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 --- arm_compute/core/utils/misc/ShapeCalculator.h | 8 ++ arm_compute/runtime/CL/CLFunctions.h | 1 + arm_compute/runtime/CL/functions/CLRNNLayer.h | 85 +++++++++++++++ src/runtime/CL/functions/CLRNNLayer.cpp | 109 +++++++++++++++++++ tests/datasets/RNNLayerDataset.h | 141 +++++++++++++++++++++++++ tests/validation/CL/RNNLayer.cpp | 138 ++++++++++++++++++++++++ tests/validation/fixtures/RNNLayerFixture.h | 145 ++++++++++++++++++++++++++ 7 files changed, 627 insertions(+) create mode 100644 arm_compute/runtime/CL/functions/CLRNNLayer.h create mode 100644 src/runtime/CL/functions/CLRNNLayer.cpp create mode 100644 tests/datasets/RNNLayerDataset.h create mode 100644 tests/validation/CL/RNNLayer.cpp create mode 100644 tests/validation/fixtures/RNNLayerFixture.h diff --git a/arm_compute/core/utils/misc/ShapeCalculator.h b/arm_compute/core/utils/misc/ShapeCalculator.h index 383fc6cda6..8816819bcd 100644 --- a/arm_compute/core/utils/misc/ShapeCalculator.h +++ b/arm_compute/core/utils/misc/ShapeCalculator.h @@ -282,6 +282,14 @@ inline TensorShape compute_min_max_shape(const ITensorInfo *input) return output_shape; } +inline TensorShape compute_rnn_shape(const ITensorInfo *input, const unsigned int batch_size) +{ + TensorShape output_shape{ input->tensor_shape() }; + output_shape.set(1, batch_size); + + return output_shape; +} + } // namespace shape_calculator } // namespace misc } // namespace arm_compute diff --git a/arm_compute/runtime/CL/CLFunctions.h b/arm_compute/runtime/CL/CLFunctions.h index a63afaac39..ffda88561d 100644 --- a/arm_compute/runtime/CL/CLFunctions.h +++ b/arm_compute/runtime/CL/CLFunctions.h @@ -93,6 +93,7 @@ #include "arm_compute/runtime/CL/functions/CLPixelWiseMultiplication.h" #include "arm_compute/runtime/CL/functions/CLPoolingLayer.h" #include "arm_compute/runtime/CL/functions/CLQuantizationLayer.h" +#include "arm_compute/runtime/CL/functions/CLRNNLayer.h" #include "arm_compute/runtime/CL/functions/CLROIPoolingLayer.h" #include "arm_compute/runtime/CL/functions/CLReductionOperation.h" #include "arm_compute/runtime/CL/functions/CLRemap.h" diff --git a/arm_compute/runtime/CL/functions/CLRNNLayer.h b/arm_compute/runtime/CL/functions/CLRNNLayer.h new file mode 100644 index 0000000000..9f239a9e64 --- /dev/null +++ b/arm_compute/runtime/CL/functions/CLRNNLayer.h @@ -0,0 +1,85 @@ +/* + * 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_CLRNN_LAYER_H__ +#define __ARM_COMPUTE_CLRNN_LAYER_H__ + +#include "arm_compute/core/CL/kernels/CLActivationLayerKernel.h" +#include "arm_compute/core/CL/kernels/CLArithmeticAdditionKernel.h" +#include "arm_compute/core/CL/kernels/CLCopyKernel.h" +#include "arm_compute/runtime/CL/ICLSimpleFunction.h" +#include "arm_compute/runtime/CL/functions/CLFullyConnectedLayer.h" +#include "arm_compute/runtime/CL/functions/CLGEMM.h" + +namespace arm_compute +{ +class ICLTensor; + +/** Basic function to run @ref CLRNNLayer */ +class CLRNNLayer : public IFunction +{ +public: + /** Default constructor */ + CLRNNLayer(std::shared_ptr memory_manager = nullptr); + /** Initialize the function + * + * @param[in] input Input is a 2-D tensor of shape [input_size, batch_size]. Data types supported: F16/F32 + * @param[in] weights Weights tensor of shape [input_size, num_units] that multiplies the input. Data types supported: Same as @p input + * @param[in] recurrent_weights Weights tensor of shape [num_units, num_units] that multiplies the current 'state'. Data types supported: Same as @p input + * @param[in] bias Bias vector of shape [num_units]. Data types supported: Same as @p input + * @param[out] output Output tensor of shape [num_units, batch_size]. Data types supported: Same as @p input + * @param[in,out] hidden_state Output tensor of shape [num_units, batch_size]. Data types supported: Same as @p input + * @param[in] info Activation layer parameter. + */ + void configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *recurrent_weights, const ICLTensor *bias, ICLTensor *hidden_state, ICLTensor *output, ActivationLayerInfo &info); + /** Initialize the function + * + * @param[in] input Input is a 2-D tensor of shape [input_size, batch_size]. Data types supported: F16/F32 + * @param[in] weights Weights tensor of shape [input_size, num_units] that multiplies the input. Data types supported: Same as @p input + * @param[in] recurrent_weights Weights tensor of shape [num_units, num_units] that multiplies the current 'state'. Data types supported: Same as @p input + * @param[in] bias