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author | Pablo Tello <pablo.tello@arm.com> | 2017-11-10 15:57:14 +0000 |
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committer | Anthony Barbier <anthony.barbier@arm.com> | 2018-11-02 16:35:24 +0000 |
commit | 4d55e0a3e848db25496b31529f4405bee7115cf8 (patch) | |
tree | 1eb7fcadf9525abab8ed2f95275fed45f5f9ead1 /src/runtime/NEON/functions/NEGEMM.cpp | |
parent | b28f29d5f5657b606921faf4c6dcc2ced1465cc7 (diff) | |
download | ComputeLibrary-4d55e0a3e848db25496b31529f4405bee7115cf8.tar.gz |
COMPMID-677: Integrate HGEMM assembly kernel (generic CPUs)
Change-Id: I39abf367fe7ea1a54475e2ac0ecec12e90806899
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/95378
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Diffstat (limited to 'src/runtime/NEON/functions/NEGEMM.cpp')
-rw-r--r-- | src/runtime/NEON/functions/NEGEMM.cpp | 31 |
1 files changed, 27 insertions, 4 deletions
diff --git a/src/runtime/NEON/functions/NEGEMM.cpp b/src/runtime/NEON/functions/NEGEMM.cpp index 2dea9317a5..950f4c9899 100644 --- a/src/runtime/NEON/functions/NEGEMM.cpp +++ b/src/runtime/NEON/functions/NEGEMM.cpp @@ -28,6 +28,7 @@ #include "arm_compute/core/ITensor.h" #include "arm_compute/core/NEON/kernels/arm32/NEGEMMAArch32Kernel.h" #include "arm_compute/core/NEON/kernels/arm64/NEGEMMAArch64Kernel.h" +#include "arm_compute/core/NEON/kernels/arm64/NEHGEMMAArch64FP16Kernel.h" #include "arm_compute/core/TensorInfo.h" #include "arm_compute/core/Types.h" #include "arm_compute/core/Validate.h" @@ -39,6 +40,7 @@ namespace arm_compute { #include "arm_compute/core/NEON/kernels/assembly/gemm_interleaved.hpp" #include "arm_compute/core/NEON/kernels/assembly/kernels/a32_sgemm_8x6.hpp" +#include "arm_compute/core/NEON/kernels/assembly/kernels/a64_hgemm_24x8.hpp" #include "arm_compute/core/NEON/kernels/assembly/kernels/a64_sgemm_12x8.hpp" } // namespace arm_compute @@ -96,6 +98,14 @@ void NEGEMM::configure(const ITensor *a, const ITensor *b, const ITensor *c, ITe { _mm_optimised_kernel = support::cpp14::make_unique<NEGEMMAArch64Kernel>(); } + else if(a->info()->data_type() == DataType::F16 && (c == nullptr || beta == 0.f)) + { +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC + _mm_optimised_kernel = support::cpp14::make_unique<NEHGEMMAArch64FP16Kernel>(); +#else /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ + ARM_COMPUTE_ERROR("Recompile the library with arch=arm64-v8.2-a to enable support for FP16."); +#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ + } #endif /* defined(__arm__) || defined(__aarch64__) */ #if defined(__arm__) || defined(__aarch64__) @@ -107,19 +117,32 @@ void NEGEMM::configure(const ITensor *a, const ITensor *b, const ITensor *c, ITe const int N = d->info()->tensor_shape().x(); const int K = a->info()->tensor_shape().x(); + size_t workbench_size = 0; + #if defined(__arm__) - GemmInterleaved<sgemm_8x6, float, float> gemm(&ci, M, N, K, false, false); + workbench_size = GemmInterleaved<sgemm_8x6, sgemm_8x6::operand_type, sgemm_8x6::result_type>(&ci, M, N, K, false, false).get_working_size(); #elif defined(__aarch64__) - GemmInterleaved<sgemm_12x8, float, float> gemm(&ci, M, N, K, false, false); + if(a->info()->data_type() == DataType::F32) + { + workbench_size = GemmInterleaved<sgemm_12x8, sgemm_12x8::operand_type, sgemm_12x8::result_type>(&ci, M, N, K, false, false).get_working_size(); + } + else if(a->info()->data_type() == DataType::F16) + { +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC + workbench_size = GemmInterleaved<hgemm_24x8, hgemm_24x8::operand_type, hgemm_24x8::result_type>(&ci, M, N, K, false, false).get_working_size(); +#else /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ + ARM_COMPUTE_ERROR("Recompile the library with arch=arm64-v8.2-a to enable support for FP16."); +#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ + } #endif /* defined(__arm__) || defined(__aarch64__) */ constexpr size_t alignment = 4096; - _workspace.allocator()->init(TensorInfo(TensorShape{ (gemm.get_working_size() + alignment - 1) * NEScheduler::get().num_threads() }, 1, DataType::S8)); + ARM_COMPUTE_ERROR_ON_MSG(workbench_size == 0, "size cannot be 0"); + _workspace.allocator()->init(TensorInfo(TensorShape{ (workbench_size + alignment - 1) * NEScheduler::get().num_threads() }, 1, DataType::S8)); _memory_group.manage(&_workspace); // Configure matrix multiplication kernel _mm_optimised_kernel->configure(a, b, d, &_workspace, alpha, 0.f); - _workspace.allocator()->allocate(); } else |