diff options
Diffstat (limited to 'tests/validation/fixtures/GEMMLowpFixture.h')
-rw-r--r-- | tests/validation/fixtures/GEMMLowpFixture.h | 1148 |
1 files changed, 893 insertions, 255 deletions
diff --git a/tests/validation/fixtures/GEMMLowpFixture.h b/tests/validation/fixtures/GEMMLowpFixture.h index 94b25e2243..aa4eedb75d 100644 --- a/tests/validation/fixtures/GEMMLowpFixture.h +++ b/tests/validation/fixtures/GEMMLowpFixture.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2020 Arm Limited. + * Copyright (c) 2017-2024 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -21,22 +21,20 @@ * 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 +#ifndef ACL_TESTS_VALIDATION_FIXTURES_GEMMLOWPFIXTURE_H +#define ACL_TESTS_VALIDATION_FIXTURES_GEMMLOWPFIXTURE_H -#include "arm_compute/core/KernelDescriptors.h" -#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 "src/core/utils/quantization/AsymmHelpers.h" #include "tests/validation/Helpers.h" +#include "tests/framework/Fixture.h" +#include "tests/validation/Validation.h" #include "tests/validation/reference/GEMMLowp.h" +#include "tests/validation/reference/ArithmeticOperations.h" +#include "tests/validation/reference/DequantizationLayer.h" -#include <random> +#include <cstdint> +#include <vector> namespace arm_compute { @@ -49,115 +47,145 @@ 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); - } + 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_a = DataType::QASYMM8, DataType data_type_b = DataType::QASYMM8, - QuantizationInfo b_qinfo = QuantizationInfo()) +template <typename U> +void fill_quantized(U &&tensor, int i) { - // Create tensors - DataType data_type_output = output_stage.type == GEMMLowpOutputStageType::NONE ? DataType::S32 : data_type_a; - - TensorType a = create_tensor<TensorType>(shape_a, data_type_a, 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, data_type_output, 1); - - a.info()->set_quantization_info(QuantizationInfo(1.0f / 255, a_offset)); + ARM_COMPUTE_ASSERT(is_data_type_quantized(tensor.data_type())); + library->fill_tensor_uniform(tensor, i); +} - if(data_type_b == DataType::QSYMM8_PER_CHANNEL) +template <typename U> +void fill(U &&tensor, int i, int32_t min, int32_t max) +{ + if (tensor.data_type() == DataType::S32) { + std::uniform_int_distribution<int32_t> distribution(min, max); + library->fill(tensor, distribution, i); + } + else if(tensor.data_type() == DataType::F32) { - b.info()->set_quantization_info(b_qinfo); + std::uniform_real_distribution<float> distribution((float)min, (float)max); + library->fill(tensor, distribution, i); } else { - b.info()->set_quantization_info(QuantizationInfo(1.0f / 255, b_offset)); + ARM_COMPUTE_ERROR("NOT SUPPORTED!"); + } +} + +/** Information about how to fill tensors */ +struct TensorFillInfo +{ + // Bias fill range. Default values are arbitrary + int32_t min_bias {-20000}; + int32_t max_bias {20000}; + + // Output fill range. Default values are arbitrary + int32_t min_output {-20000}; + int32_t max_output {20000}; + + // Optional extra hash to randomize tensor filling + int32_t hash {0}; +}; + +template <typename TensorType, typename AccessorType, typename FunctionType, bool reinterpret_input_as_3d, bool reinterpret_output_as_3d, typename OutputType, bool is_fused = false, bool run_twice = false> +TensorType compute_gemmlowp_target(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_output, const QuantizationInfo& a_qinfo, const QuantizationInfo& b_qinfo, + const QuantizationInfo& output_qinfo, DataType data_type_a = DataType::QASYMM8, DataType data_type_b = DataType::QASYMM8, + GEMMLowpOutputStageInfo output_stage = GEMMLowpOutputStageInfo(), bool reshape_b_only_on_first_run = false, const TensorFillInfo& finfo = TensorFillInfo(), + bool accumulate = false, bool dynamic_qinfo = false, DataType data_type_output = DataType::UNKNOWN) +{ + ARM_COMPUTE_ASSERT(is_data_type_quantized_asymmetric(data_type_a)); + ARM_COMPUTE_ASSERT(data_type_a == data_type_b); + // If unknown, set to sensible defaults + if (data_type_output == DataType::UNKNOWN) { + data_type_output = output_stage.type == GEMMLowpOutputStageType::NONE ? DataType::S32 : data_type_a; } + + // Create tensors + TensorType a = create_tensor<TensorType>(shape_a, data_type_a, 1, dynamic_qinfo ? QuantizationInfo(1.0,0,true) : a_qinfo); + TensorType b = create_tensor<TensorType>(shape_b, data_type_b, 1, dynamic_qinfo ? QuantizationInfo(1.0,0,true) : b_qinfo); // gemm output before output stage mismatch if i pass data_layout_output here. to be investigated + TensorType output = create_tensor<TensorType>(shape_output, data_type_output, 1, output_qinfo /* output_qinfo will be ignored when output stage type is None */); + TensorType bias; if(is_fused) { TensorShape bias_shape(shape_b[0]); - bias = create_tensor<TensorType>(bias_shape, DataType::S32, 1); + bias = create_tensor<TensorType>(bias_shape,data_type_output == DataType::F32 ? DataType::F32 : 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)); + gemmlowp.configure(&a, &b, is_fused ? &bias : nullptr, &output, GEMMInfo(false, false, reshape_b_only_on_first_run, (reinterpret_output_as_3d ? shape_output[2] : 0), reinterpret_input_as_3d, false, + output_stage, false /*fp_mixed_precision*/, false /*fast_math*/, false /*broadcast_bias*/, + arm_compute::ActivationLayerInfo(), false /* fixed_format */, arm_compute::WeightFormat::UNSPECIFIED, + false /* pretranspose_B */, accumulate)); - 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); + // If the QuantizationInfo is dynamic, it needs to be settable after configure (note that we also force it to be dynamic) + if (dynamic_qinfo) + { + a.info()->set_quantization_info(QuantizationInfo(a_qinfo.scale(), a_qinfo.offset(), true)); + b.info()->set_quantization_info(QuantizationInfo(b_qinfo.scale(), b_qinfo.offset(), true)); + } + + ARM_COMPUTE_ASSERT(a.info()->is_resizable()); + ARM_COMPUTE_ASSERT(b.info()->is_resizable()); + ARM_COMPUTE_ASSERT(output.info()->is_resizable()); + + add_padding_x({ &a, &b, &output }); // 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); + ARM_COMPUTE_ASSERT(!a.info()->is_resizable()); + ARM_COMPUTE_ASSERT(!b.info()->is_resizable()); + ARM_COMPUTE_ASSERT(!output.info()->is_resizable()); // Fill tensors - fill(AccessorType(a), 0); - fill(AccessorType(b), 1); + fill_quantized(AccessorType(a), 0 + finfo.hash); + fill_quantized(AccessorType(b), 1 + finfo.hash); + + if (accumulate) + { + ARM_COMPUTE_ASSERT(accumulate != run_twice); + fill(AccessorType(output), 6 + finfo.hash, finfo.min_output, finfo.max_output); + } if(is_fused) { - ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_ASSERT(bias.info()->is_resizable()); bias.allocator()->allocate(); - ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS); - fill(AccessorType(bias), 2); + ARM_COMPUTE_ASSERT(!bias.info()->is_resizable()); + fill(AccessorType(bias), 2 + finfo.hash, finfo.min_bias, finfo.max_bias); } + + // Run with