tqcq/llama.cpp
0
0
Fork 0
mirror of https://github.com/ggml-org/llama.cpp.git synced 2025-06-27 12:05:03 +00:00
Code Issues Packages Projects Releases Wiki Activity

llama-bench : Add --override-tensors arg (#12922)

Browse Source 
* Add --override-tensors option to llama-bench

* Correct llama-bench --override-tensors to --override-tensor

* llama-bench: Update --override-tensors parsing to match --tensor-split, appear in test matrix.

* Make new llama-bench util functions static to fix Ubuntu CI

* llama-bench: Correct -ot corner cases (No -ot calls, leading and trailing empty -ot spans, etc.)
This commit is contained in:
4onen
2025-04-27 14:48:26 -07:00
committed by GitHub
parent ced44be342
commit c0a97b762e
1 changed files with 173 additions and 4 deletions
Download Patch File Download Diff File Expand all files Collapse all files

177
examples/llama-bench/llama-bench.cpp
View File

@ -36,6 +36,46 @@ static uint64_t get_time_ns() {
return std::chrono::nanoseconds(clock::now().time_since_epoch()).count();
}
static bool tensor_buft_override_equal(const llama_model_tensor_buft_override& a, const llama_model_tensor_buft_override& b) {
if (a.pattern != b.pattern) {
// cString comparison that may be null
if (a.pattern == nullptr || b.pattern == nullptr) {
return false;
}
if (strcmp(a.pattern, b.pattern) != 0) {
return false;
}
}
if (a.buft != b.buft) {
return false;
}
return true;
}
static bool vec_tensor_buft_override_equal(const std::vector<llama_model_tensor_buft_override>& a, const std::vector<llama_model_tensor_buft_override>& b) {
if (a.size() != b.size()) {
return false;
}
for (size_t i = 0; i < a.size(); i++) {
if (!tensor_buft_override_equal(a[i], b[i])) {
return false;
}
}
return true;
}
static bool vec_vec_tensor_buft_override_equal(const std::vector<std::vector<llama_model_tensor_buft_override>>& a, const std::vector<std::vector<llama_model_tensor_buft_override>>& b) {
if (a.size() != b.size()) {
return false;
}
for (size_t i = 0; i < a.size(); i++) {
if (!vec_tensor_buft_override_equal(a[i], b[i])) {
return false;
}
}
return true;
}
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++) {
@ -175,6 +215,7 @@ struct cmd_params {
std::vector<bool> no_kv_offload;
std::vector<bool> flash_attn;
std::vector<std::vector<float>> tensor_split;
std::vector<std::vector<llama_model_tensor_buft_override>> tensor_buft_overrides;
std::vector<bool> use_mmap;
std::vector<bool> embeddings;
ggml_numa_strategy numa;
@ -207,6 +248,7 @@ static const cmd_params cmd_params_defaults = {
/* no_kv_offload */ { false },
/* flash_attn */ { false },
/* tensor_split */ { std::vector<float>(llama_max_devices(), 0.0f) },
/* tensor_buft_overrides*/ { std::vector<llama_model_tensor_buft_override>{{nullptr,nullptr}} },
/* use_mmap */ { true },
/* embeddings */ { false },
/* numa */ GGML_NUMA_STRATEGY_DISABLED,
@ -265,6 +307,7 @@ static void print_usage(int /* argc */, char ** argv) {
printf(" -embd, --embeddings <0|1> (default: %s)\n",
join(cmd_params_defaults.embeddings, ",").c_str());
printf(" -ts, --tensor-split <ts0/ts1/..> (default: 0)\n");
printf(" -ot --override-tensors <tensor name pattern>=<buffer type>;... (default: disabled)\n");
printf(" -r, --repetitions <n> (default: %d)\n", cmd_params_defaults.reps);
printf(" --prio <0|1|2|3> (default: %d)\n", cmd_params_defaults.prio);
printf(" --delay <0...N> (seconds) (default: %d)\n", cmd_params_defaults.delay);
@ -557,6 +600,87 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
}
params.tensor_split.push_back(tensor_split);
}
} else if (arg == "-ot" || arg == "--override-tensor") {
if (++i >= argc) {
invalid_param = true;
break;
}
auto value = argv[i];
/* static */ std::map<std::string, ggml_backend_buffer_type_t> buft_list;
if (buft_list.empty()) {
// enumerate all the devices and add their buffer types to the list
for (size_t i = 0; i < ggml_backend_dev_count(); ++i) {
auto * dev = ggml_backend_dev_get(i);
auto * buft = ggml_backend_dev_buffer_type(dev);
if (buft) {
buft_list[ggml_backend_buft_name(buft)] = buft;
}
