diff options
Diffstat (limited to 'src/gpu/cl/operators/ClGemm.cpp')
| -rw-r--r-- | src/gpu/cl/operators/ClGemm.cpp | 472 |
1 files changed, 299 insertions, 173 deletions
diff --git a/src/gpu/cl/operators/ClGemm.cpp b/src/gpu/cl/operators/ClGemm.cpp index 7e331a86f3..815c254c69 100644 --- a/src/gpu/cl/operators/ClGemm.cpp +++ b/src/gpu/cl/operators/ClGemm.cpp @@ -33,11 +33,12 @@ #include "arm_compute/core/TensorInfo.h" #include "arm_compute/core/Types.h" #include "arm_compute/core/Utils.h" -#include "arm_compute/core/Validate.h" #include "arm_compute/core/utils/misc/ShapeCalculator.h" +#include "arm_compute/core/Validate.h" #include "arm_compute/runtime/CL/CLScheduler.h" #include "arm_compute/runtime/ITensorAllocator.h" +#include "src/common/utils/Log.h" #include "src/core/helpers/AutoConfiguration.h" #include "src/core/helpers/MemoryHelpers.h" #include "src/core/utils/helpers/float_ops.h" @@ -45,8 +46,6 @@ #include "src/gpu/cl/utils/ClAuxTensorHandler.h" #include "src/runtime/CL/gemm/CLGEMMKernelSelection.h" #include "src/runtime/CL/gemm_auto_heuristics/CLGEMMAutoHeuristics.h" - -#include "src/common/utils/Log.h" #include "support/Cast.h" #include "utils/TypePrinter.h" @@ -67,35 +66,43 @@ inline bool validate_gemm_kernel(CLGEMMKernelType kernel_type) return kernel_type == CLGEMMKernelType::NATIVE ? false : true; } //Automatically select between mlgo (prioritized) and default heuristics for gemm kernel type -inline CLGEMMKernelType auto_select_gemm_kernel(auto_heuristics::CommonQuery query, bool reshape_b_only_on_first_run, bool constant_weights) +inline CLGEMMKernelType +auto_select_gemm_kernel(auto_heuristics::CommonQuery query, bool reshape_b_only_on_first_run, bool constant_weights) { - if(!constant_weights) + if (!constant_weights) { return CLGEMMKernelType::NATIVE; } auto gemm_kernel = auto_heuristics::select_mlgo_gemm_kernel(query, reshape_b_only_on_first_run); - if(bool(gemm_kernel)) + if (bool(gemm_kernel)) { - if(validate_gemm_kernel(gemm_kernel.gemm_type)) + if (validate_gemm_kernel(gemm_kernel.gemm_type)) { - ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use gemm kernel from mlgo heuristics: %s.", to_string(gemm_kernel.gemm_type).c_str()); + ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use gemm kernel from mlgo heuristics: %s.", + to_string(gemm_kernel.gemm_type).c_str()); return gemm_kernel.gemm_type; } } gemm_kernel = auto_heuristics::select_default_gemm_kernel(query, reshape_b_only_on_first_run); - ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use gemm kernel from default heuristics: %s.", to_string(gemm_kernel.gemm_type).c_str()); + ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use gemm kernel from default heuristics: %s.", + to_string(gemm_kernel.gemm_type).c_str()); return gemm_kernel.gemm_type; } // Validate lhs_info and rhs_info for reshaped only rhs kernel -inline bool validate_lhs_rhs_info_reshaped_only_rhs(const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, - const ITensorInfo *output, GEMMKernelInfo gemm_kernel_info) +inline bool validate_lhs_rhs_info_reshaped_only_rhs(const GEMMLHSMatrixInfo &lhs_info, + const GEMMRHSMatrixInfo &rhs_info, + const ITensorInfo *a, + const ITensorInfo *b, + const ITensorInfo *c, + const ITensorInfo *output, + GEMMKernelInfo gemm_kernel_info) { // Validate GEMMLHSMatrixInfo and GEMMRHSMatrixInfo for reshaped only rhs kernel TensorInfo tmp_b_info{}; // Validate reshape RHS kernel auto_init_if_empty(tmp_b_info, b->clone()->set_tensor_shape(compute_rhs_reshaped_shape(*b, rhs_info))); - if(!bool(ClGemmReshapeRhsMatrixKernel::validate(b, &tmp_b_info, rhs_info))) + if (!bool(ClGemmReshapeRhsMatrixKernel::validate(b, &tmp_b_info, rhs_info))) { return false; } @@ -103,12 +110,14 @@ inline bool validate_lhs_rhs_info_reshaped_only_rhs(const GEMMLHSMatrixInfo &lhs gemm_kernel_info.lhs_info = lhs_info; gemm_kernel_info.rhs_info = rhs_info; gemm_kernel_info.has_pad_y = false; - if(!bool(ClGemmMatrixMultiplyReshapedOnlyRhsKernel::validate(a, &tmp_b_info, c, output, 1.f, 0.f, lhs_info, rhs_info, gemm_kernel_info))) + if (!bool(ClGemmMatrixMultiplyReshapedOnlyRhsKernel::validate(a, &tmp_b_info, c, output, 1.f, 0.f, lhs_info, + rhs_info, gemm_kernel_info))) { return false; } gemm_kernel_info.has_pad_y = true; - if(!bool(ClGemmMatrixMultiplyReshapedOnlyRhsKernel::validate(a, &tmp_b_info, c, output, 1.f, 0.f, lhs_info, rhs_info, gemm_kernel_info))) + if (!bool(ClGemmMatrixMultiplyReshapedOnlyRhsKernel::validate(a, &tmp_b_info, c, output, 1.f, 0.f, lhs_info, + rhs_info, gemm_kernel_info))) { return false; } @@ -116,49 +125,65 @@ inline bool validate_lhs_rhs_info_reshaped_only_rhs(const GEMMLHSMatrixInfo &lhs } //Automatically select between mlgo (prioritized) and default heuristics for reshaped only rhs kernel configs -inline std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> auto_select_gemm_config_reshaped_only_rhs(auto_heuristics::CommonQuery query, GEMMKernelInfo kernel_info, const ITensorInfo *a, - const ITensorInfo *b, - const ITensorInfo *c, const ITensorInfo *output) +inline std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> +auto_select_gemm_config_reshaped_only_rhs(auto_heuristics::CommonQuery query, + GEMMKernelInfo