diff options
Diffstat (limited to 'tests/validation/fixtures')
-rw-r--r-- | tests/validation/fixtures/GEMMLowpFixture.h | 22 | ||||
-rw-r--r-- | tests/validation/fixtures/ReorderFixture.h | 27 | ||||
-rw-r--r-- | tests/validation/fixtures/ScatterLayerFixture.h | 146 |
3 files changed, 143 insertions, 52 deletions
diff --git a/tests/validation/fixtures/GEMMLowpFixture.h b/tests/validation/fixtures/GEMMLowpFixture.h index 11a491faa7..aa4eedb75d 100644 --- a/tests/validation/fixtures/GEMMLowpFixture.h +++ b/tests/validation/fixtures/GEMMLowpFixture.h @@ -31,7 +31,7 @@ #include "tests/validation/Validation.h" #include "tests/validation/reference/GEMMLowp.h" #include "tests/validation/reference/ArithmeticOperations.h" -#include "tests/validation/reference/QuantizationLayer.h" +#include "tests/validation/reference/DequantizationLayer.h" #include <cstdint> #include <vector> @@ -472,20 +472,14 @@ template <typename TensorType, typename AccessorType, typename FunctionType, boo 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) + void setup(TensorShape shape_a, TensorShape shape_b, TensorShape shape_output, int32_t a_offset, int32_t b_offset, bool accumulate) { - // Accumulation is supported for Int8/UInt8 only in aarch64 - bool accumulate = true; - // Accumulation is not supported for Int8/UInt8 in aarch32 -#ifdef __arm__ - accumulate = false; -#endif //__arm__ - bool dynamic_qinfo = false; + 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); + _reference = compute_reference(shape_a, shape_b, shape_output, a_qinfo, b_qinfo, finfo, accumulate, dynamic_qinfo); } protected: @@ -495,14 +489,16 @@ protected: 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) + 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); - QuantizationInfo dst_quant_info = QuantizationInfo(a_qinfo.uniform().scale * b_qinfo.uniform().scale, 0); - f32_ref_output = reference::quantization_layer<int32_t, float>(s32_ref_output, DataType::F32, dst_quant_info); + f32_ref_output = reference::dequantization_layer<float, int32_t>(s32_ref_output); if (accumulate) { diff --git a/tests/validation/fixtures/ReorderFixture.h b/tests/validation/fixtures/ReorderFixture.h index 36e62696bc..8e28484c48 100644 --- a/tests/validation/fixtures/ReorderFixture.h +++ b/tests/validation/fixtures/ReorderFixture.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2023 Arm Limited. + * Copyright (c) 2023-2024 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -21,8 +21,8 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#ifndef ACL_TESTS_VALIDATION_FIXTURES_REORDERFIXTURE -#define ACL_TESTS_VALIDATION_FIXTURES_REORDERFIXTURE +#ifndef ACL_TESTS_VALIDATION_FIXTURES_REORDERFIXTURE_H +#define ACL_TESTS_VALIDATION_FIXTURES_REORDERFIXTURE_H #include "arm_compute/core/TensorShape.h" #include "arm_compute/core/Types.h" @@ -32,6 +32,7 @@ #include "tests/framework/Asserts.h" #include "tests/framework/Fixture.h" #include "tests/validation/reference/Reorder.h" +#include "src/core/NEON/kernels/arm_gemm/utils.hpp" namespace arm_compute { @@ -44,10 +45,23 @@ template <typename TensorType, typename AccessorType, typename FunctionType, typ class ReorderValidationFixture : public framework::Fixture { public: + void check_hardware_supports(WeightFormat output_wf){ + if(!Scheduler::get().cpu_info().has_sve() && output_wf!