}
}
auto override_group_span_len = std::strcspn(value, ",");
bool last_group = false;
do {
if (override_group_span_len == 0) {
// Adds an empty override-tensors for an empty span
params.tensor_buft_overrides.push_back({{}});
if (value[override_group_span_len] == '\0') {
value = &value[override_group_span_len];
last_group = true;
} else {
value = &value[override_group_span_len + 1];
override_group_span_len = std::strcspn(value, ",");
}
continue;
}
// Stamps null terminators into the argv
// value for this option to avoid the
// memory leak present in the implementation
// over in arg.cpp. Acceptable because we
// only parse these args once in this program.
auto override_group = value;
if (value[override_group_span_len] == '\0') {
value = &value[override_group_span_len];
last_group = true;
} else {
value[override_group_span_len] = '\0';
value = &value[override_group_span_len + 1];
}
std::vector<llama_model_tensor_buft_override> group_tensor_buft_overrides{};
auto override_span_len = std::strcspn(override_group, ";");
while (override_span_len > 0) {
auto override = override_group;
if (override_group[override_span_len] != '\0') {
override_group[override_span_len] = '\0';
override_group = &override_group[override_span_len + 1];
} else {
override_group = &override_group[override_span_len];
}
auto tensor_name_span_len = std::strcspn(override, "=");
if (tensor_name_span_len >= override_span_len) {
invalid_param = true;
break;
}
override[tensor_name_span_len] = '\0';
auto tensor_name = override;
auto buffer_type = &override[tensor_name_span_len + 1];
if (buft_list.find(buffer_type) == buft_list.end()) {
printf("Available buffer types:\n");
for (const auto & it : buft_list) {
printf(" %s\n", ggml_backend_buft_name(it.second));
}
invalid_param = true;
break;
}
group_tensor_buft_overrides.push_back({tensor_name, buft_list.at(buffer_type)});
override_span_len = std::strcspn(override_group, ";");
}
if (invalid_param) {
break;
}
group_tensor_buft_overrides.push_back({nullptr,nullptr});
params.tensor_buft_overrides.push_back(group_tensor_buft_overrides);
override_group_span_len = std::strcspn(value, ",");
} while (!last_group);
} else if (arg == "-r" || arg == "--repetitions") {
if (++i >= argc) {
invalid_param = true;
@ -648,6 +772,9 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
if (params.tensor_split.empty()) {
params.tensor_split = cmd_params_defaults.tensor_split;
}
if (params.tensor_buft_overrides.empty()) {
params.tensor_buft_overrides = cmd_params_defaults.tensor_buft_overrides;
}
if (params.use_mmap.empty()) {
params.use_mmap = cmd_params_defaults.use_mmap;
}
@ -689,6 +816,7 @@ struct cmd_params_instance {
bool no_kv_offload;
bool flash_attn;
std::vector<float> tensor_split;
std::vector<llama_model_tensor_buft_override> tensor_buft_overrides;
bool use_mmap;
bool embeddings;
@ -733,13 +861,20 @@ struct cmd_params_instance {
mparams.tensor_split = tensor_split.data();
mparams.use_mmap = use_mmap;
if (tensor_buft_overrides.empty()) {
mparams.tensor_buft_overrides = nullptr;
} else {
GGML_ASSERT(tensor_buft_overrides.back().pattern == nullptr && "Tensor buffer overrides not terminated with empty pattern");
mparams.tensor_buft_overrides = tensor_buft_overrides.data();
}
return mparams;
}
bool equal_mparams(const cmd_params_instance & other) const {
return model == other.model && n_gpu_layers == other.n_gpu_layers && rpc_servers_str == other.rpc_servers_str &&
split_mode == other.split_mode && main_gpu == other.main_gpu && use_mmap == other.use_mmap &&
tensor_split == other.tensor_split;
tensor_split == other.tensor_split && vec_tensor_buft_override_equal(tensor_buft_overrides, other.tensor_buft_overrides);
}
llama_context_params to_llama_cparams() const {
@ -769,6 +904,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
for (const auto & sm : params.split_mode)
for (const auto & mg : params.main_gpu)
for (const auto & ts : params.tensor_split)
for (const auto & ot : params.tensor_buft_overrides)
for (const auto & mmp : params.use_mmap)
