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llm/llama.cpp/examples/llama-bench/README.md
View file @ 0ce8bcfd
# llama.cpp/example/llama-bench
# llama.cpp/examples/llama-bench
Performance testing tool for llama.cpp.
......@@ -162,7 +162,7 @@ $ ./llama-bench -o csv
```
```csv
build_commit,build_number,cuda,opencl,metal,gpu_blas,blas,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,tensor_split,n_prompt,n_gen,test_time,avg_ns,stddev_ns,avg_ts,stddev_ts
build_commit,build_number,cuda,metal,gpu_blas,blas,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,tensor_split,n_prompt,n_gen,test_time,avg_ns,stddev_ns,avg_ts,stddev_ts
"3469684","1275","1","0","0","1","1","13th Gen Intel(R) Core(TM) i9-13900K","NVIDIA GeForce RTX 3090 Ti","models/7B/ggml-model-q4_0.gguf","llama 7B mostly Q4_0","3825065984","6738415616","512","16","1","99","0","1","0.00","512","0","2023-09-23T12:09:01Z","212155977","732372","2413.341687","8.305961"
"3469684","1275","1","0","0","1","1","13th Gen Intel(R) Core(TM) i9-13900K","NVIDIA GeForce RTX 3090 Ti","models/7B/ggml-model-q4_0.gguf","llama 7B mostly Q4_0","3825065984","6738415616","512","16","1","99","0","1","0.00","0","128","2023-09-23T12:09:02Z","969320879","2728399","132.052051","0.371342"
```
......@@ -179,7 +179,6 @@ $ ./llama-bench -o json
"build_commit": "3469684",
"build_number": 1275,
"cuda": true,
"opencl": false,
"metal": false,
"gpu_blas": true,
"blas": true,
......@@ -210,7 +209,6 @@ $ ./llama-bench -o json
"build_commit": "3469684",
"build_number": 1275,
"cuda": true,
"opencl": false,
"metal": false,
"gpu_blas": true,
"blas": true,
......@@ -253,7 +251,6 @@ CREATE TABLE IF NOT EXISTS test (
build_commit TEXT,
build_number INTEGER,
cuda INTEGER,
opencl INTEGER,
metal INTEGER,
gpu_blas INTEGER,
blas INTEGER,
......@@ -279,6 +276,6 @@ CREATE TABLE IF NOT EXISTS test (
stddev_ts REAL
);
INSERT INTO test (build_commit, build_number, cuda, opencl, metal, gpu_blas, blas, 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, tensor_split, n_prompt, n_gen, test_time, avg_ns, stddev_ns, avg_ts, stddev_ts) VALUES ('3469684', '1275', '1', '0', '0', '1', '1', '13th Gen Intel(R) Core(TM) i9-13900K', 'NVIDIA GeForce RTX 3090 Ti', 'models/7B/ggml-model-q4_0.gguf', 'llama 7B mostly Q4_0', '3825065984', '6738415616', '512', '16', '1', '99', '0', '1', '0.00', '512', '0', '2023-09-23T12:10:30Z', '212693772', '743623', '2407.240204', '8.409634');
INSERT INTO test (build_commit, build_number, cuda, opencl, metal, gpu_blas, blas, 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, tensor_split, n_prompt, n_gen, test_time, avg_ns, stddev_ns, avg_ts, stddev_ts) VALUES ('3469684', '1275', '1', '0', '0', '1', '1', '13th Gen Intel(R) Core(TM) i9-13900K', 'NVIDIA GeForce RTX 3090 Ti', 'models/7B/ggml-model-q4_0.gguf', 'llama 7B mostly Q4_0', '3825065984', '6738415616', '512', '16', '1', '99', '0', '1', '0.00', '0', '128', '2023-09-23T12:10:31Z', '977925003', '4037361', '130.891159', '0.537692');
INSERT INTO test (build_commit, build_number, cuda, metal, gpu_blas, blas, 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, tensor_split, n_prompt, n_gen, test_time, avg_ns, stddev_ns, avg_ts, stddev_ts) VALUES ('3469684', '1275', '1', '0', '0', '1', '1', '13th Gen Intel(R) Core(TM) i9-13900K', 'NVIDIA GeForce RTX 3090 Ti', 'models/7B/ggml-model-q4_0.gguf', 'llama 7B mostly Q4_0', '3825065984', '6738415616', '512', '16', '1', '99', '0', '1', '0.00', '512', '0', '2023-09-23T12:10:30Z', '212693772', '743623', '2407.240204', '8.409634');
INSERT INTO test (build_commit, build_number, cuda, metal, gpu_blas, blas, 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, tensor_split, n_prompt, n_gen, test_time, avg_ns, stddev_ns, avg_ts, stddev_ts) VALUES ('3469684', '1275', '1', '0', '0', '1', '1', '13th Gen Intel(R) Core(TM) i9-13900K', 'NVIDIA GeForce RTX 3090 Ti', 'models/7B/ggml-model-q4_0.gguf', 'llama 7B mostly Q4_0', '3825065984', '6738415616', '512', '16', '1', '99', '0', '1', '0.00', '0', '128', '2023-09-23T12:10:31Z', '977925003', '4037361', '130.891159', '0.537692');
```
llm/llama.cpp/examples/llama-bench/llama-bench.cpp
View file @ 0ce8bcfd
......@@ -23,6 +23,10 @@
#include "ggml-cuda.h"
#include "ggml-sycl.h"
#ifdef GGML_USE_CANN
#include "ggml-cann.h"
#endif
// utils
static uint64_t get_time_ns() {
using clock = std::chrono::high_resolution_clock;
......@@ -41,20 +45,6 @@ static std::string join(const std::vector<T> & values, const std::string & 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, typename F>
static std::vector<std::string> transform_to_str(const std::vector<T> & values, F f) {
std::vector<std::string> str_values;
......@@ -134,30 +124,59 @@ static std::string get_gpu_info() {
id += "/";
}
}
#endif
#ifdef GGML_USE_CANN
uint32_t count = ggml_backend_cann_get_device_count();
for (uint32_t i = 0; i < count; i++) {
char buf[128];
ggml_backend_cann_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};
enum output_formats {NONE, CSV, JSON, MARKDOWN, SQL};
static const char * output_format_str(output_formats format) {
switch (format) {
case NONE: return "none";
case CSV: return "csv";
case JSON: return "json";
case MARKDOWN: return "md";
case SQL: return "sql";
default: GGML_ASSERT(!"invalid output format");
default: GGML_ABORT("invalid output format");
}
}
static bool output_format_from_str(const std::string & s, output_formats & format) {
if (s == "none") {
format = NONE;
} else if (s == "csv") {
format = CSV;
} else if (s == "json") {
format = JSON;
} else if (s == "md") {
format = MARKDOWN;
} else if (s == "sql") {
format = SQL;
} else {
return false;
}
return true;
}
static const char * split_mode_str(llama_split_mode mode) {
switch (mode) {
case LLAMA_SPLIT_MODE_NONE: return "none";
case LLAMA_SPLIT_MODE_LAYER: return "layer";
case LLAMA_SPLIT_MODE_ROW: return "row";
default: GGML_ASSERT(!"invalid split mode");
default: GGML_ABORT("invalid split mode");
}
}
......@@ -190,31 +209,33 @@ struct cmd_params {
int reps;
bool verbose;
output_formats output_format;
output_formats output_format_stderr;
};
static const cmd_params cmd_params_defaults = {
/* model */ {"models/7B/ggml-model-q4_0.gguf"},
/* n_prompt */ {512},
/* n_gen */ {128},
/* n_pg */ {},
/* n_batch */ {2048},
/* n_ubatch */ {512},
/* type_k */ {GGML_TYPE_F16},
/* type_v */ {GGML_TYPE_F16},
/* n_threads */ {cpu_get_num_math()},
/* n_gpu_layers */ {99},
/* rpc_servers */ {""},
/* split_mode */ {LLAMA_SPLIT_MODE_LAYER},
/* main_gpu */ {0},
/* no_kv_offload */ {false},
/* flash_attn */ {false},
/* tensor_split */ {std::vector<float>(llama_max_devices(), 0.0f)},
/* use_mmap */ {true},
/* embeddings */ {false},
/* numa */ GGML_NUMA_STRATEGY_DISABLED,
/* reps */ 5,
/* verbose */ false,
/* output_format */ MARKDOWN
/* model */ {"models/7B/ggml-model-q4_0.gguf"},
/* n_prompt */ {512},
/* n_gen */ {128},
/* n_pg */ {},
/* n_batch */ {2048},
/* n_ubatch */ {512},
/* type_k */ {GGML_TYPE_F16},
/* type_v */ {GGML_TYPE_F16},
/* n_threads */ {cpu_get_num_math()},
/* n_gpu_layers */ {99},
/* rpc_servers */ {""},
/* split_mode */ {LLAMA_SPLIT_MODE_LAYER},
/* main_gpu */ {0},
/* no_kv_offload */ {false},
/* flash_attn */ {false},
/* tensor_split */ {std::vector<float>(llama_max_devices(), 0.0f)},
/* use_mmap */ {true},
/* embeddings */ {false},
/* numa */ GGML_NUMA_STRATEGY_DISABLED,
/* reps */ 5,
/* verbose */ false,
/* output_format */ MARKDOWN,
/* output_format_stderr */ NONE,
};
static void print_usage(int /* argc */, char ** argv) {
......@@ -243,6 +264,7 @@ static void print_usage(int /* argc */, char ** argv) {
printf(" -ts, --tensor-split <ts0/ts1/..> (default: 0)\n");
printf(" -r, --repetitions <n> (default: %d)\n", cmd_params_defaults.reps);
printf(" -o, --output <csv|json|md|sql> (default: %s)\n", output_format_str(cmd_params_defaults.output_format));
