Overhaul the examples structure

- main -> examples
- utils -> examples (renamed to "common")
- quantize -> examples
- separate tools for "perplexity" and "embedding"

Hope I didn't break something !
This commit is contained in:
Georgi Gerganov 2023-03-25 20:26:40 +02:00
parent ecbe466a36
commit a316a425d0
No known key found for this signature in database
GPG Key ID: 449E073F9DC10735
21 changed files with 361 additions and 161 deletions

1
.gitignore vendored
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@ -19,6 +19,7 @@ models/*
/main /main
/quantize /quantize
/result /result
/perplexity
arm_neon.h arm_neon.h
compile_commands.json compile_commands.json

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@ -211,17 +211,6 @@ endif()
# Build libraries # Build libraries
# #
add_library(utils OBJECT
utils.cpp
utils.h)
target_include_directories(utils PUBLIC .)
target_compile_features(utils PUBLIC cxx_std_11) # don't bump
target_link_libraries(utils PRIVATE ${LLAMA_EXTRA_LIBS})
if (BUILD_SHARED_LIBS)
set_target_properties(utils PROPERTIES POSITION_INDEPENDENT_CODE ON)
endif()
add_library(ggml OBJECT add_library(ggml OBJECT
ggml.c ggml.c
ggml.h) ggml.h)
@ -239,22 +228,12 @@ add_library(llama
target_include_directories(llama PUBLIC .) target_include_directories(llama PUBLIC .)
target_compile_features(llama PUBLIC cxx_std_11) # don't bump target_compile_features(llama PUBLIC cxx_std_11) # don't bump
target_link_libraries(llama PRIVATE utils ggml ${LLAMA_EXTRA_LIBS}) target_link_libraries(llama PRIVATE ggml ${LLAMA_EXTRA_LIBS})
if (BUILD_SHARED_LIBS) if (BUILD_SHARED_LIBS)
set_target_properties(llama PROPERTIES POSITION_INDEPENDENT_CODE ON) set_target_properties(llama PROPERTIES POSITION_INDEPENDENT_CODE ON)
target_compile_definitions(llama PRIVATE LLAMA_SHARED LLAMA_BUILD) target_compile_definitions(llama PRIVATE LLAMA_SHARED LLAMA_BUILD)
endif() endif()
#
# Executables
#
add_executable(main main.cpp)
target_link_libraries(main PRIVATE llama ggml utils)
add_executable(quantize quantize.cpp)
target_link_libraries(quantize PRIVATE llama ggml utils)
# #
# programs, examples and tests # programs, examples and tests
# #
@ -264,6 +243,6 @@ if (LLAMA_BUILD_TESTS AND NOT CMAKE_JS_VERSION)
add_subdirectory(tests) add_subdirectory(tests)
endif () endif ()
#if (LLAMA_BUILD_EXAMPLES) if (LLAMA_BUILD_EXAMPLES)
# add_subdirectory(examples) add_subdirectory(examples)
#endif() endif()

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@ -212,7 +212,7 @@ $(info I CC: $(CCV))
$(info I CXX: $(CXXV)) $(info I CXX: $(CXXV))
$(info ) $(info )
default: main quantize default: main quantize perplexity
# #
# Build library # Build library
@ -224,20 +224,23 @@ ggml.o: ggml.c ggml.h
llama.o: llama.cpp llama.h llama.o: llama.cpp llama.h
$(CXX) $(CXXFLAGS) -c llama.cpp -o llama.o $(CXX) $(CXXFLAGS) -c llama.cpp -o llama.o
utils.o: utils.cpp utils.h common.o: examples/common.cpp examples/common.h
$(CXX) $(CXXFLAGS) -c utils.cpp -o utils.o $(CXX) $(CXXFLAGS) -c examples/common.cpp -o common.o
clean: clean:
rm -f *.o main quantize rm -vf *.o main quantize perplexity
main: main.cpp ggml.o llama.o utils.o main: examples/main/main.cpp ggml.o llama.o common.o
$(CXX) $(CXXFLAGS) main.cpp ggml.o llama.o utils.o -o main $(LDFLAGS) $(CXX) $(CXXFLAGS) examples/main/main.cpp ggml.o llama.o common.o -o main $(LDFLAGS)
@echo @echo
@echo '==== Run ./main -h for help. ====' @echo '==== Run ./main -h for help. ===='
@echo @echo
quantize: quantize.cpp ggml.o llama.o utils.o quantize: examples/quantize/quantize.cpp ggml.o llama.o
$(CXX) $(CXXFLAGS) quantize.cpp ggml.o llama.o utils.o -o quantize $(LDFLAGS) $(CXX) $(CXXFLAGS) examples/quantize/quantize.cpp ggml.o llama.o -o quantize $(LDFLAGS)
perplexity: examples/perplexity/perplexity.cpp ggml.o llama.o common.o
$(CXX) $(CXXFLAGS) examples/perplexity/perplexity.cpp ggml.o llama.o common.o -o perplexity $(LDFLAGS)
# #
# Tests # Tests

