mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-24 10:24:35 +00:00
build : fix and ignore MSVC warnings (#1889)
This commit is contained in:
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3d01122610
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9cbf50c041
@ -4,6 +4,10 @@
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#include <random>
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#include <cstring>
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#if defined(_MSC_VER)
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#pragma warning(disable: 4244 4267) // possible loss of data
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#endif
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float frand() {
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return (float)rand()/(float)RAND_MAX;
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}
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@ -1470,7 +1474,7 @@ struct ggml_tensor * square_error_loss(struct ggml_context * ctx, struct ggml_te
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}
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struct ggml_tensor * cross_entropy_loss(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) {
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const float eps = 1e-3;
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const float eps = 1e-3f;
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return
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ggml_sum(ctx,
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ggml_neg(ctx,
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@ -16,6 +16,10 @@
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#include <iterator>
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#include <algorithm>
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#if defined(_MSC_VER)
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#pragma warning(disable: 4244 4267) // possible loss of data
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#endif
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float tensor_sum_elements(const ggml_tensor * tensor) {
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float sum = 0;
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if (tensor->type==GGML_TYPE_F32) {
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@ -29,9 +33,9 @@ float tensor_sum_elements(const ggml_tensor * tensor) {
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}
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void tensor_dump(const ggml_tensor * tensor, const char * name) {
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printf("%15s: type = %i (%5s) ne = %5d x %5d x %5d, nb = (%5li, %5li, %5li) - ", name,
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printf("%15s: type = %i (%5s) ne = %5" PRIi64 " x %5" PRIi64 " x %5" PRIi64 ", nb = (%5zi, %5zi, %5zi) - ", name,
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tensor->type, ggml_type_name(tensor->type),
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(int) tensor->ne[0], (int) tensor->ne[1], (int) tensor->ne[2], tensor->nb[0], tensor->nb[1], tensor->nb[2]);
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tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->nb[0], tensor->nb[1], tensor->nb[2]);
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float sum = tensor_sum_elements(tensor);
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printf("Sum of tensor %s is %6.2f\n", name, sum);
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}
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@ -120,7 +124,7 @@ int main(int argc, char ** argv) {
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ctx_size += sizex*sizey*ggml_type_sizef(GGML_TYPE_F32); // BLAS
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ctx_size += 1024*1024*16;
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printf("Allocating Memory of size %li bytes, %li MB\n",ctx_size, (ctx_size/1024/1024));
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printf("Allocating Memory of size %zi bytes, %zi MB\n",ctx_size, (ctx_size/1024/1024));
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struct ggml_init_params params = {
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/*.mem_size =*/ ctx_size,
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@ -28,6 +28,10 @@
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#include <wchar.h>
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#endif
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#if defined(_MSC_VER)
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#pragma warning(disable: 4244 4267) // possible loss of data
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#endif
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int32_t get_num_physical_cores() {
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#ifdef __linux__
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// enumerate the set of thread siblings, num entries is num cores
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@ -373,7 +377,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
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} else {
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throw std::exception();
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}
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} catch (const std::exception &e) {
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} catch (const std::exception&) {
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invalid_param = true;
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break;
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}
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@ -4,6 +4,10 @@
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#include <ctime>
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#if defined(_MSC_VER)
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#pragma warning(disable: 4244 4267) // possible loss of data
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#endif
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int main(int argc, char ** argv) {
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gpt_params params;
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@ -28,6 +28,10 @@
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#include <signal.h>
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#endif
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#if defined(_MSC_VER)
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#pragma warning(disable: 4244 4267) // possible loss of data
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#endif
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static console_state con_st;
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static llama_context ** g_ctx;
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@ -348,7 +352,7 @@ int main(int argc, char ** argv) {
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if ((int)embd.size() > max_embd_size) {
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auto skipped_tokens = embd.size() - max_embd_size;
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console_set_color(con_st, CONSOLE_COLOR_ERROR);
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printf("<<input too long: skipped %ld token%s>>", skipped_tokens, skipped_tokens != 1 ? "s" : "");
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printf("<<input too long: skipped %" PRIu64 " token%s>>", skipped_tokens, skipped_tokens != 1 ? "s" : "");
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console_set_color(con_st, CONSOLE_COLOR_DEFAULT);
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fflush(stdout);
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embd.resize(max_embd_size);
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@ -5,6 +5,10 @@
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#include <cmath>
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#include <ctime>
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#if defined(_MSC_VER)
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#pragma warning(disable: 4244 4267) // possible loss of data
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#endif
