Merge branch 'master' into pr-train-mem-usage-improvements
This commit is contained in:
xaedes
commit
12c4e5b50f
21 changed files with 931 additions and 124 deletions
|
|
@ -13,12 +13,13 @@
|
|||
# It is up to the user to install the correct vendor-specific support.
|
||||
|
||||
Name: llama.cpp-clblast
|
||||
Version: master
|
||||
Version: %( date "+%%Y%%m%%d" )
|
||||
Release: 1%{?dist}
|
||||
Summary: OpenCL Inference of LLaMA model in pure C/C++
|
||||
Summary: OpenCL Inference of LLaMA model in C/C++
|
||||
License: MIT
|
||||
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
|
||||
|
||||
%define debug_package %{nil}
|
||||
|
|
@ -35,18 +36,43 @@ make -j LLAMA_CLBLAST=1
|
|||
|
||||
%install
|
||||
mkdir -p %{buildroot}%{_bindir}/
|
||||
cp -p main %{buildroot}%{_bindir}/llamacppclblast
|
||||
cp -p server %{buildroot}%{_bindir}/llamacppclblastserver
|
||||
cp -p simple %{buildroot}%{_bindir}/llamacppclblastsimple
|
||||
cp -p main %{buildroot}%{_bindir}/llamaclblast
|
||||
cp -p server %{buildroot}%{_bindir}/llamaclblastserver
|
||||
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
|
||||
rm -rf %{buildroot}
|
||||
rm -rf %{_builddir}/*
|
||||
|
||||
%files
|
||||
%{_bindir}/llamacppclblast
|
||||
%{_bindir}/llamacppclblastserver
|
||||
%{_bindir}/llamacppclblastsimple
|
||||
%{_bindir}/llamaclblast
|
||||
%{_bindir}/llamaclblastserver
|
||||
%{_bindir}/llamaclblastsimple
|
||||
/usr/lib/systemd/system/llamaclblast.service
|
||||
%config /etc/sysconfig/llama
|
||||
|
||||
|
||||
%pre
|
||||
|
||||
|
|
@ -13,7 +13,7 @@
|
|||
# It is up to the user to install the correct vendor-specific support.
|
||||
|
||||
Name: llama.cpp-cublas
|
||||
Version: master
|
||||
Version: %( date "+%%Y%%m%%d" )
|
||||
Release: 1%{?dist}
|
||||
Summary: CPU Inference of LLaMA model in pure C/C++ (no CUDA/OpenCL)
|
||||
License: MIT
|
||||
|
|
@ -40,6 +40,28 @@ cp -p main %{buildroot}%{_bindir}/llamacppcublas
|
|||
cp -p server %{buildroot}%{_bindir}/llamacppcublasserver
|
||||
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
|
||||
rm -rf %{buildroot}
|
||||
rm -rf %{_builddir}/*
|
||||
|
|
@ -48,6 +70,8 @@ rm -rf %{_builddir}/*
|
|||
%{_bindir}/llamacppcublas
|
||||
%{_bindir}/llamacppcublasserver
|
||||
%{_bindir}/llamacppcublassimple
|
||||
/usr/lib/systemd/system/llamacublas.service
|
||||
%config /etc/sysconfig/llama
|
||||
|
||||
%pre
|
||||
|
||||
|
|
@ -6,6 +6,7 @@
|
|||
# Notes for llama.cpp:
|
||||
# 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.
|
||||
# In the meantime, YYYYMMDD format will be used.
|
||||
# 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.
|
||||
# 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.
|
||||
|
||||
Name: llama.cpp
|
||||
Version: master
|
||||
Version: %( date "+%%Y%%m%%d" )
|
||||
Release: 1%{?dist}
|
||||
Summary: CPU Inference of LLaMA model in pure C/C++ (no CUDA/OpenCL)
|
||||
License: MIT
|
||||
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
|
||||
|
||||
%define debug_package %{nil}
|
||||
|
|
@ -26,27 +28,52 @@ URL: https://github.com/ggerganov/llama.cpp
|
|||
|
||||
%description
|
||||
CPU inference for Meta's Lllama2 models using default options.
|
||||
Models are not included in this package and must be downloaded separately.