Bias vector of shape [num_units]. Data types supported: Same as @p input + * @param[in] output Output tensor of shape [num_units, batch_size]. Data types supported: Same as @p input + * @param[in] hidden_state Output tensor of shape [num_units, batch_size]. Data types supported: Same as @p input + * @param[in] info Activation layer parameter. + * + * @return a status + */ + static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *recurrent_weights, const ITensorInfo *bias, const ITensorInfo *hidden_state, const ITensorInfo *output, + const ActivationLayerInfo &info); + + // Inherited methods overridden: + void run() override; + +private: + CLMemoryGroup _memory_group; + CLGEMM _gemm_state_f; + CLArithmeticAdditionKernel _add_kernel; + CLActivationLayerKernel _activation_kernel; + CLFullyConnectedLayer _fully_connected_kernel; + CLCopyKernel _copy_kernel; + CLTensor _fully_connected_out; + CLTensor _gemm_output; + CLTensor _add_output; +}; +} +#endif /* __ARM_COMPUTE_CLRNN_LAYER_H__ */ diff --git a/src/runtime/CL/functions/CLRNNLayer.cpp b/src/runtime/CL/functions/CLRNNLayer.cpp new file mode 100644 index 0000000000..75eac0959f --- /dev/null +++ b/src/runtime/CL/functions/CLRNNLayer.cpp @@ -0,0 +1,109 @@ +/* + * 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 "arm_compute/core/Helpers.h" +#include "arm_compute/core/Types.h" +#include "arm_compute/core/Utils.h" +#include "arm_compute/core/utils/misc/ShapeCalculator.h" +#include "arm_compute/runtime/CL/CLScheduler.h" +#include "support/ToolchainSupport.h" + +#include + +using namespace arm_compute; +using namespace arm_compute::misc::shape_calculator; + +CLRNNLayer::CLRNNLayer(std::shared_ptr memory_manager) + : _memory_group(std::move(memory_manager)), _gemm_state_f(), _add_kernel(), _activation_kernel(), _fully_connected_kernel(), _copy_kernel(), _fully_connected_out(), _gemm_output(), _add_output() +{ +} + +Status CLRNNLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *recurrent_weights, const ITensorInfo *bias, const ITensorInfo *hidden_state, + const ITensorInfo *output, const ActivationLayerInfo &info) +{ + const int idx_width = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH); + const int idx_height = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT); + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, recurrent_weights, bias, hidden_state, output); + ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(idx_width) != weights->dimension(idx_width)); + ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_height) != recurrent_weights->dimension(idx_width)); + ARM_COMPUTE_RETURN_ERROR_ON(recurrent_weights->dimension(idx_width) != recurrent_weights->dimension(1)); + ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() != 1); + ARM_COMPUTE_RETURN_ERROR_ON(bias->dimension(idx_width) != weights->dimension(idx_height)); + ARM_COMPUTE_RETURN_ERROR_ON(hidden_state->dimension(idx_width) != weights->dimension(idx_height)); + ARM_COMPUTE_RETURN_ERROR_ON(hidden_state->dimension(idx_height) != input->dimension(idx_height)); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), hidden_state->tensor_shape()); + + auto shape_info = TensorInfo(compute_rnn_shape(recurrent_weights, hidden_state->dimension(idx_height)), 1, input->data_type()); + + ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayer::validate(input, weights, bias, &shape_info, true, false)); + ARM_COMPUTE_RETURN_ON_ERROR(CLGEMM::validate(hidden_state, recurrent_weights, nullptr, &shape_info, 1.f, 0.f)); + ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAdditionKernel::validate(&shape_info, &shape_info, &shape_info, ConvertPolicy::SATURATE)); + ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayerKernel::validate(&shape_info, &shape_info, info)); + + return Status{}; +} + +void CLRNNLayer::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *recurrent_weights, const ICLTensor *bias, ICLTensor *hidden_state, ICLTensor *output, + ActivationLayerInfo &info) +{ + const int idx_height = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::HEIGHT); + TensorShape shape = compute_rnn_shape(recurrent_weights->info(), hidden_state->info()->dimension(idx_height)); + + _fully_connected_out.allocator()->init(TensorInfo(shape, 1, input->info()->data_type())); + _gemm_output.allocator()->init(TensorInfo(shape, 1, input->info()->data_type())); + + // Manage intermediate buffers and configure + _memory_group.manage(&_fully_connected_out); + _fully_connected_kernel.configure(input, weights, bias, &_fully_connected_out, true, false); + + _memory_group.manage(&_gemm_output); + _gemm_state_f.configure(hidden_state, recurrent_weights, nullptr, &_gemm_output, 1.f, 0.f); + + _add_output.allocator()->init(TensorInfo(shape, 1, input->info()->data_type())); + _memory_group.manage(&_add_output); + + _add_kernel.configure(&_fully_connected_out, &_gemm_output, &_add_output, ConvertPolicy::SATURATE); + + _fully_connected_out.allocator()->allocate(); + _gemm_output.allocator()->allocate(); + + _activation_kernel.configure(&_add_output, hidden_state, info); + _add_output.allocator()->allocate(); + + _copy_kernel.configure(hidden_state, output); +} + +void CLRNNLayer::run() +{ + _memory_group.acquire(); + _fully_connected_kernel.run(); + _gemm_state_f.run(); + CLScheduler::get().enqueue(_add_kernel); + CLScheduler::get().enqueue(_activation_kernel); + + // copy hidden out to output + CLScheduler::get().enqueue(_copy_kernel); + _memory_group.release(); +} \ No newline at end of file diff --git a/tests/datasets/RNNLayerDataset.h b/tests/datasets/RNNLayerDataset.h new file mode 100644 index 0000000000..616a69e213 --- /dev/null +++ b/tests/datasets/RNNLayerDataset.h @@ -0,0 +1,141 @@ +/* + * 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_DATASET +#define ARM_COMPUTE_TEST_RNN_LAYER_DATASET + +#include "utils/TypePrinter.h" + +#include "arm_compute/core/TensorShape.h" +#include "arm_compute/core/Types.h" + +namespace arm_compute +{ +namespace test +{ +namespace datasets +{ +class RNNLayerDataset +{ +public: + using type = std::tuple; + + struct iterator + { + iterator(std::vector::const_iterator src_it, + std::vector::const_iterator weights_it, + std::vector::const_iterator recurrent_weights_it, + std::vector::const_iterator biases_it, + std::vector::const_iterator dst_it, + std::vector::const_iterator infos_it) + : _src_it{ std::move(src_it) }, + _weights_it{ std::move(weights_it) }, + _recurrent_weights_it{ std::move(recurrent_weights_it) }, + _biases_it{ std::move(biases_it) }, + _dst_it{ std::move(dst_it) }, + _infos_it{ std::move(infos_it) } + { + } + + std::string description() const + { + std::stringstream description; + description << "In=" << *_src_it << ":"; + description << "Weights=" << *_weights_it << ":"; + description << "Biases=" << *_biases_it << ":"; + description << "Out=" << *_dst_it; + return description.str(); + } + + RNNLayerDataset::type operator*() const + { + return std::make_tuple(*_src_it, *_weights_it, *_recurrent_weights_it, *_biases_it, *_dst_it, *_infos_it); + } + + iterator &operator++() + { + ++_src_it; + ++_weights_it; + ++_recurrent_weights_it; + ++_biases_it; + ++_dst_it; + ++_infos_it; + + return *this; + } + + private: + std::vector::const_iterator _src_it; + std::vector::const_iterator _weights_it; + std::vector::const_iterator _recurrent_weights_it; + std::vector::const_iterator _biases_it; + std::vector::const_iterator _dst_it; + std::vector::const_iterator _infos_it; + }; + + iterator begin() const + { + return iterator(_src_shapes.begin(), _weight_shapes.begin(), _recurrent_weight_shapes.begin(), _bias_shapes.begin(), _dst_shapes.begin(), _infos.begin()); + } + + int size() const + { + return std::min(_src_shapes.size(), std::min(_weight_shapes.size(), std::min(_recurrent_weight_shapes.size(), std::min(_bias_shapes.size(), std::min(_dst_shapes.size(), _infos.size()))))); + } + + void add_config(TensorShape src, TensorShape weights, TensorShape recurrent_weights, TensorShape biases, TensorShape dst, ActivationLayerInfo info) + { + _src_shapes.emplace_back(std::move(src)); + _weight_shapes.emplace_back(std::move(weights)); + _recurrent_weight_shapes.emplace_back(std::move(recurrent_weights)); + _bias_shapes.emplace_back(std::move(biases)); + _dst_shapes.emplace_back(std::move(dst)); + _infos.emplace_back(std::move(info)); + } + +protected: + RNNLayerDataset() = default; + RNNLayerDataset(RNNLayerDataset &&) = default; + +private: + std::vector _src_shapes{}; + std::vector _weight_shapes{}; + std::vector _recurrent_weight_shapes{}; + std::vector _bias_shapes{}; + std::vector _dst_shapes{}; + std::vector _infos{}; +}; + +class SmallRNNLayerDataset final : public RNNLayerDataset +{ +public: + SmallRNNLayerDataset() + { + add_config(TensorShape(8U, 2U), TensorShape(8U, 16U), TensorShape(16U, 16U), TensorShape(16U), TensorShape(16U, 2U), ActivationLayerInfo()); + } +}; + +} // namespace datasets +} // namespace test +} // namespace arm_compute +#endif /* ARM_COMPUTE_TEST_RNN_LAYER_DATASET */ 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