variable inputs. + if(run_twice) + { + gemmlowp.run(); + fill_quantized(AccessorType(a), 3 + finfo.hash); // Fill tensors with new seed after run + fill_quantized(AccessorType(b), 4 + finfo.hash); + if(is_fused) + { + fill(AccessorType(bias), 5 + finfo.hash, finfo.min_bias, finfo.max_bias); + } + } + // Compute GEMM function gemmlowp.run(); return output; } -template <bool reinterpret_input_as_3d, typename TI = uint8_t, 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_a = DataType::QASYMM8, DataType data_type_b = DataType::QASYMM8, QuantizationInfo b_qinfo = QuantizationInfo()) +template <bool reinterpret_input_as_3d, typename TI = uint8_t, typename TW = uint8_t, bool pretranspose_A = false, bool pretranspose_B = false, bool run_twice = false> +SimpleTensor<int32_t> compute_gemmlowp_reference(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_output, const QuantizationInfo& a_qinfo, const QuantizationInfo& b_qinfo, + DataType data_type_a = DataType::QASYMM8, DataType data_type_b = DataType::QASYMM8, const TensorFillInfo& finfo = TensorFillInfo()) { + ARM_COMPUTE_ASSERT(is_data_type_quantized_asymmetric(data_type_a)); + ARM_COMPUTE_ASSERT(data_type_a == data_type_b); TensorShape shape_a_to_use = shape_a; if(reinterpret_input_as_3d) { @@ -166,101 +194,269 @@ SimpleTensor<int32_t> compute_gemmlowp_reference(const TensorShape &shape_a, con } // Create reference - SimpleTensor<TI> a{ shape_a_to_use, data_type_a, 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) }; + SimpleTensor<TI> a{ shape_a_to_use, data_type_a, 1, a_qinfo }; + SimpleTensor<TW> b{ shape_b, data_type_b, 1, b_qinfo }; + + TensorShape shape_a_to_use_transposed{ shape_a_to_use }; + TensorShape shape_b_transposed{ shape_b }; + + shape_a_to_use_transposed.set(0, shape_a_to_use[1]); + shape_a_to_use_transposed.set(1, shape_a_to_use[0]); + shape_b_transposed.set(0, shape_b[1]); + shape_b_transposed.set(1, shape_b[0]); + + SimpleTensor<TI> a_transposed{ shape_a_to_use_transposed, data_type_a, 1, a_qinfo }; + SimpleTensor<TW> b_transposed{ shape_b_transposed, data_type_b, 1, b_qinfo }; // Fill reference - fill(a, 0); - fill(b, 1); - return reference::gemmlowp_matrix_multiply_core<int32_t, TI, TW>(a, b, shape_output, a_offset, b_offset); -} + fill_quantized(a, 0 + finfo.hash); + fill_quantized(b, 1 + finfo.hash); + + // Transpose reference if required + /* Note: Assuming the usual batch matmul dimensions A = (B x M x K), B = (B x K x N), if pretranspose_A is set to true, then A is assumed to be (B x K x M), + therefore, A must be pre-transposed before passing it to the fixture. And, we transpose A again in the fixture to make it (B x M x K) + in order to be able to call reference implementation that works with (B x M x K) input. + Similarly, if pretranspose_B is set to true, then B is assumed to be (B x N x K), B must be pre-transposed before passing it to the fixture. */ + if(pretranspose_A) + { + transpose_matrix<TI>(a, a_transposed); + } + + if(pretranspose_B) + { + transpose_matrix<TW>(b, b_transposed); + } + + // Run with variable inputs. + const int32_t a_offset = a_qinfo.uniform().offset; + const int32_t b_offset = b_qinfo.uniform().offset; + + if(run_twice) + { + reference::gemmlowp_matrix_multiply_core<int32_t, TI, TW>((pretranspose_A ? a_transposed : a), (pretranspose_B ? b_transposed : b), shape_output, a_offset, b_offset); + fill_quantized((pretranspose_A) ? a_transposed : a, 3 + finfo.hash); + fill_quantized((pretranspose_B) ? b_transposed : b, 4 + finfo.hash); + } + + return reference::gemmlowp_matrix_multiply_core<int32_t, TI, TW>((pretranspose_A ? a_transposed : a), (pretranspose_B ? b_transposed : b), shape_output, a_offset, b_offset); } +} // namespace -template <typename TensorType, typename AccessorType, typename FunctionType, bool reinterpret_input_as_3d = false, bool reinterpret_output_as_3d = false> -class GEMMLowpMatrixMultiplyCoreValidationFixture : public framework::Fixture +template <typename TensorType, typename AccessorType, typename FunctionType, bool reinterpret_input_as_3d = false, bool reinterpret_output_as_3d = false, bool run_twice = false> +class GEMMLowpGenericMatrixMultiplyCoreValidationFixture : 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) + void setup(TensorShape shape_a, TensorShape shape_b, TensorShape shape_output, int32_t a_offset, int32_t b_offset, bool accumulate=false, bool dynamic_qinfo = false) { - _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); + const auto a_qinfo = QuantizationInfo(1.0f / 255, a_offset); + const auto b_qinfo = QuantizationInfo(1.0f / 255, b_offset); + TensorFillInfo finfo; + _target = compute_target(shape_a, shape_b, shape_output, a_qinfo, b_qinfo, finfo, accumulate, dynamic_qinfo); + _reference = compute_reference(shape_a, shape_b, shape_output, a_qinfo, b_qinfo, finfo, accumulate); } protected: - TensorType compute_target(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_output, int32_t a_offset, int32_t b_offset) + TensorType compute_target(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_output, const QuantizationInfo& a_qinfo, const QuantizationInfo& b_qinfo, const TensorFillInfo& finfo, const bool accumulate, const bool dynamic_qinfo) { - 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); + const auto output_qinfo = QuantizationInfo(); // No output stage + return compute_gemmlowp_target<TensorType, AccessorType, FunctionType, reinterpret_input_as_3d, reinterpret_output_as_3d, int32_t, false, run_twice>(shape_a, shape_b, shape_output, a_qinfo, b_qinfo, output_qinfo, DataType::QASYMM8, DataType::QASYMM8, GEMMLowpOutputStageInfo(), false, finfo, accumulate, dynamic_qinfo); } - 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) + SimpleTensor<int32_t> compute_reference(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_output, const QuantizationInfo& a_qinfo, const QuantizationInfo& b_qinfo, const TensorFillInfo& finfo, bool accumulate) { - return compute_gemmlowp_reference<reinterpret_input_as_3d>(shape_a, shape_b, shape_output, a_offset, b_offset); + SimpleTensor<int32_t> ref_output = compute_gemmlowp_reference<reinterpret_input_as_3d, uint8_t, uint8_t, false, false, run_twice>(shape_a, shape_b, shape_output, a_qinfo, b_qinfo, + DataType::QASYMM8, DataType::QASYMM8, finfo); + + if (accumulate) + { + SimpleTensor<int32_t> output{ shape_output, DataType::S32, 1 }; + fill(output, 6 + finfo.hash, finfo.min_output, finfo.max_output); + reference::arithmetic_operation<int32_t>(reference::ArithmeticOperation::ADD, output, ref_output, output, ConvertPolicy::SATURATE); + return output; + } + + return ref_output; } 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 TI = uint8_t, typename TW = uint8_t> -class GEMMLowpMatrixMultiplyCoreFusedOffsetOutputValidationFixture : public framework::Fixture +template <typename TensorType, typename AccessorType, typename FunctionType, bool reinterpret_input_as_3d = false, bool reinterpret_output_as_3d = false, bool run_twice = false> +class GEMMLowpMatrixMultiplyCoreValidationFixture : protected GEMMLowpGenericMatrixMultiplyCoreValidationFixture<TensorType, AccessorType, FunctionType, reinterpret_input_as_3d, reinterpret_output_as_3d, run_twice> { 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) + void setup(TensorShape shape_a, TensorShape shape_b, TensorShape shape_output, int32_t a_offset, int32_t b_offset) { - ARM_COMPUTE_EXPECT(output_stage.type != GEMMLowpOutputStageType::NONE, framework::LogLevel::ERRORS); - DataType data_type_a = data_type_b == DataType::QASYMM8_SIGNED ? DataType::QASYMM8_SIGNED : DataType::QASYMM8; + GEMMLowpGenericMatrixMultiplyCoreValidationFixture<TensorType, AccessorType, FunctionType, reinterpret_input_as_3d, reinterpret_output_as_3d, run_twice>::setup(shape_a, shape_b, shape_output, a_offset, b_offset, false /* accumulate */); + } +}; - 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]); - } +template <typename TensorType, typename AccessorType, typename FunctionType, bool reinterpret_input_as_3d = false, bool reinterpret_output_as_3d = false, bool run_twice = false> +class GEMMLowpMatrixMultiplyAccumulateValidationFixture : protected GEMMLowpGenericMatrixMultiplyCoreValidationFixture<TensorType, AccessorType, FunctionType, reinterpret_input_as_3d, reinterpret_output_as_3d, run_twice> +{ +public: + void setup(TensorShape shape_a, TensorShape shape_b, TensorShape shape_output, int32_t a_offset, int32_t b_offset) + { + GEMMLowpGenericMatrixMultiplyCoreValidationFixture<TensorType, AccessorType, FunctionType, reinterpret_input_as_3d, reinterpret_output_as_3d, run_twice>::setup(shape_a, shape_b, shape_output, a_offset, b_offset, true /* accumulate */); + } +}; - _reference = compute_reference(shape_a, shape_b, shape_output, a_offset, 0, output_stage, data_type_a, data_type_b, QuantizationInfo(scales)); - _target = compute_target(shape_a, shape_b, shape_output, a_offset, 0, output_stage, data_type_a, data_type_b, QuantizationInfo(scales)); - } - else - { - _reference = compute_reference(shape_a, shape_b, shape_output, a_offset, b_offset, output_stage, data_type_a, data_type_b, QuantizationInfo()); - _target = compute_target(shape_a, shape_b, shape_output, a_offset, b_offset, output_stage, data_type_a, data_type_b, QuantizationInfo()); - } +template <typename TensorType, typename AccessorType, typename FunctionType, bool reinterpret_input_as_3d = false, bool reinterpret_output_as_3d = false, bool run_twice = false> +class GEMMLowpMatrixMultiplyCoreDynamicQuantizationFixture : protected GEMMLowpGenericMatrixMultiplyCoreValidationFixture<TensorType, AccessorType, FunctionType, reinterpret_input_as_3d, reinterpret_output_as_3d, run_twice> +{ +public: + void setup(TensorShape shape_a, TensorShape shape_b, TensorShape shape_output, int32_t a_offset, int32_t b_offset) + { + GEMMLowpGenericMatrixMultiplyCoreValidationFixture<TensorType, AccessorType, FunctionType, reinterpret_input_as_3d, reinterpret_output_as_3d, run_twice>::setup(shape_a, shape_b, shape_output, a_offset, b_offset, false /* accumulate */, true /* dynamic_qinfo */); + } +}; + +template <typename TensorType, typename AccessorType, typename FunctionType, bool reinterpret_input_as_3d = false, bool reinterpret_output_as_3d = false, typename TI = uint8_t, typename TW = uint8_t, bool run_twice = false> +class GEMMLowpGenericMatrixMultiplyCoreFusedOffsetOutputValidationFixture : public framework::Fixture +{ +public: + /** Dynamically initialize the quantization info with saturation awareness + */ + template <typename T> + static void setup_quantization(DataType data_type, const TensorShape& shape_a, const TensorShape& shape_b, QuantizationInfo& a_qinfo, QuantizationInfo& b_qinfo, QuantizationInfo& output_qinfo, TensorFillInfo& finfo) + { + // This hash is used by random generators. There may be hash collisions but + // this is intentional as it's a very easy way to make the the current + // random generation process almost different for many test configurations, + // which were using the same set of values before. + finfo.hash = shape_a[0] + shape_a[1] + shape_b[0] + shape_b[1]; + + const int32_t t_max = static_cast<int32_t>(std::numeric_limits<T>::max()); + const int32_t t_min = static_cast<int32_t>(std::numeric_limits<T>::min()); + + std::mt19937 generator(library->seed() + finfo.hash); + std::uniform_real_distribution<float> distribution_float(-5.0f, 3.0f); + std::uniform_int_distribution<int32_t> distribution_t(t_min, t_max); + + const float scale_lhs = pow(2, distribution_float(generator)); // [2^-5, 2^3] + const float scale_rhs = pow(2, distribution_float(generator)); // [2^-5, 2^3] + + const int32_t offset_lhs = distribution_t(generator); + const int32_t offset_rhs = distribution_t(generator); + + a_qinfo = QuantizationInfo(scale_lhs, offset_lhs); + b_qinfo = QuantizationInfo(scale_rhs, offset_rhs); + + // reinterpret_input_as_3d or reinterpret_output_as_3d can be ignored, as the underlying gemm / matmul computation + // is equivalent to a standard 2D one with m-n-k dimensions + const int m = shape_a.y(); + const int n = shape_b.x(); + const int k = shape_a.x(); + + const float bias_fraction = 0.5f; // We enabled is_fused in compute_gemmlowp_target below, thus bias is included + + QuantizationHint q_hint = suggest_matmul_dst_q_info_and_bias(a_qinfo, b_qinfo, m, n, k, data_type, bias_fraction); + output_qinfo = q_hint.q_info; + finfo.min_bias = q_hint.bias_min; + finfo.max_bias = q_hint.bias_max; + + // Both target and reference implementations use negated offsets, i.e. + // float_val = (int_val + offset) * scale + // instead of + // float_val = (int_val - offset) * scale + // as usual. Therefore, after calculating the output quantization above, we + // negate the offsets of inputs' offsets. + a_qinfo = QuantizationInfo(scale_lhs, -offset_lhs); + b_qinfo = QuantizationInfo(scale_rhs, -offset_rhs); + } + + /** Initialize output stage info from quantization info */ + static Status init_gemmlowp_output_stage_info( + DataType data_type, + const QuantizationInfo& a_qinfo, + const QuantizationInfo& b_qinfo, + const QuantizationInfo& output_qinfo, + GEMMLowpOutputStageType type, + GEMMLowpOutputStageInfo &gemmlowp_output_stage_info) + { + ARM_COMPUTE_RETURN_ERROR_ON(!is_data_type_quantized_asymmetric(data_type)); + + const UniformQuantizationInfo aq_unif = a_qinfo.uniform(); + const UniformQuantizationInfo bq_unif = b_qinfo.uniform(); + const UniformQuantizationInfo oq_unif = output_qinfo.uniform(); + + float multiplier = (aq_unif.scale * bq_unif.scale) / oq_unif.scale; + int32_t int_multiplier; + int32_t shift; + + ARM_COMPUTE_RETURN_ON_ERROR( + quantization::calculate_quantized_multiplier(multiplier, &int_multiplier, &shift)); + + int32_t type_min = 0; + int32_t type_max = 0; + std::tie(type_min, type_max) = quantization::get_quantized_asymmetric_output_min_max(output_qinfo, ActivationLayerInfo(), data_type); + + gemmlowp_output_stage_info.gemmlowp_real_multiplier = multiplier; + gemmlowp_output_stage_info.gemmlowp_multiplier = int_multiplier; + gemmlowp_output_stage_info.gemmlowp_multipliers = { int_multiplier }; + gemmlowp_output_stage_info.gemmlowp_shift = shift; + gemmlowp_output_stage_info.gemmlowp_shifts = { shift }; + gemmlowp_output_stage_info.gemmlowp_offset = oq_unif.offset; + gemmlowp_output_stage_info.type = type; + gemmlowp_output_stage_info.gemmlowp_min_bound = type_min; + gemmlowp_output_stage_info.gemmlowp_max_bound = type_max; + + return Status{}; + } + + /** Currently this fixture only tests the following data type configurations: + * + * 1. a and b are of the same data type + * 2. The data type is quantized asymmetric + * + */ + void setup(TensorShape shape_a, TensorShape shape_b, TensorShape shape_output, GEMMLowpOutputStageType output_stage_type, DataType data_type, + bool reshape_b_only_on_first_run) + { + ARM_COMPUTE_ASSERT(output_stage_type != GEMMLowpOutputStageType::NONE); + ARM_COMPUTE_ASSERT(is_data_type_quantized_asymmetric(data_type)); + + // Randomized dynamic quantization: randomize quantization info in a way that ensures no result saturation + // most of the time + QuantizationInfo a_qinfo; + QuantizationInfo b_qinfo; + QuantizationInfo output_qinfo; + TensorFillInfo finfo; + setup_quantization<TI>(data_type, shape_a, shape_b, a_qinfo, b_qinfo, output_qinfo, finfo); + + GEMMLowpOutputStageInfo output_stage; + init_gemmlowp_output_stage_info(data_type, a_qinfo, b_qinfo, output_qinfo, output_stage_type, output_stage); + + _reference = compute_reference(shape_a, shape_b, shape_output, a_qinfo, b_qinfo, data_type, data_type, output_stage, finfo); + _target = compute_target(shape_a, shape_b, shape_output, a_qinfo, b_qinfo, output_qinfo, data_type, data_type, output_stage, reshape_b_only_on_first_run, finfo); } 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_a, DataType data_type_b, QuantizationInfo b_qinfo) + TensorType compute_target(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_output, const QuantizationInfo& a_qinfo, const QuantizationInfo& b_qinfo, const QuantizationInfo& output_qinfo, + DataType data_type_a, DataType data_type_b, const GEMMLowpOutputStageInfo& output_stage, bool reshape_b_only_on_first_run = false, const TensorFillInfo& finfo = TensorFillInfo()) { - 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_a, data_type_b, b_qinfo); + return compute_gemmlowp_target<TensorType, AccessorType, FunctionType, reinterpret_input_as_3d, reinterpret_output_as_3d, qasymm8_t, true, run_twice>(shape_a, shape_b, shape_output, a_qinfo, + b_qinfo, output_qinfo, data_type_a, data_type_b, output_stage, reshape_b_only_on_first_run, finfo); } - SimpleTensor<TI> 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_a, DataType data_type_b, QuantizationInfo b_qinfo) + SimpleTensor<TI> compute_reference(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_output, const QuantizationInfo& a_qinfo, const QuantizationInfo& b_qinfo, + DataType data_type_a, DataType data_type_b, const GEMMLowpOutputStageInfo& output_stage, const TensorFillInfo& finfo = TensorFillInfo()) { - SimpleTensor<int32_t> output = compute_gemmlowp_reference<reinterpret_input_as_3d, TI, TW>(shape_a, shape_b, shape_output, a_offset, b_offset, data_type_a, data_type_b, b_qinfo); + SimpleTensor<int32_t> output = compute_gemmlowp_reference<reinterpret_input_as_3d, TI, TW, false, false, run_twice>(shape_a, shape_b, shape_output, a_qinfo, b_qinfo, data_type_a, data_type_b, finfo); TensorShape bias_shape(shape_b[0]); SimpleTensor<int32_t> bias{ bias_shape, DataType::S32, 1 }; - fill(bias, 2); + (run_twice) ? fill(bias, 5 + finfo.hash, finfo.min_bias, finfo.max_bias) : fill(bias, 2 + finfo.hash, finfo.min_bias, finfo.max_bias); // Fill bias with same seed as last run of gemmlowp_target switch(output_stage.type) { case GEMMLowpOutputStageType::QUANTIZE_DOWN: - return reference::gemmlowp_quantize_down_scale<int32_t, TW>(output, bias, + return reference::gemmlowp_quantize_down_scale<int32_t, TI>(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, TW>(output, bias, + return reference::gemmlowp_quantize_down_scale_by_fixedpoint<int32_t, TI>(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: @@ -272,11 +468,78 @@ protected: SimpleTensor<TI> _reference{}; }; +template <typename TensorType, typename AccessorType, typename FunctionType, bool reinterpret_input_as_3d = false, bool reinterpret_output_as_3d = false, bool run_twice = false> +class GEMMLowpDequantizedMatrixMultiplyValidationFixture : public framework::Fixture +{ +public: + void setup(TensorShape shape_a, TensorShape shape_b, TensorShape shape_output, int32_t a_offset, int32_t b_offset, bool accumulate) + { + const bool dynamic_qinfo = false; + const auto a_qinfo = QuantizationInfo(1.0f / 255, a_offset); + const auto b_qinfo = QuantizationInfo(5.0f / 255, b_offset); + TensorFillInfo finfo; + _target = compute_target(shape_a, shape_b, shape_output, a_qinfo, b_qinfo, finfo, accumulate, dynamic_qinfo); + _reference = compute_reference(shape_a, shape_b, shape_output, a_qinfo, b_qinfo, finfo, accumulate, dynamic_qinfo); + } + +protected: + TensorType compute_target(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_output, const QuantizationInfo& a_qinfo, const QuantizationInfo& b_qinfo, const TensorFillInfo& finfo, const bool accumulate, const bool dynamic_qinfo) + { + const auto output_qinfo = QuantizationInfo(); + return compute_gemmlowp_target<TensorType, AccessorType, FunctionType, reinterpret_input_as_3d, reinterpret_output_as_3d, int32_t, false, run_twice>(shape_a, shape_b, shape_output, a_qinfo, b_qinfo, output_qinfo, DataType::QASYMM8_SIGNED, DataType::QASYMM8_SIGNED, GEMMLowpOutputStageInfo(), false, finfo, accumulate, dynamic_qinfo, DataType::F32); + } + + SimpleTensor<float> compute_reference(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_output, const QuantizationInfo& a_qinfo, const QuantizationInfo& b_qinfo, const TensorFillInfo& finfo, bool accumulate, const bool dynamic_qinfo) + { + QuantizationInfo s32_ref_output_quant_info = QuantizationInfo(a_qinfo.uniform().scale * b_qinfo.uniform().scale, 0, dynamic_qinfo); + + SimpleTensor<int32_t> s32_ref_output = compute_gemmlowp_reference<reinterpret_input_as_3d, int8_t, int8_t, false, false, run_twice>(shape_a, shape_b, shape_output, a_qinfo, b_qinfo, + DataType::QASYMM8_SIGNED, DataType::QASYMM8_SIGNED, finfo); + s32_ref_output.quantization_info(s32_ref_output_quant_info); + + SimpleTensor<float> f32_ref_output(s32_ref_output.shape(), DataType::F32); + f32_ref_output = reference::dequantization_layer<float, int32_t>(s32_ref_output); + + if (accumulate) + { + SimpleTensor<float> output{ shape_output, DataType::F32, 1 }; + fill(output, 6 + finfo.hash, finfo.min_output, finfo.max_output); + reference::arithmetic_operation<float>(reference::ArithmeticOperation::ADD, output, f32_ref_output, output, ConvertPolicy::SATURATE); + return