|
||||
|
||||
%prep
|
||||
%autosetup
|
||||
%setup -n llama.cpp-master
|
||||
|
||||
%build
|
||||
make -j
|
||||
|
||||
%install
|
||||
mkdir -p %{buildroot}%{_bindir}/
|
||||
cp -p main %{buildroot}%{_bindir}/llamacpp
|
||||
cp -p server %{buildroot}%{_bindir}/llamacppserver
|
||||
cp -p simple %{buildroot}%{_bindir}/llamacppsimple
|
||||
cp -p main %{buildroot}%{_bindir}/llama
|
||||
cp -p server %{buildroot}%{_bindir}/llamaserver
|
||||
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
|
||||
rm -rf %{buildroot}
|
||||
rm -rf %{_builddir}/*
|
||||
|
||||
%files
|
||||
%{_bindir}/llamacpp
|
||||
%{_bindir}/llamacppserver
|
||||
%{_bindir}/llamacppsimple
|
||||
%{_bindir}/llama
|
||||
%{_bindir}/llamaserver
|
||||
%{_bindir}/llamasimple
|
||||
/usr/lib/systemd/system/llama.service
|
||||
%config /etc/sysconfig/llama
|
||||
|
||||
%pre
|
||||
|
||||
|
|
|
|||
5
.gitignore
vendored
5
.gitignore
vendored
|
|
@ -63,10 +63,13 @@ poetry.toml
|
|||
|
||||
# Test binaries
|
||||
tests/test-grammar-parser
|
||||
tests/test-llama-grammar
|
||||
tests/test-double-float
|
||||
tests/test-grad0
|
||||
tests/test-opt
|
||||
tests/test-quantize-fns
|
||||
tests/test-quantize-perf
|
||||
tests/test-sampling
|
||||
tests/test-tokenizer-0
|
||||
tests/test-tokenizer-0-llama
|
||||
tests/test-tokenizer-0-falcon
|
||||
tests/test-tokenizer-1
|
||||
|
|
|
|||
14
Makefile
14
Makefile
|
|
@ -2,7 +2,7 @@
|
|||
BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch convert-llama2c-to-ggml simple server embd-input-test gguf llama-bench
|
||||
|
||||
# Binaries only useful for tests
|
||||
TEST_TARGETS = tests/test-llama-grammar tests/test-grammar-parser tests/test-double-float tests/test-grad0 tests/test-opt tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling tests/test-tokenizer-0
|
||||
TEST_TARGETS = tests/test-llama-grammar tests/test-grammar-parser tests/test-double-float tests/test-grad0 tests/test-opt tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling tests/test-tokenizer-0-llama tests/test-tokenizer-0-falcon tests/test-tokenizer-1
|
||||
|
||||
default: $(BUILD_TARGETS)
|
||||
|
||||
|
|
@ -442,10 +442,10 @@ benchmark-matmult: examples/benchmark/benchmark-matmult.cpp build-info.h ggml.o
|
|||
vdot: pocs/vdot/vdot.cpp ggml.o $(OBJS)
|
||||
$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
|
||||
|
||||
tests/test-llama-grammar: tests/test-llama-grammar.cpp build-info.h ggml.o llama.o common.o $(OBJS)
|
||||
tests/test-llama-grammar: tests/test-llama-grammar.cpp build-info.h ggml.o common.o grammar-parser.o $(OBJS)
|
||||
$(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS)
|
||||
|
||||
tests/test-grammar-parser: tests/test-grammar-parser.cpp build-info.h ggml.o llama.o common.o $(OBJS)
|
||||
tests/test-grammar-parser: tests/test-grammar-parser.cpp build-info.h ggml.o llama.o common.o grammar-parser.o $(OBJS)
|
||||
$(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS)
|
||||
|
||||
tests/test-double-float: tests/test-double-float.cpp build-info.h ggml.o llama.o common.o $(OBJS)
|
||||
|
|
@ -466,5 +466,11 @@ tests/test-quantize-perf: tests/test-quantize-perf.cpp build-info.h ggml.o llama
|
|||
tests/test-sampling: tests/test-sampling.cpp build-info.h ggml.o llama.o common.o $(OBJS)
|
||||
$(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS)
|
||||
|
||||
tests/test-tokenizer-0: tests/test-tokenizer-0.cpp build-info.h ggml.o llama.o common.o $(OBJS)
|
||||
tests/test-tokenizer-0-falcon: tests/test-tokenizer-0-falcon.cpp build-info.h ggml.o llama.o common.o $(OBJS)
|
||||
$(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS)
|
||||
|
||||
tests/test-tokenizer-0-llama: tests/test-tokenizer-0-llama.cpp build-info.h ggml.o llama.o common.o $(OBJS)
|
||||
$(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS)
|
||||
|
||||
tests/test-tokenizer-1: tests/test-tokenizer-1.cpp build-info.h ggml.o llama.o common.o $(OBJS)
|
||||
$(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS)
|
||||
|
|
|
|||
|
|
@ -1,15 +1,20 @@
|
|||
#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 <sstream>
|
||||
#include <unordered_set>
|
||||
#include <cassert>
|
||||
#include <cmath>
|
||||
#include <cstring>
|
||||
#include <ctime>
|
||||
#include <fstream>
|
||||
#include <iterator>
|
||||
#include <iostream>
|
||||
#include <regex>
|
||||
#include <sstream>
|
||||
#include <string>
|
||||
#include <unordered_set>
|
||||
#include <vector>
|
||||
|
||||
#if defined(__APPLE__) && defined(__MACH__)
|
||||
#include <sys/types.h>
|
||||
|
|
@ -19,11 +24,14 @@
|
|||
#if defined(_WIN32)
|
||||
#define WIN32_LEAN_AND_MEAN
|
||||
#define NOMINMAX
|
||||
#include <codecvt>
|
||||
#include <locale>
|
||||
#include <windows.h>
|
||||
#include <fcntl.h>
|
||||
#include <io.h>
|
||||