=WeightFormat::OHWIo4){ + _hardware_supports = false; + } + if (Scheduler::get().cpu_info().has_sve() && arm_gemm::utils::get_vector_length<float>() != 8 && output_wf==WeightFormat::OHWIo8) + { + _hardware_supports = false; + } + } + void setup(TensorShape input_shape, TensorShape output_shape, WeightFormat input_wf, WeightFormat output_wf, DataType data_type) { - _target = compute_target(input_shape, output_shape, input_wf, output_wf, data_type); - _reference = compute_reference(input_shape, output_shape, output_wf, data_type); + check_hardware_supports(output_wf); + if (_hardware_supports){ + _target = compute_target(input_shape, output_shape, input_wf, output_wf, data_type); + _reference = compute_reference(input_shape, output_shape, output_wf, data_type); + } } protected: @@ -98,6 +112,7 @@ public: return reference::reorder_layer<T>(src, output_shape, output_wf); } + bool _hardware_supports = true; TensorType _target{}; SimpleTensor<T> _reference{}; }; @@ -105,4 +120,4 @@ public: } // namespace validation } // namespace test } // namespace arm_compute -#endif /* ACL_TESTS_VALIDATION_FIXTURES_REORDERFIXTURE */ +#endif // ACL_TESTS_VALIDATION_FIXTURES_REORDERFIXTURE_H diff --git a/tests/validation/fixtures/ScatterLayerFixture.h b/tests/validation/fixtures/ScatterLayerFixture.h index bda5532a51..af161ef98b 100644 --- a/tests/validation/fixtures/ScatterLayerFixture.h +++ b/tests/validation/fixtures/ScatterLayerFixture.h @@ -27,8 +27,9 @@ #include "arm_compute/core/Utils.h" #include "arm_compute/runtime/CL/CLTensorAllocator.h" #include "tests/Globals.h" -#include "tests/framework/Asserts.h" // Required for ARM_COMPUTE_ASSERT +#include "tests/framework/Asserts.h" #include "tests/framework/Fixture.h" +#include "tests/validation/Helpers.h" #include "tests/validation/Validation.h" #include "tests/validation/reference/ScatterLayer.h" #include "tests/SimpleTensor.h" @@ -46,21 +47,46 @@ template <typename TensorType, typename AccessorType, typename FunctionType, typ class ScatterGenericValidationFixture : public framework::Fixture { public: - void setup(TensorShape src_shape, TensorShape updates_shape, TensorShape indices_shape, TensorShape out_shape, DataType data_type, ScatterInfo scatter_info, QuantizationInfo src_qinfo = QuantizationInfo(), QuantizationInfo o_qinfo = QuantizationInfo()) + void setup(TensorShape src_shape, TensorShape updates_shape, TensorShape indices_shape, + TensorShape out_shape, DataType data_type, ScatterInfo scatter_info, bool inplace, bool padding, + QuantizationInfo src_qinfo = QuantizationInfo(), QuantizationInfo o_qinfo = QuantizationInfo()) { - _target = compute_target(src_shape, updates_shape, indices_shape, out_shape, data_type, scatter_info, src_qinfo, o_qinfo); + // this is for improving randomness across tests + _hash = src_shape[0] + src_shape[1] + src_shape[2] + src_shape[3] + src_shape[4] + src_shape[5] + + updates_shape[0] + updates_shape[1] + updates_shape[2] + updates_shape[3] + + updates_shape[4] + updates_shape[5] + + indices_shape[0] + indices_shape[1] + indices_shape[2] + indices_shape[3]; + + _target = compute_target(src_shape, updates_shape, indices_shape, out_shape, data_type, scatter_info, inplace, padding, src_qinfo, o_qinfo); _reference = compute_reference(src_shape, updates_shape, indices_shape, out_shape, data_type,scatter_info, src_qinfo , o_qinfo); } protected: template <typename U> - void fill(U &&tensor, int i, float lo = -1.f, float hi = 1.f) + void fill(U &&tensor, int i) { switch(tensor.data_type()) { case DataType::F32: + case DataType::F16: + { + std::uniform_real_distribution<float> distribution(-10.f, 10.f); + library->fill(tensor, distribution, i); + break; + } + case DataType::S32: + case DataType::S16: + case DataType::S8: + { + std::uniform_int_distribution<int32_t> distribution(-100, 100); + library->fill(tensor, distribution, i); + break; + } + case DataType::U32: + case DataType::U16: + case DataType::U8: { - std::uniform_real_distribution<float> distribution(lo, hi); + std::uniform_int_distribution<uint32_t> distribution(0, 