diff options
-rw-r--r-- | tests/validation/CL/FullyConnectedLayer.cpp | 232 | ||||
-rw-r--r-- | tests/validation/Helpers.cpp | 10 | ||||
-rw-r--r-- | tests/validation/Helpers.h | 5 | ||||
-rw-r--r-- | tests/validation/NEON/FullyConnectedLayer.cpp | 284 | ||||
-rw-r--r-- | tests/validation/fixtures/FullyConnectedLayerFixture.h | 205 |
5 files changed, 491 insertions, 245 deletions
diff --git a/tests/validation/CL/FullyConnectedLayer.cpp b/tests/validation/CL/FullyConnectedLayer.cpp index 474a87dd1c..2f0c86499b 100644 --- a/tests/validation/CL/FullyConnectedLayer.cpp +++ b/tests/validation/CL/FullyConnectedLayer.cpp @@ -40,6 +40,7 @@ namespace test { namespace validation { +using framework::dataset::make; namespace { /** Tolerance for float operations */ @@ -51,15 +52,20 @@ constexpr float tolerance_num = 0.07f; /**< Tolerance n /** Tolerance for quantized asymmetric operations */ constexpr AbsoluteTolerance<uint8_t> tolerance_qasymm8(1); -const auto FullyConnectedParameters = combine(framework::dataset::make("TransposeWeights", { false, true }), framework::dataset::make("ReshapeWeights", { false, true })); +const auto FullyConnectedParameters = combine(make("TransposeWeights", { false, true }), make("ReshapeWeights", { false, true })); -const auto QuantizationData = framework::dataset::make("QuantizationInfo", +const auto QuantizationData = make("QuantizationInfo", { QuantizationInfo(1.f / 255.f, 10), QuantizationInfo(1.1f, 10), }); -const auto ActivationFunctionsDataset = framework::dataset::make("ActivationInfo", +const auto IgnoredQuantizationData = make("IgnoredQuantizationInfo", +{ + QuantizationInfo(), +}); + +const auto ActivationFunctionsDataset = make("ActivationInfo", { ActivationLayerInfo(), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), @@ -68,11 +74,16 @@ const auto ActivationFunctionsDataset = framework::dataset::make("ActivationInfo ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH) }); -const auto ActivationFunctionsQuantizedDataset = concat(concat(concat( - framework::dataset::make("ActivationInfo", ActivationLayerInfo()), - framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))), - framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 0.5f))), - framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 0.75f, 0.25f))); +// This dataset case only runs with dynamic quantization +const auto NoActivationFunctionsQuantizedDataset = make("ActivationInfo", +{ + ActivationLayerInfo() +}); + +const auto ActivationFunctionsQuantizedDataset = concat(concat( + make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)), + make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 0.5f))), + make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 0.75f, 0.25f))); } // namespace TEST_SUITE(CL) @@ -81,33 +92,33 @@ TEST_SUITE(FullyConnectedLayer) // *INDENT-OFF* // clang-format off DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip( - framework::dataset::make("InputInfo", { TensorInfo(TensorShape(9U, 5U, 7U, 3U), 1, DataType::F32), // Mismatching data types + make("InputInfo", { TensorInfo(TensorShape(9U, 5U, 7U, 3U), 1, DataType::F32), // Mismatching data types TensorInfo(TensorShape(8U, 4U, 6U, 4U), 1, DataType::F32), TensorInfo(TensorShape(8U, 4U, 6U, 4U), 1, DataType::F32), TensorInfo(TensorShape(9U, 5U, 7U, 3U), 1, DataType::F32), // Invalid weights dimensions TensorInfo(TensorShape(9U, 5U, 7U, 3U), 1, DataType::F32), // Wrongly reshaped weights }), - framework::dataset::make("WeightsInfo",{ TensorInfo(TensorShape(315U, 271U), 1, DataType::F16), + make("WeightsInfo",{ TensorInfo(TensorShape(315U, 271U), 1, DataType::F16), TensorInfo(TensorShape(192U, 192U), 1, DataType::F32), TensorInfo(TensorShape(192U, 192U), 1, DataType::F32), TensorInfo(TensorShape(217U, 231U), 1, DataType::F32), TensorInfo(TensorShape(217U, 315U), 1, DataType::F32), })), - framework::dataset::make("BiasInfo",{ TensorInfo(TensorShape(271U), 1, DataType::F32), + make("BiasInfo",{ TensorInfo(TensorShape(271U), 1, DataType::F32), TensorInfo(TensorShape(192U), 1, DataType::F32), TensorInfo(TensorShape(192U), 1, DataType::F32), TensorInfo(TensorShape(271U), 1, DataType::F32), TensorInfo(TensorShape(271U), 1, DataType::F32), })), - framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(271U, 3U), 1, DataType::F32), + make("OutputInfo",{ TensorInfo(TensorShape(271U, 3U), 1, DataType::F32), TensorInfo(TensorShape(192U, 4U), 1, DataType::F32), TensorInfo(TensorShape(192U, 4U), 1, DataType::F32), TensorInfo(TensorShape(271U, 3U), 1, DataType::F32), TensorInfo(TensorShape(271U, 3U), 1, DataType::F32), })), - framework::dataset::make("TransposeWeights",{ true, true, false, true, true })), - framework::dataset::make("ReshapedWeights",{ false, false, false, false, false})), - framework::dataset::make("Expected", { false, true, true, false, false })), + make("TransposeWeights",{ true, true, false, true, true })), + make("ReshapedWeights",{ false, false, false, false, false})), + make("Expected", { false, true, true, false, false })), input_info, weights_info, bias_info, output_info, transpose_weights, reshaped_weights, expected) { // Create Fully Connected layer info @@ -136,64 +147,64 @@ using CLFullyConnectedNoBiasFixture = FullyConnectedDynamicNoBiasFixture<CLTenso TEST_SUITE(Float) TEST_SUITE(FP16) -FIXTURE_DATA_TEST_CASE(RunSmall, CLFullyConnectedLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallFullyConnectedLayerDataset(), - FullyConnectedParameters), - framework::dataset::make("DataType", DataType::F16)), +FIXTURE_DATA_TEST_CASE(RunSmall, CLFullyConnectedLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallFullyConnectedLayerDataset(), + FullyConnectedParameters, + make("DataType", DataType::F16), ActivationFunctionsDataset)) { // Validate output validate(CLAccessor(_target), _reference, tolerance_f16, tolerance_num); } -FIXTURE_DATA_TEST_CASE(RunLarge, CLFullyConnectedLayerFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeFullyConnectedLayerDataset(), - FullyConnectedParameters), - framework::dataset::make("DataType", DataType::F16)), +FIXTURE_DATA_TEST_CASE(RunLarge, CLFullyConnectedLayerFixture<half>, framework::DatasetMode::NIGHTLY, combine(datasets::LargeFullyConnectedLayerDataset(), + FullyConnectedParameters, + make("DataType", DataType::F16), ActivationFunctionsDataset)) { // Validate output validate(CLAccessor(_target), _reference, tolerance_f16, tolerance_num); } -FIXTURE_DATA_TEST_CASE(RunDynamicWeights, CLFullyConnectedLayerDynamicWeightsFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallFullyConnectedLayerDataset(), - framework::dataset::make("DataType", DataType::F16)), - framework::dataset::make("ActivationInfo", ActivationLayerInfo())), - framework::dataset::make("WeightsReshaped", { false, true }))) +FIXTURE_DATA_TEST_CASE(RunDynamicWeights, CLFullyConnectedLayerDynamicWeightsFixture<half>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallFullyConnectedLayerDataset(), + make("DataType", DataType::F16), + make("ActivationInfo", ActivationLayerInfo()), + make("WeightsReshaped", { false, true }))) { } TEST_SUITE_END() TEST_SUITE(FP32) -FIXTURE_DATA_TEST_CASE(RunSmall, CLFullyConnectedLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallFullyConnectedLayerDataset(), FullyConnectedParameters), - framework::dataset::make("DataType", DataType::F32)), +FIXTURE_DATA_TEST_CASE(RunSmall, CLFullyConnectedLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallFullyConnectedLayerDataset(), FullyConnectedParameters, + make("DataType", DataType::F32), ActivationFunctionsDataset)) { // Validate output validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0, abs_tolerance_f32); } -FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, CLFullyConnectedLayerMixedDataLayoutFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(combine(combine( - framework::dataset::make("Input", TensorShape(9U, 5U, 7U)), - framework::dataset::make("Weights", TensorShape(315U, 271U))), - framework::dataset::make("Biases", TensorShape(271U))), - framework::dataset::make("Output", TensorShape(271U))), - FullyConnectedParameters), - framework::dataset::make("DataType", DataType::F32)), - framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)))) +FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, CLFullyConnectedLayerMixedDataLayoutFixture<float>, framework::DatasetMode::PRECOMMIT, combine( + make("Input", TensorShape(9U, 5U, 7U)), + make("Weights", TensorShape(315U, 271U)), + make("Biases", TensorShape(271U)), + make("Output", TensorShape(271U)), + FullyConnectedParameters, + make("DataType", DataType::F32), + make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)))) { // Validate output validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0, abs_tolerance_f32); } -FIXTURE_DATA_TEST_CASE(RunDynamicWeights, CLFullyConnectedLayerDynamicWeightsFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallFullyConnectedLayerDataset(), - framework::dataset::make("DataType", DataType::F32)), - framework::dataset::make("ActivationInfo", ActivationLayerInfo())), - framework::dataset::make("WeightsReshaped", { false, true }))) +FIXTURE_DATA_TEST_CASE(RunDynamicWeights, CLFullyConnectedLayerDynamicWeightsFixture<float>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallFullyConnectedLayerDataset(), + make("DataType", DataType::F32), + make("ActivationInfo", ActivationLayerInfo()), + make("WeightsReshaped", { false, true }))) { } -FIXTURE_DATA_TEST_CASE(RunDynamicNoBias, CLFullyConnectedNoBiasFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallFullyConnectedLayerDataset(), - framework::dataset::make("DataType", DataType::F32)), - framework::dataset::make("ActivationInfo", { ActivationLayerInfo(), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) })), - framework::dataset::make("WeightsReshaped", { false }))) +FIXTURE_DATA_TEST_CASE(RunDynamicNoBias, CLFullyConnectedNoBiasFixture<float>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallFullyConnectedLayerDataset(), + make("DataType", DataType::F32), + make("ActivationInfo", { ActivationLayerInfo(), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) }), + make("WeightsReshaped", { false }))) { } -FIXTURE_DATA_TEST_CASE(RunLarge, CLFullyConnectedLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeFullyConnectedLayerDataset(), FullyConnectedParameters), - framework::dataset::make("DataType", DataType::F32)), +FIXTURE_DATA_TEST_CASE(RunLarge, CLFullyConnectedLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(datasets::LargeFullyConnectedLayerDataset(), FullyConnectedParameters, + make("DataType", DataType::F32), ActivationFunctionsDataset)) { // Validate output @@ -209,73 +220,130 @@ using CLFullyConnectedLayerQuantizedMixedDataLayoutFixture = FullyConnectedLayer TEST_SUITE(Quantized) TEST_SUITE(QASYMM8) -FIXTURE_DATA_TEST_CASE(RunSmall, CLFullyConnectedLayerQuantizedFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, - combine(combine(combine(combine(datasets::SmallFullyConnectedLayerDataset(), FullyConnectedParameters), framework::dataset::make("DataType", DataType::QASYMM8)), QuantizationData), +FIXTURE_DATA_TEST_CASE(RunSmallWithActivation, CLFullyConnectedLayerQuantizedFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, + combine(datasets::SmallFullyConnectedLayerDataset(), FullyConnectedParameters, make("DataType", DataType::QASYMM8), QuantizationData, ActivationFunctionsQuantizedDataset)) { // Validate output validate(CLAccessor(_target), _reference, tolerance_qasymm8); } -FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, CLFullyConnectedLayerQuantizedMixedDataLayoutFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, - combine(combine(combine(combine(combine(combine(combine( - framework::dataset::make("Input", TensorShape(9U, 5U, 7U)), - framework::dataset::make("Weights", TensorShape(315U, 271U))), - framework::dataset::make("Biases", TensorShape(271U))), - framework::dataset::make("Output", TensorShape(271U))), - FullyConnectedParameters), - framework::dataset::make("DataType", DataType::QASYMM8)), - QuantizationData), - framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)))) +FIXTURE_DATA_TEST_CASE(RunMixedDataLayoutWithActivation, CLFullyConnectedLayerQuantizedMixedDataLayoutFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, + combine( + make("Input", TensorShape(9U, 5U, 7U)), + make("Weights", TensorShape(315U, 271U)), + make("Biases", TensorShape(271U)), + make("Output", TensorShape(271U)), + FullyConnectedParameters, + make("DataType", DataType::QASYMM8), + QuantizationData, + make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_qasymm8); } -FIXTURE_DATA_TEST_CASE(RunLarge, CLFullyConnectedLayerQuantizedFixture<uint8_t>, framework::DatasetMode::NIGHTLY, - combine(combine(combine(combine(datasets::LargeFullyConnectedLayerDataset(), FullyConnectedParameters), framework::dataset::make("DataType", DataType::QASYMM8)), QuantizationData), +FIXTURE_DATA_TEST_CASE(RunLargeWithActivation, CLFullyConnectedLayerQuantizedFixture<uint8_t>, framework::DatasetMode::NIGHTLY, + combine(datasets::LargeFullyConnectedLayerDataset(), FullyConnectedParameters, make("DataType", DataType::QASYMM8), QuantizationData, ActivationFunctionsQuantizedDataset)) { // Validate output validate(CLAccessor(_target), _reference, tolerance_qasymm8); } -FIXTURE_DATA_TEST_CASE(RunDynamicWeights, CLFullyConnectedLayerDynamicWeightsFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallFullyConnectedLayerDataset(), - framework::dataset::make("DataType", DataType::QASYMM8)), - framework::dataset::make("ActivationInfo", ActivationLayerInfo())), - framework::dataset::make("WeightsReshaped", { false /* COMPMID-6000: Support FullyConnected with quantized dynamic weights already reshaped */ }))) + +// Dynamic Quantization Tests +FIXTURE_DATA_TEST_CASE(RunSmall, CLFullyConnectedLayerQuantizedFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, + combine(datasets::SmallFullyConnectedLayerDataset(), FullyConnectedParameters, make("DataType", DataType::QASYMM8), IgnoredQuantizationData, + NoActivationFunctionsQuantizedDataset)) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_qasymm8); +} +FIXTURE_DATA_TEST_CASE(RunLarge, CLFullyConnectedLayerQuantizedFixture<uint8_t>, framework::DatasetMode::NIGHTLY, + combine(datasets::LargeFullyConnectedLayerDataset(), FullyConnectedParameters, make("DataType", DataType::QASYMM8), IgnoredQuantizationData, + NoActivationFunctionsQuantizedDataset)) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_qasymm8); +} +FIXTURE_DATA_TEST_CASE(RunDynamicWeights, CLFullyConnectedLayerDynamicWeightsFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallFullyConnectedLayerDataset(), + make("DataType", DataType::QASYMM8), + NoActivationFunctionsQuantizedDataset, + make("WeightsReshaped", { false /* COMPMID-6000: Support FullyConnected with quantized dynamic weights already reshaped */ }))) { } + +FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, CLFullyConnectedLayerQuantizedMixedDataLayoutFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, + combine( + make("Input", TensorShape(9U, 5U, 7U)), + make("Weights", TensorShape(315U, 271U)), + make("Biases", TensorShape(271U)), + make("Output", TensorShape(271U)), + FullyConnectedParameters, + make("DataType", DataType::QASYMM8), + IgnoredQuantizationData, + NoActivationFunctionsQuantizedDataset)) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_qasymm8); +} + TEST_SUITE_END() /* QASYMM8 */ TEST_SUITE(QASYMM8_SIGNED) -FIXTURE_DATA_TEST_CASE(RunSmall, CLFullyConnectedLayerQuantizedFixture<int8_t>, framework::DatasetMode::PRECOMMIT, - combine(combine(combine(combine(datasets::SmallFullyConnectedLayerDataset(), FullyConnectedParameters), framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)), QuantizationData), +FIXTURE_DATA_TEST_CASE(RunSmallWithActivation, CLFullyConnectedLayerQuantizedFixture<int8_t>, framework::DatasetMode::PRECOMMIT, + combine(datasets::SmallFullyConnectedLayerDataset(), FullyConnectedParameters, make("DataType", DataType::QASYMM8_SIGNED), QuantizationData, ActivationFunctionsQuantizedDataset)) { // Validate output validate(CLAccessor(_target), _reference, tolerance_qasymm8); } +FIXTURE_DATA_TEST_CASE(RunMixedDataLayoutWithActivation, CLFullyConnectedLayerQuantizedMixedDataLayoutFixture<int8_t>, framework::DatasetMode::PRECOMMIT, + combine( + make("Input", TensorShape(9U, 5U, 7U)), + make("Weights", TensorShape(315U, 271U)), + make("Biases", TensorShape(271U)), + make("Output", TensorShape(271U)), + FullyConnectedParameters, + make("DataType", DataType::QASYMM8_SIGNED), + QuantizationData, + make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_qasymm8); +} + +// Dynamic Quantization tests below +FIXTURE_DATA_TEST_CASE(RunSmall, CLFullyConnectedLayerQuantizedFixture<int8_t>, framework::DatasetMode::PRECOMMIT, + combine(datasets::SmallFullyConnectedLayerDataset(), FullyConnectedParameters, make("DataType", DataType::QASYMM8_SIGNED), IgnoredQuantizationData, + NoActivationFunctionsQuantizedDataset)) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_qasymm8); +} + FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, CLFullyConnectedLayerQuantizedMixedDataLayoutFixture<int8_t>, framework::DatasetMode::PRECOMMIT, - combine(combine(combine(combine(combine(combine(combine( - framework::dataset::make("Input", TensorShape(9U, 5U, 7U)), - framework::dataset::make("Weights", TensorShape(315U, 271U))), - framework::dataset::make("Biases", TensorShape(271U))), - framework::dataset::make("Output", TensorShape(271U))), - FullyConnectedParameters), - framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)), - QuantizationData), - framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)))) + combine( + make("Input", TensorShape(9U, 5U, 7U)), + make("Weights", TensorShape(315U, 271U)), + make("Biases", TensorShape(271U)), + make("Output", TensorShape(271U)), + FullyConnectedParameters, + make("DataType", DataType::QASYMM8_SIGNED), + IgnoredQuantizationData, + NoActivationFunctionsQuantizedDataset)) { // Validate output validate(CLAccessor(_target), _reference, tolerance_qasymm8); } -FIXTURE_DATA_TEST_CASE(RunDynamicWeights, CLFullyConnectedLayerDynamicWeightsFixture<int8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallFullyConnectedLayerDataset(), - framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)), - framework::dataset::make("ActivationInfo", ActivationLayerInfo())), - framework::dataset::make("WeightsReshaped", { false /* COMPMID-6000: Support FullyConnected with quantized dynamic weights already reshaped */ }))) + +FIXTURE_DATA_TEST_CASE(RunDynamicWeights, CLFullyConnectedLayerDynamicWeightsFixture<int8_t>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallFullyConnectedLayerDataset(), + make("DataType", DataType::QASYMM8_SIGNED), + make("ActivationInfo", ActivationLayerInfo()), + make("WeightsReshaped", { false /* COMPMID-6000: Support FullyConnected with quantized dynamic weights already reshaped */ }))) { } -FIXTURE_DATA_TEST_CASE(RunDynamicNoBias, CLFullyConnectedNoBiasFixture<int8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallFullyConnectedLayerDataset(), - framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)), - framework::dataset::make("ActivationInfo", ActivationLayerInfo())), - framework::dataset::make("WeightsReshaped", { false /* COMPMID-6000: Support FullyConnected with quantized dynamic weights already reshaped */ }))) +FIXTURE_DATA_TEST_CASE(RunDynamicNoBias, CLFullyConnectedNoBiasFixture<int8_t>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallFullyConnectedLayerDataset(), + make("DataType", DataType::QASYMM8_SIGNED), + make("ActivationInfo", ActivationLayerInfo()), + make("WeightsReshaped", { false /* COMPMID-6000: Support FullyConnected with quantized dynamic weights already reshaped */ }))) { } TEST_SUITE_END() // QASYMM8_SIGNED diff --git a/tests/validation/Helpers.cpp b/tests/validation/Helpers.cpp index cb4d87601c..560460fd33 100644 --- a/tests/validation/Helpers.cpp +++ b/tests/validation/Helpers.cpp @@ -426,7 +426,7 @@ QuantizationHint suggest_matmul_dst_q_info_and_bias(const QuantizationInfo &lhs_ } QuantizationHint suggest_mac_dst_q_info_and_bias( - const QuantizationInfo &a_q_info, const QuantizationInfo &b_q_info, int32_t K, DataType data_type, float bias_fraction) + const QuantizationInfo &a_q_info, const QuantizationInfo &b_q_info, int32_t K, DataType data_type, float bias_fraction, int num_sd) { QuantizationInfo c_q_info; @@ -554,8 +554,8 @@ QuantizationHint suggest_mac_dst_q_info_and_bias( const float var_d = std_d * std_d; // Also calculate the suggested bias range - const int32_t min_bias = mean_d_int - 2 * std_d_int; - const int32_t max_bias = mean_d_int + 2 * std_d_int; + const int32_t min_bias = mean_d_int - (num_sd * std_d_int); + const int32_t max_bias = mean_d_int + (num_sd * std_d_int); // Output/C stats const float mean_out = K * mean_a * mean_b + mean_d; @@ -563,8 +563,8 @@ QuantizationHint suggest_mac_dst_q_info_and_bias( const float std_out = sqrt(var_out); // Output quantization setup - const float scale_out = 4 * std_out / 255; - const int32_t offset_out = static_cast<int32_t>(t_min - (mean_out - 2.f * std_out) / scale_out); + const float scale_out = (2 * num_sd) * std_out / 255; + const int32_t offset_out = static_cast<int32_t>(t_min - (mean_out - (num_sd * std_out)) / scale_out); c_q_info = QuantizationInfo(scale_out, offset_out); diff --git a/tests/validation/Helpers.h b/tests/validation/Helpers.h index 5a1e69afbd..647adcdb69 100644 --- a/tests/validation/Helpers.h +++ b/tests/validation/Helpers.h @@ -286,12 +286,13 @@ QuantizationHint suggest_matmul_dst_q_info_and_bias(const QuantizationInfo &lhs_ * @param[in] k number of accumulations taking place in the sum, i.e. c_k = sum_k(a_k * b_k) * @param[in] data_type data type, only QASYMM8, QASYMM8_SIGNED are supported * @param[in] bias_fraction the fraction of bias amplitude compared to integer accummulation. + * @param[in] num_sd (Optional) number of standard deviations we allow from the mean. Default value is 2. * * @return QuantizationHint object containing the suggested output quantization info and min/max bias range */ QuantizationHint suggest_mac_dst_q_info_and_bias(const QuantizationInfo &lhs_q_info, - const QuantizationInfo &rhs_q_info, int32_t k, DataType data_type, - float bias_fraction); + const QuantizationInfo &rhs_q_info, int32_t k, DataType data_type, float bias_fraction, + int num_sd = 2); } // namespace validation } // namespace test } // namespace arm_compute diff --git a/tests/validation/NEON/FullyConnectedLayer.cpp b/tests/validation/NEON/FullyConnectedLayer.cpp index 04889a9dba..31db8f0f80 100644 --- a/tests/validation/NEON/FullyConnectedLayer.cpp +++ b/tests/validation/NEON/FullyConnectedLayer.cpp @@ -42,6 +42,7 @@ namespace test { namespace validation { +using framework::dataset::make; namespace { /** Tolerance for float operations */ @@ -58,7 +59,7 @@ constexpr AbsoluteTolerance<uint8_t> tolerance_qasymm8(1); constexpr AbsoluteTolerance<int8_t> tolerance_qasymm8_signed(1); /** CNN data types */ -const auto CNNDataTypes = framework::dataset::make("DataType", +const auto CNNDataTypes = make("DataType", { #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC DataType::F16, @@ -66,18 +67,25 @@ const auto CNNDataTypes = framework::dataset::make("DataType", DataType::F32, }); -const auto FullyConnectedParameters = combine(framework::dataset::make("TransposeWeights", { false, true }), framework::dataset::make("ReshapeWeights", { false, true })); +const auto FullyConnectedParameters = combine(make("TransposeWeights", { false, true }), make("ReshapeWeights", { false, true })); -const auto QuantizationData = framework::dataset::make("QuantizationInfo", +const auto QuantizationData = make("QuantizationInfo", { QuantizationInfo(1.f / 256.f, 10), QuantizationInfo(1.1f, 10), }); -const auto EmptyActivationFunctionDataset = framework::dataset::make("ActivationInfo", + +const auto IgnoredQuantizationData = make("IgnoredQuantizationInfo", +{ + QuantizationInfo(), +}); + +const auto NoActivationFunctionDataset = make("ActivationInfo", { ActivationLayerInfo(), }); -const auto ActivationFunctionsDataset = framework::dataset::make("ActivationInfo", + +const auto ActivationFunctionsDataset = make("ActivationInfo", { ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 0.5f), @@ -85,7 +93,7 @@ const auto ActivationFunctionsDataset = framework::dataset::make("ActivationInfo ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH), }); -const auto ActivationFunctionsQuantizedDataset = framework::dataset::make("ActivationInfo", +const auto ActivationFunctionsQuantizedDataset = make("ActivationInfo", { ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 0.5f), @@ -242,37 +250,37 @@ TEST_CASE(Quant8_Signed_Mult_gt_1, framework::DatasetMode::ALL) // *INDENT-OFF* // clang-format off DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip( - framework::dataset::make("InputInfo", { TensorInfo(TensorShape(9U, 5U, 7U, 3U), 1, DataType::F32), // Mismatching data types + make("InputInfo", { TensorInfo(TensorShape(9U, 5U, 7U, 3U), 1, DataType::F32), // Mismatching data types TensorInfo(TensorShape(8U, 4U, 6U, 4U), 1, DataType::F32), TensorInfo(TensorShape(8U, 4U, 6U, 4U), 1, DataType::F32), TensorInfo(TensorShape(9U, 5U, 7U, 3U), 1, DataType::F32), // Invalid weights dimensions TensorInfo(TensorShape(9U, 5U, 7U, 3U), 1, DataType::F32), // Wrongly reshaped weights TensorInfo(TensorShape(8U, 4U, 6U, 4U), 1, DataType::F32), }), - framework::dataset::make("WeightsInfo",{ TensorInfo(TensorShape(315U, 271U), 1, DataType::F16), + make("WeightsInfo",{ TensorInfo(TensorShape(315U, 271U), 1, DataType::F16), TensorInfo(TensorShape(192U, 192U), 1, DataType::F32), TensorInfo(TensorShape(192U, 192U), 1, DataType::F32), TensorInfo(TensorShape(217U, 315U), 1, DataType::F32), TensorInfo(TensorShape(217U, 315U), 1, DataType::F32), TensorInfo(TensorShape(192U, 192U), 1, DataType::F32), })), - framework::dataset::make("BiasInfo",{ TensorInfo(TensorShape(271U), 1, DataType::F32), + make("BiasInfo",{ TensorInfo(TensorShape(271U), 1, DataType::F32), TensorInfo(TensorShape(192U), 1, DataType::F32), TensorInfo(TensorShape(192U), 1, DataType::F32), TensorInfo(TensorShape(271U), 1, DataType::F32), TensorInfo(TensorShape(271U), 1, DataType::F32), TensorInfo(TensorShape(192U), 1, DataType::F32), })), - framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(271U, 3U), 1, DataType::F32), + make("OutputInfo",{ TensorInfo(TensorShape(271U, 3U), 1, DataType::F32), TensorInfo(TensorShape(192U, 4U), 1, DataType::F32), TensorInfo(TensorShape(192U, 4U), 1, DataType::F32), TensorInfo(TensorShape(271U, 3U), 1, DataType::F32), TensorInfo(TensorShape(271U, 3U), 1, DataType::F32), TensorInfo(TensorShape(192U, 4U), 1, DataType::F32), })), - framework::dataset::make("TransposeWeights",{ true, true, false, true, true, true })), - framework::dataset::make("ReshapedWeights",{ false, false, false, false, false , false})), - framework::dataset::make("Expected", { false, true, true, false, false, true })), + make("TransposeWeights",{ true, true, false, true, true, true })), + make("ReshapedWeights",{ false, false, false, false, false , false})), + make("Expected", { false, true, true, false, false, true })), input_info, weights_info, bias_info, output_info, transpose_weights, reshaped_weights, expected) { // Create Fully Connected layer info @@ -298,80 +306,79 @@ using NEFullyConnectedLayerDynamicBiasFixture = FullyConnectedWithDynamicBiasFix TEST_SUITE(Float) #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC TEST_SUITE(FP16) -FIXTURE_DATA_TEST_CASE(RunSmall, NEFullyConnectedLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallFullyConnectedLayerDataset(), - FullyConnectedParameters), - framework::dataset::make("DataType", DataType::F16)), - EmptyActivationFunctionDataset)) +FIXTURE_DATA_TEST_CASE(RunSmall, NEFullyConnectedLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallFullyConnectedLayerDataset(), + FullyConnectedParameters, + make("DataType", DataType::F16), + NoActivationFunctionDataset)) { // Validate output validate(Accessor(_target), _reference, rel_tolerance_f16, tolerance_num_f16, abs_tolerance_f16); } -FIXTURE_DATA_TEST_CASE(RunWithActivation, NEFullyConnectedLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine( +FIXTURE_DATA_TEST_CASE(RunWithActivation, NEFullyConnectedLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(datasets::FullyConnectedLayerWithActivationDataset(), - FullyConnectedParameters), - framework::dataset::make("DataType", DataType::F16)), + FullyConnectedParameters, + make("DataType", DataType::F16), ActivationFunctionsDataset)) { // Validate output validate(Accessor(_target), _reference, rel_tolerance_f16, tolerance_num_f16, abs_tolerance_f16); } -FIXTURE_DATA_TEST_CASE(RunLarge, NEFullyConnectedLayerFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeFullyConnectedLayerDataset(), - FullyConnectedParameters), - framework::dataset::make("DataType", DataType::F16)), - EmptyActivationFunctionDataset)) +FIXTURE_DATA_TEST_CASE(RunLarge, NEFullyConnectedLayerFixture<half>, framework::DatasetMode::NIGHTLY, combine(datasets::LargeFullyConnectedLayerDataset(), + FullyConnectedParameters, + make("DataType", DataType::F16), + NoActivationFunctionDataset)) { // Validate