llama-bench : add model sizes (#2771)

* llama-bench : add model sizes

* more compact markdown output

* back to GiB

* adjust column sizes
This commit is contained in:
slaren 2023-08-25 15:16:19 +02:00 committed by GitHub
parent 12e2e33a97
commit 154725c543
No known key found for this signature in database
GPG key ID: 4AEE18F83AFDEB23
3 changed files with 74 additions and 10 deletions

View file

@ -441,6 +441,8 @@ struct test {
static const std::string gpu_info;
std::string model_filename;
std::string model_type;
uint64_t model_size;
uint64_t model_n_params;
int n_batch;
int n_threads;
bool f32_kv;
@ -457,8 +459,10 @@ struct test {
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));
llama_model_desc(lmodel, buf, sizeof(buf));
model_type = buf;
model_size = llama_model_size(lmodel);
model_n_params = llama_model_n_params(lmodel);
n_batch = inst.n_batch;
n_threads = inst.n_threads;
f32_kv = inst.f32_kv;
@ -524,7 +528,7 @@ struct test {
"build_commit", "build_number",
"cuda", "opencl", "metal", "gpu_blas", "blas",
"cpu_info", "gpu_info",
"model_filename", "model_type",
"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_prompt", "n_gen", "test_time",
@ -538,6 +542,7 @@ struct test {
static field_type get_field_type(const std::string & field) {
if (field == "build_number" || field == "n_batch" || field == "n_threads" ||
field == "model_size" || field == "model_n_params" ||
field == "n_gpu_layers" || field == "main_gpu" ||
field == "n_prompt" || field == "n_gen" ||
field == "avg_ns" || field == "stddev_ns") {
@ -573,7 +578,7 @@ struct test {
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,
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_prompt), std::to_string(n_gen), test_time,
@ -709,8 +714,15 @@ struct markdown_printer : public printer {
return -30;
}
if (field == "t/s") {
return 15;
return 16;
}
if (field == "size" || field == "params") {
return 10;
}
if (field == "n_gpu_layers") {
return 3;
}
int width = std::max((int)field.length(), 10);
if (test::get_field_type(field) == test::STRING) {
@ -719,9 +731,28 @@ struct markdown_printer : public printer {
return width;
}
static std::string get_field_display_name(const std::string & field) {
if (field == "n_gpu_layers") {
return "ngl";
}
if (field == "n_threads") {
return "threads";
}
if (field == "mul_mat_q") {
return "mmq";
}
if (field == "tensor_split") {
return "ts";
}
return field;
}
void print_header(const cmd_params & params) override {
// select fields to print
fields = { "model", "backend" };
fields.push_back("model");
fields.push_back("size");
fields.push_back("params");
fields.push_back("backend");
bool is_cpu_backend = test::get_backend() == "CPU" || test::get_backend() == "BLAS";
if (!is_cpu_backend) {
fields.push_back("n_gpu_layers");
@ -752,7 +783,7 @@ struct markdown_printer : public printer {
fprintf(fout, "|");
for (const auto & field : fields) {
fprintf(fout, " %*s |", get_field_width(field), field.c_str());
fprintf(fout, " %*s |", get_field_width(field), get_field_display_name(field).c_str());
}
fprintf(fout, "\n");
fprintf(fout, "|");
@ -769,12 +800,26 @@ struct markdown_printer : public printer {
fprintf(fout, "|");
for (const auto & field : fields) {
std::string value;
char buf[128];
if (field == "model") {
value = t.model_type;
} else if (field == "size") {
if (t.model_size < 1024*1024*1024) {
snprintf(buf, sizeof(buf), "%.2f MiB", t.model_size / 1024.0 / 1024.0);
} else {
snprintf(buf, sizeof(buf), "%.2f GiB", t.model_size / 1024.0 / 1024.0 / 1024.0);
}
value = buf;
} else if (field == "params") {
if (t.model_n_params < 1000*1000*1000) {
snprintf(buf, sizeof(buf), "%.2f M", t.model_n_params / 1e6);
} else {
snprintf(buf, sizeof(buf), "%.2f B", t.model_n_params / 1e9);
}
value = buf;
} 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) {
@ -785,7 +830,6 @@ struct markdown_printer : public printer {
}
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()) {

View file

@ -5297,13 +5297,29 @@ int llama_model_n_embd(const struct llama_model * model) {
return model->hparams.n_embd;
}
int llama_model_type(const struct llama_model * model, char * buf, size_t buf_size) {
int llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size) {
return snprintf(buf, buf_size, "%s %s %s",
model->name.c_str(),
llama_model_type_name(model->type),
llama_model_ftype_name(model->ftype).c_str());
}
uint64_t llama_model_size(const struct llama_model * model) {
uint64_t size = 0;
for (const auto & it : model->tensors_by_name) {
size += ggml_nbytes(it.second);
}
return size;
}
uint64_t llama_model_n_params(const struct llama_model * model) {
uint64_t nparams = 0;
for (const auto & it : model->tensors_by_name) {
nparams += ggml_nelements(it.second);
}
return nparams;
}
int llama_model_quantize(
const char * fname_inp,
const char * fname_out,

View file

@ -254,7 +254,11 @@ extern "C" {
LLAMA_API int llama_model_n_embd (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);
LLAMA_API int llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size);
// Returns the total size of all the tensors in the model in bytes
LLAMA_API uint64_t llama_model_size(const struct llama_model * model);
// Returns the total number of parameters in the model
LLAMA_API uint64_t llama_model_n_params(const struct llama_model * model);
// Returns 0 on success
LLAMA_API int llama_model_quantize(