printf(" -oe, --output-err <csv|json|md|sql> (default: %s)\n", output_format_str(cmd_params_defaults.output_format_stderr));
printf(" -v, --verbose (default: %s)\n", cmd_params_defaults.verbose ? "1" : "0");
printf("\n");
printf("Multiple values can be given for each parameter by separating them with ',' or by specifying the parameter multiple times.\n");
......@@ -284,7 +306,9 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
params.verbose = cmd_params_defaults.verbose;
params.output_format = cmd_params_defaults.output_format;
params.output_format_stderr = cmd_params_defaults.output_format_stderr;
params.reps = cmd_params_defaults.reps;
params.numa = cmd_params_defaults.numa;
for (int i = 1; i < argc; i++) {
arg = argv[i];
......@@ -300,28 +324,28 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
invalid_param = true;
break;
}
auto p = split<std::string>(argv[i], split_delim);
auto p = string_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);
auto p = string_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);
auto p = string_split<int>(argv[i], split_delim);
params.n_gen.insert(params.n_gen.end(), p.begin(), p.end());
} else if (arg == "-pg") {
if (++i >= argc) {
invalid_param = true;
break;
}
auto p = split<std::string>(argv[i], ',');
auto p = string_split<std::string>(argv[i], ',');
if (p.size() != 2) {
invalid_param = true;
break;
......@@ -332,21 +356,21 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
invalid_param = true;
break;
}
auto p = split<int>(argv[i], split_delim);
auto p = string_split<int>(argv[i], split_delim);
params.n_batch.insert(params.n_batch.end(), p.begin(), p.end());
} else if (arg == "-ub" || arg == "--ubatch-size") {
if (++i >= argc) {
invalid_param = true;
break;
}
auto p = split<int>(argv[i], split_delim);
auto p = string_split<int>(argv[i], split_delim);
params.n_ubatch.insert(params.n_ubatch.end(), p.begin(), p.end());
} else if (arg == "-ctk" || arg == "--cache-type-k") {
if (++i >= argc) {
invalid_param = true;
break;
}
auto p = split<std::string>(argv[i], split_delim);
auto p = string_split<std::string>(argv[i], split_delim);
std::vector<ggml_type> types;
for (const auto & t : p) {
ggml_type gt = ggml_type_from_name(t);
......@@ -362,7 +386,7 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
invalid_param = true;
break;
}
auto p = split<std::string>(argv[i], split_delim);
auto p = string_split<std::string>(argv[i], split_delim);
std::vector<ggml_type> types;
for (const auto & t : p) {
ggml_type gt = ggml_type_from_name(t);
......@@ -378,14 +402,14 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
invalid_param = true;
break;
}
auto p = split<int>(argv[i], split_delim);
auto p = string_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);
auto p = string_split<int>(argv[i], split_delim);
params.n_gpu_layers.insert(params.n_gpu_layers.end(), p.begin(), p.end());
} else if (arg == "-rpc" || arg == "--rpc") {
if (++i >= argc) {
......@@ -398,7 +422,7 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
invalid_param = true;
break;
}
auto p = split<std::string>(argv[i], split_delim);
auto p = string_split<std::string>(argv[i], split_delim);
std::vector<llama_split_mode> modes;
for (const auto & m : p) {
llama_split_mode mode;
......@@ -420,13 +444,13 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
invalid_param = true;
break;
}
params.main_gpu = split<int>(argv[i], split_delim);
params.main_gpu = string_split<int>(argv[i], split_delim);
} else if (arg == "-nkvo" || arg == "--no-kv-offload") {
if (++i >= argc) {
invalid_param = true;
break;
}
auto p = split<bool>(argv[i], split_delim);
auto p = string_split<bool>(argv[i], split_delim);
params.no_kv_offload.insert(params.no_kv_offload.end(), p.begin(), p.end());
} else if (arg == "--numa") {
if (++i >= argc) {
......@@ -444,28 +468,28 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
invalid_param = true;
break;
}
auto p = split<bool>(argv[i], split_delim);
auto p = string_split<bool>(argv[i], split_delim);
params.flash_attn.insert(params.flash_attn.end(), p.begin(), p.end());
} else if (arg == "-mmp" || arg == "--mmap") {
if (++i >= argc) {
invalid_param = true;
break;
}
auto p = split<bool>(argv[i], split_delim);
auto p = string_split<bool>(argv[i], split_delim);
params.use_mmap.insert(params.use_mmap.end(), p.begin(), p.end());
} else if (arg == "-embd" || arg == "--embeddings") {
if (++i >= argc) {
invalid_param = true;
break;
}
auto p = split<bool>(argv[i], split_delim);
auto p = string_split<bool>(argv[i], split_delim);
params.embeddings.insert(params.embeddings.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)) {
for (auto ts : string_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};
......@@ -493,18 +517,13 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
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 = !output_format_from_str(argv[i], params.output_format);
} else if (arg == "-oe" || arg == "--output-err") {
if (++i >= argc) {
invalid_param = true;
break;
}
invalid_param = !output_format_from_str(argv[i], params.output_format_stderr);
} else if (arg == "-v" || arg == "--verbose") {
params.verbose = true;
} else {
......@@ -706,12 +725,10 @@ struct test {
static const std::string build_commit;
static const int build_number;
static const bool cuda;
static const bool opencl;
static const bool vulkan;
static const bool kompute;
static const bool metal;
static const bool sycl;
static const bool rpc;
static const bool gpu_blas;
static const bool blas;
static const std::string cpu_info;
......@@ -723,6 +740,7 @@ struct test {
int n_batch;
int n_ubatch;
int n_threads;
bool has_rpc;
ggml_type type_k;
ggml_type type_v;
int n_gpu_layers;
......@@ -748,6 +766,7 @@ struct test {
n_batch = inst.n_batch;
n_ubatch = inst.n_ubatch;
n_threads = inst.n_threads;
has_rpc = !inst.rpc_servers.empty();
type_k = inst.type_k;
type_v = inst.type_v;
n_gpu_layers = inst.n_gpu_layers;
......@@ -795,9 +814,6 @@ struct test {
if (cuda) {
return GGML_CUDA_NAME;
}
if (opencl) {
return "OpenCL";
}
if (vulkan) {
return "Vulkan";
}
......@@ -810,9 +826,6 @@ struct test {
if (sycl) {
return GGML_SYCL_NAME;
}
if (rpc) {
return "RPC";
}
if (gpu_blas) {
return "GPU BLAS";
}
......@@ -826,7 +839,7 @@ struct test {
static const std::vector<std::string> & get_fields() {
static const std::vector<std::string> fields = {
"build_commit", "build_number",
"cuda", "opencl", "vulkan", "kompute", "metal", "sycl", "rpc", "gpu_blas", "blas",
"cuda", "vulkan", "kompute", "metal", "sycl", "rpc", "gpu_blas", "blas",
"cpu_info", "gpu_info",
"model_filename", "model_type", "model_size", "model_n_params",
"n_batch", "n_ubatch",
......@@ -852,7 +865,7 @@ struct test {
field == "avg_ns" || field == "stddev_ns") {
return INT;
}
if (field == "cuda" || field == "opencl" || field == "vulkan" || field == "kompute" || field == "metal" ||
if (field == "cuda" || field == "vulkan" || field == "kompute" || field == "metal" ||
field == "gpu_blas" || field == "blas" || field == "sycl" ||field == "f16_kv" || field == "no_kv_offload" ||
field == "flash_attn" || field == "use_mmap" || field == "embeddings") {
return BOOL;
......@@ -881,8 +894,8 @@ struct test {
}
std::vector<std::string> values = {
build_commit, std::to_string(build_number),
std::to_string(cuda), std::to_string(opencl), std::to_string(vulkan), std::to_string(vulkan),
std::to_string(metal), std::to_string(sycl), std::to_string(rpc), std::to_string(gpu_blas), std::to_string(blas),
std::to_string(cuda), std::to_string(vulkan), std::to_string(vulkan),
std::to_string(metal), std::to_string(sycl), std::to_string(has_rpc), std::to_string(gpu_blas), std::to_string(blas),
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_ubatch),
......@@ -910,14 +923,12 @@ struct test {
const std::string test::build_commit = LLAMA_COMMIT;
