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Merge branch 'master' into gguf

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This commit is contained in:
Georgi Gerganov
2023-08-18 15:21:48 +03:00
parent 660ca9bbca 097e121e2f
commit 38016ed9ec
11 changed files with 1073 additions and 35 deletions
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1
.gitignore vendored
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@@ -51,6 +51,7 @@ models-mnt
/gguf
/gguf-llama-simple
/libllama.so
/llama-bench
build-info.h
arm_neon.h
compile_commands.json

7
Makefile
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@@ -1,5 +1,5 @@
# Define the default target now so that it is always the first target
BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch convert-llama2c-to-ggml simple server embd-input-test gguf
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
@@ -345,7 +345,7 @@ libllama.so: llama.o ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS)
clean:
rm -vf *.o *.so *.dll main quantize quantize-stats perplexity embedding benchmark-matmult save-load-state server simple vdot train-text-from-scratch convert-llama2c-to-ggml embd-input-test gguf build-info.h $(TEST_TARGETS)
rm -vf *.o *.so *.dll main quantize quantize-stats perplexity embedding benchmark-matmult save-load-state server simple vdot train-text-from-scratch convert-llama2c-to-ggml embd-input-test gguf llama-bench build-info.h $(TEST_TARGETS)
#
# Examples
@@ -394,6 +394,9 @@ train-text-from-scratch: examples/train-text-from-scratch/train-text-from-scratc
convert-llama2c-to-ggml: examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp build-info.h ggml.o llama.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
llama-bench: examples/llama-bench/llama-bench.cpp build-info.h ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
build-info.h: $(wildcard .git/index) scripts/build-info.sh
@sh scripts/build-info.sh > $@.tmp
@if ! cmp -s $@.tmp $@; then \

1
README.md
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@@ -96,6 +96,7 @@ as the main playground for developing new features for the [ggml](https://github
- Go: [go-skynet/go-llama.cpp](https://github.com/go-skynet/go-llama.cpp)
- Node.js: [hlhr202/llama-node](https://github.com/hlhr202/llama-node)
- Ruby: [yoshoku/llama_cpp.rb](https://github.com/yoshoku/llama_cpp.rb)
- Rust: [mdrokz/rust-llama.cpp](https://github.com/mdrokz/rust-llama.cpp)
- C#/.NET: [SciSharp/LLamaSharp](https://github.com/SciSharp/LLamaSharp)
- Scala 3: [donderom/llm4s](https://github.com/donderom/llm4s)

1
examples/CMakeLists.txt
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@@ -45,6 +45,7 @@ else()
add_subdirectory(convert-llama2c-to-ggml)
add_subdirectory(simple)
add_subdirectory(embd-input)
add_subdirectory(llama-bench)
if (LLAMA_METAL)
add_subdirectory(metal)
endif()

8
examples/llama-bench/CMakeLists.txt Normal file
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@@ -0,0 +1,8 @@
set(TARGET llama-bench)
add_executable(${TARGET} llama-bench.cpp)
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_11)
if(TARGET BUILD_INFO)
add_dependencies(${TARGET} BUILD_INFO)
endif()

