prompt-cache-all if specified, saves user input and generations to cache as well.\n");
fprintf(stdout, " not supported with --interactive or other interactive options\n"); fprintf(stdout, " not supported with --interactive or other interactive options\n");
@ -627,6 +645,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
fprintf(stdout, " --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n"); fprintf(stdout, " --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n");
fprintf(stdout, " -m FNAME, --model FNAME\n"); fprintf(stdout, " -m FNAME, --model FNAME\n");
fprintf(stdout, " model path (default: %s)\n", params.model.c_str()); fprintf(stdout, " model path (default: %s)\n", params.model.c_str());
fprintf(stdout, " -ld LOGDIR, --logdir LOGDIR\n");
fprintf(stdout, " path under which to save YAML logs (no logging if unset)\n");
fprintf(stdout, "\n"); fprintf(stdout, "\n");
} }
@ -779,3 +799,289 @@ std::string llama_detokenize_bpe(llama_context * ctx, const std::vector<llama_to
return result; return result;
} }
// returns true if successful, false otherwise
bool create_directory_with_parents(const std::string & path) {
#ifdef _WIN32
std::wstring_convert<std::codecvt_utf8<wchar_t>> converter;
std::wstring wpath = converter.from_bytes(path);
// if the path already exists, check whether it's a directory
const DWORD attributes = GetFileAttributesW(wpath.c_str());
if ((attributes != INVALID_FILE_ATTRIBUTES) && (attributes & FILE_ATTRIBUTE_DIRECTORY)) {
return true;
}
size_t pos_slash = 0;
// process path from front to back, procedurally creating directories
while ((pos_slash = path.find('\\', pos_slash)) != std::string::npos) {
const std::wstring subpath = wpath.substr(0, pos_slash);
const wchar_t * test = subpath.c_str();
const bool success = CreateDirectoryW(test, NULL);
if (!success) {
const DWORD error = GetLastError();
// if the path already exists, ensure that it's a directory
if (error == ERROR_ALREADY_EXISTS) {
const DWORD attributes = GetFileAttributesW(subpath.c_str());
if (attributes == INVALID_FILE_ATTRIBUTES || !(attributes & FILE_ATTRIBUTE_DIRECTORY)) {
return false;
}
} else {
return false;
}
}
pos_slash += 1;
}
return true;
#else
// if the path already exists, check whether it's a directory
struct stat info;
if (stat(path.c_str(), &info) == 0) {
return S_ISDIR(info.st_mode);
}
size_t pos_slash = 1; // skip leading slashes for directory creation
// process path from front to back, procedurally creating directories
while ((pos_slash = path.find('/', pos_slash)) != std::string::npos) {
const std::string subpath = path.substr(0, pos_slash);
struct stat info;
// if the path already exists, ensure that it's a directory
if (stat(subpath.c_str(), &info) == 0) {
if (!S_ISDIR(info.st_mode)) {
return false;
}
} else {
// create parent directories
const int ret = mkdir(subpath.c_str(), 0755);
if (ret != 0) {
return false;
}
}
pos_slash += 1;
}
return true;
#endif // _WIN32
}
void dump_vector_float_yaml(FILE * stream, const char * prop_name, const std::vector<float> & data) {
if (data.empty()) {
fprintf(stream, "%s:\n", prop_name);
return;
}
fprintf(stream, "%s: [", prop_name);
for (size_t i = 0; i < data.size() - 1; ++i) {
fprintf(stream, "%e, ", data[i]);
}
fprintf(stream, "%e]\n", data.back());
}
void dump_vector_int_yaml(FILE * stream, const char * prop_name, const std::vector<int> & data) {
if (data.empty()) {
fprintf(stream, "%s:\n", prop_name);
return;
}
fprintf(stream, "%s: [", prop_name);
for (size_t i = 0; i < data.size() - 1; ++i) {
fprintf(stream, "%d, ", data[i]);
}
fprintf(stream, "%d]\n", data.back());
}
void dump_string_yaml_multiline(FILE * stream, const char * prop_name, const char * data) {
std::string data_str(data == NULL ? "" : data);
if (data_str.empty()) {
fprintf(stream, "%s:\n", prop_name);
return;
}
size_t pos_start = 0;
size_t pos_found = 0;
if (!data_str.empty() && (std::isspace(data_str[0]) || std::isspace(data_str.back()))) {
data_str = std::regex_replace(data_str, std::regex("\n"), "\\n");
data_str = std::regex_replace(data_str, std::regex("\""), "\\\"");
data_str = "\"" + data_str + "\"";
fprintf(stream, "%s: %s\n", prop_name, data_str.c_str());
return;
}
if (data_str.find('\n') == std::string::npos) {
fprintf(stream, "%s: %s\n", prop_name, data_str.c_str());
return;
}
fprintf(stream, "%s: |\n", prop_name);
while ((pos_found = data_str.find('\n', pos_start)) != std::string::npos) {
fprintf(stream, " %s\n", data_str.substr(pos_start, pos_found-pos_start).c_str());
pos_start = pos_found + 1;
}
}
std::string get_sortable_timestamp() {
using clock = std::chrono::system_clock;
const clock::time_point current_time = clock::now();
const time_t as_time_t = clock::to_time_t(current_time);
char timestamp_no_ns[100];
std::strftime(timestamp_no_ns, 100, "%Y_%m_%d-%H_%M_%S", std::localtime(&as_time_t));
const int64_t ns = std::chrono::duration_cast<std::chrono::nanoseconds>(
current_time.time_since_epoch() % 1000000000).count();
char timestamp_ns[11];
snprintf(timestamp_ns, 11, "%09" PRId64, ns);
return std::string(timestamp_no_ns) + "." + std::string(timestamp_ns);
}
void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const llama_context * lctx,
const std::string & timestamp, const std::vector<int> & prompt_tokens, const char * model_desc) {
fprintf(stream, "build_commit: %s\n", BUILD_COMMIT);
fprintf(stream, "build_number: %d\n", BUILD_NUMBER);
fprintf(stream, "cpu_has_arm_fma: %s\n", ggml_cpu_has_arm_fma() ? "true" : "false");
fprintf(stream, "cpu_has_avx: %s\n", ggml_cpu_has_avx() ? "true" : "false");
fprintf(stream, "cpu_has_avx2: %s\n", ggml_cpu_has_avx2() ? "true" : "false");
fprintf(stream, "cpu_has_avx512: %s\n", ggml_cpu_has_avx512() ? "true" : "false");
fprintf(stream, "cpu_has_avx512_vbmi: %s\n", ggml_cpu_has_avx512_vbmi() ? "true" : "false");
fprintf(stream, "cpu_has_avx512_vnni: %s\n", ggml_cpu_has_avx512_vnni() ? "true" : "false");
fprintf(stream, "cpu_has_blas: %s\n", ggml_cpu_has_blas() ? "true" : "false");
fprintf(stream, "cpu_has_cublas: %s\n", ggml_cpu_has_cublas() ? "true" : "false");
fprintf(stream, "cpu_has_clblast: %s\n", ggml_cpu_has_clblast() ? "true" : "false");
fprintf(stream, "cpu_has_fma: %s\n", ggml_cpu_has_fma() ? "true" : "false");
fprintf(stream, "cpu_has_gpublas: %s\n", ggml_cpu_has_gpublas() ? "true" : "false");
fprintf(stream, "cpu_has_neon: %s\n", ggml_cpu_has_neon() ? "true" : "false");
fprintf(stream, "cpu_has_f16c: %s\n", ggml_cpu_has_f16c() ? "true" : "false");
fprintf(stream, "cpu_has_fp16_va: %s\n", ggml_cpu_has_fp16_va() ? "true" : "false");
fprintf(stream, "cpu_has_wasm_simd: %s\n", ggml_cpu_has_wasm_simd() ? "true" : "false");
fprintf(stream, "cpu_has_blas: %s\n", ggml_cpu_has_blas() ? "true" : "false");
fprintf(stream, "cpu_has_sse3: %s\n", ggml_cpu_has_sse3() ? "true" : "false");
fprintf(stream, "cpu_has_vsx: %s\n", ggml_cpu_has_vsx() ? "true" : "false");
#ifdef NDEBUG
fprintf(stream, "debug: false\n");
#else
fprintf(stream, "debug: true\n");
#endif // NDEBUG
fprintf(stream, "model_desc: %s\n", model_desc);
fprintf(stream, "n_vocab: %d # output size of the final layer, 32001 for some models\n", llama_n_vocab(lctx));
#ifdef __OPTIMIZE__
fprintf(stream, "optimize: true\n");
#else
fprintf(stream, "optimize: false\n");
#endif // __OPTIMIZE__
fprintf(stream, "time: %s\n", timestamp.c_str());
fprintf(stream, "\n");
fprintf(stream, "###############\n");
fprintf(stream, "# User Inputs #\n");
fprintf(stream, "###############\n");
fprintf(stream, "\n");
fprintf(stream, "alias: %s # default: unknown\n", params.model_alias.c_str());
fprintf(stream, "batch_size: %d # default: 512\n", params.n_batch);
dump_string_yaml_multiline(stream, "cfg_negative_prompt", params.cfg_negative_prompt.c_str());
fprintf(stream, "cfg_scale: %f # default: 1.0\n", params.cfg_scale);
fprintf(stream, "chunks: %d # default: -1 (unlimited)\n", params.n_chunks);
fprintf(stream, "color: %s # default: false\n", params.use_color ? "true" : "false");
fprintf(stream, "ctx_size: %d # default: 512\n", params.n_ctx);
fprintf(stream, "escape: %s # default: false\n", params.escape ? "true" : "false");
fprintf(stream, "export: %s # default: false\n", params.export_cgraph ? "true" : "false");
fprintf(stream, "file: # never logged, see prompt instead. Can still be specified for input.\n");
fprintf(stream, "frequency_penalty: %f # default: 0.0 \n", params.frequency_penalty);
dump_string_yaml_multiline(stream, "grammar", params.grammar.c_str());
fprintf(stream, "grammar-file: # never logged, see grammar instead. Can still be specified for input.\n");
fprintf(stream, "hellaswag: %s # default: false\n", params.hellaswag ? "true" : "false");
fprintf(stream, "hellaswag_tasks: %ld # default: 400\n", params.hellaswag_tasks);
const auto logit_bias_eos = params.logit_bias.find(llama_token_eos(lctx));
const bool ignore_eos = logit_bias_eos != params.logit_bias.end() && logit_bias_eos->second == -INFINITY;
fprintf(stream, "ignore_eos: %s # default: false\n", ignore_eos ? "true" : "false");
dump_string_yaml_multiline(stream, "in_prefix", params.input_prefix.c_str());
fprintf(stream, "in_prefix_bos: %s # default: false\n", params.input_prefix_bos ? "true" : "false");
dump_string_yaml_multiline(stream, "in_suffix", params.input_prefix.c_str());
fprintf(stream, "instruct: %s # default: false\n", params.instruct ? "true" : "false");
fprintf(stream, "interactive: %s # default: false\n", params.interactive ? "true" : "false");
fprintf(stream, "interactive_first: %s # default: false\n", params.interactive_first ? "true" : "false");
fprintf(stream, "keep: %d # default: 0\n", params.n_keep);
fprintf(stream, "logdir: %s # default: unset (no logging)\n", params.logdir.c_str());
fprintf(stream, "logit_bias:\n");
for (std::pair<llama_token, float> lb : params.logit_bias) {
if (ignore_eos && lb.first == logit_bias_eos->first) {
continue;
}
fprintf(stream, " %d: %f", lb.first, lb.second);
}
fprintf(stream, "lora: %s\n", params.lora_adapter.c_str());
fprintf(stream, "lora_base: %s\n", params.lora_base.c_str());
fprintf(stream, "low_vram: %s # default: false\n", params.low_vram ? "true" : "false");
fprintf(stream, "main_gpu: %d # default: 0\n", params.main_gpu);
fprintf(stream, "memory_f32: %s # default: false\n", !params.memory_f16 ? "true" : "false");
fprintf(stream, "mirostat: %d # default: 0 (disabled)\n", params.mirostat);
fprintf(stream, "mirostat_ent: %f # default: 5.0\n", params.mirostat_tau);
fprintf(stream, "mirostat_lr: %f # default: 0.1\n", params.mirostat_eta);
fprintf(stream, "mlock: %s # default: false\n", params.use_mlock ? "true" : "false");
fprintf(stream, "model: %s # default: models/7B/ggml-model.bin\n", params.model.c_str());
fprintf(stream, "mtest: %s # default: false\n", params.mem_test ? "true" : "false");
fprintf(stream, "multiline_input: %s # default: false\n", params.multiline_input ? "true" : "false");
fprintf(stream, "n_gpu_layers: %d # default: 0\n", params.n_gpu_layers);
fprintf(stream, "n_predict: %d # default: -1 (unlimited)\n", params.n_predict);
fprintf(stream, "n_probs: %d # only used by server binary, default: 0\n", params.n_probs);
fprintf(stream, "no_mmap: %s # default: false\n", !params.use_mmap ? "true" : "false");
fprintf(stream, "no_mul_mat_q: %s # default: false\n", !params.mul_mat_q ? "true" : "false");
fprintf(stream, "no_penalize_nl: %s # default: false\n", !params.penalize_nl ? "true" : "false");
fprintf(stream, "numa: %s # default: false\n", params.numa ? "true" : "false");
fprintf(stream, "ppl_output_type: %d # default: 0\n", params.ppl_output_type);
fprintf(stream, "ppl_stride: %d # default: 0\n", params.ppl_stride);
fprintf(stream, "presence_penalty: %f # default: 0.0\n", params.presence_penalty);
dump_string_yaml_multiline(stream, "prompt", params.prompt.c_str());
fprintf(stream, "prompt_cache: %s\n", params.path_prompt_cache.c_str());
fprintf(stream, "prompt_cache_all: %s # default: false\n", params.prompt_cache_all ? "true" : "false");
fprintf(stream, "prompt_cache_ro: %s # default: false\n", params.prompt_cache_ro ? "true" : "false");
dump_vector_int_yaml(stream, "prompt_tokens", prompt_tokens);
fprintf(stream, "random_prompt: %s # default: false\n", params.random_prompt ? "true" : "false");
fprintf(stream, "repeat_penalty: %f # default: 1.1\n", params.repeat_penalty);
fprintf(stream, "reverse_prompt:\n");
for (std::string ap : params.antiprompt) {
size_t pos = 0;
while ((pos = ap.find('\n', pos)) != std::string::npos) {
ap.replace(pos, 1, "\\n");
pos += 1;
}
fprintf(stream, " - %s\n", ap.c_str());
}
fprintf(stream, "rope_freq_base: %f # default: 10000.0\n", params.rope_freq_base);
fprintf(stream, "rope_freq_scale: %f # default: 1.0\n", params.rope_freq_scale);
fprintf(stream, "seed: %d # default: -1 (random seed)\n", params.seed);
fprintf(stream, "simple_io: %s # default: false\n", params.simple_io ? "true" : "false");
fprintf(stream, "temp: %f # default: 0.8\n", params.temp);
const std::vector<float> tensor_split_vector(params.tensor_split, params.tensor_split + LLAMA_MAX_DEVICES);
dump_vector_float_yaml(stream, "tensor_split", tensor_split_vector);
fprintf(stream, "tfs: %f # default: 1.0\n", params.tfs_z);
fprintf(stream, "threads: %d # default: %d\n", params.n_threads, std::thread::hardware_concurrency());
fprintf(stream, "top_k: %d # default: 40\n", params.top_k);
fprintf(stream, "top_p: %f # default: 0.95\n", params.top_p);
fprintf(stream, "typical_p: %f # default: 1.0\n", params.typical_p);
fprintf(stream, "verbose_prompt: %s # default: false\n", params.verbose_prompt ? "true" : "false");
}