const int test::build_number = LLAMA_BUILD_NUMBER;
const bool test::cuda = !!ggml_cpu_has_cuda();
const bool test::opencl = !!ggml_cpu_has_clblast();
const bool test::vulkan = !!ggml_cpu_has_vulkan();
const bool test::kompute = !!ggml_cpu_has_kompute();
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 bool test::sycl = !!ggml_cpu_has_sycl();
const bool test::rpc = !!ggml_cpu_has_rpc();
const std::string test::cpu_info = get_cpu_info();
const std::string test::gpu_info = get_gpu_info();
......@@ -1035,6 +1046,27 @@ struct markdown_printer : public printer {
if (field == "n_gpu_layers") {
return 3;
}
if (field == "n_threads") {
return 7;
}
if (field == "n_batch") {
return 7;
}
if (field == "n_ubatch") {
return 8;
}
if (field == "type_k" || field == "type_v") {
return 6;
}
if (field == "split_mode") {
return 5;
}
if (field == "flash_attn") {
return 2;
}
if (field == "use_mmap") {
return 4;
}
if (field == "test") {
return 13;
}
......@@ -1162,6 +1194,9 @@ struct markdown_printer : public printer {
value = buf;
} else if (field == "backend") {
value = test::get_backend();
if (t.has_rpc) {
value += "+RPC";
}
} else if (field == "test") {
if (t.n_prompt > 0 && t.n_gen == 0) {
snprintf(buf, sizeof(buf), "pp%d", t.n_prompt);
......@@ -1278,6 +1313,22 @@ static void llama_null_log_callback(enum ggml_log_level level, const char * text
(void) user_data;
}
static std::unique_ptr<printer> create_printer(output_formats format) {
switch (format) {
case NONE:
return nullptr;
case CSV:
return std::unique_ptr<printer>(new csv_printer());
case JSON:
return std::unique_ptr<printer>(new json_printer());
case MARKDOWN:
return std::unique_ptr<printer>(new markdown_printer());
case SQL:
return std::unique_ptr<printer>(new sql_printer());
}
GGML_ABORT("fatal error");
}
int main(int argc, char ** argv) {
// try to set locale for unicode characters in markdown
setlocale(LC_CTYPE, ".UTF-8");
......@@ -1304,26 +1355,18 @@ int main(int argc, char ** argv) {
llama_numa_init(params.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);
std::unique_ptr<printer> p = create_printer(params.output_format);
std::unique_ptr<printer> p_err = create_printer(params.output_format_stderr);
if (p) {
p->fout = stdout;
p->print_header(params);
}
if (p_err) {
p_err->fout = stderr;
p_err->print_header(params);
}
p->fout = stdout;
p->print_header(params);
std::vector<cmd_params_instance> params_instances = get_cmd_params_instances(params);
......@@ -1381,7 +1424,15 @@ int main(int argc, char ** argv) {
t.samples_ns.push_back(t_ns);
}
p->print_test(t);
if (p) {
p->print_test(t);
fflush(p->fout);
}
if (p_err) {
p_err->print_test(t);
fflush(p_err->fout);
}
llama_print_timings(ctx);
......@@ -1390,7 +1441,13 @@ int main(int argc, char ** argv) {
llama_free_model(lmodel);
p->print_footer();
if (p) {
p->print_footer();
}
if (p_err) {
p_err->print_footer();
}
llama_backend_free();
......
llm/llama.cpp/examples/llama.android/app/build.gradle.kts 0 → 100644
View file @ 0ce8bcfd
plugins {
id("com.android.application")
id("org.jetbrains.kotlin.android")
}
android {
namespace = "com.example.llama"
compileSdk = 34
defaultConfig {
applicationId = "com.example.llama"
minSdk = 33
targetSdk = 34
versionCode = 1
versionName = "1.0"
testInstrumentationRunner = "androidx.test.runner.AndroidJUnitRunner"
vectorDrawables {
useSupportLibrary = true
}
}
buildTypes {
release {
isMinifyEnabled = false
proguardFiles(
getDefaultProguardFile("proguard-android-optimize.txt"),
"proguard-rules.pro"
)
}
}
compileOptions {
sourceCompatibility = JavaVersion.VERSION_1_8
targetCompatibility = JavaVersion.VERSION_1_8
}
kotlinOptions {
jvmTarget = "1.8"
}
buildFeatures {
compose = true
}
composeOptions {
kotlinCompilerExtensionVersion = "1.5.1"
}
}
dependencies {
implementation("androidx.core:core-ktx:1.12.0")
implementation("androidx.lifecycle:lifecycle-runtime-ktx:2.6.2")
implementation("androidx.activity:activity-compose:1.8.2")
implementation(platform("androidx.compose:compose-bom:2023.08.00"))
implementation("androidx.compose.ui:ui")
implementation("androidx.compose.ui:ui-graphics")
implementation("androidx.compose.ui:ui-tooling-preview")
implementation("androidx.compose.material3:material3")
implementation(project(":llama"))
testImplementation("junit:junit:4.13.2")
androidTestImplementation("androidx.test.ext:junit:1.1.5")
androidTestImplementation("androidx.test.espresso:espresso-core:3.5.1")
androidTestImplementation(platform("androidx.compose:compose-bom:2023.08.00"))
androidTestImplementation("androidx.compose.ui:ui-test-junit4")
debugImplementation("androidx.compose.ui:ui-tooling")
debugImplementation("androidx.compose.ui:ui-test-manifest")
}
llm/llama.cpp/examples/llama.android/build.gradle.kts 0 → 100644
View file @ 0ce8bcfd
// Top-level build file where you can add configuration options common to all sub-projects/modules.
plugins {
id("com.android.application") version "8.2.0" apply false
id("org.jetbrains.kotlin.android") version "1.9.0" apply false
id("com.android.library") version "8.2.0" apply false
}
llm/llama.cpp/examples/llama.android/llama/build.gradle.kts 0 → 100644
View file @ 0ce8bcfd
plugins {
id("com.android.library")
id("org.jetbrains.kotlin.android")
}
android {
namespace = "android.llama.cpp"
compileSdk = 34
defaultConfig {
minSdk = 33
testInstrumentationRunner = "androidx.test.runner.AndroidJUnitRunner"
consumerProguardFiles("consumer-rules.pro")
ndk {
// Add NDK properties if wanted, e.g.
// abiFilters += listOf("arm64-v8a")
}
externalNativeBuild {
cmake {
arguments += "-DCMAKE_BUILD_TYPE=Release"
cppFlags += listOf()
arguments += listOf()
cppFlags("")
}
}
}
buildTypes {
release {
isMinifyEnabled = false
proguardFiles(
getDefaultProguardFile("proguard-android-optimize.txt"),
"proguard-rules.pro"
)
}
}
externalNativeBuild {
cmake {
path("src/main/cpp/CMakeLists.txt")
version = "3.22.1"
}
}
compileOptions {
sourceCompatibility = JavaVersion.VERSION_1_8
targetCompatibility = JavaVersion.VERSION_1_8
}
kotlinOptions {
jvmTarget = "1.8"
}
packaging {
resources {
excludes += "/META-INF/{AL2.0,LGPL2.1}"
}
}
}
dependencies {
implementation("androidx.core:core-ktx:1.12.0")
implementation("androidx.appcompat:appcompat:1.6.1")
implementation("com.google.android.material:material:1.11.0")
testImplementation("junit:junit:4.13.2")
androidTestImplementation("androidx.test.ext:junit:1.1.5")
androidTestImplementation("androidx.test.espresso:espresso-core:3.5.1")
}
llm/llama.cpp/examples/llama.android/llama/src/main/cpp/CMakeLists.txt
View file @ 0ce8bcfd
......@@ -11,15 +11,15 @@ cmake_minimum_required(VERSION 3.22.1)
# build script scope).
project("llama-android")
include(FetchContent)
FetchContent_Declare(
llama
GIT_REPOSITORY https://github.com/ggerganov/llama.cpp
GIT_TAG master
)
#include(FetchContent)
#FetchContent_Declare(
# llama
# GIT_REPOSITORY https://github.com/ggerganov/llama.cpp
# GIT_TAG master
#)
# Also provides "common"
FetchContent_MakeAvailable(llama)
#FetchContent_MakeAvailable(llama)
# Creates and names a library, sets it as either STATIC
# or SHARED, and provides the relative paths to its source code.
......@@ -30,6 +30,10 @@ FetchContent_MakeAvailable(llama)
# the target library name; in the sub-module's CMakeLists.txt, ${PROJECT_NAME}
# is preferred for the same purpose.
#
#load local llama.cpp
add_subdirectory(../../../../../../ build-llama)
# In order to load a library into your app from Java/Kotlin, you must call
# System.loadLibrary() and pass the name of the library defined here;
# for GameActivity/NativeActivity derived applications, the same library name must be
......
llm/llama.cpp/examples/llama.android/llama/src/main/cpp/llama-android.cpp
View file @ 0ce8bcfd
......@@ -5,7 +5,7 @@
#include <string>
#include <unistd.h>
#include "llama.h"
#include "common/common.h"
#include "common.h"
// Write C++ code here.
