mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-10-31 08:51:55 +00:00
58ba655af0
* ggml : disable fast-math for Metal (cmake build only) ggml-ci * metal : fix Metal API debug warnings * cmake : add -fno-inline for Metal build (#4545) * metal : fix API debug warnings * metal : fix compile warnings * metal : use uint64_t for strides * cmake : rename option to LLAMA_METAL_SHADER_DEBUG * metal : fix mat-vec Q8_0 kernel for BS > 1 * metal : normalize mat-vec kernel signatures * cmake : respect LLAMA_QKK_64 option * metal : fix mat-vec Q4_K kernel for QK_K == 64 ggml-ci
1695 lines
56 KiB
C++
1695 lines
56 KiB
C++
#include <ggml.h>
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#include <ggml-alloc.h>
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#include <ggml-backend.h>
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#include <ggml-backend-impl.h>
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#include <algorithm>
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#include <array>
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#include <cfloat>
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#include <cstring>
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#include <functional>
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#include <memory>
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#include <random>
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#include <stdio.h>
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#include <stdlib.h>
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#include <string>
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#include <thread>
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#include <vector>
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static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float max = 1.0f) {
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size_t size = ggml_nelements(tensor);
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std::vector<float> data(size);
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#if 0
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static std::default_random_engine generator(1234);
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std::uniform_real_distribution<float> distribution(min, max);
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for (size_t i = 0; i < size; i++) {
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data[i] = distribution(generator);
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}
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#else
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auto init_thread = [&](size_t start, size_t end) {
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std::random_device rd;
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std::default_random_engine generator(rd());
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std::uniform_real_distribution<float> distribution(min, max);
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for (size_t i = start; i < end; i++) {
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data[i] = distribution(generator);
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}
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};
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size_t n_threads = std::thread::hardware_concurrency();
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std::vector<std::thread> threads;
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threads.reserve(n_threads);
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for (size_t i = 0; i < n_threads; i++) {
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size_t start = i*size/n_threads;
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size_t end = (i+1)*size/n_threads;
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threads.emplace_back(init_thread, start, end);
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}
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for (auto & t : threads) {
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t.join();
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}
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#endif
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if (tensor->type == GGML_TYPE_F32 || tensor->type == GGML_TYPE_I32) {
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ggml_backend_tensor_set(tensor, data.data(), 0, size * sizeof(float));
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} else if (ggml_is_quantized(tensor->type) || tensor->type == GGML_TYPE_F16) {
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GGML_ASSERT(size % ggml_blck_size(tensor->type) == 0);
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std::vector<uint8_t> dataq(ggml_row_size(tensor->type, size));
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int64_t hist[16];
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ggml_quantize_chunk(tensor->type, data.data(), dataq.data(), 0, size, hist);
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ggml_backend_tensor_set(tensor, dataq.data(), 0, dataq.size());
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} else {
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GGML_ASSERT(false);
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}
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}
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static std::vector<float> tensor_to_float(const ggml_tensor * t) {
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std::vector<float> tv;
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tv.reserve(ggml_nelements(t));
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std::vector<uint8_t> buf(ggml_nbytes(t));
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ggml_backend_tensor_get(t, buf.data(), 0, ggml_nbytes(t));
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ggml_type_traits_t tt = ggml_internal_get_type_traits(t->type);
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size_t bs = ggml_blck_size(t->type);
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std::vector<float> vq(ggml_blck_size(t->type));
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bool quantized = ggml_is_quantized(t->type);
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// access elements by index to avoid gaps in views
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for (int64_t i3 = 0; i3 < t->ne[3]; i3++) {
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for (int64_t i2 = 0; i2 < t->ne[2]; i2++) {
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for (int64_t i1 = 0; i1 < t->ne[1]; i1++) {
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for (int64_t i0 = 0; i0 < t->ne[0]; i0 += bs) {
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size_t i = i3*t->nb[3] + i2*t->nb[2] + i1*t->nb[1] + i0/bs*t->nb[0];
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if (t->type == GGML_TYPE_F16) {
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tv.push_back(ggml_fp16_to_fp32(*(ggml_fp16_t*)&buf[i]));
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} else if (t->type == GGML_TYPE_F32) {
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tv.push_back(*(float *) &buf[i]);
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} else if (t->type == GGML_TYPE_I32) {
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tv.push_back((float)*(int32_t *) &buf[i]);
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} else if (quantized) {
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tt.to_float(&buf[i], vq.data(), bs);
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tv.insert(tv.end(), vq.begin(), vq.end());
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} else {
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GGML_ASSERT(false);
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}
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}
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}
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}
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}
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return tv;
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}
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/*
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static double cosine_similarity(const float * v1, const float * v2, size_t n) {
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double dot = 0.0;
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double mag1 = 0.0;
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double mag2 = 0.0;
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for (size_t i = 0; i < n; i++) {
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if (std::isnan(v1[i]) || std::isnan(v2[i])) {
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return -1.0f;