18
common/common.h
View file

@ -11,6 +11,12 @@
#include <unordered_map> #include <unordered_map>
#include <tuple> #include <tuple>
#ifdef _WIN32
#define DIRECTORY_SEPARATOR '\\'
#else
#define DIRECTORY_SEPARATOR '/'
#endif // _WIN32
// //
// CLI argument parsing // CLI argument parsing
// //
@ -61,6 +67,7 @@ struct gpt_params {
std::string input_suffix = ""; // string to suffix user inputs with std::string input_suffix = ""; // string to suffix user inputs with
std::string grammar = ""; // optional BNF-like grammar to constrain sampling std::string grammar = ""; // optional BNF-like grammar to constrain sampling
std::vector<std::string> antiprompt; // string upon seeing which more user input is prompted std::vector<std::string> antiprompt; // string upon seeing which more user input is prompted
std::string logdir = ""; // directory in which to save YAML log files
std::string lora_adapter = ""; // lora adapter path std::string lora_adapter = ""; // lora adapter path
std::string lora_base = ""; // base model path for the lora adapter std::string lora_base = ""; // base model path for the lora adapter
@ -82,6 +89,7 @@ struct gpt_params {
bool prompt_cache_ro = false; // open the prompt cache read-only and do not update it bool prompt_cache_ro = false; // open the prompt cache read-only and do not update it
bool embedding = false; // get only sentence embedding bool embedding = false; // get only sentence embedding
bool escape = false; // escape "\n", "\r", "\t", "\'", "\"", and "\\"
bool interactive_first = false; // wait for user input immediately bool interactive_first = false; // wait for user input immediately
bool multiline_input = false; // reverse the usage of `\` bool multiline_input = false; // reverse the usage of `\`
bool simple_io = false; // improves compatibility with subprocesses and limited consoles bool simple_io = false; // improves compatibility with subprocesses and limited consoles
@ -144,3 +152,13 @@ std::string llama_detokenize_spm(
std::string llama_detokenize_bpe( std::string llama_detokenize_bpe(
llama_context * ctx, llama_context * ctx,
const std::vector<llama_token> & tokens); const std::vector<llama_token> & tokens);
bool create_directory_with_parents(const std::string & path);
void dump_vector_float_yaml(FILE * stream, const char * prop_name, const std::vector<float> & data);
void dump_vector_int_yaml(FILE * stream, const char * prop_name, const std::vector<int> & data);
void dump_string_yaml_multiline(FILE * stream, const char * prop_name, const char * data);
std::string get_sortable_timestamp();
void dump_non_result_info_yaml(
FILE * stream, const gpt_params & params, const llama_context * lctx,
const std::string & timestamp, const std::vector<int> & prompt_tokens, const char * model_desc);

2
convert-falcon-hf-to-gguf.py
View file

@ -48,7 +48,7 @@ def count_model_parts(dir_model: str) -> int:
if len(sys.argv) < 3: if len(sys.argv) < 3:
print("Usage: convert-h5-to-ggml.py dir-model ftype\n") print(f"Usage: python {sys.argv[0]} dir-model ftype\n")
print(" ftype == 0 -> float32") print(" ftype == 0 -> float32")
print(" ftype == 1 -> float16") print(" ftype == 1 -> float16")
sys.exit(1) sys.exit(1)

2
convert-gptneox-hf-to-gguf.py
View file

@ -50,7 +50,7 @@ def count_model_parts(dir_model: str) -> int:
if len(sys.argv) < 3: if len(sys.argv) < 3:
print("Usage: convert-h5-to-ggml.py dir-model ftype\n") print(f"Usage: python {sys.argv[0]} dir-model ftype\n")
print(" ftype == 0 -> float32") print(" ftype == 0 -> float32")
print(" ftype == 1 -> float16") print(" ftype == 1 -> float16")
sys.exit(1) sys.exit(1)

2
convert-llama-7b-pth-to-gguf.py
View file

@ -32,7 +32,7 @@ def count_model_parts(dir_model: str) -> int:
if len(sys.argv) < 3: if len(sys.argv) < 3:
print("Usage: convert-h5-to-ggml.py dir-model ftype\n") print(f"Usage: python {sys.argv[0]} dir-model ftype\n")
print(" ftype == 0 -> float32") print(" ftype == 0 -> float32")
print(" ftype == 1 -> float16") print(" ftype == 1 -> float16")

2
convert-llama-hf-to-gguf.py
View file

@ -44,7 +44,7 @@ def count_model_parts(dir_model: str) -> int:
if len(sys.argv) < 3: if len(sys.argv) < 3:
print("Usage: convert-h5-to-ggml.py dir-model ftype\n") print(f"Usage: python {sys.argv[0]} dir-model ftype\n")
print(" ftype == 0 -> float32") print(" ftype == 0 -> float32")
print(" ftype == 1 -> float16") print(" ftype == 1 -> float16")

7
convert.py
View file

@ -469,7 +469,7 @@ class UnquantizedTensor(Tensor):
def permute_part(self, n_part: int, n_head: int) -> 'UnquantizedTensor': def permute_part(self, n_part: int, n_head: int) -> 'UnquantizedTensor':
r = self.ndarray.shape[0] // 3 r = self.ndarray.shape[0] // 3
return UnquantizedTensor(permute(self.ndarray[r * n_part : r * n_part + r, ...], n_head)) return UnquantizedTensor(permute(self.ndarray[r * n_part : r * n_part + r, ...], n_head, n_head))
def part(self, n_part: int) -> 'UnquantizedTensor': def part(self, n_part: int) -> 'UnquantizedTensor':
r = self.ndarray.shape[0] // 3 r = self.ndarray.shape[0] // 3
@ -952,9 +952,10 @@ def convert_model_names(model: LazyModel, params: Params) -> LazyModel:
#tmp[f"model.layers.{i}.self_attn.v_proj.weight"] = model[f"model.layers.{i}.self_attn.v_proj.weight"] #tmp[f"model.layers.{i}.self_attn.v_proj.weight"] = model[f"model.layers.{i}.self_attn.v_proj.weight"]
elif f"model.layers.{i}.self_attn.W_pack.weight" in model: elif f"model.layers.{i}.self_attn.W_pack.weight" in model:
print(f"Unpacking and permuting layer {i}") print(f"Unpacking and permuting layer {i}")
tmp[f"model.layers.{i}.self_attn.q_proj.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 0, params.n_head, params.n_head) tmp[f"model.layers.{i}.self_attn.q_proj.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 0, params.n_head)
tmp[f"model.layers.{i}.self_attn.k_proj.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 1, params.n_head, params.n_head_kv) tmp[f"model.layers.{i}.self_attn.k_proj.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 1, params.n_head)
tmp[f"model.layers.{i}.self_attn.v_proj.weight"] = part_lazy (model[f"model.layers.{i}.self_attn.W_pack.weight"], 2) tmp[f"model.layers.{i}.self_attn.v_proj.weight"] = part_lazy (model[f"model.layers.{i}.self_attn.W_pack.weight"], 2)
del tmp[f"model.layers.{i}.self_attn.W_pack.weight"]
else: else:
break break

1
examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp
View file

@ -681,7 +681,6 @@ void save_as_llama_model(struct llama_vocab * vocab, struct my_llama_model * mod
// for rms-att-weight // for rms-att-weight
int row_length = model->hparams.n_embd; int row_length = model->hparams.n_embd;
const auto & hparams = model->hparams;
int n_ff = model->hparams.n_ff; int n_ff = model->hparams.n_ff;
for (uint32_t i = 0; i < model->hparams.n_layer; ++i){ for (uint32_t i = 0; i < model->hparams.n_layer; ++i){

5
examples/gguf/CMakeLists.txt Normal file
View file

@ -0,0 +1,5 @@
set(TARGET gguf)
add_executable(${TARGET} gguf.cpp)
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_11)

7
examples/llama-bench/llama-bench.cpp
View file

@ -3,6 +3,9 @@
#include <cassert> #include <cassert>
#include <chrono> #include <chrono>
#include <cinttypes> #include <cinttypes>
#include <clocale>
#include <cmath>
#include <cstdio>
#include <cstring> #include <cstring>
#include <ctime> #include <ctime>
#include <iterator> #include <iterator>
@ -10,7 +13,6 @@
#include <numeric> #include <numeric>
#include <regex> #include <regex>
#include <sstream> #include <sstream>
#include <stdio.h>
#include <string> #include <string>
#include <vector> #include <vector>
@ -916,6 +918,9 @@ static void llama_null_log_callback(enum llama_log_level level, const char * tex
} }
int main(int argc, char ** argv) { int main(int argc, char ** argv) {
// try to set locale for unicode characters in markdown
setlocale(LC_CTYPE, ".UTF-8");
#if !defined(NDEBUG) #if !defined(NDEBUG)
fprintf(stderr, "warning: asserts enabled, performance may be affected\n"); fprintf(stderr, "warning: asserts enabled, performance may be affected\n");
#endif #endif

76
examples/main/main.cpp
View file

@ -17,6 +17,7 @@
#include <ctime> #include <ctime>
#include <fstream> #include <fstream>
#include <iostream> #include <iostream>
#include <sstream>
#include <string> #include <string>
#include <vector> #include <vector>
@ -37,8 +38,56 @@
#endif #endif
static llama_context ** g_ctx; static llama_context ** g_ctx;
static llama_model ** g_model;
static gpt_params * g_params;
static std::vector<llama_token> * g_input_tokens;
static std::ostringstream * g_output_ss;
static std::vector<llama_token> * g_output_tokens;
static bool is_interacting = false; static bool is_interacting = false;
void write_logfile(
const llama_context * ctx, const gpt_params & params, const llama_model * model,
const std::vector<llama_token> input_tokens, const std::string output, const std::vector<llama_token> output_tokens) {
if (params.logdir.empty()) {
return;
}
const std::string timestamp = get_sortable_timestamp();
const bool success = create_directory_with_parents(params.logdir);
if (!success) {
fprintf(stderr, "%s: warning: failed to create logdir %s, cannot write logfile\n",
__func__, params.logdir.c_str());
return;
}
const std::string logfile_path = params.logdir + timestamp + ".yml";
FILE * logfile = fopen(logfile_path.c_str(), "w");
if (logfile == NULL) {
fprintf(stderr, "%s: failed to open logfile %s\n", __func__, logfile_path.c_str());
return;
}
fprintf(logfile, "binary: main\n");
char model_desc[128];
llama_model_desc(model, model_desc, sizeof(model_desc));
dump_non_result_info_yaml(logfile, params, ctx, timestamp, input_tokens, model_desc);
fprintf(logfile, "\n");
fprintf(logfile, "######################\n");
fprintf(logfile, "# Generation Results #\n");
fprintf(logfile, "######################\n");
fprintf(logfile, "\n");
dump_string_yaml_multiline(logfile, "output", output.c_str());
dump_vector_int_yaml(logfile, "output_tokens", output_tokens);
llama_dump_timing_info_yaml(logfile, ctx);
fclose(logfile);
}
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32) #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
void sigint_handler(int signo) { void sigint_handler(int signo) {
if (signo == SIGINT) { if (signo == SIGINT) {
@ -48,6 +97,7 @@ void sigint_handler(int signo) {
console::cleanup(); console::cleanup();
printf("\n"); printf("\n");
llama_print_timings(*g_ctx); llama_print_timings(*g_ctx);
write_logfile(*g_ctx, *g_params, *g_model, *g_input_tokens, g_output_ss->str(), *g_output_tokens);
_exit(130); _exit(130);
} }
} }
@ -56,6 +106,7 @@ void sigint_handler(int signo) {
int main(int argc, char ** argv) { int main(int argc, char ** argv) {
gpt_params params; gpt_params params;
g_params = &params;
if (gpt_params_parse(argc, argv, params) == false) { if (gpt_params_parse(argc, argv, params) == false) {
return 1; return 1;
@ -116,6 +167,7 @@ int main(int argc, char ** argv) {
llama_model * model; llama_model * model;
llama_context * ctx; llama_context * ctx;
llama_context * ctx_guidance = NULL; llama_context * ctx_guidance = NULL;
g_model = &model;
g_ctx = &ctx; g_ctx = &ctx;
// load the model and apply lora adapter, if any // load the model and apply lora adapter, if any
@ -397,6 +449,10 @@ int main(int argc, char ** argv) {
int n_session_consumed = 0; int n_session_consumed = 0;
int n_past_guidance = 0; int n_past_guidance = 0;
std::vector<int> input_tokens; g_input_tokens = &input_tokens;
std::vector<int> output_tokens; g_output_tokens = &output_tokens;
std::ostringstream output_ss; g_output_ss = &output_ss;
// the first thing we will do is to output the prompt, so set color accordingly // the first thing we will do is to output the prompt, so set color accordingly
console::set_display(console::prompt); console::set_display(console::prompt);
@ -667,7 +723,15 @@ int main(int argc, char ** argv) {
// display text // display text
if (input_echo) { if (input_echo) {
for (auto id : embd) { for (auto id : embd) {
printf("%s", llama_token_to_piece(ctx, id).c_str()); const std::string token_str = llama_token_to_piece(ctx, id);
printf("%s", token_str.c_str());
if (embd.size() > 1) {
input_tokens.push_back(id);
} else {
output_tokens.push_back(id);
output_ss << token_str;
}
} }
fflush(stdout); fflush(stdout);
} }
@ -761,6 +825,8 @@ int main(int argc, char ** argv) {
printf("%s", params.input_suffix.c_str()); printf("%s", params.input_suffix.c_str());
} }
const size_t original_size = embd_inp.size();
// instruct mode: insert instruction prefix // instruct mode: insert instruction prefix
if (params.instruct && !is_antiprompt) { if (params.instruct && !is_antiprompt) {
n_consumed = embd_inp.size(); n_consumed = embd_inp.size();
@ -775,6 +841,12 @@ int main(int argc, char ** argv) {
embd_inp.insert(embd_inp.end(), inp_sfx.begin(), inp_sfx.end()); embd_inp.insert(embd_inp.end(), inp_sfx.begin(), inp_sfx.end());
} }
for (size_t i = original_size; i < embd_inp.size(); ++i) {
const llama_token token = embd_inp[i];
output_tokens.push_back(token);
output_ss << llama_token_to_piece(ctx, token);
}
n_remain -= line_inp.size(); n_remain -= line_inp.size();
} }
@ -817,6 +889,8 @@ int main(int argc, char ** argv) {
} }
llama_print_timings(ctx); llama_print_timings(ctx);
write_logfile(ctx, params, model, input_tokens, output_ss.str(), output_tokens);
if (ctx_guidance) { llama_free(ctx_guidance); } if (ctx_guidance) { llama_free(ctx_guidance); }
llama_free(ctx); llama_free(ctx);
llama_free_model(model); llama_free_model(model);

