mirror of
https://github.com/ggerganov/whisper.cpp.git
synced 2024-12-22 14:02:21 +00:00
2476 lines
87 KiB
C++
2476 lines
87 KiB
C++
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#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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// static RNG initialization (revisit if n_threads stops being constant)
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static const size_t n_threads = std::thread::hardware_concurrency();
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static std::vector<std::default_random_engine> generators = []() {
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std::random_device rd;
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std::vector<std::default_random_engine> vec;
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vec.reserve(n_threads);
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//for (size_t i = 0; i < n_threads; i++) { vec.emplace_back(1234 + i); } // fixed seed
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for (size_t i = 0; i < n_threads; i++) { vec.emplace_back(rd()); }
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return vec;
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}();
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size_t size = ggml_nelements(tensor);
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std::vector<float> data(size);
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auto init_thread = [&](size_t ith, size_t start, size_t end) {
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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(generators[ith]);
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}
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};
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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, i, 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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#if 0
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const char * val_str = getenv("GGML_TEST_EPS");
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float val = 1e-9f;
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if (val_str != nullptr) {
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val = std::stof(val_str);
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printf("GGML_TEST_EPS=%e\n", val);
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}
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// test quantization with very small values that may result in nan scales due to division by zero
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if (ggml_is_quantized(tensor->type)) {
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for (int i = 0; i < 256; i++) {
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data[i] = val;
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}
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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 || tensor->type == GGML_TYPE_BF16) {
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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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std::vector<float> imatrix(tensor->ne[0], 1.0f); // dummy importance matrix
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const float * im = imatrix.data();
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if (!ggml_quantize_requires_imatrix(tensor->type)) {
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// when the imatrix is optional, we want to test both quantization with and without imatrix
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// use one of the random numbers to decide
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if (data[0] > 0.5f*(min + max)) {
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im = nullptr;
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}
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}
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ggml_quantize_chunk(tensor->type, data.data(), dataq.data(), 0, size/tensor->ne[0], tensor->ne[0], im);
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GGML_ASSERT(ggml_validate_row_data(tensor->type, dataq.data(), dataq.size()));
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ggml_backend_tensor_set(tensor, dataq.data(), 0, dataq.size());
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} else if (tensor->type == GGML_TYPE_I8 || tensor->type == GGML_TYPE_I16 || tensor->type == GGML_TYPE_I32) {
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// This is going to create some weird integers though.
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ggml_backend_tensor_set(tensor, data.data(), 0, ggml_nbytes(tensor));
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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_BF16) {
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tv.push_back(ggml_bf16_to_fp32(*(ggml_bf16_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 (t->type == GGML_TYPE_I16) {
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tv.push_back((float)*(int16_t *) &buf[i]);
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} else if (t->type == GGML_TYPE_I8) {
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tv.push_back((float)*(int8_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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static std::string var_to_str(ggml_op_pool pool) {
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switch (pool) {
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case GGML_OP_POOL_AVG: return "avg";
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case GGML_OP_POOL_MAX: return "max";
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default: return std::to_string(pool);
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}
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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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#define VARS_TO_STR12(a, b, c, d, e, f, g, h, i, j, k, l) VAR_TO_STR(a) + "," + VARS_TO_STR11(b, c, d, e, f, g, h, i, j, k, l)
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#ifdef GGML_USE_SYCL
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static bool inline _isinf(float f) {
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return (*(uint32_t *)&f & 0x7fffffff) == 0x7f800000;
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}
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#else
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static bool inline _isinf(float f) { return std::isinf(f); }
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#endif
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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 _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;