969
examples/llama-bench/llama-bench.cpp Executable file
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@@ -0,0 +1,969 @@
#include <algorithm>
#include <array>
#include <cassert>
#include <chrono>
#include <cinttypes>
#include <cstring>
#include <ctime>
#include <iterator>
#include <map>
#include <numeric>
#include <regex>
#include <sstream>
#include <stdio.h>
#include <string>
#include <vector>
#include "ggml.h"
#include "llama.h"
#include "common.h"
#include "build-info.h"
#ifdef GGML_USE_CUBLAS
#include "ggml-cuda.h"
#endif
// utils
static uint64_t get_time_ns() {
using clock = std::chrono::high_resolution_clock;
return std::chrono::nanoseconds(clock::now().time_since_epoch()).count();
}
template<class T>
static std::string join(const std::vector<T> & values, const std::string & delim) {
std::ostringstream str;
for (size_t i = 0; i < values.size(); i++) {
str << values[i];
if (i < values.size() - 1) {
str << delim;
}
}
return str.str();
}
template<class T>
static std::vector<T> split(const std::string & str, char delim) {
std::vector<T> values;
std::istringstream str_stream(str);
std::string token;
while (std::getline(str_stream, token, delim)) {
T value;
std::istringstream token_stream(token);
token_stream >> value;
values.push_back(value);
}
return values;
}
template<typename T>
static T avg(const std::vector<T> & v) {
if (v.empty()) {
return 0;
}
T sum = std::accumulate(v.begin(), v.end(), T(0));
return sum / (T)v.size();
}
template<typename T>
static T stdev(const std::vector<T> & v) {
if (v.size() <= 1) {
return 0;
}
T mean = avg(v);
T sq_sum = std::inner_product(v.begin(), v.end(), v.begin(), T(0));
T stdev = std::sqrt(sq_sum / (T)(v.size() - 1) - mean * mean * (T)v.size() / (T)(v.size() - 1));
return stdev;
}
static bool ggml_cpu_has_metal() {
#if defined(GGML_USE_METAL)
return true;
#else
return false;
#endif
}
static std::string get_cpu_info() {
std::string id;
#ifdef __linux__
FILE * f = fopen("/proc/cpuinfo", "r");
if (f) {
char buf[1024];
while (fgets(buf, sizeof(buf), f)) {
if (strncmp(buf, "model name", 10) == 0) {
char * p = strchr(buf, ':');
if (p) {
p++;
while (std::isspace(*p)) {
p++;
}
while (std::isspace(p[strlen(p) - 1])) {
p[strlen(p) - 1] = '\0';
}
id = p;
break;
}
}
}
}
#endif
// TODO: other platforms
return id;
}
static std::string get_gpu_info() {
std::string id;
#ifdef GGML_USE_CUBLAS
int count = ggml_cuda_get_device_count();
for (int i = 0; i < count; i++) {
char buf[128];
ggml_cuda_get_device_description(i, buf, sizeof(buf));
id += buf;
if (i < count - 1) {
id += "/";
}
}
#endif
// TODO: other backends
return id;
}
// command line params
enum output_formats {CSV, JSON, MARKDOWN, SQL};
struct cmd_params {
std::vector<std::string> model;
std::vector<int> n_prompt;
std::vector<int> n_gen;
std::vector<int> n_batch;
std::vector<bool> f32_kv;
std::vector<int> n_threads;
std::vector<int> n_gpu_layers;
std::vector<int> main_gpu;
std::vector<bool> mul_mat_q;
std::vector<bool> low_vram;
std::vector<std::array<float, LLAMA_MAX_DEVICES>> tensor_split;
int reps;
bool verbose;
output_formats output_format;
};
static const cmd_params cmd_params_defaults = {
/* model */ {"models/7B/ggml-model-q4_0.bin"},
/* n_prompt */ {512},
/* n_gen */ {128},
/* n_batch */ {512},
/* f32_kv */ {false},
/* n_threads */ {get_num_physical_cores()},
/* n_gpu_layers */ {99},
/* main_gpu */ {0},
/* mul_mat_q */ {true},
/* low_vram */ {false},
/* tensor_split */ {{}},
/* reps */ 5,
/* verbose */ false,
/* output_format */ MARKDOWN
};
static void print_usage(int /* argc */, char ** argv) {
fprintf(stdout, "usage: %s [options]\n", argv[0]);
fprintf(stdout, "\n");
fprintf(stdout, "options:\n");
fprintf(stdout, " -h, --help\n");
fprintf(stdout, " -m, --model <filename> (default: %s)\n", join(cmd_params_defaults.model, ",").c_str());
fprintf(stdout, " -p, --n-prompt <n> (default: %s)\n", join(cmd_params_defaults.n_prompt, ",").c_str());
fprintf(stdout, " -n, --n-gen <n> (default: %s)\n", join(cmd_params_defaults.n_gen, ",").c_str());
fprintf(stdout, " -b, --batch-size <n> (default: %s)\n", join(cmd_params_defaults.n_batch, ",").c_str());
fprintf(stdout, " --memory-f32 <0|1> (default: %s)\n", join(cmd_params_defaults.f32_kv, ",").c_str());
fprintf(stdout, " -t, --threads <n> (default: %s)\n", join(cmd_params_defaults.n_threads, ",").c_str());
fprintf(stdout, " -ngl N, --n-gpu-layers <n> (default: %s)\n", join(cmd_params_defaults.n_gpu_layers, ",").c_str());
fprintf(stdout, " -mg i, --main-gpu <n> (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str());
fprintf(stdout, " -lv, --low-vram <0|1> (default: %s)\n", join(cmd_params_defaults.low_vram, ",").c_str());
fprintf(stdout, " -mmq, --mul-mat-q <0|1> (default: %s)\n", join(cmd_params_defaults.mul_mat_q, ",").c_str());
fprintf(stdout, " -ts, --tensor_split <ts> \n");
fprintf(stdout, " -r, --repetitions <n> (default: %d)\n", cmd_params_defaults.reps);
fprintf(stdout, " -o, --output <csv|json|md|sql> (default: %s)\n", cmd_params_defaults.output_format == CSV ? "csv" : cmd_params_defaults.output_format == JSON ? "json" : "md");
fprintf(stdout, " -v, --verbose (default: %s)\n", cmd_params_defaults.verbose ? "1" : "0");
fprintf(stdout, "\n");
fprintf(stdout, "Multiple values can be given for each parameter by separating them with ',' or by repeating the parameter.\n");