156
examples/perplexity/perplexity.cpp
View file

@ -3,16 +3,79 @@
#include "build-info.h" #include "build-info.h"
#include <cmath> #include <cmath>
#include <cstdio>
#include <cstring>
#include <ctime> #include <ctime>
#include <sstream> #include <sstream>
#include <cstring>
#include <thread> #include <thread>
#include <mutex> #include <mutex>
#include <vector>
#if defined(_MSC_VER) #if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data #pragma warning(disable: 4244 4267) // possible loss of data
#endif #endif
struct results_perplexity {
std::vector<llama_token> tokens;
double ppl_value;
std::vector<float> logits;
std::vector<float> probs;
};
struct results_log_softmax {
double log_softmax;
float logit;
float prob;
};
void write_logfile(const llama_context * ctx, const gpt_params & params,
const llama_model * model, const struct results_perplexity & results) {
if (params.logdir.empty()) {
return;
}
if (params.hellaswag) {
fprintf(stderr, "%s: warning: logging results is not implemented for HellaSwag. No files will be written.\n", __func__);
return;
}
const std::string timestamp = get_sortable_timestamp();
const bool success = create_directory_with_parents(params.logdir);
if (!success) {
fprintf(stderr, "%s: warning: failed to create logdir %s, cannot write logfile\n",
__func__, params.logdir.c_str());
return;
}
const std::string logfile_path = params.logdir + timestamp + ".yml";
FILE * logfile = fopen(logfile_path.c_str(), "w");
if (logfile == NULL) {
fprintf(stderr, "%s: failed to open logfile %s\n", __func__, logfile_path.c_str());
return;
}
fprintf(logfile, "binary: main\n");
char model_desc[128];
llama_model_desc(model, model_desc, sizeof(model_desc));
dump_non_result_info_yaml(logfile, params, ctx, timestamp, results.tokens, model_desc);
fprintf(logfile, "\n");
fprintf(logfile, "######################\n");
fprintf(logfile, "# Perplexity Results #\n");
fprintf(logfile, "######################\n");
fprintf(logfile, "\n");
dump_vector_float_yaml(logfile, "logits", results.logits);
fprintf(logfile, "ppl_value: %f\n", results.ppl_value);
dump_vector_float_yaml(logfile, "probs", results.probs);
llama_dump_timing_info_yaml(logfile, ctx);
fclose(logfile);
}
std::vector<float> softmax(const std::vector<float>& logits) { std::vector<float> softmax(const std::vector<float>& logits) {
std::vector<float> probs(logits.size()); std::vector<float> probs(logits.size());
float max_logit = logits[0]; float max_logit = logits[0];
@ -29,20 +92,20 @@ std::vector<float> softmax(const std::vector<float>& logits) {
return probs; return probs;
} }
float log_softmax(int n_vocab, const float * logits, int tok) { results_log_softmax log_softmax(int n_vocab, const float * logits, int tok) {
float max_logit = logits[0]; float max_logit = logits[0];
for (int i = 1; i < n_vocab; ++i) max_logit = std::max(max_logit, logits[i]); for (int i = 1; i < n_vocab; ++i) max_logit = std::max(max_logit, logits[i]);
double sum_exp = 0.0; double sum_exp = 0.0;
for (int i = 0; i < n_vocab; ++i) sum_exp += expf(logits[i] - max_logit); for (int i = 0; i < n_vocab; ++i) sum_exp += expf(logits[i] - max_logit);
return logits[tok] - max_logit - log(sum_exp); return {logits[tok] - max_logit - log(sum_exp), logits[tok], expf(logits[tok] - max_logit) / (float) sum_exp};
} }
void process_logits(int n_vocab, const float * logits, const int * tokens, int n_token, std::vector<std::thread>& workers, void process_logits(int n_vocab, const float * logits, const int * tokens, int n_token, std::vector<std::thread> & workers,
double& nll, double& nll2) { double & nll, double & nll2, float * logit_history, float * prob_history) {
std::mutex mutex; std::mutex mutex;
int counter = 0; int counter = 0;
auto compute = [&mutex, &counter, &nll, &nll2, n_vocab, logits, tokens, n_token] () { auto compute = [&mutex, &counter, &nll, &nll2, logit_history, prob_history, n_vocab, logits, tokens, n_token] () {
double local_nll = 0, local_nll2 = 0; double local_nll = 0, local_nll2 = 0;
while (true) { while (true) {
std::unique_lock<std::mutex> lock(mutex); std::unique_lock<std::mutex> lock(mutex);
@ -52,34 +115,51 @@ void process_logits(int n_vocab, const float * logits, const int * tokens, int n
break; break;
} }
lock.unlock(); lock.unlock();
double v = -log_softmax(n_vocab, logits + i*n_vocab, tokens[i+1]); const results_log_softmax results = log_softmax(n_vocab, logits + i*n_vocab, tokens[i+1]);
const double v = -results.log_softmax;
local_nll += v; local_nll += v;
local_nll2 += v*v; local_nll2 += v*v;
logit_history[i] = results.logit;
prob_history[i] = results.prob;
} }
}; };
for (auto& w : workers) w = std::thread(compute); for (auto & w : workers) w = std::thread(compute);
compute(); compute();
for (auto& w : workers) w.join(); for (auto & w : workers) w.join();
} }
void perplexity_v2(llama_context * ctx, const gpt_params & params) { results_perplexity perplexity_v2(llama_context * ctx, const gpt_params & params) {
// Download: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research // Download: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research
// Run `./perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw` // Run `./perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw`
// Output: `perplexity: 13.5106 [114/114]` // Output: `perplexity: 13.5106 [114/114]`
// BOS tokens will be added for each chunk before eval // BOS tokens will be added for each chunk before eval
if (params.ppl_stride <= 0) {
fprintf(stderr, "%s: stride is %d but must be greater than zero!\n",__func__,params.ppl_stride);
return;
}
const bool is_spm = llama_vocab_type(ctx) == LLAMA_VOCAB_TYPE_SPM; const bool is_spm = llama_vocab_type(ctx) == LLAMA_VOCAB_TYPE_SPM;
const bool add_bos = is_spm; const bool add_bos = is_spm;
fprintf(stderr, "%s: tokenizing the input ..\n", __func__); fprintf(stderr, "%s: tokenizing the input ..\n", __func__);
auto tokens = ::llama_tokenize(ctx, params.prompt, add_bos); std::vector<llama_token> tokens = ::llama_tokenize(ctx, params.prompt, add_bos);
if (int(tokens.size()) < 2*params.n_ctx) {
fprintf(stderr, "%s: you need at least %d tokens to evaluate perplexity with a context of %d\n",__func__,2*params.n_ctx,
params.n_ctx);
fprintf(stderr, "%s: the data file you provided tokenizes to only %zu tokens\n",__func__,tokens.size());
return {std::move(tokens), 0., {}, {}};
}
std::vector<float> logit_history;
std::vector<float> prob_history;
logit_history.resize(tokens.size());
prob_history.resize(tokens.size());
if (params.ppl_stride <= 0) {
fprintf(stderr, "%s: stride is %d but must be greater than zero!\n",__func__,params.ppl_stride);
return {tokens, -1, logit_history, prob_history};
}
const int calc_chunk = params.n_ctx; const int calc_chunk = params.n_ctx;
@ -88,7 +168,7 @@ void perplexity_v2(llama_context * ctx, const gpt_params & params) {
if (int(tokens.size()) <= calc_chunk) { if (int(tokens.size()) <= calc_chunk) {
fprintf(stderr, "%s: there are only %zu tokens, this is not enough for a context size of %d and stride %d\n",__func__, fprintf(stderr, "%s: there are only %zu tokens, this is not enough for a context size of %d and stride %d\n",__func__,
tokens.size(), params.n_ctx, params.ppl_stride); tokens.size(), params.n_ctx, params.ppl_stride);
return; return {tokens, -1, logit_history, prob_history};
} }
const int n_chunk_max = (tokens.size() - calc_chunk + params.ppl_stride - 1) / params.ppl_stride; const int n_chunk_max = (tokens.size() - calc_chunk + params.ppl_stride - 1) / params.ppl_stride;
@ -120,7 +200,7 @@ void perplexity_v2(llama_context * ctx, const gpt_params & params) {
//fprintf(stderr, " Batch %d: starts at %d, size is %d, n_past is %d\n",j,batch_start,batch_size,j * n_batch); //fprintf(stderr, " Batch %d: starts at %d, size is %d, n_past is %d\n",j,batch_start,batch_size,j * n_batch);
if (llama_eval(ctx, tokens.data() + batch_start, batch_size, j * n_batch, params.n_threads)) { if (llama_eval(ctx, tokens.data() + batch_start, batch_size, j * n_batch, params.n_threads)) {
//fprintf(stderr, "%s : failed to eval\n", __func__); //fprintf(stderr, "%s : failed to eval\n", __func__);
return; return {tokens, -1, logit_history, prob_history};
} }
// save original token and restore it after eval // save original token and restore it after eval
@ -161,6 +241,8 @@ void perplexity_v2(llama_context * ctx, const gpt_params & params) {
logits.begin() + (j + 1) * n_vocab); logits.begin() + (j + 1) * n_vocab);
const float prob = softmax(tok_logits)[tokens[start + j + 1]]; const float prob = softmax(tok_logits)[tokens[start + j + 1]];
logit_history[start + j + 1] = tok_logits[tokens[start + j + 1]];
prob_history[start + j + 1] = prob;
nll += -std::log(prob); nll += -std::log(prob);
++count; ++count;
@ -174,12 +256,14 @@ void perplexity_v2(llama_context * ctx, const gpt_params & params) {
fflush(stdout); fflush(stdout);
} }
printf("\n"); printf("\n");
return {tokens, std::exp(nll / count), logit_history, prob_history};
} }
void perplexity(llama_context * ctx, const gpt_params & params) { results_perplexity perplexity(llama_context * ctx, const gpt_params & params) {
if (params.ppl_stride > 0) { if (params.ppl_stride > 0) {
perplexity_v2(ctx, params); return perplexity_v2(ctx, params);
return;
} }
// Download: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research // Download: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research
@ -193,11 +277,24 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
auto tim1 = std::chrono::high_resolution_clock::now(); auto tim1 = std::chrono::high_resolution_clock::now();
fprintf(stderr, "%s: tokenizing the input ..\n", __func__); fprintf(stderr, "%s: tokenizing the input ..\n", __func__);
auto tokens = ::llama_tokenize(ctx, params.prompt, add_bos); std::vector<llama_token> tokens = ::llama_tokenize(ctx, params.prompt, add_bos);
auto tim2 = std::chrono::high_resolution_clock::now(); auto tim2 = std::chrono::high_resolution_clock::now();
fprintf(stderr, "%s: tokenization took %g ms\n",__func__,1e-3*std::chrono::duration_cast<std::chrono::microseconds>(tim2-tim1).count()); fprintf(stderr, "%s: tokenization took %g ms\n",__func__,1e-3*std::chrono::duration_cast<std::chrono::microseconds>(tim2-tim1).count());
if (int(tokens.size()) < 2*params.n_ctx) {
fprintf(stderr, "%s: you need at least %d tokens to evaluate perplexity with a context of %d\n",__func__,2*params.n_ctx,
params.n_ctx);
fprintf(stderr, "%s: the data file you provided tokenizes to only %zu tokens\n",__func__,tokens.size());
return {std::move(tokens), 0., {}, {}};
}
std::vector<float> logit_history;
logit_history.resize(tokens.size());
std::vector<float> prob_history;
prob_history.resize(tokens.size());
const int n_chunk_max = tokens.size() / params.n_ctx; const int n_chunk_max = tokens.size() / params.n_ctx;
const int n_chunk = params.n_chunks < 0 ? n_chunk_max : std::min(params.n_chunks, n_chunk_max); const int n_chunk = params.n_chunks < 0 ? n_chunk_max : std::min(params.n_chunks, n_chunk_max);
@ -236,7 +333,7 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
if (llama_eval(ctx, tokens.data() + batch_start, batch_size, j * n_batch, params.n_threads)) { if (llama_eval(ctx, tokens.data() + batch_start, batch_size, j * n_batch, params.n_threads)) {
fprintf(stderr, "%s : failed to eval\n", __func__); fprintf(stderr, "%s : failed to eval\n", __func__);
return; return {tokens, -1, logit_history, prob_history};
} }
// restore the original token in case it was set to BOS // restore the original token in case it was set to BOS
@ -272,7 +369,8 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
// last 256 tokens. Then, we split the input up into context window size chunks to // last 256 tokens. Then, we split the input up into context window size chunks to
// process the entire prompt. // process the entire prompt.
const int first = std::min(512, params.n_ctx/2); const int first = std::min(512, params.n_ctx/2);
process_logits(n_vocab, logits.data() + first*n_vocab, tokens.data() + start + first, params.n_ctx - 1 - first, workers, nll, nll2); process_logits(n_vocab, logits.data() + first*n_vocab, tokens.data() + start + first, params.n_ctx - 1 - first,
workers, nll, nll2, logit_history.data() + start + first, prob_history.data() + start + first);
count += params.n_ctx - first - 1; count += params.n_ctx - first - 1;
// perplexity is e^(average negative log-likelihood) // perplexity is e^(average negative log-likelihood)
@ -287,16 +385,19 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
fflush(stdout); fflush(stdout);
} }
printf("\n"); printf("\n");
nll2 /= count; nll2 /= count;
nll /= count; nll /= count;
const double ppl = exp(nll);
nll2 -= nll * nll; nll2 -= nll * nll;
if (nll2 > 0) { if (nll2 > 0) {
nll2 = sqrt(nll2/(count-1)); nll2 = sqrt(nll2/(count-1));
double ppl = exp(nll);
printf("Final estimate: PPL = %.4lf +/- %.5lf\n", ppl, nll2*ppl); printf("Final estimate: PPL = %.4lf +/- %.5lf\n", ppl, nll2*ppl);
} else { } else {
printf("Unexpected negative standard deviation of log(prob)\n"); printf("Unexpected negative standard deviation of log(prob)\n");
} }
return {tokens, ppl, logit_history, prob_history};
} }
std::vector<float> hellaswag_evaluate_tokens(llama_context * ctx, const std::vector<int>& tokens, int n_past, int n_batch, std::vector<float> hellaswag_evaluate_tokens(llama_context * ctx, const std::vector<int>& tokens, int n_past, int n_batch,
@ -604,13 +705,16 @@ int main(int argc, char ** argv) {
params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info()); params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info());
} }
struct results_perplexity results;
if (params.hellaswag) { if (params.hellaswag) {
hellaswag_score(ctx, params); hellaswag_score(ctx, params);
} else { } else {
perplexity(ctx, params); results = perplexity(ctx, params);
} }
llama_print_timings(ctx); llama_print_timings(ctx);
write_logfile(ctx, params, model, results);
llama_free(ctx); llama_free(ctx);
llama_free_model(model); llama_free_model(model);

4
examples/quantize/quantize.cpp
View file

@ -98,7 +98,7 @@ int main(int argc, char ** argv) {
} }
} }
if (argc - arg_idx < 3) { if (argc - arg_idx < 2) {
usage(argv[0]); usage(argv[0]);
} }
@ -112,7 +112,7 @@ int main(int argc, char ** argv) {
std::string ftype_str; std::string ftype_str;
if (try_parse_ftype(argv[arg_idx], params.ftype, ftype_str)) { if (try_parse_ftype(argv[arg_idx], params.ftype, ftype_str)) {
std::string fpath; std::string fpath;
const size_t pos = fname_inp.find_last_of('/'); const size_t pos = fname_inp.find_last_of("/\\");
if (pos != std::string::npos) { if (pos != std::string::npos) {
fpath = fname_inp.substr(0, pos + 1); fpath = fname_inp.substr(0, pos + 1);
} }

