vbatts/llama.cpp
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Merge branch 'master' into add_stop_token

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This commit is contained in:
Jason McCartney 2023-05-11 10:26:00 -07:00
commit 099a07fb87
26 changed files with 3100 additions and 928 deletions
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15
examples/common.cpp
View file

@ -139,6 +139,19 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
break;
}
params.model = argv[i];
} else if (arg == "--lora") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.lora_adapter = argv[i];
params.use_mmap = false;
} else if (arg == "--lora-base") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.lora_base = argv[i];
} else if (arg == "-i" || arg == "--interactive") {
params.interactive = true;
} else if (arg == "--embedding") {
@ -243,6 +256,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
}
fprintf(stderr, " --mtest compute maximum memory usage\n");
fprintf(stderr, " --verbose-prompt print prompt before generation\n");
fprintf(stderr, " --lora FNAME apply LoRA adapter (implies --no-mmap)\n");
fprintf(stderr, " --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n");
fprintf(stderr, " -m FNAME, --model FNAME\n");
fprintf(stderr, " model path (default: %s)\n", params.model.c_str());
fprintf(stderr, "\n");

7
examples/common.h
View file

@ -31,11 +31,12 @@ struct gpt_params {
std::string model = "models/lamma-7B/ggml-model.bin"; // model path
std::string prompt = "";
std::string input_prefix = ""; // string to prefix user inputs with
std::string input_prefix = ""; // string to prefix user inputs with
std::vector<std::string> antiprompt; // string upon seeing which more user input is prompted
std::string lora_adapter = ""; // lora adapter path
std::string lora_base = ""; // base model path for the lora adapter
bool memory_f16 = true; // use f16 instead of f32 for memory kv
bool random_prompt = false; // do not randomize prompt if none provided
bool use_color = false; // use color to distinguish generations and inputs

11
examples/main/main.cpp
View file

@ -114,6 +114,17 @@ int main(int argc, char ** argv) {
}
}
if (!params.lora_adapter.empty()) {
int err = llama_apply_lora_from_file(ctx,
params.lora_adapter.c_str(),
params.lora_base.empty() ? NULL : params.lora_base.c_str(),
params.n_threads);
if (err != 0) {
fprintf(stderr, "%s: error: failed to apply lora adapter\n", __func__);
return 1;
}
}
// print system information
{
fprintf(stderr, "\n");

11
examples/perplexity/perplexity.cpp
View file

@ -134,6 +134,17 @@ int main(int argc, char ** argv) {
}
}
if (!params.lora_adapter.empty()) {
int err = llama_apply_lora_from_file(ctx,
params.lora_adapter.c_str(),
params.lora_base.empty() ? NULL : params.lora_base.c_str(),
params.n_threads);
if (err != 0) {
fprintf(stderr, "%s: error: failed to apply lora adapter\n", __func__);
return 1;
}
}
// print system information
{
fprintf(stderr, "\n");