//
......@@ -409,7 +409,7 @@ Java_android_llama_cpp_LLamaAndroid_completion_1loop(
const auto n_cur = env->CallIntMethod(intvar_ncur, la_int_var_value);
if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) {
return env->NewStringUTF("");
return nullptr;
}
auto new_token_chars = llama_token_to_piece(context, new_token_id);
......
llm/llama.cpp/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift
View file @ 0ce8bcfd
......@@ -26,11 +26,12 @@ actor LlamaContext {
private var context: OpaquePointer
private var batch: llama_batch
private var tokens_list: [llama_token]
var is_done: Bool = false
/// This variable is used to store temporarily invalid cchars
private var temporary_invalid_cchars: [CChar]
var n_len: Int32 = 64
var n_len: Int32 = 1024
var n_cur: Int32 = 0
var n_decode: Int32 = 0
......@@ -160,6 +161,7 @@ actor LlamaContext {
if llama_token_is_eog(model, new_token_id) || n_cur == n_len {
print("\n")
is_done = true
let new_token_str = String(cString: temporary_invalid_cchars + [0])
temporary_invalid_cchars.removeAll()
return new_token_str
......@@ -322,7 +324,7 @@ actor LlamaContext {
defer {
result.deallocate()
}
let nTokens = llama_token_to_piece(model, token, result, 8, false)
let nTokens = llama_token_to_piece(model, token, result, 8, 0, false)
if nTokens < 0 {
let newResult = UnsafeMutablePointer<Int8>.allocate(capacity: Int(-nTokens))
......@@ -330,7 +332,7 @@ actor LlamaContext {
defer {
newResult.deallocate()
}
let nNewTokens = llama_token_to_piece(model, token, newResult, -nTokens, false)
let nNewTokens = llama_token_to_piece(model, token, newResult, -nTokens, 0, false)
let bufferPointer = UnsafeBufferPointer(start: newResult, count: Int(nNewTokens))
return Array(bufferPointer)
} else {
......
llm/llama.cpp/examples/llama.swiftui/llama.swiftui/Models/LlamaState.swift
View file @ 0ce8bcfd
......@@ -131,22 +131,29 @@ class LlamaState: ObservableObject {
messageLog += "\(text)"
while await llamaContext.n_cur < llamaContext.n_len {
let result = await llamaContext.completion_loop()
messageLog += "\(result)"
}
Task.detached {
while await !llamaContext.is_done {
let result = await llamaContext.completion_loop()
await MainActor.run {
self.messageLog += "\(result)"
}
}
let t_end = DispatchTime.now().uptimeNanoseconds
let t_generation = Double(t_end - t_heat_end) / NS_PER_S
let tokens_per_second = Double(await llamaContext.n_len) / t_generation
let t_end = DispatchTime.now().uptimeNanoseconds
let t_generation = Double(t_end - t_heat_end) / self.NS_PER_S
let tokens_per_second = Double(await llamaContext.n_len) / t_generation
await llamaContext.clear()
messageLog += """
\n
Done
Heat up took \(t_heat)s
Generated \(tokens_per_second) t/s\n
"""
await llamaContext.clear()
await MainActor.run {
self.messageLog += """
\n
Done
Heat up took \(t_heat)s
Generated \(tokens_per_second) t/s\n
"""
}
}
}
func bench() async {
......
llm/llama.cpp/examples/llava/CMakeLists.txt
View file @ 0ce8bcfd
......@@ -30,8 +30,9 @@ if(TARGET BUILD_INFO)
add_dependencies(llava BUILD_INFO)
endif()
set(TARGET llava-cli)
add_executable(llava-cli llava-cli.cpp)
install(TARGETS llava-cli RUNTIME)
target_link_libraries(llava-cli PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(llava PRIVATE cxx_std_11)
set(TARGET llama-llava-cli)
add_executable(${TARGET} llava-cli.cpp)
set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME llama-llava-cli)
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_11)
llm/llama.cpp/examples/llava/MobileVLM-README.md
View file @ 0ce8bcfd
......@@ -9,12 +9,12 @@ The implementation is based on llava, and is compatible with llava and mobileVLM
Notice: The overall process of model inference for both **MobileVLM** and **MobileVLM_V2** models is the same, but the process of model conversion is a little different. Therefore, using **MobileVLM-1.7B** as an example, the different conversion step will be shown.
## Usage
Build with cmake or run `make llava-cli` to build it.
Build with cmake or run `make llama-llava-cli` to build it.
After building, run: `./llava-cli` to see the usage. For example:
After building, run: `./llama-llava-cli` to see the usage. For example:
```sh
./llava-cli -m MobileVLM-1.7B/ggml-model-q4_k.gguf \
./llama-llava-cli -m MobileVLM-1.7B/ggml-model-q4_k.gguf \
--mmproj MobileVLM-1.7B/mmproj-model-f16.gguf \
--image path/to/an/image.jpg \
-p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWho is the author of this book? Answer the question using a single word or phrase. ASSISTANT:"
......@@ -30,16 +30,16 @@ git clone https://huggingface.co/mtgv/MobileVLM-1.7B
git clone https://huggingface.co/openai/clip-vit-large-patch14-336
```
2. Use `llava-surgery.py` to split the LLaVA model to LLaMA and multimodel projector constituents:
2. Use `llava_surgery.py` to split the LLaVA model to LLaMA and multimodel projector constituents:
```sh
python ./examples/llava/llava-surgery.py -m path/to/MobileVLM-1.7B
python ./examples/llava/llava_surgery.py -m path/to/MobileVLM-1.7B
```
3. Use `convert-image-encoder-to-gguf.py` with `--projector-type ldp` (for **V2** please use `--projector-type ldpv2`) to convert the LLaVA image encoder to GGUF:
3. Use `convert_image_encoder_to_gguf.py` with `--projector-type ldp` (for **V2** please use `--projector-type ldpv2`) to convert the LLaVA image encoder to GGUF:
```sh
python ./examples/llava/convert-image-encoder-to-gguf \
python ./examples/llava/convert_image_encoder_to_gguf \
-m path/to/clip-vit-large-patch14-336 \
--llava-projector path/to/MobileVLM-1.7B/llava.projector \
--output-dir path/to/MobileVLM-1.7B \
......@@ -47,22 +47,22 @@ python ./examples/llava/convert-image-encoder-to-gguf \
```
```sh
python ./examples/llava/convert-image-encoder-to-gguf \
python ./examples/llava/convert_image_encoder_to_gguf \
-m path/to/clip-vit-large-patch14-336 \
--llava-projector path/to/MobileVLM-1.7B_V2/llava.projector \
--output-dir path/to/MobileVLM-1.7B_V2 \
--projector-type ldpv2
```
4. Use `examples/convert-legacy-llama.py` to convert the LLaMA part of LLaVA to GGUF:
4. Use `examples/convert_legacy_llama.py` to convert the LLaMA part of LLaVA to GGUF:
```sh
python ./examples/convert-legacy-llama.py path/to/MobileVLM-1.7B
python ./examples/convert_legacy_llama.py path/to/MobileVLM-1.7B
```
5. Use `quantize` to convert LLaMA part's DataType from `fp16` to `q4_k`
```sh
./quantize path/to/MobileVLM-1.7B/ggml-model-f16.gguf path/to/MobileVLM-1.7B/ggml-model-q4_k.gguf q4_k_s
./llama-quantize path/to/MobileVLM-1.7B/ggml-model-f16.gguf path/to/MobileVLM-1.7B/ggml-model-q4_k.gguf q4_k_s
```
Now both the LLaMA part and the image encoder is in the `MobileVLM-1.7B` directory.