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std::vector<float> softmax(const std::vector<float>& logits) {
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std::vector<float> probs(logits.size());
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float max_logit = logits[0];
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@ -19,6 +19,10 @@
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#include <thread>
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#include <mutex>
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#if defined(_MSC_VER)
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#pragma warning(disable: 4244 4267) // possible loss of data
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#endif
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struct quantize_stats_params {
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std::string model = "models/7B/ggml-model-f16.bin";
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bool verbose = false;
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@ -37,7 +37,7 @@ int main(int argc, char ** argv) {
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// init
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auto ctx = llama_init_from_file(params.model.c_str(), lparams);
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auto tokens = std::vector<llama_token>(params.n_ctx);
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auto n_prompt_tokens = llama_tokenize(ctx, params.prompt.c_str(), tokens.data(), tokens.size(), true);
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auto n_prompt_tokens = llama_tokenize(ctx, params.prompt.c_str(), tokens.data(), int(tokens.size()), true);
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if (n_prompt_tokens < 1) {
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fprintf(stderr, "%s : failed to tokenize prompt\n", __func__);
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@ -12,6 +12,9 @@
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#include <algorithm>
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#include <string>
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#if defined(_MSC_VER)
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#pragma warning(disable: 4244 4267) // possible loss of data
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#endif
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struct random_normal_distribution {
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std::mt19937 gen;
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@ -20,7 +23,6 @@ struct random_normal_distribution {
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float max;
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};
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struct random_uniform_distribution {
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std::mt19937 gen;
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std::uniform_real_distribution<float> rd;
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@ -2366,7 +2368,7 @@ void write_tensor(struct llama_file * file, struct ggml_tensor * tensor) {
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file->write_u32(0);
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file->write_u32(0);
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file->write_u32(GGML_TYPE_F32);
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file->seek(-file->tell() & 31, SEEK_CUR);
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file->seek(0-file->tell() & 31, SEEK_CUR);
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return;
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}
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const char * name = ggml_get_name(tensor);
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@ -2381,7 +2383,7 @@ void write_tensor(struct llama_file * file, struct ggml_tensor * tensor) {
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file->write_u32(tensor->type);
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file->write_raw(ne, sizeof(ne[0]) * nd);
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file->write_raw(name, name_len);
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file->seek(-file->tell() & 31, SEEK_CUR);
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file->seek(0-file->tell() & 31, SEEK_CUR);
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file->write_raw(tensor->data, ggml_nbytes(tensor));
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}
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@ -2402,7 +2404,7 @@ void read_tensor(struct llama_file * file, struct ggml_tensor * tensor) {
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std::string name = file->read_string(name_len);
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GGML_ASSERT(strncmp(ggml_get_name(tensor), name.c_str(), sizeof(tensor->name)-1) == 0);
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file->seek(-file->tell() & 31, SEEK_CUR);
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file->seek(0-file->tell() & 31, SEEK_CUR);
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file->read_raw(tensor->data, ggml_nbytes(tensor));
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}
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@ -2756,8 +2758,8 @@ struct train_params get_default_train_params() {
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params.lbfgs_n_iter = 16;
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params.adam_n_iter = 16;
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params.adam_alpha = 1e-3;
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params.adam_decay = 1e-3;
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params.adam_alpha = 1e-3f;
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params.adam_decay = 1e-3f;
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params.mem_model_gb = 2;
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params.mem_compute_gb = 24;
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@ -3331,8 +3333,8 @@ int main(int argc, char ** argv) {
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int n_gen = params.n_predict;
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int sample_ctx = n_tokens - n_tokens/8;
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sampler.params.temp = 0.2;
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sampler.params.repeat_penalty = 1.1;
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sampler.params.temp = 0.2f;
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sampler.params.repeat_penalty = 1.1f;
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sampler.params.mirostat = 2;
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init_sampler(&sampler, lctx);
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6
ggml.c
6
ggml.c
@ -35,6 +35,12 @@
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#define static_assert(cond, msg) struct global_scope_noop_trick
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#endif
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#if defined(_MSC_VER)
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// disable "possible loss of data" to avoid hundreds of casts
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// we should just be careful :)
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#pragma warning(disable: 4244 4267)
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#endif
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#if defined(_WIN32)
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#include <windows.h>
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@ -40,6 +40,10 @@
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#include <sstream>
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#include <numeric>
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#if defined(_MSC_VER)
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#pragma warning(disable: 4244 4267) // possible loss of data
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#endif
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#define LLAMA_USE_SCRATCH
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#define LLAMA_MAX_SCRATCH_BUFFERS 16
