| /* |
| * Copyright (c) 2017 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_GEMMLOWP_FIXTURE |
| #define ARM_COMPUTE_TEST_GEMMLOWP_FIXTURE |
| |
| #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/GEMMLowp.h" |
| |
| #include <random> |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace validation |
| { |
| template <typename TensorType, typename AccessorType, typename FunctionType> |
| class GEMMLowpMatrixMultiplyCoreValidationFixture : public framework::Fixture |
| { |
| public: |
| template <typename...> |
| void setup(TensorShape shape_a, TensorShape shape_b, TensorShape shape_c, int32_t a_offset, int32_t b_offset) |
| { |
| _target = compute_target(shape_a, shape_b, shape_c, a_offset, b_offset); |
| _reference = compute_reference(shape_a, shape_b, shape_c, a_offset, b_offset); |
| } |
| |
| protected: |
| template <typename U> |
| void fill(U &&tensor, int i) |
| { |
| // Between 1 and 254 in order to avoid having -128 and 128 for the DOT product path |
| std::uniform_int_distribution<> distribution(1, 254); |
| library->fill(tensor, distribution, i); |
| } |
| |
| TensorType compute_target(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_c, |
| int32_t a_offset, int32_t b_offset) |
| { |
| // Create tensors |
| TensorType a = create_tensor<TensorType>(shape_a, DataType::QASYMM8, 1); |
| TensorType b = create_tensor<TensorType>(shape_b, DataType::QASYMM8, 1); |
| TensorType c = create_tensor<TensorType>(shape_c, DataType::S32, 1); |
| |
| a.info()->set_quantization_info(QuantizationInfo(1.0f / 255, a_offset)); |
| b.info()->set_quantization_info(QuantizationInfo(1.0f / 255, b_offset)); |
| |
| // Create and configure function |
| FunctionType gemmlowp; |
| gemmlowp.configure(&a, &b, &c); |
| |
| ARM_COMPUTE_EXPECT(a.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(b.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(c.info()->is_resizable(), framework::LogLevel::ERRORS); |
| |
| // Allocate tensors |
| a.allocator()->allocate(); |
| b.allocator()->allocate(); |
| c.allocator()->allocate(); |
| |
| ARM_COMPUTE_EXPECT(!a.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(!b.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(!c.info()->is_resizable(), framework::LogLevel::ERRORS); |
| |
| // Fill tensors |
| fill(AccessorType(a), 0); |
| fill(AccessorType(b), 1); |
| |
| // Compute GEMM function |
| gemmlowp.run(); |
| return c; |
| } |
| |
| SimpleTensor<int32_t> compute_reference(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_c, |
| int32_t a_offset, int32_t b_offset) |
| { |
| // Create reference |
| SimpleTensor<uint8_t> a{ shape_a, DataType::QASYMM8, 1 }; |
| SimpleTensor<uint8_t> b{ shape_b, DataType::QASYMM8, 1 }; |
| |
| // Fill reference |
| fill(a, 0); |
| fill(b, 1); |
| |
| return reference::gemmlowp_matrix_multiply_core<int32_t, uint8_t>(a, b, a_offset, b_offset); |
| } |
| |
| TensorType _target{}; |
| SimpleTensor<int32_t> _reference{}; |
| }; |
| |
| template <typename TensorType, typename AccessorType, typename FunctionType> |
| class GEMMLowpQuantizeDownInt32ToUint8ScaleValidationFixture : public framework::Fixture |
| { |
| public: |
| template <typename...> |
| void setup(TensorShape shape, int32_t result_offset, int32_t result_mult_int, int32_t result_shift, int32_t min, int32_t max, bool add_bias) |
| { |
| _target = compute_target(shape, result_offset, result_mult_int, result_shift, min, max, add_bias); |
| _reference = compute_reference(shape, result_offset, result_mult_int, result_shift, min, max, add_bias); |
| } |
| |
| protected: |
| template <typename U> |
| void fill(U &&tensor, int i) |
| { |
| std::uniform_int_distribution<> distribution(-6000, 6000); |
| library->fill(tensor, distribution, i); |
| } |
| |
| TensorType compute_target(const TensorShape &shape, int32_t result_offset, int32_t result_mult_int, int32_t result_shift, int32_t min, int32_t max, bool add_bias) |
| { |
| TensorShape shape_bias(shape[0]); |
| |
| // Create tensors |
| TensorType a = create_tensor<TensorType>(shape, DataType::S32, 1); |
| TensorType b = create_tensor<TensorType>(shape_bias, DataType::S32, 1); |
| TensorType c = create_tensor<TensorType>(shape, DataType::QASYMM8, 1); |
| |
| // Create and configure function |
| FunctionType output_stage; |
| output_stage.configure(&a, add_bias ? &b : nullptr, &c, result_offset, result_mult_int, result_shift, min, max); |
| |
| ARM_COMPUTE_EXPECT(a.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(c.info()->is_resizable(), framework::LogLevel::ERRORS); |
| |
| // Allocate tensors |
| a.allocator()->allocate(); |
| c.allocator()->allocate(); |
| |
| ARM_COMPUTE_EXPECT(!a.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(!c.info()->is_resizable(), framework::LogLevel::ERRORS); |