36
examples/CMakeLists.txt Normal file
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@ -0,0 +1,36 @@
# dependencies
find_package(Threads REQUIRED)
# third-party
# ...
# common
set(TARGET common)
add_library(${TARGET} OBJECT
common.h
common.cpp
)
if (BUILD_SHARED_LIBS)
set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON)
endif()
target_include_directories(${TARGET} PUBLIC .)
target_compile_features(${TARGET} PUBLIC cxx_std_11)
target_link_libraries(${TARGET} PRIVATE llama)
# examples
include_directories(${CMAKE_CURRENT_SOURCE_DIR})
if (EMSCRIPTEN)
else()
add_subdirectory(main)
add_subdirectory(quantize)
add_subdirectory(perplexity)
add_subdirectory(embedding)
endif()

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@ -1,6 +1,6 @@
#include "ggml.h" #include "common.h"
#include "utils.h" #include "ggml.h"
#include <cassert> #include <cassert>
#include <cstring> #include <cstring>

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@ -0,0 +1,4 @@
set(TARGET embedding)
add_executable(${TARGET} embedding.cpp)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_11)

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@ -0,0 +1,3 @@
# embedding
TODO

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@ -0,0 +1,106 @@
#include "common.h"
#include "llama.h"
#include <cassert>
#include <cinttypes>
#include <cmath>
#include <cstdio>
#include <cstring>
#include <fstream>
#include <string>
#include <vector>
int main(int argc, char ** argv) {
gpt_params params;
params.model = "models/llama-7B/ggml-model.bin";
if (gpt_params_parse(argc, argv, params) == false) {
return 1;
}
params.embedding = true;
if (params.n_ctx > 2048) {
fprintf(stderr, "%s: warning: model does not support context sizes greater than 2048 tokens (%d specified);"
"expect poor results\n", __func__, params.n_ctx);
}
if (params.seed <= 0) {
params.seed = time(NULL);
}
fprintf(stderr, "%s: seed = %d\n", __func__, params.seed);
std::mt19937 rng(params.seed);
if (params.random_prompt) {
params.prompt = gpt_random_prompt(rng);
}
llama_context * ctx;
// load the model
{
auto lparams = llama_context_default_params();
lparams.n_ctx = params.n_ctx;
lparams.n_parts = params.n_parts;
lparams.seed = params.seed;
lparams.f16_kv = params.memory_f16;
lparams.logits_all = params.perplexity;
lparams.use_mlock = params.use_mlock;
lparams.embedding = params.embedding;
ctx = llama_init_from_file(params.model.c_str(), lparams);
if (ctx == NULL) {
fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str());
return 1;
}
}
// print system information
{
fprintf(stderr, "\n");
fprintf(stderr, "system_info: n_threads = %d / %d | %s\n",
params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info());
}
int n_past = 0;
// Add a space in front of the first character to match OG llama tokenizer behavior
params.prompt.insert(0, 1, ' ');
// tokenize the prompt
auto embd_inp = ::llama_tokenize(ctx, params.prompt, true);
// determine newline token
auto llama_token_newline = ::llama_tokenize(ctx, "\n", false);
if (params.verbose_prompt) {
fprintf(stderr, "\n");
fprintf(stderr, "%s: prompt: '%s'\n", __func__, params.prompt.c_str());
fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
for (int i = 0; i < (int) embd_inp.size(); i++) {
fprintf(stderr, "%6d -> '%s'\n", embd_inp[i], llama_token_to_str(ctx, embd_inp[i]));
}
fprintf(stderr, "\n");
}
if (params.embedding){
if (embd_inp.size() > 0) {
if (llama_eval(ctx, embd_inp.data(), embd_inp.size(), n_past, params.n_threads)) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return 1;
}
}
const auto embeddings = llama_get_embeddings(ctx);
// TODO: print / use the embeddings
}
llama_print_timings(ctx);
llama_free(ctx);
return 0;
}

