| /* |
| * Copyright (c) 2017-2019 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 "arm_compute/core/utils/quantization/AsymmHelpers.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 |
| { |
| namespace |
| { |
| template <typename U> |
| void fill(U &&tensor, int i) |
| { |
| switch(tensor.data_type()) |
| { |
| case DataType::QSYMM8_PER_CHANNEL: |
| { |
| int min_bound = 128; |
| int max_bound = -127; |
| for(size_t j = 0; j < tensor.quantization_info().scale().size(); j++) |
| { |
| std::pair<int, int> bounds = get_symm_quantized_per_channel_bounds(tensor.quantization_info(), -1.0f, 1.0f, i); |
| if(bounds.first < min_bound) |
| { |
| min_bound = bounds.first; |
| } |
| if(bounds.second > max_bound) |
| { |
| max_bound = bounds.second; |
| } |
| } |
| std::uniform_int_distribution<int8_t> distribution(min_bound, max_bound); |
| library->fill(tensor, distribution, i); |
| break; |
| } |
| case DataType::QASYMM8: |
| { |
| std::uniform_int_distribution<uint8_t> distribution(1, 254); |
| library->fill(tensor, distribution, i); |
| break; |
| } |
| case DataType::F16: |
| case DataType::F32: |
| { |
| // Between 1 and 254 in order to avoid having -128 and 128 for the DOT product path |
| std::uniform_real_distribution<> distribution(-1.0f, 1.0f); |
| library->fill(tensor, distribution, i); |
| break; |
| } |
| default: |
| library->fill_tensor_uniform(tensor, i); |
| } |
| } |
| |
| template <typename TensorType, typename AccessorType, typename FunctionType, bool reinterpret_input_as_3d, bool reinterpret_output_as_3d, typename OutputType, bool is_fused = false> |
| TensorType compute_gemmlowp_target(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_output, int32_t a_offset, int32_t b_offset, |
| GEMMLowpOutputStageInfo output_stage = GEMMLowpOutputStageInfo(), DataType data_type_b = DataType::QASYMM8, QuantizationInfo b_qinfo = QuantizationInfo()) |
| { |
| // Create tensors |
| TensorType a = create_tensor<TensorType>(shape_a, DataType::QASYMM8, 1); |
| TensorType b = create_tensor<TensorType>(shape_b, data_type_b, 1); // gemm output before output stage mismatch if i pass data_layout_output here. to be investigated |
| TensorType output = create_tensor<TensorType>(shape_output, output_stage.type == GEMMLowpOutputStageType::NONE ? DataType::S32 : DataType::QASYMM8, 1); |
| |
| a.info()->set_quantization_info(QuantizationInfo(1.0f / 255, a_offset)); |
| |
| if(data_type_b == DataType::QSYMM8_PER_CHANNEL) |
| { |
| b.info()->set_quantization_info(b_qinfo); |
| } |
| else |
| { |
| b.info()->set_quantization_info(QuantizationInfo(1.0f / 255, b_offset)); |
| } |
| TensorType bias; |
| if(is_fused) |
| { |
| TensorShape bias_shape(shape_b[0]); |
| bias = create_tensor<TensorType>(bias_shape, DataType::S32, 1); |
| } |
| |
| // Create and configure function |
| // The GEMMinfo includes the values of the depth in case of reinterpreted 3d input/output |
| FunctionType gemmlowp; |
| // TODO (COMPMID-1672) - Extending the test to validate add bias in offset contribution |
| gemmlowp.configure(&a, &b, is_fused ? &bias : nullptr, &output, GEMMInfo(false, false, false, (reinterpret_output_as_3d ? shape_output[2] : 0), reinterpret_input_as_3d, false, output_stage)); |
| |
| ARM_COMPUTE_EXPECT(a.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(b.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(output.info()->is_resizable(), framework::LogLevel::ERRORS); |
| |
| // Allocate tensors |
| a.allocator()->allocate(); |
| b.allocator()->allocate(); |
| output.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(!output.info()->is_resizable(), framework::LogLevel::ERRORS); |
| |
| // Fill tensors |
| fill(AccessorType(a), 0); |
| fill(AccessorType(b), 1); |
| |
| if(is_fused) |
| { |
| ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS); |
| bias.allocator()->allocate(); |
| ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS); |
| fill(AccessorType(bias), 2); |
| } |
| // Compute GEMM function |
| gemmlowp.run(); |
| return output; |
| } |
| |
| template <bool reinterpret_input_as_3d, typename TW = uint8_t> |
| SimpleTensor<int32_t> compute_gemmlowp_reference(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_output, int32_t a_offset, int32_t b_offset, |