139
examples/quantize-stats/quantize-stats.cpp
View file

@ -15,6 +15,8 @@
#include <string>
#include <unordered_map>
#include <vector>
#include <thread>
#include <mutex>
struct quantize_stats_params {
std::string model = "models/7B/ggml-model-f16.bin";
@ -27,7 +29,6 @@ struct quantize_stats_params {
std::vector<enum ggml_type> include_types;
};
const int64_t SCRATCH_ELEMENTS = 32*32;
const size_t HISTOGRAM_BUCKETS = 150;
const double HISTOGRAM_RANGE = 0.03;
@ -90,6 +91,13 @@ void update_error_stats(int64_t nelements, const float * input, const float * ou
stats.num_samples += nelements;
}
void combine_error_stats(error_stats & into, const error_stats & from) {
into.num_samples += from.num_samples;
into.total_error += from.total_error;
if (from.max_error > into.max_error) into.max_error = from.max_error;
for (size_t i=0; i<HISTOGRAM_BUCKETS; ++i) into.error_histogram[i] += from.error_histogram[i];
}
double find_quantile(const error_stats & stats, double quantile) {
double sum = std::accumulate(std::begin(stats.error_histogram), std::end(stats.error_histogram), 0.0);
@ -130,6 +138,36 @@ static bool tensor_is_contiguous(const struct ggml_tensor * tensor) {
tensor->nb[3] == tensor->nb[2]*tensor->ne[2];
}
void test_roundtrip_on_chunk(
const ggml_tensor * layer,
int64_t offset,
int64_t chunk_size,
const quantize_fns_t & qfns,
bool use_reference,
float * input_scratch,
char * quantized_scratch,
float * output_scratch,
error_stats & stats) {
if (layer->type == GGML_TYPE_F16) {
for (int i = 0; i < chunk_size; i++) {
input_scratch[i] = ggml_get_f32_1d(layer, i + offset);
}
} else {
input_scratch = ggml_get_data_f32(layer) + offset;
}
if (use_reference) {
qfns.quantize_row_q_reference(input_scratch, quantized_scratch, chunk_size);
} else {
qfns.quantize_row_q(input_scratch, quantized_scratch, chunk_size);
}
qfns.dequantize_row_q(quantized_scratch, output_scratch, chunk_size);
update_error_stats(chunk_size, input_scratch, output_scratch, stats);
}
// Run quantization function for a single layer and update error stats
void test_roundtrip_on_layer(
std::string & name,
@ -137,40 +175,61 @@ void test_roundtrip_on_layer(
const quantize_fns_t & qfns,
bool use_reference,
const ggml_tensor * layer,
float * input_scratch,
char *quantized_scratch,
float * output_scratch,
error_stats & total_error) {
std::vector<float> & input_scratch,
std::vector<char> & quantized_scratch,
std::vector<float> & output_scratch,
error_stats & total_error,
int max_thread = 0) {
assert(tensor_is_contiguous(layer));
error_stats layer_error {};
int64_t nelements = ggml_nelements(layer);
uint64_t nelements = ggml_nelements(layer);
for (int64_t offset = 0; offset < nelements; offset += SCRATCH_ELEMENTS) {
int64_t chunk_size = std::min(SCRATCH_ELEMENTS, nelements - offset);
if (layer->type == GGML_TYPE_F16) {
for (int i = 0; i < chunk_size; i++) {
input_scratch[i] = ggml_get_f32_1d(layer, i + offset);
}
} else {
input_scratch = ggml_get_data_f32(layer) + offset;
}
if (use_reference) {
qfns.quantize_row_q_reference(input_scratch, quantized_scratch, chunk_size);
} else {
qfns.quantize_row_q(input_scratch, quantized_scratch, chunk_size);
}
qfns.dequantize_row_q(quantized_scratch, output_scratch, chunk_size);
update_error_stats(chunk_size, input_scratch, output_scratch, total_error);
if (print_layer_stats) {
update_error_stats(chunk_size, input_scratch, output_scratch, layer_error);
}
float* input_scratch_ptr = nullptr;
if (layer->type == GGML_TYPE_F16) {
if (input_scratch.size() < nelements) input_scratch.resize(nelements);
input_scratch_ptr = input_scratch.data();
}
if (quantized_scratch.size() < 4*nelements) quantized_scratch.resize(4*nelements);
if (output_scratch.size() < nelements) output_scratch.resize(nelements);
if (max_thread < 1) max_thread = std::thread::hardware_concurrency();
int chunk_size = 32*512;
int num_chunks = (nelements + chunk_size - 1)/chunk_size;
if (num_chunks < 2 || max_thread < 2) {
test_roundtrip_on_chunk(layer, 0, nelements, qfns, use_reference, input_scratch_ptr, quantized_scratch.data(),
output_scratch.data(), print_layer_stats ? layer_error : total_error);
} else {
auto & stats = print_layer_stats ? layer_error : total_error;
std::mutex mutex;
uint64_t counter = 0;
auto compute = [&mutex, &counter, &stats, &qfns, nelements, layer, use_reference, input_scratch_ptr,
&quantized_scratch, &output_scratch, chunk_size] () {
error_stats local_stats {};
while (true) {
std::unique_lock<std::mutex> lock(mutex);
uint64_t offset = counter; counter += chunk_size;
if (offset >= nelements) {
combine_error_stats(stats, local_stats);
break;
}
lock.unlock();
uint64_t chunk = offset + chunk_size < nelements ? chunk_size : nelements - offset;
test_roundtrip_on_chunk(layer, offset, chunk, qfns, use_reference, input_scratch_ptr + offset,
quantized_scratch.data() + 4*offset, output_scratch.data() + offset, local_stats);
}
};
int nthread = std::min(num_chunks, max_thread);
std::vector<std::thread> workers(nthread-1);
for (auto& w : workers) w = std::thread(compute);
compute();
for (auto& w : workers) w.join();
}
if (print_layer_stats) {
print_error_stats(name, layer_error, false);
combine_error_stats(total_error, layer_error);
}
}
@ -181,6 +240,7 @@ int main(int argc, char ** argv) {
// read command line
int max_thread = 0;
bool invalid_param = false;
std::string arg;
for (int i = 1; i < argc; i++) {
@ -221,7 +281,7 @@ int main(int argc, char ** argv) {
break;
}
int j;
for (j = 0; j < GGML_TYPE_COUNT && strcmp(argv[i], ggml_type_name((ggml_type) i)) != 0; j++) {
for (j = 0; j < GGML_TYPE_COUNT && strcmp(argv[i], ggml_type_name((ggml_type) j)) != 0; j++) {
// find match
}
if (j < GGML_TYPE_COUNT) {
@ -230,6 +290,12 @@ int main(int argc, char ** argv) {
fprintf(stderr, "error: %s not in list of types\n", argv[i]);
invalid_param = true;
}
} else if (arg == "-n" || arg == "--num-threads") {
if (++i >= argc) {
invalid_param = true;
break;
}
max_thread = atoi(argv[i]);
} else {
fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
quantize_stats_print_usage(argc, argv);
@ -295,9 +361,9 @@ int main(int argc, char ** argv) {
}
printf("testing %d layers with max size %" PRId64 "\n", included_layers, max_nelements);
// allocate scratch space
std::vector<float> input_scratch(SCRATCH_ELEMENTS);
std::vector<char> quantized_scratch(SCRATCH_ELEMENTS*4);
std::vector<float> output_scratch(SCRATCH_ELEMENTS);
std::vector<float> input_scratch;
std::vector<char> quantized_scratch;
std::vector<float> output_scratch;
// loop throught quantization types
for (int i = 0; i < GGML_TYPE_COUNT; i++) {
@ -328,10 +394,11 @@ int main(int argc, char ** argv) {
qfns,
params.reference,
kv_tensor.second,
input_scratch.data(),
quantized_scratch.data(),
output_scratch.data(),
global_stats
input_scratch,
quantized_scratch,
output_scratch,
global_stats,
max_thread
);
}