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@ -0,0 +1,4 @@
set(TARGET main)
add_executable(${TARGET} main.cpp)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_11)

3
examples/main/README.md Normal file
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@ -0,0 +1,3 @@
# main
TODO

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@ -1,5 +1,4 @@
#include "utils.h" #include "common.h"
#include "ggml.h"
#include "llama.h" #include "llama.h"
#include <cassert> #include <cassert>
@ -65,79 +64,6 @@ void set_console_state(console_state new_st)
} }
} }
std::vector<double> softmax(const std::vector<float>& logits) {
std::vector<double> probs(logits.size());
float max_logit = logits[0];
for (float v : logits) max_logit = std::max(max_logit, v);
double sum_exp = 0.0;
for (size_t i = 0; i < logits.size(); i++) {
// Subtract the maximum logit value from the current logit value for numerical stability
float logit = logits[i] - max_logit;
double exp_logit = std::exp(logit);
sum_exp += exp_logit;
probs[i] = exp_logit;
}
for (size_t i = 0; i < probs.size(); i++) probs[i] /= sum_exp;
return probs;
}
void perplexity(llama_context * ctx, const gpt_params & params) {
// Download: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research
// Run `./main --perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw`
// Output: `perplexity: 13.5106 [114/114]`
auto tokens = ::llama_tokenize(ctx, params.prompt, true);
int count = 0;
double nll = 0.0;
int seq_count = tokens.size() / params.n_ctx;
fprintf(stderr, "%s : calculating perplexity over %d chunks\n", __func__, seq_count);
for (int i = 0; i < seq_count; ++i) {
int start = i * params.n_ctx;
int end = start + params.n_ctx - 1;
std::vector<llama_token> embd(tokens.begin() + start, tokens.begin() + end);
auto start_t = std::chrono::high_resolution_clock::now();
if (llama_eval(ctx, embd.data(), embd.size(), 0, params.n_threads)) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return;
}
auto end_t = std::chrono::high_resolution_clock::now();
if (i == 0) {
double seconds = std::chrono::duration<double>(end_t - start_t).count();
printf("%.2f seconds per pass - ETA %.2f hours\n", seconds, (seconds * seq_count) / (60.0*60.0));
}
// We get the logits for all the tokens in the context window (params.n_ctx)
// from llama_eval above. Now, based on https://huggingface.co/docs/transformers/perplexity,
// calculate the perplexity over the last half the window (so the model always has
// some context to predict the token).
//
// We rely on the fact that attention in the forward pass only looks at previous
// tokens here, so the logits returned for each token are an accurate representation
// of what the model would have predicted at that point.
//
// Example, we have a context window of 512, we will compute perplexity for each of the
// last 256 tokens. Then, we split the input up into context window size chunks to
// process the entire prompt.
auto logits = llama_get_logits(ctx);
for (int j = params.n_ctx / 2; j < params.n_ctx - 1; ++j) {
// Calculate probability of next token, given the previous ones.
int n_vocab = llama_n_vocab(ctx);
std::vector<float> tok_logits(
logits + j * n_vocab,
logits + (j + 1) * n_vocab);
double prob = softmax(tok_logits)[tokens[start + j + 1]];
nll += -std::log(prob);
++count;
}
// perplexity is e^(average negative log-likelihood)
printf("[%d]%.4lf,", i + 1, std::exp(nll / count));
fflush(stdout);
}
printf("\n");
}
static bool is_interacting = false; static bool is_interacting = false;
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32) #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
@ -155,9 +81,6 @@ void sigint_handler(int signo) {
#endif #endif
int main(int argc, char ** argv) { int main(int argc, char ** argv) {
// has to be called once at the start of the program to init ggml stuff
ggml_time_init();
gpt_params params; gpt_params params;
params.model = "models/llama-7B/ggml-model.bin"; params.model = "models/llama-7B/ggml-model.bin";
@ -165,6 +88,14 @@ int main(int argc, char ** argv) {
return 1; return 1;
} }
if (params.perplexity) {
printf("\n************\n");
printf("%s: please use the 'perplexity' tool for perplexity calculations\n", __func__);
printf("************\n\n");
return 0;
}
if (params.n_ctx > 2048) { if (params.n_ctx > 2048) {
fprintf(stderr, "%s: warning: model does not support context sizes greater than 2048 tokens (%d specified);" fprintf(stderr, "%s: warning: model does not support context sizes greater than 2048 tokens (%d specified);"
"expect poor results\n", __func__, params.n_ctx); "expect poor results\n", __func__, params.n_ctx);
@ -198,9 +129,7 @@ int main(int argc, char ** argv) {
lparams.n_parts = params.n_parts; lparams.n_parts = params.n_parts;
lparams.seed = params.seed; lparams.seed = params.seed;
lparams.f16_kv = params.memory_f16; lparams.f16_kv = params.memory_f16;
lparams.logits_all = params.perplexity;
lparams.use_mlock = params.use_mlock; lparams.use_mlock = params.use_mlock;
lparams.embedding = params.embedding;
ctx = llama_init_from_file(params.model.c_str(), lparams); ctx = llama_init_from_file(params.model.c_str(), lparams);
@ -236,11 +165,6 @@ int main(int argc, char ** argv) {
return 0; return 0;
} }
if (params.perplexity) {
perplexity(ctx, params);
exit(0);
}
int n_past = 0; int n_past = 0;
// Add a space in front of the first character to match OG llama tokenizer behavior // Add a space in front of the first character to match OG llama tokenizer behavior
@ -346,27 +270,6 @@ int main(int argc, char ** argv) {
// the first thing we will do is to output the prompt, so set color accordingly // the first thing we will do is to output the prompt, so set color accordingly
set_console_state(CONSOLE_STATE_PROMPT); set_console_state(CONSOLE_STATE_PROMPT);
if (params.embedding){
embd = embd_inp;
if (embd.size() > 0) {
if (llama_eval(ctx, embd.data(), embd.size(), n_past, params.n_threads)) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return 1;
}
}
const auto embeddings = llama_get_embeddings(ctx);
// TODO: print / use the embeddings
if (params.use_color) {
printf(ANSI_COLOR_RESET);
}
return 0;
}
while (remaining_tokens > 0 || params.interactive) { while (remaining_tokens > 0 || params.interactive) {
// predict // predict
if (embd.size() > 0) { if (embd.size() > 0) {
@ -392,10 +295,6 @@ int main(int argc, char ** argv) {
auto logits = llama_get_logits(ctx); auto logits = llama_get_logits(ctx);
if (params.ignore_eos) { if (params.ignore_eos) {
// set the logit of the eos token to zero to avoid sampling it
//logits[logits.size() - n_vocab + EOS_TOKEN_ID] = 0;
// TODO: this does not work of params.logits_all == true
assert(params.perplexity == false);
logits[llama_token_eos()] = 0; logits[llama_token_eos()] = 0;
} }

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@ -0,0 +1,4 @@
set(TARGET perplexity)
add_executable(${TARGET} perplexity.cpp)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_11)

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@ -0,0 +1,3 @@
# perplexity
TODO

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@ -0,0 +1,146 @@
#include "common.h"
#include "llama.h"
#include <cassert>
#include <cinttypes>
#include <cmath>
#include <cstdio>
#include <cstring>
#include <string>
#include <vector>
std::vector<double> softmax(const std::vector<float>& logits) {
std::vector<double> probs(logits.size());
float max_logit = logits[0];
for (float v : logits) max_logit = std::max(max_logit, v);
double sum_exp = 0.0;
for (size_t i = 0; i < logits.size(); i++) {
// Subtract the maximum logit value from the current logit value for numerical stability
float logit = logits[i] - max_logit;
double exp_logit = std::exp(logit);
sum_exp += exp_logit;
probs[i] = exp_logit;
}
for (size_t i = 0; i < probs.size(); i++) probs[i] /= sum_exp;
return probs;
}
void perplexity(llama_context * ctx, const gpt_params & params) {
// Download: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research
// Run `./main --perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw`
// Output: `perplexity: 13.5106 [114/114]`
auto tokens = ::llama_tokenize(ctx, params.prompt, true);
int count = 0;
double nll = 0.0;
int seq_count = tokens.size() / params.n_ctx;
fprintf(stderr, "%s : calculating perplexity over %d chunks\n", __func__, seq_count);
for (int i = 0; i < seq_count; ++i) {
int start = i * params.n_ctx;
int end = start + params.n_ctx - 1;
std::vector<llama_token> embd(tokens.begin() + start, tokens.begin() + end);
auto start_t = std::chrono::high_resolution_clock::now();
if (llama_eval(ctx, embd.data(), embd.size(), 0, params.n_threads)) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return;
}
auto end_t = std::chrono::high_resolution_clock::now();
if (i == 0) {
double seconds = std::chrono::duration<double>(end_t - start_t).count();
printf("%.2f seconds per pass - ETA %.2f hours\n", seconds, (seconds * seq_count) / (60.0*60.0));
}
// We get the logits for all the tokens in the context window (params.n_ctx)
// from llama_eval above. Now, based on https://huggingface.co/docs/transformers/perplexity,
// calculate the perplexity over the last half the window (so the model always has
// some context to predict the token).
//
// We rely on the fact that attention in the forward pass only looks at previous
// tokens here, so the logits returned for each token are an accurate representation
// of what the model would have predicted at that point.
//
// Example, we have a context window of 512, we will compute perplexity for each of the
// last 256 tokens. Then, we split the input up into context window size chunks to
// process the entire prompt.
auto logits = llama_get_logits(ctx);
for (int j = params.n_ctx / 2; j < params.n_ctx - 1; ++j) {
// Calculate probability of next token, given the previous ones.
int n_vocab = llama_n_vocab(ctx);
std::vector<float> tok_logits(
logits + j * n_vocab,
logits + (j + 1) * n_vocab);
double prob = softmax(tok_logits)[tokens[start + j + 1]];
nll += -std::log(prob);
++count;
}
// perplexity is e^(average negative log-likelihood)
printf("[%d]%.4lf,", i + 1, std::exp(nll / count));
fflush(stdout);
}
printf("\n");
}
int main(int argc, char ** argv) {
gpt_params params;
params.model = "models/llama-7B/ggml-model.bin";
if (gpt_params_parse(argc, argv, params) == false) {
return 1;
}
params.perplexity = true;
if (params.n_ctx > 2048) {
fprintf(stderr, "%s: warning: model does not support context sizes greater than 2048 tokens (%d specified);"
"expect poor results\n", __func__, params.n_ctx);
}
if (params.seed <= 0) {
params.seed = time(NULL);
}
fprintf(stderr, "%s: seed = %d\n", __func__, params.seed);
std::mt19937 rng(params.seed);
if (params.random_prompt) {
params.prompt = gpt_random_prompt(rng);
}
llama_context * ctx;
// load the model
{
auto lparams = llama_context_default_params();
lparams.n_ctx = params.n_ctx;
lparams.n_parts = params.n_parts;
lparams.seed = params.seed;
lparams.f16_kv = params.memory_f16;
lparams.logits_all = params.perplexity;
lparams.use_mlock = params.use_mlock;
lparams.embedding = params.embedding;
ctx = llama_init_from_file(params.model.c_str(), lparams);
if (ctx == NULL) {
fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str());
return 1;
}
}
// print system information
{
fprintf(stderr, "\n");
fprintf(stderr, "system_info: n_threads = %d / %d | %s\n",
params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info());
}
perplexity(ctx, params);
llama_print_timings(ctx);
llama_free(ctx);
return 0;
}

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@ -0,0 +1,4 @@
set(TARGET quantize)
add_executable(${TARGET} quantize.cpp)
target_link_libraries(${TARGET} PRIVATE llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_11)

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@ -0,0 +1,3 @@
# quantize
TODO