|
||
|
|
|
||
|
|
static const int sentinel_size = 1024;
|
||
|
|
|
||
|
|
test_mode mode;
|
||
|
|
|
||
|
|
std::vector<ggml_tensor *> sentinels;
|
||
|
|
|
||
|
|
void add_sentinel(ggml_context * ctx) {
|
||
|
|
if (mode == MODE_PERF) {
|
||
|
|
return;
|
||
|
|
}
|
||
|
|
ggml_tensor * sentinel = ::ggml_new_tensor_1d(ctx, GGML_TYPE_F32, sentinel_size);
|
||
|
|
ggml_format_name(sentinel, "sent_%zu", sentinels.size());
|
||
|
|
sentinels.push_back(sentinel);
|
||
|
|
}
|
||
|
|
|
||
|
|
// hijack ggml_new_tensor to add sentinels after each tensor to check for overflows in the backend
|
||
|
|
|
||
|
|
ggml_tensor * ggml_new_tensor(ggml_context * ctx, ggml_type type, int n_dims, const int64_t * ne) {
|
||
|
|
ggml_tensor * t = ::ggml_new_tensor(ctx, type, n_dims, ne);
|
||
|
|
add_sentinel(ctx);
|
||
|
|
return t;
|
||
|
|
}
|
||
|
|
|
||
|
|
ggml_tensor * ggml_new_tensor_1d(ggml_context * ctx, ggml_type type, int64_t ne0) {
|
||
|
|
ggml_tensor * t = ::ggml_new_tensor_1d(ctx, type, ne0);
|
||
|
|
add_sentinel(ctx);
|
||
|
|
return t;
|
||
|
|
}
|
||
|
|
|
||
|
|
ggml_tensor * ggml_new_tensor_2d(ggml_context * ctx, ggml_type type, int64_t ne0, int64_t ne1) {
|
||
|
|
ggml_tensor * t = ::ggml_new_tensor_2d(ctx, type, ne0, ne1);
|
||
|
|
add_sentinel(ctx);
|
||
|
|
return t;
|
||
|
|
}
|
||
|
|
|
||
|
|
ggml_tensor * ggml_new_tensor_3d(ggml_context * ctx, ggml_type type, int64_t ne0, int64_t ne1, int64_t ne2) {
|
||
|
|
ggml_tensor * t = ::ggml_new_tensor_3d(ctx, type, ne0, ne1, ne2);
|
||
|
|
add_sentinel(ctx);
|
||
|
|
return t;
|
||
|
|
}
|
||
|
|
|
||
|
|
ggml_tensor * ggml_new_tensor_4d(ggml_context * ctx, ggml_type type, int64_t ne0, int64_t ne1, int64_t ne2, int64_t ne3) {
|
||
|
|
ggml_tensor * t = ::ggml_new_tensor_4d(ctx, type, ne0, ne1, ne2, ne3);
|
||
|
|
add_sentinel(ctx);
|
||
|
|
return t;
|
||
|
|
}
|
||
|
|
|
||
|
|
bool eval(ggml_backend_t backend1, ggml_backend_t backend2, const char * op_name) {
|
||
|
|
mode = MODE_TEST;
|
||
|
|
|
||
|
|
ggml_init_params params = {
|
||
|
|
/* .mem_size = */ ggml_tensor_overhead()*128 + ggml_graph_overhead(),
|
||
|
|
/* .mem_base = */ NULL,
|
||
|
|
/* .no_alloc = */ true,
|
||
|
|
};
|
||
|
|
ggml_context * ctx = ggml_init(params);
|
||
|
|
|
||
|
|
gf = ggml_new_graph(ctx);
|
||
|
|
|
||
|
|
// pre-graph sentinel
|
||
|
|
add_sentinel(ctx);
|
||
|
|
|
||
|
|
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;
|
||
|
|
}
|
||
|
|
|
||
|
|
printf(" %s(%s): ", op_desc(out).c_str(), vars().c_str());
|
||
|
|
fflush(stdout);
|
||
|
|
|
||
|
|
// check if the backends support the ops
|
||
|
|
bool supported = true;
|
||
|
|
for (ggml_backend_t backend : {backend1, backend2}) {
|
||
|
|
for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
|
||
|
|
if (!ggml_backend_supports_op(backend, t)) {
|
||
|
|
printf("not supported [%s] ", ggml_backend_name(backend));
|
||
|
|
supported = false;
|
||
|
|
break;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
if (!supported) {
|
||
|
|
printf("\n");
|
||
|
|
ggml_free(ctx);
|
||
|
|
return true;
|
||
|
|
}
|
||
|
|
|
||
|
|
// post-graph sentinel
|
||
|
|
add_sentinel(ctx);
|
||
|
|
|
||
|
|
// allocate
|
||
|
|
ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors(ctx, backend1);
|
||
|
|
if (buf == NULL) {
|
||
|
|
printf("failed to allocate tensors [%s] ", ggml_backend_name(backend1));
|
||
|
|
ggml_free(ctx);
|
||
|
|
return false;
|
||
|
|
}
|
||
|
|
|
||
|
|
// build graph
|
||
|
|
ggml_build_forward_expand(gf, out);
|
||
|
|
|
||
|
|
// add sentinels as graph nodes so that they are checked in the callback
|
||
|
|
for (ggml_tensor * sentinel : sentinels) {
|
||
|
|
gf->nodes[gf->n_nodes++] = sentinel;
|
||
|
|
}
|
||
|
|
|
||
|
|
// randomize tensors
|
||
|
|
initialize_tensors(ctx);
|
||
|
|
|
||
|
|
// compare
|
||
|
|
struct callback_userdata {
|
||
|
|
bool ok;
|
||
|
|
double max_err;
|
||
|
|
ggml_backend_t backend1;
|
||
|
|
ggml_backend_t backend2;
|
||
|
|
};
|
||
|
|
|
||
|
|
callback_userdata ud {
|
||
|
|
true,
|
||
|
|
max_nmse_err(),
|
||
|
|
backend1,
|
||
|
|
backend2
|
||
|
|
};
|
||
|
|
|
||
|
|
auto callback = [](int index, ggml_tensor * t1, ggml_tensor * t2, void * user_data) -> bool {
|
||
|
|
callback_userdata * ud = (callback_userdata *) user_data;
|
||
|
|
const char * bn1 = ggml_backend_name(ud->backend1);
|
||
|
|
const char * bn2 = ggml_backend_name(ud->backend2);
|
||
|
|
|
||
|
|
if (t1->op == GGML_OP_NONE) {
|
||
|
|
// sentinels must be unchanged
|
||
|
|
std::vector<uint8_t> t1_data(ggml_nbytes(t1));
|
||
|
|
std::vector<uint8_t> t2_data(ggml_nbytes(t2));
|
||
|
|
ggml_backend_tensor_get(t1, t1_data.data(), 0, ggml_nbytes(t1));
|
||
|
|
ggml_backend_tensor_get(t2, t2_data.data(), 0, ggml_nbytes(t2));
|
||
|
|
|
||
|
|
if (memcmp(t1_data.data(), t2_data.data(), ggml_nbytes(t1)) != 0) {
|
||
|
|
printf("sentinel mismatch: %s ", t1->name);
|
||
|
|
ud->ok = false;
|
||
|
|
return true;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
std::vector<float> f1 = tensor_to_float(t1);
|
||
|
|
std::vector<float> f2 = tensor_to_float(t2);
|
||
|
|
|
||
|
|
for (size_t i = 0; i < f1.size(); i++) {
|
||
|
|
// check for nans
|
||
|
|
if (std::isnan(f1[i]) || std::isnan(f2[i])) {
|
||
|
|
printf("[%s] NaN at index %zu (%s=%f %s=%f) ", ggml_op_desc(t1), i, bn1, f1[i], bn2, f2[i]);
|
||
|
|
ud->ok = false;
|
||
|
|
return true;
|
||
|
|
}
|
||
|
|
// check for infs: both must be inf of the same sign, or both must be finite
|
||
|
|
if (isinf_or_max(f1[i]) || isinf_or_max(f2[i])) {
|
||
|
|
if (isinf_or_max(f1[i]) && isinf_or_max(f2[i])) {
|
||
|
|
if (std::signbit(f1[i]) != std::signbit(f2[i])) {
|
||
|
|
printf("[%s] inf sign mismatch: %s=%f %s=%f ", ggml_op_desc(t1), bn1, f1[i], bn2, f2[i]);
|
||
|
|
ud->ok = false;
|
||
|
|
return true;
|
||
|
|
}
|
||
|
|
} else {
|
||
|
|
printf("[%s] inf mismatch: %s=%f %s=%f ", ggml_op_desc(t1), bn1, f1[i], bn2, f2[i]);
|
||
|
|
ud->ok = false;
|
||
|
|
return true;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
double err = nmse(f1.data(), f2.data(), f1.size());
|
||
|
|
if (err > ud->max_err) {
|
||
|
|
printf("[%s] NMSE = %.9f > %.9f ", ggml_op_desc(t1), err, ud->max_err);
|
||
|
|
//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);
|
||
|
|
};
|
||
|
|
|
||
|
|
const bool cmp_ok = ggml_backend_compare_graph_backend(backend1, backend2, gf, callback, &ud);
|
||
|
|
|
||
|
|
if (!cmp_ok) {
|
||
|
|
printf("compare failed ");
|
||
|
|
}
|
||
|
|
|
||
|
|
ggml_backend_buffer_free(buf);
|
||
|
|
|
||
|
|
ggml_free(ctx);
|
||
|
|
|
||
|
|
if (ud.ok && cmp_ok) {
|
||
|
|
printf("\033[1;32mOK\033[0m\n");
|
||
|
|
return true;
|
||
|
|
}
|
||
|
|
|
||
|
|
printf("\033[1;31mFAIL\033[0m\n");
|
||
|
|
return false;
|
||
|
|
}
|
||
|
|
|
||
|
|
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);
|
||
|
|
if (buf == NULL) {
|
||
|
|
printf("failed to allocate tensors\n");
|
||
|
|