14
examples/train-text-from-scratch/README.md
View file

@ -8,15 +8,15 @@ wget https://raw.githubusercontent.com/brunoklein99/deep-learning-notes/master/s
# train # train
./bin/train-text-from-scratch \ ./bin/train-text-from-scratch \
--vocab-model ../models/ggml-vocab.bin \ --vocab-model ../models/ggml-vocab-llama.gguf \
--ctx 64 --embd 256 --head 8 --layer 16 \ --ctx 64 --embd 256 --head 8 --layer 16 \
--checkpoint-in chk-shakespeare-256x16.bin \ --checkpoint-in chk-shakespeare-256x16.gguf \
--checkpoint-out chk-shakespeare-256x16.bin \ --checkpoint-out chk-shakespeare-256x16.gguf \
--model-out ggml-shakespeare-256x16-f32.bin \ --model-out ggml-shakespeare-256x16-f32.gguf \
--train-data "shakespeare.txt" \ --train-data "shakespeare.txt" \
-t 6 -b 16 -n 32 --seed 1 --adam-iter 16 \ -t 6 -b 16 --seed 1 --adam-iter 256 \
--print-details-interval 0 --predict 16 --use-flash --no-checkpointing
# predict # predict
./bin/main -m ggml-shakespeare-256x16-f32.bin ./bin/main -m ggml-shakespeare-256x16-f32.gguf
``` ```

492
examples/train-text-from-scratch/convert-train-checkpoint-to-gguf.py Normal file
View file

@ -0,0 +1,492 @@
#!/usr/bin/env python3
# train-text-from-scratch checkpoint --> gguf conversion
import argparse
import gguf
import os
import struct
import sys
import numpy as np
from pathlib import Path
# gguf constants
LLM_KV_OPTIMIZER_TYPE = "optimizer.type"
LLM_KV_OPTIMIZER_TYPE_ADAM = "adam"
LLM_KV_OPTIMIZER_TYPE_LBFGS = "lbfgs"
LLM_KV_OPTIMIZER_FILE_VERSION = "optimizer.file_version"
LLM_KV_OPTIMIZER_CONVERGENCE_PAST_COUNT = "optimizer.convergence_past_count"
LLM_KV_OPTIMIZER_PARAMETER_COUNT = "optimizer.parameter_count"
LLM_KV_OPTIMIZER_ITERATION_COUNT = "optimizer.iteration_count"
LLM_KV_OPTIMIZER_JUST_INITIALIZED = "optimizer.just_initialized"
LLM_KV_OPTIMIZER_ADAM_BEST_LOSS = "optimizer.adam.best_loss"
LLM_KV_OPTIMIZER_ADAM_PREVIOUS_LOSS = "optimizer.adam.previous_loss"
LLM_KV_OPTIMIZER_ADAM_NO_IMPROVEMENT_COUNT = "optimizer.adam.no_improvement_count"
LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT = "optimizer.lbfgs.approx_hessian_count"
LLM_KV_OPTIMIZER_LBFGS_BEST_LOSS = "optimizer.lbfgs.best_loss"
LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_STEP = "optimizer.lbfgs.line_search_step"
LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_J = "optimizer.lbfgs.line_search_j"
LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_K = "optimizer.lbfgs.line_search_k"
LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_END = "optimizer.lbfgs.line_search_end"
LLM_KV_OPTIMIZER_LBFGS_NO_IMPROVEMENT_COUNT = "optimizer.lbfgs.no_improvement_count"
LLM_TENSOR_OPTIMIZER_ADAM_FIRST_MOMENTS = "optimizer.adam.first_moments"
LLM_TENSOR_OPTIMIZER_ADAM_SECOND_MOMENTS = "optimizer.adam.second_moments"
LLM_TENSOR_OPTIMIZER_ADAM_PAST_LOSS_VALUES = "optimizer.adam.past_loss_values"
LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_PARAMETERS = "optimizer.lbfgs.current_parameters"
LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_PARAMETERS = "optimizer.lbfgs.previous_parameters"
LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_GRADIENTS = "optimizer.lbfgs.current_gradients"
LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_GRADIENTS = "optimizer.lbfgs.previous_gradients"
LLM_TENSOR_OPTIMIZER_LBFGS_SEARCH_DIRECTION = "optimizer.lbfgs.search_direction"
LLM_TENSOR_OPTIMIZER_LBFGS_PAST_LOSS_VALUES = "optimizer.lbfgs.past_loss_values"
LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_ALPHA = "optimizer.lbfgs.memory_alpha"
LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_YS = "optimizer.lbfgs.memory_ys"
LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_S = "optimizer.lbfgs.memory_s"
LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_Y = "optimizer.lbfgs.memory_y"
LLM_KV_TRAINING_FILE_VERSION = "training.file_version"
LLM_KV_TRAINING_ITERATION_COUNT = "training.iteration_count"
LLM_KV_TRAINING_SAMPLE_COUNT = "training.sample_count"
LLM_KV_TRAINING_TOKEN_COUNT = "training.token_count"
class Tensor:
def __init__(self, dtype='f', ne=None):
if ne is None:
ne = []
self.dtype = dtype
self.ne = ne
self.nbytes = 0
if self.dtype == 'f':
if len(self.ne) == 0:
self.nbytes = 0
else:
self.nbytes = int(np.product(self.ne)) * 4
else:
raise ValueError(f"Unhandled data type '{self.dtype}'")
def load(self, data, offset):
nd = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
namelen = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
dtype = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
assert(nd == len(self.ne))
ne = []
for d in range(nd):
n = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
ne.append(n)
assert(tuple(ne) == tuple(self.ne))
if self.dtype == 'f':
assert(dtype == 0)
else:
raise ValueError(f"Unhandled data type '{self.dtype}'")
self.name = bytes(data[offset:offset+namelen]); offset += namelen
# 32-byte alignment
offset += (0 - offset) & 31
self.data = data[offset:offset+self.nbytes]
offset += self.nbytes
return offset
def max_storage_size(self):
result = 0
result += 4 # nd
result += 4 # namelen
result += 4 # dtype
result += len(self.ne)*8 # ne
result += 48 # name (maximum as of commit 3b5515bbe0e2224425986ba24f1f5d84aa38dce9)
result += 31 # 32-byte alignment
result += self.nbytes
return result
def save_gguf(self, gguf_writer, name):
gguf_writer.add_tensor(
name=name,
tensor=self.data,
raw_shape=np.array(list(reversed(self.ne))),
raw_dtype=gguf.GGMLQuantizationType.F32)
class OptimizationParamsV0:
def __init__(self):
pass
def load(self, data, offset):
self.type = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
self.n_threads = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
self.past = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
self.delta = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4
self.print_forward_graph = struct.unpack('<?', bytes(data[offset:offset + 1]))[0]; offset += 4 # 32bit-aligned
self.print_backward_graph = struct.unpack('<?', bytes(data[offset:offset + 1]))[0]; offset += 4 # 32bit-aligned
self.adam_n_iter = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
self.adam_sched = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4
self.adam_decay = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4
self.adam_alpha = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4
self.adam_beta1 = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4
self.adam_beta2 = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4
self.adam_eps = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4
self.adam_eps_f = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4
self.adam_eps_g = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4
self.lbfgs_m = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
self.lbfgs_n_iter = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
self.lbfgs_max_linesearch = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
self.lbfgs_eps = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4
self.lbfgs_ftol = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4
self.lbfgs_wolfe = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4
self.lbfgs_min_step = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4
self.lbfgs_max_step = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4
self.lbfgs_linesearch = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
return offset
class OptimizationContext:
def __init__(self):
pass
def load(self, data, offset):
self.version = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]
offset += 4
if self.version == 0:
params = OptimizationParamsV0()
offset = params.load(data, offset)
self.past = params.past
self.lbfgs_m = params.lbfgs_m
self.nx = struct.unpack('N', bytes(data[offset:offset + 8]))[0]; offset += 8
self.iter = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
self.just_initialized = bool(struct.unpack('<i', bytes(data[offset:offset + 4]))[0]); offset += 4
self.type = params.type
self.adam_m = Tensor('f', [self.nx])
self.adam_v = Tensor('f', [self.nx])
self.adam_pf = Tensor('f', [self.past] if self.past > 0 else [])
self.lbfgs_x = Tensor('f', [self.nx])
self.lbfgs_xp = Tensor('f', [self.nx])
self.lbfgs_g = Tensor('f', [self.nx])
self.lbfgs_gp = Tensor('f', [self.nx])
self.lbfgs_d = Tensor('f', [self.nx])
self.lbfgs_pf = Tensor('f', [self.past] if self.past > 0 else [])
self.lbfgs_lmal = Tensor('f', [self.lbfgs_m])
self.lbfgs_lmys = Tensor('f', [self.lbfgs_m])
self.lbfgs_lms = Tensor('f', [self.nx, self.lbfgs_m])
self.lbfgs_lmy = Tensor('f', [self.nx, self.lbfgs_m])
if self.type == 0:
# these tensors are stored, but we don't need their data
x = Tensor('f', [self.nx])
g = Tensor('f', [self.nx])
g2 = Tensor('f', [self.nx])
mh = Tensor('f', [self.nx])
vh = Tensor('f', [self.nx])
offset = x.load(data, offset)
offset = g.load(data, offset)
offset = g2.load(data, offset)
offset = self.adam_m.load(data, offset)
offset = self.adam_v.load(data, offset)
offset = mh.load(data, offset)
offset = vh.load(data, offset)
offset = self.adam_pf.load(data, offset)
self.adam_fx_best = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4
self.adam_fx_prev = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4
self.adam_n_no_improvement = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
elif self.type == 1:
offset = self.lbfgs_x.load(data, offset)
offset = self.lbfgs_xp.load(data, offset)
offset = self.lbfgs_g.load(data, offset)
offset = self.lbfgs_gp.load(data, offset)
offset = self.lbfgs_d.load(data, offset)
offset = self.lbfgs_pf.load(data, offset)
offset = self.lbfgs_lmal.load(data, offset)
offset = self.lbfgs_lmys.load(data, offset)
offset = self.lbfgs_lms.load(data, offset)
offset = self.lbfgs_lmy.load(data, offset)
self.lbfgs_fx_best = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4
self.lbfgs_step = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4
self.lbfgs_j = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
self.lbfgs_k = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
self.lbfgs_end = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
self.lbfgs_n_no_improvement = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
else:
raise ValueError('Unknown optimizer type')
elif self.version == 1:
self.past = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
self.lbfgs_m = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
self.nx = struct.unpack('N', bytes(data[offset:offset + 8]))[0]; offset += 8
self.iter = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
self.just_initialized = bool(struct.unpack('<i', bytes(data[offset:offset + 4]))[0]); offset += 4
self.adam_m = Tensor('f', [self.nx])
self.adam_v = Tensor('f', [self.nx])
self.adam_pf = Tensor('f', [self.past] if self.past > 0 else [])
self.lbfgs_x = Tensor('f', [self.nx])
self.lbfgs_xp = Tensor('f', [self.nx])
self.lbfgs_g = Tensor('f', [self.nx])
self.lbfgs_gp = Tensor('f', [self.nx])
self.lbfgs_d = Tensor('f', [self.nx])
self.lbfgs_pf = Tensor('f', [self.past] if self.past > 0 else [])
self.lbfgs_lmal = Tensor('f', [self.lbfgs_m])
self.lbfgs_lmys = Tensor('f', [self.lbfgs_m])
self.lbfgs_lms = Tensor('f', [self.nx, self.lbfgs_m])
self.lbfgs_lmy = Tensor('f', [self.nx, self.lbfgs_m])
# forgot to save type in version 1:
# guess self.type from number of remaining bytes
size_type_0 = 12 + sum([t.max_storage_size() for t in
[self.adam_m, self.adam_v]
+([self.adam_pf] if (self.past > 0) else [])])
size_type_1 = 24 + sum([t.max_storage_size() for t in
[self.lbfgs_x, self.lbfgs_xp, self.lbfgs_g,
self.lbfgs_gp, self.lbfgs_d, self.lbfgs_pf,
self.lbfgs_lmal, self.lbfgs_lmys,
self.lbfgs_lms, self.lbfgs_lmy]
+([self.lbfgs_pf] if (self.past > 0) else [])])
# due to alignment padding the size might not by exact
# but the difference in size for both types is significant,
# so we can just use whichever is closest
remaining = len(data) - offset
if abs(remaining - size_type_0) < abs(remaining - size_type_1):
self.type = 0
else:
self.type = 1
if self.type == 0:
offset = self.adam_m.load(data, offset)
offset = self.adam_v.load(data, offset)
offset = self.adam_pf.load(data,offset)
self.adam_fx_best = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4
self.adam_fx_prev = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4
self.adam_n_no_improvement = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
elif self.type == 1:
offset = self.lbfgs_x.load(data, offset)
offset = self.lbfgs_xp.load(data, offset)
offset = self.lbfgs_g.load(data, offset)
offset = self.lbfgs_gp.load(data, offset)
offset = self.lbfgs_d.load(data, offset)
offset = self.lbfgs_pf.load(data, offset)
offset = self.lbfgs_lmal.load(data, offset)
offset = self.lbfgs_lmys.load(data, offset)
offset = self.lbfgs_lms.load(data, offset)
offset = self.lbfgs_lmy.load(data, offset)
self.lbfgs_fx_best = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4
self.lbfgs_step = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4
self.lbfgs_j = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
self.lbfgs_k = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
self.lbfgs_end = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
self.lbfgs_n_no_improvement = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
else:
raise ValueError('Invalid version of checkpoint file')
return offset
def save_gguf(self, gguf_writer):
gguf_writer.add_uint32(LLM_KV_OPTIMIZER_FILE_VERSION, 0)
gguf_writer.add_uint32(LLM_KV_OPTIMIZER_CONVERGENCE_PAST_COUNT, self.past)
gguf_writer.add_uint64(LLM_KV_OPTIMIZER_PARAMETER_COUNT, self.nx)
gguf_writer.add_uint32(LLM_KV_OPTIMIZER_ITERATION_COUNT, self.iter)
gguf_writer.add_bool(LLM_KV_OPTIMIZER_JUST_INITIALIZED, self.just_initialized)
if self.type == 0:
gguf_writer.add_string(LLM_KV_OPTIMIZER_TYPE, LLM_KV_OPTIMIZER_TYPE_ADAM)
gguf_writer.add_float32(LLM_KV_OPTIMIZER_ADAM_BEST_LOSS, self.adam_fx_best)
gguf_writer.add_float32(LLM_KV_OPTIMIZER_ADAM_PREVIOUS_LOSS, self.adam_fx_prev)
gguf_writer.add_uint32(LLM_KV_OPTIMIZER_ADAM_NO_IMPROVEMENT_COUNT, self.adam_n_no_improvement)
self.adam_m.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_ADAM_FIRST_MOMENTS)
self.adam_v.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_ADAM_SECOND_MOMENTS)
if self.past > 0:
