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build : fix and ignore MSVC warnings (#1889)

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Borislav Stanimirov 2023-06-16 21:23:53 +03:00 committed by GitHub
parent 3d01122610
commit 9cbf50c041
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16 changed files with 88 additions and 37 deletions
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6
examples/baby-llama/baby-llama.cpp
View File

@ -4,6 +4,10 @@
#include <random>
#include <cstring>
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
float frand() {
return (float)rand()/(float)RAND_MAX;
}
@ -1470,7 +1474,7 @@ struct ggml_tensor * square_error_loss(struct ggml_context * ctx, struct ggml_te
}
struct ggml_tensor * cross_entropy_loss(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) {
const float eps = 1e-3;
const float eps = 1e-3f;
return
ggml_sum(ctx,
ggml_neg(ctx,

10
examples/benchmark/benchmark-matmult.cpp
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@ -16,6 +16,10 @@
#include <iterator>
#include <algorithm>
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
float tensor_sum_elements(const ggml_tensor * tensor) {
float sum = 0;
if (tensor->type==GGML_TYPE_F32) {
@ -29,9 +33,9 @@ float tensor_sum_elements(const ggml_tensor * tensor) {
}
void tensor_dump(const ggml_tensor * tensor, const char * name) {
printf("%15s: type = %i (%5s) ne = %5d x %5d x %5d, nb = (%5li, %5li, %5li) - ", name,
printf("%15s: type = %i (%5s) ne = %5" PRIi64 " x %5" PRIi64 " x %5" PRIi64 ", nb = (%5zi, %5zi, %5zi) - ", name,
tensor->type, ggml_type_name(tensor->type),
(int) tensor->ne[0], (int) tensor->ne[1], (int) tensor->ne[2], tensor->nb[0], tensor->nb[1], tensor->nb[2]);
tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->nb[0], tensor->nb[1], tensor->nb[2]);
float sum = tensor_sum_elements(tensor);
printf("Sum of tensor %s is %6.2f\n", name, sum);
}
@ -120,7 +124,7 @@ int main(int argc, char ** argv) {
ctx_size += sizex*sizey*ggml_type_sizef(GGML_TYPE_F32); // BLAS
ctx_size += 1024*1024*16;
printf("Allocating Memory of size %li bytes, %li MB\n",ctx_size, (ctx_size/1024/1024));
printf("Allocating Memory of size %zi bytes, %zi MB\n",ctx_size, (ctx_size/1024/1024));
struct ggml_init_params params = {
/*.mem_size =*/ ctx_size,

6
examples/common.cpp
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@ -28,6 +28,10 @@
#include <wchar.h>
#endif
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
int32_t get_num_physical_cores() {
#ifdef __linux__
// enumerate the set of thread siblings, num entries is num cores
@ -373,7 +377,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
} else {
throw std::exception();
}
} catch (const std::exception &e) {
} catch (const std::exception&) {
invalid_param = true;
break;
}

4
examples/embedding/embedding.cpp
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@ -4,6 +4,10 @@
#include <ctime>
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
int main(int argc, char ** argv) {
gpt_params params;

6
examples/main/main.cpp
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@ -28,6 +28,10 @@
#include <signal.h>
#endif
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
static console_state con_st;
static llama_context ** g_ctx;
@ -348,7 +352,7 @@ int main(int argc, char ** argv) {
if ((int)embd.size() > max_embd_size) {
auto skipped_tokens = embd.size() - max_embd_size;
console_set_color(con_st, CONSOLE_COLOR_ERROR);
printf("<<input too long: skipped %ld token%s>>", skipped_tokens, skipped_tokens != 1 ? "s" : "");
printf("<<input too long: skipped %" PRIu64 " token%s>>", skipped_tokens, skipped_tokens != 1 ? "s" : "");
console_set_color(con_st, CONSOLE_COLOR_DEFAULT);
fflush(stdout);
embd.resize(max_embd_size);

4
examples/perplexity/perplexity.cpp
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@ -5,6 +5,10 @@
#include <cmath>
#include <ctime>
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
std::vector<float> softmax(const std::vector<float>& logits) {
std::vector<float> probs(logits.size());
float max_logit = logits[0];