output; + } + + return f32_ref_output; + } + + TensorType _target{}; + SimpleTensor<float> _reference{}; +}; + +template <typename TensorType, typename AccessorType, typename FunctionType, bool reinterpret_input_as_3d = false, bool reinterpret_output_as_3d = false, typename TI = uint8_t, typename TW = uint8_t, bool run_twice = false> +class GEMMLowpMatrixMultiplyCoreFusedOffsetOutputValidationFixture : public GEMMLowpGenericMatrixMultiplyCoreFusedOffsetOutputValidationFixture<TensorType, AccessorType, FunctionType, reinterpret_input_as_3d, reinterpret_output_as_3d, TI, TW, run_twice> +{ +public: + void setup(TensorShape shape_a, TensorShape shape_b, TensorShape shape_output, GEMMLowpOutputStageType output_stage_type, DataType data_type, bool reshape_b_only_on_first_run) + { + GEMMLowpGenericMatrixMultiplyCoreFusedOffsetOutputValidationFixture<TensorType, AccessorType, FunctionType, reinterpret_input_as_3d, reinterpret_output_as_3d, TI, TW, run_twice>::setup(shape_a, shape_b, + shape_output, output_stage_type, data_type, reshape_b_only_on_first_run); + } +}; + +template <typename TensorType, typename AccessorType, typename FunctionType, bool reinterpret_input_as_3d = false, bool reinterpret_output_as_3d = false, typename TI = uint8_t, typename TW = uint8_t, bool run_twice = false> +class GEMMLowpBatchedMatrixMultiplyCoreFusedOffsetOutputFixture : public GEMMLowpGenericMatrixMultiplyCoreFusedOffsetOutputValidationFixture<TensorType, AccessorType, FunctionType, reinterpret_input_as_3d, reinterpret_output_as_3d, TI, TW, run_twice> +{ +public: + void setup(TensorShape shape_a, TensorShape shape_b, TensorShape shape_output, GEMMLowpOutputStageType output_stage_type, DataType data_type, bool reshape_b_only_on_first_run) + { + GEMMLowpGenericMatrixMultiplyCoreFusedOffsetOutputValidationFixture<TensorType, AccessorType, FunctionType, reinterpret_input_as_3d, reinterpret_output_as_3d, TI, TW, run_twice>::setup(shape_a, shape_b, shape_output, output_stage_type, data_type, reshape_b_only_on_first_run); + } +}; + 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); @@ -312,27 +575,27 @@ protected: output_stage_info.output_data_type = DataType::QASYMM8; output_stage.configure(&a, add_bias ? &b : nullptr, &c, output_stage_info); - ARM_COMPUTE_EXPECT(a.info()->is_resizable(), framework::LogLevel::ERRORS); - ARM_COMPUTE_EXPECT(c.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_ASSERT(a.info()->is_resizable()); + ARM_COMPUTE_ASSERT(c.info()->is_resizable()); // 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); + ARM_COMPUTE_ASSERT(!a.info()->is_resizable()); + ARM_COMPUTE_ASSERT(!c.info()->is_resizable()); // Fill tensor fill(AccessorType(a), 0); if(add_bias) { - ARM_COMPUTE_EXPECT(b.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_ASSERT(b.info()->is_resizable()); // Allocate bias tensor b.allocator()->allocate(); - ARM_COMPUTE_EXPECT(!b.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_ASSERT(!b.info()->is_resizable()); // Fill tensor fill(AccessorType(b), 1); @@ -378,7 +641,6 @@ template <typename TensorType, typename AccessorType, typename FunctionType> class GEMMLowpQuantizeDownInt32ToInt8ScaleValidationFixture : 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); @@ -414,27 +676,27 @@ protected: output_stage_info.output_data_type = DataType::QASYMM8_SIGNED; output_stage.configure(&a, add_bias ? &b : nullptr, &c, output_stage_info); - ARM_COMPUTE_EXPECT(a.info()->is_resizable(), framework::LogLevel::ERRORS); - ARM_COMPUTE_EXPECT(c.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_ASSERT(a.info()->is_resizable()); + ARM_COMPUTE_ASSERT(c.info()->is_resizable()); // 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); + ARM_COMPUTE_ASSERT(!a.info()->is_resizable()); + ARM_COMPUTE_ASSERT(!c.info()->is_resizable()); // Fill tensor fill(AccessorType(a), 0); if(add_bias) { - ARM_COMPUTE_EXPECT(b.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_ASSERT(b.info()->is_resizable()); // Allocate bias tensor b.allocator()->allocate(); - ARM_COMPUTE_EXPECT(!b.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_ASSERT(!b.info()->is_resizable()); // Fill tensor fill(AccessorType(b), 1); @@ -480,7 +742,6 @@ 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); @@ -508,27 +769,27 @@ protected: 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); + ARM_COMPUTE_ASSERT(a.info()->is_resizable()); + ARM_COMPUTE_ASSERT(c.info()->is_resizable()); // 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); + ARM_COMPUTE_ASSERT(!a.info()->is_resizable()); + ARM_COMPUTE_ASSERT(!c.info()->is_resizable()); // Fill tensor fill(AccessorType(a), 0); if(add_bias) { - ARM_COMPUTE_EXPECT(b.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_ASSERT(b.info()->is_resizable()); // Allocate bias tensor b.allocator()->allocate(); - ARM_COMPUTE_EXPECT(!b.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_ASSERT(!b.info()->is_resizable()); // Fill tensor fill(AccessorType(b), 1); @@ -575,7 +836,6 @@ 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); @@ -603,27 +863,27 @@ protected: 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); + ARM_COMPUTE_ASSERT(a.info()->is_resizable()); + ARM_COMPUTE_ASSERT(c.info()->is_resizable()); // 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); + ARM_COMPUTE_ASSERT(!a.info()->is_resizable()); + ARM_COMPUTE_ASSERT(!c.info()->is_resizable()); // Fill tensor fill(AccessorType(a), 0); if(add_bias) { - ARM_COMPUTE_EXPECT(b.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_ASSERT(b.info()->is_resizable()); // Allocate bias tensor b.allocator()->allocate(); - ARM_COMPUTE_EXPECT(!b.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_ASSERT(!b.info()->is_resizable()); // Fill tensor fill(AccessorType(b), 1); @@ -670,7 +930,6 @@ template <typename TensorType, typename AccessorType, typename FunctionType, typ class GEMMLowpQuantizeDownInt32ScaleByFloatValidationFixture : public framework::Fixture { public: - template <typename...> void setup(DataType data_type, TensorShape shape, float result_real_multiplier, int32_t result_offset, int32_t min, int32_t max, bool add_bias) { _target = compute_target(data_type, shape, result_real_multiplier, result_offset, min, max, add_bias); @@ -708,27 +967,27 @@ protected: FunctionType output_stage; output_stage.configure(&a, add_bias ? &b : nullptr, &c, info); - ARM_COMPUTE_EXPECT(a.info()->is_resizable(), framework::LogLevel::ERRORS); - ARM_COMPUTE_EXPECT(c.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_ASSERT(a.info()->is_resizable()); + ARM_COMPUTE_ASSERT(c.info()->is_resizable()); // 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); + ARM_COMPUTE_ASSERT(!a.info()->is_resizable()); + ARM_COMPUTE_ASSERT(!c.info()->is_resizable()); // Fill tensor fill(AccessorType(a), 0); if(add_bias) { - ARM_COMPUTE_EXPECT(b.