self.adam_pf.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_ADAM_PAST_LOSS_VALUES)
elif self.type == 1:
gguf_writer.add_string(LLM_KV_OPTIMIZER_TYPE, LLM_KV_OPTIMIZER_TYPE_LBFGS)
gguf_writer.add_uint32(LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT, self.lbfgs_m)
gguf_writer.add_float32(LLM_KV_OPTIMIZER_LBFGS_BEST_LOSS, self.lbfgs_fx_best)
gguf_writer.add_float32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_STEP, self.lbfgs_step)
gguf_writer.add_int32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_J, self.lbfgs_j)
gguf_writer.add_int32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_K, self.lbfgs_k)
gguf_writer.add_int32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_END, self.lbfgs_end)
gguf_writer.add_uint32(LLM_KV_OPTIMIZER_LBFGS_NO_IMPROVEMENT_COUNT, self.lbfgs_n_no_improvement)
self.lbfgs_x.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_PARAMETERS)
self.lbfgs_xp.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_PARAMETERS)
self.lbfgs_g.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_GRADIENTS)
self.lbfgs_gp.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_GRADIENTS)
self.lbfgs_d.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_SEARCH_DIRECTION)
if self.past > 0:
self.lbfgs_pf.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_PAST_LOSS_VALUES)
self.lbfgs_lmal.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_ALPHA)
self.lbfgs_lmys.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_YS)
self.lbfgs_lms.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_S)
self.lbfgs_lmy.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_Y)
else:
raise ValueError('Unknown optimizer type')
class ModelParams:
def __init__(self):
pass
def load(self, data, offset):
self.n_vocab = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
self.n_embd = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
self.n_mult = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
self.n_head = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
self.n_layer = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
self.n_rot = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
return offset
def get_n_ff(self):
# struct my_llama_model::get_n_ff in train-text-from-scratch.cpp commit 3b5515bbe0e2224425986ba24f1f5d84aa38dce9
return ((2*(4*self.n_embd)//3 + self.n_mult - 1)//self.n_mult)*self.n_mult
def save_gguf(self, gguf_writer):
# self.n_vocab not saved
gguf_writer.add_embedding_length(self.n_embd)
gguf_writer.add_head_count(self.n_head)
gguf_writer.add_block_count(self.n_layer)
gguf_writer.add_rope_dimension_count(self.n_rot)
gguf_writer.add_feed_forward_length(self.get_n_ff())
def tensor_name(key, bid=None):
return gguf.MODEL_TENSOR_NAMES[gguf.MODEL_ARCH.LLAMA][key].format(bid=bid) + ".weight"
class Layer:
def __init__(self, params, bid):
self.bid = bid
self.att_norm = Tensor('f', [params.n_embd])
self.wq = Tensor('f', [params.n_embd, params.n_embd])
self.wk = Tensor('f', [params.n_embd, params.n_embd])
self.wv = Tensor('f', [params.n_embd, params.n_embd])
self.wo = Tensor('f', [params.n_embd, params.n_embd])
self.ffn_norm = Tensor('f', [params.n_embd])
self.w1 = Tensor('f', [params.n_embd, params.get_n_ff()])
self.w2 = Tensor('f', [params.get_n_ff(), params.n_embd])
self.w3 = Tensor('f', [params.n_embd, params.get_n_ff()])
def load(self, data, offset):
offset = self.att_norm.load(data, offset)
offset = self.wq.load(data, offset)
offset = self.wk.load(data, offset)
offset = self.wv.load(data, offset)
offset = self.wo.load(data, offset)
offset = self.ffn_norm.load(data, offset)
offset = self.w1.load(data, offset)
offset = self.w2.load(data, offset)
offset = self.w3.load(data, offset)
return offset
def save_gguf(self, gguf_writer):
self.att_norm.save_gguf(gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_NORM, self.bid))
self.wq.save_gguf (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_Q, self.bid))
self.wk.save_gguf (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_K, self.bid))
self.wv.save_gguf (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_V, self.bid))
self.wo.save_gguf (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_OUT, self.bid))
self.ffn_norm.save_gguf(gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_NORM, self.bid))
self.w1.save_gguf (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_GATE, self.bid))
self.w2.save_gguf (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_DOWN, self.bid))
self.w3.save_gguf (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_UP, self.bid))
class Model:
def __init__(self):
self.params = ModelParams()
self.layers = []
def load(self, data, offset):
offset = self.params.load(data, offset)
self.tok_embd = Tensor('f', [self.params.n_embd, self.params.n_vocab])
self.norm = Tensor('f', [self.params.n_embd])
self.output = Tensor('f', [self.params.n_embd, self.params.n_vocab])
offset = self.tok_embd.load(data, offset)
offset = self.norm.load(data, offset)
offset = self.output.load(data, offset)
self.layers.clear()
for bid in range(self.params.n_layer):
layer = Layer(self.params, bid)
offset = layer.load(data, offset)
self.layers.append(layer)
return offset
def save_gguf(self, gguf_writer):
self.params.save_gguf(gguf_writer)
self.tok_embd.save_gguf(gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD))
self.norm.save_gguf (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.OUTPUT_NORM))
self.output.save_gguf (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.OUTPUT))
for layer in self.layers:
layer.save_gguf(gguf_writer)
class Checkpoint:
def __init__(self):
self.model = Model()
self.opt_ctx = OptimizationContext()
def load(self, data, offset):
magic = bytes(reversed(data[offset:offset + 4])); offset += 4
if magic != b'ggcp':
raise ValueError(f"File header magic indicates, that this is no checkpoint file. Expected 'ggcp', Got '{str(magic)}'")
self.version = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
if self.version != 0:
raise ValueError('Invalid version of checkpoint file')
self.train_its = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
self.train_samples = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
self.train_tokens = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
offset = self.model.load(data, offset)
offset = self.opt_ctx.load(data, offset)
return offset
def save_gguf(self, gguf_writer):
gguf_writer.add_file_type(gguf.GGMLQuantizationType.F32)
gguf_writer.add_layer_norm_rms_eps(1e-5)
gguf_writer.add_uint32(LLM_KV_TRAINING_FILE_VERSION, 0)
gguf_writer.add_uint32(LLM_KV_TRAINING_ITERATION_COUNT, self.train_its)
gguf_writer.add_uint32(LLM_KV_TRAINING_SAMPLE_COUNT, self.train_samples)
gguf_writer.add_uint32(LLM_KV_TRAINING_TOKEN_COUNT, self.train_tokens)
self.model.save_gguf(gguf_writer)
self.opt_ctx.save_gguf(gguf_writer)
def handle_args():
parser = argparse.ArgumentParser(description = 'Convert train-text-from-scratch checkpoints to GGUF')
parser.add_argument('--input', '-i', type = Path, help = 'Input train checkpoint filename', required=True)
parser.add_argument('--output', '-o', type = Path, help ='Output GGUF filename', required=True)
return parser.parse_args()
def main():
cfg = handle_args()
data = np.memmap(cfg.input, mode = 'r')
chk = Checkpoint()
offset = 0
offset = chk.load(data, offset)
# we should have read all available data
assert(offset == len(data))
gguf_writer = gguf.GGUFWriter(cfg.output, gguf.MODEL_ARCH_NAMES[gguf.MODEL_ARCH.LLAMA], use_temp_file = False)
chk.save_gguf(gguf_writer)
print(" gguf: write header")
gguf_writer.write_header_to_file()
print(" gguf: write metadata")
gguf_writer.write_kv_data_to_file()
print(" gguf: write tensors")
gguf_writer.write_tensors_to_file()
gguf_writer.close()
if __name__ == '__main__':
main()

3486
examples/train-text-from-scratch/train-text-from-scratch.cpp
View file

File diff suppressed because it is too large Load diff

63
ggml-alloc.c
View file

@ -107,6 +107,10 @@ static size_t ggml_allocator_get_alloc_size(struct ggml_allocr * alloc, struct g
} }
void ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor) { void ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
#ifdef GGML_ALLOCATOR_DEBUG
GGML_ASSERT(ggml_is_view(tensor) == false); // views generally get data pointer from one of their sources
GGML_ASSERT(tensor->data == NULL); // avoid allocating tensor which already has memory allocated
#endif
size_t size = ggml_allocator_get_alloc_size(alloc, tensor); size_t size = ggml_allocator_get_alloc_size(alloc, tensor);
size = aligned_offset(NULL, size, alloc->alignment); size = aligned_offset(NULL, size, alloc->alignment);
@ -268,7 +272,7 @@ struct ggml_allocr * ggml_allocr_new(void * data, size_t size, size_t alignment)
/*.parse_seq = */ {0}, /*.parse_seq = */ {0},
/*.parse_seq_len = */ 0, /*.parse_seq_len = */ 0,
#ifdef GGML_ALLOCATOR_DEBUG #ifdef GGML_ALLOCATOR_DEBUG
/*.allocated_tensors = */ = {0}, /*.allocated_tensors = */ {0},
#endif #endif
}; };
@ -297,7 +301,7 @@ struct ggml_allocr * ggml_allocr_new_measure(size_t alignment) {
/*.parse_seq = */ {0}, /*.parse_seq = */ {0},
/*.parse_seq_len = */ 0, /*.parse_seq_len = */ 0,
#ifdef GGML_ALLOCATOR_DEBUG #ifdef GGML_ALLOCATOR_DEBUG
/*.allocated_tensors = */ = {0}, /*.allocated_tensors = */ {0},
#endif #endif
}; };
@ -317,8 +321,7 @@ bool ggml_allocr_is_measure(struct ggml_allocr * alloc) {
//////////// compute graph allocator //////////// compute graph allocator
static bool ggml_is_view(struct ggml_tensor * t) { static bool ggml_is_view(struct ggml_tensor * t) {
return t->op == GGML_OP_RESHAPE || t->op == GGML_OP_VIEW || t->op == GGML_OP_TRANSPOSE || return t->view_src != NULL;
t->op == GGML_OP_PERMUTE || t->op == GGML_OP_CPY;
} }
static bool ggml_are_same_layout(const struct ggml_tensor * a, const struct ggml_tensor * b) { static bool ggml_are_same_layout(const struct ggml_tensor * a, const struct ggml_tensor * b) {
@ -336,28 +339,6 @@ static bool ggml_are_same_layout(const struct ggml_tensor * a, const struct ggml
return true; return true;
} }
static struct ggml_tensor * get_view_parent(struct ggml_tensor * t) {
switch (t->op) {
case GGML_OP_PERMUTE:
case GGML_OP_RESHAPE:
case GGML_OP_TRANSPOSE:
case GGML_OP_VIEW:
return t->src[0];
case GGML_OP_CPY:
return t->src[1];
default:
return NULL;
}
}
static struct ggml_tensor * get_view_source(struct ggml_tensor * t) {
struct ggml_tensor * parent = t;
do {
parent = get_view_parent(parent);
} while (ggml_is_view(parent));
return parent;
}
static bool ggml_op_can_inplace(enum ggml_op op) { static bool ggml_op_can_inplace(enum ggml_op op) {
switch (op) { switch (op) {
case GGML_OP_SCALE: case GGML_OP_SCALE:
@ -365,7 +346,6 @@ static bool ggml_op_can_inplace(enum ggml_op op) {
case GGML_OP_DIAG_MASK_INF: case GGML_OP_DIAG_MASK_INF:
case GGML_OP_ADD: case GGML_OP_ADD:
case GGML_OP_ADD1: case GGML_OP_ADD1:
case GGML_OP_ACC:
case GGML_OP_SUB: case GGML_OP_SUB:
case GGML_OP_MUL: case GGML_OP_MUL:
case GGML_OP_DIV: case GGML_OP_DIV:
@ -375,7 +355,6 @@ static bool ggml_op_can_inplace(enum ggml_op op) {
case GGML_OP_UNARY: case GGML_OP_UNARY:
case GGML_OP_ROPE: case GGML_OP_ROPE:
case GGML_OP_RMS_NORM: case GGML_OP_RMS_NORM:
case GGML_OP_SET:
case GGML_OP_SOFT_MAX: case GGML_OP_SOFT_MAX:
case GGML_OP_CONT: case GGML_OP_CONT:
return true; return true;
@ -389,24 +368,8 @@ static void allocate_node(struct ggml_allocr * alloc, struct ggml_tensor * node)
struct hash_node * ht = alloc->hash_table; struct hash_node * ht = alloc->hash_table;
if (node->data == NULL) { if (node->data == NULL) {
if (ggml_is_view(node)) { if (ggml_is_view(node)) {
size_t offset; assert(node->view_src->data != NULL);
switch(node->op) { node->data = (char *)node->view_src->data + node->view_offs;
case GGML_OP_VIEW:
memcpy(&offset, node->op_params, sizeof(size_t));
node->data = (char *) node->src[0]->data + offset;
break;
case GGML_OP_PERMUTE:
case GGML_OP_RESHAPE:
case GGML_OP_TRANSPOSE:
node->data = node->src[0]->data;
break;
case GGML_OP_CPY:
node->data = node->src[1]->data;
break;
default:
GGML_ASSERT(!"unknown view op");
break;
}
} else { } else {
// see if we can reuse a parent's buffer (inplace) // see if we can reuse a parent's buffer (inplace)
if (ggml_op_can_inplace(node->op)) { if (ggml_op_can_inplace(node->op)) {
@ -426,7 +389,7 @@ static void allocate_node(struct ggml_allocr * alloc, struct ggml_tensor * node)
struct hash_node * p_hn = hash_get(ht, parent); struct hash_node * p_hn = hash_get(ht, parent);
if (parent->data != NULL && p_hn->n_children == 1 && p_hn->n_views == 0 && ggml_are_same_layout(node, parent)) { if (parent->data != NULL && p_hn->n_children == 1 && p_hn->n_views == 0 && ggml_are_same_layout(node, parent)) {
if (ggml_is_view(parent)) { if (ggml_is_view(parent)) {
struct ggml_tensor * view_src = get_view_source(parent); struct ggml_tensor * view_src = parent->view_src;
struct hash_node * view_src_hn = hash_get(ht, view_src); struct hash_node * view_src_hn = hash_get(ht, view_src);
if (view_src_hn->n_views == 1 && view_src_hn->n_children == 0 && view_src->data == parent->data) { if (view_src_hn->n_views == 1 && view_src_hn->n_children == 0 && view_src->data == parent->data) {
// TODO: the offset of the view parent must be kept to ensure that the op doesn't overwrite // TODO: the offset of the view parent must be kept to ensure that the op doesn't overwrite
@ -468,7 +431,7 @@ static size_t ggml_allocator_alloc_graph_tensors_n(
struct ggml_tensor * node = gf->nodes[i]; struct ggml_tensor * node = gf->nodes[i];
if (ggml_is_view(node)) { if (ggml_is_view(node)) {
struct ggml_tensor * view_src = get_view_source(node); struct ggml_tensor * view_src = node->view_src;
hash_get(ht, view_src)->n_views += 1; hash_get(ht, view_src)->n_views += 1;
} }
@ -553,10 +516,10 @@ static size_t ggml_allocator_alloc_graph_tensors_n(
if (p_hn->n_children == 0 && p_hn->n_views == 0) { if (p_hn->n_children == 0 && p_hn->n_views == 0) {
if (ggml_is_view(parent)) { if (ggml_is_view(parent)) {
struct ggml_tensor * view_src = get_view_source(parent); struct ggml_tensor * view_src = parent->view_src;
struct hash_node * view_src_hn = hash_get(ht, view_src); struct hash_node * view_src_hn = hash_get(ht, view_src);