| DataType data_type_b = DataType::QASYMM8, QuantizationInfo b_qinfo = QuantizationInfo()) |
| { |
| TensorShape shape_a_to_use = shape_a; |
| if(reinterpret_input_as_3d) |
| { |
| // Collapse the second and third dimension if the input is 3D |
| shape_a_to_use.collapse(2U, 1U); |
| } |
| |
| // Create reference |
| SimpleTensor<uint8_t> a{ shape_a_to_use, DataType::QASYMM8, 1 }; |
| SimpleTensor<TW> b{ shape_b, data_type_b, 1, data_type_b == DataType::QSYMM8_PER_CHANNEL ? b_qinfo : QuantizationInfo(1.0f / 255, b_offset) }; |
| |
| // Fill reference |
| fill(a, 0); |
| fill(b, 1); |
| return reference::gemmlowp_matrix_multiply_core<int32_t, uint8_t, TW>(a, b, shape_output, a_offset, b_offset); |
| } |
| } |
| |
| template <typename TensorType, typename AccessorType, typename FunctionType, bool reinterpret_input_as_3d = false, bool reinterpret_output_as_3d = false> |
| class GEMMLowpMatrixMultiplyCoreValidationFixture : public framework::Fixture |
| { |
| public: |
| template <typename...> |
| void setup(TensorShape shape_a, TensorShape shape_b, TensorShape shape_output, int32_t a_offset, int32_t b_offset) |
| { |
| _target = compute_target(shape_a, shape_b, shape_output, a_offset, b_offset); |
| _reference = compute_reference(shape_a, shape_b, shape_output, a_offset, b_offset); |
| } |
| |
| protected: |
| TensorType compute_target(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_output, int32_t a_offset, int32_t b_offset) |
| { |
| return compute_gemmlowp_target<TensorType, AccessorType, FunctionType, reinterpret_input_as_3d, reinterpret_output_as_3d, int32_t>(shape_a, shape_b, shape_output, a_offset, b_offset); |
| } |
| |
| SimpleTensor<int32_t> compute_reference(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_output, int32_t a_offset, int32_t b_offset) |
| { |
| return compute_gemmlowp_reference<reinterpret_input_as_3d>(shape_a, shape_b, shape_output, a_offset, b_offset); |
| } |
| |
| TensorType _target{}; |
| SimpleTensor<int32_t> _reference{}; |
| }; |
| |
| template <typename TensorType, typename AccessorType, typename FunctionType, bool reinterpret_input_as_3d = false, bool reinterpret_output_as_3d = false, typename TW = uint8_t> |
| class GEMMLowpMatrixMultiplyCoreFusedOffsetOutputValidationFixture : public framework::Fixture |
| { |
| public: |
| template <typename...> |
| void setup(TensorShape shape_a, TensorShape shape_b, TensorShape shape_output, int32_t a_offset, int32_t b_offset, GEMMLowpOutputStageInfo output_stage, DataType data_type_b) |
| { |
| ARM_COMPUTE_EXPECT(output_stage.type != GEMMLowpOutputStageType::NONE, framework::LogLevel::ERRORS); |
| if(data_type_b == DataType::QSYMM8_PER_CHANNEL) |
| { |
| output_stage.is_quantized_per_channel = true; |
| const size_t num_channels = shape_b[0]; |
| std::vector<float> scales(num_channels); |
| std::uniform_real_distribution<> distribution(0, 1); |
| library->fill(scales, distribution, 0); |
| output_stage.gemmlowp_multipliers.resize(num_channels); |
| output_stage.gemmlowp_shifts.resize(num_channels); |
| for(size_t i = 0; i < num_channels; ++i) |
| { |
| quantization::calculate_quantized_multiplier(scales[i], &output_stage.gemmlowp_multipliers[i], &output_stage.gemmlowp_shifts[i]); |
| } |
| |
| _reference = compute_reference(shape_a, shape_b, shape_output, a_offset, 0, output_stage, data_type_b, QuantizationInfo(scales)); |
| _target = compute_target(shape_a, shape_b, shape_output, a_offset, 0, output_stage, data_type_b, QuantizationInfo(scales)); |
| } |
| else |
| { |
| _reference = compute_reference(shape_a, shape_b, shape_output, a_offset, b_offset, output_stage, data_type_b, QuantizationInfo()); |
| _target = compute_target(shape_a, shape_b, shape_output, a_offset, b_offset, output_stage, data_type_b, QuantizationInfo()); |
| } |
| } |
| |
| protected: |
| TensorType compute_target(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_output, int32_t a_offset, int32_t b_offset, GEMMLowpOutputStageInfo output_stage, |
| DataType data_type_b, QuantizationInfo b_qinfo) |
| { |
| return compute_gemmlowp_target<TensorType, AccessorType, FunctionType, reinterpret_input_as_3d, reinterpret_output_as_3d, qasymm8_t, true>(shape_a, shape_b, shape_output, a_offset, b_offset, |
| output_stage, data_type_b, b_qinfo); |
| } |
| |
