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llama.cpp : split llama_context_params into model and context params (#3301)

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* llama.cpp : split llama_context_params into model and context params

ggml-ci

* fix metal build

* fix freq_base/scale default to model value

* llama-bench : keep the same model between tests when possible

* move n_threads to llama_context_params, add n_threads_batch

* fix mpi build

* remove kv_size(), cuda scratch fixes

* remove low-vram option

* add n_threads_batch to system info, refactor to get_system_info()

* add documentation about --threads-batch to the READMEs

* llama-bench fix

* main : fix rope freq/scale warning

* llama.cpp : add llama_get_model
common : add llama_tokenize from model

* remove duplicated ctx/model functions

ggml-ci

* cuda : print total VRAM used
This commit is contained in:
slaren 2023-09-28 21:42:38 +02:00 • committed by GitHub
parent 0512d66670
commit 16bc66d947
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27 changed files with 713 additions and 633 deletions
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159
examples/llama-bench/llama-bench.cpp
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@ -132,7 +132,6 @@ struct cmd_params {
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;
@ -149,7 +148,6 @@ static const cmd_params cmd_params_defaults = {
/* n_gpu_layers */ {99},
/* main_gpu */ {0},
/* mul_mat_q */ {true},
/* low_vram */ {false},
/* tensor_split */ {{}},
/* reps */ 5,
/* verbose */ false,
@ -167,9 +165,8 @@ static void print_usage(int /* argc */, char ** argv) {
printf(" -b, --batch-size <n> (default: %s)\n", join(cmd_params_defaults.n_batch, ",").c_str());
printf(" --memory-f32 <0|1> (default: %s)\n", join(cmd_params_defaults.f32_kv, ",").c_str());
printf(" -t, --threads <n> (default: %s)\n", join(cmd_params_defaults.n_threads, ",").c_str());
printf(" -ngl N, --n-gpu-layers <n> (default: %s)\n", join(cmd_params_defaults.n_gpu_layers, ",").c_str());
printf(" -mg i, --main-gpu <n> (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str());
printf(" -lv, --low-vram <0|1> (default: %s)\n", join(cmd_params_defaults.low_vram, ",").c_str());
printf(" -ngl, --n-gpu-layers <n> (default: %s)\n", join(cmd_params_defaults.n_gpu_layers, ",").c_str());
printf(" -mg, --main-gpu <i> (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str());
printf(" -mmq, --mul-mat-q <0|1> (default: %s)\n", join(cmd_params_defaults.mul_mat_q, ",").c_str());
printf(" -ts, --tensor_split <ts0/ts1/..> \n");
printf(" -r, --repetitions <n> (default: %d)\n", cmd_params_defaults.reps);
@ -255,13 +252,6 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
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;
@ -336,7 +326,6 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
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; }
@ -353,21 +342,34 @@ struct cmd_params_instance {
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();
llama_model_params to_llama_mparams() const {
llama_model_params mparams = llama_model_default_params();
return lparams;
mparams.n_gpu_layers = n_gpu_layers;
mparams.main_gpu = main_gpu;
mparams.tensor_split = tensor_split.data();
return mparams;
}
bool equal_mparams(const cmd_params_instance & other) const {
return model == other.model &&
n_gpu_layers == other.n_gpu_layers &&
main_gpu == other.main_gpu &&
tensor_split == other.tensor_split;
}
llama_context_params to_llama_cparams() const {
llama_context_params cparams = llama_context_default_params();
cparams.n_ctx = n_prompt + n_gen;
cparams.n_batch = n_batch;
cparams.f16_kv = !f32_kv;
cparams.mul_mat_q = mul_mat_q;
return cparams;
}
};
@ -375,13 +377,12 @@ static std::vector<cmd_params_instance> get_cmd_params_instances_int(const cmd_p
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 & nb : params.n_batch)
for (const auto & fk : params.f32_kv)
for (const auto & mmq : params.mul_mat_q)
for (const auto & nt : params.n_threads) {
cmd_params_instance instance = {
/* .model = */ m,
@ -393,7 +394,6 @@ static std::vector<cmd_params_instance> get_cmd_params_instances_int(const cmd_p
/* .n_gpu_layers = */ nl,
/* .main_gpu = */ mg,
/* .mul_mat_q = */ mmq,
/* .low_vram = */ lv,
/* .tensor_split = */ ts,
};
instances.push_back(instance);
@ -404,6 +404,56 @@ static std::vector<cmd_params_instance> get_cmd_params_instances_int(const cmd_p
static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_params & params) {
std::vector<cmd_params_instance> instances;
#if 1
// this ordering minimizes the number of times that each model needs to be reloaded
