diff options
Diffstat (limited to 'tests/validation/fixtures/GEMMFixture.h')
| -rw-r--r-- | tests/validation/fixtures/GEMMFixture.h | 740 |
1 files changed, 14 insertions, 726 deletions
diff --git a/tests/validation/fixtures/GEMMFixture.h b/tests/validation/fixtures/GEMMFixture.h index f1e0ee9150..afde3d8067 100644 --- a/tests/validation/fixtures/GEMMFixture.h +++ b/tests/validation/fixtures/GEMMFixture.h @@ -21,14 +21,12 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#ifndef ARM_COMPUTE_TEST_GEMM_FIXTURE -#define ARM_COMPUTE_TEST_GEMM_FIXTURE +#ifndef ACL_TESTS_VALIDATION_FIXTURES_GEMMFIXTURE_H +#define ACL_TESTS_VALIDATION_FIXTURES_GEMMFIXTURE_H #include "arm_compute/core/KernelDescriptors.h" #include "arm_compute/core/TensorShape.h" #include "arm_compute/core/Types.h" -#include "arm_compute/core/experimental/IPostOp.h" -#include "src/core/experimental/PostOpUtils.h" #include "tests/AssetsLibrary.h" #include "tests/Globals.h" #include "tests/IAccessor.h" @@ -38,7 +36,6 @@ #include "tests/validation/reference/ActivationLayer.h" #include "tests/validation/reference/ElementwiseOperations.h" #include "tests/validation/reference/GEMM.h" -#include "tests/validation/reference/PostOps.h" #include <random> @@ -304,8 +301,7 @@ protected: ITensorPack gemm_pack({ { ACL_SRC_0, &lhs }, { ACL_SRC_1, &rhs }, { ACL_SRC_2, &bias }, - { ACL_DST, &dst } - }); + { ACL_DST, &dst } }); gemm.run(gemm_pack); return dst; @@ -426,8 +422,7 @@ protected: ITensorPack gemm_pack({ { ACL_SRC_0, &lhs }, { ACL_SRC_1, &rhs }, { ACL_SRC_2, &bias }, - { ACL_DST, &dst } - }); + { ACL_DST, &dst } }); gemm.run(gemm_pack); return dst; @@ -580,8 +575,7 @@ protected: ITensorPack gemm_pack({ { ACL_SRC_0, &lhs_reshaped }, { ACL_SRC_1, &rhs_reshaped }, { ACL_SRC_2, &bias }, - { ACL_DST, &dst } - }); + { ACL_DST, &dst } }); gemm.run(gemm_pack); return dst; @@ -734,8 +728,7 @@ protected: ITensorPack gemm_pack({ { ACL_SRC_0, &lhs_reshaped }, { ACL_SRC_1, &rhs_reshaped }, { ACL_SRC_2, &bias }, - { ACL_DST, &dst } - }); + { ACL_DST, &dst } }); gemm.run(gemm_pack); return dst; @@ -908,8 +901,7 @@ protected: ITensorPack gemm_pack({ { ACL_SRC_0, &lhs_reshaped }, { ACL_SRC_1, &rhs_reshaped }, { ACL_SRC_2, &bias }, - { ACL_DST, &dst } - }); + { ACL_DST, &dst } }); gemm.run(gemm_pack); return dst; @@ -960,262 +952,6 @@ protected: SimpleTensor<T> _reference{}; }; -/** (EXPERIMENTAL_POST_OPS)*/ -template <typename TensorType, typename AccessorType, typename T, typename ReshapeLHSOperatorType, typename ReshapeRHSOperatorType, typename GEMMOperatorType, bool fp_mixed_precision = false> -class GEMMMatrixMultiplyReshapedWithPostOpsValidationFixture : public framework::Fixture -{ -public: - using PostOpArgBroadcast = std::tuple<bool, bool, bool>; // Instruct fixture if we need broadcasting in dimension 0, 1, 2 of each PostOp argument -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 v0, unsigned int h0, bool interleave_lhs, - bool interleave_rhs, bool export_to_cl_image, DataType data_type, float alpha, float beta, bool broadcast_bias, bool lhs_transpose, const ActivationLayerInfo &act_info, - const experimental::PostOpList<PostOpArgBroadcast> &post_ops) - { - GEMMLHSMatrixInfo lhs_info; - lhs_info.m0 = m0; - lhs_info.k0 = k0; - lhs_info.v0 = v0; - lhs_info.interleave = interleave_lhs; - lhs_info.transpose = lhs_transpose; - - GEMMRHSMatrixInfo rhs_info; - rhs_info.n0 = n0; - rhs_info.k0 = k0; - rhs_info.h0 = h0; - rhs_info.interleave = interleave_rhs; - rhs_info.transpose = !lhs_transpose; - rhs_info.export_to_cl_image = export_to_cl_image; - - // 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); - auto post_ops_with_shapes = experimental::transform_post_op_list_arguments<PostOpArgBroadcast, TensorShape>(post_ops, - [ = ](auto broadcast) - { - return TensorShape - { - std::get<0>(broadcast) ? 