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author | Michalis Spyrou <michalis.spyrou@arm.com> | 2018-03-20 10:30:58 +0000 |
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committer | Anthony Barbier <anthony.barbier@arm.com> | 2018-11-02 16:49:16 +0000 |
commit | 36a559e49a3d5b832b1cffd47f2298f452616bb9 (patch) | |
tree | ae09231cbc75bfe27bd1f9a2a8b92386e24fca82 /tests/validation/fixtures | |
parent | ee33ea5a6e1aa0faac1cc8b5a269bd4f89854821 (diff) | |
download | ComputeLibrary-36a559e49a3d5b832b1cffd47f2298f452616bb9.tar.gz |
COMPMID-992 Implement CL RNN function
Change-Id: I8dbada5fabedbb8523e433ba73d504bd15b81466
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/125787
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Tested-by: Jenkins <bsgcomp@arm.com>
Diffstat (limited to 'tests/validation/fixtures')
-rw-r--r-- | tests/validation/fixtures/RNNLayerFixture.h | 145 |
1 files changed, 145 insertions, 0 deletions
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 <typename TensorType, typename AccessorType, typename FunctionType, typename T> +class RNNLayerValidationFixture : public framework::Fixture +{ +public: + template <typename...> + 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 <typename U> + 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<TensorType>(input_shape, data_type); + TensorType weights = create_tensor<TensorType>(weights_shape, data_type); + TensorType recurrent_weights = create_tensor<TensorType>(recurrent_weights_shape, data_type); + TensorType bias = create_tensor<TensorType>(bias_shape, data_type); + TensorType hidden_state = create_tensor<TensorType>(output_shape, data_type); + TensorType output = create_tensor<TensorType>(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<T> 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<T> input{ input_shape, data_type }; + SimpleTensor<T> weights{ weights_shape, data_type }; + SimpleTensor<T> recurrent_weights{ recurrent_weights_shape, data_type }; + SimpleTensor<T> bias{ bias_shape, data_type }; + SimpleTensor<T> 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<T> out_w{ out_shape, data_type }; + SimpleTensor<T> fully_connected = reference::fully_connected_layer(input, weights, bias, out_shape); + SimpleTensor<T> gemm = reference::gemm(hidden_state, recurrent_weights, out_w, 1.f, 0.f); + SimpleTensor<T> add_res = reference::arithmetic_addition(fully_connected, gemm, data_type, ConvertPolicy::SATURATE); + return reference::activation_layer(add_res, info); + } + + TensorType _target{}; + SimpleTensor<T> _reference{}; +}; +} // namespace validation +} // namespace test +} // namespace arm_compute +#endif /* ARM_COMPUTE_TEST_RNN_LAYER_FIXTURE */ |