output validate(Accessor(_target), _reference, rel_tolerance_f16, tolerance_num_f16, abs_tolerance_f16); } -FIXTURE_DATA_TEST_CASE(RunDynamicWeights, NEFullyConnectedLayerDynamicWeightsFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallFullyConnectedLayerDataset(), - framework::dataset::make("DataType", DataType::F16)), - framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))), - framework::dataset::make("WeightsReshaped", { false, true }))) +FIXTURE_DATA_TEST_CASE(RunDynamicWeights, NEFullyConnectedLayerDynamicWeightsFixture<half>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallFullyConnectedLayerDataset(), + make("DataType", DataType::F16), + make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)), + make("WeightsReshaped", { false, true }))) { } TEST_SUITE_END() #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ TEST_SUITE(FP32) -FIXTURE_DATA_TEST_CASE(RunSmall, NEFullyConnectedLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallFullyConnectedLayerDataset(), FullyConnectedParameters), - framework::dataset::make("DataType", DataType::F32)), - EmptyActivationFunctionDataset)) +FIXTURE_DATA_TEST_CASE(RunSmall, NEFullyConnectedLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallFullyConnectedLayerDataset(), FullyConnectedParameters, + make("DataType", DataType::F32), + NoActivationFunctionDataset)) { // Validate output validate(Accessor(_target), _reference, rel_tolerance_f32, 0, abs_tolerance_f32); } -FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, NEFullyConnectedLayerMixedDataLayoutFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(combine(combine( - framework::dataset::make("Input", TensorShape(9U, 5U, 7U)), - framework::dataset::make("Weights", TensorShape(315U, 271U))), - framework::dataset::make("Biases", TensorShape(271U))), - framework::dataset::make("Output", TensorShape(271U))), - FullyConnectedParameters), - framework::dataset::make("DataType", DataType::F32)), - framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)))) +FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, NEFullyConnectedLayerMixedDataLayoutFixture<float>, framework::DatasetMode::PRECOMMIT, combine( + make("Input", TensorShape(9U, 5U, 7U)), + make("Weights", TensorShape(315U, 271U)), + make("Biases", TensorShape(271U)), + make("Output", TensorShape(271U)), + FullyConnectedParameters, + make("DataType", DataType::F32), + make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)))) { // Validate output validate(Accessor(_target), _reference, rel_tolerance_f32, 0, abs_tolerance_f32); } -FIXTURE_DATA_TEST_CASE(RunWithActivation, NEFullyConnectedLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine( - combine(datasets::FullyConnectedLayerWithActivationDataset(), - FullyConnectedParameters), - framework::dataset::make("DataType", DataType::F32)), +FIXTURE_DATA_TEST_CASE(RunWithActivation, NEFullyConnectedLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(datasets::FullyConnectedLayerWithActivationDataset(), + FullyConnectedParameters, + make("DataType", DataType::F32), ActivationFunctionsDataset)) { // Validate output validate(Accessor(_target), _reference, rel_tolerance_f32, 0, abs_tolerance_f32); } -FIXTURE_DATA_TEST_CASE(RunLarge, NEFullyConnectedLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeFullyConnectedLayerDataset(), FullyConnectedParameters), - framework::dataset::make("DataType", DataType::F32)), - EmptyActivationFunctionDataset)) +FIXTURE_DATA_TEST_CASE(RunLarge, NEFullyConnectedLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(datasets::LargeFullyConnectedLayerDataset(), FullyConnectedParameters, + make("DataType", DataType::F32), + NoActivationFunctionDataset)) { // Validate output validate(Accessor(_target), _reference, rel_tolerance_f32, 0, abs_tolerance_f32); } -FIXTURE_DATA_TEST_CASE(RunDynamicWeights, NEFullyConnectedLayerDynamicWeightsFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallFullyConnectedLayerDataset(), - framework::dataset::make("DataType", DataType::F32)), - framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))), - framework::dataset::make("WeightsReshaped", { false, true }))) +FIXTURE_DATA_TEST_CASE(RunDynamicWeights, NEFullyConnectedLayerDynamicWeightsFixture<float>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallFullyConnectedLayerDataset(), + make("DataType", DataType::F32), + make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)), + make("WeightsReshaped", { false, true }))) { } TEST_SUITE_END() @@ -384,103 +391,150 @@ using NEFullyConnectedLayerQuantizedMixedDataLayoutFixture = FullyConnectedLayer TEST_SUITE(Quantized) TEST_SUITE(QASYMM8) -FIXTURE_DATA_TEST_CASE(RunSmall, NEFullyConnectedLayerQuantizedFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine( - combine(datasets::SmallFullyConnectedLayerDataset(), - FullyConnectedParameters), - framework::dataset::make("DataType", DataType::QASYMM8)), - QuantizationData), - EmptyActivationFunctionDataset)) +FIXTURE_DATA_TEST_CASE(RunMixedDataLayoutWithActivation, NEFullyConnectedLayerQuantizedMixedDataLayoutFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, + combine( + make("Input", TensorShape(9U, 5U, 7U)), + make("Weights", TensorShape(315U, 271U)), + make("Biases", TensorShape(271U)), + make("Output", TensorShape(271U)), + FullyConnectedParameters, + make("DataType", DataType::QASYMM8), + QuantizationData, + make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)))) { // Validate output validate(Accessor(_target), _reference, tolerance_qasymm8); } -FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, NEFullyConnectedLayerQuantizedMixedDataLayoutFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, - combine(combine(combine(combine(combine(combine(combine( - framework::dataset::make("Input", TensorShape(9U, 5U, 7U)), - framework::dataset::make("Weights", TensorShape(315U, 271U))), - framework::dataset::make("Biases", TensorShape(271U))), - framework::dataset::make("Output", TensorShape(271U))), - FullyConnectedParameters), - framework::dataset::make("DataType", DataType::QASYMM8)), - QuantizationData), - framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)))) -{ - // Validate output - validate(Accessor(_target), _reference, tolerance_qasymm8); -} -FIXTURE_DATA_TEST_CASE(RunWithActivation, NEFullyConnectedLayerQuantizedFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine( +FIXTURE_DATA_TEST_CASE(RunSmallWithActivation, NEFullyConnectedLayerQuantizedFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(datasets::FullyConnectedLayerWithActivationDataset(), - FullyConnectedParameters), - framework::dataset::make("DataType", DataType::QASYMM8)), - QuantizationData), + FullyConnectedParameters, + make("DataType", DataType::QASYMM8), + QuantizationData, ActivationFunctionsQuantizedDataset)) { // Validate output