From 36a559e49a3d5b832b1cffd47f2298f452616bb9 Mon Sep 17 00:00:00 2001 From: Michalis Spyrou Date: Tue, 20 Mar 2018 10:30:58 +0000 Subject: COMPMID-992 Implement CL RNN function Change-Id: I8dbada5fabedbb8523e433ba73d504bd15b81466 Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/125787 Reviewed-by: Georgios Pinitas Tested-by: Jenkins --- tests/validation/CL/RNNLayer.cpp | 138 ++++++++++++++++++++++++++ tests/validation/fixtures/RNNLayerFixture.h | 145 ++++++++++++++++++++++++++++ 2 files changed, 283 insertions(+) create mode 100644 tests/validation/CL/RNNLayer.cpp create mode 100644 tests/validation/fixtures/RNNLayerFixture.h (limited to 'tests/validation') diff --git a/tests/validation/CL/RNNLayer.cpp b/tests/validation/CL/RNNLayer.cpp new file mode 100644 index 0000000000..0af6f8ea00 --- /dev/null +++ b/tests/validation/CL/RNNLayer.cpp @@ -0,0 +1,138 @@ +/* + * 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/CL/functions/CLRNNLayer.h" +#include "tests/CL/CLAccessor.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 tolerance_f32(0.001f); +RelativeTolerance tolerance_f16(half(0.1)); +} // namespace + +TEST_SUITE(CL) +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 + }), + 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), + })), + 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), + })), + 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), + })), + 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), + })), + 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), + })), + 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), + })), + framework::dataset::make("Expected", { false, false, false, false, false, false, false })), + input_info, weights_info, recurrent_weights_info, bias_info, output_info, hidden_output_info, info, expected) +{ + ARM_COMPUTE_EXPECT(bool(CLRNNLayer::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 +using CLRNNLayerFixture = RNNLayerValidationFixture; + +TEST_SUITE(FP32) +FIXTURE_DATA_TEST_CASE(RunSmall, CLRNNLayerFixture, framework::DatasetMode::ALL, combine(datasets::SmallRNNLayerDataset(), framework::dataset::make("DataType", DataType::F32))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_f32); +} +TEST_SUITE_END() // FP32 + +TEST_SUITE(FP16) +FIXTURE_DATA_TEST_CASE(RunSmall, CLRNNLayerFixture, framework::DatasetMode::ALL, combine(datasets::SmallRNNLayerDataset(), framework::dataset::make("DataType", DataType::F16))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_f16); +} +TEST_SUITE_END() // FP16 +TEST_SUITE_END() // RNNLayer +TEST_SUITE_END() // CL +} // namespace validation +} // namespace test +} // namespace arm_compute diff --git a/tests/validation/fixtures/RNNLayerFixture.h b/tests/validation/fixtures/RNNLayerFixture.h new file mode 100644 index 0000000000..42b99cce1c --- /dev/null +++ b/tests/validation/fixtures/RNNLayerFixture.h @@ -0,0 +1,145 @@ +/* + * 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. + */ +#ifndef ARM_COMPUTE_TEST_RNN_LAYER_FIXTURE +#define ARM_COMPUTE_TEST_RNN_LAYER_FIXTURE + +#include "tests/Globals.h" +#include "tests/framework/Asserts.h" +#include "tests/framework/Fixture.h" +#include "tests/validation/reference/ActivationLayer.h" +#include "tests/validation/reference/ArithmeticAddition.h" +#include "tests/validation/reference/FullyConnectedLayer.h" +#include "tests/validation/reference/GEMM.h" + +namespace arm_compute +{ +namespace test +{ +namespace validation +{ +template +class RNNLayerValidationFixture : public framework::Fixture +{ +public: + template + void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape recurrent_weights_shape, TensorShape bias_shape, TensorShape output_shape, ActivationLayerInfo info, + DataType data_type) + { + _target = compute_target(input_shape, weights_shape, recurrent_weights_shape, bias_shape, output_shape, info, data_type); + _reference = compute_reference(input_shape, weights_shape, recurrent_weights_shape, bias_shape, output_shape, info, data_type); + } + +protected: + template + void fill(U &&tensor, int i) + { + std::uniform_real_distribution<> distribution(-1.0f, 1.0f); + library->fill(tensor, distribution, i); + } + + TensorType compute_target(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &recurrent_weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, + ActivationLayerInfo info, DataType data_type) + { + // Create tensors + TensorType input = create_tensor(input_shape, data_type); + TensorType weights = create_tensor(weights_shape, data_type); + TensorType recurrent_weights = create_tensor(recurrent_weights_shape, data_type); + TensorType bias = create_tensor(bias_shape, data_type); + TensorType hidden_state = create_tensor(output_shape, data_type); + TensorType output = create_tensor(output_shape, data_type); + + // Create and configure function + FunctionType rnn; + rnn.configure(&input, &weights, &recurrent_weights, &bias, &hidden_state, &output, info); + + ARM_COMPUTE_EXPECT(input.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(weights.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(recurrent_weights.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(hidden_state.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(output.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Allocate tensors + input.allocator()->allocate(); + weights.allocator()->allocate(); + recurrent_weights.allocator()->allocate(); + bias.allocator()->allocate(); + hidden_state.allocator()->allocate(); + output.allocator()->allocate(); + + ARM_COMPUTE_EXPECT(!input.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!weights.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!recurrent_weights.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!hidden_state.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!output.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Fill tensors + fill(AccessorType(input), 0); + fill(AccessorType(weights), 0); + fill(AccessorType(recurrent_weights), 0); + fill(AccessorType(bias), 0); + fill(AccessorType(hidden_state), 0); + + // Compute function + rnn.run(); + + return output; + } + + SimpleTensor compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &recurrent_weights_shape, const TensorShape &bias_shape, + const TensorShape &output_shape, ActivationLayerInfo info, DataType data_type) + { + // Create reference + SimpleTensor input{ input_shape, data_type }; + SimpleTensor weights{ weights_shape, data_type }; + SimpleTensor recurrent_weights{ recurrent_weights_shape, data_type }; + SimpleTensor bias{ bias_shape, data_type }; + SimpleTensor hidden_state{ output_shape, data_type }; + + // Fill reference + fill(input, 0); + fill(weights, 0); + fill(recurrent_weights, 0); + fill(bias, 0); + fill(hidden_state, 0); + + TensorShape out_shape = recurrent_weights_shape; + out_shape.set(1, output_shape.y()); + + // Compute reference + SimpleTensor out_w{ out_shape, data_type }; + SimpleTensor fully_connected = reference::fully_connected_layer(input, weights, bias, out_shape); + SimpleTensor gemm = reference::gemm(hidden_state, recurrent_weights, out_w, 1.f, 0.f); + SimpleTensor add_res = reference::arithmetic_addition(fully_connected, gemm, data_type, ConvertPolicy::SATURATE); + return reference::activation_layer(add_res, info); + } + + TensorType _target{}; + SimpleTensor _reference{}; +}; +} // namespace validation +} // namespace test +} // namespace arm_compute +#endif /* ARM_COMPUTE_TEST_RNN_LAYER_FIXTURE */ -- cgit v1.2.1