COMPMID-1067 NEON RNN FP32 / FP16
Change-Id: I440df2b2af512fd874651baf28428caa6f8e0b41
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/134433
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
diff --git a/tests/validation/NEON/RNNLayer.cpp b/tests/validation/NEON/RNNLayer.cpp
new file mode 100644
index 0000000..7aa3bef
--- /dev/null
+++ b/tests/validation/NEON/RNNLayer.cpp
@@ -0,0 +1,147 @@
+/*
+ * Copyright (c) 2018 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/runtime/NEON/functions/NERNNLayer.h"
+#include "tests/NEON/Accessor.h"
+#include "tests/PaddingCalculator.h"
+#include "tests/datasets/RNNLayerDataset.h"
+#include "tests/framework/Asserts.h"
+#include "tests/framework/Macros.h"
+#include "tests/framework/datasets/Datasets.h"
+#include "tests/validation/Validation.h"
+#include "tests/validation/fixtures/RNNLayerFixture.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+namespace
+{
+RelativeTolerance<float> tolerance_f32(0.001f);
+RelativeTolerance<half> tolerance_f16(half(0.1));
+} // namespace
+
+TEST_SUITE(NEON)
+TEST_SUITE(RNNLayer)
+
+// *INDENT-OFF*
+// clang-format off
+DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zip(
+ framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U), 1, DataType::U8, 0), // Wrong data type
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Wrong input size
+ TensorInfo(TensorShape(27U, 13U), 1, DataType::F32, 0), // Wrong weights size
+ TensorInfo(TensorShape(27U, 13U), 1, DataType::F32, 0), // Wrong recurrent weights size
+ TensorInfo(TensorShape(27U, 13U), 1, DataType::F32, 0), // Wrong bias size
+ TensorInfo(TensorShape(27U, 13U), 1, DataType::F32, 0), // Wrong output size
+ TensorInfo(TensorShape(27U, 13U), 1, DataType::F32, 0), // Wrong hidden output size
+ TensorInfo(TensorShape(32U, 32U), 1, DataType::F32, 0),
+ }),
+ framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(27U, 11U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(27U, 11U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(27U, 11U, 2U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(27U, 11U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(27U, 11U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(27U, 11U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(27U, 11U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(32U, 32U), 1, DataType::F32, 0),
+ })),
+ framework::dataset::make("RecurrentWeightsInfo", { TensorInfo(TensorShape(11U, 11U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(11U, 11U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(11U, 11U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(11U, 11U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(11U, 11U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(11U, 11U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(32U, 32U), 1, DataType::F32, 0),
+ })),
+ framework::dataset::make("BiasInfo", { TensorInfo(TensorShape(11U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(11U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(11U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(11U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(30U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(11U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(11U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(32U), 1, DataType::F32, 0),
+ })),
+ framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(11U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(32U, 32U), 1, DataType::F32, 0),
+ })),
+ framework::dataset::make("HiddenStateInfo", { TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(11U, 13U, 2U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(32U, 32U), 1, DataType::F32, 0),
+ })),
+ framework::dataset::make("ActivationInfo", { ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+ })),
+ framework::dataset::make("Expected", { false, false, false, false, false, false, false, true })),
+ input_info, weights_info, recurrent_weights_info, bias_info, output_info, hidden_output_info, info, expected)
+{
+ ARM_COMPUTE_EXPECT(bool(NERNNLayer::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &recurrent_weights_info.clone()->set_is_resizable(false), &bias_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), &hidden_output_info.clone()->set_is_resizable(false), info)) == expected, framework::LogLevel::ERRORS);
+}
+// clang-format on
+// *INDENT-ON*
+
+template <typename T>
+using NERNNLayerFixture = RNNLayerValidationFixture<Tensor, Accessor, NERNNLayer, T>;
+
+TEST_SUITE(FP32)
+FIXTURE_DATA_TEST_CASE(RunSmall, NERNNLayerFixture<float>, framework::DatasetMode::ALL, combine(datasets::SmallRNNLayerDataset(), framework::dataset::make("DataType", DataType::F32)))
+{
+ // Validate output
+ validate(Accessor(_target), _reference, tolerance_f32);
+}
+TEST_SUITE_END() // FP32
+
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+TEST_SUITE(FP16)
+FIXTURE_DATA_TEST_CASE(RunSmall, NERNNLayerFixture<half>, framework::DatasetMode::ALL, combine(datasets::SmallRNNLayerDataset(), framework::dataset::make("DataType", DataType::F16)))
+{
+ // Validate output
+ validate(Accessor(_target), _reference, tolerance_f16);
+}
+TEST_SUITE_END() // FP16
+#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
+TEST_SUITE_END() // RNNLayer
+TEST_SUITE_END() // NEON
+} // namespace validation
+} // namespace test
+} // namespace arm_compute