validate(Accessor(_target), _reference, tolerance_qasymm8); } +FIXTURE_DATA_TEST_CASE(RunDynamicWeightsWithActivation, NEFullyConnectedLayerDynamicWeightsFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallFullyConnectedLayerDataset(), + make("DataType", DataType::QASYMM8), + make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)), + make("WeightsReshaped", { false }))) +{ +} +FIXTURE_DATA_TEST_CASE(RunDynamicBiasWithActivation, NEFullyConnectedLayerDynamicBiasFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallFullyConnectedLayerDataset(), + make("DataType", DataType::QASYMM8), + make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)))) +{ +} -FIXTURE_DATA_TEST_CASE(RunLarge, NEFullyConnectedLayerQuantizedFixture<uint8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine( - combine(datasets::LargeFullyConnectedLayerDataset(), - FullyConnectedParameters), - framework::dataset::make("DataType", DataType::QASYMM8)), - QuantizationData), - EmptyActivationFunctionDataset)) +// Dynamic Quantization Tests here +FIXTURE_DATA_TEST_CASE(RunSmall, NEFullyConnectedLayerQuantizedFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, + combine(datasets::SmallFullyConnectedLayerDataset(), + FullyConnectedParameters, + make("DataType", DataType::QASYMM8), + IgnoredQuantizationData, + NoActivationFunctionDataset)) { // Validate output validate(Accessor(_target), _reference, tolerance_qasymm8); } - -FIXTURE_DATA_TEST_CASE(RunDynamicBias, NEFullyConnectedLayerDynamicBiasFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::SmallFullyConnectedLayerDataset(), - framework::dataset::make("DataType", DataType::QASYMM8)), - framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)))) +FIXTURE_DATA_TEST_CASE(RunLarge, NEFullyConnectedLayerQuantizedFixture<uint8_t>, framework::DatasetMode::NIGHTLY, combine( + datasets::LargeFullyConnectedLayerDataset(), + FullyConnectedParameters, + framework::dataset::make("DataType", DataType::QASYMM8), + QuantizationData, + NoActivationFunctionDataset)) { + // Validate output + validate(Accessor(_target), _reference, tolerance_qasymm8); } -FIXTURE_DATA_TEST_CASE(RunDynamicWeights, NEFullyConnectedLayerDynamicWeightsFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallFullyConnectedLayerDataset(), - framework::dataset::make("DataType", DataType::QASYMM8)), - framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))), - framework::dataset::make("WeightsReshaped", { false }))) +FIXTURE_DATA_TEST_CASE(RunDynamicBias, NEFullyConnectedLayerDynamicBiasFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallFullyConnectedLayerDataset(), + make("DataType", DataType::QASYMM8), + NoActivationFunctionDataset)) { } -TEST_SUITE_END() -TEST_SUITE(QASYMM8_SIGNED) -FIXTURE_DATA_TEST_CASE(RunSmall, NEFullyConnectedLayerQuantizedFixture<int8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine( - combine(datasets::SmallFullyConnectedLayerDataset(), - FullyConnectedParameters), - framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)), - QuantizationData), - EmptyActivationFunctionDataset)) +FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, NEFullyConnectedLayerQuantizedMixedDataLayoutFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, + combine( + make("Input", TensorShape(9U, 5U, 7U)), + make("Weights", TensorShape(315U, 271U)), + make("Biases", TensorShape(271U)), + make("Output", TensorShape(271U)), + FullyConnectedParameters, + make("DataType", DataType::QASYMM8), + IgnoredQuantizationData, + NoActivationFunctionDataset)) { // Validate output - validate(Accessor(_target), _reference, tolerance_qasymm8_signed); + validate(Accessor(_target), _reference, tolerance_qasymm8); } -FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, NEFullyConnectedLayerQuantizedMixedDataLayoutFixture<int8_t>, framework::DatasetMode::PRECOMMIT, - combine(combine(combine(combine(combine(combine(combine( - framework::dataset::make("Input", TensorShape(9U, 5U, 7U)), - framework::dataset::make("Weights", TensorShape(315U, 271U))), - framework::dataset::make("Biases", TensorShape(271U))), - framework::dataset::make("Output", TensorShape(271U))), - FullyConnectedParameters), - framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)), - QuantizationData), - framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)))) +FIXTURE_DATA_TEST_CASE(RunDynamicWeights, NEFullyConnectedLayerDynamicWeightsFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallFullyConnectedLayerDataset(), + make("DataType", DataType::QASYMM8), + NoActivationFunctionDataset, + make("WeightsReshaped", { false }))) +{ +} +TEST_SUITE_END() // QASYMM8 +TEST_SUITE(QASYMM8_SIGNED) +FIXTURE_DATA_TEST_CASE(RunMixedDataLayoutWithActivation, NEFullyConnectedLayerQuantizedMixedDataLayoutFixture<int8_t>, framework::DatasetMode::PRECOMMIT, + combine( + make("Input", TensorShape(9U, 5U, 7U)), + make("Weights", TensorShape(315U, 271U)), + make("Biases", TensorShape(271U)), + make("Output", TensorShape(271U)), + FullyConnectedParameters, + make("DataType", DataType::QASYMM8_SIGNED), + QuantizationData, + make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)))) { // Validate output validate(Accessor(_target), _reference, tolerance_qasymm8); } -FIXTURE_DATA_TEST_CASE(RunWithActivation, NEFullyConnectedLayerQuantizedFixture<int8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine( +FIXTURE_DATA_TEST_CASE(RunWithActivation, NEFullyConnectedLayerQuantizedFixture<int8_t>, framework::DatasetMode::PRECOMMIT, combine(datasets::FullyConnectedLayerWithActivationDataset(), - FullyConnectedParameters), - framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)), - QuantizationData), + FullyConnectedParameters, + make("DataType", DataType::QASYMM8_SIGNED), + QuantizationData, ActivationFunctionsQuantizedDataset)) { // Validate output validate(Accessor(_target), _reference, tolerance_qasymm8_signed); } -FIXTURE_DATA_TEST_CASE(RunDynamicWeights, NEFullyConnectedLayerDynamicWeightsFixture<int8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallFullyConnectedLayerDataset(), - framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)), - framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))), - framework::dataset::make("WeightsReshaped", { false }))) +FIXTURE_DATA_TEST_CASE(RunDynamicWeightsWithActivation, NEFullyConnectedLayerDynamicWeightsFixture<int8_t>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallFullyConnectedLayerDataset(), + make("DataType", DataType::QASYMM8_SIGNED), + make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)), + make("WeightsReshaped", { false }))) +{ +} + +// Dynamic Quantization tests +FIXTURE_DATA_TEST_CASE(RunSmall, NEFullyConnectedLayerQuantizedFixture<int8_t>, framework::DatasetMode::PRECOMMIT, combine( + datasets::SmallFullyConnectedLayerDataset(), + FullyConnectedParameters, + make("DataType", DataType::QASYMM8_SIGNED), + IgnoredQuantizationData, + NoActivationFunctionDataset)) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_qasymm8_signed); +} +FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, NEFullyConnectedLayerQuantizedMixedDataLayoutFixture<int8_t>, framework::DatasetMode::PRECOMMIT, + combine( + make("Input", TensorShape(9U, 5U, 7U)), + make("Weights", TensorShape(315U, 