146 lines
6.7 KiB
C++
146 lines
6.7 KiB
C++
/*
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* Copyright (c) 2018 Arm Limited.
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*
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* SPDX-License-Identifier: MIT
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to
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* deal in the Software without restriction, including without limitation the
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* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
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* sell copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#ifndef ARM_COMPUTE_TEST_RNN_LAYER_FIXTURE
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#define ARM_COMPUTE_TEST_RNN_LAYER_FIXTURE
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#include "tests/Globals.h"
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#include "tests/framework/Asserts.h"
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#include "tests/framework/Fixture.h"
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#include "tests/validation/reference/ActivationLayer.h"
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#include "tests/validation/reference/ArithmeticOperations.h"
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#include "tests/validation/reference/FullyConnectedLayer.h"
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#include "tests/validation/reference/GEMM.h"
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namespace arm_compute
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{
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namespace test
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{
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namespace validation
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{
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template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
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class RNNLayerValidationFixture : public framework::Fixture
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{
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public:
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template <typename...>
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void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape recurrent_weights_shape, TensorShape bias_shape, TensorShape output_shape, ActivationLayerInfo info,
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DataType data_type)
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{
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_target = compute_target(input_shape, weights_shape, recurrent_weights_shape, bias_shape, output_shape, info, data_type);
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_reference = compute_reference(input_shape, weights_shape, recurrent_weights_shape, bias_shape, output_shape, info, data_type);
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}
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protected:
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template <typename U>
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void fill(U &&tensor, int i)
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{
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std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
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library->fill(tensor, distribution, i);
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}
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TensorType compute_target(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &recurrent_weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape,
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ActivationLayerInfo info, DataType data_type)
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{
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// Create tensors
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TensorType input = create_tensor<TensorType>(input_shape, data_type);
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TensorType weights = create_tensor<TensorType>(weights_shape, data_type);
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TensorType recurrent_weights = create_tensor<TensorType>(recurrent_weights_shape, data_type);
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TensorType bias = create_tensor<TensorType>(bias_shape, data_type);
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TensorType hidden_state = create_tensor<TensorType>(output_shape, data_type);
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TensorType output = create_tensor<TensorType>(output_shape, data_type);
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// Create and configure function
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FunctionType rnn;
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rnn.configure(&input, &weights, &recurrent_weights, &bias, &hidden_state, &output, info);
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ARM_COMPUTE_EXPECT(input.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(weights.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(recurrent_weights.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(hidden_state.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(output.info()->is_resizable(), framework::LogLevel::ERRORS);
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// Allocate tensors
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input.allocator()->allocate();
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weights.allocator()->allocate();
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recurrent_weights.allocator()->allocate();
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bias.allocator()->allocate();
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hidden_state.allocator()->allocate();
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output.allocator()->allocate();
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ARM_COMPUTE_EXPECT(!input.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!weights.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!recurrent_weights.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!hidden_state.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!output.info()->is_resizable(), framework::LogLevel::ERRORS);
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// Fill tensors
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fill(AccessorType(input), 0);
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fill(AccessorType(weights), 0);
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fill(AccessorType(recurrent_weights), 0);
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fill(AccessorType(bias), 0);
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fill(AccessorType(hidden_state), 0);
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// Compute function
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rnn.run();
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return output;
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}
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SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &recurrent_weights_shape, const TensorShape &bias_shape,
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const TensorShape &output_shape, ActivationLayerInfo info, DataType data_type)
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{
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// Create reference
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SimpleTensor<T> input{ input_shape, data_type };
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SimpleTensor<T> weights{ weights_shape, data_type };
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SimpleTensor<T> recurrent_weights{ recurrent_weights_shape, data_type };
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SimpleTensor<T> bias{ bias_shape, data_type };
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SimpleTensor<T> hidden_state{ output_shape, data_type };
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// Fill reference
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fill(input, 0);
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fill(weights, 0);
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fill(recurrent_weights, 0);
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fill(bias, 0);
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fill(hidden_state, 0);
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TensorShape out_shape = recurrent_weights_shape;
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out_shape.set(1, output_shape.y());
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// Compute reference
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SimpleTensor<T> out_w{ out_shape, data_type };
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SimpleTensor<T> fully_connected = reference::fully_connected_layer(input, weights, bias, out_shape);
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SimpleTensor<T> gemm = reference::gemm(hidden_state, recurrent_weights, out_w, 1.f, 0.f);
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SimpleTensor<T> add_res = reference::arithmetic_operation(reference::ArithmeticOperation::ADD, fully_connected, gemm, data_type, ConvertPolicy::SATURATE);
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return reference::activation_layer(add_res, info);
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}
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TensorType _target{};
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SimpleTensor<T> _reference{};
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};
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} // namespace validation
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} // namespace test
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} // namespace arm_compute
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#endif /* ARM_COMPUTE_TEST_RNN_LAYER_FIXTURE */
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