kernel_info, + const ITensorInfo *a, + const ITensorInfo *b, + const ITensorInfo *c, + const ITensorInfo *output) { auto config = auto_heuristics::select_mlgo_gemm_config_reshaped_only_rhs(query); - if(config) + if (config) { - if(validate_lhs_rhs_info_reshaped_only_rhs(config.lhs_info, config.rhs_info, a, b, c, output, kernel_info)) + if (validate_lhs_rhs_info_reshaped_only_rhs(config.lhs_info, config.rhs_info, a, b, c, output, kernel_info)) { - ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use reshaped_only_rhs config from mlgo heuristics: LHS info: %s ; RHS info: %s ", to_string(config.lhs_info).c_str(), to_string(config.rhs_info).c_str()); - return { config.lhs_info, config.rhs_info }; + ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE( + "Use reshaped_only_rhs config from mlgo heuristics: LHS info: %s ; RHS info: %s ", + to_string(config.lhs_info).c_str(), to_string(config.rhs_info).c_str()); + return {config.lhs_info, config.rhs_info}; } } config = auto_heuristics::select_default_gemm_config_reshaped_only_rhs(query); - ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use reshaped_only_rhs config from default heuristics: LHS info: %s ; RHS info: %s ", to_string(config.lhs_info).c_str(), to_string(config.rhs_info).c_str()); - return { config.lhs_info, config.rhs_info }; + ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE( + "Use reshaped_only_rhs config from default heuristics: LHS info: %s ; RHS info: %s ", + to_string(config.lhs_info).c_str(), to_string(config.rhs_info).c_str()); + return {config.lhs_info, config.rhs_info}; } // Validate lhs_info and rhs_info for reshaped kernel -inline bool validate_lhs_rhs_info_reshaped(const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, - const ITensorInfo *output, GEMMKernelInfo gemm_kernel_info, bool reinterpret_input_as_3d) +inline bool validate_lhs_rhs_info_reshaped(const GEMMLHSMatrixInfo &lhs_info, + const GEMMRHSMatrixInfo &rhs_info, + const ITensorInfo *a, + const ITensorInfo *b, + const ITensorInfo *c, + const ITensorInfo *output, + GEMMKernelInfo gemm_kernel_info, + bool reinterpret_input_as_3d) { // Validate GEMMLHSMatrixInfo and GEMMRHSMatrixInfo for reshaped kernel TensorInfo tmp_a_info{}; TensorInfo tmp_b_info{}; // Validate reshape LHS kernel - auto_init_if_empty(tmp_a_info, a->clone()->set_tensor_shape(compute_lhs_reshaped_shape(*a, lhs_info, reinterpret_input_as_3d))); - if(!bool(ClGemmReshapeLhsMatrixKernel::validate(a, &tmp_a_info, lhs_info, reinterpret_input_as_3d))) + auto_init_if_empty(tmp_a_info, + a->clone()->set_tensor_shape(compute_lhs_reshaped_shape(*a, lhs_info, reinterpret_input_as_3d))); + if (!bool(ClGemmReshapeLhsMatrixKernel::validate(a, &tmp_a_info, lhs_info, reinterpret_input_as_3d))) { return false; } // Validate reshape RHS kernel auto_init_if_empty(tmp_b_info, b->clone()->set_tensor_shape(compute_rhs_reshaped_shape(*b, rhs_info))); - if(!bool(ClGemmReshapeRhsMatrixKernel::validate(b, &tmp_b_info, rhs_info))) + if (!bool(ClGemmReshapeRhsMatrixKernel::validate(b, &tmp_b_info, rhs_info))) { return false; } // Validate mm kernel gemm_kernel_info.lhs_info = lhs_info; gemm_kernel_info.rhs_info = rhs_info; - if(!bool(ClGemmMatrixMultiplyReshapedKernel::validate(&tmp_a_info, &tmp_b_info, c, output, 1.f, 0.f, lhs_info, rhs_info, gemm_kernel_info))) + if (!bool(ClGemmMatrixMultiplyReshapedKernel::validate(&tmp_a_info, &tmp_b_info, c, output, 1.f, 0.f, lhs_info, + rhs_info, gemm_kernel_info))) { return false; } @@ -166,21 +191,32 @@ inline bool validate_lhs_rhs_info_reshaped(const GEMMLHSMatrixInfo &lhs_info, co } //Automatically select between mlgo (prioritized) and default heuristics for reshaped kernel configs -inline std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> auto_select_gemm_config_reshaped(auto_heuristics::CommonQuery query, GEMMKernelInfo kernel_info, const ITensorInfo *a, const ITensorInfo *b, - const ITensorInfo *c, const ITensorInfo *output, bool reinterpret_input_as_3d) +inline std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> +auto_select_gemm_config_reshaped(auto_heuristics::CommonQuery query, + GEMMKernelInfo kernel_info, + const ITensorInfo *a, + const ITensorInfo *b, + const ITensorInfo *c, + const ITensorInfo *output, + bool reinterpret_input_as_3d) { auto config = auto_heuristics::select_mlgo_gemm_config_reshaped(query); - if(config) + if (config) { - if(validate_lhs_rhs_info_reshaped(config.lhs_info, config.rhs_info, a, b, c, output, kernel_info, reinterpret_input_as_3d)) + if (validate_lhs_rhs_info_reshaped(config.lhs_info, config.rhs_info, a, b, c, output, kernel_info, + reinterpret_input_as_3d)) { - ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use reshaped config from mlgo heuristics: LHS info: %s ; RHS info: %s ", to_string(config.lhs_info).c_str(), to_string(config.rhs_info).c_str()); - return { config.lhs_info, config.rhs_info }; + ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE( + "Use reshaped config from mlgo heuristics: LHS info: %s ; RHS info: %s ", + to_string(config.lhs_info).c_str(), to_string(config.rhs_info).c_str()); + return {config.lhs_info, config.rhs_info}; } } config = auto_heuristics::select_default_gemm_config_reshaped(query); - ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use reshaped config from default heuristics: LHS info: %s ; RHS info: %s ", to_string(config.lhs_info).c_str(), to_string(config.rhs_info).c_str()); - return { config.lhs_info, config.rhs_info }; + ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE( + "Use reshaped config from default heuristics: LHS info: %s ; RHS info: %s ", to_string(config.lhs_info).c_str(), + to_string(config.rhs_info).c_str()); + return {config.lhs_info, config.rhs_info}; } } // namespace @@ -200,18 +236,24 @@ ClGemm::ClGemm() { } -void ClGemm::configure_native(const CLCompileContext &compile_context, ITensorInfo *a, ITensorInfo *b, ITensorInfo *c, ITensorInfo *output, float alpha, float beta, - const GEMMInfo &gemm_info) +void ClGemm::configure_native(const CLCompileContext &compile_context, + ITensorInfo *a, + ITensorInfo *b, + ITensorInfo *c, + ITensorInfo *output, + float alpha, + float beta, + const GEMMInfo &gemm_info) { DataType data_type = a->data_type(); bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d(); - const unsigned int m = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1); - const unsigned int n = b->dimension(0); - const unsigned int k = a->dimension(0); - const unsigned int batch_size = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2); - const int depth_output_gemm3d = gemm_info.depth_output_gemm3d(); - const GPUTarget gpu_target = CLScheduler::get().target(); - bool broadcast_bias = gemm_info.broadcast_bias(); + const unsigned int m = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1); + const unsigned int n = b->dimension(0); + const unsigned int k = a->dimension(0); + const unsigned int batch_size = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2); + const int depth_output_gemm3d = gemm_info.depth_output_gemm3d(); + const GPUTarget gpu_target = CLScheduler::get().target(); + bool broadcast_bias = gemm_info.broadcast_bias(); GEMMKernelInfo kernel_info; kernel_info.m = m; @@ -225,24 +267,32 @@ void ClGemm::configure_native(const CLCompileContext &compile_context, ITensorIn // Set the target for the kernels _mm_native_kernel->set_target(gpu_target); - auto config = auto_heuristics::select_mlgo_gemm_config_reshaped_only_rhs(auto_heuristics::CommonQuery{ gpu_target, data_type, m, n, k, batch_size }); + auto config = auto_heuristics::select_mlgo_gemm_config_reshaped_only_rhs( + auto_heuristics::CommonQuery{gpu_target, data_type, m, n, k, batch_size}); // Configure and tune matrix multiply kernel - _mm_native_kernel->configure(compile_context, a, b, c, output, alpha, beta, config.lhs_info, config.rhs_info, kernel_info); + _mm_native_kernel->configure(compile_context, a, b, c, output, alpha, beta, config.lhs_info, config.rhs_info, + kernel_info); } -void ClGemm::configure_reshaped(const CLCompileContext &compile_context, ITensorInfo *a, ITensorInfo *b, ITensorInfo *c, ITensorInfo *output, float alpha, float beta, - const GEMMInfo &gemm_info) +void ClGemm::configure_reshaped(const CLCompileContext &compile_context, + ITensorInfo *a, + ITensorInfo *b, + ITensorInfo *c, + ITensorInfo *output, + float alpha, + float beta, + const GEMMInfo &gemm_info) { DataType data_type = a->data_type(); bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d(); - const unsigned int m = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1); - const unsigned int n = b->dimension(0); - const unsigned int k = a->dimension(0); - const unsigned int batch_size = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2); - const int depth_output_gemm3d = gemm_info.depth_output_gemm3d(); - const GPUTarget gpu_target = CLScheduler::get().target(); - bool broadcast_bias = gemm_info.broadcast_bias(); + const unsigned int m = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1); + const unsigned int n = b->dimension(0); + const unsigned int k = a->dimension(0); + const unsigned int batch_size = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2); + const int depth_output_gemm3d = gemm_info.depth_output_gemm3d(); + const GPUTarget gpu_target = CLScheduler::get().target(); + bool broadcast_bias = gemm_info.broadcast_bias(); GEMMKernelInfo kernel_info; kernel_info.m = m; @@ -261,32 +311,42 @@ void ClGemm::configure_reshaped(const CLCompileContext &compile_context, ITensor GEMMRHSMatrixInfo rhs_info{}; // Pick up the GEMM configuration - std::tie(lhs_info, rhs_info) = auto_select_gemm_config_reshaped(auto_heuristics::CommonQuery{ gpu_target, data_type, m, n, k, batch_size }, kernel_info, a, b, - c, output, gemm_info.reinterpret_input_as_3d()); + std::tie(lhs_info, rhs_info) = + auto_select_gemm_config_reshaped(auto_heuristics::CommonQuery{gpu_target, data_type, m, n, k, batch_size}, + kernel_info, a, b, c, output, gemm_info.reinterpret_input_as_3d()); _reshape_lhs_kernel->configure(compile_context, a, &_tmp_a, lhs_info, gemm_info.reinterpret_input_as_3d()); _reshape_rhs_kernel->configure(compile_context, b, &_tmp_b, rhs_info); // Configure and tune matrix multiply kernel - _mm_reshaped_kernel->configure(compile_context, &_tmp_a, &_tmp_b, c, output, alpha, beta, lhs_info, rhs_info, kernel_info); + _mm_reshaped_kernel->configure(compile_context, &_tmp_a, &_tmp_b, c, output, alpha, beta, lhs_info, rhs_info, + kernel_info); // Request