#else
|
||||
#include <sys/ioctl.h>
|
||||
#include <sys/stat.h>
|
||||
#include <unistd.h>
|
||||
#endif
|
||||
|
||||
|
|
@ -93,7 +101,6 @@ void process_escapes(std::string& input) {
|
|||
|
||||
bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
|
||||
bool invalid_param = false;
|
||||
bool escape_prompt = false;
|
||||
std::string arg;
|
||||
gpt_params default_params;
|
||||
const std::string arg_prefix = "--";
|
||||
|
|
@ -125,8 +132,8 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
|
|||
break;
|
||||
}
|
||||
params.prompt = argv[i];
|
||||
} else if (arg == "-e") {
|
||||
escape_prompt = true;
|
||||
} else if (arg == "-e" || arg == "--escape") {
|
||||
params.escape = true;
|
||||
} else if (arg == "--prompt-cache") {
|
||||
if (++i >= argc) {
|
||||
invalid_param = true;
|
||||
|
|
@ -415,6 +422,16 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
|
|||
break;
|
||||
}
|
||||
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") {
|
||||
params.perplexity = true;
|
||||
} else if (arg == "--ppl-stride") {
|
||||
|
|
@ -520,7 +537,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
|
|||
exit(1);
|
||||
}
|
||||
|
||||
if (escape_prompt) {
|
||||
if (params.escape) {
|
||||
process_escapes(params.prompt);
|
||||
process_escapes(params.input_prefix);
|
||||
process_escapes(params.input_suffix);
|
||||
|
|
@ -546,7 +563,7 @@ 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, " -p PROMPT, --prompt PROMPT\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-all if specified, saves user input and generations to cache as well.\n");
|
||||
fprintf(stdout, " not supported with --interactive or other interactive options\n");
|
||||
|
|
@ -627,6 +644,8 @@ 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, " -m FNAME, --model FNAME\n");
|
||||
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");
|
||||
}
|
||||
|
||||
|
|
@ -779,3 +798,289 @@ std::string llama_detokenize_bpe(llama_context * ctx, const std::vector<llama_to
|
|||
|
||||
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[10];
|
||||
snprintf(timestamp_ns, 11, "%09ld", 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");
|
||||
}
|
||||
|
|
|
|||
|
|
@ -11,6 +11,12 @@
|
|||
#include <unordered_map>
|
||||
#include <tuple>
|
||||
|
||||
#ifdef _WIN32
|
||||
#define DIRECTORY_SEPARATOR '\\'
|
||||
#else
|
||||
#define DIRECTORY_SEPARATOR '/'
|
||||
#endif // _WIN32
|
||||
|
||||
//
|
||||
// CLI argument parsing
|
||||
//
|
||||
|
|
@ -61,6 +67,7 @@ struct gpt_params {
|
|||
std::string input_suffix = ""; // string to suffix user inputs with
|
||||
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::string logdir = ""; // directory in which to save YAML log files
|
||||
|
||||
std::string lora_adapter = ""; // lora adapter path
|
||||
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 embedding = false; // get only sentence embedding
|
||||
bool escape = false; // escape "\n", "\r", "\t", "\'", "\"", and "\\"
|
||||
bool interactive_first = false; // wait for user input immediately
|
||||
bool multiline_input = false; // reverse the usage of `\`
|
||||
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(
|
||||
llama_context * ctx,
|
||||
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);
|
||||
|
|
|
|||
|
|
@ -17,6 +17,7 @@
|
|||
#include <ctime>
|
||||
#include <fstream>
|
||||
#include <iostream>
|
||||
#include <sstream>
|
||||
#include <string>
|
||||
#include <vector>
|
||||
|
||||
|
|
@ -36,9 +37,57 @@
|
|||
#pragma warning(disable: 4244 4267) // possible loss of data
|
||||
#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;
|
||||
|
||||
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)
|
||||
void sigint_handler(int signo) {
|
||||
if (signo == SIGINT) {
|
||||
|
|
@ -48,6 +97,7 @@ void sigint_handler(int signo) {
|
|||
console::cleanup();
|
||||
printf("\n");
|
||||
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);
|
||||
}
|
||||
}
|
||||
|
|
@ -56,6 +106,7 @@ void sigint_handler(int signo) {
|
|||
|
||||
int main(int argc, char ** argv) {
|
||||
gpt_params params;
|
||||
g_params = ¶ms;
|
||||
|
||||
if (gpt_params_parse(argc, argv, params) == false) {
|
||||
return 1;
|
||||
|
|
@ -116,6 +167,7 @@ int main(int argc, char ** argv) {
|
|||
llama_model * model;
|
||||
llama_context * ctx;
|
||||
llama_context * ctx_guidance = NULL;
|
||||
g_model = &model;
|
||||
g_ctx = &ctx;
|
||||
|
||||
// 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_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
|
||||
console::set_display(console::prompt);
|
||||
|
||||
|
|
@ -667,7 +723,15 @@ int main(int argc, char ** argv) {
|
|||
// display text
|
||||
if (input_echo) {
|
||||
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);
|
||||
}
|
||||
|
|
