Implement address precalculation for indirect conv2d - OpenCL

- Implement kernel (ClIndirectConv2dAddressPrecalculationKernel)
- Implement OpenCL kernel (indirect_convolution.cl)
- Add test

Resolves COMPMID-5708

Change-Id: If7408e37cbc6f9ad8506ff3334bc574e5d6763fb
Signed-off-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/8661
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Viet-Hoa Do <viet-hoa.do@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Benchmark: Arm Jenkins <bsgcomp@arm.com>

1 files changed, 29 insertions, 0 deletions

diff --git a/arm_compute/core/utils/misc/ShapeCalculator.h b/arm_compute/core/utils/misc/ShapeCalculator.h
index 84c0ee5034..9e7c981814 100644
--- a/arm_compute/core/utils/misc/ShapeCalculator.h
+++ b/arm_compute/core/utils/misc/ShapeCalculator.h
@@ -746,6 +746,35 @@ inline TensorShape compute_deep_convolution_shape(const ITensorInfo &input, cons
     return compute_deep_convolution_shape(input.tensor_shape(), input.data_layout(), weights.tensor_shape(), conv_info);
 }
 
+/** Calculate the indirect buffer output shape used by the indirect convolution function
+ *
+ * @param[in] input_shape       Input tensor shape
+ * @param[in] input_data_layout Input data layout
+ * @param[in] weights_shape     Weights tensor shape
+ * @param[in] conv_info         Contains padding and stride information
+ * @param[in] desc              Contains the direct/indirect convolution compute arguments, such as the tiling dimensions
+ *
+ * @return the calculated shape
+ */
+inline TensorShape compute_indirect_buffer_shape(const TensorShape &input_shape, DataLayout input_data_layout, const TensorShape &weights_shape, const PadStrideInfo &conv_info,
+                                                 const DirectConvComputeKernelInfo &desc)
+{
+    ARM_COMPUTE_ERROR_ON_MSG(input_data_layout != DataLayout::NHWC, "The data layout can only be NHWC");
+    ARM_COMPUTE_ERROR_ON_MSG(desc.m0 <= 0 || desc.m0 > 8, "M0 can only be greater than 0 and less than or equal to 8");
+
+    const unsigned int m0 = desc.m0;
+    const unsigned int kw = weights_shape[1];
+    const unsigned int kh = weights_shape[2];
+
+    TensorShape output_conv2d_shape = compute_deep_convolution_shape(input_shape, input_data_layout, weights_shape, conv_info);
+
+    const unsigned int output_w = m0 * kw * kh;
+    const unsigned int output_h = DIV_CEIL(output_conv2d_shape[1] * output_conv2d_shape[2], m0);
+    const unsigned int output_b = output_conv2d_shape[3];
+
+    return TensorShape(output_w, output_h, output_b);
+}
+
 /** Calculate the min/max shape output shape of a tensor
  *
  * @param[in] input Input tensor info