ggml_free(ctx);
|
||
|
|
return false;
|
||
|
|
}
|
||
|
|
|
||
|
|
// 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_a;
|
||
|
|
int v; // view (1 : non-contiguous a)
|
||
|
|
|
||
|
|
std::string vars() override {
|
||
|
|
return VARS_TO_STR3(type, ne_a, v);
|
||
|
|
}
|
||
|
|
|
||
|
|
test_unary(ggml_unary_op op,
|
||
|
|
ggml_type type = GGML_TYPE_F32,
|
||
|
|
std::array<int64_t, 4> ne_a = {128, 10, 10, 10},
|
||
|
|
int v = 0)
|
||
|
|
: op(op), type(type), ne_a(ne_a), v(v) {}
|
||
|
|
|
||
|
|
ggml_tensor * build_graph(ggml_context * ctx) override {
|
||
|
|
ggml_tensor * a;
|
||
|
|
if (v & 1) {
|
||
|
|
auto ne = ne_a; ne[0] *= 3;
|
||
|
|
a = ggml_new_tensor(ctx, type, 4, ne.data());
|
||
|
|
a = ggml_view_4d(ctx, a, ne_a[0], ne_a[1], ne_a[2], ne_a[3], a->nb[1], a->nb[2], a->nb[3], 0);
|
||
|
|
} else {
|
||
|
|
a = ggml_new_tensor(ctx, type, 4, ne_a.data());
|
||
|
|
}
|
||
|
|
ggml_tensor * out = ggml_unary(ctx, a, op);
|
||
|
|
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)) {
|
||
|
|
// test extended range of values to check for NaNs in GELU
|
||
|
|
init_tensor_uniform(t, -150.f, 150.f);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
};
|
||
|
|
|
||
|
|
// 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;
|
||
|
|
const std::array<int64_t, 4> permute;
|
||
|
|
bool _use_permute;
|
||
|
|
|
||
|
|
std::string vars() override {
|
||
|
|
std::string v = VARS_TO_STR2(type, ne);
|
||
|
|
if (_use_permute) v += "," + VAR_TO_STR(permute);
|
||
|
|
return v;
|
||
|
|
}
|
||
|
|
|
||
|
|
test_dup(ggml_type type = GGML_TYPE_F32,
|
||
|
|
std::array<int64_t, 4> ne = {10, 10, 10, 1},
|
||
|
|
std::array<int64_t, 4> permute = {0, 0, 0, 0})
|
||
|
|
: type(type), ne(ne), permute(permute),
|
||
|
|
_use_permute(permute[0] + permute[1] + permute[2] + permute[3] > 0) {}
|
||
|
|
|
||
|
|
ggml_tensor * build_graph(ggml_context * ctx) override {
|
||
|
|
ggml_tensor * src = ggml_new_tensor(ctx, type, 4, ne.data());
|
||
|
|
if (_use_permute) {
|
||
|
|
src = ggml_permute(ctx, src, permute[0], permute[1], permute[2], permute[3]);
|
||
|
|
}
|
||
|
|
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);
|
||
|
|
}
|
||
|
|
|
||
|
|
double max_nmse_err() override {
|
||
|
|
return 1e-6;
|
||
|
|
}
|
||
|
|
|
||
|
|
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 n_used;
|
||
|
|
const bool b; // brodcast b matrix
|
||
|
|
const int64_t m;
|
||
|
|
const int64_t n;
|
||
|
|
const int64_t k;
|
||
|
|
|
||
|
|
std::string vars() override {
|
||
|
|
return VARS_TO_STR8(type_a, type_b, n_mats, n_used, b, m, n, k);
|
||
|
|
}
|
||
|
|
|
||
|
|
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 = 8, int n_used = 2, bool b = false,
|
||
|
|
int64_t m = 32, int64_t n = 32, int64_t k = 32)
|
||
|
|
: type_a(type_a), type_b(type_b), n_mats(n_mats), n_used(n_used), b(b),
|
||
|
|
m(m), n(n), k(k) {
|
||
|
|
GGML_ASSERT(n_used <= n_mats);
|
||
|
|
}
|
||
|
|
|
||
|
|
ggml_tensor * build_graph(ggml_context * ctx) override {
|
||
|
|
// C^T = A * B^T: (k, m) * (k, n) => (m, n)
|
||
|
|
ggml_tensor * as = ggml_new_tensor_3d(ctx, type_a, k, m, n_mats);
|
||
|
|
ggml_tensor * ids = ggml_new_tensor_2d(ctx, GGML_TYPE_I32, n_mats, n);
|
||
|
|
if (n_used != n_mats) {
|
||
|
|
ids = ggml_view_2d(ctx, ids, n_used, n, ids->nb[1], 0);
|
||
|
|
}
|
||
|
|
ggml_tensor * b = ggml_new_tensor_3d(ctx, type_b, k, this->b ? 1 : n_used, n);
|
||
|
|
ggml_tensor * out = ggml_mul_mat_id(ctx, as, b, ids);
|
||
|
|
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_SQRT
|
||
|
|
struct test_sqrt : 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_sqrt(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_sqrt(ctx, a);
|
||
|
|
return out;
|
||
|
|
}
|
||
|
|
|
||
|
|
void initialize_tensors(ggml_context * ctx) override {
|
||
|
|
// fill with positive values
|
||
|
|
for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
|
||
|
|
init_tensor_uniform(t, 0.0f, 100.0f);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
};
|
||
|
|
|
||
|
|
// 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;
|
||
|
|
const bool mask;
|
||
|
|
const float scale;
|
||
|
|
const float max_bias;
|
||
|
|
|
||
|
|
std::string vars() override {
|
||
|
|
return VARS_TO_STR5(type, ne, mask, scale, max_bias);
|
||
|
|
}
|
||
|
|
|
||
|
|
// the 1024 test with bias occasionally fails:
|
||
|
|
// SOFT_MAX(type=f32,ne=[1024,16,1,1],mask=1,scale=1.000000,max_bias=8.000000): [SOFT_MAX] NMSE = 0.000000103 > 0.000000100 FAIL
|
||
|
|
virtual double max_nmse_err() override {
|
||
|
|
return 1e-6;
|
||
|
|
}
|
||
|
|
|
||
|
|
test_soft_max(ggml_type type = GGML_TYPE_F32,
|
||
|
|
std::array<int64_t, 4> ne = {10, 10, 10, 10},
|
||
|
|
bool mask = false,
|
||
|
|
float scale = 1.0f,
|
||
|
|
float max_bias = 0.0f)
|
||
|
|
: type(type), ne(ne), mask(mask), scale(scale), max_bias(max_bias) {}
|
||
|
|
|
||
|
|
ggml_tensor * build_graph(ggml_context * ctx) override {
|
||
|
|
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
|
||
|
|
ggml_tensor * mask = nullptr;
|
||
|
|
if (this->mask) {
|
||
|
|
mask = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, ne[0], ne[1]);
|
||
|
|
}
|
||
|
|
ggml_tensor * out = ggml_soft_max_ext(ctx, a, mask, scale, max_bias);
|
||
|
|
return out;
|
||
|
|
}
|
||
|
|
};
|
||
|
|
|
||
|
|
// GGML_OP_ROPE
|
||
|
|
struct test_rope : public test_case {
|
||
|
|
const ggml_type type;
|
||
|
|
const std::array<int64_t, 4> ne_a;
|
||
|
|
int n_dims;
|
||
|
|
int mode;
|
||
|
|
int n_ctx; // used to generate positions
|
||
|
|
float fs; // freq_scale
|
||
|
|
float ef; // ext_factor
|
||
|
|
float af; // attn_factor
|
||
|
|
bool ff;
|
||
|
|
int v; // view (1 : non-contiguous a)
|
||
|
|
|
||
|
|
std::string vars() override {
|
||
|
|
return VARS_TO_STR10(type, ne_a, n_dims, mode, n_ctx, fs, ef, af, ff, v);
|
||
|
|
}
|
||
|
|
|
||
|
|
test_rope(ggml_type type = GGML_TYPE_F32,
|
||
|
|
std::array<int64_t, 4> ne_a = {10, 10, 10, 1},
|
||
|
|
int n_dims = 10, int mode = 0, int n_ctx = 512, float fs = 1.0f, float ef = 0.0f, float af = 0.0f, bool ff = false, int v = 0)
|
||
|
|
: type(type), ne_a(ne_a), n_dims(n_dims), mode(mode), n_ctx(n_ctx), fs(fs), ef(ef), af(af), ff(ff), v(v) {}
|
||
|
|
|
||
|
|
ggml_tensor * build_graph(ggml_context * ctx) override {
|
||
|
|
ggml_tensor * a;
|
||
|
|
if (v & 1) {
|
||
|
|
auto ne = ne_a; ne[0] *= 2; ne[1] *= 4; ne[2] *= 3;
|
||
|
|
a = ggml_new_tensor(ctx, type, 4, ne.data());
|
||
|
|
a = ggml_view_4d(ctx, a, ne_a[0], ne_a[1], ne_a[2], ne_a[3], a->nb[1], a->nb[2], a->nb[3], 0);
|
||
|
|
} else {
|
||
|
|
a = ggml_new_tensor(ctx, type, 4, ne_a.data());
|
||
|
|
}
|
||
|
|
ggml_tensor * pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, ne_a[2]);
|
||
|
|
ggml_tensor * freq = ff ? ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_dims/2) : nullptr;
|
||
|
|
ggml_tensor * out = ggml_rope_ext(ctx, a, pos, freq, n_dims, mode, 0, 10000.0f, fs, ef, af, 1.0f, 1.0f);