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}
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if (std::isinf(v1[i]) && std::isinf(v2[i])) {
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continue;
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}
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dot += v1[i]*v2[i];
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mag1 += v1[i]*v1[i];
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mag2 += v2[i]*v2[i];
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}
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return dot/sqrt(mag1*mag2);
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}
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static float distance(const float * v1, const float * v2, size_t n) {
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double d = 0.0;
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for (size_t i = 0; i < n; i++) {
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if (std::isnan(v1[i]) || std::isnan(v2[i])) {
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return INFINITY;
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}
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if (std::isinf(v1[i]) && std::isinf(v2[i])) {
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continue;
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}
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d += (v1[i] - v2[i])*(v1[i] - v2[i]);
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}
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return sqrt(d);
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}
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static float vec_len(const float * v, size_t n) {
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double d = 0.0;
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for (size_t i = 0; i < n; i++) {
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if (std::isnan(v[i])) {
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return INFINITY;
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}
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if (std::isinf(v[i])) {
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continue;
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}
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d += v[i]*v[i];
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}
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return sqrt(d);
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}
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*/
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// normalized mean squared error = mse(a, b) / mse(a, 0)
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static double nmse(const float * a, const float * b, size_t n) {
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double mse_a_b = 0.0;
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double mse_a_0 = 0.0;
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for (size_t i = 0; i < n; i++) {
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float a_i = a[i];
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float b_i = b[i];
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mse_a_b += (a_i - b_i) * (a_i - b_i);
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mse_a_0 += a_i * a_i;
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}
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return mse_a_b / mse_a_0;
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}
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// utils for printing the variables of the test cases
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#define VAR_TO_STR(x) (#x "=" + var_to_str(x))
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template<typename T>
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static std::string var_to_str(const T & x) {
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return std::to_string(x);
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}
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template<typename T, size_t N>
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static std::string var_to_str(const T (&x)[N]) {
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std::string s = "[";
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for (size_t i = 0; i < N; i++) {
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if (i > 0) {
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s += ",";
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}
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s += var_to_str(x[i]);
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}
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s += "]";
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return s;
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}
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template<typename T, size_t N>
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static std::string var_to_str(const std::array<T, N> & x) {
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std::string s = "[";
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for (size_t i = 0; i < N; i++) {
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if (i > 0) {
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s += ",";
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}
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s += var_to_str(x[i]);
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}
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s += "]";
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return s;
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}
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//static std::string var_to_str(ggml_unary_op unary_op) {
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// return ggml_unary_op_name(unary_op);
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//}
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static std::string var_to_str(ggml_type type) {
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return ggml_type_name(type);
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}
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#define VARS_TO_STR1(a) VAR_TO_STR(a)
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#define VARS_TO_STR2(a, b) VAR_TO_STR(a) + "," + VAR_TO_STR(b)
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#define VARS_TO_STR3(a, b, c) VAR_TO_STR(a) + "," + VARS_TO_STR2(b, c)
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#define VARS_TO_STR4(a, b, c, d) VAR_TO_STR(a) + "," + VARS_TO_STR3(b, c, d)
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#define VARS_TO_STR5(a, b, c, d, e) VAR_TO_STR(a) + "," + VARS_TO_STR4(b, c, d, e)
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#define VARS_TO_STR6(a, b, c, d, e, f) VAR_TO_STR(a) + "," + VARS_TO_STR5(b, c, d, e, f)
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#define VARS_TO_STR7(a, b, c, d, e, f, g) VAR_TO_STR(a) + "," + VARS_TO_STR6(b, c, d, e, f, g)
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#define VARS_TO_STR8(a, b, c, d, e, f, g, h) VAR_TO_STR(a) + "," + VARS_TO_STR7(b, c, d, e, f, g, h)
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#define VARS_TO_STR9(a, b, c, d, e, f, g, h, i) VAR_TO_STR(a) + "," + VARS_TO_STR8(b, c, d, e, f, g, h, i)
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#define VARS_TO_STR10(a, b, c, d, e, f, g, h, i, j) VAR_TO_STR(a) + "," + VARS_TO_STR9(b, c, d, e, f, g, h, i, j)
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#define VARS_TO_STR11(a, b, c, d, e, f, g, h, i, j, k) VAR_TO_STR(a) + "," + VARS_TO_STR10(b, c, d, e, f, g, h, i, j, k)
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// accept FLT_MAX as infinity
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static bool isinf_or_max(float f) {
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return std::isinf(f) || f == FLT_MAX || f == -FLT_MAX;
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}
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static bool ggml_is_view_op(enum ggml_op op) {
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return op == GGML_OP_VIEW || op == GGML_OP_RESHAPE || op == GGML_OP_PERMUTE || op == GGML_OP_TRANSPOSE;
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}
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enum test_mode {
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MODE_TEST,
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MODE_PERF,
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};
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struct test_case {
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virtual ~test_case() {}