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#include <ggml.h>
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#if defined(_MSC_VER)
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#pragma warning(disable: 4244 4267) // possible loss of data
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#endif
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constexpr int kVecSize = 1 << 18;
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float drawFromGaussianPdf(std::mt19937& rndm) {
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@ -9,12 +9,15 @@
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#include <string>
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#include <vector>
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#if defined(_MSC_VER)
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#pragma warning(disable: 4244 4267) // possible loss of data
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#endif
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const float MAX_QUANTIZATION_REFERENCE_ERROR = 0.0001;
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const float MAX_QUANTIZATION_TOTAL_ERROR = 0.002;
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const float MAX_QUANTIZATION_TOTAL_ERROR_2BITS = 0.0075;
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const float MAX_QUANTIZATION_TOTAL_ERROR_3BITS = 0.0040;
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const float MAX_DOT_PRODUCT_ERROR = 0.02;
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const float MAX_QUANTIZATION_REFERENCE_ERROR = 0.0001f;
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const float MAX_QUANTIZATION_TOTAL_ERROR = 0.002f;
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const float MAX_QUANTIZATION_TOTAL_ERROR_2BITS = 0.0075f;
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const float MAX_QUANTIZATION_TOTAL_ERROR_3BITS = 0.0040f;
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const float MAX_DOT_PRODUCT_ERROR = 0.02f;
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const char* RESULT_STR[] = {"ok", "FAILED"};
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#include <string>
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#include <vector>
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#if defined(_MSC_VER)
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#pragma warning(disable: 4244 4267) // possible loss of data
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#endif
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#define MAX_ALIGNMENT 64
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#define QK 32
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#define WARMUP 5
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@ -176,27 +176,27 @@ void test_frequency_presence_penalty(
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int main(void) {
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ggml_time_init();
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test_top_k({0.1, 0.2, 0.3, 0.4}, {0.4}, 1);
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test_top_k({0.1, 0.2, 0.3, 0.4}, {0.4, 0.3, 0.2}, 3);
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test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f}, 1);
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test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f}, 3);
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test_top_p({0.1, 0.2, 0.3, 0.4}, {0.4}, 0);
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test_top_p({0.1, 0.2, 0.3, 0.4}, {0.4, 0.3}, 0.7);
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test_top_p({0.1, 0.2, 0.3, 0.4}, {0.4, 0.3, 0.2, 0.1}, 1);
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test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f}, 0);
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test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f}, 0.7f);
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test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f, 0.1f}, 1);
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test_tfs({0.1, 0.15, 0.2, 0.25, 0.3}, {0.3}, 0.25);
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test_tfs({0.1, 0.15, 0.2, 0.25, 0.3}, {0.3, 0.25}, 0.75);
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test_tfs({0.1, 0.15, 0.2, 0.25, 0.3}, {0.3, 0.25}, 0.99);
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test_tfs({0.1f, 0.15f, 0.2f, 0.25f, 0.3f}, {0.3f}, 0.25f);
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test_tfs({0.1f, 0.15f, 0.2f, 0.25f, 0.3f}, {0.3f, 0.25f}, 0.75f);
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test_tfs({0.1f, 0.15f, 0.2f, 0.25f, 0.3f}, {0.3f, 0.25f}, 0.99f);
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test_typical({0.97, 0.01, 0.01, 0.01}, {0.97}, 0.5);
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test_typical({0.4, 0.2, 0.2, 0.2}, {0.2, 0.2, 0.2}, 0.5);
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test_typical({0.97f, 0.01f, 0.01f, 0.01f}, {0.97f}, 0.5f);
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test_typical({0.4f, 0.2f, 0.2f, 0.2f}, {0.2f, 0.2f, 0.2f}, 0.5f);
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test_repetition_penalty({0.2, 0.2, 0.2, 0.2, 0.2}, {0}, {0.25, 0.25, 0.25, 0.25, 0}, 50.0);
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test_repetition_penalty({0.2, 0.2, 0.2, 0.2, 0.2}, {0, 1, 2}, {0.5, 0.5, 0, 0, 0}, 50.0);
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test_repetition_penalty({0.2, 0.2, 0.2, 0.2, 0.2}, {0, 1, 2, 0, 0}, {0.5, 0.5, 0, 0, 0}, 50.0);
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test_repetition_penalty({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0}, {0.25f, 0.25f, 0.25f, 0.25f, 0}, 50.0f);
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test_repetition_penalty({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2}, {0.5f, 0.5f, 0, 0, 0}, 50.0f);
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test_repetition_penalty({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 0, 0}, {0.5f, 0.5f, 0, 0, 0}, 50.0f);
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test_frequency_presence_penalty({0.2, 0.2, 0.2, 0.2, 0.2}, {0}, {0.249997, 0.249997, 0.249997, 0.249997, 0.000011}, 5.0, 5.0);
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test_frequency_presence_penalty({0.2, 0.2, 0.2, 0.2, 0.2}, {0, 1, 2}, {0.499966, 0.499966, 0.000023, 0.000023, 0.000023}, 5.0, 5.0);
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test_frequency_presence_penalty({0.2, 0.2, 0.2, 0.2, 0.2}, {0, 1, 2, 0, 0}, {0.499977, 0.499977, 0.000023, 0.000023, 0.000000}, 5.0, 5.0);
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test_frequency_presence_penalty({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0}, {0.249997f, 0.249997f, 0.249997f, 0.249997f, 0.000011f}, 5.0f, 5.0f);
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test_frequency_presence_penalty({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2}, {0.499966f, 0.499966f, 0.000023f, 0.000023f, 0.000023f}, 5.0f, 5.0f);
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test_frequency_presence_penalty({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 0, 0}, {0.499977f, 0.499977f, 0.000023f, 0.000023f, 0.000000f}, 5.0f, 5.0f);
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printf("OK\n");
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}
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@ -53,7 +53,7 @@ int main(int argc, char **argv) {
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for (const auto & test_kv : k_tests()) {
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std::vector<llama_token> res(test_kv.first.size());
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const int n = llama_tokenize(ctx, test_kv.first.c_str(), res.data(), res.size(), true);
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const int n = llama_tokenize(ctx, test_kv.first.c_str(), res.data(), int(res.size()), true);
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res.resize(n);
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bool correct = res.size() == test_kv.second.size();
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