|
||
|
|
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_a[2]);
|
||
|
|
for (int i = 0; i < ne_a[2]; i++) {
|
||
|
|
data[i] = rand() % n_ctx;
|
||
|
|
}
|
||
|
|
ggml_backend_tensor_set(t, data.data(), 0, ne_a[2] * sizeof(int));
|
||
|
|
} else {
|
||
|
|
if (t->ne[0] == n_dims/2) {
|
||
|
|
// frequency factors in the range [0.9f, 1.1f]
|
||
|
|
init_tensor_uniform(t, 0.9f, 1.1f);
|
||
|
|
} else {
|
||
|
|
init_tensor_uniform(t);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
};
|
||
|
|
|
||
|
|
// GGML_OP_POOL2D
|
||
|
|
struct test_pool2d : public test_case {
|
||
|
|
enum ggml_op_pool pool_type;
|
||
|
|
const ggml_type type_input;
|
||
|
|
const std::array<int64_t, 4> ne_input;
|
||
|
|
// kernel size
|
||
|
|
const int k0;
|
||
|
|
const int k1;
|
||
|
|
// stride
|
||
|
|
const int s0;
|
||
|
|
const int s1;
|
||
|
|
// padding
|
||
|
|
const int p0;
|
||
|
|
const int p1;
|
||
|
|
|
||
|
|
std::string vars() override {
|
||
|
|
return VARS_TO_STR9(pool_type, type_input, ne_input, k0, k1, s0, s1, p0, p1);
|
||
|
|
}
|
||
|
|
|
||
|
|
test_pool2d(ggml_op_pool pool_type = GGML_OP_POOL_AVG,
|
||
|
|
ggml_type type_input = GGML_TYPE_F32,
|
||
|
|
std::array<int64_t, 4> ne_input = {10, 10, 3, 1}, // [input_width, input_height, input_channels, 1]
|
||
|
|
int k0 = 3, int k1 = 3,
|
||
|
|
int s0 = 1, int s1 = 1,
|
||
|
|
int p0 = 1, int p1 = 1)
|
||
|
|
: pool_type(pool_type), type_input(type_input), ne_input(ne_input), k0(k0), k1(k1), s0(s0), s1(s1), p0(p0), p1(p1) {}
|
||
|
|
|
||
|
|
ggml_tensor * build_graph(ggml_context * ctx) override {
|
||
|
|
ggml_tensor * input = ggml_new_tensor(ctx, type_input, 4, ne_input.data());
|
||
|
|
ggml_tensor * out = ggml_pool_2d(ctx, input, pool_type, k0, k1, s0, s1, p0, p1);
|
||
|
|
return out;
|
||
|
|
}
|
||
|
|
};
|
||
|
|
|
||
|
|
// GGML_OP_IM2COL
|
||
|
|
struct test_im2col : public test_case {
|
||
|
|
const ggml_type type_input;
|
||
|
|
const ggml_type type_kernel;
|
||
|
|
const ggml_type dst_type;
|
||
|
|
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_STR12(type_input, type_kernel, dst_type, 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, ggml_type dst_type = GGML_TYPE_F32,
|
||
|
|
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), dst_type(dst_type), 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, dst_type);
|
||
|
|
return out;
|
||
|
|
}
|
||
|
|
};
|
||
|
|
|
||
|
|
// GGML_OP_CONCAT
|
||
|
|
struct test_concat : public test_case {
|
||
|
|
const ggml_type type;
|
||
|
|
const std::array<int64_t, 4> ne_a;
|
||
|
|
const int64_t ne_b_d;
|
||
|
|
const int dim;
|
||
|
|
const int v; // view (1 << 0: non-cont a, 1 << 1: non-cont b)
|
||
|
|
|
||
|
|
std::string vars() override {
|
||
|
|
return VARS_TO_STR5(type, ne_a, ne_b_d, dim, v);
|
||
|
|
}
|
||
|
|
|
||
|
|
test_concat(ggml_type type = GGML_TYPE_F32,
|
||
|
|
std::array<int64_t, 4> ne_a = {10, 10, 10, 10},
|
||
|
|
int64_t ne_b_d = 10,
|
||
|
|
int dim = 2, int v = 0)
|
||
|
|
: type(type), ne_a(ne_a), ne_b_d(ne_b_d), dim(dim), v(v) {}
|
||
|
|
|
||
|
|
ggml_tensor * build_graph(ggml_context * ctx) override {
|
||
|
|
auto ne_b = ne_a;
|
||
|
|
ne_b[dim] = ne_b_d;
|
||
|
|
ggml_tensor * a;
|
||
|
|
if (v & 1) {
|
||
|
|
auto ne = ne_a; ne[0] *= 2; ne[1] *= 4; ne[2] *= 3;
|
||
|
|
a = ggml_new_tensor(ctx, type, 4, ne.data());
|
||
|
|
a = ggml_view_4d(ctx, a, ne_a[0], ne_a[1], ne_a[2], ne_a[3], a->nb[1], a->nb[2], a->nb[3], 0);
|
||
|
|
} else {
|
||
|
|
a = ggml_new_tensor(ctx, type, 4, ne_a.data());
|
||
|
|
}
|
||
|
|
ggml_tensor * b;
|
||
|
|
if (v & 2) {
|
||
|
|
auto ne = ne_b; ne[0] *= 3; ne[1] *= 2; ne[2] *= 4;
|
||
|
|
b = ggml_new_tensor(ctx, type, 4, ne.data());
|
||
|
|
b = ggml_view_4d(ctx, b, ne_b[0], ne_b[1], ne_b[2], ne_b[3], b->nb[1], b->nb[2], b->nb[3], 0);
|
||
|
|
} else {
|
||
|
|
b = ggml_new_tensor(ctx, type, 4, ne_b.data());
|
||
|
|
}
|
||
|
|
ggml_tensor * out = ggml_concat(ctx, a, b, dim);
|
||
|
|
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_ORDER_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;
|
||
|
|
const bool transpose;
|
||
|
|
|
||
|
|
std::string vars() override {
|
||
|
|
return VARS_TO_STR4(type, ne, scale_factor, transpose);
|
||
|
|
}
|
||
|
|
|
||
|
|
test_upscale(ggml_type type = GGML_TYPE_F32,
|
||
|
|
std::array<int64_t, 4> ne = {512, 512, 3, 1},
|
||
|
|
int32_t scale_factor = 2, bool transpose = false)
|
||
|
|
: type(type), ne(ne), scale_factor(scale_factor), transpose(transpose) {}
|
||
|
|
|
||
|
|
ggml_tensor * build_graph(ggml_context * ctx) override {
|
||
|
|
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
|
||
|
|
if (transpose) a = ggml_transpose(ctx, a);
|
||
|
|
ggml_tensor * out = ggml_upscale(ctx, a, scale_factor);
|
||
|
|
return out;
|
||
|
|
}
|
||
|
|
};
|
||
|
|
|
||
|
|
// GGML_OP_UPSCALE (ext)
|
||
|
|
struct test_upscale_ext : public test_case {
|
||
|
|
const ggml_type type;
|
||
|
|
const std::array<int64_t, 4> ne;
|
||
|
|
const std::array<int64_t, 4> ne_tgt;
|
||
|
|
|
||
|
|
std::string vars() override {
|
||
|
|
return VARS_TO_STR3(type, ne, ne_tgt);
|
||
|
|
}
|
||
|
|
|
||
|
|
test_upscale_ext(ggml_type type = GGML_TYPE_F32,
|
||
|
|
std::array<int64_t, 4> ne = {2, 5, 7, 11},
|
||
|
|
std::array<int64_t, 4> ne_tgt = {5, 7, 11, 13})
|
||
|
|
: type(type), ne(ne), ne_tgt(ne_tgt) {}
|
||
|
|
|
||
|
|
ggml_tensor * build_graph(ggml_context * ctx) override {
|
||
|
|
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
|
||
|
|
ggml_tensor * out = ggml_upscale_ext(ctx, a, ne_tgt[0], ne_tgt[1],ne_tgt[2], ne_tgt[3]);
|
||
|
|
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_ARANGE
|
||
|
|
struct test_arange : public test_case {
|
||
|
|
const ggml_type type;
|
||
|
|
const float start;
|
||
|
|
const float stop;
|
||
|
|
const float step;
|
||
|
|
|
||
|
|
std::string vars() override {
|
||
|
|
return VARS_TO_STR4(type, start, stop, step);
|
||
|
|
}
|
||
|
|
|
||
|
|
test_arange(ggml_type type = GGML_TYPE_F32,
|
||
|
|
float start = 0.f, float stop = 10.f, float step = 1.f)
|
||
|
|
: type(type), start(start), stop(stop), step(step) {}
|
||
|
|
|
||
|
|
ggml_tensor * build_graph(ggml_context * ctx) override {
|
||
|
|
ggml_tensor * out = ggml_arange(ctx, start, stop, step);
|
||
|
|
return out;
|
||
|
|
}
|
||
|
|
};
|
||
|
|
|
||
|
|
// GGML_OP_TIMESTEP_EMBEDDING
|
||
|
|
struct test_timestep_embedding : public test_case {
|
||
|
|
const ggml_type type;
|
||
|
|
const std::array<int64_t, 4> ne_a;
|
||
|
|
const int dim;
|
||
|
|
const int max_period;
|
||
|
|
|
||
|
|
std::string vars() override {
|
||
|
|
return VARS_TO_STR4(type, ne_a, dim, max_period);
|
||
|
|
}
|
||
|
|
|
||
|
|
test_timestep_embedding(ggml_type type = GGML_TYPE_F32,
|
||
|
|
std::array<int64_t, 4> ne_a = {2, 1, 1, 1},
|
||
|
|
int dim = 320, int max_period=10000)
|
||
|
|
: type(type), ne_a(ne_a), dim(dim), max_period(max_period) {}