}
static cmd_params parse_cmd_params(int argc, char ** argv) {
cmd_params params;
std::string arg;
bool invalid_param = false;
const std::string arg_prefix = "--";
const char split_delim = ',';
params.verbose = cmd_params_defaults.verbose;
params.output_format = cmd_params_defaults.output_format;
params.reps = cmd_params_defaults.reps;
for (int i = 1; i < argc; i++) {
arg = argv[i];
if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
std::replace(arg.begin(), arg.end(), '_', '-');
}
if (arg == "-h" || arg == "--help") {
print_usage(argc, argv);
exit(0);
} else if (arg == "-m" || arg == "--model") {
if (++i >= argc) {
invalid_param = true;
break;
}
auto p = split<std::string>(argv[i], split_delim);
params.model.insert(params.model.end(), p.begin(), p.end());
} else if (arg == "-p" || arg == "--n-prompt") {
if (++i >= argc) {
invalid_param = true;
break;
}
auto p = split<int>(argv[i], split_delim);
params.n_prompt.insert(params.n_prompt.end(), p.begin(), p.end());
} else if (arg == "-n" || arg == "--n-gen") {
if (++i >= argc) {
invalid_param = true;
break;
}
auto p = split<int>(argv[i], split_delim);
params.n_gen.insert(params.n_gen.end(), p.begin(), p.end());
} else if (arg == "-b" || arg == "--batch-size") {
if (++i >= argc) {
invalid_param = true;
break;
}
auto p = split<int>(argv[i], split_delim);
params.n_batch.insert(params.n_batch.end(), p.begin(), p.end());
} else if (arg == "--memory-f32") {
if (++i >= argc) {
invalid_param = true;
break;
}
auto p = split<int>(argv[i], split_delim);
params.f32_kv.insert(params.f32_kv.end(), p.begin(), p.end());
} else if (arg == "-t" || arg == "--threads") {
if (++i >= argc) {
invalid_param = true;
break;
}
auto p = split<int>(argv[i], split_delim);
params.n_threads.insert(params.n_threads.end(), p.begin(), p.end());
} else if (arg == "-ngl" || arg == "--n-gpu-layers") {
if (++i >= argc) {
invalid_param = true;
break;
}
auto p = split<int>(argv[i], split_delim);
params.n_gpu_layers.insert(params.n_gpu_layers.end(), p.begin(), p.end());
} else if (arg == "-mg" || arg == "--main-gpu") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.main_gpu = split<int>(argv[i], split_delim);
} else if (arg == "-lv" || arg == "--low-vram") {
if (++i >= argc) {
invalid_param = true;
break;
}
auto p = split<bool>(argv[i], split_delim);
params.low_vram.insert(params.low_vram.end(), p.begin(), p.end());
} else if (arg == "-mmq" || arg == "--mul-mat-q") {
if (++i >= argc) {
invalid_param = true;
break;
}
auto p = split<bool>(argv[i], split_delim);
params.mul_mat_q.insert(params.mul_mat_q.end(), p.begin(), p.end());
} else if (arg == "-ts" || arg == "--tensor-split") {
if (++i >= argc) {
invalid_param = true;
break;
}
for (auto ts : split<std::string>(argv[i], split_delim)) {
// split string by ; and /
const std::regex regex{R"([;/]+)"};
std::sregex_token_iterator it{ts.begin(), ts.end(), regex, -1};
std::vector<std::string> split_arg{it, {}};
GGML_ASSERT(split_arg.size() <= LLAMA_MAX_DEVICES);
std::array<float, LLAMA_MAX_DEVICES> tensor_split;
for (size_t i = 0; i < LLAMA_MAX_DEVICES; ++i) {
if (i < split_arg.size()) {
tensor_split[i] = std::stof(split_arg[i]);
} else {
tensor_split[i] = 0.0f;
}
}
params.tensor_split.push_back(tensor_split);
}
} else if (arg == "-r" || arg == "--repetitions") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.reps = std::stoi(argv[i]);
} else if (arg == "-o" || arg == "--output") {
if (++i >= argc) {
invalid_param = true;
break;
}
if (argv[i] == std::string("csv")) {
params.output_format = CSV;
} else if (argv[i] == std::string("json")) {
params.output_format = JSON;
} else if (argv[i] == std::string("md")) {
params.output_format = MARKDOWN;
} else if (argv[i] == std::string("sql")) {
params.output_format = SQL;
} else {
invalid_param = true;
break;
}
} else if (arg == "-v" || arg == "--verbose") {
params.verbose = true;
} else {
invalid_param = true;
break;
}
}
if (invalid_param) {
fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str());
print_usage(argc, argv);
exit(1);
}
// set defaults
if (params.model.empty()) { params.model = cmd_params_defaults.model; }
if (params.n_prompt.empty()) { params.n_prompt = cmd_params_defaults.n_prompt; }
if (params.n_gen.empty()) { params.n_gen = cmd_params_defaults.n_gen; }
if (params.n_batch.empty()) { params.n_batch = cmd_params_defaults.n_batch; }
if (params.f32_kv.empty()) { params.f32_kv = cmd_params_defaults.f32_kv; }
if (params.n_gpu_layers.empty()) { params.n_gpu_layers = cmd_params_defaults.n_gpu_layers; }
if (params.main_gpu.empty()) { params.main_gpu = cmd_params_defaults.main_gpu; }
if (params.mul_mat_q.empty()) { params.mul_mat_q = cmd_params_defaults.mul_mat_q; }
if (params.low_vram.empty()) { params.low_vram = cmd_params_defaults.low_vram; }
if (params.tensor_split.empty()) { params.tensor_split = cmd_params_defaults.tensor_split; }
if (params.n_threads.empty()) { params.n_threads = cmd_params_defaults.n_threads; }
return params;
}
struct cmd_params_instance {
std::string model;
int n_prompt;
int n_gen;
int n_batch;
bool f32_kv;
int n_threads;
int n_gpu_layers;
int main_gpu;
bool mul_mat_q;
bool low_vram;
std::array<float, LLAMA_MAX_DEVICES> tensor_split;