271U)), + make("Biases", TensorShape(271U)), + make("Output", TensorShape(271U)), + FullyConnectedParameters, + make("DataType", DataType::QASYMM8_SIGNED), + QuantizationData, + NoActivationFunctionDataset)) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_qasymm8); +} +FIXTURE_DATA_TEST_CASE(RunDynamicWeights, NEFullyConnectedLayerDynamicWeightsFixture<int8_t>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallFullyConnectedLayerDataset(), + make("DataType", DataType::QASYMM8_SIGNED), + NoActivationFunctionDataset, + make("WeightsReshaped", { false }))) { } TEST_SUITE_END() // QASYMM8_SIGNED diff --git a/tests/validation/fixtures/FullyConnectedLayerFixture.h b/tests/validation/fixtures/FullyConnectedLayerFixture.h index 7cfe6e49b9..05f20ac12b 100644 --- a/tests/validation/fixtures/FullyConnectedLayerFixture.h +++ b/tests/validation/fixtures/FullyConnectedLayerFixture.h @@ -55,6 +55,40 @@ public: using TBias = typename std::conditional < (std::is_same<TDecay, uint8_t>::value || std::is_same<TDecay, int8_t>::value), int32_t, T >::type; public: + void setup_quantization(TensorShape weights_shape, TensorShape output_shape, QuantizationInfo &input_q_info, QuantizationInfo &weights_q_info, DataType data_type) + { + _hash = weights_shape[0] + weights_shape[1] + output_shape[0] + output_shape[1]; + const int32_t t_max = static_cast<int32_t>(std::numeric_limits<T>::max()); + const int32_t t_min = static_cast<int32_t>(std::numeric_limits<T>::min()); + + std::mt19937 generator(library->seed() + _hash); + std::uniform_real_distribution<float> distribution_float(-5.0f, 3.0f); + std::uniform_int_distribution<int32_t> distribution_t(t_min, t_max); + + const float scale_lhs = pow(2, distribution_float(generator)); // [2^-5, 2^3] + const float scale_rhs = pow(2, distribution_float(generator)); // [2^-5, 2^3] + const int32_t offset_lhs = distribution_t(generator); + const int32_t offset_rhs = distribution_t(generator); + + input_q_info = QuantizationInfo(scale_lhs, offset_lhs); + weights_q_info = QuantizationInfo(scale_rhs, offset_rhs); + + + const int k = weights_shape.x(); + QuantizationHint q_hint = suggest_mac_dst_q_info_and_bias(input_q_info, weights_q_info, k, data_type, 0.1f /* bias_fraction */, 4 /* number of standard deviations*/); + + _dst_q_info = q_hint.q_info; + _min_bias = q_hint.bias_min; + _max_bias = q_hint.bias_max; + + // Do not change here as these limits are the natural limits of the associated data types and + // are embedded in the computation of the dst quantization info. + _min_u8 = 0; + _max_u8 = 255; + _min_s8 = -128; + _max_s8 = 127; + } + void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, bool transpose_weights, bool reshape_weights, DataType data_type, QuantizationInfo quantization_info, ActivationLayerInfo activation_info, bool mixed_layout = false) { @@ -64,7 +98,20 @@ public: _mixed_layout = mixed_layout; _data_type = data_type; _bias_data_type = is_data_type_quantized_asymmetric(data_type) ? DataType::S32 : data_type; - _quantization_info = quantization_info; + + // Note : Quantization Info parameter from setup function is only used when quant datatype and activation function is not enabled or is identity. + if(is_data_type_quantized(data_type) && (!activation_info.enabled() || activation_info.activation() == ActivationFunction::IDENTITY)) + { + // Initialises quantization info with appropriate scale and offset for given input shapes. + setup_quantization(weights_shape, output_shape,_input_q_info, _weight_q_info, data_type); + } + else + { + _input_q_info = quantization_info; + _weight_q_info = quantization_info; + _dst_q_info = quantization_info; + } + _activation_info = activation_info; _target = compute_target(input_shape, weights_shape, bias_shape, output_shape, transpose_weights, reshape_weights); @@ -92,17 +139,17 @@ protected: { if(_data_type == DataType::QASYMM8) { - std::uniform_int_distribution<uint32_t> distribution(0, 30); + std::uniform_int_distribution<uint32_t> distribution(_min_u8, _max_u8); library->fill(tensor, distribution, i); } else if(_data_type == DataType::QASYMM8_SIGNED) { - std::uniform_int_distribution<int32_t> distribution(-15, 15); + std::uniform_int_distribution<int32_t> distribution(_min_s8, _max_s8); library->fill(tensor, distribution, i); } else if(_data_type == DataType::S32) { - std::uniform_int_distribution<int32_t> distribution(-50, 50); + std::uniform_int_distribution<int32_t> distribution(_min_bias, _max_bias); library->fill(tensor, distribution, i); } else if(_data_type == DataType::F16) @@ -144,10 +191,10 @@ protected: } // Create tensors - TensorType src = create_tensor<TensorType>(input_shape, _data_type, 1, _quantization_info); - TensorType weights = create_tensor<TensorType>(reshaped_weights_shape, _data_type, 1, _quantization_info); - TensorType bias = create_tensor<TensorType>(bias_shape, _bias_data_type, 1, _quantization_info); - TensorType dst = create_tensor<TensorType>(output_shape, _data_type, 1, _quantization_info); + TensorType src = create_tensor<TensorType>(input_shape, _data_type, 1, _input_q_info); + TensorType weights = create_tensor<TensorType>(reshaped_weights_shape, _data_type, 1, _weight_q_info); + TensorType bias = create_tensor<TensorType>(bias_shape, _bias_data_type, 1); + TensorType dst = create_tensor<TensorType>(output_shape, _data_type, 1, _dst_q_info); // Create Fully Connected layer info FullyConnectedLayerInfo fc_info; @@ -178,8 +225,8 @@ protected: ARM_COMPUTE_ASSERT(!dst.info()->is_resizable()); // Fill tensors - fill(AccessorType(src), 0); - fill(AccessorType(bias), 2); + fill(AccessorType(src), 0 + _hash); + fill(AccessorType(bias), 2 + _hash); if(!reshape_weights || !transpose_weights) { @@ -187,7 +234,7 @@ protected: RawTensor tmp(tmp_shape, _data_type, 1); // Fill with original shape - fill(tmp, 1); + fill(tmp, 1 + _hash); // Transpose elementwise tmp = transpose(tmp); @@ -204,7 +251,7 @@ protected: } else { - fill(AccessorType(weights), 1); + fill(AccessorType(weights), 1 + _hash); } if(_mixed_layout) @@ -223,16 +270,16 @@ protected: SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape) { // Create reference - SimpleTensor<T> src{ input_shape, _data_type, 1, _quantization_info }; - SimpleTensor<T> weights{ weights_shape, _data_type, 1, _quantization_info }; - SimpleTensor<TBias> bias{ bias_shape, _bias_data_type, 1, _quantization_info }; + SimpleTensor<T> src{ input_shape, _data_type, 1, _input_q_info }; + SimpleTensor<T> weights{ weights_shape, _data_type, 1, _weight_q_info }; + SimpleTensor<TBias> bias{ bias_shape, _bias_data_type, 1, QuantizationInfo() }; // Fill reference - fill(src, 0); - fill(weights, 1); - fill(bias, 2); + fill(src, 0 + _hash); + fill(weights, 1 + _hash); + fill(bias, 2 + _hash); - return reference::activation_layer(reference::fully_connected_layer<T>(src, weights, bias, output_shape, _quantization_info), _activation_info, _quantization_info); + return reference::activation_layer(reference::fully_connected_layer<T>(src, weights, bias, output_shape, _dst_q_info), _activation_info, _dst_q_info); } TensorType _target{}; @@ -240,8 +287,22 @@ protected: DataType _data_type{}; DataType _bias_data_type{}; bool _mixed_layout{ false }; - QuantizationInfo _quantization_info{}; + QuantizationInfo _input_q_info{}; + QuantizationInfo _weight_q_info{}; + QuantizationInfo _dst_q_info{}; ActivationLayerInfo _activation_info{}; + + // Random initialization