|
||
|
|
|
||
|
|
ggml_tensor * build_graph(ggml_context * ctx) override {
|
||
|
|
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne_a.data());
|
||
|
|
ggml_tensor * out = ggml_timestep_embedding(ctx, a, dim, max_period);
|
||
|
|
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;
|
||
|
|
}
|
||
|
|
};
|
||
|
|
|
||
|
|
// GGML_OP_FLASH_ATTN_EXT
|
||
|
|
struct test_flash_attn_ext : public test_case {
|
||
|
|
const int64_t hs; // head size
|
||
|
|
const int64_t nh; // num heads
|
||
|
|
const int64_t kv; // kv size
|
||
|
|
const int64_t nb; // batch size
|
||
|
|
|
||
|
|
const bool mask; // use mask
|
||
|
|
|
||
|
|
const float max_bias; // ALiBi
|
||
|
|
|
||
|
|
const ggml_type type_KV;
|
||
|
|
|
||
|
|
std::string vars() override {
|
||
|
|
return VARS_TO_STR7(hs, nh, kv, nb, mask, max_bias, type_KV);
|
||
|
|
}
|
||
|
|
|
||
|
|
double max_nmse_err() override {
|
||
|
|
return 5e-4;
|
||
|
|
}
|
||
|
|
|
||
|
|
test_flash_attn_ext(int64_t hs = 128, int64_t nh = 32, int64_t kv = 96, int64_t nb = 8, bool mask = true, float max_bias = 0.0f, ggml_type type_KV = GGML_TYPE_F16)
|
||
|
|
: hs(hs), nh(nh), kv(kv), nb(nb), mask(mask), max_bias(max_bias), type_KV(type_KV) {}
|
||
|
|
|
||
|
|
ggml_tensor * build_graph(ggml_context * ctx) override {
|
||
|
|
const int64_t hs_padded = GGML_PAD(hs, ggml_blck_size(type_KV));
|
||
|
|
|
||
|
|
ggml_tensor * q = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, hs_padded, nb, nh, 1);
|
||
|
|
ggml_tensor * k = ggml_new_tensor_4d(ctx, type_KV, hs_padded, kv, nh, 1);
|
||
|
|
ggml_tensor * v = ggml_new_tensor_4d(ctx, type_KV, hs_padded, kv, nh, 1);
|
||
|
|
ggml_tensor * m = mask ? ggml_new_tensor_4d(ctx, GGML_TYPE_F16, kv, GGML_PAD(nb, GGML_KQ_MASK_PAD), 1, 1) : nullptr;
|
||
|
|
ggml_tensor * out = ggml_flash_attn_ext(ctx, q, k, v, m, 1.0f/sqrtf(hs), max_bias);
|
||
|
|
return out;
|
||
|
|
}
|
||
|
|
};
|
||
|
|
|
||
|
|
enum llm_norm_type {
|
||
|
|
LLM_NORM,
|
||
|
|
LLM_NORM_RMS,
|
||
|
|
};
|
||
|
|
|
||
|
|
struct llama_hparams {
|
||
|
|
uint32_t n_vocab;
|
||
|
|
uint32_t n_embd;
|
||
|
|
uint32_t n_head;
|
||
|
|
uint32_t n_head_kv;
|
||
|
|
static constexpr uint32_t n_layer = 1;
|
||
|
|
uint32_t n_rot;
|
||
|
|
uint32_t n_embd_head; // dimension of values (d_v)
|
||
|
|
uint32_t n_ff;
|
||
|
|
|
||
|
|
float f_norm_eps;
|
||
|
|
float f_norm_rms_eps;
|
||
|
|
|
||
|
|
// cparams
|
||
|
|
static constexpr uint32_t n_ctx = 512; // user-specified context size
|
||
|
|
static constexpr uint32_t n_ctx_orig = n_ctx;
|
||
|
|
|
||
|
|
// batch
|
||
|
|
int32_t n_tokens;
|
||
|
|
|
||
|
|
// llm_build_context
|
||
|
|
static constexpr int32_t n_kv = 32; // size of KV cache to consider (n_kv <= n_ctx
|
||
|
|
static constexpr int32_t kv_head = 1; // index of where we store new KV data in the cache
|
||
|
|
|
||
|
|
uint32_t n_embd_gqa() const { // dimension of key embeddings across all k-v heads
|
||
|
|
return n_embd_head * n_head_kv;
|
||
|
|
}
|
||
|
|
};
|
||
|
|
|
||
|
|
// LLM base class
|
||
|
|
struct test_llm : public test_case {
|
||
|
|
llama_hparams hp;
|
||
|
|
|
||
|
|
protected:
|
||
|
|
test_llm(llama_hparams hp)
|
||
|
|
: hp(std::move(hp)) {
|
||
|
|
}
|
||
|
|
|
||
|
|
public:
|
||
|
|
struct ggml_tensor * llm_build_norm(
|
||
|
|
struct ggml_context * ctx,
|
||
|
|
struct ggml_tensor * cur,
|
||
|
|
struct ggml_tensor * mw,
|
||
|
|
struct ggml_tensor * mb,
|
||
|
|
llm_norm_type type) {
|
||
|
|
switch (type) {
|
||
|
|
case LLM_NORM: cur = ggml_norm (ctx, cur, hp.f_norm_eps); break;
|
||
|
|
case LLM_NORM_RMS: cur = ggml_rms_norm(ctx, cur, hp.f_norm_rms_eps); break;
|
||
|
|
}
|
||
|
|
cur = ggml_mul(ctx, cur, mw);
|
||
|
|
if (mb) {
|
||
|
|
cur = ggml_add(ctx, cur, mb);
|
||
|
|
}
|
||
|
|
return cur;
|
||
|
|
}
|
||
|
|
|
||
|
|
void llm_build_kv_store(
|
||
|
|
struct ggml_context * ctx,
|
||
|
|
struct ggml_tensor * k_l,
|
||
|
|
struct ggml_tensor * v_l,
|
||
|
|
struct ggml_tensor * k_cur,
|
||
|
|
struct ggml_tensor * v_cur) {
|
||
|
|
// compute the transposed [n_tokens, n_embd] V matrix
|
||
|
|
struct ggml_tensor * v_cur_t = ggml_transpose(ctx, ggml_reshape_2d(ctx, v_cur, hp.n_embd_gqa(), hp.n_tokens));
|
||
|
|
|
||
|
|
struct ggml_tensor * k_cache_view = ggml_view_1d(ctx, k_l, hp.n_tokens*hp.n_embd_gqa(),
|
||
|
|
(ggml_row_size(k_l->type, hp.n_embd_gqa()))*hp.kv_head);
|
||
|
|
|
||
|
|
struct ggml_tensor * v_cache_view = ggml_view_2d(ctx, v_l, hp.n_tokens, hp.n_embd_gqa(),
|
||
|
|
( hp.n_ctx)*ggml_element_size(v_l),
|
||
|
|
(hp.kv_head)*ggml_element_size(v_l));
|
||
|
|
|
||
|
|
// important: storing RoPE-ed version of K in the KV cache!
|
||
|
|
ggml_cpy(ctx, k_cur, k_cache_view);
|
||
|
|
ggml_cpy(ctx, v_cur_t, v_cache_view);
|
||
|
|
}
|
||
|
|
|
||
|
|
struct ggml_tensor * llm_build_kqv(
|
||
|
|
struct ggml_context * ctx,
|
||
|
|
struct ggml_tensor * k_l,
|
||
|
|
struct ggml_tensor * v_l,
|
||
|
|
struct ggml_tensor * q_cur,
|
||
|
|
struct ggml_tensor * kq_mask,
|
||
|
|
float kq_scale) {
|
||
|
|
struct ggml_tensor * q = ggml_permute(ctx, q_cur, 0, 2, 1, 3);
|
||
|
|
|
||
|
|
struct ggml_tensor * k =
|
||
|
|
ggml_view_3d(ctx, k_l,
|
||
|
|
hp.n_embd_head, hp.n_kv, hp.n_head_kv,
|
||
|
|
ggml_row_size(k_l->type, hp.n_embd_gqa()),
|
||
|
|
ggml_row_size(k_l->type, hp.n_embd_head),
|
||
|
|
0);
|
||
|
|
|
||
|
|
struct ggml_tensor * kq = ggml_mul_mat(ctx, k, q);
|
||
|
|
|
||
|
|
kq = ggml_soft_max_ext(ctx, kq, kq_mask, kq_scale, 0.0f);
|
||
|
|
|
||
|
|
// split cached v into n_head heads
|
||
|
|
struct ggml_tensor * v =
|
||
|
|
ggml_view_3d(ctx, v_l,
|
||
|
|
hp.n_kv, hp.n_embd_head, hp.n_head_kv,
|
||
|
|
ggml_element_size(v_l)*hp.n_ctx,
|
||
|
|
ggml_element_size(v_l)*hp.n_ctx*hp.n_embd_head,
|
||
|
|
0);
|
||
|
|
|
||
|
|
struct ggml_tensor * kqv = ggml_mul_mat(ctx, v, kq);
|
||
|
|
|
||
|
|
struct ggml_tensor * kqv_merged = ggml_permute(ctx, kqv, 0, 2, 1, 3);
|
||
|
|
|
||
|
|
struct ggml_tensor * cur = ggml_cont_2d(ctx, kqv_merged, hp.n_embd_head*hp.n_head, hp.n_tokens);
|
||
|
|
|
||
|
|
struct ggml_tensor * wo = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, hp.n_embd, hp.n_embd);
|
||
|
|
cur = ggml_mul_mat(ctx, wo, cur);
|
||
|
|
|
||
|
|
return cur;
|
||
|
|
}
|
||
|
|
|
||
|
|
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(hp.n_tokens);
|
||
|
|
for (int i = 0; i < hp.n_tokens; i++) {
|
||
|
|
data[i] = rand() % hp.n_ctx;
|
||
|
|
}
|
||
|
|
ggml_backend_tensor_set(t, data.data(), 0, hp.n_tokens * sizeof(int));
|
||
|
|
} else {
|
||
|
|
init_tensor_uniform(t);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
};
|
||
|
|
|
||
|
|
// Llama
|
||
|
|
struct test_llama : public test_llm {
|
||
|
|
static constexpr float freq_base = 10000.0f;
|
||
|
|
static constexpr float freq_scale = 1.0f;
|
||
|
|
static constexpr float ext_factor = 0.0f;
|
||
|
|
static constexpr float attn_factor = 1.0f;
|
||
|
|
