Provide tests for fusing pad layer in graph API

There are two tests:
    - A unit test that checks if certain padding configurations are to be
      fused or not
    - A fixture test that compares a reference implementation of pad+conv
      vs target implementation using the same fusing logic as graph API

Tests are written for CL backend only to prevent code duplication.
The code written in the graph API remains untested.

Resolves: COMPMID-4702
Change-Id: Ie84d1cb910013033b46ac9d66cf5fc556d4963d2
Signed-off-by: Gunes Bayir <gunes.bayir@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/6252
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Sheri Zhang <sheri.zhang@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>

1 files changed, 64 insertions, 6 deletions

diff --git a/tests/validation/fixtures/ConvolutionLayerFixture.h b/tests/validation/fixtures/ConvolutionLayerFixture.h
index 6dbf3d5731..0b3f070e9c 100644
--- a/tests/validation/fixtures/ConvolutionLayerFixture.h
+++ b/tests/validation/fixtures/ConvolutionLayerFixture.h
@@ -26,7 +26,9 @@
 
 #include "arm_compute/core/TensorShape.h"
 #include "arm_compute/core/Types.h"
+#include "arm_compute/graph/Utils.h"
 #include "arm_compute/runtime/NEON/NEScheduler.h"
+#include "src/graph/mutators/MutatorUtils.h"
 #include "tests/AssetsLibrary.h"
 #include "tests/Globals.h"
 #include "tests/IAccessor.h"
@@ -35,6 +37,7 @@
 #include "tests/validation/Helpers.h"
 #include "tests/validation/reference/ActivationLayer.h"
 #include "tests/validation/reference/ConvolutionLayer.h"
+#include "tests/validation/reference/PadLayer.h"
 #include "tests/validation/reference/Permute.h"
 #include "tests/validation/reference/Utils.h"
 
@@ -70,7 +73,7 @@ public:
     template <typename...>
     void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, Size2D dilation, bool reshape_weights,
                DataType data_type, DataType weights_data_type, DataLayout data_layout, QuantizationInfo quantization_info, QuantizationInfo weight_quantization_info, ActivationLayerInfo act_info,
-               bool mixed_layout = false)
+               bool mixed_layout = false, PaddingList pre_pad_layer = PaddingList({}))
     {
         _mixed_layout             = mixed_layout;
         _data_type                = data_type;
@@ -83,8 +86,8 @@ public:
         _weight_quantization_info = weight_quantization_info;
         _data_layout              = data_layout;
 
-        _target    = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, reshape_weights, dilation, act_info);
-        _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, dilation, act_info);
+        _target    = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, reshape_weights, dilation, act_info, pre_pad_layer);
+        _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, dilation, act_info, pre_pad_layer);
     }
 
 protected:
@@ -179,8 +182,9 @@ protected:
         }
     }
 
+    // given input is IN nchw format
     TensorType compute_target(TensorShape input_shape, TensorShape weights_shape, const TensorShape &bias_shape, TensorShape output_shape, const PadStrideInfo &info,
-                              bool reshape_weights, const Size2D &dilation, const ActivationLayerInfo act_info)
+                              bool reshape_weights, const Size2D &dilation, const ActivationLayerInfo act_info, PaddingList pre_pad_layer = PaddingList({}))
     {
         ARM_COMPUTE_ERROR_ON((input_shape[2] % weights_shape[2]) != 0);
 
@@ -191,6 +195,18 @@ protected:
             permute(input_shape, PermutationVector(2U, 0U, 1U));
             permute(weights_shape, PermutationVector(2U, 0U, 1U));
             permute(output_shape, PermutationVector(2U, 0U, 1U));
+
+            if(pre_pad_layer.size() > 0)
+            {
+                // make sure paddings exist for each c,h,w dimensions
+                for(unsigned int i = 0; i < 3 - pre_pad_layer.size(); ++i)
+                {
+                    pre_pad_layer.push_back({ 0, 0 });
+                }
+
+                // rotate padding info from nchw to nhwc
+                std::rotate(pre_pad_layer.begin(), pre_pad_layer.begin() + 2, pre_pad_layer.begin() + 3);
+            }
         }
 
         const int idx_width  = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH);
@@ -207,7 +223,30 @@ protected:
 