26
ggml.c
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@ -5741,8 +5741,8 @@ static bool ggml_compute_forward_mul_mat_use_blas(
const struct ggml_tensor * src0, const struct ggml_tensor * src0,
const struct ggml_tensor * src1, const struct ggml_tensor * src1,
struct ggml_tensor * dst) { struct ggml_tensor * dst) {
const int ne00 = src0->ne[0]; //const int ne00 = src0->ne[0];
const int ne01 = src0->ne[1]; //const int ne01 = src0->ne[1];
const int ne10 = src1->ne[0]; const int ne10 = src1->ne[0];
@ -5776,16 +5776,16 @@ static void ggml_compute_forward_mul_mat_f32(
const int ne10 = src1->ne[0]; const int ne10 = src1->ne[0];
const int ne11 = src1->ne[1]; const int ne11 = src1->ne[1];
const int ne12 = src1->ne[2]; //const int ne12 = src1->ne[2];
const int ne13 = src1->ne[3]; //const int ne13 = src1->ne[3];
const int ne0 = dst->ne[0]; //const int ne0 = dst->ne[0];
const int ne1 = dst->ne[1]; //const int ne1 = dst->ne[1];
const int ne2 = dst->ne[2]; //const int ne2 = dst->ne[2];
const int ne3 = dst->ne[3]; //const int ne3 = dst->ne[3];
const int ne = ne0*ne1*ne2*ne3; //const int ne = ne0*ne1*ne2*ne3;
const int nb00 = src0->nb[0]; //const int nb00 = src0->nb[0];
const int nb01 = src0->nb[1]; const int nb01 = src0->nb[1];
const int nb02 = src0->nb[2]; const int nb02 = src0->nb[2];
const int nb03 = src0->nb[3]; const int nb03 = src0->nb[3];
@ -5947,7 +5947,7 @@ static void ggml_compute_forward_mul_mat_f16_f32(
const int ne1 = dst->ne[1]; const int ne1 = dst->ne[1];
const int ne2 = dst->ne[2]; const int ne2 = dst->ne[2];
const int ne3 = dst->ne[3]; const int ne3 = dst->ne[3];
const int ne = ne0*ne1*ne2*ne3; //const int ne = ne0*ne1*ne2*ne3;
const int nb00 = src0->nb[0]; const int nb00 = src0->nb[0];
const int nb01 = src0->nb[1]; const int nb01 = src0->nb[1];
@ -6137,7 +6137,7 @@ static void ggml_compute_forward_mul_mat_q4_0_f32(
const int ne1 = dst->ne[1]; const int ne1 = dst->ne[1];
const int ne2 = dst->ne[2]; const int ne2 = dst->ne[2];
const int ne3 = dst->ne[3]; const int ne3 = dst->ne[3];
const int ne = ne0*ne1*ne2*ne3; //const int ne = ne0*ne1*ne2*ne3;
const int nb00 = src0->nb[0]; const int nb00 = src0->nb[0];
const int nb01 = src0->nb[1]; const int nb01 = src0->nb[1];
@ -6322,7 +6322,7 @@ static void ggml_compute_forward_mul_mat_q4_1_f32(
const int ne1 = dst->ne[1]; const int ne1 = dst->ne[1];
const int ne2 = dst->ne[2]; const int ne2 = dst->ne[2];
const int ne3 = dst->ne[3]; const int ne3 = dst->ne[3];
const int ne = ne0*ne1*ne2*ne3; //const int ne = ne0*ne1*ne2*ne3;
const int nb00 = src0->nb[0]; const int nb00 = src0->nb[0];
const int nb01 = src0->nb[1]; const int nb01 = src0->nb[1];

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@ -1,7 +1,7 @@
function(llama_add_test source) function(llama_add_test source)
get_filename_component(TEST_TARGET ${source} NAME_WE) get_filename_component(TEST_TARGET ${source} NAME_WE)
add_executable(${TEST_TARGET} ${source}) add_executable(${TEST_TARGET} ${source})
target_link_libraries(${TEST_TARGET} PRIVATE llama ggml utils) target_link_libraries(${TEST_TARGET} PRIVATE llama)
add_test(NAME ${TEST_TARGET} COMMAND $<TARGET_FILE:${TEST_TARGET}> ${ARGN}) add_test(NAME ${TEST_TARGET} COMMAND $<TARGET_FILE:${TEST_TARGET}> ${ARGN})
endfunction() endfunction()

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@ -1,9 +1,9 @@
#include "utils.h"
#include "llama.h" #include "llama.h"
#include <cstdio> #include <cstdio>
#include <string> #include <string>
#include <map> #include <map>
#include <vector>
static const std::map<std::string, std::vector<llama_token>> k_tests = { static const std::map<std::string, std::vector<llama_token>> k_tests = {
{ "Hello World", { 1, 10994, 2787, }, }, { "Hello World", { 1, 10994, 2787, }, },
@ -48,7 +48,9 @@ int main(int argc, char **argv) {
} }
for (const auto & test_kv : k_tests) { for (const auto & test_kv : k_tests) {
const auto res = ::llama_tokenize(ctx, test_kv.first, true); 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);
res.resize(n);
bool correct = res.size() == test_kv.second.size(); bool correct = res.size() == test_kv.second.size();