llama_context_params to_llama_params() const {
llama_context_params lparams = llama_context_default_params();
lparams.n_ctx = n_prompt + n_gen;
lparams.n_batch = n_batch;
lparams.f16_kv = !f32_kv;
lparams.n_gpu_layers = n_gpu_layers;
lparams.main_gpu = main_gpu;
lparams.mul_mat_q = mul_mat_q;
lparams.low_vram = low_vram;
lparams.tensor_split = tensor_split.data();
return lparams;
}
};
static std::vector<cmd_params_instance> get_cmd_params_instances_int(const cmd_params & params, int n_gen, int n_prompt) {
std::vector<cmd_params_instance> instances;
for (const auto & m : params.model)
for (const auto & nb : params.n_batch)
for (const auto & fk : params.f32_kv)
for (const auto & nl : params.n_gpu_layers)
for (const auto & mg : params.main_gpu)
for (const auto & mmq : params.mul_mat_q)
for (const auto & lv : params.low_vram)
for (const auto & ts : params.tensor_split)
for (const auto & nt : params.n_threads) {
cmd_params_instance instance = {
/* .model = */ m,
/* .n_prompt = */ n_prompt,
/* .n_gen = */ n_gen,
/* .n_batch = */ nb,
/* .f32_kv = */ fk,
/* .n_threads = */ nt,
/* .n_gpu_layers = */ nl,
/* .main_gpu = */ mg,
/* .mul_mat_q = */ mmq,
/* .low_vram = */ lv,
/* .tensor_split = */ ts,
};
instances.push_back(instance);
}
return instances;
}
static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_params & params) {
std::vector<cmd_params_instance> instances;
for (const auto & n_prompt : params.n_prompt) {
if (n_prompt == 0) {
continue;
}
auto instances_prompt = get_cmd_params_instances_int(params, 0, n_prompt);
instances.insert(instances.end(), instances_prompt.begin(), instances_prompt.end());
}
for (const auto & n_gen : params.n_gen) {
if (n_gen == 0) {
continue;
}
auto instances_gen = get_cmd_params_instances_int(params, n_gen, 0);
instances.insert(instances.end(), instances_gen.begin(), instances_gen.end());
}
return instances;
}
struct test {
static const std::string build_commit;
static const int build_number;
static const bool cuda;
static const bool opencl;
static const bool metal;
static const bool gpu_blas;
static const bool blas;
static const std::string cpu_info;
static const std::string gpu_info;
std::string model_filename;
std::string model_type;
int n_batch;
int n_threads;
bool f32_kv;
int n_gpu_layers;
int main_gpu;
bool mul_mat_q;
bool low_vram;
std::array<float, LLAMA_MAX_DEVICES> tensor_split;
int n_prompt;
int n_gen;
std::string test_time;
std::vector<uint64_t> samples_ns;
test(const cmd_params_instance & inst, const llama_model * lmodel, const llama_context * ctx) {
model_filename = inst.model;
char buf[128];
llama_model_type(lmodel, buf, sizeof(buf));
model_type = buf;
n_batch = inst.n_batch;
n_threads = inst.n_threads;
f32_kv = inst.f32_kv;
n_gpu_layers = inst.n_gpu_layers;
main_gpu = inst.main_gpu;
mul_mat_q = inst.mul_mat_q;
low_vram = inst.low_vram;
tensor_split = inst.tensor_split;
n_prompt = inst.n_prompt;
n_gen = inst.n_gen;
// RFC 3339 date-time format
time_t t = time(NULL);
std::strftime(buf, sizeof(buf), "%FT%TZ", gmtime(&t));
test_time = buf;
(void) ctx;
}
uint64_t avg_ns() const {
return ::avg(samples_ns);
}
uint64_t stdev_ns() const {
return ::stdev(samples_ns);
}
std::vector<double> get_ts() const {
int n_tokens = n_prompt + n_gen;
std::vector<double> ts;
std::transform(samples_ns.begin(), samples_ns.end(), std::back_inserter(ts), [n_tokens](uint64_t t) { return 1e9 * n_tokens / t; });
return ts;
}
double avg_ts() const {
return ::avg(get_ts());
}
double stdev_ts() const {
return ::stdev(get_ts());
}
static std::string get_backend() {
if (cuda) {
return "CUDA";
}
if (opencl) {
return "OpenCL";
}
if (metal) {
return "Metal";
}
if (gpu_blas) {
return "GPU BLAS";
}
if (blas) {
return "BLAS";
}
return "CPU";
}
static const std::vector<std::string> & get_fields() {
static const std::vector<std::string> fields = {
"build_commit", "build_number",
"cuda", "opencl", "metal", "gpu_blas", "blas",
"cpu_info", "gpu_info",
"model_filename", "model_type",
"n_batch", "n_threads", "f16_kv",
"n_gpu_layers", "main_gpu", "mul_mat_q", "low_vram", "tensor_split",
"n_prompt", "n_gen", "test_time",
"avg_ns", "stddev_ns",
"avg_ts", "stddev_ts"
};
return fields;
}
enum field_type {STRING, BOOL, INT, FLOAT};
static field_type get_field_type(const std::string & field) {
if (field == "build_number" || field == "n_batch" || field == "n_threads" ||
field == "n_gpu_layers" || field == "main_gpu" ||
field == "n_prompt" || field == "n_gen" ||
field == "avg_ns" || field == "stddev_ns") {
return INT;
}
if (field == "cuda" || field == "opencl" || field == "metal" || field == "gpu_blas" || field == "blas" ||
field == "f16_kv" || field == "mul_mat_q" || field == "low_vram") {
return BOOL;
}
if (field == "avg_ts" || field == "stddev_ts") {
return FLOAT;
}
return STRING;
}
std::vector<std::string> get_values() const {
std::string tensor_split_str;
int max_nonzero = 0;
for (int i = 0; i < LLAMA_MAX_DEVICES; i++) {
if (tensor_split[i] > 0) {
max_nonzero = i;
}
}
for (int i = 0; i <= max_nonzero; i++) {
char buf[32];
snprintf(buf, sizeof(buf), "%.2f", tensor_split[i]);
tensor_split_str += buf;
if (i < max_nonzero) {
tensor_split_str += "/";
}
}
std::vector<std::string> values = {
build_commit, std::to_string(build_number),