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virtual std::string op_desc(ggml_tensor * t) {
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return ggml_op_desc(t);
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}
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virtual std::string vars() {
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return "";
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}
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virtual ggml_tensor * build_graph(ggml_context * ctx) = 0;
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virtual double max_nmse_err() {
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return 1e-7;
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}
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virtual void initialize_tensors(ggml_context * ctx) {
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for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != nullptr; t = ggml_get_next_tensor(ctx, t)) {
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init_tensor_uniform(t);
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}
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}
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virtual size_t op_size(ggml_tensor * t) {
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size_t size = ggml_nbytes(t);
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// add source tensors
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for (int i = 0; i < GGML_MAX_SRC; i++) {
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if (t->src[i] != NULL) {
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size += ggml_nbytes(t->src[i]);
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}
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}
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return size;
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}
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ggml_cgraph * gf = nullptr;
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static const int sentinel_size = 1024;
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test_mode mode;
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std::vector<ggml_tensor *> sentinels;
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void add_sentinel(ggml_context * ctx) {
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if (mode == MODE_PERF) {
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return;
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}
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ggml_tensor * sentinel = ::ggml_new_tensor_1d(ctx, GGML_TYPE_F32, sentinel_size);
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ggml_format_name(sentinel, "sent_%zu", sentinels.size());
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sentinels.push_back(sentinel);
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}
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// hijack ggml_new_tensor to add sentinels after each tensor to check for overflows in the backend
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ggml_tensor * ggml_new_tensor(ggml_context * ctx, ggml_type type, int n_dims, const int64_t * ne) {
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ggml_tensor * t = ::ggml_new_tensor(ctx, type, n_dims, ne);
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add_sentinel(ctx);
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return t;
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}
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ggml_tensor * ggml_new_tensor_1d(ggml_context * ctx, ggml_type type, int64_t ne0) {
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ggml_tensor * t = ::ggml_new_tensor_1d(ctx, type, ne0);
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add_sentinel(ctx);
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return t;
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}
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ggml_tensor * ggml_new_tensor_2d(ggml_context * ctx, ggml_type type, int64_t ne0, int64_t ne1) {
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ggml_tensor * t = ::ggml_new_tensor_2d(ctx, type, ne0, ne1);
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add_sentinel(ctx);
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return t;
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}
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ggml_tensor * ggml_new_tensor_3d(ggml_context * ctx, ggml_type type, int64_t ne0, int64_t ne1, int64_t ne2) {
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ggml_tensor * t = ::ggml_new_tensor_3d(ctx, type, ne0, ne1, ne2);
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add_sentinel(ctx);
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return t;
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}
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ggml_tensor * ggml_new_tensor_4d(ggml_context * ctx, ggml_type type, int64_t ne0, int64_t ne1, int64_t ne2, int64_t ne3) {
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ggml_tensor * t = ::ggml_new_tensor_4d(ctx, type, ne0, ne1, ne2, ne3);
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add_sentinel(ctx);
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return t;
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}
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bool eval(ggml_backend_t backend1, ggml_backend_t backend2, const char * op_name) {
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mode = MODE_TEST;
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ggml_init_params params = {
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/* .mem_size = */ ggml_tensor_overhead()*128 + ggml_graph_overhead(),
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/* .mem_base = */ NULL,
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/* .no_alloc = */ true,
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};
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ggml_context * ctx = ggml_init(params);
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gf = ggml_new_graph(ctx);
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// pre-graph sentinel
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add_sentinel(ctx);
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ggml_tensor * out = build_graph(ctx);
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if (op_name != nullptr && op_desc(out) != op_name) {
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//printf(" %s: skipping\n", op_desc(out).c_str());
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ggml_free(ctx);
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return true;
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}
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printf(" %s(%s): ", op_desc(out).c_str(), vars().c_str());
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fflush(stdout);
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// check if backends support op
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bool supported = true;
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for (ggml_backend_t backend : {backend1, backend2}) {
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if (!ggml_backend_supports_op(backend, out)) {
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printf("not supported [%s] ", ggml_backend_name(backend));
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supported = false;
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}
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}
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if (!supported) {
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printf("\n");
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ggml_free(ctx);
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return true;
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}
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// post-graph sentinel
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add_sentinel(ctx);
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// allocate
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ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors(ctx, backend1);
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// build graph
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ggml_build_forward_expand(gf, out);
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// add sentinels as graph nodes so that they are checked in the callback
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for (ggml_tensor * sentinel : sentinels) {
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gf->nodes[gf->n_nodes++] = sentinel;
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}
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// randomize tensors
