COMPMID-481: Add AArch64 GEMM
Change-Id: I34f94f99cb05f0eabafee13c5e623ee779b72360
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/83741
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Reviewed-by: Pablo Tello <pablo.tello@arm.com>
diff --git a/src/runtime/NEON/functions/NEGEMM.cpp b/src/runtime/NEON/functions/NEGEMM.cpp
index 85b283c..1d6aa65 100644
--- a/src/runtime/NEON/functions/NEGEMM.cpp
+++ b/src/runtime/NEON/functions/NEGEMM.cpp
@@ -26,18 +26,27 @@
#include "arm_compute/core/Error.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/NEON/kernels/arm64/NEGEMMAArch64Kernel.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/Validate.h"
#include "arm_compute/runtime/NEON/NEScheduler.h"
#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_sgemm_12x8.hpp"
+} // namespace arm_compute
#include <cmath>
-using namespace arm_compute;
-
+namespace arm_compute
+{
NEGEMM::NEGEMM(std::shared_ptr<IMemoryManager> memory_manager)
- : _memory_group(std::move(memory_manager)), _interleave_kernel(), _transpose_kernel(), _mm_kernel(), _ma_kernel(), _tmp_a(), _tmp_b(), _run_vector_matrix_multiplication(false), _run_addition(false)
+ : _memory_group(std::move(memory_manager)), _interleave_kernel(), _transpose_kernel(), _mm_kernel(), _mm_optimised_kernel(nullptr), _ma_kernel(), _tmp_a(), _tmp_b(), _workspace(),
+ _run_vector_matrix_multiplication(false), _run_addition(false)
{
}
@@ -57,57 +66,94 @@
ARM_COMPUTE_ERROR_ON_MSG(c->info()->dimension(1) != d->info()->dimension(1), "The C matrix must have the same number of columns as the output matrix");
}
- // Check if the first input tensor is a vector. If so, all the kernels for reshaping the tensors can be skipped
- if((a->info()->dimension(1) == 1))
- {
- _run_vector_matrix_multiplication = true;
+ _run_vector_matrix_multiplication = a->info()->dimension(1) < 2;
+#if defined(__aarch64__)
+ if(NEScheduler::get().cpu_info().CPU >= CPUTarget::ARMV8 && a->info()->data_type() == DataType::F32 && (c == nullptr || beta == 0.f))
+ {
+ _mm_optimised_kernel = support::cpp14::make_unique<NEGEMMAArch64Kernel>();
+ }
+#endif /* defined(__aarch64__) */
+
+ // Check if the first input tensor is a vector.
+ // If so, all the kernels for reshaping the tensors can be skipped
+ if(_run_vector_matrix_multiplication)
+ {
// Configure the matrix multiply kernel
_mm_kernel.configure(a, b, d, alpha);
+
+ // Configure matrix addition kernel
+ if(beta != 0 && c != nullptr)
+ {
+ _ma_kernel.configure(c, d, beta);
+ _run_addition = true;
+ }
}
else
{
- _run_vector_matrix_multiplication = false;
+#if defined(__aarch64__)
+ if(_mm_optimised_kernel != nullptr)
+ {
+ struct CPUInfo ci = NEScheduler::get().cpu_info();
- TensorShape shape_tmp_a = a->info()->tensor_shape();
- TensorShape shape_tmp_b = b->info()->tensor_shape();
+ const int M = d->info()->tensor_shape().y();
+ const int N = d->info()->tensor_shape().x();
+ const int K = a->info()->tensor_shape().x();
- shape_tmp_a.set(0, a->info()->dimension(0) * 4);
- shape_tmp_a.set(1, std::ceil(a->info()->dimension(1) / 4.0f));
+ GemmInterleaved<sgemm_12x8, float, float> gemm(&ci, M, N, K, false, false);
- const unsigned int transpose_w = 16 / data_size_from_type(b->info()->data_type());
- shape_tmp_b.set(0, b->info()->dimension(1) * transpose_w);
- shape_tmp_b.set(1, std::ceil(b->info()->dimension(0) / static_cast<float>(transpose_w)));
+ 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);
- TensorInfo info_a(shape_tmp_a, 1, a->info()->data_type(), a->info()->fixed_point_position());