std::to_string(cuda), std::to_string(opencl), std::to_string(metal), std::to_string(gpu_blas), std::to_string(blas),
cpu_info, gpu_info,
model_filename, model_type,
std::to_string(n_batch), std::to_string(n_threads), std::to_string(!f32_kv),
std::to_string(n_gpu_layers), std::to_string(main_gpu), std::to_string(mul_mat_q), std::to_string(low_vram), tensor_split_str,
std::to_string(n_prompt), std::to_string(n_gen), test_time,
std::to_string(avg_ns()), std::to_string(stdev_ns()),
std::to_string(avg_ts()), std::to_string(stdev_ts())
};
return values;
}
std::map<std::string, std::string> get_map() const {
std::map<std::string, std::string> map;
auto fields = get_fields();
auto values = get_values();
std::transform(fields.begin(), fields.end(), values.begin(),
std::inserter(map, map.end()), std::make_pair<const std::string &, const std::string &>);
return map;
}
};
const std::string test::build_commit = BUILD_COMMIT;
const int test::build_number = BUILD_NUMBER;
const bool test::cuda = !!ggml_cpu_has_cublas();
const bool test::opencl = !!ggml_cpu_has_clblast();
const bool test::metal = !!ggml_cpu_has_metal();
const bool test::gpu_blas = !!ggml_cpu_has_gpublas();
const bool test::blas = !!ggml_cpu_has_blas();
const std::string test::cpu_info = get_cpu_info();
const std::string test::gpu_info = get_gpu_info();
struct printer {
virtual ~printer() {}
FILE * fout;
virtual void print_header(const cmd_params & params) { (void) params; };
virtual void print_test(const test & t) = 0;
virtual void print_footer() { };
};
struct csv_printer : public printer {
static std::string escape_csv(const std::string & field) {
std::string escaped = "\"";
for (auto c : field) {
if (c == '"') {
escaped += "\"";
}
escaped += c;
}
escaped += "\"";
return escaped;
}
void print_header(const cmd_params & params) override {
std::vector<std::string> fields = test::get_fields();
fprintf(fout, "%s\n", join(fields, ",").c_str());
(void) params;
}
void print_test(const test & t) override {
std::vector<std::string> values = t.get_values();
std::transform(values.begin(), values.end(), values.begin(), escape_csv);
fprintf(fout, "%s\n", join(values, ",").c_str());
}
};
struct json_printer : public printer {
bool first = true;
static std::string escape_json(const std::string & value) {
std::string escaped;
for (auto c : value) {
if (c == '"') {
escaped += "\\\"";
} else if (c == '\\') {
escaped += "\\\\";
} else if (c <= 0x1f) {
char buf[8];
snprintf(buf, sizeof(buf), "\\u%04x", c);
escaped += buf;
} else {
escaped += c;
}
}
return escaped;
}
static std::string format_value(const std::string & field, const std::string & value) {
switch (test::get_field_type(field)) {
case test::STRING:
return "\"" + escape_json(value) + "\"";
case test::BOOL:
return value == "0" ? "false" : "true";
default:
return value;
}
}
void print_header(const cmd_params & params) override {
fprintf(fout, "[\n");
(void) params;
}
void print_fields(const std::vector<std::string> & fields, const std::vector<std::string> & values) {
assert(fields.size() == values.size());
for (size_t i = 0; i < fields.size(); i++) {
fprintf(fout, " \"%s\": %s,\n", fields.at(i).c_str(), format_value(fields.at(i), values.at(i)).c_str());
}
}
void print_test(const test & t) override {
if (first) {
first = false;
} else {
fprintf(fout, ",\n");
}
fprintf(fout, " {\n");
print_fields(test::get_fields(), t.get_values());
fprintf(fout, " \"samples_ns\": [ %s ],\n", join(t.samples_ns, ", ").c_str());
fprintf(fout, " \"samples_ts\": [ %s ]\n", join(t.get_ts(), ", ").c_str());
fprintf(fout, " }");
fflush(fout);
}
void print_footer() override {
fprintf(fout, "\n]\n");
}
};
struct markdown_printer : public printer {
std::vector<std::string> fields;
static int get_field_width(const std::string & field) {
if (field == "model") {
return -30;
}
if (field == "t/s") {
return 15;
}
int width = std::max((int)field.length(), 10);
if (test::get_field_type(field) == test::STRING) {
return -width;
}
return width;
}
void print_header(const cmd_params & params) override {
// select fields to print
fields = { "model", "backend" };
bool is_cpu_backend = test::get_backend() == "CPU" || test::get_backend() == "BLAS";
if (!is_cpu_backend) {
fields.push_back("n_gpu_layers");
}
if (params.n_batch.size() > 1 || params.n_threads != cmd_params_defaults.n_threads || is_cpu_backend) {
fields.push_back("n_threads");
}
if (params.n_batch.size() > 1 || params.n_batch != cmd_params_defaults.n_batch) {
fields.push_back("n_batch");
}
if (params.f32_kv.size() > 1 || params.f32_kv != cmd_params_defaults.f32_kv) {
fields.push_back("f16_kv");
}
if (params.main_gpu.size() > 1 || params.main_gpu != cmd_params_defaults.main_gpu) {
fields.push_back("main_gpu");
}
if (params.mul_mat_q.size() > 1 || params.mul_mat_q != cmd_params_defaults.mul_mat_q) {
fields.push_back("mul_mat_q");
}
if (params.low_vram.size() > 1 || params.low_vram != cmd_params_defaults.low_vram) {
fields.push_back("low_vram");
}
if (params.tensor_split.size() > 1 || params.tensor_split != cmd_params_defaults.tensor_split) {
fields.push_back("tensor_split");
}
fields.push_back("test");
fields.push_back("t/s");
fprintf(fout, "|");
for (const auto & field : fields) {
fprintf(fout, " %*s |", get_field_width(field), field.c_str());
}
fprintf(fout, "\n");
fprintf(fout, "|");
