Handle Conv2d layer with implicit output padding in NHWC

Corner cases exist when output top/bottom padding is non-zero for
Convolution Layer. This can cause invalid output from the
NEGEMMConvolutionLayer as assembly kernel integration does not
efficiently handles such cases.

As a workaround we always allocate a memory-managed auxiliary tensor
which we use as an output for GEMM when padding exists and then we copy
to the padded output. If no padding exists we import the output tensor
memory to the temporary buffer and perform calculation as we did before.

Resolves: COMPMID-4114

Signed-off-by: Georgios Pinitas <georgios.pinitas@arm.com>
Change-Id: If82d0e115b8369b91d775895d5315b044306cc74
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5083
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Reviewed-by: Giorgio Arena <giorgio.arena@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>

diff --git a/arm_compute/runtime/NEON/functions/NEGEMMConvolutionLayer.h b/arm_compute/runtime/NEON/functions/NEGEMMConvolutionLayer.h
index aadc429864db008e9b6ef5ea3f34ee4c82b0081b..65c2ef7e0b48f9330f3ec839e280f753b406d744 100644 (file)
--- a/arm_compute/runtime/NEON/functions/NEGEMMConvolutionLayer.h
+++ b/arm_compute/runtime/NEON/functions/NEGEMMConvolutionLayer.h
@@ -275,10 +275,12 @@ private:
     NEReshapeLayer                                                     _reshape_layer;
 
     const ITensor *_original_weights;
+    const ITensor *_original_output;
 
     Tensor _im2col_output;
     Tensor _weights_reshaped;
     Tensor _gemm_output;
+    Tensor _gemm_output_3d;
     Tensor _tmp_output;
 
     DataLayout _data_layout;

diff --git a/src/runtime/NEON/functions/NEGEMMAssemblyDispatch.h b/src/runtime/NEON/functions/NEGEMMAssemblyDispatch.h
index 466e60183a093a7f01fef7710e73acd4871b0963..381fa4de311a6880655c5467ca16c06b941dcd89 100644 (file)
--- a/src/runtime/NEON/functions/NEGEMMAssemblyDispatch.h
+++ b/src/runtime/NEON/functions/NEGEMMAssemblyDispatch.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2018-2020 Arm Limited.
+ * Copyright (c) 2018-2021 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -117,7 +117,7 @@ public:
     void run() override;
 
 private:
-    std::unique_ptr<IFallback> _arm_gemm;        /** Interface for the arm_gemm fallback */
+    std::unique_ptr<IFallback> _arm_gemm;        /**< Interface for the arm_gemm fallback */
     MemoryGroup                _memory_group;    /**< Function memory group */
     IWeightsManager           *_weights_manager; /**< Pointer to the weights manager */
 };

diff --git a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
index 8fc788c402e2a4d9cab7f1c498f10782798c439e..74ef3eef5670384dee842131c3ceadd781f39503 100644 (file)
--- a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
+++ b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017-2020 Arm Limited.
+ * Copyright (c) 2017-2021 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -109,8 +109,8 @@ NEGEMMConvolutionLayer::~NEGEMMConvolutionLayer() = default;
 
 NEGEMMConvolutionLayer::NEGEMMConvolutionLayer(const std::shared_ptr<IMemoryManager> &memory_manager, IWeightsManager *weights_manager)
     : _memory_group(memory_manager), _weights_manager(weights_manager), _reshape_weights(), _reshape_weights_managed(), _im2col_kernel(), _mm_gemm(memory_manager), _mm_gemmlowp(memory_manager),
-      _col2im_kernel(), _reshape_layer(), _original_weights(nullptr), _im2col_output(), _weights_reshaped(), _gemm_output(), _tmp_output(), _data_layout(DataLayout::NCHW), _skip_im2col(false),
-      _skip_col2im(false), _is_quantized(false), _is_prepared(false)
+      _col2im_kernel(), _reshape_layer(), _original_weights(nullptr), _original_output(nullptr), _im2col_output(), _weights_reshaped(), _gemm_output(), _gemm_output_3d(), _tmp_output(),
+      _data_layout(DataLayout::NCHW), _skip_im2col(false), _skip_col2im(false), _is_quantized(false), _is_prepared(false)
 {
 }
 
@@ -281,6 +281,7 @@ void NEGEMMConvolutionLayer::configure(const ITensor *input, const ITensor *weig
 
     _is_prepared      = weights_info.retain_internal_weights();
     _original_weights = weights;
+    _original_output  = output;
     _is_quantized     = is_data_type_quantized_asymmetric(input->info()->data_type());
     _data_layout      = data_layout;
     _skip_im2col      = (data_layout == DataLayout::NHWC && kernel_width == 1 && kernel_height == 1 && conv_info.stride().first == 1 && conv_info.stride().second == 1);
@@ -368,6 +369,15 @@ void NEGEMMConvolutionLayer::configure(const ITensor *input, const ITensor *weig
         // Update GEMM output
         gemm_output_to_use = &_gemm_output;
     }
+    else
+    {
+        _gemm_output.allocator()->init(*output->info());
+        _memory_group.manage(&_gemm_output);
+        _gemm_output_3d.allocator()->init(*output->info());
+
+        // Update GEMM output
+        gemm_output_to_use = &_gemm_output_3d;
+    }
 
     // Configure GEMM
     // In case we need to skip col2im, GEMM3D (gemm_3d_depth != 0) must be called in order to avoid reshaping the output matrix
@@ -393,16 +403,18 @@ void NEGEMMConvolutionLayer::configure(const ITensor *input, const ITensor *weig
             _reshape_layer.configure(gemm_output_to_use, output);
         }
     }
+    else
+    {
+        // Configure reshape layer
+        _reshape_layer.configure(gemm_output_to_use, output);
+    }
 
     if(_is_quantized && !_skip_col2im)
     {
         _tmp_output.allocator()->allocate();
     }
 
-    if(!_skip_col2im || _is_quantized)
-    {
-        _gemm_output.allocator()->allocate();
-    }
+    _gemm_output.allocator()->allocate();
 
     ARM_COMPUTE_ERROR_ON_MSG((output->info()->dimension(idx_width) != conv_w) || (output->info()->dimension(idx_height) != conv_h),
                              "Output shape does not match the expected one");
@@ -554,6 +566,8 @@ void NEGEMMConvolutionLayer::run()
 
     MemoryGroupResourceScope scope_mg(_memory_group);
 
+    bool out_has_padding = _skip_col2im && (_original_output->info()->padding().bottom != 0 || _original_output->info()->padding().top != 0);
+
     if(!_skip_im2col)
     {
         // Run input reshaping
@@ -561,6 +575,10 @@ void NEGEMMConvolutionLayer::run()
         NEScheduler::get().schedule(_im2col_kernel.get(), y_dim);
     }
 
+    // Handle the case where output has top/bottom padding
+    const ITensor *out_to_use = out_has_padding ? &_gemm_output : _original_output;
+    _gemm_output_3d.allocator()->import_memory(out_to_use->buffer());
+
     // Runs NEGEMM or NEGEMMLowpMatrixMultiplyCore functions
     if(_is_quantized)
     {
@@ -585,6 +603,10 @@ void NEGEMMConvolutionLayer::run()
             _reshape_layer.run();
         }
     }
+    else if(out_has_padding)
+    {
+        _reshape_layer.run();
+    }
 }
 
 void NEGEMMConvolutionLayer::prepare()