1 : n, - std::get<1>(broadcast) ? 1 : m, - std::get<2>(broadcast) ? 1 : batch_size, - }; - }); - - _target = compute_target(lhs_shape, rhs_shape, bias_shape, lhs_info, rhs_info, data_type, alpha, beta, broadcast_bias, act_info, post_ops_with_shapes); - if(validate_result) - { - _reference = compute_reference(lhs_shape, rhs_shape, data_type, alpha, beta, broadcast_bias, act_info, post_ops_with_shapes); - } - } - -protected: - template <typename U> - void fill(U &&tensor, int i) - { - static_assert(std::is_floating_point<T>::value || std::is_same<T, half>::value, "Only floating point data types supported."); - using DistributionType = typename std::conditional<std::is_same<T, half>::value, arm_compute::utils::uniform_real_distribution_16bit<T>, std::uniform_real_distribution<T>>::type; - - DistributionType distribution{ T(-1.0f), T(1.0f) }; - library->fill(tensor, distribution, i); - - // Fill border with infinity in order to check the presence of NaN values (i.e. inf * 0) - DistributionType distribution_inf{ T(std::numeric_limits<float>::infinity()), T(std::numeric_limits<float>::infinity()) }; - library->fill_borders_with_garbage(tensor, distribution_inf, i); - } - - 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, float alpha, float beta, bool broadcast_bias, const ActivationLayerInfo &act_info, const experimental::PostOpList<TensorShape> &post_ops) - { - // Create tensors - TensorType lhs = create_tensor<TensorType>(lhs_shape, data_type, 1); - TensorType rhs = create_tensor<TensorType>(rhs_shape, data_type, 1); - TensorType bias = create_tensor<TensorType>(bias_shape, data_type, 1); - - // Create post op tensors and populate post op with them - std::vector<TensorType> post_op_tensors_holder{}; - auto populated_post_ops = experimental::transform_post_op_list_arguments<TensorShape, ITensorInfo *>(post_ops, - [&post_op_tensors_holder, &data_type](auto shape) - { - auto t = create_tensor<TensorType>(shape, data_type, 1); - post_op_tensors_holder.push_back(std::move(t)); - return post_op_tensors_holder.back().info(); - }); - TensorType lhs_reshaped; - TensorType rhs_reshaped; - TensorType dst; - - const unsigned int M = lhs_shape[1]; - const unsigned int N = rhs_shape[0]; - const unsigned int K = lhs_shape[0]; - GEMMKernelInfo kernel_info; - kernel_info.m = M; - kernel_info.n = N; - kernel_info.k = K; - kernel_info.depth_output_gemm3d = 0; - kernel_info.reinterpret_input_as_3d = false; - kernel_info.broadcast_bias = broadcast_bias; - kernel_info.activation_info = act_info; - kernel_info.fp_mixed_precision = fp_mixed_precision; - kernel_info.post_ops = populated_post_ops; - - // The output tensor will be auto-initialized within the function - - // Create and configure function - ReshapeLHSOperatorType reshape_lhs; - ReshapeRHSOperatorType reshape_rhs; - GEMMOperatorType gemm; - - validate_result = bool(reshape_rhs.validate(rhs.info(), rhs_reshaped.info(), rhs_info)); - validate_result = validate_result || !rhs_info.export_to_cl_image; - if(!validate_result) - { - return nullptr; - } - - 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(), bias.info(), dst.info(), alpha, beta, lhs_info, rhs_info, kernel_info); - - ARM_COMPUTE_ASSERT(lhs.info()->is_resizable()); - ARM_COMPUTE_ASSERT(rhs.info()->is_resizable()); - ARM_COMPUTE_ASSERT(bias.info()->is_resizable()); - for(const auto &tensor : post_op_tensors_holder) - { - ARM_COMPUTE_ASSERT(tensor.info()->is_resizable()); - } - - // We do not pad when using image as it needs to comply to strict pitch alignment restrictions - if(!rhs_info.export_to_cl_image) - { - add_padding_x({ &lhs, &rhs, &lhs_reshaped, &rhs_reshaped, &bias, &dst }); - for(auto &tensor : post_op_tensors_holder) - { - add_padding_x({ &tensor }); - } - } - - // Allocate tensors - lhs.allocator()->allocate(); - rhs.allocator()->allocate(); - lhs_reshaped.allocator()->allocate(); - rhs_reshaped.allocator()->allocate(); - bias.allocator()->allocate(); - dst.allocator()->allocate(); - for(auto &tensor : post_op_tensors_holder) - { - tensor.allocator()->allocate(); - } - - ARM_COMPUTE_ASSERT(!lhs.info()->is_resizable()); - ARM_COMPUTE_ASSERT(!rhs.info()->is_resizable()); - ARM_COMPUTE_ASSERT(!bias.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()); - for(const auto &tensor : post_op_tensors_holder) - { - ARM_COMPUTE_ASSERT(!tensor.info()->is_resizable()); - } - - // Fill tensors - fill(AccessorType(lhs), 0); - fill(AccessorType(rhs), 1); - fill(AccessorType(bias), 2); - for(size_t i = 0; i < post_op_tensors_holder.size(); ++i) - { - fill(AccessorType(post_op_tensors_holder.at(i)), 3 + i); - } - - // Compute GEMM - 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_SRC_2, &bias }, - { ACL_DST, &dst } - }); - for(size_t i = 0; i < post_op_tensors_holder.size(); ++i) - { - gemm_pack.add_tensor(experimental::get_post_op_arg_type(i), &post_op_tensors_holder.at(i)); - } - gemm.run(gemm_pack); - - return dst; - } - - SimpleTensor<T> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, DataType data_type, float alpha, float beta, bool broadcast_bias, - const ActivationLayerInfo &act_info, const experimental::PostOpList<TensorShape> &post_ops) - { - 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 }; - SimpleTensor<T> rhs{ rhs_shape, data_type, 1 }; - SimpleTensor<T> bias{ dst_shape, data_type, 1 }; - // Create post op tensors and populate post op with them - auto populated_post_ops = experimental::transform_post_op_list_arguments<TensorShape, SimpleTensor<T>>(post_ops, [&data_type](auto shape) - { - return SimpleTensor<T> { shape, data_type, 1 }; - }); - - const int n = rhs_shape[0]; - const int m = lhs_shape[1]; - const int batch_size = lhs_shape[2]; - - // Fill reference - int tensor_idx = 0; - fill(lhs, tensor_idx++); - fill(rhs, tensor_idx++); - fill(bias, tensor_idx++); - for(auto &op : populated_post_ops.get_list()) - { - for(auto tensor : op->arguments()) - { - fill(*tensor, tensor_idx++); - } - } - - if(broadcast_bias) - { - // In case of broadcast, we need to simply copy the first into the following "M" ones - for(int i = 1; i < m * batch_size; i++) - { - memcpy(bias.data() + i * n, bias.data(), n * sizeof(T)); - } - } - - SimpleTensor<T> out; - if(fp_mixed_precision) - { - out = reference::gemm_mixed_precision<T>(lhs, rhs, bias, alpha, beta); - } - else - { - out = reference::gemm<T>(lhs, rhs, bias, alpha, beta); - } - // Ignore activation info if post ops are used instead - if(populated_post_ops.size() > 0) - { - out = reference::post_ops<T>(out, populated_post_ops); - } - else - { - out = reference::activation_layer(out, act_info); - } - return out; - } - - bool validate_result = true; - TensorType _target{}; - SimpleTensor<T> _reference{}; -}; - template <typename TensorType, typename AccessorType, typename T, typename ReshapeLHSOperatorType, typename ReshapeRHSOperatorType, typename GEMMOperatorType, bool fp_mixed_precision = false> class GEMMMatrixMultiplyReshaped3DValidationFixture : public framework::Fixture { @@ -1344,8 +1080,7 @@ protected: ITensorPack gemm_pack({ { ACL_SRC_0, &lhs_reshaped }, { ACL_SRC_1, &rhs_reshaped }, { ACL_SRC_2, &bias }, - { ACL_DST, &dst } - }); + { ACL_DST, &dst } }); gemm.run(gemm_pack); return dst; @@ -1515,8 +1250,7 @@ protected: ITensorPack gemm_pack({ { ACL_SRC_0, &lhs }, { ACL_SRC_1, &rhs_reshaped }, { ACL_SRC_2, &bias }, - { ACL_DST, &dst } - }); + { ACL_DST, &dst } }); gemm.run(gemm_pack); return dst; @@ -1560,242 +1294,6 @@ protected: SimpleTensor<T> _reference{}; }; -/** (EXPERIMENTAL_POST_OPS)*/ -template <typename TensorType, typename AccessorType, typename T, typename ReshapeRHSOperatorType, typename GEMMOperatorType> -class GEMMMatrixMultiplyReshapedOnlyRHSWithPostOpsValidationFixture : public framework::Fixture -{ -public: - using PostOpArgBroadcast = std::tuple<bool, bool, bool>; // Instruct fixture if we need broadcasting in dimension 0, 1, 2 of each PostOp argument - 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 export_to_cl_image, DataType data_type, float alpha, float beta, bool broadcast_bias, const ActivationLayerInfo &act_info, - const experimental::PostOpList<PostOpArgBroadcast> &post_ops) - { - 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; - rhs_info.export_to_cl_image = export_to_cl_image; - - // 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); - auto post_ops_with_shapes = experimental::transform_post_op_list_arguments<PostOpArgBroadcast, TensorShape>(post_ops, - [ = ](auto broadcast) - { - return TensorShape - { - std::get<0>(broadcast) ? 1 : n, - std::get<1>(broadcast) ? 1 : m, - std::get<2>(broadcast) ? 1 : batch_size, - }; - }); - - _target = compute_target(lhs_shape, rhs_shape, bias_shape, lhs_info, rhs_info, data_type, alpha, beta, broadcast_bias, act_info, post_ops_with_shapes); - if(validate_result) - { - _reference = compute_reference(lhs_shape, rhs_shape, data_type, alpha, beta, broadcast_bias, act_info, post_ops_with_shapes); - } - } - -protected: - template <typename U> - void fill(U &&tensor, int i) - { - static_assert(std::is_floating_point<T>::value || std::is_same<T, half>::value, "Only floating point data types supported."); - using DistributionType = typename std::conditional<std::is_same<T, half>::value, arm_compute::utils::uniform_real_distribution_16bit<T>, std::uniform_real_distribution<T>>::type; - - DistributionType distribution{ T(-1.0f), T(1.0f) }; - library->fill(tensor, distribution, i); - - // Fill border with infinity in order to check the presence of NaN values (i.e. inf * 0) - DistributionType distribution_inf{ T(std::numeric_limits<float>::infinity()), T(std::numeric_limits<float>::infinity()) }; - library->fill_borders_with_garbage(tensor, distribution_inf, i); - } - - 