diff options
author | Michalis Spyrou <michalis.spyrou@arm.com> | 2018-06-05 11:45:48 +0100 |
---|---|---|
committer | Anthony Barbier <anthony.barbier@arm.com> | 2018-11-02 16:53:09 +0000 |
commit | 542e92d95536f2ab7fc6f1cc1aa1bd4f1d471212 (patch) | |
tree | a4c03807d9731c1305b6f446282d3f4b97cfb595 | |
parent | 72219330fd85b1271e714d4ba894d6d8e26340c9 (diff) | |
download | ComputeLibrary-542e92d95536f2ab7fc6f1cc1aa1bd4f1d471212.tar.gz |
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>
-rw-r--r-- | arm_compute/runtime/NEON/NEFunctions.h | 1 | ||||
-rw-r--r-- | arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h | 2 | ||||
-rw-r--r-- | arm_compute/runtime/NEON/functions/NERNNLayer.h | 96 | ||||
-rw-r--r-- | src/runtime/NEON/functions/NERNNLayer.cpp | 125 | ||||
-rw-r--r-- | tests/datasets/RNNLayerDataset.h | 2 | ||||
-rw-r--r-- | tests/validation/NEON/RNNLayer.cpp | 147 |
6 files changed, 371 insertions, 2 deletions
diff --git a/arm_compute/runtime/NEON/NEFunctions.h b/arm_compute/runtime/NEON/NEFunctions.h index bd4097c6d3..b83675b779 100644 --- a/arm_compute/runtime/NEON/NEFunctions.h +++ b/arm_compute/runtime/NEON/NEFunctions.h @@ -95,6 +95,7 @@ #include "arm_compute/runtime/NEON/functions/NEPixelWiseMultiplication.h" #include "arm_compute/runtime/NEON/functions/NEPoolingLayer.h" #include "arm_compute/runtime/NEON/functions/NEQuantizationLayer.h" +#include "arm_compute/runtime/NEON/functions/NERNNLayer.h" #include "arm_compute/runtime/NEON/functions/NEROIPoolingLayer.h" #include "arm_compute/runtime/NEON/functions/NEReductionOperation.h" #include "arm_compute/runtime/NEON/functions/NERemap.h" diff --git a/arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h b/arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h index 2739f5ebef..42c9e2d3e9 100644 --- a/arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h +++ b/arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h @@ -104,7 +104,7 @@ public: NEFullyConnectedLayer &operator=(NEFullyConnectedLayer &&) = default; /** Set the input and output tensors. * - * @param[in] input Source tensor. Data type supported: QS8/QS16/F32. + * @param[in] input Source tensor. Data type supported: QS8/QS16/F16/F32. * @param[in] weights Weights tensor. The weights must be 2 dimensional. Data type supported: Same as @p input. * @param[in] biases Bias tensor. Can be nullptr. Data type supported:Same as @p input. * @param[out] output Destination tensor. Data type supported: Same as @p input. diff --git a/arm_compute/runtime/NEON/functions/NERNNLayer.h b/arm_compute/runtime/NEON/functions/NERNNLayer.h new file mode 100644 index 0000000000..f1398eb3cc --- /dev/null +++ b/arm_compute/runtime/NEON/functions/NERNNLayer.h @@ -0,0 +1,96 @@ +/* + * 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_NERNNLAYER_H__ +#define __ARM_COMPUTE_NERNNLAYER_H__ + +#include "arm_compute/core/NEON/kernels/NEActivationLayerKernel.h" +#include "arm_compute/core/NEON/kernels/NEArithmeticAdditionKernel.h" +#include "arm_compute/runtime/NEON/INESimpleFunction.h" + +#include "arm_compute/core/Types.h" +#include "arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h" +#include "arm_compute/runtime/NEON/functions/NEGEMM.h" + +namespace arm_compute +{ +// Forward declarations +class ITensor; + +/** Basic function to run @ref NERNNLayer */ +class NERNNLayer : public IFunction +{ +public: + /** Default constructor */ + NERNNLayer(std::shared_ptr<IMemoryManager> memory_manager = nullptr); + /** Prevent instances of this class from being copied (As this class contains pointers) */ + NERNNLayer(const NERNNLayer &) = delete; + /** Default move constructor */ + NERNNLayer(NERNNLayer &&) = default; + /** Prevent instances of this class from being copied (As this class contains pointers) */ + NERNNLayer &operator=(const NERNNLayer &) = delete; + /** Default move assignment operator */ + NERNNLayer &operator=(NERNNLayer &&) = default; + /** 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 ITensor *input, const ITensor *weights, const ITensor *recurrent_weights, const ITensor *bias, ITensor *hidden_state, ITensor *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: + MemoryGroup _memory_group; + NEGEMM _gemm_state_f; + NEArithmeticAdditionKernel _add_kernel; + NEActivationLayerKernel _activation_kernel; + NEFullyConnectedLayer _fully_connected_kernel; + Tensor _fully_connected_out; + Tensor _gemm_output; + Tensor _add_output; + ITensor *_hidden_state; + ITensor *_output; +}; +} // namespace arm_compute +#endif /* __ARM_COMPUTE_NERNNLAYER_H__ */ diff --git a/src/runtime/NEON/functions/NERNNLayer.cpp b/src/runtime/NEON/functions/NERNNLayer.cpp