for (const auto & m : params.model)
for (const auto & nl : params.n_gpu_layers)
for (const auto & mg : params.main_gpu)
for (const auto & ts : params.tensor_split)
for (const auto & nb : params.n_batch)
for (const auto & fk : params.f32_kv)
for (const auto & mmq : params.mul_mat_q)
for (const auto & nt : params.n_threads) {
for (const auto & n_prompt : params.n_prompt) {
if (n_prompt == 0) {
continue;
}
cmd_params_instance instance = {
/* .model = */ m,
/* .n_prompt = */ n_prompt,
/* .n_gen = */ 0,
/* .n_batch = */ nb,
/* .f32_kv = */ fk,
/* .n_threads = */ nt,
/* .n_gpu_layers = */ nl,
/* .main_gpu = */ mg,
/* .mul_mat_q = */ mmq,
/* .tensor_split = */ ts,
};
instances.push_back(instance);
}
for (const auto & n_gen : params.n_gen) {
if (n_gen == 0) {
continue;
}
cmd_params_instance instance = {
/* .model = */ m,
/* .n_prompt = */ 0,
/* .n_gen = */ n_gen,
/* .n_batch = */ nb,
/* .f32_kv = */ fk,
/* .n_threads = */ nt,
/* .n_gpu_layers = */ nl,
/* .main_gpu = */ mg,
/* .mul_mat_q = */ mmq,
/* .tensor_split = */ ts,
};
instances.push_back(instance);
}
}
#else
// this ordering separates the prompt and generation tests
for (const auto & n_prompt : params.n_prompt) {
if (n_prompt == 0) {
continue;
@ -419,6 +469,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
auto instances_gen = get_cmd_params_instances_int(params, n_gen, 0);
instances.insert(instances.end(), instances_gen.begin(), instances_gen.end());
}
#endif
return instances;
}
@ -443,7 +494,6 @@ struct test {
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;
@ -463,7 +513,6 @@ struct test {
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;
@ -524,7 +573,7 @@ struct test {
"cpu_info", "gpu_info",
"model_filename", "model_type", "model_size", "model_n_params",
"n_batch", "n_threads", "f16_kv",
"n_gpu_layers", "main_gpu", "mul_mat_q", "low_vram", "tensor_split",
"n_gpu_layers", "main_gpu", "mul_mat_q", "tensor_split",
"n_prompt", "n_gen", "test_time",
"avg_ns", "stddev_ns",
"avg_ts", "stddev_ts"
@ -543,7 +592,7 @@ struct test {
return INT;
}
if (field == "cuda" || field == "opencl" || field == "metal" || field == "gpu_blas" || field == "blas" ||
field == "f16_kv" || field == "mul_mat_q" || field == "low_vram") {
field == "f16_kv" || field == "mul_mat_q") {
return BOOL;
}
if (field == "avg_ts" || field == "stddev_ts") {
@ -574,7 +623,7 @@ struct test {
cpu_info, gpu_info,
model_filename, model_type, std::to_string(model_size), std::to_string(model_n_params),
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_gpu_layers), std::to_string(main_gpu), std::to_string(mul_mat_q), 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())
@ -766,9 +815,6 @@ struct markdown_printer : public printer {
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");
}
@ -889,17 +935,23 @@ struct sql_printer : public printer {
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(ctx));
int n_processed = 0;
llama_set_n_threads(ctx, n_threads, n_threads);
while (n_processed < n_prompt) {
int n_tokens = std::min(n_prompt - n_processed, n_batch);
llama_decode(ctx, llama_batch_get_one(tokens.data(), n_tokens, n_past + n_processed, 0), n_threads);
llama_decode(ctx, llama_batch_get_one(tokens.data(), n_tokens, n_past + n_processed, 0));
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(ctx);
llama_set_n_threads(ctx, n_threads, n_threads);
for (int i = 0; i < n_gen; i++) {
llama_decode(ctx, llama_batch_get_one(&token, 1, n_past + i, 0), n_threads);
llama_decode(ctx, llama_batch_get_one(&token, 1, n_past + i, 0));
}
}
@ -958,17 +1010,25 @@ int main(int argc, char ** argv) {
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 = nullptr;
const cmd_params_instance * prev_inst = nullptr;
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;
for (const auto & inst : params_instances) {
// keep the same model between tests when possible
if (!lmodel || !prev_inst || !inst.equal_mparams(*prev_inst)) {
if (lmodel) {
llama_free_model(lmodel);
}
lmodel = llama_load_model_from_file(inst.model.c_str(), inst.to_llama_mparams());
if (lmodel == NULL) {
fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, inst.model.c_str());
return 1;
}
prev_inst = &inst;
}
llama_context * ctx = llama_new_context_with_model(lmodel, lparams);
llama_context * ctx = llama_new_context_with_model(lmodel, inst.to_llama_cparams());
if (ctx == NULL) {
fprintf(stderr, "%s: error: failed to create context with model '%s'\n", __func__, inst.model.c_str());
llama_free_model(lmodel);
@ -1006,9 +1066,10 @@ int main(int argc, char ** argv) {
llama_print_timings(ctx);
llama_free(ctx);
llama_free_model(lmodel);
}
llama_free_model(lmodel);
p->print_footer();
llama_backend_free();