COMPMID-3732: Remove OpenCL padding from CLPoolingLayer

- Refactor pooling layer kernels on OpenCL (F32/F16/QASYMM8) to avoid
  padding and improve performance
- Add test for checking zero padding requirement
- Fix issue with extracting the index. The issue was caused by the
  padding passed at compile time
- auto_init indices tensor in CLPoolingLayerKernel

Change-Id: I1ae5a2ef8c4ce787c80dcd73e35c17bb34623cb5
Signed-off-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/4188
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Reviewed-by: Giorgio Arena <giorgio.arena@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>

1 files changed, 71 insertions, 63 deletions

diff --git a/src/core/CL/kernels/CLPoolingLayerKernel.cpp b/src/core/CL/kernels/CLPoolingLayerKernel.cpp
index d60e196b7f..1771834aac 100644
--- a/src/core/CL/kernels/CLPoolingLayerKernel.cpp
+++ b/src/core/CL/kernels/CLPoolingLayerKernel.cpp
@@ -50,10 +50,14 @@ namespace
 // Internal window config info
 using CLPoolingConfig = std::pair<unsigned int, BorderSize>; //num_elems_processed_per_iteration, border_size
 
-void auto_init(const ITensorInfo *input, ITensorInfo *output, PoolingLayerInfo pool_info)
+void auto_init(const ITensorInfo *input, ITensorInfo *output, ITensorInfo *indices, PoolingLayerInfo pool_info)
 {
     TensorShape out_shape = compute_pool_shape(*input, pool_info);
     auto_init_if_empty(*output, input->clone()->set_tensor_shape(out_shape));
+    if(indices)
+    {
+        auto_init_if_empty(*indices, input->clone()->set_tensor_shape(out_shape).set_data_type(DataType::U32));
+    }
 }
 
 Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const PoolingLayerInfo &pool_info, const ITensorInfo *indices)
@@ -63,16 +67,19 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, c
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32);
     ARM_COMPUTE_RETURN_ERROR_ON_MSG((is_data_type_quantized_asymmetric(input->data_type()) && pool_info.pool_type == PoolingType::L2),
                                     "Unsupported combination of parameters!");
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(is_data_type_quantized(input->data_type()) && !pool_info.exclude_padding && (pool_info.pool_type == PoolingType::AVG) && pool_info.pad_stride_info.has_padding()
-                                    && (input->data_layout() == DataLayout::NHWC),
-                                    "exclude_padding equal false is not supported for AVG Pooling with padding on quantized types");
+
     // Check indices
     if(indices)
     {
-        ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(indices, 1, DataType::U32);
         ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
         ARM_COMPUTE_RETURN_ERROR_ON_MSG(pool_info.pool_type != PoolingType::MAX, "Pooling indices only supported for MAX pooling method");
         ARM_COMPUTE_RETURN_ERROR_ON_MSG((pool_info.pool_size != Size2D(2, 2)), "Pooling indices only supported for pool size 2x2");
+
+        if(indices->total_size() != 0)
+        {
+            TensorInfo idx_info(TensorInfo(compute_pool_shape(*input, pool_info), 1, DataType::U32));
+            ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(indices, &idx_info);
+        }
     }
 
     // Checks performed when output is configured
@@ -108,9 +115,9 @@ std::tuple<Status, Window, CLPoolingConfig> validate_and_configure_window(ITenso
     const int  pool_pad_top    = pad_stride_info.pad_top();
     const int  pool_pad_left   = pad_stride_info.pad_left();
     const int  pool_pad_bottom = pad_stride_info.pad_bottom();
-    BorderSize border_size     = BorderSize(pool_pad_top, pool_pad_right, pool_pad_bottom, pool_pad_left);
+    BorderSize border_size     = BorderSize();
 
-    auto_init(input, output, pool_info);
+    auto_init(input, output, indices, pool_info);
     pooled_w = output->tensor_shape()[idx_width];
     pooled_h = output->tensor_shape()[idx_height];
 