- TensorInfo info_b(shape_tmp_b, 1, b->info()->data_type(), a->info()->fixed_point_position());
+ // Configure matrix multiplication kernel
+ _mm_optimised_kernel->configure(a, b, d, &_workspace, alpha, 0.f);
- _tmp_a.allocator()->init(info_a);
- _tmp_b.allocator()->init(info_b);
+ _workspace.allocator()->allocate();
+ }
+ else
+#endif /* defined(__aarch64__) */
+ {
+ TensorShape shape_tmp_a = a->info()->tensor_shape();
+ TensorShape shape_tmp_b = b->info()->tensor_shape();
- // Manage intermediate buffers
- _memory_group.manage(&_tmp_a);
- _memory_group.manage(&_tmp_b);
+ shape_tmp_a.set(0, a->info()->dimension(0) * 4);
+ shape_tmp_a.set(1, std::ceil(a->info()->dimension(1) / 4.0f));
- // Configure interleave kernel
- _interleave_kernel.configure(a, &_tmp_a);
+ const unsigned int transpose_w = 16 / data_size_from_type(b->info()->data_type());
+ shape_tmp_b.set(0, b->info()->dimension(1) * transpose_w);
+ shape_tmp_b.set(1, std::ceil(b->info()->dimension(0) / static_cast<float>(transpose_w)));
- // Configure transpose kernel
- _transpose_kernel.configure(b, &_tmp_b);
+ TensorInfo info_a(shape_tmp_a, 1, a->info()->data_type(), a->info()->fixed_point_position());
+ TensorInfo info_b(shape_tmp_b, 1, b->info()->data_type(), a->info()->fixed_point_position());
- // Configure matrix multiplication kernel
- _mm_kernel.configure(&_tmp_a, &_tmp_b, d, alpha);
+ _tmp_a.allocator()->init(info_a);
+ _tmp_b.allocator()->init(info_b);
- // Allocate once the all configure methods have been called
- _tmp_a.allocator()->allocate();
- _tmp_b.allocator()->allocate();
- }
+ // Manage intermediate buffers
+ _memory_group.manage(&_tmp_a);
+ _memory_group.manage(&_tmp_b);
- // Configure matrix addition kernel
- if(beta != 0 && c != nullptr)
- {
- _ma_kernel.configure(c, d, beta);
- _run_addition = true;
+ // Configure interleave kernel
+ _interleave_kernel.configure(a, &_tmp_a);
+
+ // Configure transpose kernel
+ _transpose_kernel.configure(b, &_tmp_b);
+
+ // Configure matrix multiplication kernel
+ _mm_kernel.configure(&_tmp_a, &_tmp_b, d, alpha);
+
+ // Allocate once the all configure methods have been called
+ _tmp_a.allocator()->allocate();
+ _tmp_b.allocator()->allocate();
+
+ // Configure matrix addition kernel
+ if(beta != 0 && c != nullptr)
+ {
+ _ma_kernel.configure(c, d, beta);
+ _run_addition = true;
+ }
+ }
}
}
@@ -115,23 +161,31 @@
{
_memory_group.acquire();
- if(!_run_vector_matrix_multiplication)
+ if(_mm_optimised_kernel != nullptr)
{
- // Run interleave kernel
- NEScheduler::get().schedule(&_interleave_kernel, Window::DimY);
-
- // Run transpose kernel
- NEScheduler::get().schedule(&_transpose_kernel, Window::DimY);
+ NEScheduler::get().schedule(_mm_optimised_kernel.get(), Window::DimY);
+ _memory_group.release();
}
-
- // Run matrix multiply kernel
- NEScheduler::get().schedule(&_mm_kernel, _run_vector_matrix_multiplication ? Window::DimX : Window::DimY);
-
- _memory_group.release();
-
- // Run matrix addition kernel
- if(_run_addition)
+ else
{
- NEScheduler::get().schedule(&_ma_kernel, Window::DimY);
+ if(!_run_vector_matrix_multiplication)
+ {
+ // Run interleave kernel
+ NEScheduler::get().schedule(&_interleave_kernel, Window::DimY);
+
+ // Run transpose kernel
+ NEScheduler::get().schedule(&_transpose_kernel, Window::DimY);
+ }
+
+ NEScheduler::get().schedule(&_mm_kernel, _run_vector_matrix_multiplication ? Window::DimX : Window::DimY);
+
+ _memory_group.release();
+
+ // Run matrix addition kernel
+ if(_run_addition)
+ {
+ NEScheduler::get().schedule(&_ma_kernel, Window::DimY);
+ }
}
}
+} // namespace arm_compute