| SimpleTensor<qasymm8_t> compute_reference(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_output, int32_t a_offset, int32_t b_offset, |
| GEMMLowpOutputStageInfo output_stage, DataType data_type_b, QuantizationInfo b_qinfo) |
| { |
| SimpleTensor<int32_t> output = compute_gemmlowp_reference<reinterpret_input_as_3d, TW>(shape_a, shape_b, shape_output, a_offset, b_offset, data_type_b, b_qinfo); |
| |
| TensorShape bias_shape(shape_b[0]); |
| SimpleTensor<int32_t> bias{ bias_shape, DataType::S32, 1 }; |
| fill(bias, 2); |
| |
| switch(output_stage.type) |
| { |
| case GEMMLowpOutputStageType::QUANTIZE_DOWN: |
| return reference::gemmlowp_quantize_down_int32_to_uint8_scale<int32_t>(output, bias, |
| output_stage.gemmlowp_offset, output_stage.gemmlowp_multipliers, output_stage.gemmlowp_shifts, output_stage.gemmlowp_min_bound, output_stage.gemmlowp_max_bound); |
| break; |
| case GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT: |
| return reference::gemmlowp_quantize_down_scale_by_fixedpoint<int32_t, uint8_t>(output, bias, |
| output_stage.gemmlowp_multipliers, output_stage.gemmlowp_shifts, output_stage.gemmlowp_offset, output_stage.gemmlowp_min_bound, output_stage.gemmlowp_max_bound); |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Not Supported!"); |
| } |
| } |
| |
| TensorType _target{}; |
| SimpleTensor<qasymm8_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); |
| |
| const std::vector<int32_t> result_mult_int_vec = { result_mult_int }; |
| const std::vector<int32_t> result_shift_vec = { result_shift }; |
| |
| 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_vec, result_shift_vec, min, max); |
| } |
| else |
| { |
| return reference::gemmlowp_quantize_down_int32_to_uint8_scale<int32_t>(a, result_offset, result_mult_int_vec, result_shift_vec, min, max); |
| } |
| } |
| |
| TensorType _target{}; |
| SimpleTensor<uint8_t> _reference{}; |
| }; |
| |
| template <typename TensorType, typename AccessorType, typename FunctionType> |
| class GEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointValidationFixture : 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_SIGNED, 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<int8_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); |
| |
| const std::vector<int32_t> result_fixed_point_multiplier_vec = { result_fixed_point_multiplier }; |
| const std::vector<int32_t> result_shift_vec = { result_shift }; |
| |
| if(add_bias) |
| { |
| // Fill bias |
| fill(b, 1); |
| |
| return reference::gemmlowp_quantize_down_scale_by_fixedpoint<int32_t, int8_t>(a, b, result_fixed_point_multiplier_vec, result_shift_vec, result_offset_after_shift, min, max); |
| } |
| else |
| { |
| return reference::gemmlowp_quantize_down_scale_by_fixedpoint<int32_t, int8_t>(a, result_fixed_point_multiplier_vec, result_shift_vec, result_offset_after_shift, min, max); |
| } |
| } |
| |
| TensorType _target{}; |
| SimpleTensor<int8_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); |
| |
| const std::vector<int32_t> result_fixed_point_multiplier_vec = { result_fixed_point_multiplier }; |
| const std::vector<int32_t> result_shift_vec = { result_shift }; |
| |
| if(add_bias) |
| { |
| // Fill bias |
| fill(b, 1); |
| |
| return reference::gemmlowp_quantize_down_scale_by_fixedpoint<int32_t, uint8_t>(a, b, result_fixed_point_multiplier_vec, result_shift_vec, result_offset_after_shift, min, max); |
| } |
| else |
| { |
| return reference::gemmlowp_quantize_down_scale_by_fixedpoint<int32_t, uint8_t>(a, result_fixed_point_multiplier_vec, result_shift_vec, result_offset_after_shift, min, max); |
| } |
| } |
| |
| TensorType _target{}; |
| SimpleTensor<uint8_t> _reference{}; |
| }; |
| |
| template <typename TensorType, typename AccessorType, typename FunctionType> |
| class GEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointValidationFixture : public framework::Fixture |
| { |
| public: |
| template <typename...> |
| void setup(TensorShape shape, int32_t result_fixedpoint_multiplier, int32_t result_shift, int32_t min, int32_t max, bool add_bias) |
| { |
| _target = compute_target(shape, result_fixedpoint_multiplier, result_shift, min, max, add_bias); |
| _reference = compute_reference(shape, result_fixedpoint_multiplier, 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_fixedpoint_multiplier, 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::QSYMM16, 1); |
| |
| // Create and configure function |
| FunctionType output_stage; |
| output_stage.configure(&a, add_bias ? &b : nullptr, &c, result_fixedpoint_multiplier, 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<int16_t> compute_reference(const TensorShape &shape, int32_t result_fixed_point_multiplier, 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); |
| |
| const std::vector<int32_t> result_fixed_point_multiplier_vec = { result_fixed_point_multiplier }; |
| const std::vector<int32_t> result_shift_vec = { result_shift }; |
| |
| if(add_bias) |
| { |
| // Fill bias |
| fill(b, 1); |
| |
| return reference::gemmlowp_quantize_down_scale_by_fixedpoint<int32_t, int16_t>(a, b, result_fixed_point_multiplier_vec, result_shift_vec, 0, min, max); |
| } |
| else |
| { |
| return reference::gemmlowp_quantize_down_scale_by_fixedpoint<int32_t, int16_t>(a, result_fixed_point_multiplier_vec, result_shift_vec, 0, min, max); |
| } |
| } |
| |
| TensorType _target{}; |
| SimpleTensor<int16_t> _reference{}; |
| }; |
| |
| template <typename TensorType, typename AccessorType, typename ReshapeLHSFunctionType, typename ReshapeRHSFunctionType, typename GEMMFunctionType> |
| class GEMMLowpMatrixMultiplyReshapedValidationFixture : public framework::Fixture |
| { |
| public: |
| template <typename...> |
| void setup(unsigned int m, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0, unsigned int k0, unsigned int v0, unsigned int h0, bool interleave_lhs, |
| bool interleave_rhs) |
| { |
| GEMMLHSMatrixInfo lhs_info; |
| lhs_info.m0 = m0; |
| lhs_info.k0 = k0; |
| lhs_info.v0 = v0; |
| lhs_info.interleave = interleave_lhs; |
| lhs_info.transpose = false; |
| |
| GEMMRHSMatrixInfo rhs_info; |
| rhs_info.n0 = n0; |
| rhs_info.k0 = k0; |
| rhs_info.h0 = h0; |
| rhs_info.interleave = interleave_rhs; |
| rhs_info.transpose = true; |
| |
| // Set the tensor shapes for LHS and RHS matrices |
| const TensorShape lhs_shape(k, m, batch_size); |
| const TensorShape rhs_shape(n, k, batch_size); |
| |
| _target = compute_target(lhs_shape, rhs_shape, lhs_info, rhs_info); |
| _reference = compute_reference(lhs_shape, rhs_shape); |
| } |
| |
| 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 &lhs_shape, const TensorShape &rhs_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info) |
| { |
| // Create tensors |
| TensorType lhs = create_tensor<TensorType>(lhs_shape, DataType::QASYMM8, 1); |
| TensorType rhs = create_tensor<TensorType>(rhs_shape, DataType::QASYMM8, 1); |
| TensorType lhs_reshaped; |
| TensorType rhs_reshaped; |
| TensorType dst; |
| |
| const unsigned int M = lhs_shape[1]; |
| const unsigned int N = rhs_shape[0]; |
| const unsigned int K = lhs_shape[0]; |
| |
| // The output tensor will be auto-initialized within the function |
| |
| // Create and configure function |
| ReshapeLHSFunctionType reshape_lhs; |
| ReshapeRHSFunctionType reshape_rhs; |
| GEMMFunctionType gemm; |
| reshape_lhs.configure(&lhs, &lhs_reshaped, lhs_info); |
| reshape_rhs.configure(&rhs, &rhs_reshaped, rhs_info); |
| gemm.configure(&lhs_reshaped, &rhs_reshaped, &dst, lhs_info, rhs_info, GEMMReshapeInfo(M, N, K)); |
| |
| ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS); |
| |
| // Allocate tensors |
| lhs.allocator()->allocate(); |
| rhs.allocator()->allocate(); |
| lhs_reshaped.allocator()->allocate(); |
| rhs_reshaped.allocator()->allocate(); |
| dst.allocator()->allocate(); |
| |
| ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(!lhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(!rhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS); |
| |
| // Fill tensors |
| fill(AccessorType(lhs), 0); |
| fill(AccessorType(rhs), 1); |
| |
| // Compute GEMM |
| reshape_lhs.run(); |
| reshape_rhs.run(); |
| gemm.run(); |
| |
| return dst; |
| } |
| |
| SimpleTensor<int32_t> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape) |
| { |
| TensorShape dst_shape = lhs_shape; |
| dst_shape[0] = rhs_shape[0]; |
| dst_shape[1] = lhs_shape[1]; |
| |
| // Create reference |
| SimpleTensor<uint8_t> lhs{ lhs_shape, DataType::QASYMM8, 1 }; |
| SimpleTensor<uint8_t> rhs{ rhs_shape, DataType::QASYMM8, 1 }; |
| |
| // Fill reference |
| fill(lhs, 0); |
| fill(rhs, 1); |
| |