memory for LHS and RHS reshape matrix _aux_mem[LhsReshape] = MemoryInfo(offset_int_vec(LhsReshape), MemoryLifetime::Temporary, _tmp_a.total_size()); - _aux_mem[RhsReshape] = MemoryInfo(offset_int_vec(RhsReshape), _reshape_b_only_on_first_run ? MemoryLifetime::Persistent : MemoryLifetime::Temporary, _tmp_b.total_size()); + _aux_mem[RhsReshape] = MemoryInfo( + offset_int_vec(RhsReshape), + _reshape_b_only_on_first_run ? MemoryLifetime::Persistent : MemoryLifetime::Temporary, _tmp_b.total_size()); } -void ClGemm::configure_reshaped_only_rhs(const CLCompileContext &compile_context, ITensorInfo *a, ITensorInfo *b, ITensorInfo *c, ITensorInfo *output, float alpha, float beta, - const GEMMInfo &gemm_info) +void ClGemm::configure_reshaped_only_rhs(const CLCompileContext &compile_context, + ITensorInfo *a, + ITensorInfo *b, + ITensorInfo *c, + ITensorInfo *output, + float alpha, + float beta, + const GEMMInfo &gemm_info) { DataType data_type = a->data_type(); bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d(); - const unsigned int m = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1); - const unsigned int n = b->dimension(0); - const unsigned int k = a->dimension(0); - const unsigned int batch_size = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2); - const int depth_output_gemm3d = gemm_info.depth_output_gemm3d(); - const GPUTarget gpu_target = CLScheduler::get().target(); - bool broadcast_bias = gemm_info.broadcast_bias(); + const unsigned int m = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1); + const unsigned int n = b->dimension(0); + const unsigned int k = a->dimension(0); + const unsigned int batch_size = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2); + const int depth_output_gemm3d = gemm_info.depth_output_gemm3d(); + const GPUTarget gpu_target = CLScheduler::get().target(); + bool broadcast_bias = gemm_info.broadcast_bias(); GEMMKernelInfo kernel_info; kernel_info.m = m; @@ -304,7 +364,8 @@ void ClGemm::configure_reshaped_only_rhs(const CLCompileContext &compile_context GEMMRHSMatrixInfo rhs_info{}; // Pick up the GEMM configuration - std::tie(lhs_info, rhs_info) = auto_select_gemm_config_reshaped_only_rhs(auto_heuristics::CommonQuery{ gpu_target, data_type, m, n, k, batch_size }, kernel_info, a, b, c, output); + std::tie(lhs_info, rhs_info) = auto_select_gemm_config_reshaped_only_rhs( + auto_heuristics::CommonQuery{gpu_target, data_type, m, n, k, batch_size}, kernel_info, a, b, c, output); // Transpose matrix _reshape_rhs_kernel->configure(compile_context, b, &_tmp_b, rhs_info); @@ -315,24 +376,33 @@ void ClGemm::configure_reshaped_only_rhs(const CLCompileContext &compile_context // Configure matrix multiply kernel with no y padding support kernel_info.has_pad_y = false; - _mm_reshaped_only_rhs_kernel->configure(compile_context, a, &_tmp_b, c, output, alpha, beta, lhs_info, rhs_info, kernel_info); + _mm_reshaped_only_rhs_kernel->configure(compile_context, a, &_tmp_b, c, output, alpha, beta, lhs_info, rhs_info, + kernel_info); // Request memory for RHS reshape matrix - _aux_mem[RhsReshape] = MemoryInfo(offset_int_vec(RhsReshape), _reshape_b_only_on_first_run ? MemoryLifetime::Persistent : MemoryLifetime::Temporary, _tmp_b.total_size()); + _aux_mem[RhsReshape] = MemoryInfo( + offset_int_vec(RhsReshape), + _reshape_b_only_on_first_run ? MemoryLifetime::Persistent : MemoryLifetime::Temporary, _tmp_b.total_size()); } -void ClGemm::configure_reshaped_only_rhs_mmul(const CLCompileContext &compile_context, ITensorInfo *a, ITensorInfo *b, ITensorInfo *c, ITensorInfo *output, float alpha, float beta, - const GEMMInfo &gemm_info) +void ClGemm::configure_reshaped_only_rhs_mmul(const CLCompileContext &compile_context, + ITensorInfo *a, + ITensorInfo *b, + ITensorInfo *c, + ITensorInfo *output, + float alpha, + float beta, + const GEMMInfo &gemm_info) { DataType data_type = a->data_type(); bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d(); - const unsigned int m = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1); - const unsigned int n = b->dimension(0); - const unsigned int k = a->dimension(0); - const unsigned int batch_size = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2); - const int depth_output_gemm3d = gemm_info.depth_output_gemm3d(); - const GPUTarget gpu_target = CLScheduler::get().target(); - bool broadcast_bias = gemm_info.broadcast_bias(); + const unsigned int m = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1); + const unsigned int n = b->dimension(0); + const unsigned int k = a->dimension(0); + const unsigned int batch_size = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2); + const int depth_output_gemm3d = gemm_info.depth_output_gemm3d(); + const GPUTarget gpu_target = CLScheduler::get().target(); + bool broadcast_bias = gemm_info.broadcast_bias(); GEMMKernelInfo kernel_info; kernel_info.m = m; @@ -350,9 +420,10 @@ void ClGemm::configure_reshaped_only_rhs_mmul(const CLCompileContext &compile_co GEMMRHSMatrixInfo rhs_info{}; // Pick up the GEMM configuration - auto gemm_config = select_default_gemm_config_reshaped_only_rhs(auto_heuristics::CommonQuery{ gpu_target, data_type, m, n, k, batch_size }); - lhs_info = gemm_config.lhs_info; - rhs_info = gemm_config.rhs_info; + auto gemm_config = select_default_gemm_config_reshaped_only_rhs( + auto_heuristics::CommonQuery{gpu_target, data_type, m, n, k, batch_size}); + lhs_info = gemm_config.lhs_info; + rhs_info = gemm_config.rhs_info; // Force H0 to 4 in order to use the MMUL extension rhs_info.h0 = 4; @@ -361,13 +432,22 @@ void ClGemm::configure_reshaped_only_rhs_mmul(const CLCompileContext &compile_co // Configure matrix multiply kernel with no y padding support kernel_info.has_pad_y = false; - _mm_reshaped_only_rhs_mmul_kernel->configure(compile_context, a, &_tmp_b, c, output, alpha, beta, lhs_info, rhs_info, kernel_info); + _mm_reshaped_only_rhs_mmul_kernel->configure(compile_context, a, &_tmp_b, c, output, alpha, beta, lhs_info, + rhs_info, kernel_info); // Request memory for RHS reshape matrix - _aux_mem[RhsReshape] = MemoryInfo(offset_int_vec(RhsReshape), _reshape_b_only_on_first_run ? MemoryLifetime::Persistent : MemoryLifetime::Temporary, _tmp_b.total_size()); + _aux_mem[RhsReshape] = MemoryInfo( + offset_int_vec(RhsReshape), + _reshape_b_only_on_first_run ? MemoryLifetime::Persistent : MemoryLifetime::Temporary, _tmp_b.total_size()); } -Status ClGemm::validate_native(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info) +Status ClGemm::validate_native(const ITensorInfo *a, + const ITensorInfo *b, + const ITensorInfo *c, + const ITensorInfo *output, + float alpha, + float beta, + const GEMMInfo &gemm_info) { ARM_COMPUTE_UNUSED(alpha); ARM_COMPUTE_UNUSED(output); @@ -376,12 +456,12 @@ Status ClGemm::validate_native(const ITensorInfo *a, const ITensorInfo *b, const const GPUTarget gpu_target = CLScheduler::get().target(); DataType data_type = a->data_type(); bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d(); - const unsigned int m = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1); - const unsigned int n = b->dimension(0); - const unsigned int k = a->dimension(0); - const unsigned int batch_size = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2); - const int depth_output_gemm3d = gemm_info.depth_output_gemm3d(); - const bool broadcast_bias = gemm_info.broadcast_bias(); + const unsigned int m = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1); + const unsigned int n = b->dimension(0); + const unsigned int k = a->dimension(0); + const unsigned int batch_size = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2); + const int depth_output_gemm3d = gemm_info.depth_output_gemm3d(); + const bool broadcast_bias = gemm_info.broadcast_bias(); GEMMKernelInfo kernel_info; kernel_info.m = m; @@ -392,15 +472,23 @@ Status ClGemm::validate_native(const ITensorInfo *a, const ITensorInfo *b, const kernel_info.broadcast_bias = broadcast_bias; kernel_info.activation_info = gemm_info.activation_info(); - auto config = auto_heuristics::select_mlgo_gemm_config_reshaped_only_rhs(auto_heuristics::CommonQuery{ gpu_target, data_type, m, n, k, batch_size }); + auto config = auto_heuristics::select_mlgo_gemm_config_reshaped_only_rhs( + auto_heuristics::CommonQuery{gpu_target, data_type, m, n, k, batch_size}); // Validate matrix multiply - ARM_COMPUTE_RETURN_ON_ERROR(ClGemmMatrixMultiplyNativeKernel::validate(a, b, c, output, alpha, beta, config.lhs_info, config.rhs_info, kernel_info)); + ARM_COMPUTE_RETURN_ON_ERROR(ClGemmMatrixMultiplyNativeKernel::validate( + a, b, c, output, alpha, beta, config.lhs_info, config.rhs_info, kernel_info)); return Status{}; } -Status ClGemm::validate_reshaped(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info) +Status ClGemm::validate_reshaped(const ITensorInfo *a, + const ITensorInfo *b, + const ITensorInfo *c, + const ITensorInfo *output, + float alpha, + float beta, + const GEMMInfo &gemm_info) { ARM_COMPUTE_UNUSED(alpha); ARM_COMPUTE_UNUSED(output); @@ -412,12 +500,12 @@ Status ClGemm::validate_reshaped(const ITensorInfo *a, const ITensorInfo *b, con const GPUTarget gpu_target = CLScheduler::get().target(); DataType data_type = a->data_type(); bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d(); - const unsigned int m = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1); - const unsigned int n = b->dimension(0); - const unsigned int k = a->dimension(0); - const unsigned int batch_size = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2); - const int depth_output_gemm3d = gemm_info.depth_output_gemm3d(); - const bool broadcast_bias = gemm_info.broadcast_bias(); + const unsigned int m = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1); + const unsigned int n = b->dimension(0); + const unsigned int k = a->dimension(0); + const unsigned int batch_size = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2); + const int depth_output_gemm3d = gemm_info.depth_output_gemm3d(); + const bool broadcast_bias = gemm_info.broadcast_bias(); GEMMKernelInfo kernel_info; kernel_info.m = m; @@ -433,23 +521,33 @@ Status ClGemm::validate_reshaped(const ITensorInfo *a, const ITensorInfo *b, con // Pick up the GEMM configuration // NOTE: No need to validate mlgo configurations as they automatically fall back to default heuristics if validation fails - const auto gemm_config = select_default_gemm_config_reshaped(auto_heuristics::CommonQuery{ gpu_target, data_type, m, n, k, batch_size }); - lhs_info = gemm_config.lhs_info; - rhs_info = gemm_config.rhs_info; + const auto gemm_config = + select_default_gemm_config_reshaped(auto_heuristics::CommonQuery{gpu_target, data_type, m, n, k, batch_size}); + lhs_info = gemm_config.lhs_info; + rhs_info = gemm_config.rhs_info; - auto_init_if_empty(tmp_a_info, a->clone()->set_tensor_shape(compute_lhs_reshaped_shape(*a, lhs_info, gemm_info.reinterpret_input_as_3d()))); - ARM_COMPUTE_RETURN_ON_ERROR(ClGemmReshapeLhsMatrixKernel::validate(a, &tmp_a_info, lhs_info, gemm_info.reinterpret_input_as_3d())); + auto_init_if_empty(tmp_a_info, a->clone()->set_tensor_shape( + compute_lhs_reshaped_shape(*a, lhs_info, gemm_info.reinterpret_input_as_3d()))); + ARM_COMPUTE_RETURN_ON_ERROR( + ClGemmReshapeLhsMatrixKernel::validate(a, &tmp_a_info, lhs_info, gemm_info.reinterpret_input_as_3d())); auto_init_if_empty(tmp_b_info, b->clone()->set_tensor_shape(compute_rhs_reshaped_shape(*b, rhs_info))); ARM_COMPUTE_RETURN_ON_ERROR(ClGemmReshapeRhsMatrixKernel::validate(b, &tmp_b_info, rhs_info)); // Validate matrix multiply - ARM_COMPUTE_RETURN_ON_ERROR(ClGemmMatrixMultiplyReshapedKernel::validate(&tmp_a_info, &tmp_b_info, c, output, alpha, beta, lhs_info, rhs_info, kernel_info)); + ARM_COMPUTE_RETURN_ON_ERROR(ClGemmMatrixMultiplyReshapedKernel::validate(&tmp_a_info, &tmp_b_info, c, output, alpha, + beta, lhs_info, rhs_info, kernel_info)); return Status{}; } -Status ClGemm::validate_reshaped_only_rhs(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info) +Status ClGemm::validate_reshaped_only_rhs(const ITensorInfo *a, + const ITensorInfo *b, + const ITensorInfo *c, + const ITensorInfo *output, + float alpha, + float beta, + const GEMMInfo &gemm_info) { ARM_COMPUTE_UNUSED(alpha); ARM_COMPUTE_UNUSED(output); @@ -460,12 +558,12 @@ Status ClGemm::validate_reshaped_only_rhs(const ITensorInfo *a, const ITensorInf const GPUTarget gpu_target = CLScheduler::get().target(); const DataType data_type = a->data_type(); bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d(); - const unsigned int m = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1); - const unsigned int n = b->dimension(0); - const unsigned int k = a->dimension(0); - const unsigned int batch_size = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2); - const int depth_output_gemm3d = gemm_info.depth_output_gemm3d(); - const bool broadcast_bias = gemm_info.broadcast_bias(); + const unsigned int m = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1); + const unsigned int n = b->dimension(0); + const unsigned int k = a->dimension(0); + const unsigned int batch_size = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2); + const int depth_output_gemm3d = gemm_info.depth_output_gemm3d(); + const bool broadcast_bias = gemm_info.broadcast_bias(); GEMMKernelInfo kernel_info; kernel_info.m = m; @@ -481,24 +579,33 @@ Status ClGemm::validate_reshaped_only_rhs(const ITensorInfo *a, const ITensorInf // Pick up the GEMM configuration // NOTE: No need to validate mlgo configurations as they automatically fall back to default heuristics if validation fails - const auto gemm_config = select_default_gemm_config_reshaped_only_rhs(auto_heuristics::CommonQuery{ gpu_target, data_type, m, n, k, batch_size }); - lhs_info = gemm_config.lhs_info; - rhs_info = gemm_config.rhs_info; + const auto gemm_config = select_default_gemm_config_reshaped_only_rhs( + auto_heuristics::CommonQuery{gpu_target, data_type, m, n, k, batch_size}); + lhs_info = gemm_config.lhs_info; + rhs_info = gemm_config.rhs_info; auto_init_if_empty(tmp_b_info, b->clone()->set_tensor_shape(compute_rhs_reshaped_shape(*b, rhs_info))); ARM_COMPUTE_RETURN_ON_ERROR(ClGemmReshapeRhsMatrixKernel::validate(b, &tmp_b_info, rhs_info)); // Validate matrix multiply kernel_info.has_pad_y = false; - ARM_COMPUTE_RETURN_ON_ERROR(ClGemmMatrixMultiplyReshapedOnlyRhsKernel::validate(a, &tmp_b_info, c, output, alpha, beta, lhs_info, rhs_info, kernel_info)); + ARM_COMPUTE_RETURN_ON_ERROR(ClGemmMatrixMultiplyReshapedOnlyRhsKernel::validate( + a, &tmp_b_info, c, output, alpha, beta, lhs_info, rhs_info, kernel_info)); kernel_info.has_pad_y = true; - ARM_COMPUTE_RETURN_ON_ERROR(ClGemmMatrixMultiplyReshapedOnlyRhsKernel::validate(a, &tmp_b_info, c, output, alpha, beta, lhs_info, rhs_info, kernel_info)); + ARM_COMPUTE_RETURN_ON_ERROR(ClGemmMatrixMultiplyReshapedOnlyRhsKernel::validate( + a, &tmp_b_info, c, output, alpha, beta, lhs_info, rhs_info, kernel_info)); return Status{}; } -Status ClGemm::validate_reshaped_only_rhs_mmul(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info) +Status ClGemm::validate_reshaped_only_rhs_mmul(const ITensorInfo *a, + const ITensorInfo *b, + const ITensorInfo *c, + const ITensorInfo *output, + float alpha, + float beta, + const GEMMInfo &gemm_info) { ARM_COMPUTE_UNUSED(alpha); ARM_COMPUTE_UNUSED(output); @@ -508,12 +615,12 @@ Status ClGemm::validate_reshaped_only_rhs_mmul(const ITensorInfo *a, const ITens const GPUTarget gpu_target = CLScheduler::get().target(); const DataType data_type = a->data_type(); bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d(); - const unsigned int m = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1); - const unsigned int n = b->dimension(0); - const unsigned int k = a->dimension(0); - const unsigned int batch_size = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2); - const int depth_output_gemm3d = gemm_info.depth_output_gemm3d(); - const bool broadcast_bias = gemm_info.broadcast_bias(); + const unsigned int m = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1); + const unsigned int n = b->dimension(0); + const unsigned int k = a->dimension(0); + const unsigned int batch_size = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2); + const int depth_output_gemm3d = gemm_info.depth_output_gemm3d(); + const bool broadcast_bias = gemm_info.broadcast_bias(); GEMMKernelInfo kernel_info; kernel_info.m = m; @@ -529,9 +636,10 @@ Status ClGemm::validate_reshaped_only_rhs_mmul(const ITensorInfo *a, const ITens // Pick up the GEMM configuration // NOTE: No need to validate mlgo configurations as they automatically fall back to default heuristics if validation fails - const auto gemm_config = select_default_gemm_config_reshaped_only_rhs(auto_heuristics::CommonQuery{ gpu_target, data_type, m, n, k, batch_size }); - lhs_info = gemm_config.lhs_info; - rhs_info = gemm_config.rhs_info; + const auto gemm_config = select_default_gemm_config_reshaped_only_rhs( + auto_heuristics::CommonQuery{gpu_target, data_type, m, n, k, batch_size}); + lhs_info = gemm_config.lhs_info; + rhs_info = gemm_config.rhs_info; // Force H0 to 4 in order to use the MMUL extension rhs_info.h0 = 4; @@ -540,12 +648,20 @@ Status ClGemm::validate_reshaped_only_rhs_mmul(const ITensorInfo *a, const ITens // Validate matrix multiply kernel_info.has_pad_y = false; - ARM_COMPUTE_RETURN_ON_ERROR(ClGemmMatrixMultiplyReshapedOnlyRhsMMULKernel::validate(a, &tmp_b_info, c, output, alpha, beta, lhs_info, rhs_info, kernel_info)); + ARM_COMPUTE_RETURN_ON_ERROR(ClGemmMatrixMultiplyReshapedOnlyRhsMMULKernel::validate( + a, &tmp_b_info, c, output, alpha, beta, lhs_info, rhs_info, kernel_info)); return Status{}; } -void ClGemm::configure(const CLCompileContext &compile_context, ITensorInfo *a, ITensorInfo *b, ITensorInfo *c, ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info) +void ClGemm::configure(const CLCompileContext &compile_context, + ITensorInfo *a, + ITensorInfo *b, + ITensorInfo *c, + ITensorInfo *output, + float alpha, + float beta, + const GEMMInfo &gemm_info) { ARM_COMPUTE_ERROR_ON_NULLPTR(a, b, output); @@ -558,20 +674,21 @@ void ClGemm::configure(const CLCompileContext &compile_context, ITensorInfo *a, _is_prepared = gemm_info.retain_internal_weights(); bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d(); - const unsigned int m = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1); - const unsigned int n = b->dimension(0); - const unsigned int k = a->dimension(0); - const unsigned int batch_size = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2); + const unsigned int m = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1); + const unsigned int n = b->dimension(0); + const unsigned int k = a->dimension(0); + const unsigned int batch_size = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2); // Select GEMMType - _gemm_kernel_type = auto_select_gemm_kernel(auto_heuristics::CommonQuery{ CLScheduler::get().target(), a->data_type(), m, n, k, batch_size }, _reshape_b_only_on_first_run, - b->are_values_constant()); + _gemm_kernel_type = auto_select_gemm_kernel( + auto_heuristics::CommonQuery{CLScheduler::get().target(), a->data_type(), m, n, k, batch_size}, + _reshape_b_only_on_first_run, b->are_values_constant()); const bool fuse_add_c = (!(helpers::float_ops::is_zero(beta)) && c != nullptr); ITensorInfo *c_to_use = fuse_add_c ? c : nullptr; - switch(_gemm_kernel_type) + switch (_gemm_kernel_type) { case CLGEMMKernelType::NATIVE: { @@ -600,35 +717,41 @@ void ClGemm::configure(const CLCompileContext &compile_context, ITensorInfo *a, } } -Status ClGemm::validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info) +Status ClGemm::validate(const ITensorInfo *a, + const ITensorInfo *b, + const ITensorInfo *c, + const ITensorInfo *output, + float alpha, + float beta, + const GEMMInfo &gemm_info) { // Get the GPU target bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d(); - const unsigned int m = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1); - const unsigned int n = b->dimension(0); - const unsigned int k = a->dimension(0); - const unsigned int batch_size = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2); + const unsigned int m = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1); + const unsigned int n = b->dimension(0); + const unsigned int k = a->dimension(0); + const unsigned int batch_size = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2); // Check data type early because the auto_select_gemm_kernel has assertions on supported data types ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::F32, DataType::F16); // Select GEMMType - CLGEMMKernelType gemm_kernel_type = auto_select_gemm_kernel(auto_heuristics::CommonQuery - { - CLScheduler::get().target(), - a->data_type(), - m, - n, - k, - batch_size, - }, - gemm_info.reshape_b_only_on_first_run(), b->are_values_constant()); + CLGEMMKernelType gemm_kernel_type = auto_select_gemm_kernel( + auto_heuristics::CommonQuery{ + CLScheduler::get().target(), + a->data_type(), + m, + n, + k, + batch_size, + }, + gemm_info.reshape_b_only_on_first_run(), b->are_values_constant()); const bool fuse_add_c = (!