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-rw-r--r-- | third_party/aom/test/av1_nn_predict_test.cc | 228 |
1 files changed, 228 insertions, 0 deletions
diff --git a/third_party/aom/test/av1_nn_predict_test.cc b/third_party/aom/test/av1_nn_predict_test.cc new file mode 100644 index 0000000000..4201ea6ce6 --- /dev/null +++ b/third_party/aom/test/av1_nn_predict_test.cc @@ -0,0 +1,228 @@ +/* + * Copyright (c) 2018, Alliance for Open Media. All rights reserved + * + * This source code is subject to the terms of the BSD 2 Clause License and + * the Alliance for Open Media Patent License 1.0. If the BSD 2 Clause License + * was not distributed with this source code in the LICENSE file, you can + * obtain it at www.aomedia.org/license/software. If the Alliance for Open + * Media Patent License 1.0 was not distributed with this source code in the + * PATENTS file, you can obtain it at www.aomedia.org/license/patent. + */ + +#include <tuple> + +#include "third_party/googletest/src/googletest/include/gtest/gtest.h" + +#include "aom/aom_integer.h" +#include "aom_ports/aom_timer.h" +#include "av1/encoder/ml.h" +#include "config/aom_config.h" +#include "config/aom_dsp_rtcd.h" +#include "config/av1_rtcd.h" +#include "test/util.h" +#include "test/register_state_check.h" +#include "test/acm_random.h" + +namespace { +typedef void (*NnPredict_Func)(const float *const input_nodes, + const NN_CONFIG *const nn_config, + int reduce_prec, float *const output); + +typedef std::tuple<const NnPredict_Func> NnPredictTestParam; + +const float epsilon = 1e-3f; // Error threshold for functional equivalence + +class NnPredictTest : public ::testing::TestWithParam<NnPredictTestParam> { + public: + void SetUp() override { + const int MAX_NODES2 = NN_MAX_NODES_PER_LAYER * NN_MAX_NODES_PER_LAYER; + // Allocate two massive buffers on the heap for edge weights and node bias + // Then set-up the double-dimension arrays pointing into the big buffers + weights_buf = (float *)aom_malloc(MAX_NODES2 * (NN_MAX_HIDDEN_LAYERS + 1) * + sizeof(*weights_buf)); + bias_buf = + (float *)aom_malloc(NN_MAX_NODES_PER_LAYER * + (NN_MAX_HIDDEN_LAYERS + 1) * sizeof(*bias_buf)); + ASSERT_NE(weights_buf, nullptr); + ASSERT_NE(bias_buf, nullptr); + for (int i = 0; i < NN_MAX_HIDDEN_LAYERS + 1; i++) { + weights[i] = &weights_buf[i * MAX_NODES2]; + bias[i] = &bias_buf[i * NN_MAX_NODES_PER_LAYER]; + } + target_func_ = GET_PARAM(0); + } + void TearDown() override { + aom_free(weights_buf); + aom_free(bias_buf); + } + void RunNnPredictTest(const NN_CONFIG *const shape); + void RunNnPredictSpeedTest(const NN_CONFIG *const shape, const int run_times); + void RunNnPredictTest_all(const NN_CONFIG *const shapes, + const int num_shapes); + void RunNnPredictSpeedTest_all(const NN_CONFIG *const shapes, + const int num_shapes, const int run_times); + + private: + NnPredict_Func target_func_; + libaom_test::ACMRandom rng_; + float *weights[NN_MAX_HIDDEN_LAYERS + 1] = {}; + float *bias[NN_MAX_HIDDEN_LAYERS + 1] = {}; + float *weights_buf = nullptr, *bias_buf = nullptr; +}; +GTEST_ALLOW_UNINSTANTIATED_PARAMETERIZED_TEST(NnPredictTest); + +void NnPredictTest::RunNnPredictTest(const NN_CONFIG *const shape) { + float inputs[NN_MAX_NODES_PER_LAYER] = { 0 }; + float outputs_test[NN_MAX_NODES_PER_LAYER] = { 0 }; + float outputs_ref[NN_MAX_NODES_PER_LAYER] = { 0 }; + + NN_CONFIG nn_config; + memcpy(&nn_config, shape, sizeof(nn_config)); + + char shape_str[32] = { 0 }; + snprintf(shape_str, sizeof(shape_str), "%d", shape->num_inputs); + for (int layer = 0; layer < shape->num_hidden_layers; layer++) + snprintf(&shape_str[strlen(shape_str)], + sizeof(shape_str) - strlen(shape_str), "x%d", + shape->num_hidden_nodes[layer]); + snprintf(&shape_str[strlen(shape_str)], sizeof(shape_str) - strlen(shape_str), + "x%d", shape->num_outputs); + + for (int i = 0; i < NN_MAX_HIDDEN_LAYERS + 1; i++) { + nn_config.weights[i] = weights[i]; + nn_config.bias[i] = bias[i]; + } + + for (int iter = 0; iter < 10000 && !HasFatalFailure(); ++iter) { + for (int node = 0; node < shape->num_inputs; node++) { + inputs[node] = ((float)rng_.Rand31() - (1 << 30)) / (1u << 31); + } + for (int layer = 0; layer < shape->num_hidden_layers; layer++) { + for (int node = 0; node < NN_MAX_NODES_PER_LAYER; node++) { + bias[layer][node] = ((float)rng_.Rand31() - (1 << 