for (const auto & embd : params.embeddings)
for (const auto & nb : params.n_batch)
@ -804,6 +940,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
/* .no_kv_offload= */ nkvo,
/* .flash_attn = */ fa,
/* .tensor_split = */ ts,
/* .tensor_buft_overrides = */ ot,
/* .use_mmap = */ mmp,
/* .embeddings = */ embd,
};
@ -833,6 +970,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
/* .no_kv_offload= */ nkvo,
/* .flash_attn = */ fa,
/* .tensor_split = */ ts,
/* .tensor_buft_overrides = */ ot,
/* .use_mmap = */ mmp,
/* .embeddings = */ embd,
};
@ -862,6 +1000,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
/* .no_kv_offload= */ nkvo,
/* .flash_attn = */ fa,
/* .tensor_split = */ ts,
/* .tensor_buft_overrides = */ ot,
/* .use_mmap = */ mmp,
/* .embeddings = */ embd,
};
@ -896,6 +1035,7 @@ struct test {
bool no_kv_offload;
bool flash_attn;
std::vector<float> tensor_split;
std::vector<llama_model_tensor_buft_override> tensor_buft_overrides;
bool use_mmap;
bool embeddings;
int n_prompt;
@ -927,6 +1067,7 @@ struct test {
no_kv_offload = inst.no_kv_offload;
flash_attn = inst.flash_attn;
tensor_split = inst.tensor_split;
tensor_buft_overrides = inst.tensor_buft_overrides;
use_mmap = inst.use_mmap;
embeddings = inst.embeddings;
n_prompt = inst.n_prompt;
@ -972,9 +1113,9 @@ struct test {
"build_commit", "build_number", "cpu_info", "gpu_info", "backends", "model_filename",
"model_type", "model_size", "model_n_params", "n_batch", "n_ubatch", "n_threads",
"cpu_mask", "cpu_strict", "poll", "type_k", "type_v", "n_gpu_layers",
"split_mode", "main_gpu", "no_kv_offload", "flash_attn", "tensor_split", "use_mmap",
"embeddings", "n_prompt", "n_gen", "test_time", "avg_ns", "stddev_ns",
"avg_ts", "stddev_ts",
"split_mode", "main_gpu", "no_kv_offload", "flash_attn", "tensor_split", "tensor_buft_overrides",
"use_mmap", "embeddings", "n_prompt", "n_gen", "test_time", "avg_ns",
"stddev_ns", "avg_ts", "stddev_ts",
};
return fields;
}
@ -1000,6 +1141,7 @@ struct test {
std::vector<std::string> get_values() const {
std::string tensor_split_str;
std::string tensor_buft_overrides_str;
int max_nonzero = 0;
for (size_t i = 0; i < llama_max_devices(); i++) {
if (tensor_split[i] > 0) {
@ -1014,6 +1156,26 @@ struct test {
tensor_split_str += "/";
}
}
if (tensor_buft_overrides.size() == 1) {
// Last element of tensor_buft_overrides is always a null pattern
// so if it is only one element long, it must be a null pattern.
GGML_ASSERT(tensor_buft_overrides[0].pattern == nullptr);
tensor_buft_overrides_str += "none";
} else {
for (size_t i = 0; i < tensor_buft_overrides.size()-1; i++) {
// Last element of tensor_buft_overrides is always a null pattern
if (tensor_buft_overrides[i].pattern == nullptr) {
tensor_buft_overrides_str += "none";
} else {
tensor_buft_overrides_str += tensor_buft_overrides[i].pattern;
tensor_buft_overrides_str += "=";
tensor_buft_overrides_str += ggml_backend_buft_name(tensor_buft_overrides[i].buft);
}
if (i + 2 < tensor_buft_overrides.size()) {
tensor_buft_overrides_str += ";";
}
}
}
std::vector<std::string> values = { build_commit,
std::to_string(build_number),
cpu_info,
@ -1037,6 +1199,7 @@ struct test {
std::to_string(no_kv_offload),
std::to_string(flash_attn),
tensor_split_str,
tensor_buft_overrides_str,
std::to_string(use_mmap),
std::to_string(embeddings),
std::to_string(n_prompt),
@ -1254,6 +1417,9 @@ struct markdown_printer : public printer {
if (field == "tensor_split") {
return "ts";
}
if (field == "tensor_buft_overrides") {
return "ot";
}
return field;
}
@ -1307,6 +1473,9 @@ struct markdown_printer : public printer {
if (params.tensor_split.size() > 1 || params.tensor_split != cmd_params_defaults.tensor_split) {
fields.emplace_back("tensor_split");
}
if (params.tensor_buft_overrides.size() > 1 || !vec_vec_tensor_buft_override_equal(params.tensor_buft_overrides, cmd_params_defaults.tensor_buft_overrides)) {
fields.emplace_back("tensor_buft_overrides");
}
if (params.use_mmap.size() > 1 || params.use_mmap != cmd_params_defaults.use_mmap) {
fields.emplace_back("use_mmap");
}