static constexpr float beta_fast = 32.0f;
|
||
|
|
static constexpr float beta_slow = 1.0f;
|
||
|
|
|
||
|
|
std::string op_desc(ggml_tensor * t) override {
|
||
|
|
GGML_UNUSED(t);
|
||
|
|
return "LLAMA";
|
||
|
|
}
|
||
|
|
|
||
|
|
std::string vars() override {
|
||
|
|
auto n_tokens = hp.n_tokens;
|
||
|
|
return VARS_TO_STR1(n_tokens);
|
||
|
|
}
|
||
|
|
|
||
|
|
double max_nmse_err() override {
|
||
|
|
return 2e-3;
|
||
|
|
}
|
||
|
|
|
||
|
|
test_llama(int n_tokens = 1)
|
||
|
|
: test_llm({
|
||
|
|
/*n_vocab =*/ 32000,
|
||
|
|
/*n_embd =*/ 3200,
|
||
|
|
/*n_head =*/ 32,
|
||
|
|
/*n_head_kv =*/ 32,
|
||
|
|
/*n_rot =*/ 100,
|
||
|
|
/*n_embd_head =*/ 100,
|
||
|
|
/*n_ff =*/ 8640,
|
||
|
|
/*f_norm_eps =*/ 0.f,
|
||
|
|
/*f_norm_rms_eps =*/ 1e-5f,
|
||
|
|
/*n_tokens =*/ n_tokens,
|
||
|
|
}) {
|
||
|
|
}
|
||
|
|
|
||
|
|
ggml_tensor * build_graph(ggml_context * ctx) override {
|
||
|
|
struct ggml_tensor * cur;
|
||
|
|
struct ggml_tensor * inpL;
|
||
|
|
|
||
|
|
inpL = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, hp.n_embd, hp.n_tokens);
|
||
|
|
|
||
|
|
// inp_pos - contains the positions
|
||
|
|
struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, hp.n_tokens);
|
||
|
|
|
||
|
|
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
||
|
|
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx, GGML_TYPE_F16, hp.n_kv, hp.n_tokens, 1);
|
||
|
|
|
||
|
|
ggml_tensor * k_l = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, 1638400);
|
||
|
|
ggml_tensor * v_l = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, 1638400);
|
||
|
|
|
||
|
|
for (uint32_t il = 0; il < hp.n_layer; ++il) {
|
||
|
|
struct ggml_tensor * inpSA = inpL;
|
||
|
|
|
||
|
|
// norm
|
||
|
|
ggml_tensor * attn_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hp.n_embd);
|
||
|
|
cur = llm_build_norm(ctx, inpL, attn_norm, nullptr, LLM_NORM_RMS);
|
||
|
|
|
||
|
|
// self-attention
|
||
|
|
{
|
||
|
|
ggml_tensor * wq = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, hp.n_embd, hp.n_embd);
|
||
|
|
ggml_tensor * wk = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, hp.n_embd, hp.n_embd_gqa());
|
||
|
|
ggml_tensor * wv = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, hp.n_embd, hp.n_embd_gqa());
|
||
|
|
|
||
|
|
// compute Q and K and RoPE them
|
||
|
|
struct ggml_tensor * Qcur = ggml_mul_mat(ctx, wq, cur);
|
||
|
|
struct ggml_tensor * Kcur = ggml_mul_mat(ctx, wk, cur);
|
||
|
|
struct ggml_tensor * Vcur = ggml_mul_mat(ctx, wv, cur);
|
||
|
|
|
||
|
|
Qcur = ggml_rope_ext(
|
||
|
|
ctx, ggml_reshape_3d(ctx, Qcur, hp.n_embd_head, hp.n_head, hp.n_tokens), inp_pos, nullptr,
|
||
|
|
hp.n_rot, 0, hp.n_ctx_orig, freq_base, freq_scale,
|
||
|
|
ext_factor, attn_factor, beta_fast, beta_slow
|
||
|
|
);
|
||
|
|
|
||
|
|
Kcur = ggml_rope_ext(
|
||
|
|
ctx, ggml_reshape_3d(ctx, Kcur, hp.n_embd_head, hp.n_head_kv, hp.n_tokens), inp_pos, nullptr,
|
||
|
|
hp.n_rot, 0, hp.n_ctx_orig, freq_base, freq_scale,
|
||
|
|
ext_factor, attn_factor, beta_fast, beta_slow
|
||
|
|
);
|
||
|
|
|
||
|
|
llm_build_kv_store(ctx, k_l, v_l, Kcur, Vcur);
|
||
|
|
|
||
|
|
cur = llm_build_kqv(ctx, k_l, v_l, Qcur, KQ_mask, 1.0f/sqrtf(float(hp.n_embd_head)));
|
||
|
|
}
|
||
|
|
|
||
|
|
struct ggml_tensor * ffn_inp = ggml_add(ctx, cur, inpSA);
|
||
|
|
|
||
|
|
// feed-forward network
|
||
|
|
ggml_tensor * ffn_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hp.n_embd);
|
||
|
|
cur = llm_build_norm(ctx, ffn_inp, ffn_norm, nullptr, LLM_NORM_RMS);
|
||
|
|
|
||
|
|
ggml_tensor * ffn_gate = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, hp.n_embd, hp.n_ff);
|
||
|
|
ggml_tensor * ffn_down = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, hp.n_ff, hp.n_embd);
|
||
|
|
ggml_tensor * ffn_up = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, hp.n_embd, hp.n_ff);
|
||
|
|
struct ggml_tensor * tmp = ggml_mul_mat(ctx, ffn_up, cur);
|
||
|
|
cur = ggml_mul_mat(ctx, ffn_gate, cur);
|
||
|
|
cur = ggml_silu(ctx, cur);
|
||
|
|
cur = ggml_mul(ctx, cur, tmp);
|
||
|
|
cur = ggml_mul_mat(ctx, ffn_down, cur);
|
||
|
|
|
||
|
|
cur = ggml_add(ctx, cur, ffn_inp);
|
||
|
|
|
||
|
|
// input for next layer
|
||
|
|
inpL = cur;
|
||
|
|
}
|
||
|
|
|
||
|
|
cur = inpL;
|
||
|
|
|
||
|
|
ggml_tensor * output_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hp.n_embd);
|
||
|
|
cur = llm_build_norm(ctx, cur, output_norm, nullptr, LLM_NORM_RMS);
|
||
|
|
|
||
|
|
// lm_head
|
||
|
|
ggml_tensor * output = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, hp.n_embd, hp.n_vocab);
|
||
|
|
cur = ggml_mul_mat(ctx, output, cur);
|
||
|
|
|
||
|
|
return cur;
|
||
|
|
}
|
||
|
|
};
|
||
|
|
|
||
|
|
// Falcon
|
||
|
|
struct test_falcon : public test_llm {
|
||
|
|
static constexpr float freq_base = 10000.0f;
|
||
|
|
static constexpr float freq_scale = 1.0f;
|
||
|
|
static constexpr float ext_factor = 0.0f;
|
||
|
|
static constexpr float attn_factor = 1.0f;
|
||
|
|
static constexpr float beta_fast = 32.0f;
|
||
|
|
static constexpr float beta_slow = 1.0f;
|
||
|
|
|
||
|
|
std::string op_desc(ggml_tensor * t) override {
|
||
|
|
GGML_UNUSED(t);
|
||
|
|
return "FALCON";
|
||
|
|
}
|
||
|
|
|
||
|
|
std::string vars() override {
|
||
|
|
auto n_tokens = hp.n_tokens;
|
||
|
|
return VARS_TO_STR1(n_tokens);
|
||
|
|
}
|
||
|
|
|
||
|
|
double max_nmse_err() override {
|
||
|
|
return 2e-3;
|
||
|
|
}
|
||
|
|
|
||
|
|
test_falcon(int n_tokens = 1)
|
||
|
|
: test_llm({
|
||
|
|
/*n_vocab =*/ 32000,
|
||
|
|
/*n_embd =*/ 3200,
|
||
|
|
/*n_head =*/ 50,
|
||
|
|
/*n_head_kv =*/ 1,
|
||
|
|
/*n_rot =*/ 64,
|
||
|
|
/*n_embd_head =*/ 64,
|
||
|
|
/*n_ff =*/ 8640,
|
||
|
|
/*f_norm_eps =*/ 1e-5f,
|
||
|
|
/*f_norm_rms_eps =*/ 0.f,
|
||
|
|
/*n_tokens =*/ n_tokens,
|
||
|
|
}) {
|
||
|
|
}
|
||
|
|
|
||
|
|
ggml_tensor * build_graph(ggml_context * ctx) override {
|
||
|
|
struct ggml_tensor * cur;
|
||
|
|
struct ggml_tensor * inpL;
|
||
|
|
|
||
|
|
inpL = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, hp.n_embd, hp.n_tokens);
|
||
|
|
|
||
|
|
// inp_pos - contains the positions
|
||
|
|
struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, hp.n_tokens);
|
||
|
|
|
||
|
|
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
||
|
|
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx, GGML_TYPE_F16, hp.n_kv, hp.n_tokens, 1);
|
||
|
|
|
||
|
|
ggml_tensor * k_l = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, 1638400);
|
||
|
|
ggml_tensor * v_l = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, 1638400);
|
||
|
|
|
||
|
|
for (uint32_t il = 0; il < hp.n_layer; ++il) {
|
||
|
|
// norm
|
||
|
|
ggml_tensor * attn_norm_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hp.n_embd);
|
||
|
|
ggml_tensor * attn_norm_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hp.n_embd);
|
||
|
|
ggml_tensor * attn_norm = llm_build_norm(ctx, inpL, attn_norm_w, attn_norm_b, LLM_NORM);