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, float alpha, float beta, bool broadcast_bias, const ActivationLayerInfo &act_info, const experimental::PostOpList<TensorShape> &post_ops) - { - // Create tensors - TensorType lhs = create_tensor<TensorType>(lhs_shape, data_type, 1); - TensorType rhs = create_tensor<TensorType>(rhs_shape, data_type, 1); - TensorType bias = create_tensor<TensorType>(bias_shape, data_type, 1); - TensorType rhs_reshaped; - TensorType dst; - // Create post op tensors and populate post op with them - std::vector<TensorType> post_op_tensors_holder{}; - auto populated_post_ops = experimental::transform_post_op_list_arguments<TensorShape, ITensorInfo *>(post_ops, - [&post_op_tensors_holder, &data_type](auto shape) - { - auto t = create_tensor<TensorType>(shape, data_type, 1); - post_op_tensors_holder.push_back(std::move(t)); - return post_op_tensors_holder.back().info(); - }); - - const unsigned int M = lhs_shape[1]; - const unsigned int N = rhs_shape[0]; - const unsigned int K = lhs_shape[0]; - GEMMKernelInfo kernel_info; - kernel_info.m = M; - kernel_info.n = N; - kernel_info.k = K; - kernel_info.depth_output_gemm3d = 0; - kernel_info.reinterpret_input_as_3d = false; - kernel_info.broadcast_bias = broadcast_bias; - kernel_info.activation_info = act_info; - kernel_info.post_ops = populated_post_ops; - - // The output tensor will be auto-initialized within the function - - // Create and configure function - ReshapeRHSOperatorType reshape_rhs; - GEMMOperatorType gemm; - - validate_result = bool(reshape_rhs.validate(rhs.info(), rhs_reshaped.info(), rhs_info)); - validate_result = validate_result || !rhs_info.export_to_cl_image; - if(!validate_result) - { - return nullptr; - } - - reshape_rhs.configure(rhs.info(), rhs_reshaped.info(), rhs_info); - gemm.configure(lhs.info(), rhs_reshaped.info(), bias.info(), dst.info(), alpha, beta, lhs_info, rhs_info, kernel_info); - - ARM_COMPUTE_ASSERT(lhs.info()->is_resizable()); - ARM_COMPUTE_ASSERT(rhs.info()->is_resizable()); - ARM_COMPUTE_ASSERT(bias.info()->is_resizable()); - for(const auto &tensor : post_op_tensors_holder) - { - ARM_COMPUTE_ASSERT(tensor.info()->is_resizable()); - } - - // We do not pad when using image as it needs to comply to strict pitch alignment restrictions - if(!rhs_info.export_to_cl_image) - { - add_padding_x({ &lhs, &rhs, &rhs_reshaped, &bias, &dst }); - for(auto &tensor : post_op_tensors_holder) - { - add_padding_x({ &tensor }); - } - } - - // Allocate tensors - lhs.allocator()->allocate(); - rhs.allocator()->allocate(); - rhs_reshaped.allocator()->allocate(); - bias.allocator()->allocate(); - dst.allocator()->allocate(); - for(auto &tensor : post_op_tensors_holder) - { - tensor.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(!dst.info()->is_resizable()); - for(const auto &tensor : post_op_tensors_holder) - { - ARM_COMPUTE_ASSERT(!tensor.info()->is_resizable()); - } - - // Fill tensors - fill(AccessorType(lhs), 0); - fill(AccessorType(rhs), 1); - fill(AccessorType(bias), 2); - for(size_t i = 0; i < post_op_tensors_holder.size(); ++i) - { - fill(AccessorType(post_op_tensors_holder.at(i)), 3 + i); - } - - // 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 } - }); - for(size_t i = 0; i < post_op_tensors_holder.size(); ++i) - { - gemm_pack.add_tensor(experimental::get_post_op_arg_type(i), &post_op_tensors_holder.at(i)); - } - gemm.run(gemm_pack); - - return