commit edfa9f463bed084f8b0953557202b2a1e56da817
author Gian Marco Iodice <gianmarco.iodice@arm.com> Tue Aug 15 11:45:22 2017 +0100
committer Anthony Barbier <anthony.barbier@arm.com> Fri Nov 02 16:35:24 2018 +0000
tree 5d1e92926d112fde05dcbc61324d96f73f692390
parent dc460f13ee65e27b2a428e44c2d80afb1f516a99

COMPMID-477 - Optimized batched case in CLConvolutionLayer

Change-Id: I4ef18f49f1da0cb816aaa0762466b940792c15ed
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/84162
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>

src/runtime/CL/functions/CLGEMM.cpp

diff --git a/src/runtime/CL/functions/CLGEMM.cpp b/src/runtime/CL/functions/CLGEMM.cpp
index e81d8a6..9867229 100644
--- a/src/runtime/CL/functions/CLGEMM.cpp
+++ b/src/runtime/CL/functions/CLGEMM.cpp
@@ -39,7 +39,7 @@
 using namespace arm_compute;
 
 CLGEMM::CLGEMM()
-    : _interleave_kernel(), _transpose_kernel(), _mm_kernel(), _ma_kernel(), _tmp_a(), _tmp_b(), _run_vector_matrix_multiplication(false), _run_addition(false)
+    : _interleave_kernel(), _transpose_kernel(), _mm_kernel(), _ma_kernel(), _tmp_a(), _tmp_b(), _is_interleaved_transposed(false), _run_addition(false)
 {
 }
 
@@ -59,12 +59,16 @@
 
     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");
 
-    _mm_kernel.set_target(CLScheduler::get().target());
+    // If the input tensor has less than 16 rows, we run a special version of GEMM without reshaping the input tensors
+    _is_interleaved_transposed = a->info()->dimension(1) > 16;
 
-    // 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)
+    const ICLTensor *matrix_a = a;
+    const ICLTensor *matrix_b = b;
+
+    if(_is_interleaved_transposed)
     {
-        _run_vector_matrix_multiplication = false;
+        matrix_a = &_tmp_a;
+        matrix_b = &_tmp_b;
 
         TensorShape shape_tmp_a = a->info()->tensor_shape();
         TensorShape shape_tmp_b = b->info()->tensor_shape();
@@ -89,19 +93,17 @@
         _transpose_kernel.configure(b, &_tmp_b);
 
         // Configure matrix multiply kernel
-        _mm_kernel.configure(&_tmp_a, &_tmp_b, output, alpha);
+        _mm_kernel.set_target(CLScheduler::get().target());
+    }
 
+    _mm_kernel.configure(matrix_a, matrix_b, output, alpha, _is_interleaved_transposed);
+
+    if(_is_interleaved_transposed)
+    {
         // Allocate intermediate tensors
         _tmp_a.allocator()->allocate();
         _tmp_b.allocator()->allocate();
     }
-    else // The first input tensor is a vector
-    {
-        _run_vector_matrix_multiplication = true;
-
-        // Configure the matrix multiply kernel
-        _mm_kernel.configure(a, b, output, alpha);
-    }
 
     // Configure matrix addition kernel
     if(beta != 0 && c != nullptr)
@@ -113,7 +115,7 @@
 
 void CLGEMM::run()
 {
-    if(!_run_vector_matrix_multiplication)
+    if(_is_interleaved_transposed)
     {
         // Run interleave kernel
         CLScheduler::get().enqueue(_interleave_kernel, false);