| return reference::gemmlowp_matrix_multiply_core<int32_t, uint8_t>(lhs, rhs, dst_shape, 0, 0); |
| } |
| |
| TensorType _target{}; |
| SimpleTensor<int32_t> _reference{}; |
| }; |
| |
| template <typename TensorType, typename AccessorType, typename ReshapeLHSFunctionType, typename ReshapeRHSFunctionType, typename GEMMFunctionType> |
| class GEMMLowpMatrixMultiplyReshaped3DValidationFixture : public framework::Fixture |
| { |
| public: |
| template <typename...> |
| void setup(unsigned int m_w, unsigned int m_h, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0, unsigned int k0, unsigned int v0, unsigned int h0, |
| bool interleave_lhs, bool interleave_rhs) |
| { |
| GEMMLHSMatrixInfo lhs_info; |
| lhs_info.m0 = m0; |
| lhs_info.k0 = k0; |
| lhs_info.v0 = v0; |
| lhs_info.interleave = interleave_lhs; |
| lhs_info.transpose = false; |
| |
| GEMMRHSMatrixInfo rhs_info; |
| rhs_info.n0 = n0; |
| rhs_info.k0 = k0; |
| rhs_info.h0 = h0; |
| rhs_info.interleave = interleave_rhs; |
| rhs_info.transpose = true; |
| |
| // In case of GEMM3D, m is the product between m_w and m_h |
| const unsigned int m = m_w * m_h; |
| |
| // Set the tensor shapes for LHS and RHS matrices |
| const TensorShape lhs_shape(k, m, batch_size); |
| const TensorShape rhs_shape(n, k, batch_size); |
| |
| _target = compute_target(lhs_shape, rhs_shape, lhs_info, rhs_info, m_h); |
| _reference = compute_reference(lhs_shape, rhs_shape, m_h); |
| } |
| |
| 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 &lhs_shape, const TensorShape &rhs_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, unsigned int m_h) |
| { |
| // Create tensors |
| TensorType lhs = create_tensor<TensorType>(lhs_shape, DataType::QASYMM8, 1); |
| TensorType rhs = create_tensor<TensorType>(rhs_shape, DataType::QASYMM8, 1); |
| TensorType lhs_reshaped; |
| TensorType rhs_reshaped; |
| TensorType dst; |
| |
| const unsigned int M = lhs_shape[1]; |
| const unsigned int N = rhs_shape[0]; |
| const unsigned int K = lhs_shape[0]; |
| |
| // The output tensor will be auto-initialized within the function |
| |
| // Create and configure function |
| ReshapeLHSFunctionType reshape_lhs; |
| ReshapeRHSFunctionType reshape_rhs; |
| GEMMFunctionType gemm; |
| reshape_lhs.configure(&lhs, &lhs_reshaped, lhs_info); |
| reshape_rhs.configure(&rhs, &rhs_reshaped, rhs_info); |
| gemm.configure(&lhs_reshaped, &rhs_reshaped, &dst, lhs_info, rhs_info, GEMMReshapeInfo(M, N, K, 1, 1, m_h)); |
| |
| ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS); |
| |
| // Allocate tensors |
| lhs.allocator()->allocate(); |
| rhs.allocator()->allocate(); |
| lhs_reshaped.allocator()->allocate(); |
| rhs_reshaped.allocator()->allocate(); |
| dst.allocator()->allocate(); |
| |
| ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(!lhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(!rhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS); |
| |
| // Fill tensors |
| fill(AccessorType(lhs), 0); |
| fill(AccessorType(rhs), 1); |
| |
| // Compute GEMM |
| reshape_lhs.run(); |
| reshape_rhs.run(); |
| gemm.run(); |
| |
| return dst; |
| } |
| |
| SimpleTensor<int32_t> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, unsigned int m_h) |
| { |
| TensorShape dst_shape = lhs_shape; |
| dst_shape.set(0, rhs_shape[0]); |
| dst_shape.set(1, lhs_shape[1] / m_h); |
| dst_shape.set(2, m_h); |
| dst_shape.set(3, lhs_shape[2]); |
| |
| // Create reference |
| SimpleTensor<uint8_t> lhs{ lhs_shape, DataType::QASYMM8, 1 }; |
| SimpleTensor<uint8_t> rhs{ rhs_shape, DataType::QASYMM8, 1 }; |
| |
| // Fill reference |
| fill(lhs, 0); |
| fill(rhs, 1); |
| |
| return reference::gemmlowp_matrix_multiply_core<int32_t, uint8_t>(lhs, rhs, dst_shape, 0, 0); |
| } |
| |
| TensorType _target{}; |
| SimpleTensor<int32_t> _reference{}; |
| }; |
| |
| template <typename TensorType, typename AccessorType, typename ReshapeRHSFunctionType, typename GEMMFunctionType> |
| class GEMMLowpMatrixMultiplyReshapedOnlyRHSValidationFixture : public framework::Fixture |
| { |
| public: |
| template <typename...> |
| void setup(unsigned int m, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0, |
| unsigned int k0, unsigned int h0, bool interleave_rhs, bool transpose_rhs, DataType data_type) |
| { |
| GEMMLHSMatrixInfo lhs_info; |
| lhs_info.m0 = m0; |