         // Create and configure function
         FunctionType conv;
-        detail::configure_conv_function(conv, &src, &weights, &bias, &dst, info, weights_info, dilation, act_info, num_groups);
+
+        const unsigned int height_index = arm_compute::graph::get_dimension_idx(_data_layout, DataLayoutDimension::HEIGHT);
+        const unsigned int width_index  = arm_compute::graph::get_dimension_idx(_data_layout, DataLayoutDimension::WIDTH);
+
+        const PaddingInfo pad_w = width_index < pre_pad_layer.size() ? pre_pad_layer[width_index] : PaddingInfo(0, 0);
+        const PaddingInfo pad_h = height_index < pre_pad_layer.size() ? pre_pad_layer[height_index] : PaddingInfo(0, 0);
+
+        if(pre_pad_layer.size() > 0 && arm_compute::graph::is_padding_in_height_or_width(_data_layout, pre_pad_layer))
+        {
+            // this is the logic implemented in NodeFusionMutator -> fuse_pad_with_convolution
+            const PadStrideInfo new_conv_info(
+                info.stride().first,
+                info.stride().second,
+                info.pad_left() + pad_w.first,
+                info.pad_right() + pad_w.second,
+                info.pad_top() + pad_h.first,
+                info.pad_bottom() + pad_h.second,
+                info.round());
+            detail::configure_conv_function(conv, &src, &weights, &bias, &dst, new_conv_info, weights_info, dilation, act_info, num_groups);
+        }
+        else
+        {
+            detail::configure_conv_function(conv, &src, &weights, &bias, &dst, info, weights_info, dilation, act_info, num_groups);
+        }
 
         ARM_COMPUTE_ASSERT(src.info()->is_resizable());
         ARM_COMPUTE_ASSERT(weights.info()->is_resizable());
@@ -246,7 +285,7 @@ protected:
     }
 
     SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, const PadStrideInfo &info,
-                                      const Size2D &dilation, const ActivationLayerInfo act_info)
+                                      const Size2D &dilation, const ActivationLayerInfo act_info, PaddingList pre_pad_layer = PaddingList({}))
     {
         ARM_COMPUTE_ERROR_ON((input_shape[2] % weights_shape[2]) != 0);
 
@@ -273,6 +312,11 @@ protected:
             regularize_values(static_cast<void *>(weights.data()), weights.num_elements());
         }
 
+        if(pre_pad_layer.size() > 0)
+        {
+            src = reference::pad_layer<T>(src, pre_pad_layer, PixelValue(0), PaddingMode::CONSTANT);
+        }
+
         return (act_info.enabled()) ? reference::activation_layer<T>(reference::convolution_layer<T>(src, weights, bias, output_shape, info, dilation, num_groups),
                                                                      act_info) :
                reference::convolution_layer<T>(src, weights, bias, output_shape, info, dilation, num_groups);
@@ -307,6 +351,20 @@ public:
 };
 
 template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool mixed_layout = false>
+class ConvolutionValidationWithPaddingFixture : public ConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T, T>
+{
+public:
+    template <typename...>
+    void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, Size2D dilation, bool reshape_weights, DataType data_type,
+               DataLayout data_layout, ActivationLayerInfo act_info, PaddingList pre_pad_layer = PaddingList({}))
+    {
+        ConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, dilation, reshape_weights,
+                                                                                                 data_type, data_type, data_layout,
+                                                                                                 QuantizationInfo(), QuantizationInfo(), act_info, mixed_layout, pre_pad_layer);
+    }
+};
+
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool mixed_layout = false>
 class ConvolutionValidationQuantizedFixture : public ConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T, T>
 {
 public: