diff options
Diffstat (limited to 'tests/validation/fixtures/GEMMLowpFixture.h')
| -rw-r--r-- | tests/validation/fixtures/GEMMLowpFixture.h | 375 |
1 files changed, 365 insertions, 10 deletions
diff --git a/tests/validation/fixtures/GEMMLowpFixture.h b/tests/validation/fixtures/GEMMLowpFixture.h index 5fe7d83efd..6d073cd361 100644 --- a/tests/validation/fixtures/GEMMLowpFixture.h +++ b/tests/validation/fixtures/GEMMLowpFixture.h @@ -24,19 +24,10 @@ #ifndef ARM_COMPUTE_TEST_GEMMLOWP_FIXTURE #define ARM_COMPUTE_TEST_GEMMLOWP_FIXTURE -#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 "tests/validation/Helpers.h" #include "tests/validation/reference/GEMMLowp.h" - -#include <random> +#include "tests/validation/Validation.h" namespace arm_compute { @@ -1362,6 +1353,370 @@ protected: SimpleTensor<int32_t> _reference{}; }; +template <typename T, typename TensorType, typename AccessorType, typename ReshapeRHSOperatorType, typename GEMMFunctionType, typename ReduceOperation, typename CastOperation> +class GEMMLowpMatrixMultiplyReshapedOnlyRHSMMULOutputStageValidationFixture : 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, 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: + 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); + 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 { |