4
examples/quantize-stats/quantize-stats.cpp
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@ -19,6 +19,10 @@
#include <thread>
#include <mutex>
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
struct quantize_stats_params {
std::string model = "models/7B/ggml-model-f16.bin";
bool verbose = false;

2
examples/save-load-state/save-load-state.cpp
View File

@ -37,7 +37,7 @@ int main(int argc, char ** argv) {
// init
auto ctx = llama_init_from_file(params.model.c_str(), lparams);
auto tokens = std::vector<llama_token>(params.n_ctx);
auto n_prompt_tokens = llama_tokenize(ctx, params.prompt.c_str(), tokens.data(), tokens.size(), true);
auto n_prompt_tokens = llama_tokenize(ctx, params.prompt.c_str(), tokens.data(), int(tokens.size()), true);
if (n_prompt_tokens < 1) {
fprintf(stderr, "%s : failed to tokenize prompt\n", __func__);

18
examples/train-text-from-scratch/train-text-from-scratch.cpp
View File

@ -12,6 +12,9 @@
#include <algorithm>
#include <string>
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
struct random_normal_distribution {
std::mt19937 gen;
@ -20,7 +23,6 @@ struct random_normal_distribution {
float max;
};
struct random_uniform_distribution {
std::mt19937 gen;
std::uniform_real_distribution<float> rd;
@ -2366,7 +2368,7 @@ void write_tensor(struct llama_file * file, struct ggml_tensor * tensor) {
file->write_u32(0);
file->write_u32(0);
file->write_u32(GGML_TYPE_F32);
file->seek(-file->tell() & 31, SEEK_CUR);
file->seek(0-file->tell() & 31, SEEK_CUR);
return;
}
const char * name = ggml_get_name(tensor);
@ -2381,7 +2383,7 @@ void write_tensor(struct llama_file * file, struct ggml_tensor * tensor) {
file->write_u32(tensor->type);
file->write_raw(ne, sizeof(ne[0]) * nd);
file->write_raw(name, name_len);
file->seek(-file->tell() & 31, SEEK_CUR);
file->seek(0-file->tell() & 31, SEEK_CUR);
file->write_raw(tensor->data, ggml_nbytes(tensor));
}
@ -2402,7 +2404,7 @@ void read_tensor(struct llama_file * file, struct ggml_tensor * tensor) {
std::string name = file->read_string(name_len);
GGML_ASSERT(strncmp(ggml_get_name(tensor), name.c_str(), sizeof(tensor->name)-1) == 0);
file->seek(-file->tell() & 31, SEEK_CUR);
file->seek(0-file->tell() & 31, SEEK_CUR);
file->read_raw(tensor->data, ggml_nbytes(tensor));
}
@ -2756,8 +2758,8 @@ struct train_params get_default_train_params() {
params.lbfgs_n_iter = 16;
params.adam_n_iter = 16;
params.adam_alpha = 1e-3;
params.adam_decay = 1e-3;
params.adam_alpha = 1e-3f;
params.adam_decay = 1e-3f;
params.mem_model_gb = 2;
params.mem_compute_gb = 24;
@ -3331,8 +3333,8 @@ int main(int argc, char ** argv) {
int n_gen = params.n_predict;
int sample_ctx = n_tokens - n_tokens/8;
sampler.params.temp = 0.2;
sampler.params.repeat_penalty = 1.1;
sampler.params.temp = 0.2f;
sampler.params.repeat_penalty = 1.1f;
sampler.params.mirostat = 2;
init_sampler(&sampler, lctx);

6
ggml.c
View File

@ -35,6 +35,12 @@
#define static_assert(cond, msg) struct global_scope_noop_trick
#endif
#if defined(_MSC_VER)
// disable "possible loss of data" to avoid hundreds of casts
// we should just be careful :)
#pragma warning(disable: 4244 4267)
#endif
#if defined(_WIN32)
#include <windows.h>

4
llama.cpp
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@ -40,6 +40,10 @@
#include <sstream>
#include <numeric>
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
#define LLAMA_USE_SCRATCH
#define LLAMA_MAX_SCRATCH_BUFFERS 16