@@ -126,6 +133,8 @@ std::tuple<Status, Window, CLPoolingConfig> validate_and_configure_window(ITenso
     {
         case DataLayout::NCHW:
         {
+            // Initialize border size
+            border_size = BorderSize(pool_pad_top, pool_pad_right, pool_pad_bottom, pool_pad_left);
             // Change the number of elements processed per iteration
             // for pooling 3x3 with stride less equal than 3
             const bool can_optimize                         = (pool_size_x == 3) && (pool_size_y == 3) && (pool_stride_x <= 3) && !is_data_type_quantized(data_type);
@@ -165,27 +174,17 @@ std::tuple<Status, Window, CLPoolingConfig> validate_and_configure_window(ITenso
         }
         case DataLayout::NHWC:
         {
-            num_elems_processed_per_iteration = 8;
+            // Initialize border size
+            border_size = BorderSize();
+            num_elems_processed_per_iteration = adjust_vec_size(4, output->dimension(0));
             win                               = calculate_max_window(*output, Steps(num_elems_processed_per_iteration));
 
-            AccessWindowStatic input_access(input,
-                                            0, -1,
-                                            ceil_to_multiple(input->dimension(0), num_elems_processed_per_iteration), input->dimension(1));
-            AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration);
-
-            // Update indices window
-            if(indices)
+            if(indices != nullptr)
             {
-                AccessWindowHorizontal indices_access(indices, 0, num_elems_processed_per_iteration);
-                window_changed = update_window_and_padding(win, input_access, output_access, indices_access);
-                indices_access.set_valid_region(win, ValidRegion(Coordinates(), indices->tensor_shape()));
-            }
-            else
-            {
-                window_changed = update_window_and_padding(win, input_access, output_access);
+                indices->set_valid_region(ValidRegion(Coordinates(), indices->tensor_shape()));
             }
 
-            output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape()));
+            output->set_valid_region(ValidRegion(Coordinates(), output->tensor_shape()));
             break;
         }
         default:
@@ -228,6 +227,7 @@ void CLPoolingLayerKernel::configure(const CLCompileContext &compile_context, co
     const int           idx_width       = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH);
     const int           idx_height      = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT);
     const int           idx_channel     = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::CHANNEL);
+    const int           idx_batch_size  = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::BATCHES);
     const int           pool_size_x     = pool_info.is_global_pooling ? input->info()->dimension(idx_width) : pool_info.pool_size.width;
     const int           pool_size_y     = pool_info.is_global_pooling ? input->info()->dimension(idx_height) : pool_info.pool_size.height;
     const PadStrideInfo pad_stride_info = pool_info.pad_stride_info;
@@ -246,17 +246,11 @@ void CLPoolingLayerKernel::configure(const CLCompileContext &compile_context, co
     ARM_COMPUTE_ERROR_THROW_ON(std::get<0>(win_config));
     ICLKernel::configure_internal(std::get<1>(win_config));
 
-    if(_data_layout == DataLayout::NCHW)
-    {
-        CLPoolingConfig pooling_config     = std::get<2>(win_config);
-        _num_elems_processed_per_iteration = pooling_config.first;
-        _border_size                       = pooling_config.second;
-    }
-    else
-    {
-        _border_size                       = BorderSize(1, 0, 0, 0);
-        _num_elems_processed_per_iteration = 8;
-    }
+    CLPoolingConfig pooling_config     = std::get<2>(win_config);
+    _num_elems_processed_per_iteration = pooling_config.first;
+    _border_size                       = pooling_config.second;
+
+    build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(_num_elems_processed_per_iteration));
 
     // Tensor paddings are used to calculate the indicies for MAX pooling
     if(pool_info.pool_size == Size2D(2, 2) && pool_type == PoolingType::MAX && _indices && is_data_type_float(data_type))
@@ -282,7 +276,8 @@ void CLPoolingLayerKernel::configure(const CLCompileContext &compile_context, co
     }
 