view_src_hn->n_views -= 1; view_src_hn->n_views -= 1;
AT_PRINTF("view_src %s\n", view_src->name); AT_PRINTF("view_src %s: %d children, %d views\n", view_src->name, view_src_hn->n_children, view_src_hn->n_views);
if (view_src_hn->n_views == 0 && view_src_hn->n_children == 0 && view_src->data != node->data) { if (view_src_hn->n_views == 0 && view_src_hn->n_children == 0 && view_src->data != node->data) {
ggml_allocator_free_tensor(alloc, view_src); ggml_allocator_free_tensor(alloc, view_src);
} }

7
ggml-cuda.cu
View file

@ -4908,8 +4908,8 @@ static void scale_f32_cuda(const float * x, float * dst, const float scale, cons
static void rope_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float p0, static void rope_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float p0,
const float p_delta, const int p_delta_rows, const float theta_scale, cudaStream_t stream) { const float p_delta, const int p_delta_rows, const float theta_scale, cudaStream_t stream) {
GGML_ASSERT(nrows % 2 == 0); // GG: is this assert really needed? I don't see why GGML_ASSERT(ncols % 2 == 0);
const dim3 block_dims(1, 2*CUDA_ROPE_BLOCK_SIZE, 1); const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
const int num_blocks_x = (ncols + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE); const int num_blocks_x = (ncols + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
const dim3 block_nums(nrows, num_blocks_x, 1); const dim3 block_nums(nrows, num_blocks_x, 1);
rope_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, p0, p_delta, p_delta_rows, theta_scale); rope_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, p0, p_delta, p_delta_rows, theta_scale);
@ -4917,7 +4917,8 @@ static void rope_f32_cuda(const float * x, float * dst, const int ncols, const i
static void rope_neox_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float p0, static void rope_neox_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float p0,
const float p_delta, const int p_delta_rows, const float theta_scale, cudaStream_t stream) { const float p_delta, const int p_delta_rows, const float theta_scale, cudaStream_t stream) {
const dim3 block_dims(1, 2*CUDA_ROPE_BLOCK_SIZE, 1); GGML_ASSERT(ncols % 2 == 0);
const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
const int num_blocks_x = (ncols + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE); const int num_blocks_x = (ncols + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
const dim3 block_nums(nrows, num_blocks_x, 1); const dim3 block_nums(nrows, num_blocks_x, 1);
rope_neox_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, p0, p_delta, p_delta_rows, theta_scale); rope_neox_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, p0, p_delta, p_delta_rows, theta_scale);

1
ggml-metal.h
View file

@ -24,6 +24,7 @@
// max memory buffers that can be mapped to the device // max memory buffers that can be mapped to the device
#define GGML_METAL_MAX_BUFFERS 16 #define GGML_METAL_MAX_BUFFERS 16
#define GGML_METAL_MAX_COMMAND_BUFFERS 32
struct ggml_tensor; struct ggml_tensor;
struct ggml_cgraph; struct ggml_cgraph;

169
ggml-metal.m
View file

@ -11,6 +11,7 @@
#define MIN(a, b) ((a) < (b) ? (a) : (b)) #define MIN(a, b) ((a) < (b) ? (a) : (b))
#define MAX(a, b) ((a) > (b) ? (a) : (b)) #define MAX(a, b) ((a) > (b) ? (a) : (b))
// TODO: temporary - reuse llama.cpp logging
#ifdef GGML_METAL_NDEBUG #ifdef GGML_METAL_NDEBUG
#define metal_printf(...) #define metal_printf(...)
#else #else
@ -33,12 +34,15 @@ struct ggml_metal_buffer {
struct ggml_metal_context { struct ggml_metal_context {
int n_cb; int n_cb;
float * logits;
id<MTLDevice> device; id<MTLDevice> device;
id<MTLCommandQueue> queue; id<MTLCommandQueue> queue;
id<MTLLibrary> library; id<MTLLibrary> library;
id<MTLCommandBuffer> command_buffers [GGML_METAL_MAX_COMMAND_BUFFERS];
id<MTLComputeCommandEncoder> command_encoders[GGML_METAL_MAX_COMMAND_BUFFERS];
dispatch_queue_t d_queue;
int n_buffers; int n_buffers;
struct ggml_metal_buffer buffers[GGML_METAL_MAX_BUFFERS]; struct ggml_metal_buffer buffers[GGML_METAL_MAX_BUFFERS];
@ -110,16 +114,17 @@ static NSString * const msl_library_source = @"see metal.metal";
@end @end
struct ggml_metal_context * ggml_metal_init(int n_cb) { struct ggml_metal_context * ggml_metal_init(int n_cb) {
fprintf(stderr, "%s: allocating\n", __func__); metal_printf("%s: allocating\n", __func__);
struct ggml_metal_context * ctx = malloc(sizeof(struct ggml_metal_context)); struct ggml_metal_context * ctx = malloc(sizeof(struct ggml_metal_context));
ctx->n_cb = n_cb; ctx->n_cb = MIN(n_cb, GGML_METAL_MAX_BUFFERS);
ctx->device = MTLCreateSystemDefaultDevice(); ctx->device = MTLCreateSystemDefaultDevice();
ctx->queue = [ctx->device newCommandQueue]; ctx->queue = [ctx->device newCommandQueue];
ctx->n_buffers = 0; ctx->n_buffers = 0;
ctx->concur_list_len = 0; ctx->concur_list_len = 0;
ctx->d_queue = dispatch_queue_create("llama.cpp", DISPATCH_QUEUE_CONCURRENT);
#if 0 #if 0
// compile from source string and show compile log // compile from source string and show compile log
@ -128,7 +133,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
ctx->library = [ctx->device newLibraryWithSource:msl_library_source options:nil error:&error]; ctx->library = [ctx->device newLibraryWithSource:msl_library_source options:nil error:&error];
if (error) { if (error) {
fprintf(stderr, "%s: error: %s\n", __func__, [[error description] UTF8String]); metal_printf("%s: error: %s\n", __func__, [[error description] UTF8String]);
return NULL; return NULL;
} }
} }
@ -142,11 +147,11 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
//NSString * path = [[NSBundle mainBundle] pathForResource:@"../../examples/metal/metal" ofType:@"metal"]; //NSString * path = [[NSBundle mainBundle] pathForResource:@"../../examples/metal/metal" ofType:@"metal"];
NSBundle * bundle = [NSBundle bundleForClass:[GGMLMetalClass class]]; NSBundle * bundle = [NSBundle bundleForClass:[GGMLMetalClass class]];
NSString * path = [bundle pathForResource:@"ggml-metal" ofType:@"metal"]; NSString * path = [bundle pathForResource:@"ggml-metal" ofType:@"metal"];
fprintf(stderr, "%s: loading '%s'\n", __func__, [path UTF8String]); metal_printf("%s: loading '%s'\n", __func__, [path UTF8String]);
NSString * src = [NSString stringWithContentsOfFile:path encoding:NSUTF8StringEncoding error:&error]; NSString * src = [NSString stringWithContentsOfFile:path encoding:NSUTF8StringEncoding error:&error];
if (error) { if (error) {
fprintf(stderr, "%s: error: %s\n", __func__, [[error description] UTF8String]); metal_printf("%s: error: %s\n", __func__, [[error description] UTF8String]);
return NULL; return NULL;
} }
@ -158,7 +163,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
ctx->library = [ctx->device newLibraryWithSource:src options:nil error:&error]; ctx->library = [ctx->device newLibraryWithSource:src options:nil error:&error];
#endif #endif
if (error) { if (error) {
fprintf(stderr, "%s: error: %s\n", __func__, [[error description] UTF8String]); metal_printf("%s: error: %s\n", __func__, [[error description] UTF8String]);
return NULL; return NULL;
} }
} }
@ -170,11 +175,11 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
#define GGML_METAL_ADD_KERNEL(name) \ #define GGML_METAL_ADD_KERNEL(name) \
ctx->function_##name = [ctx->library newFunctionWithName:@"kernel_"#name]; \ ctx->function_##name = [ctx->library newFunctionWithName:@"kernel_"#name]; \
ctx->pipeline_##name = [ctx->device newComputePipelineStateWithFunction:ctx->function_##name error:&error]; \ ctx->pipeline_##name = [ctx->device newComputePipelineStateWithFunction:ctx->function_##name error:&error]; \
fprintf(stderr, "%s: loaded %-32s %16p | th_max = %4d | th_width = %4d\n", __func__, "kernel_"#name, (void *) ctx->pipeline_##name, \ metal_printf("%s: loaded %-32s %16p | th_max = %4d | th_width = %4d\n", __func__, "kernel_"#name, (void *) ctx->pipeline_##name, \
(int) ctx->pipeline_##name.maxTotalThreadsPerThreadgroup, \ (int) ctx->pipeline_##name.maxTotalThreadsPerThreadgroup, \
(int) ctx->pipeline_##name.threadExecutionWidth); \ (int) ctx->pipeline_##name.threadExecutionWidth); \
if (error) { \ if (error) { \
fprintf(stderr, "%s: load pipeline error: %s\n", __func__, [[error description] UTF8String]); \ metal_printf("%s: load pipeline error: %s\n", __func__, [[error description] UTF8String]); \
return NULL; \ return NULL; \
} }
@ -226,22 +231,80 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
#undef GGML_METAL_ADD_KERNEL #undef GGML_METAL_ADD_KERNEL
} }
fprintf(stderr, "%s: recommendedMaxWorkingSetSize = %8.2f MB\n", __func__, ctx->device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0); metal_printf("%s: recommendedMaxWorkingSetSize = %8.2f MB\n", __func__, ctx->device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0);
fprintf(stderr, "%s: hasUnifiedMemory = %s\n", __func__, ctx->device.hasUnifiedMemory ? "true" : "false"); metal_printf("%s: hasUnifiedMemory = %s\n", __func__, ctx->device.hasUnifiedMemory ? "true" : "false");
if (ctx->device.maxTransferRate != 0) { if (ctx->device.maxTransferRate != 0) {
fprintf(stderr, "%s: maxTransferRate = %8.2f MB/s\n", __func__, ctx->device.maxTransferRate / 1024.0 / 1024.0); metal_printf("%s: maxTransferRate = %8.2f MB/s\n", __func__, ctx->device.maxTransferRate / 1024.0 / 1024.0);
} else { } else {
fprintf(stderr, "%s: maxTransferRate = built-in GPU\n", __func__); metal_printf("%s: maxTransferRate = built-in GPU\n", __func__);
} }
return ctx; return ctx;
} }
void ggml_metal_free(struct ggml_metal_context * ctx) { void ggml_metal_free(struct ggml_metal_context * ctx) {
fprintf(stderr, "%s: deallocating\n", __func__); metal_printf("%s: deallocating\n", __func__);
#define GGML_METAL_DEL_KERNEL(name) \
[ctx->function_##name release]; \
[ctx->pipeline_##name release];
GGML_METAL_DEL_KERNEL(add);
GGML_METAL_DEL_KERNEL(add_row);
GGML_METAL_DEL_KERNEL(mul);
GGML_METAL_DEL_KERNEL(mul_row);
GGML_METAL_DEL_KERNEL(scale);
GGML_METAL_DEL_KERNEL(silu);
GGML_METAL_DEL_KERNEL(relu);
GGML_METAL_DEL_KERNEL(gelu);
GGML_METAL_DEL_KERNEL(soft_max);
GGML_METAL_DEL_KERNEL(diag_mask_inf);
GGML_METAL_DEL_KERNEL(get_rows_f16);
GGML_METAL_DEL_KERNEL(get_rows_q4_0);
GGML_METAL_DEL_KERNEL(get_rows_q4_1);
GGML_METAL_DEL_KERNEL(get_rows_q8_0);
GGML_METAL_DEL_KERNEL(get_rows_q2_K);
GGML_METAL_DEL_KERNEL(get_rows_q3_K);
GGML_METAL_DEL_KERNEL(get_rows_q4_K);
GGML_METAL_DEL_KERNEL(get_rows_q5_K);
GGML_METAL_DEL_KERNEL(get_rows_q6_K);
GGML_METAL_DEL_KERNEL(rms_norm);
GGML_METAL_DEL_KERNEL(norm);
GGML_METAL_DEL_KERNEL(mul_mat_f16_f32);
GGML_METAL_DEL_KERNEL(mul_mat_q4_0_f32);
GGML_METAL_DEL_KERNEL(mul_mat_q4_1_f32);
GGML_METAL_DEL_KERNEL(mul_mat_q8_0_f32);
GGML_METAL_DEL_KERNEL(mul_mat_q2_K_f32);
GGML_METAL_DEL_KERNEL(mul_mat_q3_K_f32);
GGML_METAL_DEL_KERNEL(mul_mat_q4_K_f32);
GGML_METAL_DEL_KERNEL(mul_mat_q5_K_f32);
GGML_METAL_DEL_KERNEL(mul_mat_q6_K_f32);
GGML_METAL_DEL_KERNEL(mul_mm_f16_f32);
GGML_METAL_DEL_KERNEL(mul_mm_q4_0_f32);
GGML_METAL_DEL_KERNEL(mul_mm_q8_0_f32);
GGML_METAL_DEL_KERNEL(mul_mm_q4_1_f32);
GGML_METAL_DEL_KERNEL(mul_mm_q2_K_f32);
GGML_METAL_DEL_KERNEL(mul_mm_q3_K_f32);
GGML_METAL_DEL_KERNEL(mul_mm_q4_K_f32);
GGML_METAL_DEL_KERNEL(mul_mm_q5_K_f32);
GGML_METAL_DEL_KERNEL(mul_mm_q6_K_f32);
GGML_METAL_DEL_KERNEL(rope);
GGML_METAL_DEL_KERNEL(alibi_f32);
GGML_METAL_DEL_KERNEL(cpy_f32_f16);
GGML_METAL_DEL_KERNEL(cpy_f32_f32);
GGML_METAL_DEL_KERNEL(cpy_f16_f16);
#undef GGML_METAL_DEL_KERNEL
for (int i = 0; i < ctx->n_buffers; ++i) { for (int i = 0; i < ctx->n_buffers; ++i) {
[ctx->buffers[i].metal release]; [ctx->buffers[i].metal release];
} }
[ctx->library release];
[ctx->queue release];
[ctx->device release];
dispatch_release(ctx->d_queue);
free(ctx); free(ctx);
} }
@ -249,7 +312,7 @@ void * ggml_metal_host_malloc(size_t n) {
void * data = NULL; void * data = NULL;
const int result = posix_memalign((void **) &data, getpagesize(), n); const int result = posix_memalign((void **) &data, getpagesize(), n);
if (result != 0) { if (result != 0) {
fprintf(stderr, "%s: error: posix_memalign failed\n", __func__); metal_printf("%s: error: posix_memalign failed\n", __func__);
return NULL; return NULL;
} }
@ -261,7 +324,7 @@ void ggml_metal_host_free(void * data) {
} }
void ggml_metal_set_n_cb(struct ggml_metal_context * ctx, int n_cb) { void ggml_metal_set_n_cb(struct ggml_metal_context * ctx, int n_cb) {
ctx->n_cb = n_cb; ctx->n_cb = MIN(n_cb, GGML_METAL_MAX_BUFFERS);
} }
int ggml_metal_if_optimized(struct ggml_metal_context * ctx) { int ggml_metal_if_optimized(struct ggml_metal_context * ctx) {
@ -277,7 +340,7 @@ int * ggml_metal_get_concur_list(struct ggml_metal_context * ctx) {
// Metal buffer based on the host memory pointer // Metal buffer based on the host memory pointer
// //
static id<MTLBuffer> ggml_metal_get_buffer(struct ggml_metal_context * ctx, struct ggml_tensor * t, size_t * offs) { static id<MTLBuffer> ggml_metal_get_buffer(struct ggml_metal_context * ctx, struct ggml_tensor * t, size_t * offs) {
//fprintf(stderr, "%s: data tensor '%16s', offs_data = %8ld, offs_eval = %8ld, offs_cach = %8ld\n", __func__, t->name, offs_data, offs_eval, offs_cach); //metal_printf("%s: data tensor '%16s', offs_data = %8ld, offs_eval = %8ld, offs_cach = %8ld\n", __func__, t->name, offs_data, offs_eval, offs_cach);
const int64_t tsize = ggml_nbytes(t); const int64_t tsize = ggml_nbytes(t);
@ -288,13 +351,13 @@ static id<MTLBuffer> ggml_metal_get_buffer(struct ggml_metal_context * ctx, stru
if (ioffs >= 0 && ioffs + tsize <= (int64_t) ctx->buffers[i].size) { if (ioffs >= 0 && ioffs + tsize <= (int64_t) ctx->buffers[i].size) {
*offs = (size_t) ioffs; *offs = (size_t) ioffs;
//fprintf(stderr, "%s: '%s' tensor '%16s', offs = %8ld\n", __func__, ctx->buffers[i].name, t->name, *offs); //metal_printf("%s: '%s' tensor '%16s', offs = %8ld\n", __func__, ctx->buffers[i].name, t->name, *offs);
return ctx->buffers[i].metal; return ctx->buffers[i].metal;
} }
} }
fprintf(stderr, "%s: error: buffer is nil\n", __func__); metal_printf("%s: error: buffer is nil\n", __func__);
return nil; return nil;
} }
