COMPMID-481: Add gemmlowp_aarch64_v8p4 kernel.
Change-Id: I15496b16ffd636f5bff76572e750df7e15c80830
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/90532
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
diff --git a/src/runtime/NEON/functions/NEGEMMLowp.cpp b/src/runtime/NEON/functions/NEGEMMLowp.cpp
index 7413b28..90e47ce 100644
--- a/src/runtime/NEON/functions/NEGEMMLowp.cpp
+++ b/src/runtime/NEON/functions/NEGEMMLowp.cpp
@@ -26,28 +26,100 @@
#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/NEGEMMLowpAArch64V8P4Kernel.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"
using namespace arm_compute;
+#define NEGEMMLOWP_VALIDATE_DIMENSIONS(a, b, output) \
+ ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN((a), 1, DataType::U8); \
+ ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN((b), 1, DataType::U8); \
+ 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"); \
+ ARM_COMPUTE_ERROR_ON_MSG((a)->info()->dimension(1) != (output)->info()->dimension(1), "The C matrix must have the same number of rows as the matrix A"); \
+ ARM_COMPUTE_ERROR_ON_MSG((b)->info()->dimension(0) != (output)->info()->dimension(0), "The C matrix must have the same number of columns as the matrix C");
+
NEGEMMLowp::NEGEMMLowp(std::shared_ptr<IMemoryManager> memory_manager)
- : _memory_group(std::move(memory_manager)), _interleave_kernel(), _transpose_kernel(), _mm_kernel(), _tmp_a(), _tmp_b()
+ : _memory_group(std::move(memory_manager)), _interleave_kernel(), _transpose_kernel(), _mm_kernel(), _mm_optimised_kernel(nullptr), _interleave_blocked(), _interleave_blocked_transposed(), _tmp_a(),
+ _tmp_b()
{
}
+void NEGEMMLowp::configure(const ITensor *a, const ITensor *b, ITensor *output)
+{
+ NEGEMMLOWP_VALIDATE_DIMENSIONS(a, b, output);
+ ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U32);
+
+ const struct CPUInfo ci = NEScheduler::get().cpu_info();
+ const int cpu_has_dotprod = static_cast<int>(ci.CPU) & static_cast<int>(CPUTarget::DOT);
+ if(cpu_has_dotprod != 0)
+ {
+#if defined(__aarch64__)
+ // NEGEMMLowpAArch64V8P4Kernel only compiled in AArch64 targets
+ _mm_optimised_kernel = support::cpp14::make_unique<NEGEMMLowpAArch64V8P4Kernel>();
+ 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);
+
+ _interleave_blocked.configure(a, &_tmp_a, 8, 4, false);
+ _interleave_blocked_transposed.configure(b, &_tmp_b, 12, 4, true);
+ _mm_optimised_kernel->configure(&_tmp_a, &_tmp_b, output);
+
+ _tmp_a.allocator()->allocate();
+ _tmp_b.allocator()->allocate();
+#endif /* defined(__aarch64__) */
+ }
+ else
+ {
+ ARM_COMPUTE_ERROR("Not implemented");
+ // This is in the process of being updated, for more info please refer to COMPMID-624.
+ }
+}
+
+void NEGEMMLowp::run()
+{
+ _memory_group.acquire();
+
+ if(_mm_optimised_kernel != nullptr)
+ {
+ NEScheduler::get().schedule(&_interleave_blocked, Window::DimY);
+ NEScheduler::get().schedule(&_interleave_blocked_transposed, Window::DimY);
+ NEScheduler::get().schedule(_mm_optimised_kernel.get(), Window::DimY);
+ }
+ else
+ {
+ /* Run interleave kernel */
+ NEScheduler::get().schedule(&_interleave_kernel, Window::DimY);
+ /* Run transpose kernel */
+ NEScheduler::get().schedule(&_transpose_kernel, Window::DimY);
+ /* Run matrix multiply kernel */
+ NEScheduler::get().schedule(&_mm_kernel, Window::DimY);
+ }
+
+ _memory_group.release();
+}
+
void NEGEMMLowp::configure(const ITensor *a, const ITensor *b, ITensor *output, int32_t a_offset, int32_t b_offset, int32_t output_offset, int32_t output_mult_int, int32_t shift)
{
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::U8);
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(b, 1, DataType::U8);
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8);
+ NEGEMMLOWP_VALIDATE_DIMENSIONS(a, b, output);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(a, b, output);
- 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");
- ARM_COMPUTE_ERROR_ON_MSG(a->info()->dimension(1) != output->info()->dimension(1), "The C matrix must have the same number of rows as the matrix A");
- ARM_COMPUTE_ERROR_ON_MSG(b->info()->dimension(0) != output->info()->dimension(0), "The C matrix must have the same number of columns as the matrix C");
+ ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8);
/* The interleaved output matrix will have the following shape: [ a_height * 4, ceil(a_width / 4.0f) ] */
TensorShape shape_tmp_a = a->info()->tensor_shape();
@@ -75,18 +147,4 @@
_tmp_b.allocator()->allocate();
}
-void NEGEMMLowp::run()
-{
- _memory_group.acquire();
-
- /* Run interleave kernel */
- NEScheduler::get().schedule(&_interleave_kernel, Window::DimY);
-
- /* Run transpose kernel */
- NEScheduler::get().schedule(&_transpose_kernel, Window::DimY);
-
- /* Run matrix multiply kernel */
- NEScheduler::get().schedule(&_mm_kernel, Window::DimY);
-
- _memory_group.release();
-}
+#undef NEGEMMLOWP_VALIDATE_DIMENSIONS