@ -761,6 +825,8 @@ int main(int argc, char ** argv) {
|
|||
printf("%s", params.input_suffix.c_str());
|
||||
}
|
||||
|
||||
const size_t original_size = embd_inp.size();
|
||||
|
||||
// instruct mode: insert instruction prefix
|
||||
if (params.instruct && !is_antiprompt) {
|
||||
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());
|
||||
}
|
||||
|
||||
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();
|
||||
}
|
||||
|
||||
|
|
@ -817,6 +889,8 @@ int main(int argc, char ** argv) {
|
|||
}
|
||||
|
||||
llama_print_timings(ctx);
|
||||
write_logfile(ctx, params, model, input_tokens, output_ss.str(), output_tokens);
|
||||
|
||||
if (ctx_guidance) { llama_free(ctx_guidance); }
|
||||
llama_free(ctx);
|
||||
llama_free_model(model);
|
||||
|
|
|
|||
|
|
@ -3,16 +3,79 @@
|
|||
#include "build-info.h"
|
||||
|
||||
#include <cmath>
|
||||
#include <cstdio>
|
||||
#include <cstring>
|
||||
#include <ctime>
|
||||
#include <sstream>
|
||||
#include <cstring>
|
||||
#include <thread>
|
||||
#include <mutex>
|
||||
#include <vector>
|
||||
|
||||
#if defined(_MSC_VER)
|
||||
#pragma warning(disable: 4244 4267) // possible loss of data
|
||||
#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> probs(logits.size());
|
||||
float max_logit = logits[0];
|
||||
|
|
@ -29,20 +92,20 @@ std::vector<float> softmax(const std::vector<float>& logits) {
|
|||
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];
|
||||
for (int i = 1; i < n_vocab; ++i) max_logit = std::max(max_logit, logits[i]);
|
||||
double sum_exp = 0.0;
|
||||
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,
|
||||
double& nll, double& nll2) {
|
||||
void process_logits(int n_vocab, const float * logits, const int * tokens, int n_token, std::vector<std::thread> & workers,
|
||||
double & nll, double & nll2, float * logit_history, float * prob_history) {
|
||||
|
||||
std::mutex mutex;
|
||||
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;
|
||||
while (true) {
|
||||
std::unique_lock<std::mutex> lock(mutex);
|
||||
|
|
@ -52,34 +115,43 @@ void process_logits(int n_vocab, const float * logits, const int * tokens, int n
|
|||
break;
|
||||
}
|
||||
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_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();
|
||||
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
|
||||
// Run `./perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw`
|
||||
// Output: `perplexity: 13.5106 [114/114]`
|
||||
// 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 add_bos = is_spm;
|
||||
|
||||
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);
|
||||
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;
|
||||
|
||||
|
|
@ -88,7 +160,7 @@ void perplexity_v2(llama_context * ctx, const gpt_params & params) {
|
|||
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__,
|
||||
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;
|
||||
|
|
@ -120,7 +192,7 @@ 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);
|
||||
if (llama_eval(ctx, tokens.data() + batch_start, batch_size, j * n_batch, params.n_threads)) {
|
||||
//fprintf(stderr, "%s : failed to eval\n", __func__);
|
||||
return;
|
||||
return {tokens, -1, logit_history, prob_history};
|
||||
}
|
||||
|
||||
// save original token and restore it after eval
|
||||
|
|
@ -161,6 +233,8 @@ void perplexity_v2(llama_context * ctx, const gpt_params & params) {
|
|||
logits.begin() + (j + 1) * n_vocab);
|
||||
|
||||
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);
|
||||
++count;
|
||||
|
|
@ -174,12 +248,14 @@ void perplexity_v2(llama_context * ctx, const gpt_params & params) {
|
|||
fflush(stdout);
|
||||
}
|
||||
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) {
|
||||
perplexity_v2(ctx, params);
|
||||
return;
|
||||
return perplexity_v2(ctx, params);
|
||||
}
|
||||
|
||||
// Download: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research
|
||||
|
|
@ -193,11 +269,17 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
|
|||
auto tim1 = std::chrono::high_resolution_clock::now();
|
||||
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();
|
||||
fprintf(stderr, "%s: tokenization took %g ms\n",__func__,1e-3*std::chrono::duration_cast<std::chrono::microseconds>(tim2-tim1).count());
|
||||
|
||||
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 = params.n_chunks < 0 ? n_chunk_max : std::min(params.n_chunks, n_chunk_max);
|
||||
|
|
@ -236,7 +318,7 @@ 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)) {
|
||||
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
|
||||
|
|
@ -272,7 +354,8 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
|
|||
// last 256 tokens. Then, we split the input up into context window size chunks to
|
||||
// process the entire prompt.
|
||||
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;
|
||||
|
||||
// perplexity is e^(average negative log-likelihood)
|
||||
|
|
@ -287,16 +370,19 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
|
|||
fflush(stdout);
|
||||
}
|
||||
printf("\n");
|
||||
|
||||
nll2 /= count;
|
||||
nll /= count;
|
||||
const double ppl = exp(nll);
|
||||
nll2 -= nll * nll;
|
||||
if (nll2 > 0) {
|
||||
nll2 = sqrt(nll2/(count-1));
|
||||
double ppl = exp(nll);
|
||||
printf("Final estimate: PPL = %.4lf +/- %.5lf\n", ppl, nll2*ppl);
|
||||
} else {
|
||||
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,
|
||||
|
|
@ -604,13 +690,16 @@ int main(int argc, char ** argv) {
|
|||
params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info());
|
||||
}
|
||||
|
||||
struct results_perplexity results;
|
||||
if (params.hellaswag) {
|
||||
hellaswag_score(ctx, params);
|
||||
} else {
|
||||
perplexity(ctx, params);
|
||||
results = perplexity(ctx, params);
|
||||
}
|
||||
|
||||
llama_print_timings(ctx);
|
||||
write_logfile(ctx, params, model, results);
|
||||
|
||||
llama_free(ctx);
|
||||
llama_free_model(model);
|
||||
|
||||
|
|
|
|||
|
|
@ -100,7 +100,7 @@ int main(int argc, char ** argv) {
|
|||
}
|
||||
}
|
||||
|
||||
if (argc - arg_idx < 3) {
|
||||
if (argc - arg_idx < 2) {
|
||||
usage(argv[0]);
|
||||
}
|
||||
|
||||
|
|
@ -114,7 +114,7 @@ int main(int argc, char ** argv) {
|
|||
std::string ftype_str;
|
||||
if (try_parse_ftype(argv[arg_idx], params.ftype, ftype_str)) {
|
||||
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) {
|
||||
fpath = fname_inp.substr(0, pos + 1);
|
||||
}
|
||||
|
|
|
|||
|
|
@ -719,7 +719,7 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms,
|
|||
fprintf(stdout, " -ts SPLIT --tensor-split SPLIT\n");
|
||||
fprintf(stdout, " how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
|
||||
fprintf(stdout, " -mg i, --main-gpu i the GPU to use for scratch and small tensors\n");
|
||||
fprintf(stdout, " -lv, --low-vram don't allocate VRAM scratch buffer\n");
|
||||
fprintf(stdout, " -lv, --low-vram don't allocate VRAM scratch buffer\n");
|
||||
fprintf(stdout, " -nommq, --no-mul-mat-q\n");
|
||||
fprintf(stdout, " use cuBLAS instead of custom mul_mat_q CUDA kernels.\n");
|
||||
fprintf(stdout, " Not recommended since this is both slower and uses more VRAM.\n");
|
||||
|
|
|
|||
|
|
@ -272,7 +272,7 @@ struct ggml_allocr * ggml_allocr_new(void * data, size_t size, size_t alignment)
|
|||
/*.parse_seq = */ {0},
|
||||
/*.parse_seq_len = */ 0,
|
||||
#ifdef GGML_ALLOCATOR_DEBUG
|
||||
/*.allocated_tensors = */ = {0},
|
||||
/*.allocated_tensors = */ {0},
|
||||
#endif
|
||||
};
|
||||
|
||||
|
|
@ -301,7 +301,7 @@ struct ggml_allocr * ggml_allocr_new_measure(size_t alignment) {
|
|||
/*.parse_seq = */ {0},
|
||||
/*.parse_seq_len = */ 0,
|
||||
#ifdef GGML_ALLOCATOR_DEBUG
|
||||
/*.allocated_tensors = */ = {0},
|
||||
/*.allocated_tensors = */ {0},
|
||||
#endif
|
||||
};
|
||||
|
||||
|
|
@ -560,7 +560,7 @@ static size_t ggml_allocator_alloc_graph_tensors_n(
|
|||
struct ggml_tensor * view_src = get_view_source(parent);
|
||||
struct hash_node * view_src_hn = hash_get(ht, view_src);
|
||||
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) {
|
||||
ggml_allocator_free_tensor(alloc, view_src);
|
||||
}
|
||||
|
|
|
|||
|
|
@ -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,
|
||||
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
|
||||
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 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);
|
||||
|
|
@ -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,
|
||||
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 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);
|
||||
|
|
|
|||
|
|
@ -24,6 +24,7 @@
|
|||
|
||||
// max memory buffers that can be mapped to the device
|
||||
#define GGML_METAL_MAX_BUFFERS 16
|
||||
#define GGML_METAL_MAX_COMMAND_BUFFERS 32
|
||||
|
||||
struct ggml_tensor;
|
||||
struct ggml_cgraph;
|
||||
|
|
|
|||
100
ggml-metal.m
100
ggml-metal.m
|
|
@ -33,12 +33,15 @@ struct ggml_metal_buffer {
|
|||
struct ggml_metal_context {
|
||||
int n_cb;
|
||||
|
||||
float * logits;