     // Check output dimensions
-    auto_init(input->info(), output->info(), pool_info);
+    auto_init(input->info(), output->info(), indices ? indices->info() : nullptr, pool_info);
+
     ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), pool_info, (indices) ? indices->info() : nullptr));
 
     build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type));
@@ -314,19 +309,20 @@ void CLPoolingLayerKernel::configure(const CLCompileContext &compile_context, co
         build_opts.add_option("-DINITIAL_VALUE=0");
     }
 
-    const auto use_fp_mixed_precision = (data_type == DataType::F16) && pool_info.fp_mixed_precision;
-    const auto use_wider_accumulator  = use_fp_mixed_precision && (pool_type != PoolingType::MAX);
-    const auto acc_data_type          = get_cl_type_from_data_type(use_wider_accumulator ? DataType::F32 : data_type);
-    build_opts.add_option("-DACC_DATA_TYPE=" + acc_data_type);
-    build_opts.add_option_if(use_wider_accumulator, "-DFP_MIXED_PRECISION");
+    build_opts.add_option("-DMAX_WIDTH=" + support::cpp11::to_string(input->info()->dimension(idx_width) + (exclude_padding ? 0 : pool_pad_left)));
+    build_opts.add_option("-DMAX_HEIGHT=" + support::cpp11::to_string(input->info()->dimension(idx_height) + (exclude_padding ? 0 : pool_pad_top)));
 