dst; - } - - SimpleTensor<T> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, DataType data_type, float alpha, float beta, bool broadcast_bias, - const ActivationLayerInfo &act_info, const experimental::PostOpList<TensorShape> &post_ops) - { - 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 }; - SimpleTensor<T> rhs{ rhs_shape, data_type, 1 }; - SimpleTensor<T> bias{ dst_shape, data_type, 1 }; - // Create post op tensors and populate post op with them - auto populated_post_ops = experimental::transform_post_op_list_arguments<TensorShape, SimpleTensor<T>>(post_ops, [&data_type](auto shape) - { - return SimpleTensor<T> { shape, data_type, 1 }; - }); - - const int n = rhs_shape[0]; - const int m = lhs_shape[1]; - const int batch_size = lhs_shape[2]; - - // Fill reference - int tensor_idx = 0; - fill(lhs, tensor_idx++); - fill(rhs, tensor_idx++); - fill(bias, tensor_idx++); - for(auto &op : populated_post_ops.get_list()) - { - for(auto tensor : op->arguments()) - { - fill(*tensor, tensor_idx++); - } - } - - if(broadcast_bias) - { - // In case of broadcast, we need to simply copy the first into the following "M" ones - for(int i = 1; i < m * batch_size; i++) - { - memcpy(bias.data() + i * n, bias.data(), n * sizeof(T)); - } - } - - SimpleTensor<T> out; - out = reference::gemm<T>(lhs, rhs, bias, alpha, beta); - // Ignore activation info if post ops are used instead - if(populated_post_ops.size() > 0) - { - out = reference::post_ops<T>(out, populated_post_ops); - } - else - { - out = reference::activation_layer(out, act_info); - } - return out; - } - - bool validate_result = true; - TensorType _target{}; - SimpleTensor<T> _reference{}; -}; - template <typename TensorType, typename AccessorType, typename T, typename ReshapeRHSOperatorType, typename GEMMOperatorType> class GEMMMatrixMultiplyReshapedOnlyRHS3DValidationFixture : public framework::Fixture { @@ -1921,8 +1419,7 @@ protected: ITensorPack gemm_pack({ { ACL_SRC_0, &lhs }, { ACL_SRC_1, &rhs_reshaped }, { ACL_SRC_2, &bias }, - { ACL_DST, &dst } - }); + { ACL_DST, &dst } }); gemm.run(gemm_pack); return dst; @@ -2057,8 +1554,7 @@ protected: ITensorPack gemm_pack({ { ACL_SRC_0, &lhs }, { ACL_SRC_1, &rhs }, { ACL_SRC_2, &bias }, - { ACL_DST, &dst } - }); + { ACL_DST, &dst } }); gemm.run(gemm_pack); return dst; @@ -2102,212 +1598,6 @@ protected: }; template <typename TensorType, typename AccessorType, typename T, typename GEMMOperatorType> -class GEMMMatrixMultiplyNativeWithPostOpsValidationFixture : public framework::Fixture -{ -public: - using PostOpArgBroadcast = std::tuple<bool, bool, bool>; // Instruct fixture if we need broadcasting in dimension 0, 1, 2 of each PostOp argument -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, DataType data_type, float alpha, float beta, bool broadcast_bias, - const ActivationLayerInfo &act_info, const experimental::PostOpList<PostOpArgBroadcast> &post_ops) - { - GEMMLHSMatrixInfo lhs_info; - lhs_info.m0 = m0; - lhs_info.k0 = k0; - - GEMMRHSMatrixInfo rhs_info; - rhs_info.n0 = n0; - rhs_info.k0 = k0; - - // Set the tensor shapes for LHS and RHS matrices - const TensorShape lhs_shape(k, m, batch_size); - const TensorShape rhs_shape(n, k, batch_size); - const TensorShape bias_shape(n, - broadcast_bias ? 1 : m, - broadcast_bias ? 