|
||
|
|
|
||
|
|
// self-attention
|
||
|
|
{
|
||
|
|
cur = attn_norm;
|
||
|
|
|
||
|
|
ggml_tensor * wqkv = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, hp.n_embd, hp.n_embd + 2*hp.n_embd_gqa());
|
||
|
|
|
||
|
|
cur = ggml_mul_mat(ctx, wqkv, cur);
|
||
|
|
|
||
|
|
struct ggml_tensor * Qcur = ggml_cont(ctx, ggml_view_2d(ctx, cur, hp.n_embd, hp.n_tokens, cur->nb[1], 0*sizeof(float)*(hp.n_embd)));
|
||
|
|
struct ggml_tensor * Kcur = ggml_cont(ctx, ggml_view_2d(ctx, cur, hp.n_embd_gqa(), hp.n_tokens, cur->nb[1], 1*sizeof(float)*(hp.n_embd)));
|
||
|
|
struct ggml_tensor * Vcur = ggml_cont(ctx, ggml_view_2d(ctx, cur, hp.n_embd_gqa(), hp.n_tokens, cur->nb[1], 1*sizeof(float)*(hp.n_embd + hp.n_embd_gqa())));
|
||
|
|
|
||
|
|
Qcur = ggml_reshape_3d(ctx, Qcur, hp.n_embd_head, hp.n_head, hp.n_tokens);
|
||
|
|
Kcur = ggml_reshape_3d(ctx, Kcur, hp.n_embd_head, hp.n_head_kv, hp.n_tokens);
|
||
|
|
|
||
|
|
// using mode = 2 for neox mode
|
||
|
|
Qcur = ggml_rope_ext(
|
||
|
|
ctx, Qcur, inp_pos, nullptr, hp.n_rot, 2, hp.n_ctx_orig,
|
||
|
|
freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
|
||
|
|
);
|
||
|
|
|
||
|
|
Kcur = ggml_rope_ext(
|
||
|
|
ctx, Kcur, inp_pos, nullptr, hp.n_rot, 2, hp.n_ctx_orig,
|
||
|
|
freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
|
||
|
|
);
|
||
|
|
|
||
|
|
llm_build_kv_store(ctx, k_l, v_l, Kcur, Vcur);
|
||
|
|
|
||
|
|
cur = llm_build_kqv(ctx, k_l, v_l, Qcur, KQ_mask, 1.0f/sqrtf(float(hp.n_embd_head)));
|
||
|
|
}
|
||
|
|
|
||
|
|
struct ggml_tensor * ffn_inp = cur;
|
||
|
|
|
||
|
|
// feed forward
|
||
|
|
{
|
||
|
|
ggml_tensor * ffn_up = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, hp.n_embd, hp.n_ff);
|
||
|
|
ggml_tensor * ffn_down = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, hp.n_ff, hp.n_embd);
|
||
|
|
cur = attn_norm;
|
||
|
|
cur = ggml_mul_mat(ctx, ffn_up, cur);
|
||
|
|
cur = ggml_gelu(ctx, cur);
|
||
|
|
cur = ggml_mul_mat(ctx, ffn_down, cur);
|
||
|
|
}
|
||
|
|
|
||
|
|
cur = ggml_add(ctx, cur, ffn_inp);
|
||
|
|
|
||
|
|
cur = ggml_add(ctx, cur, inpL);
|
||
|
|
|
||
|
|
// input for next layer
|
||
|
|
inpL = cur;
|
||
|
|
}
|
||
|
|
|
||
|
|
cur = inpL;
|
||
|
|
|
||
|
|
ggml_tensor * output_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hp.n_embd);
|
||
|
|
ggml_tensor * output_norm_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hp.n_embd);
|
||
|
|
cur = llm_build_norm(ctx, cur, output_norm, output_norm_b, LLM_NORM);
|
||
|
|
|
||
|
|
// lm_head
|
||
|
|
ggml_tensor * output = ggml_new_tensor_2d(ctx, GGML_TYPE_Q8_0, hp.n_embd, hp.n_vocab);
|
||
|
|
cur = ggml_mul_mat(ctx, output, cur);
|
||
|
|
|
||
|
|
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;
|
||
|
|
std::default_random_engine rng(0);
|
||
|
|
|
||
|
|
const ggml_type all_types[] = {
|
||
|
|
GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_BF16,
|
||
|
|
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,
|
||
|
|
GGML_TYPE_IQ2_XXS, GGML_TYPE_IQ2_XS, GGML_TYPE_IQ2_S,
|
||
|
|
GGML_TYPE_IQ3_XXS, GGML_TYPE_IQ1_S, GGML_TYPE_IQ1_M,
|
||
|
|
GGML_TYPE_IQ4_NL, GGML_TYPE_IQ3_S, GGML_TYPE_IQ4_XS,
|
||
|
|
};
|
||
|
|
|
||
|
|
const ggml_type base_types[] = {
|
||
|
|
GGML_TYPE_F32, GGML_TYPE_F16,
|
||
|
|
GGML_TYPE_Q4_0,
|
||
|
|
GGML_TYPE_Q4_K,
|
||
|
|
GGML_TYPE_IQ2_XXS
|
||
|
|
};
|
||
|
|
|
||
|
|
const ggml_type other_types[] = {
|
||
|
|
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_Q5_K,
|
||
|
|
GGML_TYPE_Q6_K,
|
||
|
|
GGML_TYPE_IQ2_XS, GGML_TYPE_IQ2_S,
|
||
|
|
GGML_TYPE_IQ3_XXS, GGML_TYPE_IQ1_S, GGML_TYPE_IQ1_M,
|
||
|
|
GGML_TYPE_IQ4_NL, GGML_TYPE_IQ3_S, GGML_TYPE_IQ4_XS,
|
||
|
|
};
|
||
|
|
|
||
|
|
// unary ops
|
||
|
|
for (int v : {0, 1}) {
|
||
|
|
for (int op = 0; op < GGML_UNARY_OP_COUNT; op++) {
|
||
|
|
test_cases.emplace_back(new test_unary((ggml_unary_op) op, GGML_TYPE_F32, { 128, 10, 10, 10 }, v));
|
||
|
|
test_cases.emplace_back(new test_unary((ggml_unary_op) op, GGML_TYPE_F32, { 7, 13, 19, 23 }, v));
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
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));
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
for (int b : {1, 7}) {
|
||
|
|
for (bool v : {false, true}) {
|
||
|
|
test_cases.emplace_back(new test_get_rows(GGML_TYPE_I32, 256, 5, 4, b, v));
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
for (ggml_type type_input : {GGML_TYPE_F32}) {
|
||
|
|
for (ggml_op_pool pool_type : {GGML_OP_POOL_AVG, GGML_OP_POOL_MAX}) {
|
||
|
|
for (int k0 : {1, 3}) {
|
||
|
|
for (int k1 : {1, 3}) {
|
||
|
|
for (int s0 : {1, 2}) {
|
||
|
|
for (int s1 : {1, 2}) {
|
||
|
|
for (int p0 : {0, 1}) {
|
||
|
|
for (int p1 : {0, 1}) {
|
||
|
|
test_cases.emplace_back(new test_pool2d(pool_type, type_input, {10, 10, 3, 1}, k0, k1, s0, s1, p0, p1));
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F32));
|
||
|
|
test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16));
|
||
|
|
|
||
|
|
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_repeat(GGML_TYPE_I32, {10, 10, 10, 10}, {2, 1, 1, 1}));
|
||
|
|
test_cases.emplace_back(new test_repeat(GGML_TYPE_I16, {10, 10, 10, 10}, {1, 1, 1, 2}));
|
||
|
|
|
||
|
|
test_cases.emplace_back(new test_dup(GGML_TYPE_F32));
|
||
|
|
test_cases.emplace_back(new test_dup(GGML_TYPE_F16));
|
||
|
|
test_cases.emplace_back(new test_dup(GGML_TYPE_I32));
|
||
|
|
test_cases.emplace_back(new test_dup(GGML_TYPE_I16));
|
||
|
|
test_cases.emplace_back(new test_dup(GGML_TYPE_I16, {10, 8, 3, 1}, {0, 2, 1, 3}));
|
||
|
|
test_cases.emplace_back(new test_dup(GGML_TYPE_I16, {10, 8, 3, 1}, {1, 2, 0, 3}));
|
||
|
|
|
||
|
|
for (ggml_type type_src : {GGML_TYPE_F16, GGML_TYPE_F32}) {
|
||
|
|
for (ggml_type type_dst : all_types) {
|
||
|
|
test_cases.emplace_back(new test_cpy(type_src, type_dst, {256, 4, 4, 4}));
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
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 : base_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 : other_types) {
|
||
|
|
for (ggml_type type_b : {GGML_TYPE_F32}) {
|
||
|
|
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(GGML_TYPE_F16, GGML_TYPE_F32, 64, 2, 128, { 8, 1}, {1, 1}));
|
||
|
|
test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 83, 2, 128, { 8, 1}, {4, 1}));
|
||
|
|
test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 64, 2, 64, { 8, 1}, {4, 1}));
|
||
|
|
test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 83, 2, 64, { 8, 1}, {4, 1}));
|
||
|
|
test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 64, 45, 128, { 8, 1}, {4, 1}));
|
||
|
|
test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 128, 45, 64, { 8, 1}, {4, 1}));
|
||
|
|
|
||
|
|
for (ggml_type type_a : base_types) {
|
||
|
|
for (ggml_type type_b : {GGML_TYPE_F32 /*, GGML_TYPE_F16 */}) {
|
||
|
|
for (int n_mats : {4, 8}) {
|
||
|
|
for (int n_used : {1, 2, 4}) {
|
||
|
|