for (const auto & field : fields) {
int width = get_field_width(field);
fprintf(fout, " %s%s |", std::string(std::abs(width) - 1, '-').c_str(), width > 0 ? ":" : "-");
}
fprintf(fout, "\n");
}
void print_test(const test & t) override {
std::map<std::string, std::string> vmap = t.get_map();
fprintf(fout, "|");
for (const auto & field : fields) {
std::string value;
if (field == "model") {
value = t.model_type;
} else if (field == "backend") {
value = test::get_backend();
} else if (field == "test") {
char buf[128];
if (t.n_prompt > 0 && t.n_gen == 0) {
snprintf(buf, sizeof(buf), "pp %d", t.n_prompt);
} else if (t.n_gen > 0 && t.n_prompt == 0) {
snprintf(buf, sizeof(buf), "tg %d", t.n_gen);
} else {
assert(false);
exit(1);
}
value = buf;
} else if (field == "t/s") {
char buf[128];
snprintf(buf, sizeof(buf), "%.2f ± %.2f", t.avg_ts(), t.stdev_ts());
value = buf;
} else if (vmap.find(field) != vmap.end()) {
value = vmap.at(field);
} else {
assert(false);
exit(1);
}
int width = get_field_width(field);
if (field == "t/s") {
// HACK: the utf-8 character is 2 bytes
width += 1;
}
fprintf(fout, " %*s |", width, value.c_str());
}
fprintf(fout, "\n");
}
void print_footer() override {
fprintf(fout, "\nbuild: %s (%d)\n", test::build_commit.c_str(), test::build_number);
}
};
struct sql_printer : public printer {
static std::string get_sql_field_type(const std::string & field) {
switch (test::get_field_type(field)) {
case test::STRING:
return "TEXT";
case test::BOOL:
case test::INT:
return "INTEGER";
case test::FLOAT:
return "REAL";
default:
assert(false);
exit(1);
}
}
void print_header(const cmd_params & params) override {
std::vector<std::string> fields = test::get_fields();
fprintf(fout, "CREATE TABLE IF NOT EXISTS test (\n");
for (size_t i = 0; i < fields.size(); i++) {
fprintf(fout, " %s %s%s\n", fields.at(i).c_str(), get_sql_field_type(fields.at(i)).c_str(), i < fields.size() - 1 ? "," : "");
}
fprintf(fout, ");\n");
fprintf(fout, "\n");
(void) params;
}
void print_test(const test & t) override {
fprintf(fout, "INSERT INTO test (%s) ", join(test::get_fields(), ", ").c_str());
fprintf(fout, "VALUES (");
std::vector<std::string> values = t.get_values();
for (size_t i = 0; i < values.size(); i++) {
fprintf(fout, "'%s'%s", values.at(i).c_str(), i < values.size() - 1 ? ", " : "");
}
fprintf(fout, ");\n");
}
};
static void test_prompt(llama_context * ctx, int n_prompt, int n_past, int n_batch, int n_threads) {
std::vector<llama_token> tokens(n_batch, llama_token_bos());
int n_processed = 0;
while (n_processed < n_prompt) {
int n_tokens = std::min(n_prompt - n_processed, n_batch);
llama_eval(ctx, tokens.data(), n_tokens, n_past + n_processed, n_threads);
n_processed += n_tokens;
}
}
static void test_gen(llama_context * ctx, int n_gen, int n_past, int n_threads) {
llama_token token = llama_token_bos();
for (int i = 0; i < n_gen; i++) {
llama_eval(ctx, &token, 1, n_past + i, n_threads);
}
}
static void llama_null_log_callback(enum llama_log_level level, const char * text, void * user_data) {
(void) level;
(void) text;
(void) user_data;
}
int main(int argc, char ** argv) {
#if !defined(NDEBUG)
fprintf(stderr, "warning: asserts enabled, performance may be affected\n");
#endif
#if (defined(_MSC_VER) && defined(_DEBUG)) || (!defined(_MSC_VER) && !defined(__OPTIMIZE__))
fprintf(stderr, "warning: debug build, performance may be affected\n");
#endif
#if defined(__SANITIZE_ADDRESS__) || defined(__SANITIZE_THREAD__)
fprintf(stderr, "warning: sanitizer enabled, performance may be affected\n");
#endif
cmd_params params = parse_cmd_params(argc, argv);
// initialize llama.cpp
if (!params.verbose) {
llama_log_set(llama_null_log_callback, NULL);
}
bool numa = false;
llama_backend_init(numa);
// initialize printer
std::unique_ptr<printer> p;
switch (params.output_format) {
case CSV:
p.reset(new csv_printer());
break;
case JSON:
p.reset(new json_printer());
break;
case MARKDOWN:
p.reset(new markdown_printer());
break;
case SQL:
p.reset(new sql_printer());
break;
default:
assert(false);
exit(1);
}
p->fout = stdout;
p->print_header(params);
std::vector<cmd_params_instance> params_instances = get_cmd_params_instances(params);
for (const auto & inst : params_instances) {
// TODO: keep the model between tests when possible
llama_context_params lparams = inst.to_llama_params();
llama_model * lmodel = llama_load_model_from_file(inst.model.c_str(), lparams);
if (lmodel == NULL) {
fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, inst.model.c_str());
return 1;
}
llama_context * ctx = llama_new_context_with_model(lmodel, lparams);
if (ctx == NULL) {
fprintf(stderr, "%s: error: failed to create context with model '%s'\n", __func__, inst.model.c_str());
llama_free_model(lmodel);
return 1;
}
test t(inst, lmodel, ctx);
// warmup run
test_gen(ctx, 1, 0, t.n_threads);
for (int i = 0; i < params.reps; i++) {
uint64_t t_start = get_time_ns();
if (t.n_prompt > 0) {
test_prompt(ctx, t.n_prompt, 0, t.n_batch, t.n_threads);
}
if (t.n_gen > 0) {
test_gen(ctx, t.n_gen, t.n_prompt, t.n_threads);
}
uint64_t t_ns = get_time_ns() - t_start;
t.samples_ns.push_back(t_ns);
}
p->print_test(t);
llama_print_timings(ctx);
llama_free(ctx);
llama_free_model(lmodel);
}
p->print_footer();
llama_backend_free();
return 0;
}

2
examples/perplexity/perplexity.cpp
View File

@@ -88,7 +88,7 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
fprintf(stderr, "%d hours ", total_seconds / (60*60));
total_seconds = total_seconds % (60*60);
}
fprintf(stderr, "%d minutes\n", total_seconds / 60);
fprintf(stderr, "%.2f minutes\n", total_seconds / 60.0);
}
// We get the logits for all the tokens in the context window (params.n_ctx)

12
ggml-cuda.cu
View File

@@ -6469,3 +6469,15 @@ bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_
func(tensor->src[0], tensor->src[1], tensor);
return true;
}
int ggml_cuda_get_device_count() {
int device_count;
CUDA_CHECK(cudaGetDeviceCount(&device_count));
return device_count;
}
void ggml_cuda_get_device_description(int device, char * description, size_t description_size) {
cudaDeviceProp prop;
CUDA_CHECK(cudaGetDeviceProperties(&prop, device));
snprintf(description, description_size, "%s", prop.name);
}

38
ggml-cuda.h
View File

@@ -8,29 +8,25 @@ extern "C" {
#define GGML_CUDA_MAX_DEVICES 16
void ggml_init_cublas(void);
void ggml_cuda_set_tensor_split(const float * tensor_split);
GGML_API void ggml_init_cublas(void);
GGML_API void * ggml_cuda_host_malloc(size_t size);
GGML_API void ggml_cuda_host_free(void * ptr);
void ggml_cuda_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
size_t ggml_cuda_mul_mat_get_wsize(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
void ggml_cuda_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst, void * wdata, size_t wsize);
GGML_API bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
GGML_API void ggml_cuda_set_tensor_split(const float * tensor_split);
GGML_API void ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor);
GGML_API void ggml_cuda_free_data(struct ggml_tensor * tensor);
GGML_API void ggml_cuda_assign_buffers(struct ggml_tensor * tensor);
GGML_API void ggml_cuda_assign_buffers_no_scratch(struct ggml_tensor * tensor);
GGML_API void ggml_cuda_assign_buffers_force_inplace(struct ggml_tensor * tensor);
GGML_API void ggml_cuda_set_main_device(int main_device);
GGML_API void ggml_cuda_set_mul_mat_q(bool mul_mat_q);
GGML_API void ggml_cuda_set_scratch_size(size_t scratch_size);
GGML_API void ggml_cuda_free_scratch(void);
GGML_API bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor);
// TODO: export these with GGML_API
void * ggml_cuda_host_malloc(size_t size);
void ggml_cuda_host_free(void * ptr);
void ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor);
void ggml_cuda_free_data(struct ggml_tensor * tensor);
void ggml_cuda_assign_buffers(struct ggml_tensor * tensor);
void ggml_cuda_assign_buffers_no_scratch(struct ggml_tensor * tensor);
void ggml_cuda_assign_buffers_force_inplace(struct ggml_tensor * tensor);
void ggml_cuda_set_main_device(int main_device);
void ggml_cuda_set_mul_mat_q(bool mul_mat_q);
void ggml_cuda_set_scratch_size(size_t scratch_size);
void ggml_cuda_free_scratch(void);
bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor);
GGML_API int ggml_cuda_get_device_count(void);
GGML_API void ggml_cuda_get_device_description(int device, char * description, size_t description_size);
#ifdef __cplusplus
}

