IVGCVSW-863 Broadcast support in CL/NEON Arithmetic Add

Also, added instrumentation to support generic tensor broadcasting for
NEON and CL backends.

Change-Id: I1bc5747a286e1a4b464c209067581e103d473b9a
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/114201
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Tested-by: Jenkins <bsgcomp@arm.com>

1 files changed, 58 insertions, 0 deletions

diff --git a/arm_compute/core/TensorShape.h b/arm_compute/core/TensorShape.h
index 50f1211c18..dc836c98da 100644
--- a/arm_compute/core/TensorShape.h
+++ b/arm_compute/core/TensorShape.h
@@ -26,6 +26,7 @@
 
 #include "arm_compute/core/Dimensions.h"
 #include "arm_compute/core/Error.h"
+#include "arm_compute/core/utils/misc/utility.h"
 
 #include <algorithm>
 #include <array>
@@ -132,6 +133,19 @@ public:
         std::fill(_id.begin() + _num_dimensions, _id.end(), 1);
     }
 
+    /** Return a copy with collapsed dimensions starting from a given point.
+     *
+     * @param[in] start Starting point of collapsing dimensions.
+     *
+     * @return A copy with collapse dimensions starting from start.
+     */
+    TensorShape collapsed_from(size_t start) const
+    {
+        TensorShape copy(*this);
+        copy.collapse(num_dimensions(), start);
+        return copy;
+    }
+
     /** Collapses all dimensions to a single linear total size.
      *
      * @return The total tensor size in terms of elements.
@@ -164,6 +178,50 @@ public:
         return std::accumulate(_id.begin(), _id.begin() + dimension, 1, std::multiplies<size_t>());
     }
 
+    /** If shapes are broadcast compatible, return the broadcasted shape.
+     *
+     * Two tensor shapes are broadcast compatible if for each dimension, they're equal or one of them is 1.
+     *
+     * If two shapes are compatible, each dimension in the broadcasted shape is the max of the original dimensions.
+     *
+     * @param[in] shapes Tensor shapes.
+     *
+     * @return The broadcasted shape or an empty shape if the shapes are not broadcast compatible.
+     */
+    template <typename... Shapes>
+    static TensorShape broadcast_shape(const Shapes &... shapes)
+    {
+        TensorShape bc_shape;
+
+        auto broadcast = [&bc_shape](const TensorShape & other)
+        {
+            if(bc_shape.num_dimensions() == 0)
+            {
+                bc_shape = other;
+            }
+            else if(other.num_dimensions() != 0)
+            {
+                for(size_t d = 0; d < TensorShape::num_max_dimensions; ++d)
+                {
+                    const size_t dim_min = std::min(bc_shape[d], other[d]);
+                    const size_t dim_max = std::max(bc_shape[d], other[d]);
+
+                    if((dim_min != 1) && (dim_min != dim_max))
+                    {
+                        bc_shape = TensorShape{ 0U };
+                        break;
+                    }
+
+                    bc_shape.set(d, dim_max);
+                }
+            }
+        };
+
+        utility::for_each(broadcast, shapes...);
+
+        return bc_shape;
+    }
+
 private:
     /** Remove trailing dimensions of size 1 from the reported number of dimensions. */
     void apply_dimension_correction()