for (bool b : {false, true}) {
|
||
|
|
for (int n : {1, 32}) {
|
||
|
|
int m = 512;
|
||
|
|
int k = 256;
|
||
|
|
test_cases.emplace_back(new test_mul_mat_id(type_a, type_b, n_mats, n_used, b, m, n, k));
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
for (ggml_type type_a : other_types) {
|
||
|
|
for (ggml_type type_b : {GGML_TYPE_F32 /*, GGML_TYPE_F16 */}) {
|
||
|
|
for (int n_mats : {4}) {
|
||
|
|
for (int n_used : {2}) {
|
||
|
|
for (bool b : {false}) {
|
||
|
|
for (int n : {1}) {
|
||
|
|
int m = 512;
|
||
|
|
int k = 256;
|
||
|
|
test_cases.emplace_back(new test_mul_mat_id(type_a, type_b, n_mats, n_used, b, m, n, k));
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
test_cases.emplace_back(new test_sqr());
|
||
|
|
test_cases.emplace_back(new test_sqrt());
|
||
|
|
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));
|
||
|
|
|
||
|
|
#if 0
|
||
|
|
std::uniform_int_distribution<> dist_ne1(1, 50);
|
||
|
|
int exponent = 1;
|
||
|
|
while (exponent < (1 << 17)) {
|
||
|
|
std::uniform_int_distribution<> dist_ne0(exponent, 2*exponent);
|
||
|
|
|
||
|
|
for (int n = 0; n < 10; ++n) {
|
||
|
|
int64_t ne0 = dist_ne0(rng);
|
||
|
|
int64_t ne1 = dist_ne1(rng);
|
||
|
|
test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {ne0, ne1, 1, 1}, n/2 == 0, 0.1f, ne0 < 1000 ? 4.0f : 0.0f));
|
||
|
|
}
|
||
|
|
|
||
|
|
exponent <<= 1;
|
||
|
|
}
|
||
|
|
#endif
|
||
|
|
for (bool mask : {false, true}) {
|
||
|
|
for (float max_bias : {0.0f, 8.0f}) {
|
||
|
|
if (!mask && max_bias > 0.0f) continue;
|
||
|
|
for (float scale : {1.0f, 0.1f}) {
|
||
|
|
for (int64_t ne0 : {16, 1024}) {
|
||
|
|
for (int64_t ne1 : {16, 1024}) {
|
||
|
|
test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {ne0, ne1, 1, 1}, mask, scale, max_bias));
|
||
|
|
test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {ne0-1, ne1-1, 1, 1}, mask, scale, max_bias));
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {16, 2, 32, 1}, false, 0.1f, 0.0f));
|
||
|
|
test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {32, 2, 32, 1}, true, 0.1f, 0.0f));
|
||
|
|
test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {32, 2, 32, 1}, true, 0.1f, 8.0f));
|
||
|
|
|
||
|
|
{
|
||
|
|
bool all = true;
|
||
|
|
|
||
|
|
for (float v : { 0, 1 }) {
|
||
|
|
for (float fs : { 1.0f, 1.4245f }) {
|
||
|
|
for (float ef : { 0.0f, 0.7465f }) {
|
||
|
|
for (float af : { 1.0f, 1.4245f }) {
|
||
|
|
for (ggml_type type : {GGML_TYPE_F32, GGML_TYPE_F16}) {
|
||
|
|
for (bool ff : {false, true}) { // freq_factors
|
||
|
|
test_cases.emplace_back(new test_rope(type, {128, 32, 10, 1}, 128, 0, 512, fs, ef, af, ff, v)); // llama 7B
|
||
|
|
|
||
|
|
if (all) {
|
||
|
|
test_cases.emplace_back(new test_rope(type, {128, 40, 10, 1}, 128, 0, 512, fs, ef, af, ff, v)); // llama 13B
|
||
|
|
test_cases.emplace_back(new test_rope(type, {128, 52, 10, 1}, 128, 0, 512, fs, ef, af, ff, v)); // llama 30B
|
||
|
|
test_cases.emplace_back(new test_rope(type, {128, 64, 10, 1}, 128, 0, 512, fs, ef, af, ff, v)); // llama 65B
|
||
|
|
}
|
||
|
|
|
||
|
|
if (all) {
|
||
|
|
test_cases.emplace_back(new test_rope(type, { 64, 1, 10, 1}, 64, 2, 512, fs, ef, af, ff, v)); // neox (falcon 7B)
|
||
|
|
test_cases.emplace_back(new test_rope(type, { 64, 71, 10, 1}, 64, 2, 512, fs, ef, af, ff, v)); // neox (falcon 7B)
|
||
|
|
test_cases.emplace_back(new test_rope(type, { 64, 8, 10, 1}, 64, 2, 512, fs, ef, af, ff, v)); // neox (falcon 40B)
|
||
|
|
test_cases.emplace_back(new test_rope(type, { 80, 32, 10, 1}, 20, 2, 512, fs, ef, af, ff, v)); // neox (stablelm)
|
||
|
|
test_cases.emplace_back(new test_rope(type, { 80, 32, 10, 1}, 32, 2, 512, fs, ef, af, ff, v)); // neox (phi-2)
|
||
|
|
}
|
||
|
|
|
||
|
|
test_cases.emplace_back(new test_rope(type, { 64, 128, 10, 1}, 64, 2, 512, fs, ef, af, ff, v)); // neox (falcon 40B)
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
all = false;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
for (int v : { 0, 1, 2, 3 }) {
|
||
|
|
for (int dim : { 0, 1, 2, 3, }) {
|
||
|
|
test_cases.emplace_back(new test_concat(GGML_TYPE_F32, {11, 12, 13, 14}, 7, dim, v));
|
||
|
|
test_cases.emplace_back(new test_concat(GGML_TYPE_I32, {11, 12, 13, 14}, 7, dim, v));
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
for (ggml_sort_order order : {GGML_SORT_ORDER_ASC, GGML_SORT_ORDER_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_argsort(GGML_TYPE_F32, {60, 10, 10, 10}, order)); // qwen
|
||
|
|
}
|
||
|
|
|
||
|
|
test_cases.emplace_back(new test_sum_rows());
|
||
|
|
test_cases.emplace_back(new test_upscale());
|
||
|
|
test_cases.emplace_back(new test_upscale(GGML_TYPE_F32, { 512, 512, 3, 1 }, 2, true));
|
||
|
|
test_cases.emplace_back(new test_upscale_ext());
|
||
|
|
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_arange());
|
||
|
|
test_cases.emplace_back(new test_timestep_embedding());
|
||
|
|
test_cases.emplace_back(new test_leaky_relu());
|
||
|
|
|
||
|
|
for (int hs : { 64, 80, 128, 256, }) {
|
||
|
|
for (bool mask : { true, false } ) {
|
||
|
|
for (float max_bias : { 0.0f, 8.0f }) {
|
||
|
|
if (!mask && max_bias > 0.0f) continue;
|
||
|
|
for (int nh : { 32, }) {
|
||
|
|
for (int kv : { 512, 1024, }) {
|
||
|
|
for (int nb : { 1, 2, 4, 8, }) {
|
||
|
|
for (ggml_type type_KV : {GGML_TYPE_F16, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0}) {
|
||
|
|
test_cases.emplace_back(new test_flash_attn_ext(hs, nh, kv, nb, mask, max_bias, type_KV));
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
// these tests are disabled to save execution time, but they can be handy for debugging
|
||
|
|
#if 0
|
||
|
|
test_cases.emplace_back(new test_llama(1));
|
||
|
|
test_cases.emplace_back(new test_llama(2));
|
||
|
|
test_cases.emplace_back(new test_falcon(1));
|
||
|
|
test_cases.emplace_back(new test_falcon(2));
|
||
|
|
#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_filter = NULL;
|
||
|
|
const char * backend_filter = 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_filter = argv[++i];
|
||
|
|
} else {
|
||
|
|
usage(argv);
|
||
|
|
return 1;
|
||
|
|
}
|
||
|
|
} else if (strcmp(argv[i], "-b") == 0) {
|
||
|
|
if (i + 1 < argc) {
|
||
|
|
backend_filter = 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_filter != NULL && strcmp(backend_filter, 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);
|
||
|
|
|
||
|
|
if (backend_filter == NULL && ggml_backend_is_cpu(backend)) {
|
||
|
|
printf(" Skipping CPU backend\n");
|
||
|
|
ggml_backend_free(backend);
|
||
|
|
n_ok++;
|
||
|
|
continue;
|
||
|
|
}
|
||
|
|
|
||
|
|
printf(" Backend name: %s\n", ggml_backend_name(backend));
|
||
|
|
|
||
|
|
bool ok = test_backend(backend, mode, op_name_filter);
|
||
|
|
|
||
|
|
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;
|
||
|
|
}
|
||
|
|
|
||
|
|
ggml_quantize_free();
|
||
|
|
|
||
|
|
printf("\033[1;32mOK\033[0m\n");
|
||
|
|
return 0;
|
||
|
|
}
|