......@@ -82,7 +82,7 @@ refer to `android/adb_run.sh`, modify resources' `name` and `path`
### case 1
**input**
```sh
/data/local/tmp/llava-cli \
/data/local/tmp/llama-llava-cli \
-m /data/local/tmp/ggml-model-q4_k.gguf \
--mmproj /data/local/tmp/mmproj-model-f16.gguf \
-t 4 \
......@@ -102,7 +102,7 @@ llama_print_timings: total time = 34731.93 ms
### case 2
**input**
```sh
/data/local/tmp/llava-cli \
/data/local/tmp/llama-llava-cli \
-m /data/local/tmp/ggml-model-q4_k.gguf \
--mmproj /data/local/tmp/mmproj-model-f16.gguf \
-t 4 \
......@@ -126,7 +126,7 @@ llama_print_timings: total time = 34570.79 ms
#### llava-cli release-b2005
**input**
```sh
/data/local/tmp/llava-cli \
/data/local/tmp/llama-llava-cli \
-m /data/local/tmp/ggml-model-q4_k.gguf \
--mmproj /data/local/tmp/mmproj-model-f16.gguf \
-t 4 \
......@@ -194,13 +194,13 @@ llama_print_timings: total time = 44411.01 ms / 377 tokens
## Orin compile and run
### compile
```sh
make LLAMA_CUDA=1 CUDA_DOCKER_ARCH=sm_87 LLAMA_CUDA_F16=1 -j 32
make GGML_CUDA=1 CUDA_DOCKER_ARCH=sm_87 GGML_CUDA_F16=1 -j 32
```
### run on Orin
### case 1
**input**
```sh
./llava-cli \
./llama-llava-cli \
-m /data/local/tmp/ggml-model-q4_k.gguf \
--mmproj /data/local/tmp/mmproj-model-f16.gguf \
--image /data/local/tmp/demo.jpeg \
......@@ -224,7 +224,7 @@ llama_print_timings: total time = 1352.63 ms / 252 tokens
### case 2
**input**
```sh
./llava-cli \
./llama-llava-cli \
-m /data/local/tmp/ggml-model-q4_k.gguf \
--mmproj /data/local/tmp/mmproj-model-f16.gguf \
-p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWhat is in the image? ASSISTANT:" \
......
llm/llama.cpp/examples/llava/README.md
View file @ 0ce8bcfd
......@@ -11,12 +11,12 @@ For llava-1.6 a variety of prepared gguf models are available as well [7b-34b](h
After API is confirmed, more models will be supported / uploaded.
## Usage
Build with cmake or run `make llava-cli` to build it.
Build with cmake or run `make llama-llava-cli` to build it.
After building, run: `./llava-cli` to see the usage. For example:
After building, run: `./llama-llava-cli` to see the usage. For example:
```sh
./llava-cli -m ../llava-v1.5-7b/ggml-model-f16.gguf --mmproj ../llava-v1.5-7b/mmproj-model-f16.gguf --image path/to/an/image.jpg
./llama-llava-cli -m ../llava-v1.5-7b/ggml-model-f16.gguf --mmproj ../llava-v1.5-7b/mmproj-model-f16.gguf --image path/to/an/image.jpg
```
**note**: A lower temperature like 0.1 is recommended for better quality. add `--temp 0.1` to the command to do so.
......@@ -38,22 +38,22 @@ git clone https://huggingface.co/openai/clip-vit-large-patch14-336
pip install -r examples/llava/requirements.txt
```
3. Use `llava-surgery.py` to split the LLaVA model to LLaMA and multimodel projector constituents:
3. Use `llava_surgery.py` to split the LLaVA model to LLaMA and multimodel projector constituents:
```sh
python ./examples/llava/llava-surgery.py -m ../llava-v1.5-7b
python ./examples/llava/llava_surgery.py -m ../llava-v1.5-7b
```
4. Use `convert-image-encoder-to-gguf.py` to convert the LLaVA image encoder to GGUF:
4. Use `convert_image_encoder_to_gguf.py` to convert the LLaVA image encoder to GGUF:
```sh
python ./examples/llava/convert-image-encoder-to-gguf.py -m ../clip-vit-large-patch14-336 --llava-projector ../llava-v1.5-7b/llava.projector --output-dir ../llava-v1.5-7b
python ./examples/llava/convert_image_encoder_to_gguf.py -m ../clip-vit-large-patch14-336 --llava-projector ../llava-v1.5-7b/llava.projector --output-dir ../llava-v1.5-7b
```
5. Use `examples/convert-legacy-llama.py` to convert the LLaMA part of LLaVA to GGUF:
5. Use `examples/convert_legacy_llama.py` to convert the LLaMA part of LLaVA to GGUF:
```sh
python ./examples/convert-legacy-llama.py ../llava-v1.5-7b --skip-unknown
python ./examples/convert_legacy_llama.py ../llava-v1.5-7b --skip-unknown
```
Now both the LLaMA part and the image encoder are in the `llava-v1.5-7b` directory.
......@@ -70,9 +70,9 @@ git clone https://huggingface.co/liuhaotian/llava-v1.6-vicuna-7b
pip install -r examples/llava/requirements.txt
```
3) Use `llava-surgery-v2.py` which also supports llava-1.5 variants pytorch as well as safetensor models:
3) Use `llava_surgery_v2.py` which also supports llava-1.5 variants pytorch as well as safetensor models:
```console
python examples/llava/llava-surgery-v2.py -C -m ../llava-v1.6-vicuna-7b/
python examples/llava/llava_surgery_v2.py -C -m ../llava-v1.6-vicuna-7b/
```
- you will find a llava.projector and a llava.clip file in your model directory
......@@ -86,18 +86,18 @@ curl -s -q https://huggingface.co/cmp-nct/llava-1.6-gguf/raw/main/config_vit.jso
5) Create the visual gguf model:
```console
python ./examples/llava/convert-image-encoder-to-gguf.py -m vit --llava-projector vit/llava.projector --output-dir vit --clip-model-is-vision
python ./examples/llava/convert_image_encoder_to_gguf.py -m vit --llava-projector vit/llava.projector --output-dir vit --clip-model-is-vision
```
- This is similar to llava-1.5, the difference is that we tell the encoder that we are working with the pure vision model part of CLIP
6) Then convert the model to gguf format:
```console
python ./examples/convert-legacy-llama.py ../llava-v1.6-vicuna-7b/ --skip-unknown
python ./examples/convert_legacy_llama.py ../llava-v1.6-vicuna-7b/ --skip-unknown
```
7) And finally we can run the llava-cli using the 1.6 model version:
7) And finally we can run the llava cli using the 1.6 model version:
```console
./llava-cli -m ../llava-v1.6-vicuna-7b/ggml-model-f16.gguf --mmproj vit/mmproj-model-f16.gguf --image some-image.jpg -c 4096
./llama-llava-cli -m ../llava-v1.6-vicuna-7b/ggml-model-f16.gguf --mmproj vit/mmproj-model-f16.gguf --image some-image.jpg -c 4096
```
**note** llava-1.6 needs more context than llava-1.5, at least 3000 is needed (just run it at -c 4096)
......
llm/llama.cpp/examples/llava/android/adb_run.sh
View file @ 0ce8bcfd
......@@ -10,7 +10,7 @@ prompt="A chat between a curious user and an artificial intelligence assistant.
# prompt="A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWhat is in the image? ASSISTANT:"
program_dir="build_64/bin"
binName="llava-cli"
binName="llama-llava-cli"
n_threads=4
......
