vbatts/llama.cpp
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

ggml : add IQ2 to test-backend-ops + refactoring (#4990)

Browse source 
* ggml : add IQ2 to test-backend-ops + refactoring

ggml-ci

* cuda : update supports_op for IQ2

ggml-ci

* ci : enable LLAMA_CUBLAS=1 for CUDA nodes

ggml-ci

* cuda : fix out-of-bounds-access in `mul_mat_vec_q`

ggml-ci

* tests : avoid creating RNGs for each Q tensor

ggml-ci

* tests : avoid creating RNGs for each tensor

ggml-ci
This commit is contained in:
Georgi Gerganov 2024-01-17 18:54:56 +02:00 • committed by GitHub
parent ba69bbc84c
commit 38566680cd
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
9 changed files with 128 additions and 87 deletions
Download patch file Download diff file Expand all files Collapse all files

46
tests/test-backend-ops.cpp
View file

@ -16,39 +16,37 @@
#include <vector>
static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float max = 1.0f) {
// static RNG initialization (revisit if n_threads stops being constant)
static const size_t n_threads = std::thread::hardware_concurrency();
static std::vector<std::default_random_engine> generators = []() {
std::random_device rd;
std::vector<std::default_random_engine> vec;
vec.reserve(n_threads);
//for (size_t i = 0; i < n_threads; i++) { vec.emplace_back(1234 + i); } // fixed seed
for (size_t i = 0; i < n_threads; i++) { vec.emplace_back(rd()); }
return vec;
}();
size_t size = ggml_nelements(tensor);
std::vector<float> data(size);
#if 0
static std::default_random_engine generator(1234);
std::uniform_real_distribution<float> distribution(min, max);
for (size_t i = 0; i < size; i++) {
data[i] = distribution(generator);
}
#else
auto init_thread = [&](size_t start, size_t end) {
std::random_device rd;
std::default_random_engine generator(rd());
auto init_thread = [&](size_t ith, size_t start, size_t end) {
std::uniform_real_distribution<float> distribution(min, max);
for (size_t i = start; i < end; i++) {
data[i] = distribution(generator);
data[i] = distribution(generators[ith]);
}
};
size_t n_threads = std::thread::hardware_concurrency();
std::vector<std::thread> threads;
threads.reserve(n_threads);
for (size_t i = 0; i < n_threads; i++) {
size_t start = i*size/n_threads;
size_t end = (i+1)*size/n_threads;
threads.emplace_back(init_thread, start, end);
threads.emplace_back(init_thread, i, start, end);
}
for (auto & t : threads) {
t.join();
}
#endif
if (tensor->type == GGML_TYPE_F32 || tensor->type == GGML_TYPE_I32) {
ggml_backend_tensor_set(tensor, data.data(), 0, size * sizeof(float));
@ -56,7 +54,16 @@ static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float m
GGML_ASSERT(size % ggml_blck_size(tensor->type) == 0);
std::vector<uint8_t> dataq(ggml_row_size(tensor->type, size));
int64_t hist[16];
ggml_quantize_chunk(tensor->type, data.data(), dataq.data(), 0, size/tensor->ne[0], tensor->ne[0], hist, nullptr);
std::vector<float> imatrix(tensor->ne[0], 1.0f); // dummy importance matrix
const float * im = imatrix.data();
if (!ggml_quantize_requires_imatrix(tensor->type)) {
// when the imatrix is optional, we want to test both quantization with and without imatrix
// use one of the random numbers to decide
if (data[0] > 0.5f*(min + max)) {
im = nullptr;
}
}
ggml_quantize_chunk(tensor->type, data.data(), dataq.data(), 0, size/tensor->ne[0], tensor->ne[0], hist, im);
ggml_backend_tensor_set(tensor, dataq.data(), 0, dataq.size());
} else if (tensor->type == GGML_TYPE_I8 || tensor->type == GGML_TYPE_I16 || tensor->type == GGML_TYPE_I32) {
// This is going to create some weird integers though.
@ -1472,7 +1479,8 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
GGML_TYPE_Q8_0,
GGML_TYPE_Q2_K, GGML_TYPE_Q3_K,
GGML_TYPE_Q4_K, GGML_TYPE_Q5_K,
GGML_TYPE_Q6_K
GGML_TYPE_Q6_K,
GGML_TYPE_IQ2_XXS, GGML_TYPE_IQ2_XS,
};
// unary ops
@ -1752,6 +1760,8 @@ int main(int argc, char ** argv) {
return 1;
}
ggml_quantize_free();
printf("\033[1;32mOK\033[0m\n");
return 0;
}