@ -306,7 +369,7 @@ bool ggml_metal_add_buffer(
size_t size, size_t size,
size_t max_size) { size_t max_size) {
if (ctx->n_buffers >= GGML_METAL_MAX_BUFFERS) { if (ctx->n_buffers >= GGML_METAL_MAX_BUFFERS) {
fprintf(stderr, "%s: too many buffers\n", __func__); metal_printf("%s: too many buffers\n", __func__);
return false; return false;
} }
@ -316,7 +379,7 @@ bool ggml_metal_add_buffer(
const int64_t ioffs = (int64_t) data - (int64_t) ctx->buffers[i].data; const int64_t ioffs = (int64_t) data - (int64_t) ctx->buffers[i].data;
if (ioffs >= 0 && ioffs < (int64_t) ctx->buffers[i].size) { if (ioffs >= 0 && ioffs < (int64_t) ctx->buffers[i].size) {
fprintf(stderr, "%s: error: buffer '%s' overlaps with '%s'\n", __func__, name, ctx->buffers[i].name); metal_printf("%s: error: buffer '%s' overlaps with '%s'\n", __func__, name, ctx->buffers[i].name);
return false; return false;
} }
} }
@ -337,11 +400,11 @@ bool ggml_metal_add_buffer(
ctx->buffers[ctx->n_buffers].metal = [ctx->device newBufferWithBytesNoCopy:data length:size_aligned options:MTLResourceStorageModeShared deallocator:nil]; ctx->buffers[ctx->n_buffers].metal = [ctx->device newBufferWithBytesNoCopy:data length:size_aligned options:MTLResourceStorageModeShared deallocator:nil];
if (ctx->buffers[ctx->n_buffers].metal == nil) { if (ctx->buffers[ctx->n_buffers].metal == nil) {
fprintf(stderr, "%s: failed to allocate '%-16s' buffer, size = %8.2f MB\n", __func__, name, size_aligned / 1024.0 / 1024.0); metal_printf("%s: failed to allocate '%-16s' buffer, size = %8.2f MB\n", __func__, name, size_aligned / 1024.0 / 1024.0);
return false; return false;
} }
fprintf(stderr, "%s: allocated '%-16s' buffer, size = %8.2f MB", __func__, name, size_aligned / 1024.0 / 1024.0); metal_printf("%s: allocated '%-16s' buffer, size = %8.2f MB", __func__, name, size_aligned / 1024.0 / 1024.0);
++ctx->n_buffers; ++ctx->n_buffers;
} else { } else {
@ -361,27 +424,27 @@ bool ggml_metal_add_buffer(
ctx->buffers[ctx->n_buffers].metal = [ctx->device newBufferWithBytesNoCopy:(void *) ((uint8_t *) data + i) length:size_step_aligned options:MTLResourceStorageModeShared deallocator:nil]; ctx->buffers[ctx->n_buffers].metal = [ctx->device newBufferWithBytesNoCopy:(void *) ((uint8_t *) data + i) length:size_step_aligned options:MTLResourceStorageModeShared deallocator:nil];
if (ctx->buffers[ctx->n_buffers].metal == nil) { if (ctx->buffers[ctx->n_buffers].metal == nil) {
fprintf(stderr, "%s: failed to allocate '%-16s' buffer, size = %8.2f MB\n", __func__, name, size_step_aligned / 1024.0 / 1024.0); metal_printf("%s: failed to allocate '%-16s' buffer, size = %8.2f MB\n", __func__, name, size_step_aligned / 1024.0 / 1024.0);
return false; return false;
} }
fprintf(stderr, "%s: allocated '%-16s' buffer, size = %8.2f MB, offs = %12ld", __func__, name, size_step_aligned / 1024.0 / 1024.0, i); metal_printf("%s: allocated '%-16s' buffer, size = %8.2f MB, offs = %12ld", __func__, name, size_step_aligned / 1024.0 / 1024.0, i);
if (i + size_step < size) { if (i + size_step < size) {
fprintf(stderr, "\n"); metal_printf("\n");
} }
++ctx->n_buffers; ++ctx->n_buffers;
} }
} }
fprintf(stderr, ", (%8.2f / %8.2f)", metal_printf(", (%8.2f / %8.2f)",
ctx->device.currentAllocatedSize / 1024.0 / 1024.0, ctx->device.currentAllocatedSize / 1024.0 / 1024.0,
ctx->device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0); ctx->device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0);
if (ctx->device.currentAllocatedSize > ctx->device.recommendedMaxWorkingSetSize) { if (ctx->device.currentAllocatedSize > ctx->device.recommendedMaxWorkingSetSize) {
fprintf(stderr, ", warning: current allocated size is greater than the recommended max working set size\n"); metal_printf(", warning: current allocated size is greater than the recommended max working set size\n");
} else { } else {
fprintf(stderr, "\n"); metal_printf("\n");
} }
} }
@ -391,8 +454,6 @@ bool ggml_metal_add_buffer(
void ggml_metal_set_tensor( void ggml_metal_set_tensor(
struct ggml_metal_context * ctx, struct ggml_metal_context * ctx,
struct ggml_tensor * t) { struct ggml_tensor * t) {
metal_printf("%s: set input for tensor '%s'\n", __func__, t->name);
size_t offs; size_t offs;
id<MTLBuffer> id_dst = ggml_metal_get_buffer(ctx, t, &offs); id<MTLBuffer> id_dst = ggml_metal_get_buffer(ctx, t, &offs);
@ -402,8 +463,6 @@ void ggml_metal_set_tensor(
void ggml_metal_get_tensor( void ggml_metal_get_tensor(
struct ggml_metal_context * ctx, struct ggml_metal_context * ctx,
struct ggml_tensor * t) { struct ggml_tensor * t) {
metal_printf("%s: extract results for tensor '%s'\n", __func__, t->name);
size_t offs; size_t offs;
id<MTLBuffer> id_src = ggml_metal_get_buffer(ctx, t, &offs); id<MTLBuffer> id_src = ggml_metal_get_buffer(ctx, t, &offs);
@ -498,14 +557,14 @@ void ggml_metal_graph_find_concurrency(
} }
if (ctx->concur_list_len > GGML_MAX_CONCUR) { if (ctx->concur_list_len > GGML_MAX_CONCUR) {
fprintf(stderr, "%s: too many elements for metal ctx->concur_list!\n", __func__); metal_printf("%s: too many elements for metal ctx->concur_list!\n", __func__);
} }
} }
void ggml_metal_graph_compute( void ggml_metal_graph_compute(
struct ggml_metal_context * ctx, struct ggml_metal_context * ctx,
struct ggml_cgraph * gf) { struct ggml_cgraph * gf) {
metal_printf("%s: evaluating graph\n", __func__); @autoreleasepool {
// if there is ctx->concur_list, dispatch concurrently // if there is ctx->concur_list, dispatch concurrently
// else fallback to serial dispatch // else fallback to serial dispatch
@ -521,29 +580,25 @@ void ggml_metal_graph_compute(
const int n_cb = ctx->n_cb; const int n_cb = ctx->n_cb;
NSMutableArray * command_buffers = [NSMutableArray arrayWithCapacity:n_cb];
for (int i = 0; i < n_cb; ++i) { for (int i = 0; i < n_cb; ++i) {
command_buffers[i] = [ctx->queue commandBuffer]; ctx->command_buffers[i] = [ctx->queue commandBuffer];
// enqueue the command buffers in order to specify their execution order // enqueue the command buffers in order to specify their execution order
[command_buffers[i] enqueue]; [ctx->command_buffers[i] enqueue];
}
// TODO: is this the best way to start threads? ctx->command_encoders[i] = [ctx->command_buffers[i] computeCommandEncoderWithDescriptor: edesc];
dispatch_queue_t queue = dispatch_queue_create("llama.cpp", DISPATCH_QUEUE_CONCURRENT); }
for (int cb_idx = 0; cb_idx < n_cb; ++cb_idx) { for (int cb_idx = 0; cb_idx < n_cb; ++cb_idx) {
const int n_nodes_per_cb = (n_nodes + n_cb - 1) / n_cb; const int n_nodes_per_cb = (n_nodes + n_cb - 1) / n_cb;
dispatch_async(queue, ^{ dispatch_async(ctx->d_queue, ^{
size_t offs_src0 = 0; size_t offs_src0 = 0;
size_t offs_src1 = 0; size_t offs_src1 = 0;
size_t offs_dst = 0; size_t offs_dst = 0;
id<MTLCommandBuffer> command_buffer = command_buffers[cb_idx]; id<MTLCommandBuffer> command_buffer = ctx->command_buffers[cb_idx];
id<MTLComputeCommandEncoder> encoder = ctx->command_encoders[cb_idx];
id<MTLComputeCommandEncoder> encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
const int node_start = (cb_idx + 0) * n_nodes_per_cb; const int node_start = (cb_idx + 0) * n_nodes_per_cb;
const int node_end = MIN((cb_idx == n_cb - 1) ? n_nodes : (cb_idx + 1) * n_nodes_per_cb, n_nodes); const int node_end = MIN((cb_idx == n_cb - 1) ? n_nodes : (cb_idx + 1) * n_nodes_per_cb, n_nodes);
@ -556,7 +611,7 @@ void ggml_metal_graph_compute(
continue; continue;
} }
metal_printf("%s: encoding node %3d, op = %8s\n", __func__, i, ggml_op_name(gf->nodes[i]->op)); //metal_printf("%s: encoding node %3d, op = %8s\n", __func__, i, ggml_op_name(gf->nodes[i]->op));
struct ggml_tensor * src0 = gf->nodes[i]->src[0]; struct ggml_tensor * src0 = gf->nodes[i]->src[0];
struct ggml_tensor * src1 = gf->nodes[i]->src[1]; struct ggml_tensor * src1 = gf->nodes[i]->src[1];
@ -704,7 +759,7 @@ void ggml_metal_graph_compute(
} break; } break;
default: default:
{ {
fprintf(stderr, "%s: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op)); metal_printf("%s: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op));
GGML_ASSERT(false); GGML_ASSERT(false);
} }
} break; } break;
@ -863,7 +918,7 @@ void ggml_metal_graph_compute(
} break; } break;
default: default:
{ {
fprintf(stderr, "Asserting on type %d\n",(int)src0t); metal_printf("Asserting on type %d\n",(int)src0t);
GGML_ASSERT(false && "not implemented"); GGML_ASSERT(false && "not implemented");
} }
}; };
@ -1101,7 +1156,7 @@ void ggml_metal_graph_compute(
} break; } break;
default: default:
{ {
fprintf(stderr, "%s: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op)); metal_printf("%s: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op));
GGML_ASSERT(false); GGML_ASSERT(false);
} }
} }
@ -1117,17 +1172,19 @@ void ggml_metal_graph_compute(
} }
// wait for all threads to finish // wait for all threads to finish
dispatch_barrier_sync(queue, ^{}); dispatch_barrier_sync(ctx->d_queue, ^{});
[command_buffers[n_cb - 1] waitUntilCompleted];
// check status of command buffers // check status of command buffers
// needed to detect if the device ran out-of-memory for example (#1881) // needed to detect if the device ran out-of-memory for example (#1881)
for (int i = 0; i < n_cb; i++) { for (int i = 0; i < n_cb; i++) {
MTLCommandBufferStatus status = (MTLCommandBufferStatus) [command_buffers[i] status]; [ctx->command_buffers[i] waitUntilCompleted];
MTLCommandBufferStatus status = (MTLCommandBufferStatus) [ctx->command_buffers[i] status];
if (status != MTLCommandBufferStatusCompleted) { if (status != MTLCommandBufferStatusCompleted) {
fprintf(stderr, "%s: command buffer %d failed with status %lu\n", __func__, i, status); metal_printf("%s: command buffer %d failed with status %lu\n", __func__, i, status);
GGML_ASSERT(false); GGML_ASSERT(false);
} }
} }
}
} }

561
ggml.c
View file

File diff suppressed because it is too large Load diff

46
ggml.h
View file

@ -130,13 +130,16 @@
// The data of the tensor is accessed via the "data" pointer. For example: // The data of the tensor is accessed via the "data" pointer. For example:
// //
// { // {
// struct ggml_tensor * a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 2, 3); // const int nx = 2;
// const int ny = 3;
// //
// // a[2, 1] = 1.0f; // struct ggml_tensor * a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, nx, ny);
// *(float *) ((char *) a->data + 2*a->nb[1] + 1*a->nb[0]) = 1.0f;
// //
// // a[0, 2] = 2.0f; // for (int y = 0; y < ny; y++) {
// *(float *) ((char *) a->data + 0*a->nb[1] + 2*a->nb[0]) = 2.0f; // for (int x = 0; x < nx; x++) {
// *(float *) ((char *) a->data + y*a->nb[1] + x*a->nb[0]) = x + y;
// }
// }
// //
// ... // ...
// } // }
@ -211,6 +214,11 @@
#define GGML_MAX_OP_PARAMS 32 #define GGML_MAX_OP_PARAMS 32
#define GGML_DEFAULT_N_THREADS 4 #define GGML_DEFAULT_N_THREADS 4
#if UINTPTR_MAX == 0xFFFFFFFF
#define GGML_MEM_ALIGN 4
#else
#define GGML_MEM_ALIGN 16
#endif
#define GGML_EXIT_SUCCESS 0 #define GGML_EXIT_SUCCESS 0
#define GGML_EXIT_ABORTED 1 #define GGML_EXIT_ABORTED 1
@ -471,6 +479,9 @@ extern "C" {
int64_t perf_cycles; int64_t perf_cycles;
int64_t perf_time_us; int64_t perf_time_us;
struct ggml_tensor * view_src;
size_t view_offs;
void * data; void * data;
char name[GGML_MAX_NAME]; char name[GGML_MAX_NAME];
@ -653,7 +664,7 @@ extern "C" {
GGML_API struct ggml_tensor * ggml_new_f32(struct ggml_context * ctx, float value); GGML_API struct ggml_tensor * ggml_new_f32(struct ggml_context * ctx, float value);
GGML_API struct ggml_tensor * ggml_dup_tensor (struct ggml_context * ctx, const struct ggml_tensor * src); GGML_API struct ggml_tensor * ggml_dup_tensor (struct ggml_context * ctx, const struct ggml_tensor * src);
GGML_API struct ggml_tensor * ggml_view_tensor(struct ggml_context * ctx, const struct ggml_tensor * src); GGML_API struct ggml_tensor * ggml_view_tensor(struct ggml_context * ctx, struct ggml_tensor * src);
GGML_API struct ggml_tensor * ggml_get_tensor(struct ggml_context * ctx, const char * name); GGML_API struct ggml_tensor * ggml_get_tensor(struct ggml_context * ctx, const char * name);
@ -944,11 +955,11 @@ extern "C" {
// a - x // a - x
// b - dy // b - dy
// TODO: update with configurable eps
GGML_API struct ggml_tensor * ggml_rms_norm_back( GGML_API struct ggml_tensor * ggml_rms_norm_back(
struct ggml_context * ctx, struct ggml_context * ctx,
struct ggml_tensor * a, struct ggml_tensor * a,
struct ggml_tensor * b); struct ggml_tensor * b,
float eps);
// A: n columns, m rows // A: n columns, m rows
// B: n columns, p rows (i.e. we transpose it internally) // B: n columns, p rows (i.e. we transpose it internally)
@ -1604,7 +1615,8 @@ extern "C" {
struct ggml_tensor * tensor); struct ggml_tensor * tensor);
GGML_API void ggml_build_forward_expand(struct ggml_cgraph * cgraph, struct ggml_tensor * tensor); GGML_API void ggml_build_forward_expand (struct ggml_cgraph * cgraph, struct ggml_tensor * tensor);
GGML_API void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * gf, struct ggml_cgraph * gb, bool keep);
GGML_API struct ggml_cgraph ggml_build_forward (struct ggml_tensor * tensor); GGML_API struct ggml_cgraph ggml_build_forward (struct ggml_tensor * tensor);
GGML_API struct ggml_cgraph ggml_build_backward(struct ggml_context * ctx, struct ggml_cgraph * gf, bool keep); GGML_API struct ggml_cgraph ggml_build_backward(struct ggml_context * ctx, struct ggml_cgraph * gf, bool keep);
@ -1669,6 +1681,8 @@ extern "C" {
GGML_LINESEARCH_INVALID_PARAMETERS, GGML_LINESEARCH_INVALID_PARAMETERS,
}; };
typedef void (*ggml_opt_callback)(void * data, float * sched);
// optimization parameters // optimization parameters
// //
// see ggml.c (ggml_opt_default_params) for default values // see ggml.c (ggml_opt_default_params) for default values
@ -1704,12 +1718,14 @@ extern "C" {
float sched; // schedule multiplier (fixed, decay or warmup) float sched; // schedule multiplier (fixed, decay or warmup)
float decay; // weight decay for AdamW, use 0.0f to disable float decay; // weight decay for AdamW, use 0.0f to disable
int decay_min_ndim; // minimum number of tensor dimension to apply weight decay
float alpha; // learning rate float alpha; // learning rate