llm/llama.cpp/examples/llava/android/build_64.sh 0 → 100644
View file @ 0ce8bcfd
#!/bin/bash
cmake ../../../../ \
-DCMAKE_TOOLCHAIN_FILE=$ANDROID_NDK/build/cmake/android.toolchain.cmake \
-DCMAKE_BUILD_TYPE=Release \
-DANDROID_ABI="arm64-v8a" \
-DANDROID_PLATFORM=android-23 $1
make -j4
llm/llama.cpp/examples/llava/clip.cpp
View file @ 0ce8bcfd
......@@ -16,6 +16,10 @@
#include "ggml-metal.h"
#endif
#ifdef GGML_USE_CANN
#include "ggml-cann.h"
#endif
#define STB_IMAGE_IMPLEMENTATION
#include "stb_image.h"
......@@ -865,7 +869,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
embeddings = peg_0;
}
else {
GGML_ASSERT(false);
GGML_ABORT("fatal error");
}
}
......@@ -1001,6 +1005,11 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
LOG_TEE("%s: CLIP using Metal backend\n", __func__);
#endif
#ifdef GGML_USE_CANN
new_clip->backend = ggml_backend_cann_init(0);
LOG_TEE("%s: CLIP using CANN backend\n", __func__);
#endif
if (!new_clip->backend) {
new_clip->backend = ggml_backend_cpu_init();
......@@ -1121,20 +1130,20 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
}
if (n < 32)
hparams.image_grid_pinpoints[n] = 0;
} catch (std::runtime_error & e) {
} catch (std::runtime_error & /*e*/) {
hparams.image_grid_pinpoints[0]=0;
}
try {
int idx = get_key_idx(ctx, KEY_MM_PATCH_MERGE_TYPE);
strcpy(hparams.mm_patch_merge_type, gguf_get_val_str(ctx, idx));
} catch (std::runtime_error & e) {
} catch (std::runtime_error & /*e*/) {
strcpy(hparams.mm_patch_merge_type, "flat");
}
try {
hparams.image_crop_resolution = get_u32(ctx, KEY_IMAGE_CROP_RESOLUTION); // llava-1.6
} catch(const std::exception& e) {
} catch(const std::exception& /*e*/) {
hparams.image_crop_resolution = hparams.image_size;
}
......@@ -1173,7 +1182,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
try {
vision_model.class_embedding = get_tensor(new_clip->ctx_data, TN_CLASS_EMBD);
new_clip->has_class_embedding = true;
} catch (const std::exception& e) {
} catch (const std::exception& /*e*/) {
new_clip->has_class_embedding = false;
}
......@@ -1181,7 +1190,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
vision_model.pre_ln_w = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "weight"));
vision_model.pre_ln_b = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "bias"));
new_clip->has_pre_norm = true;
} catch (std::exception & e) {
} catch (std::exception & /*e*/) {
new_clip->has_pre_norm = false;
}
......@@ -1189,21 +1198,21 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
vision_model.post_ln_w = get_tensor(new_clip->ctx_data, format(TN_LN_POST, "v", "weight"));
vision_model.post_ln_b = get_tensor(new_clip->ctx_data, format(TN_LN_POST, "v", "bias"));
new_clip->has_post_norm = true;
} catch (std::exception & e) {
} catch (std::exception & /*e*/) {
new_clip->has_post_norm = false;
}
try {
vision_model.patch_bias = get_tensor(new_clip->ctx_data, TN_PATCH_BIAS);
new_clip->has_patch_bias = true;
} catch (std::exception & e) {
} catch (std::exception & /*e*/) {
new_clip->has_patch_bias = false;
}
try {
vision_model.patch_embeddings = get_tensor(new_clip->ctx_data, TN_PATCH_EMBD);
vision_model.position_embeddings = get_tensor(new_clip->ctx_data, format(TN_POS_EMBD, "v"));
} catch(const std::exception& e) {
} catch(const std::exception& /*e*/) {
LOG_TEE("%s: failed to load vision model tensors\n", __func__);
}
......@@ -1215,26 +1224,26 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
// Yi-type llava
vision_model.mm_1_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 1, "weight"));
vision_model.mm_1_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 1, "bias"));
} catch (std::runtime_error & e) { }
} catch (std::runtime_error & /*e*/) { }
try {
// missing in Yi-type llava
vision_model.mm_2_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "weight"));
vision_model.mm_2_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "bias"));
} catch (std::runtime_error & e) { }
} catch (std::runtime_error & /*e*/) { }
try {
// Yi-type llava
vision_model.mm_3_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 3, "weight"));
vision_model.mm_3_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 3, "bias"));
} catch (std::runtime_error & e) { }
} catch (std::runtime_error & /*e*/) { }
try {
// Yi-type llava
vision_model.mm_4_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 4, "weight"));
vision_model.mm_4_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 4, "bias"));
} catch (std::runtime_error & e) { }
} catch (std::runtime_error & /*e*/) { }
try {
vision_model.image_newline = get_tensor(new_clip->ctx_data, TN_IMAGE_NEWLINE);
// LOG_TEE("%s: image_newline tensor (llava-1.6) found\n", __func__);
} catch (std::runtime_error & e) { }
} catch (std::runtime_error & /*e*/) { }
} else if (new_clip->proj_type == PROJECTOR_TYPE_LDP) {
// MobileVLM projection
vision_model.mm_model_mlp_1_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 1, "weight"));
......
llm/llama.cpp/examples/llava/convert_image_encoder_to_gguf.py 0 → 100644
View file @ 0ce8bcfd
import argparse
import os
import json
import re
import torch
import numpy as np
from gguf import *
from transformers import CLIPModel, CLIPProcessor, CLIPVisionModel
TEXT = "clip.text"
VISION = "clip.vision"
def k(raw_key: str, arch: str) -> str:
return raw_key.format(arch=arch)
def should_skip_tensor(name: str, has_text: bool, has_vision: bool, has_llava: bool) -> bool:
if name in (
"logit_scale",
"text_model.embeddings.position_ids",
"vision_model.embeddings.position_ids",
):
return True
if has_llava and name in ["visual_projection.weight", "vision_model.post_layernorm.weight", "vision_model.post_layernorm.bias"]:
return True
if name.startswith("v") and not has_vision:
return True
if name.startswith("t") and not has_text:
return True
return False
def get_tensor_name(name: str) -> str:
if "projection" in name:
return name
if "mm_projector" in name:
name = name.replace("model.mm_projector", "mm")
name = re.sub(r'mm\.mlp\.mlp', 'mm.model.mlp', name, count=1)
name = re.sub(r'mm\.peg\.peg', 'mm.model.peg', name, count=1)
return name
return name.replace("text_model", "t").replace("vision_model", "v").replace("encoder.layers", "blk").replace("embeddings.", "").replace("_proj", "").replace("self_attn.", "attn_").replace("layer_norm", "ln").replace("layernorm", "ln").replace("mlp.fc1", "ffn_down").replace("mlp.fc2", "ffn_up").replace("embedding", "embd").replace("final", "post").replace("layrnorm", "ln")
def bytes_to_unicode():
"""
Returns list of utf-8 byte and a corresponding list of unicode strings.
The reversible bpe codes work on unicode strings.
This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
This is a significant percentage of your normal, say, 32K bpe vocab.
To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
And avoids mapping to whitespace/control characters the bpe code barfs on.
"""
bs = (
list(range(ord("!"), ord("~") + 1))
+ list(range(ord("¡"), ord("¬") + 1))
+ list(range(ord("®"), ord("ÿ") + 1))
)
cs = bs[:]
n = 0
for b in range(2**8):
if b not in bs:
bs.append(b)
cs.append(2**8 + n)
n += 1
cs = [chr(n) for n in cs]
return dict(zip(bs, cs))
ap = argparse.ArgumentParser()
ap.add_argument("-m", "--model-dir", help="Path to model directory cloned from HF Hub", required=True)
ap.add_argument("--use-f32", action="store_true", default=False, help="Use f32 instead of f16")
ap.add_argument("--text-only", action="store_true", required=False,
help="Save a text-only model. It can't be used to encode images")
ap.add_argument("--vision-only", action="store_true", required=False,
help="Save a vision-only model. It can't be used to encode texts")
ap.add_argument("--clip-model-is-vision", action="store_true", required=False,
help="The clip model is a pure vision model (ShareGPT4V vision extract for example)")
ap.add_argument("--clip-model-is-openclip", action="store_true", required=False,
help="The clip model is from openclip (for ViT-SO400M type))")
ap.add_argument("--llava-projector", help="Path to llava.projector file. If specified, save an image encoder for LLaVA models.")
ap.add_argument("--projector-type", help="Type of projector. Possible values: mlp, ldp, ldpv2", choices=["mlp", "ldp", "ldpv2"], default="mlp")
ap.add_argument("-o", "--output-dir", help="Directory to save GGUF files. Default is the original model directory", default=None)
# Example --image_mean 0.48145466 0.4578275 0.40821073 --image_std 0.26862954 0.26130258 0.27577711
# Example --image_mean 0.5 0.5 0.5 --image_std 0.5 0.5 0.5
default_image_mean = [0.48145466, 0.4578275, 0.40821073]
default_image_std = [0.26862954, 0.26130258, 0.27577711]
ap.add_argument('--image-mean', type=float, nargs='+', help='Mean of the images for normalization (overrides processor) ', default=None)
ap.add_argument('--image-std', type=float, nargs='+', help='Standard deviation of the images for normalization (overrides processor)', default=None)
# with proper
args = ap.parse_args()
if args.text_only and args.vision_only:
print("--text-only and --image-only arguments cannot be specified at the same time.")
exit(1)
if args.use_f32:
print("WARNING: Weights for the convolution op is always saved in f16, as the convolution op in GGML does not support 32-bit kernel weights yet.")