30)) / (1u << 31); + } + for (int node = 0; node < NN_MAX_NODES_PER_LAYER * NN_MAX_NODES_PER_LAYER; + node++) { + weights[layer][node] = ((float)rng_.Rand31() - (1 << 30)) / (1u << 31); + } + } + // Now the outputs: + int layer = shape->num_hidden_layers; + for (int node = 0; node < NN_MAX_NODES_PER_LAYER; node++) { + bias[layer][node] = ((float)rng_.Rand31() - (1 << 30)) / (1u << 31); + } + for (int node = 0; node < NN_MAX_NODES_PER_LAYER * NN_MAX_NODES_PER_LAYER; + node++) { + weights[layer][node] = ((float)rng_.Rand31() - (1 << 30)) / (1u << 31); + } + + av1_nn_predict_c(inputs, &nn_config, 0, outputs_ref); + target_func_(inputs, &nn_config, 0, outputs_test); + + for (int node = 0; node < shape->num_outputs; node++) { + if (outputs_ref[node] < epsilon) { + ASSERT_LE(outputs_test[node], epsilon) + << "Reference output was near-zero, test output was not (" + << shape_str << ")"; + } else { + const float error = outputs_ref[node] - outputs_test[node]; + const float relative_error = fabsf(error / outputs_ref[node]); + ASSERT_LE(relative_error, epsilon) + << "Excessive relative error between reference and test (" + << shape_str << ")"; + } + } + } +} + +void NnPredictTest::RunNnPredictSpeedTest(const NN_CONFIG *const shape, + const int run_times) { + float inputs[NN_MAX_NODES_PER_LAYER] = { 0 }; + float outputs_test[NN_MAX_NODES_PER_LAYER] = { 0 }; + float outputs_ref[NN_MAX_NODES_PER_LAYER] = { 0 }; + + NN_CONFIG nn_config; + memcpy(&nn_config, shape, sizeof(nn_config)); + + for (int i = 0; i < NN_MAX_HIDDEN_LAYERS; i++) { + nn_config.weights[i] = weights[i]; + nn_config.bias[i] = bias[i]; + } + // Don't bother actually changing the values for inputs/weights/bias: it + // shouldn't make any difference for a speed test. + + aom_usec_timer timer; + aom_usec_timer_start(&timer); + for (int i = 0; i < run_times; ++i) { + av1_nn_predict_c(inputs, &nn_config, 0, outputs_ref); + } + aom_usec_timer_mark(&timer); + const double time1 = static_cast<double>(aom_usec_timer_elapsed(&timer)); + aom_usec_timer_start(&timer); + for (int i = 0; i < run_times; ++i) { + target_func_(inputs, &nn_config, 0, outputs_test); + } + aom_usec_timer_mark(&timer); + const double time2 = static_cast<double>(aom_usec_timer_elapsed(&timer)); + + printf("%d", shape->num_inputs); + for (int layer = 0; layer < shape->num_hidden_layers; layer++) + printf("x%d", shape->num_hidden_nodes[layer]); + printf("x%d: ", shape->num_outputs); + printf("%7.2f/%7.2fns (%3.2f)\n", time1, time2, time1 / time2); +} + +// This is all the neural network shapes observed executed in a few different +// runs of the encoder. It also conveniently covers all the kernels +// implemented. +static const NN_CONFIG kShapes[] = { + { 37, 1, 2, { 16, 24 }, {}, {} }, { 24, 24, 1, { 12 }, {}, {} }, + { 10, 16, 1, { 64 }, {}, {} }, { 12, 1, 1, { 12 }, {}, {} }, + { 12, 1, 1, { 24 }, {}, {} }, { 12, 1, 1, { 32 }, {}, {} }, + { 18, 4, 1, { 24 }, {}, {} }, { 18, 4, 1, { 32 }, {}, {} }, + { 4, 1, 1, { 16 }, {}, {} }, { 8, 1, 0, { 0 }, {}, {} }, + { 8, 4, 1, { 16 }, {}, {} }, { 8, 1, 1, { 32 }, {}, {} }, + { 9, 3, 1, { 32 }, {}, {} }, { 8, 4, 0, { 0 }, {}, {} }, + { 8, 8, 0, { 0 }, {}, {} }, { 4, 4, 1, { 8 }, {}, {} }, + { 4, 3, 0, { 64 }, {}, {} }, +}; + +void NnPredictTest::RunNnPredictTest_all(const NN_CONFIG *const shapes, + const int num_shapes) { + for (int i = 0; i < num_shapes; i++) RunNnPredictTest(&shapes[i]); +} + +void NnPredictTest::RunNnPredictSpeedTest_all(const NN_CONFIG *const shapes, + const int num_shapes, + const int run_times) { + for (int i = 0; i < num_shapes; i++) + NnPredictTest::RunNnPredictSpeedTest(&shapes[i], run_times); +} + +TEST_P(NnPredictTest, RandomValues) { + RunNnPredictTest_all(kShapes, sizeof(kShapes) / sizeof(kShapes[0])); +} + +TEST_P(NnPredictTest, DISABLED_Speed) { + RunNnPredictSpeedTest_all(kShapes, sizeof(kShapes) / sizeof(kShapes[0]), + 10000000); +} + +#if !CONFIG_EXCLUDE_SIMD_MISMATCH +#if HAVE_SSE3 +INSTANTIATE_TEST_SUITE_P(SSE3, NnPredictTest, + ::testing::Values(av1_nn_predict_sse3)); +#endif + +#if HAVE_AVX2 +INSTANTIATE_TEST_SUITE_P(AVX2, NnPredictTest, + ::testing::Values(av1_nn_predict_avx2)); +#endif + +#if HAVE_NEON +INSTANTIATE_TEST_SUITE_P(NEON, NnPredictTest, + ::testing::Values(av1_nn_predict_neon)); +#endif +#endif // !CONFIG_EXCLUDE_SIMD_MISMATCH + +} // namespace |