| lhs_info.k0 = k0; |
| |
| GEMMRHSMatrixInfo rhs_info; |
| rhs_info.n0 = n0; |
| rhs_info.k0 = k0; |
| rhs_info.h0 = h0; |
| rhs_info.interleave = interleave_rhs; |
| rhs_info.transpose = transpose_rhs; |
| |
| // Set the tensor shapes for LHS and RHS matrices |
| const TensorShape lhs_shape(k, m, batch_size); |
| const TensorShape rhs_shape(n, k, batch_size); |
| |
| _target = compute_target(lhs_shape, rhs_shape, lhs_info, rhs_info, data_type); |
| _reference = compute_reference(lhs_shape, rhs_shape, data_type); |
| } |
| |
| protected: |
| template <typename U> |
| void fill(U &&tensor, int i) |
| { |
| switch(tensor.data_type()) |
| { |
| case DataType::QASYMM8: |
| { |
| // 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); |
| } |
| break; |
| case DataType::QASYMM8_SIGNED: |
| { |
| std::uniform_int_distribution<> distribution(-127, 126); |
| library->fill(tensor, distribution, i); |
| } |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Unsupported data type"); |
| } |
| } |
| |
| TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const GEMMLHSMatrixInfo &lhs_info, |
| const GEMMRHSMatrixInfo &rhs_info, DataType data_type) |
| { |
| // Create tensors |
| TensorType lhs = create_tensor<TensorType>(lhs_shape, data_type, 1); |
| TensorType rhs = create_tensor<TensorType>(rhs_shape, data_type, 1); |
| TensorType rhs_reshaped; |
| TensorType dst; |
| |
| const unsigned int M = lhs_shape[1]; |
| const unsigned int N = rhs_shape[0]; |
| const unsigned int K = lhs_shape[0]; |
| |
| // The output tensor will be auto-initialized within the function |
| |
| // Create and configure function |
| ReshapeRHSFunctionType reshape_rhs; |
| GEMMFunctionType gemm; |
| reshape_rhs.configure(&rhs, &rhs_reshaped, rhs_info); |
| gemm.configure(&lhs, &rhs_reshaped, &dst, lhs_info, rhs_info, GEMMReshapeInfo(M, N, K)); |
| |
| ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS); |
| |
| // Allocate tensors |
| lhs.allocator()->allocate(); |
| rhs.allocator()->allocate(); |
| rhs_reshaped.allocator()->allocate(); |
| dst.allocator()->allocate(); |
| |
| ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(!rhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS); |
| |
| // Fill tensors |
| fill(AccessorType(lhs), 0); |
| fill(AccessorType(rhs), 1); |
| |
| // Compute GEMM |
| reshape_rhs.run(); |
| gemm.run(); |
| |
| return dst; |
| } |
| |
| SimpleTensor<int32_t> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, DataType data_type) |
| { |
| TensorShape dst_shape = lhs_shape; |
| dst_shape[0] = rhs_shape[0]; |
| dst_shape[1] = lhs_shape[1]; |
| |
| if(data_type == DataType::QASYMM8) |
| { |
| // Create reference |
| SimpleTensor<uint8_t> lhs{ lhs_shape, data_type, 1 }; |
| SimpleTensor<uint8_t> rhs{ rhs_shape, data_type, 1 }; |
| |
| // Fill reference |
| fill(lhs, 0); |
| fill(rhs, 1); |
| |
| return reference::gemmlowp_matrix_multiply_core<int32_t, uint8_t>(lhs, rhs, dst_shape, 0, 0); |
| } |
| else |
| { |
| // Create reference |
| SimpleTensor<int8_t> lhs{ lhs_shape, data_type, 1 }; |
| SimpleTensor<int8_t> rhs{ rhs_shape, data_type, 1 }; |
| |
| // Fill reference |
| fill(lhs, 0); |
| fill(rhs, 1); |
| |
| return reference::gemmlowp_matrix_multiply_core<int32_t, int8_t>(lhs, rhs, dst_shape, 0, 0); |
| } |
| } |
| |
| TensorType _target{}; |
| SimpleTensor<int32_t> _reference{}; |
| }; |
| |
| template <typename TensorType, typename AccessorType, typename ReshapeRHSFunctionType, typename GEMMFunctionType> |
| class GEMMLowpMatrixMultiplyReshapedOnlyRHS3DValidationFixture : public framework::Fixture |
| { |
| public: |
| template <typename...> |
| void setup(unsigned int m_w, unsigned int m_h, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0, |
| unsigned int k0, unsigned int h0, bool interleave_rhs, bool transpose_rhs, DataType data_type) |
| { |
| GEMMLHSMatrixInfo lhs_info; |
| lhs_info.m0 = m0; |
| lhs_info.k0 = k0; |
| |
| GEMMRHSMatrixInfo rhs_info; |
| rhs_info.n0 = n0; |
| rhs_info.k0 = k0; |
| rhs_info.h0 = h0; |
| rhs_info.interleave = interleave_rhs; |
| rhs_info.transpose = transpose_rhs; |
| |
| // In case of GEMM3D, m is the product between m_w and m_h |
| const unsigned int m = m_w * m_h; |
| |
| // Set the tensor shapes for LHS and RHS matrices |
| const TensorShape lhs_shape(k, m, batch_size); |