# output in the same directory as the model if output_dir is None
dir_model = args.model_dir
if args.clip_model_is_vision or not os.path.exists(dir_model + "/vocab.json") or args.clip_model_is_openclip:
vocab = None
tokens = None
else:
with open(dir_model + "/vocab.json", "r", encoding="utf-8") as f:
vocab = json.load(f)
tokens = [key for key in vocab]
with open(dir_model + "/config.json", "r", encoding="utf-8") as f:
config = json.load(f)
if args.clip_model_is_vision:
v_hparams = config
t_hparams = None
else:
v_hparams = config["vision_config"]
t_hparams = config["text_config"]
# possible data types
# ftype == 0 -> float32
# ftype == 1 -> float16
#
# map from ftype to string
ftype_str = ["f32", "f16"]
ftype = 1
if args.use_f32:
ftype = 0
if args.clip_model_is_vision or args.clip_model_is_openclip:
model = CLIPVisionModel.from_pretrained(dir_model)
processor = None
else:
model = CLIPModel.from_pretrained(dir_model)
processor = CLIPProcessor.from_pretrained(dir_model)
fname_middle = None
has_text_encoder = True
has_vision_encoder = True
has_llava_projector = False
if args.text_only:
fname_middle = "text-"
has_vision_encoder = False
elif args.llava_projector is not None:
fname_middle = "mmproj-"
has_text_encoder = False
has_llava_projector = True
elif args.vision_only:
fname_middle = "vision-"
has_text_encoder = False
else:
fname_middle = ""
output_dir = args.output_dir if args.output_dir is not None else dir_model
os.makedirs(output_dir, exist_ok=True)
output_prefix = os.path.basename(output_dir).replace("ggml_", "")
fname_out = os.path.join(output_dir, f"{fname_middle}model-{ftype_str[ftype]}.gguf")
fout = GGUFWriter(path=fname_out, arch="clip")
fout.add_bool("clip.has_text_encoder", has_text_encoder)
fout.add_bool("clip.has_vision_encoder", has_vision_encoder)
fout.add_bool("clip.has_llava_projector", has_llava_projector)
fout.add_file_type(ftype)
model_name = config["_name_or_path"] if "_name_or_path" in config else os.path.basename(dir_model)
fout.add_name(model_name)
if args.text_only:
fout.add_description("text-only CLIP model")
elif args.vision_only and not has_llava_projector:
fout.add_description("vision-only CLIP model")
elif has_llava_projector:
fout.add_description("image encoder for LLaVA")
# add projector type
fout.add_string("clip.projector_type", args.projector_type)
else:
fout.add_description("two-tower CLIP model")
if has_text_encoder:
assert t_hparams is not None
assert tokens is not None
# text_model hparams
fout.add_uint32(k(KEY_CONTEXT_LENGTH, TEXT), t_hparams["max_position_embeddings"])
fout.add_uint32(k(KEY_EMBEDDING_LENGTH, TEXT), t_hparams["hidden_size"])
fout.add_uint32(k(KEY_FEED_FORWARD_LENGTH, TEXT), t_hparams["intermediate_size"])
fout.add_uint32("clip.text.projection_dim", t_hparams.get("projection_dim", config["projection_dim"]))
fout.add_uint32(k(KEY_ATTENTION_HEAD_COUNT, TEXT), t_hparams["num_attention_heads"])
fout.add_float32(k(KEY_ATTENTION_LAYERNORM_EPS, TEXT), t_hparams["layer_norm_eps"])
fout.add_uint32(k(KEY_BLOCK_COUNT, TEXT), t_hparams["num_hidden_layers"])
fout.add_token_list(tokens)
if has_vision_encoder:
# vision_model hparams
fout.add_uint32("clip.vision.image_size", v_hparams["image_size"])
fout.add_uint32("clip.vision.patch_size", v_hparams["patch_size"])
fout.add_uint32(k(KEY_EMBEDDING_LENGTH, VISION), v_hparams["hidden_size"])
fout.add_uint32(k(KEY_FEED_FORWARD_LENGTH, VISION), v_hparams["intermediate_size"])
fout.add_uint32("clip.vision.projection_dim", v_hparams.get("projection_dim", config["projection_dim"]))
fout.add_uint32(k(KEY_ATTENTION_HEAD_COUNT, VISION), v_hparams["num_attention_heads"])
fout.add_float32(k(KEY_ATTENTION_LAYERNORM_EPS, VISION), v_hparams["layer_norm_eps"])
block_count = v_hparams["num_hidden_layers"] - 1 if has_llava_projector else v_hparams["num_hidden_layers"]
fout.add_uint32(k(KEY_BLOCK_COUNT, VISION), block_count)
# /**
# "image_grid_pinpoints": [
# [
# 336,
# 672
# ],
# [
# 672,
# 336
# ],
# [
# 672,
# 672
# ],
# [
# 1008,
# 336
# ],
# [
# 336,
# 1008
# ]
# ],
# Flattened:
# [
# 336, 672,
# 672, 336,
# 672, 672,
# 1008, 336,
# 336, 1008
# ]
# *
# */
if "image_grid_pinpoints" in v_hparams:
# flatten it
image_grid_pinpoints = []
for pinpoint in v_hparams["image_grid_pinpoints"]:
for p in pinpoint:
image_grid_pinpoints.append(p)
fout.add_array("clip.vision.image_grid_pinpoints", image_grid_pinpoints)
if "image_crop_resolution" in v_hparams:
fout.add_uint32("clip.vision.image_crop_resolution", v_hparams["image_crop_resolution"])
if "image_aspect_ratio" in v_hparams:
fout.add_string("clip.vision.image_aspect_ratio", v_hparams["image_aspect_ratio"])
if "image_split_resolution" in v_hparams:
fout.add_uint32("clip.vision.image_split_resolution", v_hparams["image_split_resolution"])
if "mm_patch_merge_type" in v_hparams:
fout.add_string("clip.vision.mm_patch_merge_type", v_hparams["mm_patch_merge_type"])
if "mm_projector_type" in v_hparams:
fout.add_string("clip.vision.mm_projector_type", v_hparams["mm_projector_type"])
if processor is not None:
image_mean = processor.image_processor.image_mean if args.image_mean is None or args.image_mean == default_image_mean else args.image_mean # pyright: ignore[reportAttributeAccessIssue]
image_std = processor.image_processor.image_std if args.image_std is None or args.image_std == default_image_std else args.image_std # pyright: ignore[reportAttributeAccessIssue]
else:
image_mean = args.image_mean if args.image_mean is not None else default_image_mean
image_std = args.image_std if args.image_std is not None else default_image_std
fout.add_array("clip.vision.image_mean", image_mean)
fout.add_array("clip.vision.image_std", image_std)
use_gelu = v_hparams["hidden_act"] == "gelu"
fout.add_bool("clip.use_gelu", use_gelu)
if has_llava_projector:
model.vision_model.encoder.layers.pop(-1) # pyright: ignore[reportAttributeAccessIssue]
projector = torch.load(args.llava_projector)
for name, data in projector.items():
name = get_tensor_name(name)
# pw and dw conv ndim==4
if data.ndim == 2 or data.ndim == 4:
data = data.squeeze().numpy().astype(np.float16)
else:
data = data.squeeze().numpy().astype(np.float32)
fout.add_tensor(name, data)
print("Projector tensors added\n")
state_dict = model.state_dict() # pyright: ignore[reportAttributeAccessIssue]
for name, data in state_dict.items():
if should_skip_tensor(name, has_text_encoder, has_vision_encoder, has_llava_projector):
# we don't need this
print(f"skipping parameter: {name}")
continue
name = get_tensor_name(name)
data = data.squeeze().numpy()
n_dims = len(data.shape)
# ftype == 0 -> float32, ftype == 1 -> float16
ftype_cur = 0
if n_dims == 4:
print(f"tensor {name} is always saved in f16")
data = data.astype(np.float16)
ftype_cur = 1
elif ftype == 1:
if name[-7:] == ".weight" and n_dims == 2:
print(" Converting to float16")
data = data.astype(np.float16)
ftype_cur = 1
else:
print(" Converting to float32")
data = data.astype(np.float32)
ftype_cur = 0
else:
if data.dtype != np.float32:
print(" Converting to float32")
data = data.astype(np.float32)
ftype_cur = 0
print(f"{name} - {ftype_str[ftype_cur]} - shape = {data.shape}")
fout.add_tensor(name, data)
fout.write_header_to_file()
fout.write_kv_data_to_file()
fout.write_tensors_to_file()
fout.close()
print("Done. Output file: " + fname_out)
llm/llama.cpp/examples/llava/llava-cli.cpp
View file @ 0ce8bcfd
......@@ -112,9 +112,12 @@ struct llava_context {
struct llama_model * model = NULL;
};
static void show_additional_info(int /*argc*/, char ** argv) {
LOG_TEE("\n example usage: %s -m <llava-v1.5-7b/ggml-model-q5_k.gguf> --mmproj <llava-v1.5-7b/mmproj-model-f16.gguf> --image <path/to/an/image.jpg> --image <path/to/another/image.jpg> [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]);
LOG_TEE(" note: a lower temperature value like 0.1 is recommended for better quality.\n");
static void print_usage(int argc, char ** argv, const gpt_params & params) {
gpt_params_print_usage(argc, argv, params);
LOG_TEE("\n example usage:\n");
LOG_TEE("\n %s -m <llava-v1.5-7b/ggml-model-q5_k.gguf> --mmproj <llava-v1.5-7b/mmproj-model-f16.gguf> --image <path/to/an/image.jpg> --image <path/to/another/image.jpg> [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]);
LOG_TEE("\n note: a lower temperature value like 0.1 is recommended for better quality.\n");
}
static struct llava_image_embed * load_image(llava_context * ctx_llava, gpt_params * params, const std::string & fname) {
......@@ -278,7 +281,7 @@ int main(int argc, char ** argv) {
gpt_params params;
if (!gpt_params_parse(argc, argv, params)) {
show_additional_info(argc, argv);
print_usage(argc, argv, params);
return 1;
}
......@@ -290,8 +293,7 @@ int main(int argc, char ** argv) {
#endif // LOG_DISABLE_LOGS
if (params.mmproj.empty() || (params.image.empty() && !prompt_contains_image(params.prompt))) {
gpt_params_print_usage(argc, argv, params);
show_additional_info(argc, argv);
print_usage(argc, argv, {});
return 1;
}
auto model = llava_init(&params);
......