4
pocs/vdot/vdot.cpp
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@ -10,6 +10,10 @@
#include <ggml.h>
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
constexpr int kVecSize = 1 << 18;
float drawFromGaussianPdf(std::mt19937& rndm) {

13
tests/test-quantize-fns.cpp
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@ -9,12 +9,15 @@
#include <string>
#include <vector>
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
const float MAX_QUANTIZATION_REFERENCE_ERROR = 0.0001;
const float MAX_QUANTIZATION_TOTAL_ERROR = 0.002;
const float MAX_QUANTIZATION_TOTAL_ERROR_2BITS = 0.0075;
const float MAX_QUANTIZATION_TOTAL_ERROR_3BITS = 0.0040;
const float MAX_DOT_PRODUCT_ERROR = 0.02;
const float MAX_QUANTIZATION_REFERENCE_ERROR = 0.0001f;
const float MAX_QUANTIZATION_TOTAL_ERROR = 0.002f;
const float MAX_QUANTIZATION_TOTAL_ERROR_2BITS = 0.0075f;
const float MAX_QUANTIZATION_TOTAL_ERROR_3BITS = 0.0040f;
const float MAX_DOT_PRODUCT_ERROR = 0.02f;
const char* RESULT_STR[] = {"ok", "FAILED"};

4
tests/test-quantize-perf.cpp
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@ -13,6 +13,10 @@
#include <string>
#include <vector>
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
#define MAX_ALIGNMENT 64
#define QK 32
#define WARMUP 5

32
tests/test-sampling.cpp
View File

@ -176,27 +176,27 @@ void test_frequency_presence_penalty(
int main(void) {
ggml_time_init();
test_top_k({0.1, 0.2, 0.3, 0.4}, {0.4}, 1);
test_top_k({0.1, 0.2, 0.3, 0.4}, {0.4, 0.3, 0.2}, 3);
test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f}, 1);
test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f}, 3);
test_top_p({0.1, 0.2, 0.3, 0.4}, {0.4}, 0);
test_top_p({0.1, 0.2, 0.3, 0.4}, {0.4, 0.3}, 0.7);
test_top_p({0.1, 0.2, 0.3, 0.4}, {0.4, 0.3, 0.2, 0.1}, 1);
test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f}, 0);
test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f}, 0.7f);
test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f, 0.1f}, 1);
test_tfs({0.1, 0.15, 0.2, 0.25, 0.3}, {0.3}, 0.25);
test_tfs({0.1, 0.15, 0.2, 0.25, 0.3}, {0.3, 0.25}, 0.75);
test_tfs({0.1, 0.15, 0.2, 0.25, 0.3}, {0.3, 0.25}, 0.99);
test_tfs({0.1f, 0.15f, 0.2f, 0.25f, 0.3f}, {0.3f}, 0.25f);
test_tfs({0.1f, 0.15f, 0.2f, 0.25f, 0.3f}, {0.3f, 0.25f}, 0.75f);
test_tfs({0.1f, 0.15f, 0.2f, 0.25f, 0.3f}, {0.3f, 0.25f}, 0.99f);
test_typical({0.97, 0.01, 0.01, 0.01}, {0.97}, 0.5);
test_typical({0.4, 0.2, 0.2, 0.2}, {0.2, 0.2, 0.2}, 0.5);
test_typical({0.97f, 0.01f, 0.01f, 0.01f}, {0.97f}, 0.5f);
test_typical({0.4f, 0.2f, 0.2f, 0.2f}, {0.2f, 0.2f, 0.2f}, 0.5f);
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);
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);
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);
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);
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);
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);
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);
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);
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);
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);
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);
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);
printf("OK\n");
}

2
tests/test-tokenizer-0.cpp
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@ -53,7 +53,7 @@ int main(int argc, char **argv) {
for (const auto & test_kv : k_tests()) {
std::vector<llama_token> res(test_kv.first.size());
const int n = llama_tokenize(ctx, test_kv.first.c_str(), res.data(), res.size(), true);
const int n = llama_tokenize(ctx, test_kv.first.c_str(), res.data(), int(res.size()), true);
res.resize(n);
bool correct = res.size() == test_kv.second.size();