COMPMID-662: Integrated the new a64_s8_gemm_12x8 + dot product kernel into ACL.
Change-Id: Id8f919e486a132fc58346c9f84fccbeeb83d19b3
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/94233
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
diff --git a/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp b/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp
index 11ae054..29104cc 100644
--- a/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp
+++ b/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp
@@ -26,9 +26,8 @@
#include "arm_compute/core/Error.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/NEON/kernels/NEGEMMAssemblyBaseKernel.h"
#include "arm_compute/core/NEON/kernels/NEGEMMInterleave4x4Kernel.h"
-#include "arm_compute/core/NEON/kernels/NEGEMMInterleaveBlockedKernel.h"
-#include "arm_compute/core/NEON/kernels/NEGEMMLowpAssemblyBaseKernel.h"
#include "arm_compute/core/NEON/kernels/NEGEMMLowpMatrixMultiplyKernel.h"
#include "arm_compute/core/NEON/kernels/NEGEMMTranspose1xWKernel.h"
#include "arm_compute/core/NEON/kernels/arm64/NEGEMMLowpAArch64V8P4Kernel.h"
@@ -39,16 +38,22 @@
#include "arm_compute/runtime/TensorAllocator.h"
#include "support/ToolchainSupport.h"
+namespace arm_compute
+{
+#include "arm_compute/core/NEON/kernels/assembly/gemm_interleaved.hpp"
+#include "arm_compute/core/NEON/kernels/assembly/kernels/a64_gemm_s8_12x8.hpp"
+} // namespace arm_compute
+
using namespace arm_compute;
NEGEMMLowpMatrixMultiplyCore::NEGEMMLowpMatrixMultiplyCore(std::shared_ptr<IMemoryManager> memory_manager)
- : _memory_group(std::move(memory_manager)), _mm_kernel(nullptr), _mtx_a_reshape_kernel(nullptr), _mtx_b_reshape_kernel(nullptr), _tmp_a(), _tmp_b()
+ : _memory_group(std::move(memory_manager)), _mm_kernel(nullptr), _mtx_a_reshape_kernel(nullptr), _mtx_b_reshape_kernel(nullptr), _tmp_a(), _tmp_b(), _workspace()
{
}
void NEGEMMLowpMatrixMultiplyCore::configure(const ITensor *a, const ITensor *b, ITensor *output)
{
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::U8);
+ ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::S8);
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(a, b);
ARM_COMPUTE_ERROR_ON_MSG((a)->info()->dimension(0) != (b)->info()->dimension(1), "The product AB is defined only if the number of columns in A is equal to the number of rows in B");
@@ -62,42 +67,22 @@
if(cpu_has_dotprod != 0)
{
- TensorShape shape_a_int = a->info()->tensor_shape();
- shape_a_int.set(0, a->info()->dimension(0) * 8.f);
- shape_a_int.set(1, std::ceil(a->info()->dimension(1) / 8.f));
-
- TensorShape shape_b_int = b->info()->tensor_shape();
- shape_b_int.set(0, b->info()->dimension(0) * 12.f);
- shape_b_int.set(1, std::ceil(b->info()->dimension(1) / 12.f));
-
- TensorInfo info_a_int(shape_a_int, 1, a->info()->data_type());
- TensorInfo info_b_int(shape_b_int, 1, b->info()->data_type());
- _tmp_a.allocator()->init(info_a_int);
- _tmp_b.allocator()->init(info_b_int);
- _memory_group.manage(&_tmp_a);
- _memory_group.manage(&_tmp_b);
-
- // Configure interleave blocked kernel for matrix A
- {
- auto k = arm_compute::support::cpp14::make_unique<NEGEMMInterleaveBlockedKernel>();
- k->configure(a, &_tmp_a, 8, 4, false);
- _mtx_a_reshape_kernel = std::move(k);
- }
-
- // Configure interleave blocked kernel for matrix B
- {
- auto k = arm_compute::support::cpp14::make_unique<NEGEMMInterleaveBlockedKernel>();
- k->configure(b, &_tmp_b, 12, 4, true);
- _mtx_b_reshape_kernel = std::move(k);
- }
-
// Configure matrix multiply kernel
- {
- // NEGEMMLowpAArch64V8P4Kernel only compiled in AArch64 targets
- auto k = arm_compute::support::cpp14::make_unique<NEGEMMLowpAArch64V8P4Kernel>();
- k->configure(&_tmp_a, &_tmp_b, output);
- _mm_kernel = std::move(k);
- }
+ struct CPUInfo ci = NEScheduler::get().cpu_info();
+ const int M = output->info()->tensor_shape().y();
+ const int N = output->info()->tensor_shape().x();
+ const int K = a->info()->tensor_shape().x();
+
+ GemmInterleaved<gemm_s8_12x8, int8_t, int32_t> gemm(&ci, M, N, K, false, false);
+ constexpr size_t alignment = 4096;
+ _workspace.allocator()->init(TensorInfo(TensorShape{ (gemm.get_working_size() + alignment - 1) * NEScheduler::get().num_threads() }, 1, DataType::U8));
+ _memory_group.manage(&_workspace);
+ // Configure matrix multiplication kernel
+ auto k = arm_compute::support::cpp14::make_unique<NEGEMMLowpAArch64V8P4Kernel>();
+ k->configure(a, b, output, &_workspace, 1.f, 1.f);
+ _mm_kernel = std::move(k);
+
+ _workspace.allocator()->allocate();
}
else
#endif /* ARM_COMPUTE_AARCH64_V8_2 */
@@ -139,25 +124,28 @@
k->configure(&_tmp_a, &_tmp_b, output);
_mm_kernel = std::move(k);
}
- }
- // Allocate tensors
- _tmp_a.allocator()->allocate();
- _tmp_b.allocator()->allocate();
+ // Allocate tensors
+ _tmp_a.allocator()->allocate();
+ _tmp_b.allocator()->allocate();
+ }
}
void NEGEMMLowpMatrixMultiplyCore::run()
{
_memory_group.acquire();
- // Run reshape matrix A
- NEScheduler::get().schedule(_mtx_a_reshape_kernel.get(), Window::DimY);
+ if(_mtx_a_reshape_kernel)
+ {
+ NEScheduler::get().schedule(_mtx_a_reshape_kernel.get(), Window::DimY);
+ }
- // Run reshape matrix B
- NEScheduler::get().schedule(_mtx_b_reshape_kernel.get(), Window::DimY);
+ if(_mtx_b_reshape_kernel)
+ {
+ NEScheduler::get().schedule(_mtx_b_reshape_kernel.get(), Window::DimY);
+ }
- // Run matrix multiply kernel
NEScheduler::get().schedule(_mm_kernel.get(), Window::DimY);
_memory_group.release();
-}
\ No newline at end of file
+}