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Diffstat (limited to 'tests/validation/fixtures/AddMulAddFixture.h')
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diff --git a/tests/validation/fixtures/AddMulAddFixture.h b/tests/validation/fixtures/AddMulAddFixture.h new file mode 100644 index 0000000000..d13fef2f02 --- /dev/null +++ b/tests/validation/fixtures/AddMulAddFixture.h @@ -0,0 +1,270 @@ +/* + * Copyright (c) 2023 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 ACL_TESTS_VALIDATION_FIXTURES_ADDMULADDFIXTURE_H +#define ACL_TESTS_VALIDATION_FIXTURES_ADDMULADDFIXTURE_H + +#include "arm_compute/core/TensorShape.h" +#include "arm_compute/core/Types.h" +#include "tests/AssetsLibrary.h" +#include "tests/Globals.h" +#include "tests/IAccessor.h" +#include "tests/framework/Asserts.h" +#include "tests/framework/Fixture.h" +#include "tests/validation/Helpers.h" +#include "tests/validation/reference/ActivationLayer.h" +#include "tests/validation/reference/ArithmeticOperations.h" +#include "tests/validation/reference/DequantizationLayer.h" +#include "tests/validation/reference/PixelWiseMultiplication.h" +#include "tests/validation/reference/QuantizationLayer.h" + +namespace arm_compute +{ +namespace test +{ +namespace validation +{ +template <typename TensorType, typename AccessorType, typename FunctionType, typename T> +class AddMulAddGenericFixture : public framework::Fixture +{ +public: + void setup(const TensorShape &shape, DataType data_type, ActivationLayerInfo &act_info, bool interm_out) + { + compute_target(shape, data_type, act_info, interm_out); + } + +protected: + template <typename U> + void fill(U &&tensor, int i, DataType data_type) + { + switch(data_type) + { + case DataType::F32: + library->fill_tensor_uniform(tensor, i, -10.f, 10.f); + break; + case DataType::F16: + library->fill_tensor_uniform(tensor, i, -1.f, 1.f); + break; + default: + library->fill_tensor_uniform(tensor, i); + break; + } + } + + void compute_target(const TensorShape &shape, DataType data_type, ActivationLayerInfo &act_info, bool interm_out) + { + TensorShape b_shape(shape.x()); + + // Create tensors + TensorType input1 = create_tensor<TensorType>(shape, data_type, 1, _input1_qinfo); + TensorType input2 = create_tensor<TensorType>(shape, data_type, 1, _input2_qinfo); + TensorType bn_mul = create_tensor<TensorType>(b_shape, data_type, 1, _bn_mul_qinfo); + TensorType bn_add = create_tensor<TensorType>(b_shape, data_type, 1, _bn_add_qinfo); + TensorType add_output = create_tensor<TensorType>(shape, data_type, 1, _add_output_qinfo); + TensorType final_output = create_tensor<TensorType>(shape, data_type, 1, _final_output_qinfo); + + // Create and configure function + FunctionType add_mul_add; + ARM_COMPUTE_ERROR_THROW_ON(add_mul_add.validate(input1.info(), input2.info(), bn_mul.info(), + bn_add.info(), interm_out ? add_output.info() : nullptr, final_output.info(), + ConvertPolicy::SATURATE, act_info)); + + add_mul_add.configure(&input1, &input2, &bn_mul, &bn_add, interm_out ? &add_output : nullptr, + &final_output, ConvertPolicy::SATURATE, act_info); + + // Allocate tensors + input1.allocator()->allocate(); + input2.allocator()->allocate(); + bn_mul.allocator()->allocate(); + bn_add.allocator()->allocate(); + + if(interm_out) + { + add_output.allocator()->allocate(); + } + + final_output.allocator()->allocate(); + + // Fill tensors + fill(AccessorType(input1), 0, data_type); + fill(AccessorType(input2), 1, data_type); + fill(AccessorType(bn_mul), 2, data_type); + fill(AccessorType(bn_add), 3, data_type); + + // // Compute function + add_mul_add.run(); + + _target = std::move(final_output); + + if(interm_out) + { + _interm_target = std::move(add_output); + } + } + + TensorType _target{}; + TensorType _interm_target{}; + SimpleTensor<T> _reference{}; + SimpleTensor<T> _interm_reference{}; + + QuantizationInfo _input1_qinfo{}; + QuantizationInfo _input2_qinfo{}; + QuantizationInfo _bn_mul_qinfo{}; + QuantizationInfo _bn_add_qinfo{}; + QuantizationInfo _add_output_qinfo{}; + QuantizationInfo _final_output_qinfo{}; +}; + +template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool interm_out> +class AddMulAddFloatValidationFixture : public AddMulAddGenericFixture<TensorType, AccessorType, FunctionType, T> +{ +public: + using Parent = AddMulAddGenericFixture<TensorType, AccessorType, FunctionType, T>; + + void setup(const TensorShape &shape, DataType data_type, ActivationLayerInfo act_info) + { + Parent::setup(shape, data_type, act_info, interm_out); + compute_reference(shape, data_type, act_info); + } + + // Compute Reference is moved outside of the generic fixture because with the quantized data types, + // it becomes a very different implementation with intermediate tensors' data types being always float. + // This way the reference calculations are more readable and the size of the classes will be smaller + // due to unrepeated fill() and target() methods. + void compute_reference(const TensorShape &shape, DataType data_type, ActivationLayerInfo &act_info) + { + TensorShape b_shape(shape.x()); + + // Create reference + SimpleTensor<T> input1{ shape, data_type }; + SimpleTensor<T> input2{ shape, data_type }; + SimpleTensor<T> bn_mul{ b_shape, data_type }; + SimpleTensor<T> bn_add{ b_shape, data_type }; + SimpleTensor<T> add_output{ shape, data_type, 1 }; + + SimpleTensor<T> bn_mul_out{ shape, data_type }; + SimpleTensor<T> bn_add_out{ shape, data_type }; + + // Fill reference + Parent::fill(input1, 0, data_type); + Parent::fill(input2, 1, data_type); + Parent::fill(bn_mul, 2, data_type); + Parent::fill(bn_add, 3, data_type); + + reference::arithmetic_operation<T>(reference::ArithmeticOperation::ADD, input1, input2, add_output, ConvertPolicy::SATURATE); + bn_mul_out = reference::pixel_wise_multiplication<T, T, T>(add_output, bn_mul, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_UP, data_type); + reference::arithmetic_operation<T>(reference::ArithmeticOperation::ADD, bn_mul_out, bn_add, bn_add_out, ConvertPolicy::SATURATE); + + if(interm_out) + { + Parent::_interm_reference = std::move(add_output); + } + + if(act_info.enabled() && act_info.activation() != ActivationLayerInfo::ActivationFunction::IDENTITY) + { + Parent::_reference = reference::activation_layer(bn_add_out, act_info); + } + else + { + Parent::_reference = std::move(bn_add_out); + } + } +}; + +template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool interm_out> +class AddMulAddQuantizedValidationFixture : public AddMulAddGenericFixture<TensorType, AccessorType, FunctionType, T> +{ +public: + using Parent = AddMulAddGenericFixture<TensorType, AccessorType, FunctionType, T>; + + void setup(const TensorShape &shape, DataType data_type, ActivationLayerInfo act_info, + QuantizationInfo input1_qinfo, QuantizationInfo input2_qinfo, QuantizationInfo bn_mul_qinfo, + QuantizationInfo bn_add_qinfo, QuantizationInfo add_output_qinfo, QuantizationInfo final_output_qinfo) + { + // Quantization arguments moved to class attributes to prevent long function declerations + Parent::_input1_qinfo = input1_qinfo; + Parent::_input2_qinfo = input2_qinfo; + Parent::_bn_mul_qinfo = bn_mul_qinfo; + Parent::_bn_add_qinfo = bn_add_qinfo; + Parent::_add_output_qinfo = add_output_qinfo; + Parent::_final_output_qinfo = final_output_qinfo; + + Parent::setup(shape, data_type, act_info, interm_out); + compute_reference(shape, data_type, act_info); + } + + // Compute Reference is moved outside of the generic fixture because with the quantized data types, + // it becomes a very different implementation with intermediate tensors' data types being always float. + // This way the reference calculations are more readable and the size of the classes will be smaller + // due to unrepeated fill() and target() methods. + void compute_reference(const TensorShape &shape, DataType data_type, ActivationLayerInfo &act_info) + { + TensorShape b_shape(shape.x()); + + // Create reference + SimpleTensor<T> input1{ shape, data_type, 1, Parent::_input1_qinfo }; + SimpleTensor<T> input2{ shape, data_type, 1, Parent::_input2_qinfo }; + SimpleTensor<T> bn_mul{ b_shape, data_type, 1, Parent::_bn_mul_qinfo }; + SimpleTensor<T> bn_add{ b_shape, data_type, 1, Parent::_bn_add_qinfo }; + + // Fill input tensors + Parent::fill(input1, 0, data_type); + Parent::fill(input2, 1, data_type); + Parent::fill(bn_mul, 2, data_type); + Parent::fill(bn_add, 3, data_type); + + SimpleTensor<float> input1_dequantized = reference::dequantization_layer<float>(input1); + SimpleTensor<float> input2_dequantized = reference::dequantization_layer<float>(input2); + SimpleTensor<float> bn_mul_dequantized = reference::dequantization_layer<float>(bn_mul); + SimpleTensor<float> bn_add_dequantized = reference::dequantization_layer<float>(bn_add); + + SimpleTensor<float> add_output_dequantized{ shape, DataType::F32 }; + SimpleTensor<float> bn_add_out_dequantized{ shape, DataType::F32 }; + + reference::arithmetic_operation<float>(reference::ArithmeticOperation::ADD, input1_dequantized, input2_dequantized, add_output_dequantized, ConvertPolicy::SATURATE); + SimpleTensor<float> bn_mul_out_dequantized = reference::pixel_wise_multiplication<float, float, float>(add_output_dequantized, bn_mul_dequantized, 1.f, ConvertPolicy::SATURATE, + RoundingPolicy::TO_NEAREST_UP, DataType::F32); + reference::arithmetic_operation<float>(reference::ArithmeticOperation::ADD, bn_mul_out_dequantized, bn_add_dequantized, bn_add_out_dequantized, ConvertPolicy::SATURATE); + + if(interm_out) + { + Parent::_interm_reference = reference::quantization_layer<float, T>(add_output_dequantized, data_type, Parent::_add_output_qinfo); + } + + if(act_info.enabled() && act_info.activation() != ActivationLayerInfo::ActivationFunction::IDENTITY) + { + SimpleTensor<T> ref = reference::quantization_layer<float, T>(bn_add_out_dequantized, data_type, Parent::_final_output_qinfo); + Parent::_reference = reference::activation_layer(ref, act_info); + } + else + { + Parent::_reference = reference::quantization_layer<float, T>(bn_add_out_dequantized, data_type, Parent::_final_output_qinfo); + } + } +}; +} // namespace validation +} // namespace test +} // namespace arm_compute + +#endif // ACL_TESTS_VALIDATION_FIXTURES_ADDMULADDFIXTURE_H |