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initialize_tensors(ctx);
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// compare
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struct callback_userdata {
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bool ok;
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double max_err;
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};
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callback_userdata ud {
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true,
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max_nmse_err(),
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};
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auto callback = [](int index, ggml_tensor * t1, ggml_tensor * t2, void * user_data) -> bool {
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callback_userdata * ud = (callback_userdata *) user_data;
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if (t1->op == GGML_OP_NONE) {
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// sentinels must be unchanged
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std::vector<uint8_t> t1_data(ggml_nbytes(t1));
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std::vector<uint8_t> t2_data(ggml_nbytes(t2));
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ggml_backend_tensor_get(t1, t1_data.data(), 0, ggml_nbytes(t1));
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ggml_backend_tensor_get(t2, t2_data.data(), 0, ggml_nbytes(t2));
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if (memcmp(t1_data.data(), t2_data.data(), ggml_nbytes(t1)) != 0) {
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printf("sentinel mismatch: %s ", t1->name);
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ud->ok = false;
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return true;
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}
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}
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std::vector<float> f1 = tensor_to_float(t1);
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std::vector<float> f2 = tensor_to_float(t2);
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for (size_t i = 0; i < f1.size(); i++) {
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// check for nans
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if (std::isnan(f1[i]) || std::isnan(f2[i])) {
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printf("[%s] NaN at index %zu (%f %f) ", ggml_op_desc(t1), i, f1[i], f2[i]);
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ud->ok = false;
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return true;
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}
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// check for infs: both must be inf of the same sign, or both must be finite
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if (isinf_or_max(f1[i]) || isinf_or_max(f2[i])) {
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if (isinf_or_max(f1[i]) && isinf_or_max(f2[i])) {
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if (std::signbit(f1[i]) != std::signbit(f2[i])) {
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printf("[%s] inf sign mismatch: %f %f ", ggml_op_desc(t1), f1[i], f2[i]);
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ud->ok = false;
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return true;
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}
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} else {
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printf("[%s] inf mismatch: %f %f ", ggml_op_desc(t1), f1[i], f2[i]);
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ud->ok = false;
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return true;
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}
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}
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}
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double err = nmse(f1.data(), f2.data(), f1.size());
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if (err > ud->max_err) {
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printf("[%s] NMSE = %f ", ggml_op_desc(t1), err);
|
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//for (int i = 0; i < (int) f1.size(); i++) {
|
|
// printf("%5d %9.6f %9.6f, diff = %9.6f\n", i, f1[i], f2[i], f1[i] - f2[i]);
|
|
//}
|
|
//printf("\n");
|
|
//exit(1);
|
|
ud->ok = false;
|
|
}
|
|
return true;
|
|
|
|
GGML_UNUSED(index);
|
|
};
|
|
|
|
ggml_backend_compare_graph_backend(backend1, backend2, gf, callback, &ud);
|
|
|
|
if (ud.ok) {
|
|
printf("\033[1;32mOK\033[0m\n");
|
|
} else {
|
|
printf("\033[1;31mFAIL\033[0m\n");
|
|
}
|
|
|
|
ggml_backend_buffer_free(buf);
|
|
|
|
ggml_free(ctx);
|
|
|
|
return ud.ok;
|
|
}
|
|
|
|
bool eval_perf(ggml_backend_t backend, const char * op_name) {
|
|
mode = MODE_PERF;
|
|
|
|
static const size_t graph_nodes = 8192;
|
|
|
|
ggml_init_params params = {
|
|
/* .mem_size = */ ggml_tensor_overhead()*128 + ggml_graph_overhead_custom(graph_nodes, false),
|
|
/* .mem_base = */ NULL,
|
|
/* .no_alloc = */ true,
|
|
};
|
|
ggml_context * ctx = ggml_init(params);
|
|
|
|
ggml_tensor * out = build_graph(ctx);
|
|
|
|
if (op_name != nullptr && op_desc(out) != op_name) {
|
|
//printf(" %s: skipping\n", op_desc(out).c_str());
|
|
ggml_free(ctx);
|
|
return true;
|
|
}
|
|
|
|
int len = printf(" %s(%s): ", op_desc(out).c_str(), vars().c_str());
|
|
fflush(stdout);
|
|
|
|
// check if backends support op
|
|
if (!ggml_backend_supports_op(backend, out)) {
|
|
printf("not supported\n");
|
|
ggml_free(ctx);
|
|
return true;
|
|
}
|
|
|
|
// align while also leaving some margin for variations in parameters
|
|
int align = 20;
|
|
int last = (len + align - 1) / align * align;
|
|
if (last - len < 5) {
|
|
last += align;
|
|
}
|
|
last = std::max(last, 60);
|
|
printf("%*s", last - len, "");
|
|
|
|
// allocate
|
|
ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors(ctx, backend);
|
|
|
|
// randomize tensors
|
|
initialize_tensors(ctx);
|
|
|
|
// build graph
|
|
ggml_cgraph * gf = ggml_new_graph_custom(ctx, graph_nodes, false);
|
|
ggml_build_forward_expand(gf, out);
|
|
|
|
// warmup run
|
|
ggml_backend_graph_compute(backend, gf);
|
|
|
|
// duplicate the op
|
|
size_t target_size = ggml_backend_is_cpu(backend) ? 1ULL << 33 : 1ULL << 35; // 8 GB CPU, 32 GB GPU
|
|
int n_runs = std::min((size_t)gf->size - gf->n_nodes, target_size / op_size(out)) + 1;
|
|
for (int i = 1; i < n_runs; i++) {
|
|
gf->nodes[gf->n_nodes++] = out;
|
|
}
|
|
|
|
// calculate memory
|
|
size_t mem = n_runs * op_size(out);
|
|
auto tensor_op_size = [](ggml_tensor * t) {
|
|
size_t size = ggml_nbytes(t);
|
|
// add source tensors
|
|
for (int i = 0; i < GGML_MAX_SRC; i++) {
|
|
if (t->src[i] != NULL) {
|
|
size += ggml_nbytes(t->src[i]);
|
|
}
|
|
}
|
|
return size;
|
|
};
|
|
for (int i = 0; i < gf->n_nodes; i++) {
|
|
if (ggml_is_view_op(gf->nodes[i]->op) || gf->nodes[i] == out) {
|
|
continue;
|
|
}
|
|
mem += tensor_op_size(gf->nodes[i]);
|
|
}
|
|
|
|
// run
|
|
ggml_backend_synchronize(backend);
|
|
|
|
int64_t start_time = ggml_time_us();
|
|
ggml_backend_graph_compute(backend, gf);
|
|
ggml_backend_synchronize(backend);
|
|
int64_t end_time = ggml_time_us();
|
|
double time_us = end_time - start_time;
|
|
|
|
printf(" %5d runs - %8.2f us/run - %8zu kB/run - \033[1;34m%7.2f GB/s\033[0m\n",
|
|
n_runs,
|
|
time_us / n_runs,
|
|
op_size(out) / 1024,
|
|
mem / (time_us/1e6) / 1024.0 / 1024.0 / 1024.0);
|
|
|
|
ggml_backend_buffer_free(buf);
|
|
|
|
ggml_free(ctx);
|
|
|
|
return true;
|
|
}
|
|
};
|
|
|
|
// GGML_OP_UNARY
|
|
struct test_unary : public test_case {
|
|
const ggml_unary_op op;
|
|
const ggml_type type;
|
|
const std::array<int64_t, 4> ne;
|
|
|
|
std::string vars() override {
|
|
return VARS_TO_STR2(type, ne);
|
|
}
|
|
|
|
test_unary(ggml_unary_op op,
|
|
ggml_type type = GGML_TYPE_F32,
|
|
std::array<int64_t, 4> ne = {128, 10, 10, 10})
|
|
: op(op), type(type), ne(ne) {}
|
|
|
|
ggml_tensor * build_graph(ggml_context * ctx) override {
|
|
ggml_tensor * in = ggml_new_tensor(ctx, type, 4, ne.data());
|
|
ggml_tensor * out = ggml_unary(ctx, in, op);
|
|
return out;
|
|
}
|
|
};
|
|
|
|
// GGML_OP_GET_ROWS
|
|
struct test_get_rows : public test_case {
|
|
const ggml_type type;
|
|
const int n; // cols
|
|
const int m; // rows
|
|
const int r; // rows to get
|
|
const int b; // batch size
|
|
const bool v; // view (non-contiguous src1)
|
|
|
|
std::string vars() override {
|
|
return VARS_TO_STR6(type, n, m, r, b, v);
|
|
}
|
|
|
|
test_get_rows(ggml_type type = GGML_TYPE_F32, int n = 10, int m = 5, int r = 3, int b = 1, bool v = false)
|
|
: type(type), n(n), m(m), r(r), b(b), v(v) {}
|
|
|
|
ggml_tensor * build_graph(ggml_context * ctx) override {
|
|
ggml_tensor * in = ggml_new_tensor_3d(ctx, type, n, m, b);
|
|
ggml_tensor * rows = ggml_new_tensor_2d(ctx, GGML_TYPE_I32, r, b);
|
|
if (v) {
|
|
rows = ggml_view_2d(ctx, rows, r/2, b, rows->nb[1], 0);
|
|
}
|
|
ggml_tensor * out = ggml_get_rows(ctx, in, rows);
|
|
return out;
|
|
}
|
|
|
|
void initialize_tensors(ggml_context * ctx) override {
|
|
for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
|
|
if (t->type == GGML_TYPE_I32) {
|
|
if (ggml_is_view_op(t->op)) { continue; }
|
|
// rows
|
|
std::vector<int> data(r*b);
|
|
for (int i = 0; i < r*b; i++) {
|
|
data[i] = rand() % m;
|
|
}
|
|
ggml_backend_tensor_set(t, data.data(), 0, r * b * sizeof(int));
|
|
} else {
|
|
init_tensor_uniform(t);
|
|
}
|
|
}
|
|
}
|
|
};
|
|
|
|
// GGML_OP_REPEAT
|
|
struct test_repeat : public test_case {
|
|
const ggml_type type;
|
|
const std::array<int64_t, 4> ne;
|
|
const std::array<int, 4> nr;
|
|
|
|
std::string vars() override {
|
|
return VARS_TO_STR3(type, ne, nr);
|
|
}
|
|
|
|
size_t op_size(ggml_tensor * t) override {
|
|
return ggml_nbytes(t) * 2;
|
|
}
|
|
|
|
test_repeat(ggml_type type = GGML_TYPE_F32,
|
|
std::array<int64_t, 4> ne = {10, 10, 10, 10},
|
|
std::array<int, 4> nr = {2, 2, 2, 2})
|
|
: type(type), ne(ne), nr(nr) {}
|
|
|
|
ggml_tensor * build_graph(ggml_context * ctx) override {
|
|
ggml_tensor * target = ggml_new_tensor_4d(ctx, type, ne[0]*nr[0], ne[1]*nr[1], ne[2]*nr[2], ne[3]*nr[3]);
|
|
ggml_tensor * src = ggml_new_tensor(ctx, type, 4, ne.data());
|
|
ggml_tensor * out = ggml_repeat(ctx, src, target);
|
|
return out;
|
|
}
|
|
};
|
|
|
|
// GGML_OP_DUP
|
|
struct test_dup : public test_case {
|
|
const ggml_type type;
|
|
const std::array<int64_t, 4> ne;
|
|
|
|
std::string vars() override {
|
|
return VARS_TO_STR2(type, ne);
|
|
}
|
|
|
|
test_dup(ggml_type type = GGML_TYPE_F32,
|
|
std::array<int64_t, 4> ne = {10, 10, 10, 1})
|
|
: type(type), ne(ne) {}
|
|
|
|
ggml_tensor * build_graph(ggml_context * ctx) override {
|
|
ggml_tensor * src = ggml_new_tensor(ctx, type, 4, ne.data());
|
|
ggml_tensor * out = ggml_dup(ctx, src);
|
|
return out;
|
|
}
|
|
};
|
|
|
|
// GGML_OP_CPY
|
|
struct test_cpy : public test_case {
|
|
const ggml_type type_src;
|
|
const ggml_type type_dst;
|
|
const std::array<int64_t, 4> ne;
|
|
|
|
std::string vars() override {
|
|
return VARS_TO_STR3(type_src, type_dst, ne);
|
|
}
|
|
|
|
size_t op_size(ggml_tensor * t) override {
|
|
return ggml_nbytes(t) + ggml_nbytes(t->src[0]);
|
|
}
|
|
|
|
test_cpy(ggml_type type_src = GGML_TYPE_F32, ggml_type type_dst = GGML_TYPE_F32,
|
|
std::array<int64_t, 4> ne = {10, 10, 10, 1})
|
|
: type_src(type_src), type_dst(type_dst), ne(ne) {}
|
|
|
|
ggml_tensor * build_graph(ggml_context * ctx) override {
|
|
ggml_tensor * src = ggml_new_tensor(ctx, type_src, 4, ne.data());
|
|
ggml_tensor * dst = ggml_new_tensor(ctx, type_dst, 4, ne.data());
|
|
ggml_tensor * out = ggml_cpy(ctx, src, dst);
|
|
return out;
|
|
}
|
|
};
|
|
|
|
// GGML_OP_CONT
|
|
struct test_cont : public test_case {
|
|
const ggml_type type;
|
|
const std::array<int64_t, 4> ne;
|
|
|
|
std::string vars() override {
|
|
return VARS_TO_STR2(type, ne);
|
|
}
|
|
|
|
test_cont(ggml_type type = GGML_TYPE_F32,
|
|
std::array<int64_t, 4> ne = {10, 10, 10, 1})
|
|
: type(type), ne(ne) {}
|
|
|
|
ggml_tensor * build_graph(ggml_context * ctx) override {
|
|
ggml_tensor * src = ggml_new_tensor(ctx, type, 4, ne.data());
|
|
src = ggml_transpose(ctx, src);
|
|
ggml_tensor * out = ggml_cont(ctx, src);
|
|
|
|
return out;
|
|
}
|
|
};
|
|
|
|
// GGML_OP_ADD
|
|
// GGML_OP_MUL
|
|
// GGML_OP_DIV
|
|
struct test_bin_bcast : public test_case {
|
|
using op_t = ggml_tensor * (*) (ggml_context *, ggml_tensor *, ggml_tensor *);
|
|
op_t op;
|
|
const ggml_type type;
|
|
const std::array<int64_t, 4> ne;
|
|
const std::array<int, 4> nr;
|
|
|
|
std::string vars() override {
|
|
return VARS_TO_STR3(type, ne, nr);
|
|
}
|
|
|
|
size_t op_size(ggml_tensor * t) override {
|
|
return ggml_nbytes(t) * 3;
|
|
}
|
|
|
|
test_bin_bcast(op_t op, ggml_type type = GGML_TYPE_F32,
|
|
std::array<int64_t, 4> ne = {10, 10, 1, 1},
|
|
std::array<int, 4> nr = {1, 2, 1, 1})
|
|
: op(op), type(type), ne(ne), nr(nr) {}
|
|
|
|
ggml_tensor * build_graph(ggml_context * ctx) override {
|
|
ggml_tensor * a = ggml_new_tensor_4d(ctx, type, ne[0]*nr[0], ne[1]*nr[1], ne[2]*nr[2], ne[3]*nr[3]);
|
|
ggml_tensor * b = ggml_new_tensor(ctx, type, 4, ne.data());
|
|
ggml_tensor * out = op(ctx, a, b);
|
|
return out;
|
|
}
|
|
|
|
void initialize_tensors(ggml_context * ctx) override {
|
|
for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
|
|
if (op == ggml_div) {
|
|
// avoid division by zero
|
|
init_tensor_uniform(t, 1.0f, 2.0f);
|
|
} else {
|
|
init_tensor_uniform(t);
|
|
}
|
|
}
|
|
}
|
|
};
|
|
|
|
// GGML_OP_SCALE
|
|
struct test_scale : public test_case {
|
|
const ggml_type type;
|
|
const std::array<int64_t, 4> ne;
|
|
float scale;
|
|
|
|
std::string vars() override {
|
|
return VARS_TO_STR3(type, ne, scale);
|
|
}
|
|
|
|
test_scale(ggml_type type = GGML_TYPE_F32,
|
|
std::array<int64_t, 4> ne = {10, 10, 10, 10},
|
|
float scale = 2.0f)
|
|
: type(type), ne(ne), scale(scale) {}
|
|
|
|
ggml_tensor * build_graph(ggml_context * ctx) override {
|
|
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
|
|
ggml_tensor * out = ggml_scale(ctx, a, scale);
|
|
return out;
|
|
}
|
|
};
|
|
|
|
// GGML_OP_NORM
|
|
struct test_norm : public test_case {
|
|
const ggml_type type;
|
|
const std::array<int64_t, 4> ne;
|
|
float eps;
|
|
|
|
std::string vars() override {
|
|
return VARS_TO_STR3(type, ne, eps);
|
|
}
|
|
|
|
test_norm(ggml_type type = GGML_TYPE_F32,
|
|
std::array<int64_t, 4> ne = {64, 10, 10, 10},
|
|
float eps = 1e-6f)
|
|
: type(type), ne(ne), eps(eps) {}
|
|
|
|
ggml_tensor * build_graph(ggml_context * ctx) override {
|
|
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
|
|
ggml_tensor * out = ggml_norm(ctx, a, eps);
|
|
return out;
|
|
}
|
|
};
|
|
|
|
// GGML_OP_RMS_NORM
|
|
struct test_rms_norm : public test_case {
|
|
const ggml_type type;
|
|
const std::array<int64_t, 4> ne;
|
|
float eps;
|
|
|
|
std::string vars() override {
|
|
return VARS_TO_STR3(type, ne, eps);
|
|
}
|
|
|
|
test_rms_norm(ggml_type type = GGML_TYPE_F32,
|
|
std::array<int64_t, 4> ne = {64, 10, 10, 10},
|
|
float eps = 1e-6f)
|
|
: type(type), ne(ne), eps(eps) {}
|
|
|
|
ggml_tensor * build_graph(ggml_context * ctx) override {
|
|
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
|
|
ggml_tensor * out = ggml_rms_norm(ctx, a, eps);
|
|
return out;
|
|
}
|
|
};
|
|
|
|
// GGML_OP_MUL_MAT
|
|
struct test_mul_mat : public test_case {
|
|
const ggml_type type_a;
|
|
const ggml_type type_b;
|
|
const int64_t m;
|
|
const int64_t n;
|
|
const int64_t k;
|
|
const std::array<int64_t, 2> bs; // dims 3 and 4
|
|
const std::array<int64_t, 2> nr; // repeat in dims 3 and 4
|
|
|
|
std::string vars() override {
|
|
return VARS_TO_STR7(type_a, type_b, m, n, k, bs, nr);
|
|
}
|
|
|
|
double max_nmse_err() override {
|
|
return 5e-4;
|
|
}
|
|
|
|
size_t op_size(ggml_tensor * t) override {
|
|
size_t a = ggml_nbytes(t->src[0]) * n * nr[0] * nr[1];
|
|
size_t b = ggml_nbytes(t->src[1]) * m;
|
|
size_t c = ggml_nbytes(t);
|
|
return a + b + c;
|
|
|
|
GGML_UNUSED(t);
|
|
}
|
|
|
|
test_mul_mat(ggml_type type_a = GGML_TYPE_F32, ggml_type type_b = GGML_TYPE_F32,
|
|
int64_t m = 32, int64_t n = 32, int64_t k = 32,
|
|
std::array<int64_t, 2> bs = {10, 10},
|
|
std::array<int64_t, 2> nr = {2, 2})
|
|
: type_a(type_a), type_b(type_b), m(m), n(n), k(k), bs(bs), nr(nr) {}
|
|
|
|
ggml_tensor * build_graph(ggml_context * ctx) override {
|
|
// C^T = A * B^T: (k, m) * (k, n) => (m, n)
|
|
ggml_tensor * a = ggml_new_tensor_4d(ctx, type_a, k, m, bs[0] , bs[1]);
|
|
ggml_tensor * b = ggml_new_tensor_4d(ctx, type_b, k, n, bs[0]*nr[0], bs[1]*nr[1]);
|
|
ggml_tensor * out = ggml_mul_mat(ctx, a, b);
|
|
return out;
|
|
}
|
|
};
|
|
|
|
// GGML_OP_MUL_MAT_ID
|
|
struct test_mul_mat_id : public test_case {
|
|
const ggml_type type_a;
|
|
const ggml_type type_b;
|
|
const int n_mats;
|
|
const int id;
|
|
const int64_t m;
|
|
const int64_t n;
|
|
const int64_t k;
|
|
const bool v; // view (non-contiguous ids)
|
|
|
|
std::string vars() override {
|
|
return VARS_TO_STR8(type_a, type_b, n_mats, id, m, n, k, v);
|
|
}
|
|
|
|
double max_nmse_err() override {
|
|
return 5e-4;
|
|
}
|
|
|
|
size_t op_size(ggml_tensor * t) override {
|
|
size_t a = ggml_nbytes(t->src[2]) * n;
|
|
size_t b = ggml_nbytes(t->src[1]) * m;
|
|
size_t c = ggml_nbytes(t);
|
|
return a + b + c;
|
|
|
|
GGML_UNUSED(t);
|
|
}
|
|
|
|
test_mul_mat_id(ggml_type type_a = GGML_TYPE_F32, ggml_type type_b = GGML_TYPE_F32,
|
|
int n_mats = 2, int id = 0,
|
|
int64_t m = 32, int64_t n = 32, int64_t k = 32, bool v = false)
|
|
: type_a(type_a), type_b(type_b), n_mats(n_mats), id(id),
|
|
m(m), n(n), k(k), v(v) {}
|
|
|
|
ggml_tensor * build_graph(ggml_context * ctx) override {
|
|
// C^T = A * B^T: (k, m) * (k, n) => (m, n)
|
|
std::vector<ggml_tensor *> mats;
|
|
for (int i = 0; i < n_mats; i++) {
|
|
ggml_tensor * a = ggml_new_tensor_2d(ctx, type_a, k, m);
|
|
mats.push_back(a);
|
|
}
|
|
ggml_tensor * ids = ggml_new_tensor_2d(ctx, GGML_TYPE_I32, n_mats, n);
|
|
if (v) {
|
|
ids = ggml_view_2d(ctx, ids, n_mats/2, ids->ne[1], ids->nb[1], 0);
|
|
}
|
|
ggml_tensor * b = ggml_new_tensor_2d(ctx, type_b, k, n);
|
|
ggml_tensor * out = ggml_mul_mat_id(ctx, mats.data(), n_mats, ids, v ? id/2 : id, b);
|
|
return out;
|
|
}
|
|
|
|
void initialize_tensors(ggml_context * ctx) override {
|
|
std::random_device rd;
|
|
std::default_random_engine rng(rd());
|
|
for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
|
|
if (t->type == GGML_TYPE_I32) {
|
|
if (ggml_is_view_op(t->op)) { continue; }
|
|
// ids
|
|
for (int64_t r = 0; r < ggml_nrows(t); r++) {
|
|
std::vector<int32_t> data(t->ne[0]);
|
|
for (int i = 0; i < t->ne[0]; i++) {
|
|
data[i] = i % n_mats;
|
|
}
|
|
std::shuffle(data.begin(), data.end(), rng);
|
|
ggml_backend_tensor_set(t, data.data(), r * t->nb[1], t->ne[0] * sizeof(int32_t));
|
|
}
|
|
} else {
|
|
init_tensor_uniform(t);
|
|
}
|
|
}
|
|
}
|
|
};
|
|
|
|
// GGML_OP_SQR
|
|
struct test_sqr : public test_case {
|
|
const ggml_type type;
|
|
const std::array<int64_t, 4> ne;
|
|
|
|
std::string vars() override {
|
|
return VARS_TO_STR2(type, ne);
|
|
}
|
|
|
|
test_sqr(ggml_type type = GGML_TYPE_F32,
|
|
std::array<int64_t, 4> ne = {10, 10, 10, 10})
|
|
: type(type), ne(ne) {}
|
|
|
|
ggml_tensor * build_graph(ggml_context * ctx) override {
|
|
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
|
|
ggml_tensor * out = ggml_sqr(ctx, a);
|
|
return out;
|
|
}
|
|
};
|
|
|
|
// GGML_OP_CLAMP
|
|
struct test_clamp : public test_case {
|
|
const ggml_type type;
|
|
const std::array<int64_t, 4> ne;
|
|
float min;
|
|
float max;
|
|
|
|
std::string vars() override {
|
|
return VARS_TO_STR4(type, ne, min, max);
|
|
}
|
|
|
|
test_clamp(ggml_type type = GGML_TYPE_F32,
|
|
std::array<int64_t, 4> ne = {10, 10, 10, 10},
|
|
float min = -0.5f, float max = 0.5f)
|
|
: type(type), ne(ne), min(min), max(max) {}
|
|
|
|
ggml_tensor * build_graph(ggml_context * ctx) override {
|
|
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
|
|
ggml_tensor * out = ggml_clamp(ctx, a, min, max);
|
|
return out;
|
|
}
|
|
};
|
|
|
|
// GGML_OP_DIAG_MASK_INF
|
|
struct test_diag_mask_inf : public test_case {
|
|
const ggml_type type;
|
|
const std::array<int64_t, 4> ne;
|
|
const int n_past;
|
|
|
|
std::string vars() override {
|
|
return VARS_TO_STR3(type, ne, n_past);
|
|
}
|
|
|
|
test_diag_mask_inf(ggml_type type = GGML_TYPE_F32,
|
|
std::array<int64_t, 4> ne = {10, 10, 10, 10},
|
|
int n_past = 5)
|
|
: type(type), ne(ne), n_past(n_past) {}
|
|
|
|
ggml_tensor * build_graph(ggml_context * ctx) override {
|
|
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
|
|
ggml_tensor * out = ggml_diag_mask_inf(ctx, a, n_past);
|
|
return out;
|
|
}
|
|
};
|
|
|
|
// GGML_OP_SOFT_MAX
|
|
struct test_soft_max : public test_case {
|
|
const ggml_type type;
|
|
const std::array<int64_t, 4> ne;
|
|
|
|
std::string vars() override {
|
|
return VARS_TO_STR2(type, ne);
|
|
}
|
|
|
|
test_soft_max(ggml_type type = GGML_TYPE_F32,
|
|
std::array<int64_t, 4> ne = {10, 10, 10, 10})
|
|
: type(type), ne(ne) {}
|
|
|
|
ggml_tensor * build_graph(ggml_context * ctx) override {
|
|
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
|
|
ggml_tensor * out = ggml_soft_max(ctx, a);
|
|
return out;
|
|
}
|
|
};
|
|
|
|
// GGML_OP_ROPE
|
|
struct test_rope : public test_case {
|
|
const ggml_type type;
|
|
const std::array<int64_t, 4> ne;
|
|
int n_dims;
|
|
int mode;
|
|
int n_ctx;
|
|
|
|
std::string vars() override {
|
|
return VARS_TO_STR5(type, ne, n_dims, mode, n_ctx);
|
|
}
|
|
|
|
test_rope(ggml_type type = GGML_TYPE_F32,
|
|
std::array<int64_t, 4> ne = {10, 10, 10, 1},
|
|
int n_dims = 10, int mode = 0, int n_ctx = 512)
|
|
: type(type), ne(ne), n_dims(n_dims), mode(mode), n_ctx(n_ctx) {}
|
|
|
|
ggml_tensor * build_graph(ggml_context * ctx) override {
|
|
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
|
|
ggml_tensor * pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, ne[2]);
|
|
ggml_tensor * out = ggml_rope(ctx, a, pos, n_dims, mode, n_ctx);
|
|
return out;
|
|
}
|
|
|
|
void initialize_tensors(ggml_context * ctx) override {
|
|
for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
|
|
if (t->type == GGML_TYPE_I32) {
|
|
// pos
|
|
std::vector<int> data(ne[2]);
|
|
for (int i = 0; i < ne[2]; i++) {
|
|
data[i] = rand() % n_ctx;
|
|
}
|
|
ggml_backend_tensor_set(t, data.data(), 0, ne[2] * sizeof(int));
|
|
} else {
|
|
init_tensor_uniform(t);
|
|
}
|
|
}
|
|
}
|
|
};
|
|
|
|
// GGML_OP_ALIBI
|
|
struct test_alibi : public test_case {
|
|
const ggml_type type;
|
|
const std::array<int64_t, 4> ne;
|
|
int n_past;
|
|
int n_head;
|
|
float bias_max;
|
|
|
|
std::string vars() override {
|
|
return VARS_TO_STR5(type, ne, n_past, n_head, bias_max);
|
|
}
|
|
|
|
test_alibi(ggml_type type = GGML_TYPE_F32,
|
|
std::array<int64_t, 4> ne = {10, 10, 10, 10},
|
|
int n_past = 512, int n_head = 10, float bias_max = 0.5f)
|
|
: type(type), ne(ne), n_past(n_past), n_head(n_head), bias_max(bias_max) {}
|
|
|
|
ggml_tensor * build_graph(ggml_context * ctx) override {
|
|
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
|
|
ggml_tensor * out = ggml_alibi(ctx, a, n_past, n_head, bias_max);
|
|
return out;
|
|
}
|
|
};
|
|
|
|
// GGML_OP_IM2COL
|
|
struct test_im2col : public test_case {
|
|
const ggml_type type_input;
|
|
const ggml_type type_kernel;
|
|
const std::array<int64_t, 4> ne_input;
|
|
const std::array<int64_t, 4> ne_kernel;
|
|
// stride
|
|
const int s0;
|
|
const int s1;
|
|
// padding
|
|
const int p0;
|
|
const int p1;
|
|
// dilatation
|
|
const int d0;
|
|
const int d1;
|
|
// mode
|
|
const bool is_2D;
|
|
|
|
std::string vars() override {
|
|
return VARS_TO_STR11(type_input, type_kernel, ne_input, ne_kernel, s0, s1, p0, p1, d0, d1, is_2D);
|
|
}
|
|
|
|
test_im2col(ggml_type type_input = GGML_TYPE_F32, ggml_type type_kernel = GGML_TYPE_F16,
|
|
std::array<int64_t, 4> ne_input = {10, 10, 3, 1}, // [input_width, input_height, input_channels, 1]
|
|
std::array<int64_t, 4> ne_kernel = {3, 3, 3, 1}, // [kernel_width, kernel_height, input_channels, 1]
|
|
int s0 = 1, int s1 = 1,
|
|
int p0 = 1, int p1 = 1,
|
|
int d0 = 1, int d1 = 1,
|
|
bool is_2D = true)
|
|
: type_input(type_input), type_kernel(type_kernel), ne_input(ne_input), ne_kernel(ne_kernel), s0(s0), s1(s1), p0(p0), p1(p1), d0(d0), d1(d1), is_2D(is_2D) {}
|
|
|
|
ggml_tensor * build_graph(ggml_context * ctx) override {
|
|
ggml_tensor * input = ggml_new_tensor(ctx, type_input, 4, ne_input.data());
|
|
ggml_tensor * kernel = ggml_new_tensor(ctx, type_kernel, 4, ne_kernel.data());
|
|
ggml_tensor * out = ggml_im2col(ctx, kernel, input, s0, s1, p0, p1, d0, d1, is_2D);
|
|
return out;
|
|
}
|
|
};
|
|
|
|
// GGML_OP_CONCAT
|
|
struct test_concat : public test_case {
|
|
const ggml_type type;
|
|
const std::array<int64_t, 4> ne;
|
|
const int64_t b_ne2;
|
|
|
|
std::string vars() override {
|
|
return VARS_TO_STR3(type, ne, b_ne2);
|
|
}
|
|
|
|
test_concat(ggml_type type = GGML_TYPE_F32,
|
|
std::array<int64_t, 4> ne = {10, 10, 10, 10},
|
|
int64_t b_ne2 = 10)
|
|
: type(type), ne(ne), b_ne2(b_ne2) {}
|
|
|
|
ggml_tensor * build_graph(ggml_context * ctx) override {
|
|
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
|
|
ggml_tensor * b = ggml_new_tensor_4d(ctx, type, ne[0], ne[1], b_ne2, ne[3]);
|
|
ggml_tensor * out = ggml_concat(ctx, a, b);
|
|
return out;
|
|
}
|
|
};
|
|
|
|
// GGML_OP_ARGSORT
|
|
struct test_argsort : public test_case {
|
|
const ggml_type type;
|
|
const std::array<int64_t, 4> ne;
|
|
ggml_sort_order order;
|
|
|
|
std::string vars() override {
|
|
return VARS_TO_STR3(type, ne, order);
|
|
}
|
|
|
|
test_argsort(ggml_type type = GGML_TYPE_F32,
|
|
std::array<int64_t, 4> ne = {16, 10, 10, 10},
|
|
ggml_sort_order order = GGML_SORT_ASC)
|
|
: type(type), ne(ne), order(order) {}
|
|
|
|
ggml_tensor * build_graph(ggml_context * ctx) override {
|
|
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
|
|
ggml_tensor * out = ggml_argsort(ctx, a, order);
|
|
return out;
|
|
}
|
|
|
|
void initialize_tensors(ggml_context * ctx) override {
|
|
std::random_device rd;
|
|
std::default_random_engine rng(rd());
|
|
for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
|
|
if (t->type == GGML_TYPE_I32) {
|
|
// indices
|
|
std::vector<int> data(ggml_nelements(t));
|
|
for (int i = 0; i < ggml_nelements(t); i++) {
|
|
data[i] = rand();
|
|
}
|
|
std::shuffle(data.begin(), data.end(), rng);
|
|
ggml_backend_tensor_set(t, data.data(), 0, ne[0]*ne[1]*ne[2]*ne[3] * sizeof(int));
|
|
} else if (t->type == GGML_TYPE_F32) {
|
|
// initialize with unique values to avoid ties
|
|
