1 files changed, 72 insertions, 88 deletions

diff --git a/src/runtime/NEON/functions/NEGEMMLowp.cpp b/src/runtime/NEON/functions/NEGEMMLowp.cpp
index 12136cbcb5..ab7fa079b1 100644
--- a/src/runtime/NEON/functions/NEGEMMLowp.cpp
+++ b/src/runtime/NEON/functions/NEGEMMLowp.cpp
@@ -31,120 +31,104 @@
 #include "arm_compute/core/Types.h"
 #include "arm_compute/core/Validate.h"
 #include "arm_compute/runtime/NEON/NEScheduler.h"
+#include "arm_compute/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.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(), _mm_optimised_kernel(nullptr), _interleave_blocked(), _interleave_blocked_transposed(), _tmp_a(),
-      _tmp_b()
+    : _memory_group(std::move(memory_manager)), _mm_func(), _mtx_a_reduction_kernel(), _mtx_b_reduction_kernel(), _finalize_kernel(), _vector_sum_col(), _vector_sum_row(), _mm_output(), _a_offset(0),
+      _b_offset(0)
 {
 }
 
-void NEGEMMLowp::configure(const ITensor *a, const ITensor *b, ITensor *output)
+void NEGEMMLowp::configure(const ITensor *a, const ITensor *b, ITensor *output, int32_t a_offset, int32_t b_offset, int32_t c_offset, int32_t output_mult_int, int32_t shift)
 {
-    NEGEMMLOWP_VALIDATE_DIMENSIONS(a, b, output);
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN((a), 1, DataType::U8);
+    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 output 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 output matrix must have the same number of columns as the matrix B");
+
+    _a_offset = a_offset;
+    _b_offset = b_offset;
+
+    // Initialize matrix multiply output tensor
+    const TensorShape &shape_mm_output = output->info()->tensor_shape();
+    TensorInfo         info_mm_output(shape_mm_output, 1, DataType::S32);
+    _mm_output.allocator()->init(info_mm_output);
+    _memory_group.manage(&_mm_output);
 
-    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)
+    // Initialize Matrix B reduction kernel only if _a_offset is not equal to 0
+    if(_a_offset != 0)
     {
-#ifdef ARM_COMPUTE_AARCH64_V8_2
-        // 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 /* ARM_COMPUTE_AARCH64_V8_2 */
+        TensorShape shape_vector_sum_col = b->info()->tensor_shape();
+        shape_vector_sum_col.remove_dimension(1);
+        TensorInfo info_vector_sum_col(shape_vector_sum_col, 1, DataType::S32);
+        _vector_sum_col.allocator()->init(info_vector_sum_col);
+        _memory_group.manage(&_vector_sum_col);
+
+        // Configure Matrix B reduction kernel
+        _mtx_b_reduction_kernel.configure(b, &_vector_sum_col, a->info()->dimension(0), false);
     }
-    else
+
+    // Initialize Matrix A reduction kernel only if _b_offset is not equal to 0
+    if(_b_offset != 0)
     {
-        ARM_COMPUTE_ERROR("Not implemented");
-        //FIXME: This is in the process of being updated, for more info please refer to COMPMID-624.
+        TensorShape shape_vector_sum_row = a->info()->tensor_shape();
+        shape_vector_sum_row.set(Window::DimX, a->info()->dimension(1));
+        shape_vector_sum_row.remove_dimension(1);
+        TensorInfo info_vector_sum_row(shape_vector_sum_row, 1, DataType::S32);
+        _vector_sum_row.allocator()->init(info_vector_sum_row);
+        _memory_group.manage(&_vector_sum_row);
+
+        // Configure Matrix A reduction kernel
+        _mtx_a_reduction_kernel.configure(a, &_vector_sum_row, a->info()->dimension(0), false);
     }
-}
 
-void NEGEMMLowp::run()
-{
-    _memory_group.acquire();
+    // Configure matrix multiply function
+    _mm_func.configure(a, b, &_mm_output);
+
+    // Configure finalize kernel
+    _finalize_kernel.configure(_a_offset == 0 ? nullptr : &_vector_sum_col, _b_offset == 0 ? nullptr : &_vector_sum_row, &_mm_output, output, a->info()->dimension(0), a_offset, b_offset, c_offset,
+                               output_mult_int, shift);
 
-    if(_mm_optimised_kernel != nullptr)
+    // Allocate tensors
+    _mm_output.allocator()->allocate();
+
+    if(_a_offset != 0)
     {
-        NEScheduler::get().schedule(&_interleave_blocked, Window::DimY);
-        NEScheduler::get().schedule(&_interleave_blocked_transposed, Window::DimY);
-        NEScheduler::get().schedule(_mm_optimised_kernel.get(), Window::DimY);
+        _vector_sum_col.allocator()->allocate();
     }
-    else
+
+    if(_b_offset != 0)
     {
-        /* 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);
+        _vector_sum_row.allocator()->allocate();
     }
-
-    _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)
+void NEGEMMLowp::run()
 {
-    NEGEMMLOWP_VALIDATE_DIMENSIONS(a, b, output);
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(a, b, output);
-    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();
-    shape_tmp_a.set(0, a->info()->dimension(0) * 4);
-    shape_tmp_a.set(1, std::ceil(a->info()->dimension(1) / 4.f));
-
-    TensorShape shape_tmp_b = b->info()->tensor_shape();
-    shape_tmp_b.set(0, b->info()->dimension(1) * 16);
-    shape_tmp_b.set(1, std::ceil(b->info()->dimension(0) / 16.f));
+    _memory_group.acquire();
 
-    TensorInfo info_a(shape_tmp_a, 1, a->info()->data_type());
-    TensorInfo info_b(shape_tmp_b, 1, b->info()->data_type());
-    _tmp_a.allocator()->init(info_a);
-    _tmp_b.allocator()->init(info_b);
+    // Run matrix A reduction kernel only if _b_offset is not equal to 0
+    if(_b_offset != 0)
+    {
+        NEScheduler::get().schedule(&_mtx_a_reduction_kernel, Window::DimX);
+    }
 
-    // Manage intermediate buffers
-    _memory_group.manage(&_tmp_a);
-    _memory_group.manage(&_tmp_b);
+    // Run matrix B reduction kernel only if _a_offset is not equal to 0
+    if(_a_offset != 0)
+    {
+        NEScheduler::get().schedule(&_mtx_b_reduction_kernel, Window::DimX);
+    }
 
-    _interleave_kernel.configure(a, &_tmp_a);
-    _transpose_kernel.configure(b, &_tmp_b);
-    _mm_kernel.configure(&_tmp_a, &_tmp_b, output, a_offset, b_offset, output_offset, output_mult_int, shift);
+    // Run matrix multiply core function
+    _mm_func.run();
 
-    _tmp_a.allocator()->allocate();
-    _tmp_b.allocator()->allocate();
-}
+    // Run finalise kernel
+    NEScheduler::get().schedule(&_finalize_kernel, Window::DimY);
 
-#undef NEGEMMLOWP_VALIDATE_DIMENSIONS
+    _memory_group.release();
+}
\ No newline at end of file