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_ASSERT(b.info()->is_resizable()); // Allocate bias tensor b.allocator()->allocate(); - ARM_COMPUTE_EXPECT(!b.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_ASSERT(!b.info()->is_resizable()); // Fill tensor fill(AccessorType(b), 1); @@ -773,7 +1032,6 @@ 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); @@ -801,27 +1059,27 @@ protected: 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); + ARM_COMPUTE_ASSERT(a.info()->is_resizable()); + ARM_COMPUTE_ASSERT(c.info()->is_resizable()); // 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); + ARM_COMPUTE_ASSERT(!a.info()->is_resizable()); + ARM_COMPUTE_ASSERT(!c.info()->is_resizable()); // Fill tensor fill(AccessorType(a), 0); if(add_bias) { - ARM_COMPUTE_EXPECT(b.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_ASSERT(b.info()->is_resizable()); // Allocate bias tensor b.allocator()->allocate(); - ARM_COMPUTE_EXPECT(!b.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_ASSERT(!b.info()->is_resizable()); // Fill tensor fill(AccessorType(b), 1); @@ -864,11 +1122,10 @@ protected: SimpleTensor<int16_t> _reference{}; }; -template <typename TensorType, typename AccessorType, typename ReshapeLHSFunctionType, typename ReshapeRHSFunctionType, typename GEMMFunctionType> +template <typename TensorType, typename AccessorType, typename ReshapeLHSOperatorType, typename ReshapeRHSOperatorType, 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, DataType data_type) { @@ -934,15 +1191,17 @@ protected: // The output tensor will be auto-initialized within the function // Create and configure function - ReshapeLHSFunctionType reshape_lhs; - ReshapeRHSFunctionType reshape_rhs; + ReshapeLHSOperatorType reshape_lhs; + ReshapeRHSOperatorType 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)); + reshape_lhs.configure(lhs.info(), lhs_reshaped.info(), lhs_info); + reshape_rhs.configure(rhs.info(), rhs_reshaped.info(), rhs_info); + gemm.configure(lhs_reshaped.info(), rhs_reshaped.info(), dst.info(), lhs_info, rhs_info, GEMMReshapeInfo(M, N, K)); + + ARM_COMPUTE_ASSERT(lhs.info()->is_resizable()); + ARM_COMPUTE_ASSERT(rhs.info()->is_resizable()); - ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS); - ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS); + add_padding_x({ &lhs, &rhs, &lhs_reshaped, &rhs_reshaped, &dst }); // Allocate tensors lhs.allocator()->allocate(); @@ -951,20 +1210,23 @@ protected: 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); + ARM_COMPUTE_ASSERT(!lhs.info()->is_resizable()); + ARM_COMPUTE_ASSERT(!rhs.info()->is_resizable()); + ARM_COMPUTE_ASSERT(!lhs_reshaped.info()->is_resizable()); + ARM_COMPUTE_ASSERT(!rhs_reshaped.info()->is_resizable()); + ARM_COMPUTE_ASSERT(!dst.info()->is_resizable()); // Fill tensors fill(AccessorType(lhs), 0); fill(AccessorType(rhs), 1); // Compute GEMM - reshape_lhs.run(); - reshape_rhs.run(); - gemm.run(); + ITensorPack reshape_lhs_pack = { { ACL_SRC, &lhs }, { ACL_DST, &lhs_reshaped } }; + reshape_lhs.run(reshape_lhs_pack); + ITensorPack reshape_rhs_pack = { { ACL_SRC, &rhs }, { ACL_DST, &rhs_reshaped } }; + reshape_rhs.run(reshape_rhs_pack); + ITensorPack gemm_pack({ { ACL_SRC_0, &lhs_reshaped }, { ACL_SRC_1, &rhs_reshaped }, { ACL_DST, &dst } }); + gemm.run(gemm_pack); return dst; } @@ -1010,11 +1272,10 @@ protected: SimpleTensor<int32_t> _reference{}; }; -template <typename TensorType, typename AccessorType, typename ReshapeLHSFunctionType, typename ReshapeRHSFunctionType, typename GEMMFunctionType> +template <typename TensorType, typename AccessorType, typename ReshapeLHSOperatorType, typename ReshapeRHSOperatorType, 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, DataType data_type) { @@ -1084,15 +1345,17 @@ protected: // The output tensor will be auto-initialized within the function // Create and configure function - ReshapeLHSFunctionType reshape_lhs; - ReshapeRHSFunctionType reshape_rhs; + ReshapeLHSOperatorType reshape_lhs; + ReshapeRHSOperatorType 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)); + reshape_lhs.configure(lhs.info(), lhs_reshaped.info(), lhs_info); + reshape_rhs.configure(rhs.info(), rhs_reshaped.info(), rhs_info); + gemm.configure(lhs_reshaped.info(), rhs_reshaped.info(), dst.info(), 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); + ARM_COMPUTE_ASSERT(lhs.info()->is_resizable()); + ARM_COMPUTE_ASSERT(rhs.info()->is_resizable()); + + add_padding_x({ &lhs, &rhs, &lhs_reshaped, &rhs_reshaped, &dst }); // Allocate tensors lhs.allocator()->allocate(); @@ -1101,20 +1364,23 @@ protected: 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); + ARM_COMPUTE_ASSERT(!lhs.info()->is_resizable()); + ARM_COMPUTE_ASSERT(!rhs.info()->is_resizable()); + ARM_COMPUTE_ASSERT(!lhs_reshaped.info()->is_resizable()); + ARM_COMPUTE_ASSERT(!rhs_reshaped.info()->is_resizable()); + ARM_COMPUTE_ASSERT(!dst.info()->is_resizable()); // Fill tensors fill(AccessorType(lhs), 0); fill(AccessorType(rhs), 1); // Compute GEMM - reshape_lhs.run(); - reshape_rhs.run(); - gemm.run(); + ITensorPack reshape_lhs_pack = { { ACL_SRC, &lhs }, { ACL_DST, &lhs_reshaped } }; + reshape_lhs.run(reshape_lhs_pack); + ITensorPack reshape_rhs_pack = { { ACL_SRC, &rhs }, { ACL_DST, &rhs_reshaped } }; + reshape_rhs.run(reshape_rhs_pack); + ITensorPack gemm_pack({ { ACL_SRC_0, &lhs_reshaped }, { ACL_SRC_1, &rhs_reshaped }, { ACL_DST, &dst } }); + gemm.run(gemm_pack); return dst; } @@ -1162,11 +1428,10 @@ protected: SimpleTensor<int32_t> _reference{}; }; -template <typename TensorType, typename AccessorType, typename ReshapeRHSFunctionType, typename GEMMFunctionType> +template <typename TensorType, typename AccessorType, typename ReshapeRHSOperatorType, 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) { @@ -1235,13 +1500,15 @@ protected: // The output tensor will be auto-initialized within the function // Create and configure function - ReshapeRHSFunctionType reshape_rhs; + ReshapeRHSOperatorType reshape_rhs; GEMMFunctionType gemm; - reshape_rhs.configure(&rhs, &rhs_reshaped, rhs_info); - gemm.configure(&lhs, &rhs_reshaped, &dst, gemm_info); + reshape_rhs.configure(rhs.info(), rhs_reshaped.info(), rhs_info); + gemm.configure(lhs.info(), rhs_reshaped.info(), dst.info(), gemm_info); + + ARM_COMPUTE_ASSERT(lhs.info()->is_resizable()); + ARM_COMPUTE_ASSERT(rhs.info()->is_resizable()); - ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS); - ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS); + add_padding_x({ &lhs, &rhs, &rhs_reshaped, &dst }); // Allocate tensors lhs.allocator()->allocate(); @@ -1249,18 +1516,20 @@ protected: 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); + ARM_COMPUTE_ASSERT(!lhs.info()->is_resizable()); + ARM_COMPUTE_ASSERT(!rhs.info()->is_resizable()); + ARM_COMPUTE_ASSERT(!rhs_reshaped.info()->is_resizable()); + ARM_COMPUTE_ASSERT(!dst.info()->is_resizable()); // Fill tensors fill(AccessorType(lhs), 0); fill(AccessorType(rhs), 1); // Compute GEMM - reshape_rhs.run(); - gemm.run(); + ITensorPack reshape_rhs_pack = { { ACL_SRC, &rhs }, { ACL_DST, &rhs_reshaped } }; + reshape_rhs.run(reshape_rhs_pack); + ITensorPack gemm_pack({ { ACL_SRC_0, &lhs }, { ACL_SRC_1, &rhs_reshaped }, { ACL_DST, &dst } }); + gemm.run(gemm_pack); return dst; } @@ -1301,11 +1570,372 @@ protected: SimpleTensor<int32_t> _reference{}; }; -template <typename TensorType, typename AccessorType, typename ReshapeRHSFunctionType, typename GEMMFunctionType> +template <typename T, typename TensorType, typename AccessorType, typename ReshapeRHSOperatorType, typename GEMMFunctionType, typename ReduceOperation, typename CastOperation> +class GEMMLowpMatrixMultiplyReshapedOnlyRHSMMULOutputStageValidationFixture : public framework::Fixture +{ +public: + 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, bool broadcast_bias, DataType data_type) + { + GEMMLowpOutputStageInfo output_stage; + output_stage.