for (int64_t r = 0; r < ggml_nrows(t); r++) {
|
|
std::vector<float> data(t->ne[0]);
|
|
for (int i = 0; i < t->ne[0]; i++) {
|
|
data[i] = i;
|
|
}
|
|
std::shuffle(data.begin(), data.end(), rng);
|
|
ggml_backend_tensor_set(t, data.data(), r * t->nb[1], t->ne[0] * sizeof(float));
|
|
}
|
|
} else {
|
|
GGML_ASSERT(false);
|
|
}
|
|
}
|
|
}
|
|
};
|
|
|
|
// GGML_OP_SUM_ROWS
|
|
struct test_sum_rows : public test_case {
|
|
const ggml_type type;
|
|
const std::array<int64_t, 4> ne;
|
|
|
|
std::string vars() override {
|
|
return VARS_TO_STR2(type, ne);
|
|
}
|
|
|
|
test_sum_rows(ggml_type type = GGML_TYPE_F32,
|
|
std::array<int64_t, 4> ne = {10, 10, 10, 10})
|
|
: type(type), ne(ne) {}
|
|
|
|
ggml_tensor * build_graph(ggml_context * ctx) override {
|
|
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
|
|
ggml_tensor * out = ggml_sum_rows(ctx, a);
|
|
return out;
|
|
}
|
|
};
|
|
|
|
// GGML_OP_UPSCALE
|
|
struct test_upscale : public test_case {
|
|
const ggml_type type;
|
|
const std::array<int64_t, 4> ne;
|
|
const int32_t scale_factor;
|
|
|
|
std::string vars() override {
|
|
return VARS_TO_STR3(type, ne, scale_factor);
|
|
}
|
|
|
|
test_upscale(ggml_type type = GGML_TYPE_F32,
|
|
std::array<int64_t, 4> ne = {512, 512, 3, 1},
|
|
int32_t scale_factor = 2)
|
|
: type(type), ne(ne), scale_factor(scale_factor) {}
|
|
|
|
ggml_tensor * build_graph(ggml_context * ctx) override {
|
|
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
|
|
ggml_tensor * out = ggml_upscale(ctx, a, scale_factor);
|
|
return out;
|
|
}
|
|
};
|
|
|
|
// GGML_OP_GROUP_NORM
|
|
struct test_group_norm : public test_case {
|
|
const ggml_type type;
|
|
const std::array<int64_t, 4> ne;
|
|
const int32_t num_groups;
|
|
|
|
std::string vars() override {
|
|
return VARS_TO_STR3(type, ne, num_groups);
|
|
}
|
|
|
|
test_group_norm(ggml_type type = GGML_TYPE_F32,
|
|
std::array<int64_t, 4> ne = {64, 64, 320, 1},
|
|
int32_t num_groups = 32)
|
|
: type(type), ne(ne), num_groups(num_groups) {}
|
|
|
|
ggml_tensor * build_graph(ggml_context * ctx) override {
|
|
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
|
|
ggml_tensor * out = ggml_group_norm(ctx, a, num_groups);
|
|
return out;
|
|
}
|
|
};
|
|
|
|
// GGML_OP_ACC
|
|
struct test_acc : public test_case {
|
|
const ggml_type type;
|
|
const std::array<int64_t, 4> ne_a;
|
|
const std::array<int64_t, 4> ne_b;
|
|
|
|
std::string vars() override {
|
|
return VARS_TO_STR3(type, ne_a, ne_b);
|
|
}
|
|
|
|
test_acc(ggml_type type = GGML_TYPE_F32,
|
|
std::array<int64_t, 4> ne_a = {1024, 577, 1, 1},
|
|
std::array<int64_t, 4> ne_b = {1024, 576, 1, 1})
|
|
: type(type), ne_a(ne_a), ne_b(ne_b) {}
|
|
|
|
ggml_tensor * build_graph(ggml_context * ctx) override {
|
|
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne_a.data());
|
|
ggml_tensor * b = ggml_new_tensor(ctx, type, 4, ne_b.data());
|
|
ggml_tensor * out = ggml_acc(ctx, a, b, a->nb[1], a->nb[2], a->nb[3], b->nb[1]);
|
|
return out;
|
|
}
|
|
};
|
|
|
|
// GGML_OP_PAD
|
|
struct test_pad : public test_case {
|
|
const ggml_type type;
|
|
const std::array<int64_t, 4> ne_a;
|
|
const int pad_0;
|
|
const int pad_1;
|
|
|
|
std::string vars() override {
|
|
return VARS_TO_STR4(type, ne_a, pad_0, pad_1);
|
|
}
|
|
|
|
test_pad(ggml_type type = GGML_TYPE_F32,
|
|
std::array<int64_t, 4> ne_a = {512, 512, 1, 1},
|
|
int pad_0 = 1, int pad_1 = 1)
|
|
: type(type), ne_a(ne_a), pad_0(pad_0), pad_1(pad_1) {}
|
|
|
|
ggml_tensor * build_graph(ggml_context * ctx) override {
|
|
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne_a.data());
|
|
ggml_tensor * out = ggml_pad(ctx, a, pad_0, pad_1, 0, 0);
|
|
return out;
|
|
}
|
|
};
|
|
|
|
// GGML_OP_LEAKY_RELU
|
|
struct test_leaky_relu : public test_case {
|
|
const ggml_type type;
|
|
const std::array<int64_t, 4> ne_a;
|
|
const float negative_slope;
|
|
|
|
std::string vars() override {
|
|
return VARS_TO_STR3(type, ne_a, negative_slope);
|
|
}
|
|
|
|
test_leaky_relu(ggml_type type = GGML_TYPE_F32,
|
|
std::array<int64_t, 4> ne_a = {10, 10, 10, 10},
|
|
float negative_slope = 0.1f)
|
|
: type(type), ne_a(ne_a), negative_slope(negative_slope) {}
|
|
|
|
ggml_tensor * build_graph(ggml_context * ctx) override {
|
|
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne_a.data());
|
|
ggml_tensor * out = ggml_leaky_relu(ctx, a, negative_slope, true);
|
|
return out;
|
|
}
|
|
};
|
|
|
|
// Mixtral MOE
|
|
struct test_moe : public test_case {
|
|
const int n_experts;
|
|
const int n_experts_per_tok;
|
|
const int n_tokens;
|
|
const int n_embd;
|
|
const int n_ff;
|
|
|
|
std::string op_desc(ggml_tensor * t) override {
|
|
return "MOE";
|
|
|
|
GGML_UNUSED(t);
|
|
}
|
|
|
|
std::string vars() override {
|
|
return VARS_TO_STR5(n_experts, n_experts_per_tok, n_tokens, n_embd, n_ff);
|
|
}
|
|
|
|
test_moe(int n_experts = 8, int n_experts_per_tok = 2, int n_tokens = 1, int n_embd = 4096, int n_ff = 14336)
|
|
: n_experts(n_experts), n_experts_per_tok(n_experts_per_tok), n_tokens(n_tokens), n_embd(n_embd), n_ff(n_ff) {
|
|
}
|
|
|
|
ggml_tensor * build_graph(ggml_context * ctx) override {
|
|
ggml_tensor * ffn_gate_inp = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_experts);
|
|
|
|
std::vector<ggml_tensor *> ffn_up_exp(n_experts);
|
|
std::vector<ggml_tensor *> ffn_gate_exp(n_experts);
|
|
std::vector<ggml_tensor *> ffn_down_exp(n_experts);
|
|
|
|
for (int i = 0; i < n_experts; ++i) {
|
|
ffn_up_exp[i] = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff);
|
|
ffn_gate_exp[i] = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff);
|
|
ffn_down_exp[i] = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_ff, n_embd);
|
|
}
|
|
|
|
ggml_tensor * cur = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_tokens);
|
|
|
|
ggml_tensor * logits = ggml_mul_mat(ctx, ffn_gate_inp, cur);
|
|
ggml_tensor * probs = ggml_soft_max_ext(ctx, logits, nullptr, 1.0f/sqrtf(n_embd));
|
|
|
|
// select experts
|
|
ggml_tensor * selected_experts = ggml_top_k(ctx, probs, n_experts_per_tok);
|
|
|
|
ggml_tensor * weights = ggml_get_rows(ctx,
|
|
ggml_reshape_3d(ctx, probs, 1, n_experts, n_tokens), selected_experts);
|
|
|
|
weights = ggml_reshape_2d(ctx, weights, n_experts_per_tok, n_tokens);
|
|
|
|
ggml_tensor * weights_sum = ggml_sum_rows(ctx, weights);
|
|
|
|
weights = ggml_div(ctx, weights, weights_sum);
|
|
|
|
// compute expert outputs
|
|
ggml_tensor * moe_out = nullptr;
|
|
|
|
for (int i = 0; i < n_experts_per_tok; ++i) {
|
|
ggml_tensor * cur_expert;
|
|
|
|
ggml_tensor * cur_up = ggml_mul_mat_id(ctx, ffn_up_exp.data(), n_experts, selected_experts, i, cur);
|
|
|
|
ggml_tensor * cur_gate = ggml_mul_mat_id(ctx, ffn_gate_exp.data(), n_experts, selected_experts, i, cur);
|
|
|
|
cur_gate = ggml_silu(ctx, cur_gate);
|
|
|
|
cur_expert = ggml_mul(ctx, cur_up, cur_gate);
|
|
|
|
cur_expert = ggml_mul_mat_id(ctx, ffn_down_exp.data(), n_experts, selected_experts, i, cur_expert);
|
|
|
|
cur_expert = ggml_mul(ctx, cur_expert,
|
|
ggml_view_2d(ctx, weights, 1, n_tokens, weights->nb[1], i*weights->nb[0]));
|
|
|
|
if (i == 0) {
|
|
moe_out = cur_expert;
|
|
} else {
|
|
moe_out = ggml_add(ctx, moe_out, cur_expert);
|
|
}
|
|
}
|
|
|
|
cur = moe_out;
|
|
|
|
return cur;
|
|
}
|
|
};
|
|
|
|
static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op_name) {
|
|
std::vector<std::unique_ptr<test_case>> test_cases;
|
|
|
|
const ggml_type all_types[] = {
|
|
GGML_TYPE_F32, GGML_TYPE_F16,
|
|
GGML_TYPE_Q4_0, GGML_TYPE_Q4_1,
|
|
GGML_TYPE_Q5_0, GGML_TYPE_Q5_1,
|
|
GGML_TYPE_Q8_0,
|
|
GGML_TYPE_Q2_K, GGML_TYPE_Q3_K,
|
|
GGML_TYPE_Q4_K, GGML_TYPE_Q5_K,
|
|
GGML_TYPE_Q6_K
|
|
};
|
|
|
|
// unary ops
|
|
for (int op = 0; op < GGML_UNARY_OP_COUNT; op++) {
|
|
test_cases.emplace_back(new test_unary((ggml_unary_op) op));
|
|
}
|
|
|
|
test_cases.emplace_back(new test_get_rows(GGML_TYPE_F32, 1, 8, 2, 1, false));
|
|
for (ggml_type type : all_types) {
|
|
for (int b : {1, 7}) {
|
|
for (bool v : {false, true}) {
|
|
test_cases.emplace_back(new test_get_rows(type, 256, 5, 4, b, v));
|
|
}
|
|
}
|
|
}
|
|
|
|
test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {1, 1, 1, 1}));
|
|
test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {2, 1, 1, 1}));
|
|
test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {1, 2, 1, 1}));
|
|
test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {1, 1, 2, 1}));
|
|
test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {1, 1, 1, 2}));
|
|
|
|
test_cases.emplace_back(new test_dup());
|
|
|
|
for (ggml_type type : all_types) {
|
|
test_cases.emplace_back(new test_cpy(GGML_TYPE_F32, type, {256, 10, 10, 1}));
|
|
}
|
|
|
|
test_cases.emplace_back(new test_cont());
|
|
|
|
auto add_test_bin_bcast = [&](ggml_type type, std::array<int64_t, 4> ne, std::array<int, 4> nr) {
|
|
for (auto op : {ggml_add, ggml_mul, ggml_div}) {
|
|
test_cases.emplace_back(new test_bin_bcast(op, type, ne, nr));
|
|
}
|
|
};
|
|
|
|