|
||||
|
||||
id<MTLDevice> device;
|
||||
id<MTLCommandQueue> queue;
|
||||
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;
|
||||
struct ggml_metal_buffer buffers[GGML_METAL_MAX_BUFFERS];
|
||||
|
||||
|
|
@ -114,12 +117,13 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
|
|||
|
||||
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->queue = [ctx->device newCommandQueue];
|
||||
ctx->n_buffers = 0;
|
||||
ctx->concur_list_len = 0;
|
||||
|
||||
ctx->d_queue = dispatch_queue_create("llama.cpp", DISPATCH_QUEUE_CONCURRENT);
|
||||
|
||||
#if 0
|
||||
// compile from source string and show compile log
|
||||
|
|
@ -239,9 +243,67 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
|
|||
|
||||
void ggml_metal_free(struct ggml_metal_context * ctx) {
|
||||
fprintf(stderr, "%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) {
|
||||
[ctx->buffers[i].metal release];
|
||||
}
|
||||
|
||||
[ctx->library release];
|
||||
[ctx->queue release];
|
||||
[ctx->device release];
|
||||
|
||||
dispatch_release(ctx->d_queue);
|
||||
|
||||
free(ctx);
|
||||
}
|
||||
|
||||
|
|
@ -261,7 +323,7 @@ void ggml_metal_host_free(void * data) {
|
|||
}
|
||||
|
||||
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) {
|
||||
|
|
@ -507,6 +569,8 @@ void ggml_metal_graph_compute(
|
|||
struct ggml_cgraph * gf) {
|
||||
metal_printf("%s: evaluating graph\n", __func__);
|
||||
|
||||
@autoreleasepool {
|
||||
|
||||
// if there is ctx->concur_list, dispatch concurrently
|
||||
// else fallback to serial dispatch
|
||||
MTLComputePassDescriptor * edesc = MTLComputePassDescriptor.computePassDescriptor;
|
||||
|
|
@ -521,29 +585,25 @@ void ggml_metal_graph_compute(
|
|||
|
||||
const int n_cb = ctx->n_cb;
|
||||
|
||||
NSMutableArray * command_buffers = [NSMutableArray arrayWithCapacity:n_cb];
|
||||
|
||||
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
|
||||
[command_buffers[i] enqueue];
|
||||
}
|
||||
[ctx->command_buffers[i] enqueue];
|
||||
|
||||
// TODO: is this the best way to start threads?
|
||||
dispatch_queue_t queue = dispatch_queue_create("llama.cpp", DISPATCH_QUEUE_CONCURRENT);
|
||||
ctx->command_encoders[i] = [ctx->command_buffers[i] computeCommandEncoderWithDescriptor: edesc];
|
||||
}
|
||||
|
||||
for (int cb_idx = 0; cb_idx < n_cb; ++cb_idx) {
|
||||
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_src1 = 0;
|
||||
size_t offs_dst = 0;
|
||||
|
||||
id<MTLCommandBuffer> command_buffer = command_buffers[cb_idx];
|
||||
|
||||
id<MTLComputeCommandEncoder> encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
|
||||
id<MTLCommandBuffer> command_buffer = ctx->command_buffers[cb_idx];
|
||||
id<MTLComputeCommandEncoder> encoder = ctx->command_encoders[cb_idx];
|
||||
|
||||
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);
|
||||
|
|
@ -1117,17 +1177,19 @@ void ggml_metal_graph_compute(
|
|||
}
|
||||
|
||||
// wait for all threads to finish
|
||||
dispatch_barrier_sync(queue, ^{});
|
||||
|
||||
[command_buffers[n_cb - 1] waitUntilCompleted];
|
||||
dispatch_barrier_sync(ctx->d_queue, ^{});
|
||||
|
||||
// check status of command buffers
|
||||
// needed to detect if the device ran out-of-memory for example (#1881)
|
||||
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) {
|
||||
fprintf(stderr, "%s: command buffer %d failed with status %lu\n", __func__, i, status);
|
||||
GGML_ASSERT(false);
|
||||
}
|
||||
}
|
||||
|
||||
}
|
||||
}
|
||||
|
|
|
|||
33
ggml.c
33
ggml.c
|
|
@ -159,12 +159,6 @@ typedef void * thread_ret_t;
|
|||
//#define GGML_SOFT_MAX_ACCELERATE
|
||||
#endif
|
||||
|
||||
#if UINTPTR_MAX == 0xFFFFFFFF
|
||||
#define GGML_MEM_ALIGN 4
|
||||
#else
|
||||
#define GGML_MEM_ALIGN 16
|
||||
#endif
|
||||
|
||||
//
|
||||
// logging
|
||||
//
|
||||
|
|
@ -2447,7 +2441,6 @@ static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void *
|
|||
const int nb = n / qk;
|
||||
|
||||
assert(n % qk == 0);
|
||||
assert(nb % 2 == 0);
|
||||
|
||||
const block_q4_0 * restrict x = vx;
|
||||
const block_q8_0 * restrict y = vy;
|
||||
|
|
@ -2456,6 +2449,7 @@ static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void *
|
|||
float32x4_t sumv0 = vdupq_n_f32(0.0f);