| const TensorShape rhs_shape(n, k, batch_size); |
| |
| _target = compute_target(lhs_shape, rhs_shape, lhs_info, rhs_info, m_h, data_type); |
| _reference = compute_reference(lhs_shape, rhs_shape, m_h, data_type); |
| } |
| |
| protected: |
| template <typename U> |
| void fill(U &&tensor, int i) |
| { |
| switch(tensor.data_type()) |
| { |
| case DataType::QASYMM8: |
| { |
| // 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); |
| } |
| break; |
| case DataType::QASYMM8_SIGNED: |
| { |
| std::uniform_int_distribution<> distribution(-127, 126); |
| library->fill(tensor, distribution, i); |
| } |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Unsupported data type"); |
| } |
| } |
| |
| TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const GEMMLHSMatrixInfo &lhs_info, |
| const GEMMRHSMatrixInfo &rhs_info, unsigned int m_h, DataType data_type) |
| { |
| // Create tensors |
| TensorType lhs = create_tensor<TensorType>(lhs_shape, data_type, 1); |
| TensorType rhs = create_tensor<TensorType>(rhs_shape, data_type, 1); |
| TensorType rhs_reshaped; |
| TensorType dst; |
| |
| const unsigned int M = lhs_shape[1]; |
| const unsigned int N = rhs_shape[0]; |
| const unsigned int K = lhs_shape[0]; |
| |
| // The output tensor will be auto-initialized within the function |
| |
| // Create and configure function |
| ReshapeRHSFunctionType reshape_rhs; |
| GEMMFunctionType gemm; |
| reshape_rhs.configure(&rhs, &rhs_reshaped, rhs_info); |
| gemm.configure(&lhs, &rhs_reshaped, &dst, lhs_info, rhs_info, GEMMReshapeInfo(M, N, K, 1, 1, m_h)); |
| |
| ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS); |
| |
| // Allocate tensors |
| lhs.allocator()->allocate(); |
| rhs.allocator()->allocate(); |
| rhs_reshaped.allocator()->allocate(); |
| dst.allocator()->allocate(); |
| |
| ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(!rhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS); |
| |
| // Fill tensors |
| fill(AccessorType(lhs), 0); |
| fill(AccessorType(rhs), 1); |
| |
| // Compute GEMM |
| reshape_rhs.run(); |
| gemm.run(); |
| |
| return dst; |
| } |
| |
| SimpleTensor<int32_t> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, unsigned int m_h, DataType data_type) |
| { |
| TensorShape dst_shape = lhs_shape; |
| dst_shape.set(0, rhs_shape[0]); |
| dst_shape.set(1, lhs_shape[1] / m_h); |
| dst_shape.set(2, m_h); |
| dst_shape.set(3, lhs_shape[2]); |
| |
| if(data_type == DataType::QASYMM8) |
| { |
| // Create reference |
| SimpleTensor<uint8_t> lhs{ lhs_shape, data_type, 1 }; |
| SimpleTensor<uint8_t> rhs{ rhs_shape, data_type, 1 }; |
| |
| // Fill reference |
| fill(lhs, 0); |
| fill(rhs, 1); |
| |
| return reference::gemmlowp_matrix_multiply_core<int32_t, uint8_t>(lhs, rhs, dst_shape, 0, 0); |
| } |
| else |
| { |
| // Create reference |
| SimpleTensor<int8_t> lhs{ lhs_shape, data_type, 1 }; |
| SimpleTensor<int8_t> rhs{ rhs_shape, data_type, 1 }; |
| |
| // Fill reference |
| fill(lhs, 0); |
| fill(rhs, 1); |
| |
| return reference::gemmlowp_matrix_multiply_core<int32_t, int8_t>(lhs, rhs, dst_shape, 0, 0); |
| } |
| } |
| |
| TensorType _target{}; |
| SimpleTensor<int32_t> _reference{}; |
| }; |
| |
| template <typename TensorType, typename AccessorType, typename GEMMFunctionType> |
| class GEMMLowpMatrixMultiplyNativeValidationFixture : public framework::Fixture |
| { |
| public: |
| template <typename...> |
| void setup(unsigned int m, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0, unsigned int k0) |
| { |
| GEMMLHSMatrixInfo lhs_info; |
| lhs_info.m0 = m0; |
| lhs_info.k0 = k0; |
| |
| GEMMRHSMatrixInfo rhs_info; |
| rhs_info.n0 = n0; |
| rhs_info.k0 = k0; |
| |
| // Set the tensor shapes for LHS and RHS matrices |
| const TensorShape lhs_shape(k, m, batch_size); |
| const TensorShape rhs_shape(n, k, batch_size); |
| |
| _target = compute_target(lhs_shape, rhs_shape, lhs_info, rhs_info); |
| _reference = compute_reference(lhs_shape, rhs_shape); |
| } |
| |
| 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 &lhs_shape, const TensorShape &rhs_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info) |
| { |
| // Create tensors |
| TensorType lhs = create_tensor<TensorType>(lhs_shape, DataType::QASYMM8, 1); |
| TensorType rhs = create_tensor<TensorType>(rhs_shape, DataType::QASYMM8, 1); |
