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>

1 files changed, 80 insertions, 22 deletions

diff --git a/src/runtime/NEON/functions/NEGEMMLowp.cpp b/src/runtime/NEON/functions/NEGEMMLowp.cpp
index 7413b28d03..90e47ceca0 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 @@ void NEGEMMLowp::configure(const ITensor *a, const ITensor *b, ITensor *output,
     _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