new file mode 100644 index 0000000000..08017e20c3 --- /dev/null +++ b/src/runtime/NEON/functions/NERNNLayer.cpp @@ -0,0 +1,125 @@ +/* + * 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 "arm_compute/core/Error.h" +#include "arm_compute/core/TensorInfo.h" +#include "arm_compute/core/Types.h" +#include "arm_compute/core/Validate.h" +#include "arm_compute/core/utils/misc/ShapeCalculator.h" +#include "arm_compute/runtime/NEON/NEScheduler.h" + +namespace arm_compute +{ +NERNNLayer::NERNNLayer(std::shared_ptr<IMemoryManager> memory_manager) + : _memory_group(std::move(memory_manager)), _gemm_state_f(), _add_kernel(), _activation_kernel(), _fully_connected_kernel(), _fully_connected_out(), _gemm_output(), _add_output(), _hidden_state(), + _output() +{ +} + +Status NERNNLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *recurrent_weights, const ITensorInfo *bias, const ITensorInfo *hidden_state, + const ITensorInfo *output, const ActivationLayerInfo &info) +{ + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, recurrent_weights, bias, hidden_state, output); + + 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(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(idx_height)); + 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(misc::shape_calculator::compute_rnn_shape(recurrent_weights, hidden_state->dimension(idx_height)), 1, input->data_type()); + + ARM_COMPUTE_RETURN_ON_ERROR(NEFullyConnectedLayer::validate(input, weights, bias, &shape_info, true, false)); + ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAdditionKernel::validate(&shape_info, &shape_info, &shape_info, ConvertPolicy::SATURATE)); + ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayerKernel::validate(&shape_info, &shape_info, info)); + + return Status{}; +} + +void NERNNLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *recurrent_weights, const ITensor *bias, ITensor *hidden_state, ITensor *output, + ActivationLayerInfo &info) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, recurrent_weights, bias, hidden_state, output); + ARM_COMPUTE_ERROR_THROW_ON(NERNNLayer::validate(input->info(), weights->info(), recurrent_weights->info(), bias->info(), hidden_state->info(), output->info(), info)); + + _hidden_state = hidden_state; + _output = output; + + const int idx_height = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::HEIGHT); + TensorShape shape = misc::shape_calculator::compute_rnn_shape(recurrent_weights->info(), hidden_state->info()->dimension(idx_height)); + + // Manage intermediate buffers and configure + _fully_connected_out.allocator()->init(TensorInfo(shape, 1, input->info()->data_type())); + _memory_group.manage(&_fully_connected_out); + _fully_connected_kernel.configure(input, weights, bias, &_fully_connected_out, true, false); + + _gemm_output.allocator()->init(TensorInfo(shape, 1, input->info()->data_type())); + _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(); +} + +void NERNNLayer::run() +{ + _memory_group.acquire(); + + _fully_connected_kernel.run(); + _gemm_state_f.run(); + NEScheduler::get().schedule(&_add_kernel, Window::DimY); + NEScheduler::get().schedule(&_activation_kernel, Window::DimY); + + // copy hidden out to output + Window hidden_state_window; + Window output_window; + hidden_state_window.use_tensor_dimensions(_hidden_state->info()->tensor_shape(), Window::DimY); + output_window.use_tensor_dimensions(_output->info()->tensor_shape(), Window::DimY); + + Iterator hidden_state_it(_hidden_state, output_window); + Iterator output_it(_output, output_window); + + execute_window_loop(output_window, [&](const Coordinates & id) + { + memcpy(output_it.ptr(), hidden_state_it.ptr(), _output->info()->dimension(0) * _output->info()->element_size()); + }, + hidden_state_it, output_it); + + _memory_group.release(); +} +} // namespace arm_compute diff --git a/tests/datasets/RNNLayerDataset.h b/tests/datasets/RNNLayerDataset.h index 616a69e213..40d1b934f3 100644 --- a/tests/datasets/RNNLayerDataset.h +++ b/tests/datasets/RNNLayerDataset.h @@ -131,7 +131,7 @@ class SmallRNNLayerDataset final : public RNNLayerDataset public: SmallRNNLayerDataset() { - add_config(TensorShape(8U, 2U), TensorShape(8U, 16U), TensorShape(16U, 16U), TensorShape(16U), TensorShape(16U, 2U), ActivationLayerInfo()); + add_config(TensorShape(128U, 16U), TensorShape(128U, 32U), TensorShape(32U, 32U), TensorShape(32U), TensorShape(32U, 16U), ActivationLayerInfo()); } }; diff --git a/tests/validation/NEON/RNNLayer.cpp b/tests/validation/NEON/RNNLayer.cpp new file mode 100644 index 0000000000..7aa3befd03 --- /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 |