float beta1; float beta1;
float beta2; float beta2;
float eps; // epsilon for numerical stability float eps; // epsilon for numerical stability
float eps_f; // epsilon for convergence test float eps_f; // epsilon for convergence test
float eps_g; // epsilon for convergence test float eps_g; // epsilon for convergence test
float gclip; // gradient clipping
} adam; } adam;
// LBFGS parameters // LBFGS parameters
@ -1737,14 +1753,12 @@ extern "C" {
bool just_initialized; bool just_initialized;
float loss_before;
float loss_after;
struct { struct {
struct ggml_tensor * x; // view of the parameters
struct ggml_tensor * g1; // gradient
struct ggml_tensor * g2; // gradient squared
struct ggml_tensor * m; // first moment struct ggml_tensor * m; // first moment
struct ggml_tensor * v; // second moment struct ggml_tensor * v; // second moment
struct ggml_tensor * mh; // first moment hat
struct ggml_tensor * vh; // second moment hat
struct ggml_tensor * pf; // past function values struct ggml_tensor * pf; // past function values
float fx_best; float fx_best;
float fx_prev; float fx_prev;
@ -1798,7 +1812,9 @@ extern "C" {
struct ggml_opt_context * opt, struct ggml_opt_context * opt,
struct ggml_tensor * f, struct ggml_tensor * f,
struct ggml_cgraph * gf, struct ggml_cgraph * gf,
struct ggml_cgraph * gb); struct ggml_cgraph * gb,
ggml_opt_callback callback,
void * callback_data);
// //
// quantization // quantization

4
k_quants.c
View file

@ -2694,13 +2694,13 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri
const __m256i q8l = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; const __m256i q8l = _mm256_loadu_si256((const __m256i*)q8); q8 += 32;
__m256i p16l = _mm256_maddubs_epi16(q4l, q8l); __m256i p16l = _mm256_maddubs_epi16(q4l, q8l);
p16l = _mm256_madd_epi16(scale_l, p16l); p16l = _mm256_madd_epi16(scale_l, p16l);
sumi = _mm256_add_epi32(sumi, p16l);
const __m256i q8h = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; const __m256i q8h = _mm256_loadu_si256((const __m256i*)q8); q8 += 32;
__m256i p16h = _mm256_maddubs_epi16(q4h, q8h); __m256i p16h = _mm256_maddubs_epi16(q4h, q8h);
p16h = _mm256_madd_epi16(scale_h, p16h); p16h = _mm256_madd_epi16(scale_h, p16h);
sumi = _mm256_add_epi32(sumi, p16h); const __m256i sumj = _mm256_add_epi32(p16l, p16h);
sumi = _mm256_add_epi32(sumi, sumj);
} }
__m256 vd = _mm256_set1_ps(d); __m256 vd = _mm256_set1_ps(d);

51
llama.cpp
View file

@ -3216,7 +3216,7 @@ private:
struct llm_bigram_bpe { struct llm_bigram_bpe {
struct comparator { struct comparator {
bool operator()(llm_bigram_bpe & l, llm_bigram_bpe & r) { bool operator()(const llm_bigram_bpe & l, const llm_bigram_bpe & r) const {
return l.rank > r.rank || (l.rank == r.rank && l.left > r.left); return l.rank > r.rank || (l.rank == r.rank && l.left > r.left);
} }
}; };
@ -3364,23 +3364,22 @@ private:
} }
// probably not 100% correct // probably not 100% correct
// TODO: this is quite slow - how to make it more efficient? static std::vector<std::string> bpe_gpt2_preprocess(const std::string & text) {
static std::vector<std::string> bpe_gpt2_preprocess(std::string text) {
std::vector<std::string> words; std::vector<std::string> words;
// ref: https://github.com/openai/gpt-2/blob/a74da5d99abaaba920de8131d64da2862a8f213b/src/encoder.py#L53 // ref: https://github.com/openai/gpt-2/blob/a74da5d99abaaba920de8131d64da2862a8f213b/src/encoder.py#L53
const std::string pattern = R"('s|'t|'re|'ve|'m|'ll|'d| ?[[:alpha:]]+| ?[[:digit:]]+| ?[^\s[:alpha:][:digit:]]+|\s+(?!\S)|\s+)"; const std::string pattern = R"('s|'t|'re|'ve|'m|'ll|'d| ?[[:alpha:]]+| ?[[:digit:]]+| ?[^\s[:alpha:][:digit:]]+|\s+(?!\S)|\s+)";
const std::regex re(pattern); const std::regex re(pattern);
std::smatch m;
while (std::regex_search(text, m, re)) { auto words_begin = std::sregex_iterator(text.begin(), text.end(), re);
for (auto x : m) { auto words_end = std::sregex_iterator();
words.push_back(x); auto n_words = std::distance(words_begin, words_end);
words.reserve(n_words);
for (auto it = words_begin; it != words_end; ++it) {
words.push_back(it->str());
} }
text = m.suffix();
}
return words; return words;
} }
const llama_vocab & vocab; const llama_vocab & vocab;
@ -6252,6 +6251,34 @@ const char * llama_print_system_info(void) {
return s.c_str(); return s.c_str();
} }
void llama_dump_timing_info_yaml(FILE * stream, const llama_context * ctx) {
fprintf(stream, "\n");
fprintf(stream, "###########\n");
fprintf(stream, "# Timings #\n");
fprintf(stream, "###########\n");
fprintf(stream, "\n");
fprintf(stream, "mst_eval: %.2f # ms / token during generation\n",
1.0e-3 * ctx->t_eval_us / ctx->n_eval);
fprintf(stream, "mst_p_eval: %.2f # ms / token during prompt processing\n",
1.0e-3 * ctx->t_p_eval_us / ctx->n_p_eval);
fprintf(stream, "mst_sample: %.2f # ms / token during sampling\n",
1.0e-3 * ctx->t_sample_us / ctx->n_sample);
fprintf(stream, "n_eval: %d # number of tokens generated (excluding the first one)\n", ctx->n_eval);
fprintf(stream, "n_p_eval: %d # number of tokens processed in batches at the beginning\n", ctx->n_p_eval);
fprintf(stream, "n_sample: %d # number of sampled tokens\n", ctx->n_sample);
fprintf(stream, "t_eval_us: %" PRId64 " # total microseconds spent generating tokens\n", ctx->t_eval_us);
fprintf(stream, "t_load_us: %" PRId64 " # total microseconds spent loading the model\n", ctx->t_load_us);
fprintf(stream, "t_p_eval_us: %" PRId64 " # total microseconds spent prompt processing\n", ctx->t_p_eval_us);
fprintf(stream, "t_sample_us: %" PRId64 " # total microseconds spent sampling\n", ctx->t_sample_us);
fprintf(stream, "ts_eval: %.2f # tokens / second during generation\n",
1.0e6 * ctx->n_eval / ctx->t_eval_us);
fprintf(stream, "ts_p_eval: %.2f # tokens / second during prompt processing\n",
1.0e6 * ctx->n_p_eval / ctx->t_p_eval_us);
fprintf(stream, "ts_sample: %.2f # tokens / second during sampling\n",
1.0e6 * ctx->n_sample / ctx->t_sample_us);
}
// For internal test use // For internal test use
const std::vector<std::pair<std::string, struct ggml_tensor *>>& llama_internal_get_tensor_map(struct llama_context * ctx) { const std::vector<std::pair<std::string, struct ggml_tensor *>>& llama_internal_get_tensor_map(struct llama_context * ctx) {
return ctx->model.tensors_by_name; return ctx->model.tensors_by_name;
@ -6262,10 +6289,6 @@ void llama_log_set(llama_log_callback log_callback, void * user_data) {
g_state.log_callback_user_data = user_data; g_state.log_callback_user_data = user_data;
} }
#if defined(_MSC_VER) && !defined(vsnprintf)
#define vsnprintf _vsnprintf
#endif
static void llama_log_internal_v(llama_log_level level, const char * format, va_list args) { static void llama_log_internal_v(llama_log_level level, const char * format, va_list args) {
va_list args_copy; va_list args_copy;
va_copy(args_copy, args); va_copy(args_copy, args);

5
llama.h
View file

@ -10,6 +10,7 @@
#endif // GGML_USE_CUBLAS #endif // GGML_USE_CUBLAS
#include <stddef.h> #include <stddef.h>
#include <stdint.h> #include <stdint.h>
#include <stdio.h>
#include <stdbool.h> #include <stdbool.h>
#ifdef LLAMA_SHARED #ifdef LLAMA_SHARED
@ -496,7 +497,7 @@ extern "C" {
// Type of pointer to the beam_search_callback function. // Type of pointer to the beam_search_callback function.
// void* callback_data is any custom data passed to llama_beam_search, that is subsequently // void* callback_data is any custom data passed to llama_beam_search, that is subsequently
// passed back to beam_search_callback. This avoids having to use global variables in the callback. // passed back to beam_search_callback. This avoids having to use global variables in the callback.
typedef void (*llama_beam_search_callback_fn_t)(void * callback_data, llama_beams_state); typedef void (*llama_beam_search_callback_fn_t)(void * callback_data, struct llama_beams_state);
/// @details Deterministically returns entire sentence constructed by a beam search. /// @details Deterministically returns entire sentence constructed by a beam search.
/// @param ctx Pointer to the llama_context. /// @param ctx Pointer to the llama_context.
@ -520,6 +521,8 @@ extern "C" {
// If this is not called, or NULL is supplied, everything is output on stderr. // If this is not called, or NULL is supplied, everything is output on stderr.
LLAMA_API void llama_log_set(llama_log_callback log_callback, void * user_data); LLAMA_API void llama_log_set(llama_log_callback log_callback, void * user_data);
LLAMA_API void llama_dump_timing_info_yaml(FILE * stream, const struct llama_context * ctx);
#ifdef __cplusplus #ifdef __cplusplus
} }
#endif #endif

140
run_with_preset.py Executable file
View file

@ -0,0 +1,140 @@
#!/usr/bin/env python3
import argparse
import os
import subprocess
import sys
import yaml
CLI_ARGS_MAIN_PERPLEXITY = [
"batch-size", "cfg-negative-prompt", "cfg-scale", "chunks", "color", "ctx-size", "escape",
"export", "file", "frequency-penalty", "grammar", "grammar-file", "hellaswag",
"hellaswag-tasks", "ignore-eos", "in-prefix", "in-prefix-bos", "in-suffix", "instruct",
"interactive", "interactive-first", "keep", "logdir", "logit-bias", "lora", "lora-base",
"low-vram", "main-gpu", "memory-f32", "mirostat", "mirostat-ent", "mirostat-lr", "mlock",
"model", "mtest", "multiline-input", "n-gpu-layers", "n-predict", "no-mmap", "no-mul-mat-q",
"np-penalize-nl", "numa", "ppl-output-type", "ppl-stride", "presence-penalty", "prompt",
"prompt-cache", "prompt-cache-all", "prompt-cache-ro", "random-prompt", "repeat-last-n",
"repeat-penalty", "reverse-prompt", "rope-freq-base", "rope-freq-scale", "rope-scale", "seed",
"simple-io", "tensor-split", "threads", "temp", "tfs", "top-k", "top-p", "typical",
"verbose-prompt"
]
CLI_ARGS_LLAMA_BENCH = [
"batch-size", "memory-f32", "low-vram", "model", "mul-mat-q", "n-gen", "n-gpu-layers",
"n-prompt", "output", "repetitions", "tensor-split", "threads", "verbose"
]
CLI_ARGS_SERVER = [
"alias", "batch-size", "ctx-size", "embedding", "host", "memory-f32", "lora", "lora-base",
"low-vram", "main-gpu", "mlock", "model", "n-gpu-layers", "n-probs", "no-mmap", "no-mul-mat-q",
"numa", "path", "port", "rope-freq-base", "timeout", "rope-freq-scale", "tensor-split",
"threads", "verbose"
]
description = """Run llama.cpp binaries with presets from YAML file(s).
To specify which binary should be run, specify the "binary" property (main, perplexity, llama-bench, and server are supported).
To get a preset file template, run a llama.cpp binary with the "--logdir" CLI argument.
Formatting considerations:
- The YAML property names are the same as the CLI argument names of the corresponding binary.
- Properties must use the long name of their corresponding llama.cpp CLI arguments.
- Like the llama.cpp binaries the property names do not differentiate between hyphens and underscores.
- Flags must be defined as "<PROPERTY_NAME>: true" to be effective.
- To define the logit_bias property, the expected format is "<TOKEN_ID>: <BIAS>" in the "logit_bias" namespace.
- To define multiple "reverse_prompt" properties simultaneously the expected format is a list of strings.
- To define a tensor split, pass a list of floats.
"""
usage = "run_with_preset.py [-h] [yaml_files ...] [--<ARG_NAME> <ARG_VALUE> ...]"
epilog = (" --<ARG_NAME> specify additional CLI ars to be passed to the binary (override all preset files). "
"Unknown args will be ignored.")
parser = argparse.ArgumentParser(
description=description, usage=usage, epilog=epilog, formatter_class=argparse.RawTextHelpFormatter)
parser.add_argument("-bin", "--binary", help="The binary to run.")
parser.add_argument("yaml_files", nargs="*",
help="Arbitrary number of YAML files from which to read preset values. "
"If two files specify the same values the later one will be used.")
known_args, unknown_args = parser.parse_known_args()
if not known_args.yaml_files and not unknown_args:
parser.print_help()
sys.exit(0)
props = dict()
for yaml_file in known_args.yaml_files:
with open(yaml_file, "r") as f:
props.update(yaml.load(f, yaml.SafeLoader))
props = {prop.replace("_", "-"): val for prop, val in props.items()}
binary = props.pop("binary", "main")
if known_args.binary:
binary = known_args.binary
if os.path.exists(f"./{binary}"):
binary = f"./{binary}"
if binary.lower().endswith("main") or binary.lower().endswith("perplexity"):
cli_args = CLI_ARGS_MAIN_PERPLEXITY
elif binary.lower().endswith("llama-bench"):
cli_args = CLI_ARGS_LLAMA_BENCH
elif binary.lower().endswith("server"):
cli_args = CLI_ARGS_SERVER
else:
print(f"Unknown binary: {binary}")
sys.exit(1)
command_list = [binary]
for cli_arg in cli_args:
value = props.pop(cli_arg, None)
if not value or value == -1:
continue
if cli_arg == "logit-bias":
for token, bias in value.items():
command_list.append("--logit-bias")
command_list.append(f"{token}{bias:+}")
continue
if cli_arg == "reverse-prompt" and not isinstance(value, str):
for rp in value:
command_list.append("--reverse-prompt")
command_list.append(str(rp))
continue
command_list.append(f"--{cli_arg}")
if cli_arg == "tensor-split":
command_list.append(",".join([str(v) for v in value]))
continue
value = str(value)
if value != "True":
command_list.append(str(value))
num_unused = len(props)
if num_unused > 10:
print(f"The preset file contained a total of {num_unused} unused properties.")
elif num_unused > 0:
print("The preset file contained the following unused properties:")
for prop, value in props.items():
print(f" {prop}: {value}")
command_list += unknown_args
sp = subprocess.Popen(command_list)
while sp.returncode is None:
try:
sp.wait()
except KeyboardInterrupt:
pass
sys.exit(sp.returncode)

1
scripts/qnt-all.sh
View file

@ -20,6 +20,7 @@ fi
model="$1" model="$1"
out="../tmp/results-${model}" out="../tmp/results-${model}"
set -o pipefail
set -e set -e
mkdir -p ${out} mkdir -p ${out}

1
scripts/run-all-perf.sh
View file

@ -20,6 +20,7 @@ fi
model="$1" model="$1"
out="../tmp/results-${model}" out="../tmp/results-${model}"
set -o pipefail
set -e set -e
mkdir -p ${out} mkdir -p ${out}

1
scripts/run-all-ppl.sh
View file

@ -17,6 +17,7 @@ if [ ! -z "$3" ]; then
args="$3" args="$3"
fi fi
set -o pipefail
set -e set -e
model="$1" model="$1"