vbatts/llama.cpp
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init the test array with const values

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hongruichen 2024-06-15 12:55:06 +08:00
parent 5598fbd15d
commit 5e18cdc268
1 changed files with 5 additions and 31 deletions
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36
tests/ggml-qnn/ggml-qnn-ut.cpp
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@ -266,37 +266,12 @@ static uint32_t get_tensor_data_size(const ggml_tensor * tensor) {
//ref: https://github.com/ggerganov/llama.cpp/blob/master/tests/test-backend-ops.cpp#L20 //ref: https://github.com/ggerganov/llama.cpp/blob/master/tests/test-backend-ops.cpp#L20
static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float max = 1.0f) { 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); size_t size = ggml_nelements(tensor);
std::vector<float> data(size); std::vector<float> data(size);
for (size_t i = 0; i < size; i++) {
data[i] = i + 1;
}
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(generators[ith]);
}
};
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, i, start, end);
}
for (auto & t : threads) {
t.join();
}
if (tensor->type == GGML_TYPE_F32 || tensor->type == GGML_TYPE_I32) { if (tensor->type == GGML_TYPE_F32 || tensor->type == GGML_TYPE_I32) {
#ifdef GGML_USE_QNN #ifdef GGML_USE_QNN
memcpy((char*)tensor->data, data.data(), size * sizeof(float)); memcpy((char*)tensor->data, data.data(), size * sizeof(float));
@ -378,7 +353,6 @@ static int qnn_op_ut(int num_threads, int n_backend_type, int n_ggml_op_type) {
QNN_LOG_DEBUG("ggml op:%d(%s)\n", n_ggml_op_type, ggml_op_name((enum ggml_op) n_ggml_op_type)); QNN_LOG_DEBUG("ggml op:%d(%s)\n", n_ggml_op_type, ggml_op_name((enum ggml_op) n_ggml_op_type));
n_begin_time = ggml_time_us(); n_begin_time = ggml_time_us();
srand(time(NULL));
ctx_size += 1024 * 1024 * 32; ctx_size += 1024 * 1024 * 32;
QNN_LOG_DEBUG("Allocating Memory of size %zi bytes, %zi MB\n", ctx_size, QNN_LOG_DEBUG("Allocating Memory of size %zi bytes, %zi MB\n", ctx_size,
@ -460,11 +434,11 @@ static int qnn_op_ut(int num_threads, int n_backend_type, int n_ggml_op_type) {
initialize_tensors(ctx); initialize_tensors(ctx);
} else { } else {
if (qtype == GGML_TYPE_F32) { if (qtype == GGML_TYPE_F32) {
ggml_set_f32(src0, (rand() % 100 + 1)); ggml_set_f32(src0, 2.f);
} else { } else {
initialize_tensors(ctx); initialize_tensors(ctx);
} }
ggml_set_f32(src1, (rand() % 100 + 1)); ggml_set_f32(src1, 3.f);
} }
ggml_graph_compute_helper(backend, gf, work_buffer, num_threads, nullptr, nullptr); ggml_graph_compute_helper(backend, gf, work_buffer, num_threads, nullptr, nullptr);