llm/llama.cpp/examples/llava/llava_surgery.py 0 → 100644
View file @ 0ce8bcfd
import argparse
import glob
import os
import torch
ap = argparse.ArgumentParser()
ap.add_argument("-m", "--model", help="Path to LLaVA v1.5 model")
args = ap.parse_args()
# find the model part that includes the the multimodal projector weights
path = sorted(glob.glob(f"{args.model}/pytorch_model*.bin"))[-1]
checkpoint = torch.load(path)
# get a list of mm tensor names
mm_tensors = [k for k, v in checkpoint.items() if k.startswith("model.mm_projector")]
# store these tensors in a new dictionary and torch.save them
projector = {name: checkpoint[name].float() for name in mm_tensors}
torch.save(projector, f"{args.model}/llava.projector")
# BakLLaVA models contain CLIP tensors in it
clip_tensors = [k for k, v in checkpoint.items() if k.startswith("model.vision_tower")]
if len(clip_tensors) > 0:
clip = {name.replace("vision_tower.vision_tower.", ""): checkpoint[name].float() for name in clip_tensors}
torch.save(clip, f"{args.model}/llava.clip")
# added tokens should be removed to be able to convert Mistral models
if os.path.exists(f"{args.model}/added_tokens.json"):
with open(f"{args.model}/added_tokens.json", "w") as f:
f.write("{}\n")
print("Done!")
print(f"Now you can convert {args.model} to a regular LLaMA GGUF file.")
print(f"Also, use {args.model}/llava.projector to prepare a llava-encoder.gguf file.")
llm/llama.cpp/examples/llava/llava_surgery_v2.py 0 → 100644
View file @ 0ce8bcfd
import argparse
import glob
import os
import torch
from safetensors import safe_open
from safetensors.torch import save_file
from typing import Any, ContextManager, cast
# Function to determine if file is a SafeTensor file
def is_safetensor_file(file_path):
return file_path.endswith('.safetensors')
# Unified loading function
def load_model(file_path):
if is_safetensor_file(file_path):
tensors = {}
with cast(ContextManager[Any], safe_open(file_path, framework="pt", device="cpu")) as f:
for key in f.keys():
tensors[key] = f.get_tensor(key).clone()
# output shape
print(f"{key} : {tensors[key].shape}")
return tensors, 'safetensor'
else:
return torch.load(file_path, map_location=torch.device('cpu')), 'pytorch'
# Unified saving function
def save_model(model, file_path, file_type):
if file_type == 'safetensor':
# safe_save(model, file_path)
save_file(model, file_path)
else:
torch.save(model, file_path)
# Adapted function to clean vision tower from checkpoint
def clean_vision_tower_from_checkpoint(checkpoint_path):
checkpoint, file_type = load_model(checkpoint_path)
# file_type = 'pytorch'
model_path = os.path.dirname(checkpoint_path)
print(f"Searching for vision tower tensors in {checkpoint_path}")
clip_tensors = [k for k, v in checkpoint.items() if (k.startswith("model.vision_tower") or k.startswith("vit."))]
if len(clip_tensors) > 0:
print(f"Found {len(clip_tensors)} tensors to extract from {checkpoint_path}")
# Adapted for file type
clip_path = os.path.join(model_path, "llava.clip")
if os.path.exists(clip_path):
print(f"Loading existing llava.clip from {clip_path}")
existing_clip, _ = load_model(clip_path)
else:
print(f"Creating new llava.clip at {clip_path}")
existing_clip = {}
# Update existing_clip with new tensors, avoid duplicates
for name in clip_tensors:
simple_name = name[name.index('vision_model.'):] if 'vision_model.' in name else name
print(f"Adding {simple_name} to llava.clip")
if simple_name not in existing_clip:
existing_clip[simple_name] = checkpoint[name]
# Save the updated clip tensors back to llava.clip
save_model(existing_clip, clip_path, 'pytorch')
# Remove the tensors from the original checkpoint
for name in clip_tensors:
del checkpoint[name]
checkpoint_path = checkpoint_path
return True
return False
def find_relevant_checkpoints(checkpoint_paths, newline_criteria, projector):
newline_checkpoint_path = None
projector_checkpoint_path = None
for path in checkpoint_paths:
checkpoint, _ = load_model(path)
if newline_criteria(checkpoint) and newline_checkpoint_path is None:
newline_checkpoint_path = path
if projector(checkpoint):
projector_checkpoint_path = path
return newline_checkpoint_path, projector_checkpoint_path
def newline_criteria(checkpoint):
return any(k.startswith("model.image_newline") for k in checkpoint.keys())
def proj_criteria(checkpoint):
return any(k.startswith("model.mm_projector") or k.startswith("vision_proj.") for k in checkpoint.keys())
# Command-line interface setup
ap = argparse.ArgumentParser()
ap.add_argument("-m", "--model", required=True, help="Path to LLaVA v1.5+ model")
ap.add_argument("-C", "--clean-vision-tower", action="store_true", help="Remove any vision tower from the model files")
args = ap.parse_args()
if args.clean_vision_tower:
# Generalized to handle both PyTorch and SafeTensors models
model_files = sorted(glob.glob(f"{args.model}/*"), key=os.path.getmtime, reverse=True)
# checkpoint_paths = [path for path in model_files if (path.endswith('.bin') and path.startswith('pytorch')) or (path.endswith('.safetensors') and path.startswith('model'))]
checkpoint_paths = [path for path in model_files if (path.endswith('.bin') and 'pytorch' in path.split('/')[-1].split('\\')[-1]) or (path.endswith('.safetensors') and 'model' in path.split('/')[-1].split('\\')[-1])]
for projector_checkpoint_path in checkpoint_paths:
print(f"Cleaning {projector_checkpoint_path}")
if not clean_vision_tower_from_checkpoint(projector_checkpoint_path):
print(f"No vision tower found in {projector_checkpoint_path}")
# we break once none is found, so far all models append them at the end
# break
print("Done! All vision tower tensors are removed from the model files and stored in llava.clip file.")
# Now we look for the projector in the last checkpoint
model_files = sorted(glob.glob(f"{args.model}/*"), key=os.path.getmtime, reverse=True)
checkpoint_paths = [path for path in model_files if (path.endswith('.bin') and 'pytorch' in path.split('/')[-1].split('\\')[-1]) or (path.endswith('.safetensors') and 'model' in path.split('/')[-1].split('\\')[-1])]
# last_checkpoint_path = checkpoint_paths[0]
# first_checkpoint_path = checkpoint_paths[-1]
newline_checkpoint_path, projector_checkpoint_path = find_relevant_checkpoints(checkpoint_paths, newline_criteria, proj_criteria)
print(f"Taking projector from {projector_checkpoint_path}")
first_mm_tensors = []
first_checkpoint = None
if newline_checkpoint_path is not None:
print(f"Taking newline from {newline_checkpoint_path}")
first_checkpoint, file_type = load_model(newline_checkpoint_path)
first_mm_tensors = [k for k, v in first_checkpoint.items() if k.startswith("model.image_newline")]
# Load the checkpoint
mm_tensors = []
last_checkpoint = None
if projector_checkpoint_path is not None:
last_checkpoint, file_type = load_model(projector_checkpoint_path)
mm_tensors = [k for k, v in last_checkpoint.items() if k.startswith("model.mm_projector") or k.startswith("vision_proj.")]
if len(mm_tensors) == 0:
if last_checkpoint is not None:
for k, v in last_checkpoint.items():
print(k)
print(f"Found {len(mm_tensors)} tensors to extract out of {len(last_checkpoint) if last_checkpoint is not None else 0} tensors.")
print("No tensors found. Is this a LLaVA model?")
exit()
print(f"Found {len(mm_tensors)} tensors to extract.")
print(f"Found additional {len(first_mm_tensors)} tensors to extract.")
# projector = {name: checkpoint.[name].float() for name in mm_tensors}
projector = {}
for name in mm_tensors:
assert last_checkpoint is not None
projector[name] = last_checkpoint[name].float()
for name in first_mm_tensors:
assert first_checkpoint is not None
projector[name] = first_checkpoint[name].float()
if len(projector) > 0:
save_model(projector, f"{args.model}/llava.projector", 'pytorch')
print("Done!")
print(f"Now you can convert {args.model} to a a regular LLaMA GGUF file.")
print(f"Also, use {args.model}/llava.projector to prepare a llava-encoder.gguf file.")
llm/llama.cpp/examples/llava/requirements.txt
View file @ 0ce8bcfd
-r ../../requirements/requirements-convert-legacy-llama.txt
-r ../../requirements/requirements-convert_legacy_llama.txt
--extra-index-url https://download.pytorch.org/whl/cpu
pillow~=10.2.0
torch~=2.1.1
torch~=2.2.1
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