| TensorType dst; |
| |
| const unsigned int M = lhs_shape[1]; |
| const unsigned int N = rhs_shape[0]; |
| const unsigned int K = lhs_shape[0]; |
| |
| // The output tensor will be auto-initialized within the function |
| |
| // Create and configure function |
| GEMMFunctionType gemm; |
| gemm.configure(&lhs, &rhs, &dst, lhs_info, rhs_info, GEMMReshapeInfo(M, N, K)); |
| |
| ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS); |
| |
| // Allocate tensors |
| lhs.allocator()->allocate(); |
| rhs.allocator()->allocate(); |
| dst.allocator()->allocate(); |
| |
| ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS); |
| |
| // Fill tensors |
| fill(AccessorType(lhs), 0); |
| fill(AccessorType(rhs), 1); |
| |
| // Compute GEMM |
| gemm.run(); |
| |
| return dst; |
| } |
| |
| SimpleTensor<int32_t> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape) |
| { |
| TensorShape dst_shape = lhs_shape; |
| dst_shape[0] = rhs_shape[0]; |
| dst_shape[1] = lhs_shape[1]; |
| |
| // Create reference |
| SimpleTensor<uint8_t> lhs{ lhs_shape, DataType::QASYMM8, 1 }; |
| SimpleTensor<uint8_t> rhs{ rhs_shape, DataType::QASYMM8, 1 }; |
| |
| // Fill reference |
| fill(lhs, 0); |
| fill(rhs, 1); |
| |
| return reference::gemmlowp_matrix_multiply_core<int32_t, uint8_t>(lhs, rhs, dst_shape, 0, 0); |
| } |
| |
| TensorType _target{}; |
| SimpleTensor<int32_t> _reference{}; |
| }; |
| |
| template <typename TensorType, typename AccessorType, typename GEMMFunctionType> |
| class GEMMLowpMatrixMultiplyNative3DValidationFixture : public framework::Fixture |
| { |
| public: |
| template <typename...> |
| void setup(unsigned int m_w, unsigned int m_h, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0, unsigned int k0) |
| { |
| GEMMLHSMatrixInfo lhs_info; |
| lhs_info.m0 = m0; |
| lhs_info.k0 = k0; |
| |
| GEMMRHSMatrixInfo rhs_info; |
| rhs_info.n0 = n0; |
| rhs_info.k0 = k0; |
| |
| // In case of GEMM3D, m is the product between m_w and m_h |
| const unsigned int m = m_w * m_h; |
| |
| // Set the tensor shapes for LHS and RHS matrices |
| const TensorShape lhs_shape(k, m, batch_size); |
| const TensorShape rhs_shape(n, k, batch_size); |
| |
| _target = compute_target(lhs_shape, rhs_shape, lhs_info, rhs_info, m_h); |
| _reference = compute_reference(lhs_shape, rhs_shape, m_h); |
| } |
| |
| 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 &lhs_shape, const TensorShape &rhs_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, unsigned int m_h) |
| { |
| // Create tensors |
| TensorType lhs = create_tensor<TensorType>(lhs_shape, DataType::QASYMM8, 1); |
| TensorType rhs = create_tensor<TensorType>(rhs_shape, DataType::QASYMM8, 1); |
| TensorType dst; |
| |
| const unsigned int M = lhs_shape[1]; |
| const unsigned int N = rhs_shape[0]; |
| const unsigned int K = lhs_shape[0]; |
| |
| // The output tensor will be auto-initialized within the function |
| |
| // Create and configure function |
| GEMMFunctionType gemm; |
| gemm.configure(&lhs, &rhs, &dst, lhs_info, rhs_info, GEMMReshapeInfo(M, N, K, 1, 1, m_h)); |
| |
| ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS); |
| |
| // Allocate tensors |
| lhs.allocator()->allocate(); |
| rhs.allocator()->allocate(); |
| dst.allocator()->allocate(); |
| |
| ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS); |
| |
| // Fill tensors |
| fill(AccessorType(lhs), 0); |
| fill(AccessorType(rhs), 1); |
| |
| // Compute GEMM |
| gemm.run(); |
| |
| return dst; |
| } |
| |
| SimpleTensor<int32_t> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, unsigned int m_h) |
| { |
| TensorShape dst_shape = lhs_shape; |
| dst_shape.set(0, rhs_shape[0]); |
| dst_shape.set(1, lhs_shape[1] / m_h); |
| dst_shape.set(2, m_h); |
| dst_shape.set(3, lhs_shape[2]); |
| |
| // Create reference |
| SimpleTensor<uint8_t> lhs{ lhs_shape, DataType::QASYMM8, 1 }; |
| SimpleTensor<uint8_t> rhs{ rhs_shape, DataType::QASYMM8, 1 }; |
| |
| // Fill reference |
| fill(lhs, 0); |
| fill(rhs, 1); |
| |
| return reference::gemmlowp_matrix_multiply_core<int32_t, uint8_t>(lhs, rhs, dst_shape, 0, 0); |
| } |
| |
| TensorType _target{}; |
| SimpleTensor<int32_t> _reference{}; |
| }; |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |
| #endif /* ARM_COMPUTE_TEST_GEMMLOWP_FIXTURE */ |