|
||||
float32x4_t sumv1 = vdupq_n_f32(0.0f);
|
||||
|
||||
GGML_ASSERT(nb % 2 == 0); // TODO: handle odd nb
|
||||
for (int i = 0; i < nb; i += 2) {
|
||||
const block_q4_0 * restrict x0 = &x[i + 0];
|
||||
const block_q4_0 * restrict x1 = &x[i + 1];
|
||||
|
|
@ -2634,6 +2628,7 @@ static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void *
|
|||
}
|
||||
|
||||
// Main loop
|
||||
GGML_ASSERT(nb % 2 == 0); // TODO: handle odd nb
|
||||
for (int i = 2; i < nb; i+=2) {
|
||||
_mm_prefetch(&x[i] + sizeof(block_q4_0), _MM_HINT_T0);
|
||||
_mm_prefetch(&y[i] + sizeof(block_q8_0), _MM_HINT_T0);
|
||||
|
|
@ -2717,7 +2712,6 @@ static void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void *
|
|||
const int nb = n / qk;
|
||||
|
||||
assert(n % qk == 0);
|
||||
assert(nb % 2 == 0);
|
||||
|
||||
const block_q4_1 * restrict x = vx;
|
||||
const block_q8_1 * restrict y = vy;
|
||||
|
|
@ -2729,6 +2723,7 @@ static void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void *
|
|||
|
||||
float summs = 0;
|
||||
|
||||
GGML_ASSERT(nb % 2 == 0); // TODO: handle odd nb
|
||||
for (int i = 0; i < nb; i += 2) {
|
||||
const block_q4_1 * restrict x0 = &x[i + 0];
|
||||
const block_q4_1 * restrict x1 = &x[i + 1];
|
||||
|
|
@ -2843,7 +2838,6 @@ static void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void *
|
|||
const int nb = n / qk;
|
||||
|
||||
assert(n % qk == 0);
|
||||
assert(nb % 2 == 0);
|
||||
assert(qk == QK5_0);
|
||||
|
||||
const block_q5_0 * restrict x = vx;
|
||||
|
|
@ -2859,6 +2853,7 @@ static void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void *
|
|||
uint64_t tmp0[4];
|
||||
uint64_t tmp1[4];
|
||||
|
||||
GGML_ASSERT(nb % 2 == 0); // TODO: handle odd nb
|
||||
for (int i = 0; i < nb; i += 2) {
|
||||
const block_q5_0 * restrict x0 = &x[i];
|
||||
const block_q5_0 * restrict x1 = &x[i + 1];
|
||||
|
|
@ -3083,7 +3078,6 @@ static void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void *
|
|||
const int nb = n / qk;
|
||||
|
||||
assert(n % qk == 0);
|
||||
assert(nb % 2 == 0);
|
||||
assert(qk == QK5_1);
|
||||
|
||||
const block_q5_1 * restrict x = vx;
|
||||
|
|
@ -3102,6 +3096,7 @@ static void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void *
|
|||
uint64_t tmp0[4];
|
||||
uint64_t tmp1[4];
|
||||
|
||||
GGML_ASSERT(nb % 2 == 0); // TODO: handle odd nb
|
||||
for (int i = 0; i < nb; i += 2) {
|
||||
const block_q5_1 * restrict x0 = &x[i];
|
||||
const block_q5_1 * restrict x1 = &x[i + 1];
|
||||
|
|
@ -3339,7 +3334,6 @@ static void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void *
|
|||
const int nb = n / qk;
|
||||
|
||||
assert(n % qk == 0);
|
||||
assert(nb % 2 == 0);
|
||||
|
||||
const block_q8_0 * restrict x = vx;
|
||||
const block_q8_0 * restrict y = vy;
|
||||
|
|
@ -3348,6 +3342,7 @@ static void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void *
|
|||
float32x4_t sumv0 = vdupq_n_f32(0.0f);
|
||||
float32x4_t sumv1 = vdupq_n_f32(0.0f);
|
||||
|
||||
GGML_ASSERT(nb % 2 == 0); // TODO: handle odd nb
|
||||
for (int i = 0; i < nb; i += 2) {
|
||||
const block_q8_0 * restrict x0 = &x[i + 0];
|
||||
const block_q8_0 * restrict x1 = &x[i + 1];
|
||||
|
|
@ -7111,11 +7106,13 @@ struct ggml_tensor * ggml_conv_transpose_2d_p0(
|
|||
};
|
||||
|
||||
struct ggml_tensor* result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
|
||||
|
||||
ggml_set_op_params_i32(result, 0, stride);
|
||||
|
||||
result->op = GGML_OP_CONV_TRANSPOSE_2D;
|
||||
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
||||
result->src[0] = a;
|
||||
result->src[1] = b;
|
||||
result->src[2] = ggml_new_i32(ctx, stride);
|
||||
|
||||
return result;
|
||||
}
|
||||
|
|
@ -13513,7 +13510,6 @@ static void ggml_compute_forward_conv_transpose_2d(
|
|||
const struct ggml_compute_params * params,
|
||||
const struct ggml_tensor * src0,
|
||||
const struct ggml_tensor * src1,
|
||||
const struct ggml_tensor * opt0,
|
||||
struct ggml_tensor * dst) {
|
||||
GGML_ASSERT(src0->type == GGML_TYPE_F16);
|
||||
GGML_ASSERT(src1->type == GGML_TYPE_F32);
|
||||
|
|
@ -13573,7 +13569,7 @@ static void ggml_compute_forward_conv_transpose_2d(
|
|||
return;
|
||||
}
|
||||
|
||||
const int32_t stride = ((const int32_t*)(opt0->data))[0];
|
||||
const int32_t stride = ggml_get_op_params_i32(dst, 0);
|
||||
|
||||
// total patches in dst
|
||||
const int np = ne2;
|
||||
|
|
@ -13586,7 +13582,7 @@ static void ggml_compute_forward_conv_transpose_2d(
|
|||
const int ip1 = MIN(ip0 + dp, np);
|
||||
|
||||
ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0;
|
||||
ggml_fp16_t * const wdata_src = (ggml_fp16_t *) params->wdata + nk;
|
||||
ggml_fp16_t * const wdata_src = wdata + nk;
|
||||
|
||||
for (int i2 = ip0; i2 < ip1; i2++) { // Cout
|
||||
float * dst_data = (float *)((char *) dst->data + i2*nb2);
|
||||
|
|
@ -13598,9 +13594,8 @@ static void ggml_compute_forward_conv_transpose_2d(
|
|||
for (int i00 = 0; i00 < ne00; i00++) {
|
||||
float v = 0;
|
||||
ggml_vec_dot_f16(ne03, &v,
|
||||
(ggml_fp16_t *) wdata_src + i1n,
|
||||
(ggml_fp16_t *) wdata_kernel + i01*ne00*ne03 + i00*ne03);
|
||||
|
||||
wdata_src + i1n,
|
||||
wdata_kernel + i01*ne00*ne03 + i00*ne03);
|
||||
dst_data[(i11*stride + i01)*ne0 + i10*stride + i00] += v;
|
||||
}
|
||||
}
|
||||
|
|
@ -15712,7 +15707,7 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
|
|||
} break;
|
||||
case GGML_OP_CONV_TRANSPOSE_2D:
|
||||
{
|
||||
ggml_compute_forward_conv_transpose_2d(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor);
|
||||
ggml_compute_forward_conv_transpose_2d(params, tensor->src[0], tensor->src[1], tensor);
|
||||
} break;
|
||||
case GGML_OP_POOL_1D:
|
||||
{
|
||||
|
|
|
|||
18
ggml.h
18
ggml.h
|
|
@ -130,13 +130,16 @@
|
|||
// 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;
|
||||
// *(float *) ((char *) a->data + 2*a->nb[1] + 1*a->nb[0]) = 1.0f;
|
||||
// struct ggml_tensor * a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, nx, ny);
|
||||
//
|
||||
// // a[0, 2] = 2.0f;
|
||||
// *(float *) ((char *) a->data + 0*a->nb[1] + 2*a->nb[0]) = 2.0f;
|
||||
// for (int y = 0; y < ny; y++) {
|
||||
// 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_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_ABORTED 1
|
||||
|
|
|
|||
|
|
@ -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;
|
||||
__m256i p16l = _mm256_maddubs_epi16(q4l, q8l);
|
||||
p16l = _mm256_madd_epi16(scale_l, p16l);
|
||||
sumi = _mm256_add_epi32(sumi, p16l);
|
||||
|
||||
const __m256i q8h = _mm256_loadu_si256((const __m256i*)q8); q8 += 32;
|
||||
__m256i p16h = _mm256_maddubs_epi16(q4h, q8h);
|
||||
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);
|
||||
|
|
|
|||
33
llama.cpp
33
llama.cpp
|
|
@ -6247,6 +6247,35 @@ const char * llama_print_system_info(void) {
|
|||
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: %ld # total microseconds spent generating tokens\n", ctx->t_eval_us);
|
||||
fprintf(stream, "t_load_us: %ld # total microseconds spent loading the model\n", ctx->t_load_us);
|
||||
fprintf(stream, "t_p_eval_us: %ld # total microseconds spent prompt processing\n", ctx->t_p_eval_us);
|
||||
fprintf(stream, "t_sample_us: %ld # 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
|
||||
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;
|
||||
|
|
@ -6257,10 +6286,6 @@ void llama_log_set(llama_log_callback log_callback, void * 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) {
|
||||
va_list args_copy;
|
||||
va_copy(args_copy, args);
|
||||
|
|
|
|||
5
llama.h
5
llama.h
|
|
@ -10,6 +10,7 @@
|
|||
#endif // GGML_USE_CUBLAS
|
||||
#include <stddef.h>
|
||||
#include <stdint.h>
|
||||
#include <stdio.h>
|
||||
#include <stdbool.h>
|
||||
|
||||
#ifdef LLAMA_SHARED
|
||||
|
|
@ -496,7 +497,7 @@ extern "C" {
|
|||
// Type of pointer to the beam_search_callback function.
|
||||
// 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.
|
||||
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.
|
||||
/// @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.
|
||||
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 llama_context * ctx);
|
||||
|
||||
#ifdef __cplusplus
|
||||
}
|
||||
#endif
|
||||
|
|
|
|||
140
run_with_preset.py
Executable file
140
run_with_preset.py
Executable 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)
|
||||
Loading…
Add table
Add a link
Reference in a new issue