(helpers::float_ops::is_zero(beta)) && c != nullptr); const ITensorInfo *c_to_use = fuse_add_c ? c : nullptr; - switch(gemm_kernel_type) + switch (gemm_kernel_type) { case CLGEMMKernelType::NATIVE: { @@ -647,7 +770,8 @@ Status ClGemm::validate(const ITensorInfo *a, const ITensorInfo *b, const ITenso } case CLGEMMKernelType::RESHAPED_ONLY_RHS_MMUL: { - ARM_COMPUTE_RETURN_ON_ERROR(validate_reshaped_only_rhs_mmul(a, b, c_to_use, output, alpha, beta, gemm_info)); + ARM_COMPUTE_RETURN_ON_ERROR( + validate_reshaped_only_rhs_mmul(a, b, c_to_use, output, alpha, beta, gemm_info)); break; } default: @@ -674,7 +798,7 @@ void ClGemm::run(ITensorPack &tensors) prepare(tensors); // Run matrix multiply kernel - switch(_gemm_kernel_type) + switch (_gemm_kernel_type) { case CLGEMMKernelType::NATIVE: { @@ -684,13 +808,13 @@ void ClGemm::run(ITensorPack &tensors) case CLGEMMKernelType::RESHAPED: { // Run interleave kernel - ITensorPack reshape_lhs_pack{ { ACL_SRC, lhs }, { ACL_DST, lhs_reshaped.get() } }; + ITensorPack reshape_lhs_pack{{ACL_SRC, lhs}, {ACL_DST, lhs_reshaped.get()}}; CLScheduler::get().enqueue_op(*_reshape_lhs_kernel, reshape_lhs_pack, false); - if(!_reshape_b_only_on_first_run) + if (!_reshape_b_only_on_first_run) { // Run transpose kernel - ITensorPack reshape_rhs_pack{ { ACL_SRC, rhs }, { ACL_DST, rhs_reshaped.get() } }; + ITensorPack reshape_rhs_pack{{ACL_SRC, rhs}, {ACL_DST, rhs_reshaped.get()}}; CLScheduler::get().enqueue_op(*_reshape_rhs_kernel, reshape_rhs_pack, false); } // Copy original tensor pack and overwrite lhs and rhs with reshaped counterparts @@ -698,7 +822,7 @@ void ClGemm::run(ITensorPack &tensors) gemm_reshaped_pack.add_const_tensor(ACL_SRC_0, lhs_reshaped.get()); gemm_reshaped_pack.add_const_tensor(ACL_SRC_1, rhs_reshaped.get()); - if(_gemm_kernel_type == CLGEMMKernelType::RESHAPED) + if (_gemm_kernel_type == CLGEMMKernelType::RESHAPED) { CLScheduler::get().enqueue_op(*_mm_reshaped_kernel, gemm_reshaped_pack, true); } @@ -706,10 +830,10 @@ void ClGemm::run(ITensorPack &tensors) } case CLGEMMKernelType::RESHAPED_ONLY_RHS: { - if(!_reshape_b_only_on_first_run) + if (!_reshape_b_only_on_first_run) { // Run transpose kernel - ITensorPack reshape_rhs_pack{ { ACL_SRC, rhs }, { ACL_DST, rhs_reshaped.get() } }; + ITensorPack reshape_rhs_pack{{ACL_SRC, rhs}, {ACL_DST, rhs_reshaped.get()}}; CLScheduler::get().enqueue_op(*_reshape_rhs_kernel, reshape_rhs_pack, false); } // In case of RESHAPED_ONLY_RHS, we need to check the padding requirement @@ -722,7 +846,7 @@ void ClGemm::run(ITensorPack &tensors) ITensorPack gemm_reshaped_onlyrhs_pack(tensors); gemm_reshaped_onlyrhs_pack.add_const_tensor(ACL_SRC_1, rhs_reshaped.get()); - if(has_pad_y) + if (has_pad_y) { ARM_COMPUTE_ERROR_ON(has_pad_y); } @@ -734,10 +858,10 @@ void ClGemm::run(ITensorPack &tensors) } case CLGEMMKernelType::RESHAPED_ONLY_RHS_MMUL: { - if(!_reshape_b_only_on_first_run) + if (!_reshape_b_only_on_first_run) { // Run transpose kernel - ITensorPack reshape_rhs_pack{ { ACL_SRC, rhs }, { ACL_DST, rhs_reshaped.get() } }; + ITensorPack reshape_rhs_pack{{ACL_SRC, rhs}, {ACL_DST, rhs_reshaped.get()}}; CLScheduler::get().enqueue_op(*_reshape_rhs_kernel, reshape_rhs_pack, false); } // In case of RESHAPED_ONLY_RHS, we need to check the padding requirement @@ -750,7 +874,7 @@ void ClGemm::run(ITensorPack &tensors) ITensorPack gemm_reshaped_onlyrhs_pack(tensors); gemm_reshaped_onlyrhs_pack.add_const_tensor(ACL_SRC_1, rhs_reshaped.get()); - if(has_pad_y) + if (has_pad_y) { ARM_COMPUTE_ERROR_ON(has_pad_y); } @@ -769,20 +893,22 @@ void ClGemm::run(ITensorPack &tensors) void ClGemm::prepare(ITensorPack &constants) { - if(!_is_prepared) + if (!_is_prepared) { - const ITensor *src1 = constants.get_const_tensor(ACL_SRC_1); - ICLTensor *rhs_aux = utils::cast::polymorphic_downcast<ICLTensor *>(constants.get_tensor(offset_int_vec(RhsReshape))); + const ITensor *src1 = constants.get_const_tensor(ACL_SRC_1); + ICLTensor *rhs_aux = + utils::cast::polymorphic_downcast<ICLTensor *>(constants.get_tensor(offset_int_vec(RhsReshape))); // If memory for RHS is persistent and src1 is provided re-transform else assume that RHS is transformed - if((_aux_mem[AuxTensorIdx::RhsReshape].lifetime == MemoryLifetime::Persistent) && (src1 != nullptr && rhs_aux != nullptr) && rhs_aux) + if ((_aux_mem[AuxTensorIdx::RhsReshape].lifetime == MemoryLifetime::Persistent) && + (src1 != nullptr && rhs_aux != nullptr) && rhs_aux) { ARM_COMPUTE_LOG_INFO_WITH_FUNCNAME_ACL("Transforming RHS Matrix!"); CLAuxTensorHandler rhs_reshaped(_tmp_b, *rhs_aux); ARM_COMPUTE_ERROR_ON(rhs_reshaped.get()->cl_buffer().get() == nullptr); - ITensorPack reshape_rhs_pack{ { ACL_SRC, src1 }, { ACL_DST, rhs_reshaped.get() } }; + ITensorPack reshape_rhs_pack{{ACL_SRC, src1}, {ACL_DST, rhs_reshaped.get()}}; CLScheduler::get().enqueue_op(*_reshape_rhs_kernel, reshape_rhs_pack, true); } _is_prepared = true; |