Fabio
c36510c21e
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85 lines
4 KiB
C++
85 lines
4 KiB
C++
#include "test_assert.h"
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#include "ggml.h"
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#include "ggml-cpu.h"
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#include "ggml-quants.h"
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#include <cmath>
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#include <cstring>
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#include <iostream>
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#include <memory>
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#include <vector>
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namespace {
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void test_matrix(int64_t columns, int64_t rows, int64_t batch) {
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std::vector<float> weights(static_cast<size_t>(columns * rows));
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for (size_t i = 0; i < weights.size(); ++i) {
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const float scale = (i / 128) % 3 == 0 ? 0.0f : ((i / 128) % 2 ? 0.125f : 0.5f);
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weights[i] = (static_cast<int>(i % 3) - 1) * scale;
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}
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std::vector<block_tq2_1> packed(weights.size() / 256);
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quantize_row_tq2_1_ref(weights.data(), packed.data(), static_cast<int64_t>(weights.size()));
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engine::test::require(ggml_validate_row_data(GGML_TYPE_TQ2_1, packed.data(), packed.size() * sizeof(block_tq2_1)),
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"valid ternary blocks rejected");
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std::vector<float> restored(weights.size());
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dequantize_row_tq2_1(packed.data(), restored.data(), static_cast<int64_t>(weights.size()));
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engine::test::require(restored == weights, "independent 128-weight scales were not preserved exactly");
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auto invalid = packed;
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invalid.front().qs[0] |= 3;
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engine::test::require(!ggml_validate_row_data(GGML_TYPE_TQ2_1, invalid.data(), invalid.size() * sizeof(block_tq2_1)),
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"reserved ternary code accepted");
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invalid = packed;
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invalid.front().d[1] = ggml_fp32_to_fp16(INFINITY);
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engine::test::require(!ggml_validate_row_data(GGML_TYPE_TQ2_1, invalid.data(), invalid.size() * sizeof(block_tq2_1)),
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"nonfinite second scale accepted");
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std::vector<float> input(static_cast<size_t>(columns * batch));
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for (size_t i = 0; i < input.size(); ++i) input[i] = (static_cast<int>((i * 17) % 129) - 64) * 0.125f;
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std::vector<block_q8_K> activation(input.size() / 256);
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quantize_row_q8_K_ref(input.data(), activation.data(), static_cast<int64_t>(input.size()));
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std::vector<float> quantized_input(input.size());
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dequantize_row_q8_K(activation.data(), quantized_input.data(), static_cast<int64_t>(input.size()));
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std::unique_ptr<ggml_context, decltype(&ggml_free)> context(
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ggml_init({16 * 1024 * 1024, nullptr, false}), ggml_free);
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engine::test::require(context != nullptr, "context allocation failed");
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auto * w = ggml_new_tensor_2d(context.get(), GGML_TYPE_TQ2_1, columns, rows);
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auto * x = ggml_new_tensor_2d(context.get(), GGML_TYPE_F32, columns, batch);
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std::memcpy(w->data, packed.data(), packed.size() * sizeof(block_tq2_1));
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std::memcpy(x->data, input.data(), input.size() * sizeof(float));
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auto * output = ggml_mul_mat(context.get(), w, x);
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auto * graph = ggml_new_graph(context.get());
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ggml_build_forward_expand(graph, output);
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engine::test::require(ggml_graph_compute_with_ctx(context.get(), graph, 2) == GGML_STATUS_SUCCESS,
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"ternary CPU graph failed");
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const auto * actual = static_cast<const float *>(output->data);
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for (int64_t b = 0; b < batch; ++b) {
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for (int64_t r = 0; r < rows; ++r) {
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float expected = 0.0f;
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for (int64_t c = 0; c < columns; ++c) {
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expected += weights[r * columns + c] * quantized_input[b * columns + c];
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}
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engine::test::require_close(actual[b * rows + r], expected,
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1e-4f + 1e-5f * std::abs(expected), "ternary matrix multiplication");
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}
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}
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}
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} // namespace
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int main() {
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try {
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engine::test::require(GGML_TYPE_I2_S == 43 && GGML_TYPE_TQ2_1 == 44,
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"existing GGUF type IDs changed");
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engine::test::require(ggml_row_size(GGML_TYPE_TQ2_1, 256) == 68, "incorrect ternary block size");
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for (const int64_t columns : {256, 512, 1536}) {
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for (const int64_t batch : {1, 3, 32}) test_matrix(columns, 7, batch);
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}
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std::cout << "tq2_1_test passed\n";
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return 0;
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} catch (const std::exception & error) {
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std::cerr << error.what() << '\n';
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return 1;
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}
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}
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