limits + // Default values are previously handcrafted limits + // that sould be used when we don't use dynamic quantization + int32_t _min_bias{-50}; + int32_t _max_bias{50}; + + int32_t _min_u8{0}; + int32_t _max_u8{30}; + int32_t _min_s8{-15}; + int32_t _max_s8{15}; + int _hash{0}; }; template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool mixed_layout = false> @@ -289,12 +350,17 @@ private: } else if(_data_type == DataType::QASYMM8) { - std::uniform_int_distribution<uint32_t> distribution(0, 30); + std::uniform_int_distribution<uint32_t> distribution(_min_u8, _max_u8); + library->fill(tensor, distribution, i); + } + else if(_data_type == DataType::QASYMM8_SIGNED) + { + std::uniform_int_distribution<int32_t> distribution(_min_s8, _max_s8); library->fill(tensor, distribution, i); } else if(_data_type == DataType::S32) { - std::uniform_int_distribution<int32_t> distribution(-50, 50); + std::uniform_int_distribution<int32_t> distribution(_min_bias, _max_bias); library->fill(tensor, distribution, i); } else @@ -352,6 +418,40 @@ private: validate(AccessorType(target), ref, tolerance_qasymm8_signed); } + void setup_quantization(TensorShape weights_shape, TensorShape output_shape, QuantizationInfo &input_q_info, QuantizationInfo &weights_q_info, DataType data_type) + { + _hash = weights_shape[0] + weights_shape[1] + output_shape[0] + output_shape[1]; + + const int32_t t_max = static_cast<int32_t>(std::numeric_limits<T>::max()); + const int32_t t_min = static_cast<int32_t>(std::numeric_limits<T>::min()); + + std::mt19937 generator(library->seed() + _hash); + std::uniform_real_distribution<float> distribution_float(-5.0f, 3.0f); + std::uniform_int_distribution<int32_t> distribution_t(t_min, t_max); + + const float scale_lhs = pow(2, distribution_float(generator)); // [2^-5, 2^3] + const float scale_rhs = pow(2, distribution_float(generator)); // [2^-5, 2^3] + const int32_t offset_lhs = distribution_t(generator); + const int32_t offset_rhs = distribution_t(generator); + + input_q_info = QuantizationInfo(scale_lhs, offset_lhs); + weights_q_info = QuantizationInfo(scale_rhs, offset_rhs); + + const int k = weights_shape.x(); + QuantizationHint q_hint = suggest_mac_dst_q_info_and_bias(input_q_info, weights_q_info, k, data_type, 0.1f /* bias_fraction */, 4 /* number of standard deviations*/); + + _dst_q_info = q_hint.q_info; + _min_bias = q_hint.bias_min; + _max_bias = q_hint.bias_max; + + // Do not change here as these limits are the natural limits of the associated data types and + // are embedded in the computation of the dst quantization info. + _min_u8 = 0; + _max_u8 = 255; + _min_s8 = -128; + _max_s8 = 127; + } + public: using TDecay = typename std::decay<T>::type; using TBias = typename std::conditional < (std::is_same<TDecay, uint8_t>::value || std::is_same<TDecay, int8_t>::value), int32_t, T >::type; @@ -364,15 +464,22 @@ public: const bool is_quantized = is_data_type_quantized(data_type); const DataType bias_data_type = (is_quantized) ? DataType::S32 : data_type; - const QuantizationInfo src_qinfo = is_quantized ? QuantizationInfo(0.1f, 10) : QuantizationInfo(); - const QuantizationInfo weights_qinfo = is_quantized ? QuantizationInfo(0.3f, 20) : QuantizationInfo(); - const QuantizationInfo dst_qinfo = is_quantized ? QuantizationInfo(0.2f, 5) : QuantizationInfo(); + if (is_quantized && (!activation_info.enabled() || activation_info.activation() == ActivationFunction::IDENTITY)) + { + setup_quantization(weights_shape, dst_shape, _src_q_info, _weights_q_info, data_type); + } + else + { + _src_q_info = QuantizationInfo(0.1f, 10); + _dst_q_info = QuantizationInfo(0.3f, 20); + _weights_q_info = QuantizationInfo(0.2f, 5); + } // Configure TensorInfo Objects - const TensorInfo src_info(src_shape, 1, data_type, src_qinfo); - const TensorInfo dst_info(dst_shape, 1, data_type, dst_qinfo); + const TensorInfo src_info(src_shape, 1, data_type, _src_q_info); + const TensorInfo dst_info(dst_shape, 1, data_type, _dst_q_info); TensorInfo bias_info(bias_shape, 1, bias_data_type); - TensorInfo wei_info(weights_shape, 1, data_type, weights_qinfo); + TensorInfo wei_info(weights_shape, 1, data_type, _weights_q_info); if(!constant_weights && weights_reshaped) { @@ -412,20 +519,20 @@ public: int randomizer_offset = 0; // Create reference tensors - SimpleTensor<T> src{ src_shape, data_type, 1, src_qinfo }; - SimpleTensor<T> weights{ weights_shape, data_type, 1, weights_qinfo }; + SimpleTensor<T> src{ src_shape, data_type, 1, _src_q_info }; + SimpleTensor<T> weights{ weights_shape, data_type, 1, _weights_q_info }; SimpleTensor<TBias> bias{ bias_shape, bias_data_type }; // Fill weights and/or bias if they remain constant if(constant_weights) { - fill(AccessorType(_weights), 1); - fill(weights, 1); + fill(AccessorType(_weights), 1 + _hash); + fill(weights, 1 + _hash); } if(constant_bias && !remove_bias) { - fill(AccessorType(_bias), 2); - fill(bias, 2); + fill(AccessorType(_bias), 2 + _hash); + fill(bias, 2 + _hash); } // To remove bias, fill with 0 if(remove_bias && is_quantized) @@ -446,16 +553,16 @@ public: { if(weights_reshaped) { - fill_transposed_weights(_weights, weights_shape, randomizer_offset + 1); + fill_transposed_weights(_weights, weights_shape, randomizer_offset + 1 + _hash); } else { - fill(AccessorType(_weights), randomizer_offset + 1); + fill(AccessorType(_weights), randomizer_offset + 1 +_hash); } } if(!constant_bias && !remove_bias) { - fill(AccessorType(_bias), randomizer_offset + 2); + fill(AccessorType(_bias), randomizer_offset + 2 + _hash); } fc.run(); @@ -467,14 +574,14 @@ public: fill(src, randomizer_offset); if(!constant_weights) { - fill(weights, randomizer_offset + 1); + fill(weights, randomizer_offset + 1 + _hash); } if(!constant_bias && !remove_bias) { - fill(bias, randomizer_offset + 2); + fill(bias, randomizer_offset + 2 + _hash); } - auto dst = reference::activation_layer(reference::fully_connected_layer<T>(src, weights, bias, dst_shape, dst_qinfo), activation_info, dst_qinfo); + auto dst = reference::activation_layer(reference::fully_connected_layer<T>(src, weights, bias, dst_shape, _dst_q_info), activation_info, _dst_q_info); // Validate validate_with_tolerance(_dst, dst); @@ -487,6 +594,22 @@ public: private: TensorType _src{}, _weights{}, _bias{}, _dst{}; DataType _data_type{ DataType::UNKNOWN }; + + QuantizationInfo _src_q_info{}; + QuantizationInfo _weights_q_info{}; + QuantizationInfo _dst_q_info{}; + + // Random initialization limits + // Default values are previously handcrafted limits + // that sould be used when we don't use dynamic quantization + int32_t _min_bias{-50}; + int32_t _max_bias{50}; + + int32_t _min_u8{0}; + int32_t _max_u8{30}; + int32_t _min_s8{-15}; + int32_t _max_s8{15}; + int _hash{0}; }; template <typename TensorType, typename AccessorType, typename FunctionType, typename T> @@ -521,7 +644,7 @@ public: DataType data_type, ActivationLayerInfo activation_info) { FullyConnectedWithDynamicTensorsFixture<TensorType, AccessorType, FunctionType, T>::setup(src_shape, weights_shape, bias_shape, - dst_shape, data_type, activation_info, true, false, false, false /* weights_reshaped (not used) */); + dst_shape, data_type, activation_info, true, false, false, false); } }; } // namespace validation |