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT; + output_stage.output_data_type = data_type; + output_stage.gemmlowp_multipliers = std::vector<int32_t> { 1 }; + output_stage.gemmlowp_shifts = std::vector<int32_t> { 1 }; + output_stage.gemmlowp_multipliers[0] = 1; + output_stage.gemmlowp_shifts[0] = 1; + output_stage.gemmlowp_offset = 0; + constexpr float scale = 0.001f; + quantization::calculate_quantized_multiplier(scale, &output_stage.gemmlowp_multipliers[0], &output_stage.gemmlowp_shifts[0]); + output_stage.gemmlowp_min_bound = -100; + output_stage.gemmlowp_max_bound = 100; + + 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; + + int a_offset = 1; + int b_offset = 1; + + // 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); + const TensorShape bias_shape(n, + broadcast_bias ? 1 : m, + broadcast_bias ? 1 : batch_size); + + _target = compute_target(lhs_shape, rhs_shape, bias_shape, lhs_info, rhs_info, data_type, output_stage, a_offset, b_offset); + if(gemm_validated == true) + { + _reference = compute_reference(lhs_shape, rhs_shape, bias_shape, data_type, output_stage, a_offset, b_offset); + } + } + +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; + case DataType::S32: + { + std::uniform_int_distribution<> distribution(-10000, 10000); + 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 TensorShape &bias_shape, const GEMMLHSMatrixInfo &lhs_info, + const GEMMRHSMatrixInfo &rhs_info, DataType data_type, GEMMLowpOutputStageInfo output_stage, const int a_offset, const int b_offset) + { + // Create tensors + TensorType lhs = create_tensor<TensorType>(lhs_shape, data_type, 1, QuantizationInfo(1.0f / 255, a_offset)); + TensorType rhs = create_tensor<TensorType>(rhs_shape, data_type, 1, QuantizationInfo(1.0f / 255, b_offset)); + TensorType bias = create_tensor<TensorType>(bias_shape, DataType::S32, 1); + TensorType dst; + TensorType rhs_reshaped; + + const unsigned int M = lhs_shape[1]; + const unsigned int N = rhs_shape[0]; + const unsigned int K = lhs_shape[0]; + + // Tensors for precomputing sum of lhs rows / rhs columns + TensorType vec_sum_rows = create_tensor<TensorType>(TensorShape(M, 1, lhs_shape[2]), DataType::S32, 1); + TensorType vec_sum_cols = create_tensor<TensorType>(TensorShape(N, 1, rhs_shape[2]), DataType::S32, 1); + + GEMMKernelInfo gemm_info; + gemm_info.m = M; + gemm_info.n = N; + gemm_info.k = K; + gemm_info.lhs_info = lhs_info; + gemm_info.rhs_info = rhs_info; + gemm_info.output_stage = output_stage; + gemm_info.a_offset = a_offset; + gemm_info.b_offset = b_offset; + // The output tensor will be auto-initialized within the function + + // Create and configure function + ReshapeRHSOperatorType reshape_rhs; + GEMMFunctionType gemm; + reshape_rhs.configure(rhs.info(), rhs_reshaped.info(), rhs_info); + + // If GEMM is not validated, do not try to run. The validation will check + // if the technology supports this extension. If not, the test will be skipped. + // If it supports, the test will fail anyway because target and reference + // will not match. + gemm_validated = bool(gemm.validate(lhs.info(), rhs_reshaped.info(), dst.info(), gemm_info, vec_sum_cols.info(), vec_sum_rows.info(), bias.info())); + if(gemm_validated == true) + { + gemm.configure(lhs.info(), rhs_reshaped.info(), dst.info(), gemm_info, vec_sum_cols.info(), vec_sum_rows.info(), bias.info()); + + ARM_COMPUTE_ASSERT(lhs.info()->is_resizable()); + ARM_COMPUTE_ASSERT(rhs.info()->is_resizable()); + ARM_COMPUTE_ASSERT(bias.info()->is_resizable()); + + // Allocate tensors + lhs.allocator()->allocate(); + rhs.allocator()->allocate(); + rhs_reshaped.allocator()->allocate(); + bias.allocator()->allocate(); + vec_sum_cols.allocator()->allocate(); + vec_sum_rows.allocator()->allocate(); + dst.allocator()->allocate(); + + ARM_COMPUTE_ASSERT(!lhs.info()->is_resizable()); + ARM_COMPUTE_ASSERT(!rhs.info()->is_resizable()); + ARM_COMPUTE_ASSERT(!rhs_reshaped.info()->is_resizable()); + ARM_COMPUTE_ASSERT(!bias.info()->is_resizable()); + ARM_COMPUTE_ASSERT(!vec_sum_cols.info()->is_resizable()); + ARM_COMPUTE_ASSERT(!vec_sum_rows.info()->is_resizable()); + ARM_COMPUTE_ASSERT(!dst.info()->is_resizable()); + + // Fill tensors + fill(AccessorType(lhs), 0); + fill(AccessorType(rhs), 1); + fill(AccessorType(bias), 2); + + TensorType lhs_32 = create_tensor<TensorType>(lhs_shape, DataType::S32, 1); + TensorType rhs_32 = create_tensor<TensorType>(rhs_shape, DataType::S32, 1); + CastOperation cast_lhs; + CastOperation cast_rhs; + cast_lhs.configure(&lhs, &lhs_32, ConvertPolicy::SATURATE); + cast_rhs.configure(&rhs, &rhs_32, ConvertPolicy::SATURATE); + lhs_32.allocator()->allocate(); + rhs_32.allocator()->allocate(); + cast_lhs.run(); + cast_rhs.run(); + + ReduceOperation lhs_sum_rows; + ReduceOperation rhs_sum_cols; + + lhs_sum_rows.configure(&lhs_32, &vec_sum_rows, 0, ReductionOperation::SUM, false); + rhs_sum_cols.configure(&rhs_32, &vec_sum_cols, 1, ReductionOperation::SUM); + + lhs_sum_rows.run(); + rhs_sum_cols.run(); + + // Compute GEMM + ITensorPack reshape_rhs_pack = { { ACL_SRC, &rhs }, { ACL_DST, &rhs_reshaped } }; + reshape_rhs.run(reshape_rhs_pack); + ITensorPack gemm_pack({ { ACL_SRC_0, &lhs }, { ACL_SRC_1, &rhs_reshaped }, { ACL_SRC_2, &bias }, { ACL_DST, &dst }, { ACL_VEC_COL_SUM, &vec_sum_cols }, { ACL_VEC_ROW_SUM, &vec_sum_rows } }); + gemm.run(gemm_pack); + } + + return dst; + } + + SimpleTensor<T> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, DataType data_type, GEMMLowpOutputStageInfo output_stage, + const int a_offset, const int b_offset) + { + TensorShape dst_shape = lhs_shape; + dst_shape[0] = rhs_shape[0]; + dst_shape[1] = lhs_shape[1]; + + // Create reference + SimpleTensor<T> lhs{ lhs_shape, data_type, 1, QuantizationInfo(1.0f / 255, a_offset) }; + SimpleTensor<T> rhs{ rhs_shape, data_type, 1, QuantizationInfo(1.0f / 255, b_offset) }; + SimpleTensor<int32_t> bias{ bias_shape, DataType::S32, 1 }; + SimpleTensor<int32_t> dst{ dst_shape, DataType::S32, 1 }; + SimpleTensor<T> dst_final{ dst_shape, data_type, 1 }; + + // Fill reference + fill(lhs, 0); + fill(rhs, 1); + fill(bias, 2); + + dst = reference::gemmlowp_matrix_multiply_core<int32_t, T>(lhs, rhs, dst_shape, a_offset, b_offset); + dst_final = reference::gemmlowp_quantize_down_scale_by_fixedpoint<int32_t, T>(dst, bias, + output_stage.gemmlowp_multipliers, output_stage.gemmlowp_shifts, output_stage.gemmlowp_offset, output_stage.gemmlowp_min_bound, output_stage.gemmlowp_max_bound); + return dst_final; + } + + bool gemm_validated = true; + TensorType _target{}; + SimpleTensor<T> _reference{}; +}; + +template <typename TensorType, typename AccessorType, typename ReshapeRHSOperatorType, typename GEMMFunctionType> +class GEMMLowpMatrixMultiplyReshapedOnlyRHSMMULValidationFixture : public framework::Fixture +{ +public: + 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); + if(gemm_validated == true) + { + _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]; + + GEMMKernelInfo gemm_info; + gemm_info.m = M; + gemm_info.n = N; + gemm_info.k = K; + gemm_info.lhs_info = lhs_info; + gemm_info.rhs_info = rhs_info; + // The output tensor will be auto-initialized within the function + + // Create and configure function + ReshapeRHSOperatorType reshape_rhs; + GEMMFunctionType gemm; + reshape_rhs.configure(rhs.info(), rhs_reshaped.info(), rhs_info); + + // If GEMM is not validated, do not try to run. The validation will check + // if the technology supports this extension. If not, the test will be skipped. + // If it supports, the test will fail anyway because target and reference + // will not match. + gemm_validated = bool(gemm.validate(lhs.info(), rhs_reshaped.info(), dst.info(), gemm_info, nullptr, nullptr, nullptr)); + if(gemm_validated == true) + { + gemm.configure(lhs.info(), rhs_reshaped.info(), dst.info(), gemm_info, nullptr, nullptr, nullptr); + + ARM_COMPUTE_ASSERT(lhs.info()->is_resizable()); + ARM_COMPUTE_ASSERT(rhs.info()->is_resizable()); + + // Allocate tensors + lhs.allocator()->allocate(); + rhs.allocator()->allocate(); + rhs_reshaped.allocator()->allocate(); + dst.allocator()->allocate(); + + ARM_COMPUTE_ASSERT(!lhs.info()->is_resizable()); + ARM_COMPUTE_ASSERT(!rhs.info()->is_resizable()); + ARM_COMPUTE_ASSERT(!rhs_reshaped.info()->is_resizable()); + ARM_COMPUTE_ASSERT(!dst.info()->is_resizable()); + + // Fill tensors + fill(AccessorType(lhs), 0); + fill(AccessorType(rhs), 1); + + // Compute GEMM + ITensorPack reshape_rhs_pack = { { ACL_SRC, &rhs }, { ACL_DST, &rhs_reshaped } }; + reshape_rhs.run(reshape_rhs_pack); + ITensorPack gemm_pack({ { ACL_SRC_0, &lhs }, { ACL_SRC_1, &rhs_reshaped }, { ACL_DST, &dst } }); + gemm.run(gemm_pack); + } + + 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 }; + SimpleTensor<int32_t> dst{ dst_shape, DataType::S32, 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 }; + SimpleTensor<int32_t> dst{ dst_shape, DataType::S32, 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); + } + } + + bool gemm_validated = true; + TensorType _target{}; + SimpleTensor<int32_t> _reference{}; +}; + +template <typename TensorType, typename AccessorType, typename ReshapeRHSOperatorType, 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) { @@ -1378,13 +2008,15 @@ protected: // The output tensor will be auto-initialized within the function // Create and configure function - ReshapeRHSFunctionType reshape_rhs; + ReshapeRHSOperatorType reshape_rhs; GEMMFunctionType gemm; - reshape_rhs.configure(&rhs, &rhs_reshaped, rhs_info); - gemm.configure(&lhs, &rhs_reshaped, &dst, gemm_info); + reshape_rhs.configure(rhs.info(), rhs_reshaped.info(), rhs_info); + gemm.configure(lhs.info(), rhs_reshaped.info(), dst.info(), gemm_info); + + ARM_COMPUTE_ASSERT(lhs.info()->is_resizable()); + ARM_COMPUTE_ASSERT(rhs.info()->is_resizable()); - ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS); - ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS); + add_padding_x({ &lhs, &rhs, &rhs_reshaped, &dst }); // Allocate tensors lhs.allocator()->allocate(); @@ -1392,18 +2024,20 @@ protected: 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); + ARM_COMPUTE_ASSERT(!lhs.info()->is_resizable()); + ARM_COMPUTE_ASSERT(!rhs.info()->is_resizable()); + ARM_COMPUTE_ASSERT(!rhs_reshaped.info()->is_resizable()); + ARM_COMPUTE_ASSERT(!dst.info()->is_resizable()); // Fill tensors fill(AccessorType(lhs), 0); fill(AccessorType(rhs), 1); // Compute GEMM - reshape_rhs.run(); - gemm.run(); + ITensorPack reshape_rhs_pack = { { ACL_SRC, &rhs }, { ACL_DST, &rhs_reshaped } }; + reshape_rhs.run(reshape_rhs_pack); + ITensorPack gemm_pack({ { ACL_SRC_0, &lhs }, { ACL_SRC_1, &rhs_reshaped }, { ACL_DST, &dst } }); + gemm.run(gemm_pack); return dst; } @@ -1450,7 +2084,6 @@ 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; @@ -1493,26 +2126,29 @@ protected: // Create and configure function GEMMFunctionType gemm; - gemm.configure(&lhs, &rhs, &dst, lhs_info, rhs_info, GEMMReshapeInfo(M, N, K)); + gemm.configure(lhs.info(), rhs.info(), dst.info(), 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); + ARM_COMPUTE_ASSERT(lhs.info()->is_resizable()); + ARM_COMPUTE_ASSERT(rhs.info()->is_resizable()); + + add_padding_x({ &lhs, &rhs, &dst }); // 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); + ARM_COMPUTE_ASSERT(!lhs.info()->is_resizable()); + ARM_COMPUTE_ASSERT(!rhs.info()->is_resizable()); + ARM_COMPUTE_ASSERT(!dst.info()->is_resizable()); // Fill tensors fill(AccessorType(lhs), 0); fill(AccessorType(rhs), 1); // Compute GEMM - gemm.run(); + ITensorPack gemm_pack({ { ACL_SRC_0, &lhs }, { ACL_SRC_1, &rhs }, { ACL_DST, &dst } }); + gemm.run(gemm_pack); return dst; } @@ -1542,7 +2178,6 @@ 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; @@ -1588,26 +2223,29 @@ protected: // Create and configure function GEMMFunctionType gemm; - gemm.configure(&lhs, &rhs, &dst, lhs_info, rhs_info, GEMMReshapeInfo(M, N, K, 1, 1, m_h)); + gemm.configure(lhs.info(), rhs.info(), dst.info(), lhs_info, rhs_info, GEMMReshapeInfo(M, N, K, 1, 1, m_h)); + + ARM_COMPUTE_ASSERT(lhs.info()->is_resizable()); + ARM_COMPUTE_ASSERT(rhs.info()->is_resizable()); - ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS); - ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS); + add_padding_x({ &lhs, &rhs, &dst }); // 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); + ARM_COMPUTE_ASSERT(!lhs.info()->is_resizable()); + ARM_COMPUTE_ASSERT(!rhs.info()->is_resizable()); + ARM_COMPUTE_ASSERT(!dst.info()->is_resizable()); // Fill tensors fill(AccessorType(lhs), 0); fill(AccessorType(rhs), 1); // Compute GEMM - gemm.run(); + ITensorPack gemm_pack({ { ACL_SRC_0, &lhs }, { ACL_SRC_1, &rhs }, { ACL_DST, &dst } }); + gemm.run(gemm_pack); return dst; } @@ -1637,4 +2275,4 @@ protected: } // namespace validation } // namespace test } // namespace arm_compute -#endif /* ARM_COMPUTE_TEST_GEMMLOWP_FIXTURE */ +#endif // ACL_TESTS_VALIDATION_FIXTURES_GEMMLOWPFIXTURE_H |