add_test_bin_bcast(GGML_TYPE_F32, {1, 1, 8, 1}, {1, 1, 1, 1});
|
|
add_test_bin_bcast(GGML_TYPE_F32, {1, 1, 1, 1}, {32, 1, 1, 1});
|
|
add_test_bin_bcast(GGML_TYPE_F32, {1, 1, 320, 320}, {1, 1, 1, 1});
|
|
add_test_bin_bcast(GGML_TYPE_F32, {16, 10, 1, 1}, {1, 1, 1, 1});
|
|
add_test_bin_bcast(GGML_TYPE_F32, {16, 10, 10, 1}, {1, 1, 1, 1});
|
|
add_test_bin_bcast(GGML_TYPE_F32, {16, 10, 10, 10}, {1, 1, 1, 1});
|
|
add_test_bin_bcast(GGML_TYPE_F32, {16, 10, 10, 10}, {2, 1, 1, 1});
|
|
add_test_bin_bcast(GGML_TYPE_F32, {16, 10, 10, 10}, {1, 2, 1, 1});
|
|
add_test_bin_bcast(GGML_TYPE_F32, {16, 10, 10, 10}, {1, 1, 2, 1});
|
|
add_test_bin_bcast(GGML_TYPE_F32, {16, 10, 10, 10}, {1, 1, 1, 2});
|
|
add_test_bin_bcast(GGML_TYPE_F32, {16, 10, 10, 10}, {1, 1, 2, 2});
|
|
add_test_bin_bcast(GGML_TYPE_F32, {16, 10, 10, 10}, {1, 2, 2, 2});
|
|
add_test_bin_bcast(GGML_TYPE_F32, {16, 10, 10, 10}, {2, 2, 2, 2});
|
|
|
|
// stable diffusion
|
|
add_test_bin_bcast(GGML_TYPE_F32, {1280, 1, 1, 1}, {1, 1, 1, 1});
|
|
add_test_bin_bcast(GGML_TYPE_F32, {1280, 1, 1, 1}, {1, 16, 16, 1});
|
|
add_test_bin_bcast(GGML_TYPE_F32, {1280, 16, 16, 1}, {1, 1, 1, 1});
|
|
add_test_bin_bcast(GGML_TYPE_F32, {1280, 1, 1, 1}, {1, 256, 1, 1});
|
|
add_test_bin_bcast(GGML_TYPE_F32, {1, 1, 1280, 1}, {16, 16, 1, 1});
|
|
add_test_bin_bcast(GGML_TYPE_F32, {16, 16, 1280, 1}, {1, 1, 1, 1});
|
|
add_test_bin_bcast(GGML_TYPE_F32, {1, 1, 1920, 1}, {16, 16, 1, 1});
|
|
add_test_bin_bcast(GGML_TYPE_F32, {1, 1, 2560, 1}, {16, 16, 1, 1});
|
|
add_test_bin_bcast(GGML_TYPE_F32, {1, 1, 1280, 1}, {32, 32, 1, 1});
|
|
add_test_bin_bcast(GGML_TYPE_F32, {1, 1, 1920, 1}, {32, 32, 1, 1});
|
|
add_test_bin_bcast(GGML_TYPE_F32, {1, 1, 640, 1}, {32, 32, 1, 1});
|
|
add_test_bin_bcast(GGML_TYPE_F32, {5120, 1, 1, 1}, {1, 256, 1, 1});
|
|
add_test_bin_bcast(GGML_TYPE_F32, {640, 1, 1, 1}, {1, 1, 1, 1});
|
|
//add_test_bin_bcast(GGML_TYPE_F32, {3, 3, 2560, 1280}, {1, 1, 1, 1});
|
|
//add_test_bin_bcast(GGML_TYPE_F32, {3, 3, 2560, 1280}, {2, 1, 1, 1});
|
|
|
|
test_cases.emplace_back(new test_scale());
|
|
|
|
for (float eps : {1e-6f, 1e-5f, 1e-3f, 1e-1f}) {
|
|
test_cases.emplace_back(new test_norm(GGML_TYPE_F32, {64, 10, 10, 10}, eps));
|
|
test_cases.emplace_back(new test_rms_norm(GGML_TYPE_F32, {64, 10, 10, 10}, eps));
|
|
}
|
|
|
|
for (ggml_type type_a : all_types) {
|
|
for (ggml_type type_b : {GGML_TYPE_F32, GGML_TYPE_F16}) {
|
|
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, { 1, 1}, {1, 1}));
|
|
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 1}, {1, 1}));
|
|
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 1}, {2, 1}));
|
|
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 10}, {1, 1}));
|
|
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 10}, {2, 1}));
|
|
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 10}, {1, 2}));
|
|
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 10}, {2, 2}));
|
|
|
|
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, { 1, 1}, {1, 1}));
|
|
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {10, 1}, {1, 1}));
|
|
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {10, 1}, {2, 1}));
|
|
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {10, 10}, {1, 1}));
|
|
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {10, 10}, {2, 1}));
|
|
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {10, 10}, {1, 2}));
|
|
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {10, 10}, {2, 2}));
|
|
}
|
|
}
|
|
|
|
for (ggml_type type_a : all_types) {
|
|
for (ggml_type type_b : {GGML_TYPE_F32 /*, GGML_TYPE_F16 */}) {
|
|
for (int n_mats : {2, 4, 8}) {
|
|
for (int id = 0; id < n_mats; id++) {
|
|
for (bool v : {false, true}) {
|
|
test_cases.emplace_back(new test_mul_mat_id(type_a, type_b, n_mats, id, 16, 16, 256, v));
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
test_cases.emplace_back(new test_sqr());
|
|
test_cases.emplace_back(new test_clamp());
|
|
|
|
test_cases.emplace_back(new test_diag_mask_inf(GGML_TYPE_F32, {10, 10, 1, 1}, 5));
|
|
test_cases.emplace_back(new test_diag_mask_inf(GGML_TYPE_F32, {10, 10, 10, 1}, 5));
|
|
test_cases.emplace_back(new test_diag_mask_inf(GGML_TYPE_F32, {10, 10, 10, 10}, 5));
|
|
|
|
test_cases.emplace_back(new test_soft_max());
|
|
|
|
for (ggml_type type : {GGML_TYPE_F32, GGML_TYPE_F16}) {
|
|
test_cases.emplace_back(new test_rope(type, {128, 32, 10, 1}, 128, 0, 512)); // llama 7B
|
|
test_cases.emplace_back(new test_rope(type, {128, 40, 10, 1}, 128, 0, 512)); // llama 13B
|
|
test_cases.emplace_back(new test_rope(type, {128, 52, 10, 1}, 128, 0, 512)); // llama 30B
|
|
test_cases.emplace_back(new test_rope(type, {128, 64, 10, 1}, 128, 0, 512)); // llama 65B
|
|
test_cases.emplace_back(new test_rope(type, { 64, 1, 10, 1}, 64, 2, 512)); // neox (falcon 7B)
|
|
test_cases.emplace_back(new test_rope(type, { 64, 71, 10, 1}, 64, 2, 512)); // neox (falcon 7B)
|
|
test_cases.emplace_back(new test_rope(type, { 64, 8, 10, 1}, 64, 2, 512)); // neox (falcon 40B)
|
|
test_cases.emplace_back(new test_rope(type, { 64, 128, 10, 1}, 64, 2, 512)); // neox (falcon 40B)
|
|
test_cases.emplace_back(new test_rope(type, { 80, 32, 10, 1}, 20, 2, 512)); // neox (stablelm)
|
|
test_cases.emplace_back(new test_rope(type, { 80, 32, 10, 1}, 32, 2, 512)); // neox (phi-2)
|
|
}
|
|
|
|
test_cases.emplace_back(new test_alibi());
|
|
test_cases.emplace_back(new test_im2col());
|
|
test_cases.emplace_back(new test_concat());
|
|
|
|
for (ggml_sort_order order : {GGML_SORT_ASC, GGML_SORT_DESC}) {
|
|
test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {8, 1, 1, 1}, order));
|
|
test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {16, 10, 10, 10}, order));
|
|
}
|
|
|
|
test_cases.emplace_back(new test_sum_rows());
|
|
test_cases.emplace_back(new test_upscale());
|
|
test_cases.emplace_back(new test_group_norm());
|
|
test_cases.emplace_back(new test_acc());
|
|
test_cases.emplace_back(new test_pad());
|
|
test_cases.emplace_back(new test_leaky_relu());
|
|
|
|
#if !defined(__SANITIZE_THREAD__)
|
|
// FIXME: these tests use too much memory with thread sanitizer
|
|
test_cases.emplace_back(new test_moe(8, 2, 1, 4096, 8*1024));
|
|
//test_cases.emplace_back(new test_moe(8, 2, 8, 4096, 14336));
|
|
#endif
|
|
|
|
// run tests
|
|
if (mode == MODE_TEST) {
|
|
ggml_backend_t backend_cpu = ggml_backend_cpu_init();
|
|
|
|
size_t n_ok = 0;
|
|
for (auto & test : test_cases) {
|
|
if (test->eval(backend, backend_cpu, op_name)) {
|
|
n_ok++;
|
|
}
|
|
}
|
|
printf(" %zu/%zu tests passed\n", n_ok, test_cases.size());
|
|
|
|
ggml_backend_free(backend_cpu);
|
|
|
|
return n_ok == test_cases.size();
|
|
}
|
|
|
|
if (mode == MODE_PERF) {
|
|
for (auto & test : test_cases) {
|
|
test->eval_perf(backend, op_name);
|
|
}
|
|
return true;
|
|
}
|
|
|
|
GGML_ASSERT(false);
|
|
return false;
|
|
}
|
|
|
|
static void usage(char ** argv) {
|
|
printf("Usage: %s [mode] [-o op] [-b backend]\n", argv[0]);
|
|
printf(" valid modes are: test (compare with CPU backend for correctness) or perf (performance evaluation)\n");
|
|
printf(" op names are as given by ggml_op_desc()\n");
|
|
}
|
|
|
|
int main(int argc, char ** argv) {
|
|
test_mode mode = MODE_TEST;
|
|
const char * op_name = NULL;
|
|
const char * backend = NULL;
|
|
|
|
for (int i = 1; i < argc; i++) {
|
|
if (strcmp(argv[i], "test") == 0) {
|
|
mode = MODE_TEST;
|
|
} else if (strcmp(argv[i], "perf") == 0) {
|
|
mode = MODE_PERF;
|
|
} else if (strcmp(argv[i], "-o") == 0) {
|
|
if (i + 1 < argc) {
|
|
op_name = argv[++i];
|
|
} else {
|
|
usage(argv);
|
|
return 1;
|
|
}
|
|
} else if (strcmp(argv[i], "-b") == 0) {
|
|
if (i + 1 < argc) {
|
|
backend = argv[++i];
|
|
} else {
|
|
usage(argv);
|
|
return 1;
|
|
}
|
|
} else {
|
|
usage(argv);
|
|
return 1;
|
|
}
|
|
}
|
|
|
|
// enumerate backends
|
|
printf("Testing %zu backends\n\n", ggml_backend_reg_get_count());
|
|
|
|
size_t n_ok = 0;
|
|
|
|
for (size_t i = 0; i < ggml_backend_reg_get_count(); i++) {
|
|
printf("Backend %zu/%zu (%s)\n", i + 1, ggml_backend_reg_get_count(), ggml_backend_reg_get_name(i));
|
|
|
|
if (backend != NULL && strcmp(backend, ggml_backend_reg_get_name(i)) != 0) {
|
|
printf(" Skipping\n");
|
|
n_ok++;
|
|
continue;
|
|
}
|
|
|
|
ggml_backend_t backend = ggml_backend_reg_init_backend(i, NULL);
|
|
GGML_ASSERT(backend != NULL);
|
|
printf(" Backend name: %s\n", ggml_backend_name(backend));
|
|
|
|
bool ok = test_backend(backend, mode, op_name);
|
|
|
|
printf(" Backend %s: ", ggml_backend_name(backend));
|
|
if (ok) {
|
|
printf("\033[1;32mOK\033[0m\n");
|
|
n_ok++;
|
|
} else {
|
|
printf("\033[1;31mFAIL\033[0m\n");
|
|
}
|
|
|
|
printf("\n");
|
|
|
|
ggml_backend_free(backend);
|
|
}
|
|
|
|
printf("%zu/%zu backends passed\n", n_ok, ggml_backend_reg_get_count());
|
|
|
|
if (n_ok != ggml_backend_reg_get_count()) {
|
|
printf("\033[1;31mFAIL\033[0m\n");
|
|
return 1;
|
|
}
|
|
|
|
printf("\033[1;32mOK\033[0m\n");
|
|
return 0;
|
|
}
|