1 : batch_size); - const auto post_ops_with_shapes = experimental::transform_post_op_list_arguments<PostOpArgBroadcast, TensorShape>(post_ops, - [ = ](auto broadcast) - { - return TensorShape - { - std::get<0>(broadcast) ? 1 : n, - std::get<1>(broadcast) ? 1 : m, - std::get<2>(broadcast) ? 1 : batch_size, - }; - }); - - _target = compute_target(lhs_shape, rhs_shape, bias_shape, lhs_info, rhs_info, data_type, alpha, beta, broadcast_bias, act_info, post_ops_with_shapes); - _reference = compute_reference(lhs_shape, rhs_shape, data_type, alpha, beta, broadcast_bias, act_info, post_ops_with_shapes); - } - -protected: - template <typename U> - void fill(U &&tensor, int i) - { - static_assert(std::is_floating_point<T>::value || std::is_same<T, half>::value, "Only floating point data types supported."); - using DistributionType = typename std::conditional<std::is_same<T, half>::value, arm_compute::utils::uniform_real_distribution_16bit<T>, std::uniform_real_distribution<T>>::type; - - DistributionType distribution{ T(-1.0f), T(1.0f) }; - library->fill(tensor, distribution, i); - - // Fill border with infinity in order to check the presence of NaN values (i.e. inf * 0) - DistributionType distribution_inf{ T(std::numeric_limits<float>::infinity()), T(std::numeric_limits<float>::infinity()) }; - library->fill_borders_with_garbage(tensor, distribution_inf, i); - } - - 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, float alpha, float beta, bool broadcast_bias, const ActivationLayerInfo &act_info, const experimental::PostOpList<TensorShape> &post_ops) - { - // Create tensors - TensorType lhs = create_tensor<TensorType>(lhs_shape, data_type, 1); - TensorType rhs = create_tensor<TensorType>(rhs_shape, data_type, 1); - TensorType bias = create_tensor<TensorType>(bias_shape, data_type, 1); - TensorType dst; - // Create post op tensors and populate post op with them - std::vector<TensorType> post_op_tensors_holder{}; - auto populated_post_ops = experimental::transform_post_op_list_arguments<TensorShape, ITensorInfo *>(post_ops, - [&post_op_tensors_holder, &data_type](auto shape) - { - auto t = create_tensor<TensorType>(shape, data_type, 1); - post_op_tensors_holder.push_back(std::move(t)); - return post_op_tensors_holder.back().info(); - }); - - const unsigned int M = lhs_shape[1]; - const unsigned int N = rhs_shape[0]; - const unsigned int K = lhs_shape[0]; - GEMMKernelInfo kernel_info; - kernel_info.m = M; - kernel_info.n = N; - kernel_info.k = K; - kernel_info.depth_output_gemm3d = 0; - kernel_info.reinterpret_input_as_3d = false; - kernel_info.broadcast_bias = broadcast_bias; - kernel_info.activation_info = act_info; - kernel_info.post_ops = populated_post_ops; - - // Create and configure function - GEMMOperatorType gemm; - gemm.configure(lhs.info(), rhs.info(), bias.info(), dst.info(), alpha, beta, lhs_info, rhs_info, kernel_info); - - ARM_COMPUTE_ASSERT(lhs.info()->is_resizable()); - ARM_COMPUTE_ASSERT(rhs.info()->is_resizable()); - ARM_COMPUTE_ASSERT(bias.info()->is_resizable()); - for(const auto &tensor : post_op_tensors_holder) - { - ARM_COMPUTE_ASSERT(tensor.info()->is_resizable()); - } - - add_padding_x({ &lhs, &rhs, &bias, &dst }); - for(auto &tensor : post_op_tensors_holder) - { - add_padding_x({ &tensor }); - } - - // Allocate tensors - lhs.allocator()->allocate(); - rhs.allocator()->allocate(); - bias.allocator()->allocate(); - dst.allocator()->allocate(); - for(auto &tensor : post_op_tensors_holder) - { - tensor.allocator()->allocate(); - } - - ARM_COMPUTE_ASSERT(!lhs.info()->is_resizable()); - ARM_COMPUTE_ASSERT(!rhs.info()->is_resizable()); - ARM_COMPUTE_ASSERT(!bias.info()->is_resizable()); - ARM_COMPUTE_ASSERT(!dst.info()->is_resizable()); - for(const auto &tensor : post_op_tensors_holder) - { - ARM_COMPUTE_ASSERT(!tensor.info()->is_resizable()); - } - - // Fill tensors - fill(AccessorType(lhs), 0); - fill(AccessorType(rhs), 1); - fill(AccessorType(bias), 2); - for(size_t i = 0; i < post_op_tensors_holder.size(); ++i) - { - fill(AccessorType(post_op_tensors_holder.at(i)), 3 + i); - } - - // Compute GEMM - ITensorPack gemm_pack({ { ACL_SRC_0, &lhs }, - { ACL_SRC_1, &rhs }, - { ACL_SRC_2, &bias }, - { ACL_DST, &dst } - }); - for(size_t i = 0; i < post_op_tensors_holder.size(); ++i) - { - gemm_pack.add_tensor(experimental::get_post_op_arg_type(i), &post_op_tensors_holder.at(i)); - } - gemm.run(gemm_pack); - - return dst; - } - - SimpleTensor<T> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, DataType data_type, float alpha, float beta, bool broadcast_bias, - const ActivationLayerInfo &act_info, const experimental::PostOpList<TensorShape> &post_ops) - { - 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 }; - SimpleTensor<T> rhs{ rhs_shape, data_type, 1 }; - SimpleTensor<T> bias{ dst_shape, data_type, 1 }; - // Create post op tensors and populate post op with them - auto populated_post_ops = experimental::transform_post_op_list_arguments<TensorShape, SimpleTensor<T>>(post_ops, [&data_type](auto shape) - { - return SimpleTensor<T> { shape, data_type, 1 }; - }); - - const int n = rhs_shape[0]; - const int m = lhs_shape[1]; - const int batch_size = lhs_shape[2]; - - // Fill reference - int tensor_idx = 0; - fill(lhs, tensor_idx++); - fill(rhs, tensor_idx++); - fill(bias, tensor_idx++); - for(auto &op : populated_post_ops.get_list()) - { - for(auto tensor : op->arguments()) - { - fill(*tensor, tensor_idx++); - } - } - - if(broadcast_bias) - { - // In case of broadcast, we need to simply copy the first into the following "M" ones - for(int i = 1; i < m * batch_size; i++) - { - memcpy(bias.data() + i * n, bias.data(), n * sizeof(T)); - } - } - - SimpleTensor<T> out; - out = reference::gemm<T>(lhs, rhs, bias, alpha, beta); - // Ignore activation info if post ops are used instead - if(populated_post_ops.size() > 0) - { - out = reference::post_ops<T>(out, populated_post_ops); - } - else - { - out = reference::activation_layer(out, act_info); - } - return out; - } - - TensorType _target{}; - SimpleTensor<T> _reference{}; -}; - -template <typename TensorType, typename AccessorType, typename T, typename GEMMOperatorType> class GEMMMatrixMultiplyNative3DValidationFixture : public framework::Fixture { public: @@ -2398,8 +1688,7 @@ protected: ITensorPack gemm_pack({ { ACL_SRC_0, &lhs }, { ACL_SRC_1, &rhs }, { ACL_SRC_2, &bias }, - { ACL_DST, &dst } - }); + { ACL_DST, &dst } }); gemm.run(gemm_pack); return dst; @@ -2557,8 +1846,7 @@ protected: ITensorPack gemm_pack({ { ACL_SRC_0, &lhs }, { ACL_SRC_1, &rhs_reshaped }, { ACL_SRC_2, &bias }, - { ACL_DST, &dst } - }); + { ACL_DST, &dst } }); gemm.run(gemm_pack); return dst; @@ -2608,4 +1896,4 @@ protected: } // namespace validation } // namespace test } // namespace arm_compute -#endif /* ARM_COMPUTE_TEST_GEMM_FIXTURE */ +#endif // ACL_TESTS_VALIDATION_FIXTURES_GEMMFIXTURE_H |