64
llama.cpp
View File

@@ -597,9 +597,9 @@ enum e_model {
static const size_t kB = 1024;
static const size_t MB = 1024*1024;
static const std::map<e_model, size_t> & MEM_REQ_SCRATCH0(int n_ctx)
static std::map<e_model, size_t> MEM_REQ_SCRATCH0(int n_ctx)
{
static std::map<e_model, size_t> k_sizes = {
std::map<e_model, size_t> k_sizes = {
{ MODEL_3B, ((size_t) n_ctx / 16ull + 92ull) * MB },
{ MODEL_7B, ((size_t) n_ctx / 16ull + 100ull) * MB },
{ MODEL_13B, ((size_t) n_ctx / 12ull + 120ull) * MB },
@@ -778,6 +778,7 @@ struct llama_vocab {
struct llama_model {
e_model type = MODEL_UNKNOWN;
llama_ftype ftype = LLAMA_FTYPE_ALL_F32;
llama_hparams hparams;
llama_vocab vocab;
@@ -1027,7 +1028,8 @@ struct llama_model_loader {
bool use_mmap = false;
llama_file file;
llama_file_version file_version;
llama_ftype ftype;
llama_file_version fver;
std::unique_ptr<llama_mmap> mapping;
@@ -1048,7 +1050,7 @@ struct llama_model_loader {
n_kv = gguf_get_n_kv(ctx_gguf);
n_tensors = gguf_get_n_tensors(ctx_gguf);
file_version = (enum llama_file_version) gguf_get_version(ctx_gguf);
fver = (enum llama_file_version) gguf_get_version(ctx_gguf);
for (int i = 0; i < n_tensors; i++) {
const char * name = gguf_get_tensor_name(ctx_gguf, i);
@@ -1056,23 +1058,51 @@ struct llama_model_loader {
n_elements += ggml_nelements(t);
}
// print meta data
LLAMA_LOG_INFO("%s: loaded meta data with %d key-value pairs and %d tensors from %s (version %s)\n",
__func__, n_kv, n_tensors, fname.c_str(), llama_file_version_name(fver));
// determine file type based on the number of tensors for each quantization and print meta data
// TODO: make optional
{
LLAMA_LOG_INFO("%s: loaded meta data with %d key-value pairs and %d tensors from %s (version %s)\n",
__func__, n_kv, n_tensors, fname.c_str(), llama_file_version_name(file_version));
std::map<enum ggml_type, uint32_t> n_type;
uint32_t n_type_max = 0;
enum ggml_type type_max = GGML_TYPE_F32;
for (int i = 0; i < n_tensors; i++) {
const char * name = gguf_get_tensor_name(ctx_gguf, i);
struct ggml_tensor * meta = ggml_get_tensor(ctx_meta, name);
n_type[meta->type]++;
if (n_type_max < n_type[meta->type]) {
n_type_max = n_type[meta->type];
type_max = meta->type;
}
LLAMA_LOG_INFO("%s: - tensor %4d: %32s %-8s [ %s ]\n", __func__, i, name, ggml_type_name(meta->type), llama_format_tensor_shape(meta).c_str());
}
switch (type_max) {
case GGML_TYPE_F32: ftype = LLAMA_FTYPE_ALL_F32; break;
case GGML_TYPE_F16: ftype = LLAMA_FTYPE_MOSTLY_F16; break;
case GGML_TYPE_Q4_0: ftype = LLAMA_FTYPE_MOSTLY_Q4_0; break;
case GGML_TYPE_Q4_1: ftype = LLAMA_FTYPE_MOSTLY_Q4_1; break;
case GGML_TYPE_Q5_0: ftype = LLAMA_FTYPE_MOSTLY_Q5_0; break;
case GGML_TYPE_Q5_1: ftype = LLAMA_FTYPE_MOSTLY_Q5_1; break;
case GGML_TYPE_Q8_0: ftype = LLAMA_FTYPE_MOSTLY_Q8_0; break;
case GGML_TYPE_Q2_K: ftype = LLAMA_FTYPE_MOSTLY_Q2_K; break;
case GGML_TYPE_Q3_K: ftype = LLAMA_FTYPE_MOSTLY_Q3_K_M; break;
case GGML_TYPE_Q4_K: ftype = LLAMA_FTYPE_MOSTLY_Q4_K_M; break;
case GGML_TYPE_Q5_K: ftype = LLAMA_FTYPE_MOSTLY_Q5_K_M; break;
case GGML_TYPE_Q6_K: ftype = LLAMA_FTYPE_MOSTLY_Q6_K; break;
default:
{
LLAMA_LOG_WARN("%s: unknown type %s\n", __func__, ggml_type_name(type_max));
ftype = LLAMA_FTYPE_ALL_F32;
} break;
}
for (int i = 0; i < n_kv; i++) {
const char * name = gguf_get_key(ctx_gguf, i);
const enum gguf_type type = gguf_get_kv_type(ctx_gguf, i);
@@ -1275,7 +1305,7 @@ struct llama_model_loader {
// load LLaMA models
//
static const char * llama_ftype_name(enum llama_ftype ftype) {
const char * llama_model_ftype_name(enum llama_ftype ftype) {
switch (ftype) {
case LLAMA_FTYPE_ALL_F32: return "all F32";
case LLAMA_FTYPE_MOSTLY_F16: return "mostly F16";
@@ -1403,6 +1433,8 @@ static void llama_model_load_internal(
} break;
}
model.ftype = ml->ftype;
hparams.n_ctx = n_ctx;
// LLaMAv2
@@ -1456,7 +1488,7 @@ static void llama_model_load_internal(
{
// hparams
LLAMA_LOG_INFO("%s: format = %s\n", __func__, llama_file_version_name(ml->file_version));
LLAMA_LOG_INFO("%s: format = %s\n", __func__, llama_file_version_name(ml->fver));
LLAMA_LOG_INFO("%s: arch = %s\n", __func__, general_arch.c_str());
LLAMA_LOG_INFO("%s: n_vocab = %u\n", __func__, hparams.n_vocab);
LLAMA_LOG_INFO("%s: n_ctx_train = %u\n", __func__, hparams.n_ctx_train);
@@ -1472,6 +1504,7 @@ static void llama_model_load_internal(
LLAMA_LOG_INFO("%s: freq_base = %.1f\n", __func__, hparams.rope_freq_base);
LLAMA_LOG_INFO("%s: freq_scale = %g\n", __func__, hparams.rope_freq_scale);
LLAMA_LOG_INFO("%s: model type = %s\n", __func__, llama_model_type_name(model.type));
LLAMA_LOG_INFO("%s: model ftype = %s\n", __func__, llama_model_ftype_name(model.ftype));
LLAMA_LOG_INFO("%s: model size = %.2f B\n", __func__, ml->n_elements*1e-9);
// general kv
@@ -2142,6 +2175,13 @@ static bool llama_eval_internal(
GGML_ASSERT((!tokens && embd) || (tokens && !embd)); // NOLINT
GGML_ASSERT(n_tokens > 0);
GGML_ASSERT(n_past >= 0);
GGML_ASSERT(n_threads > 0);
// TODO: keep the values of n_batch and n_ctx
// GGML_ASSERT(n_tokens <= n_batch);
// GGML_ASSERT(n_past + n_tokens <= n_ctx);
const int64_t t_start_us = ggml_time_us();
#ifdef GGML_USE_MPI
@@ -4915,6 +4955,10 @@ int llama_get_vocab(
return llama_get_vocab_from_model(&ctx->model, strings, scores, capacity);
}
int llama_model_type(const struct llama_model * model, char * buf, size_t buf_size) {
return snprintf(buf, buf_size, "LLaMA %s %s", llama_model_type_name(model->type), llama_model_ftype_name(model->ftype));
}
int llama_get_vocab_from_model(
const struct llama_model * model,
const char * * strings,

3
llama.h
View File

@@ -235,6 +235,9 @@ extern "C" {
LLAMA_API int llama_n_ctx_from_model (const struct llama_model * model);
LLAMA_API int llama_n_embd_from_model (const struct llama_model * model);
// Get a string describing the model type
LLAMA_API int llama_model_type(const struct llama_model * model, char * buf, size_t buf_size);
// Returns 0 on success
LLAMA_API int llama_model_quantize(
const char * fname_inp,