| |
| // Fill tensor |
| fill(AccessorType(a), 0); |
| |
| if(add_bias) |
| { |
| ARM_COMPUTE_EXPECT(b.info()->is_resizable(), framework::LogLevel::ERRORS); |
| |
| // Allocate bias tensor |
| b.allocator()->allocate(); |
| |
| ARM_COMPUTE_EXPECT(!b.info()->is_resizable(), framework::LogLevel::ERRORS); |
| |
| // Fill tensor |
| fill(AccessorType(b), 1); |
| } |
| |
| // Compute GEMM function |
| output_stage.run(); |
| return c; |
| } |
| |
| SimpleTensor<uint8_t> compute_reference(const TensorShape &shape, int32_t result_offset, int32_t result_mult_int, int32_t result_shift, int32_t min, int32_t max, bool add_bias) |
| { |
| // Create reference |
| TensorShape shape_bias(shape[0]); |
| |
| SimpleTensor<int32_t> a{ shape, DataType::S32, 1 }; |
| SimpleTensor<int32_t> b{ shape_bias, DataType::S32, 1 }; |
| |
| // Fill reference |
| fill(a, 0); |
| |
| if(add_bias) |
| { |
| // Fill bias |
| fill(b, 1); |
| |
| return reference::gemmlowp_quantize_down_int32_to_uint8_scale<int32_t>(a, b, result_offset, result_mult_int, result_shift, min, max); |
| } |
| else |
| { |
| return reference::gemmlowp_quantize_down_int32_to_uint8_scale<int32_t>(a, result_offset, result_mult_int, result_shift, min, max); |
| } |
| } |
| |
| TensorType _target{}; |
| SimpleTensor<uint8_t> _reference{}; |
| }; |
| |
| template <typename TensorType, typename AccessorType, typename FunctionType> |
| class GEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointValidationFixture : public framework::Fixture |
| { |
| public: |
| template <typename...> |
| void setup(TensorShape shape, int32_t result_fixedpoint_multiplier, int32_t result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max, bool add_bias) |
| { |
| _target = compute_target(shape, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max, add_bias); |
| _reference = compute_reference(shape, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max, add_bias); |
| } |
| |
| protected: |
| template <typename U> |
| void fill(U &&tensor, int i) |
| { |
| std::uniform_int_distribution<> distribution(-6000, 6000); |
| library->fill(tensor, distribution, i); |
| } |
| |
| TensorType compute_target(const TensorShape &shape, int32_t result_fixedpoint_multiplier, int32_t result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max, bool add_bias) |
| { |
| TensorShape shape_bias(shape[0]); |
| |
| // Create tensors |
| TensorType a = create_tensor<TensorType>(shape, DataType::S32, 1); |
| TensorType b = create_tensor<TensorType>(shape_bias, DataType::S32, 1); |
| TensorType c = create_tensor<TensorType>(shape, DataType::QASYMM8, 1); |
| |
| // Create and configure function |
| FunctionType output_stage; |
| output_stage.configure(&a, add_bias ? &b : nullptr, &c, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max); |
| |
| ARM_COMPUTE_EXPECT(a.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(c.info()->is_resizable(), framework::LogLevel::ERRORS); |
| |
| // Allocate tensors |
| a.allocator()->allocate(); |
| c.allocator()->allocate(); |
| |
| ARM_COMPUTE_EXPECT(!a.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(!c.info()->is_resizable(), framework::LogLevel::ERRORS); |
| |
| // Fill tensor |
| fill(AccessorType(a), 0); |
| |
| if(add_bias) |
| { |
| ARM_COMPUTE_EXPECT(b.info()->is_resizable(), framework::LogLevel::ERRORS); |
| |
| // Allocate bias tensor |
| b.allocator()->allocate(); |
| |
| ARM_COMPUTE_EXPECT(!b.info()->is_resizable(), framework::LogLevel::ERRORS); |
| |
| // Fill tensor |
| fill(AccessorType(b), 1); |
| } |
| |
| // Compute GEMM function |
| output_stage.run(); |
| return c; |
| } |
| |
| SimpleTensor<uint8_t> compute_reference(const TensorShape &shape, int32_t result_fixed_point_multiplier, int32_t result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max, |
| bool add_bias) |
| { |
| // Create reference |
| TensorShape shape_bias(shape[0]); |
| |
| SimpleTensor<int32_t> a{ shape, DataType::S32, 1 }; |
| SimpleTensor<int32_t> b{ shape_bias, DataType::S32, 1 }; |
| |
| // Fill reference |
| fill(a, 0); |
| |
| if(add_bias) |
| { |
| // Fill bias |
| fill(b, 1); |
| |
| return reference::gemmlowp_quantize_down_int32_to_uint8_scale_by_fixedpoint<int32_t>(a, b, result_fixed_point_multiplier, result_shift, result_offset_after_shift, min, max); |
| } |
| else |
| { |
| return reference::gemmlowp_quantize_down_int32_to_uint8_scale_by_fixedpoint<int32_t>(a, result_fixed_point_multiplier, result_shift, result_offset_after_shift, min, max); |
| } |
| } |
| |
| TensorType _target{}; |
| SimpleTensor<uint8_t> _reference{}; |
| }; |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |
| #endif /* ARM_COMPUTE_TEST_GEMMLOWP_FIXTURE */ |