9
examples/quantize/quantize.cpp
View file

@ -10,10 +10,12 @@
int main(int argc, char ** argv) {
ggml_time_init();
if (argc != 4) {
fprintf(stderr, "usage: %s model-f32.bin model-quant.bin type\n", argv[0]);
if (argc < 4) {
fprintf(stderr, "usage: %s model-f32.bin model-quant.bin type [nthread]\n", argv[0]);
fprintf(stderr, " type = %d - q4_0\n", LLAMA_FTYPE_MOSTLY_Q4_0);
fprintf(stderr, " type = %d - q4_1\n", LLAMA_FTYPE_MOSTLY_Q4_1);
fprintf(stderr, " type = %d - q4_2\n", LLAMA_FTYPE_MOSTLY_Q4_2);
fprintf(stderr, " type = %d - q4_3\n", LLAMA_FTYPE_MOSTLY_Q4_3);
return 1;
}
@ -28,6 +30,7 @@ int main(int argc, char ** argv) {
const std::string fname_out = argv[2];
const enum llama_ftype ftype = (enum llama_ftype)atoi(argv[3]);
int nthread = argc > 4 ? atoi(argv[4]) : 0;
const int64_t t_main_start_us = ggml_time_us();
@ -37,7 +40,7 @@ int main(int argc, char ** argv) {
{
const int64_t t_start_us = ggml_time_us();
if (llama_model_quantize(fname_inp.c_str(), fname_out.c_str(), ftype)) {
if (llama_model_quantize(fname_inp.c_str(), fname_out.c_str(), ftype, nthread)) {
fprintf(stderr, "%s: failed to quantize model from '%s'\n", __func__, fname_inp.c_str());
return 1;
}