200); library->fill(tensor, distribution, i); break; } @@ -71,37 +97,47 @@ protected: } } - // This is used to fill indices tensor with U32 datatype. + // This is used to fill indices tensor with S32 datatype. // Used to prevent ONLY having values that are out of bounds. template <typename U> void fill_indices(U &&tensor, int i, const TensorShape &shape) { - // Calculate max indices the shape should contain. Add an arbitrary constant to allow testing for some out of bounds values. - const uint32_t max = std::max({shape[0] , shape[1], shape[2]}) + 5; - library->fill_tensor_uniform(tensor, i, static_cast<uint32_t>(0), static_cast<uint32_t>(max)); + // Calculate max indices the shape should contain. Add an arbitrary value to allow testing for some out of bounds values (In this case min dimension) + const int32_t max = std::min({shape[0] , shape[1], shape[2]}) + 1; + library->fill_tensor_uniform(tensor, i, static_cast<int32_t>(0), static_cast<int32_t>(max)); } - TensorType compute_target(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_c, const TensorShape &out_shape, DataType data_type, const ScatterInfo info, QuantizationInfo a_qinfo, QuantizationInfo o_qinfo) + TensorType compute_target(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_c, + const TensorShape &out_shape, DataType data_type, const ScatterInfo info, bool inplace, bool padding, + QuantizationInfo a_qinfo, QuantizationInfo o_qinfo) { // 1. Create relevant tensors using ScatterInfo data structure. // ---------------------------------------------------- // In order - src, updates, indices, output. TensorType src = create_tensor<TensorType>(shape_a, data_type, 1, a_qinfo); TensorType updates = create_tensor<TensorType>(shape_b, data_type, 1, a_qinfo); - TensorType indices = create_tensor<TensorType>(shape_c, DataType::U32, 1, QuantizationInfo()); + TensorType indices = create_tensor<TensorType>(shape_c, DataType::S32, 1, QuantizationInfo()); TensorType dst = create_tensor<TensorType>(out_shape, data_type, 1, o_qinfo); FunctionType scatter; // Configure operator - // When scatter_info.zero_initialization is true, pass nullptr to scatter function. + // When scatter_info.zero_initialization is true, pass nullptr for src + // because dst does not need to be initialized with src values. if(info.zero_initialization) { scatter.configure(nullptr, &updates, &indices, &dst, info); } else { - scatter.configure(&src, &updates, &indices, &dst, info); + if(inplace) + { + scatter.configure(&src, &updates, &indices, &src, info); + } + else + { + scatter.configure(&src, &updates, &indices, &dst, info); + } } // Assertions @@ -110,51 +146,92 @@ protected: ARM_COMPUTE_ASSERT(indices.info()->is_resizable()); ARM_COMPUTE_ASSERT(dst.info()->is_resizable()); + if(padding) + { + add_padding_x({ &src, &updates, &indices}); + + if(!inplace) + { + add_padding_x({ &dst }); + } + } + // Allocate tensors src.allocator()->allocate(); updates.allocator()->allocate(); indices.allocator()->allocate(); - dst.allocator()->allocate(); + + if(!inplace) + { + dst.allocator()->allocate(); + } ARM_COMPUTE_ASSERT(!src.info()->is_resizable()); ARM_COMPUTE_ASSERT(!updates.info()->is_resizable()); ARM_COMPUTE_ASSERT(!indices.info()->is_resizable()); - ARM_COMPUTE_ASSERT(!dst.info()->is_resizable()); + + if(!inplace) + { + ARM_COMPUTE_ASSERT(!dst.info()->is_resizable()); + } // Fill update (a) and indices (b) tensors. - fill(AccessorType(src), 0); - fill(AccessorType(updates), 1); - fill_indices(AccessorType(indices), 2, out_shape); + fill(AccessorType(src), 0 + _hash); + fill(AccessorType(updates), 1+ _hash); + fill_indices(AccessorType(indices), 2 + _hash, out_shape); scatter.run(); - return dst; + if(inplace) + { + return src; + } + else + { + return dst; + } } - SimpleTensor<T> compute_reference(const TensorShape &a_shape, const TensorShape &b_shape, const TensorShape &c_shape, const TensorShape &out_shape, DataType data_type, - ScatterInfo info, QuantizationInfo a_qinfo, QuantizationInfo o_qinfo) + SimpleTensor<T> compute_reference(const TensorShape &a_shape, const TensorShape &b_shape, const TensorShape &c_shape, + const TensorShape &out_shape, DataType data_type, ScatterInfo info, QuantizationInfo a_qinfo, QuantizationInfo o_qinfo) { // Output Quantization not currently in use - fixture should be extended to support this. ARM_COMPUTE_UNUSED(o_qinfo); + TensorShape src_shape = a_shape; + TensorShape updates_shape = b_shape; + TensorShape indices_shape = c_shape; + const int num_ind_dims = c_shape.num_dimensions(); + + // 1. Collapse batch index into a single dim if necessary for update tensor and indices tensor. + if(num_ind_dims >= 3) + { + indices_shape = indices_shape.collapsed_from(1); + updates_shape = updates_shape.collapsed_from(updates_shape.num_dimensions() - (num_ind_dims -1)); // Collapses batch dims + } + + // 2. Collapse data dims into a single dim. + // Collapse all src dims into 2 dims. First one holding data, the other being the index we iterate over. + src_shape.collapse(updates_shape.num_dimensions() - 1); // Collapse all data dims into single dim. + src_shape = src_shape.collapsed_from(1); // Collapse all index dims into a single dim + updates_shape.collapse(updates_shape.num_dimensions() - 1); // Collapse data dims (all except last dim which is batch dim) // Create reference tensors - SimpleTensor<T> src{ a_shape, data_type, 1, a_qinfo }; - SimpleTensor<T> updates{b_shape, data_type, 1, QuantizationInfo() }; - SimpleTensor<uint32_t> indices{ c_shape, DataType::U32, 1, QuantizationInfo() }; + SimpleTensor<T> src{ src_shape, data_type, 1, a_qinfo }; + SimpleTensor<T> updates{updates_shape, data_type, 1, QuantizationInfo() }; + SimpleTensor<int32_t> indices{ indices_shape, DataType::S32, 1, QuantizationInfo() }; // Fill reference - fill(src, 0); - fill(updates, 1); - fill_indices(indices, 2, out_shape); - - // Calculate individual reference. - auto result = reference::scatter_layer<T>(src, updates, indices, out_shape, info); + fill(src, 0 + _hash); + fill(updates, 1 + _hash); + fill_indices(indices, 2 + _hash, out_shape); - return result; + // Calculate individual reference using collapsed shapes + return reference::scatter_layer<T>(src, updates, indices, out_shape, info); } TensorType _target{}; SimpleTensor<T> _reference{}; + int32_t _hash{}; }; // This fixture will use the same shape for updates as indices. @@ -162,9 +239,12 @@ template <typename TensorType, typename AccessorType, typename FunctionType, typ class ScatterValidationFixture : public ScatterGenericValidationFixture<TensorType, AccessorType, FunctionType, T> { public: - void setup(TensorShape src_shape, TensorShape update_shape, TensorShape indices_shape, TensorShape out_shape, DataType data_type, ScatterFunction func, bool zero_init) + void setup(TensorShape src_shape, TensorShape update_shape, TensorShape indices_shape, + TensorShape out_shape, DataType data_type, ScatterFunction func, bool zero_init, bool inplace, bool padding) { - ScatterGenericValidationFixture<TensorType, AccessorType, FunctionType, T>::setup(src_shape, update_shape, indices_shape, out_shape, data_type, ScatterInfo(func, zero_init), QuantizationInfo(), QuantizationInfo()); + ScatterGenericValidationFixture<TensorType, AccessorType, FunctionType, T>::setup(src_shape, update_shape, + indices_shape, out_shape, data_type, ScatterInfo(func, zero_init), inplace, padding, + QuantizationInfo(), QuantizationInfo()); } }; |