     // Create kernel
     switch(_data_layout)
     {
         case DataLayout::NCHW:
         {
-            build_opts.add_option("-DMAX_WIDTH=" + support::cpp11::to_string(input->info()->dimension(idx_width) + (exclude_padding ? 0 : pool_pad_left)));
-            build_opts.add_option("-DMAX_HEIGHT=" + support::cpp11::to_string(input->info()->dimension(idx_height) + (exclude_padding ? 0 : pool_pad_top)));
+            const auto use_fp_mixed_precision = (data_type == DataType::F16) && pool_info.fp_mixed_precision;
+            const auto use_wider_accumulator  = use_fp_mixed_precision && (pool_type != PoolingType::MAX);
+            const auto acc_data_type          = get_cl_type_from_data_type(use_wider_accumulator ? DataType::F32 : data_type);
+            build_opts.add_option("-DACC_DATA_TYPE=" + acc_data_type);
+            build_opts.add_option_if(use_wider_accumulator, "-DFP_MIXED_PRECISION");
+
             if(pool_type != PoolingType::MAX)
             {
                 build_opts.add_option_if(exclude_padding, "-DEXCLUDE_PADDING");
@@ -365,26 +361,38 @@ void CLPoolingLayerKernel::configure(const CLCompileContext &compile_context, co
         }
         case DataLayout::NHWC:
         {
+            // Floating point mixed precision is support on F16 only
+            const auto use_fp_mixed_precision = (data_type == DataType::F16) && pool_info.fp_mixed_precision && pool_type != PoolingType::MAX;
+
+            // Wider accumulation is required to avoid accuracy loss
+            // Case 1: Floating point mixed precision (fp16 input data and fp32 accumulation)
+            // Cast 2: Quantized (int8/uint8 input data and int32 accumulation )
+            DataType acc_data_type = data_type;
+
+            if(use_fp_mixed_precision)
+            {
+                acc_data_type = DataType::F32;
+            }
+            else if(is_data_type_quantized(data_type) && pool_type != PoolingType::MAX)
+            {
+                acc_data_type = DataType::S32;
+            }
+
+            build_opts.add_option("-DACC_DATA_TYPE=" + get_cl_type_from_data_type(acc_data_type));
+            build_opts.add_option_if(use_fp_mixed_precision, "-DFP_MIXED_PRECISION");
             build_opts.add_option_if(exclude_padding, "-DEXCLUDE_PADDING");
-            build_opts.add_option("-DMAX_WIDTH=" + support::cpp11::to_string(input->info()->dimension(idx_width)));
-            build_opts.add_option("-DMAX_HEIGHT=" + support::cpp11::to_string(input->info()->dimension(idx_height)));
-            build_opts.add_option_if(output->info()->tensor_shape().total_size_upper(3) > 1,
-                                     "-DDST_DEPTH=" + support::cpp11::to_string(output->info()->dimension(idx_height)));
-            build_opts.add_option_if(output->info()->tensor_shape().total_size_upper(3) > 1,
-                                     "-DBATCH_SIZE=" + support::cpp11::to_string(output->info()->tensor_shape().total_size_upper(3)));
-
-            if(pool_info.pool_size == Size2D(2, 2) && pool_type == PoolingType::MAX && _indices && is_data_type_float(data_type))
+            build_opts.add_option("-DSRC_WIDTH=" + support::cpp11::to_string(input->info()->dimension(idx_width)));
+            build_opts.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(input->info()->dimension(idx_height)));
+            build_opts.add_option("-DDST_HEIGHT=" + support::cpp11::to_string(output->info()->dimension(idx_height)));
+            build_opts.add_option("-DDST_CHANNELS=" + support::cpp11::to_string(output->info()->dimension(idx_channel)));
+            build_opts.add_option("-DDST_BATCH_SIZE=" + support::cpp11::to_string(output->info()->dimension(idx_batch_size)));
+            build_opts.add_option("-DVEC_SIZE_LEFTOVER=" + support::cpp11::to_string(input->info()->dimension(0) % _num_elems_processed_per_iteration));
+            if(pool_info.pool_size == Size2D(2, 2) && is_data_type_float(data_type))
             {
-                if(data_type == DataType::F32)
-                {
-                    std::string kernel_name = "pooling_layer_2_nhwc_indices_fp32";
-                    _kernel                 = create_kernel(compile_context, kernel_name, build_opts.options());
-                }
-                else if(data_type == DataType::F16)
-                {
-                    std::string kernel_name = "pooling_layer_2_nhwc_indices_fp16";
-                    _kernel                 = create_kernel(compile_context, kernel_name, build_opts.options());
-                }
+                build_opts.add_option_if(_indices != nullptr && pool_type == PoolingType::MAX, "-DEXTRACT_MAX_INDEX");
+
+                std::string kernel_name = "pooling_layer_2x2_nhwc";
+                _kernel                 = create_kernel(compile_context, kernel_name, build_opts.options());
             }
             else
             {
@@ -452,7 +460,7 @@ void CLPoolingLayerKernel::run(const Window &window, cl::CommandQueue &queue)
                 unsigned int idx = 0;
                 add_3D_tensor_argument(idx, _input, in_slice);
                 add_3D_tensor_argument(idx, _output, slice);
-                if(_indices && is_data_type_float(_input->info()->data_type()) && (_pool_info.pool_type == PoolingType::MAX) && (_pool_info.pool_size == Size2D(2, 2)))
+                if(_indices && is_data_type_float(_input->info()->data_type()) && (_pool_info.pool_size == Size2D(2, 2)))
                 {
                     add_3D_tensor_argument(idx, _indices, slice);
                 }
@@ -463,14 +471,14 @@ void CLPoolingLayerKernel::run(const Window &window, cl::CommandQueue &queue)
         }
         case DataLayout::NHWC:
         {
-            const size_t total_batches = _output->info()->tensor_shape().total_size_upper(3);
+            const size_t batch_size = _output->info()->tensor_shape().total_size_upper(3);
 
             Window slice    = window_collapsed.first_slice_window_4D();
             Window in_slice = window_collapsed.first_slice_window_4D();
             in_slice.set(Window::DimX, Window::Dimension(0, _input->info()->dimension(0), _num_elems_processed_per_iteration));
             in_slice.set(Window::DimY, Window::Dimension(0, _input->info()->dimension(1), pool_stride_x));
             in_slice.set(Window::DimZ, Window::Dimension(0, _input->info()->dimension(2), pool_stride_y));
-            in_slice.set(3, Window::Dimension(0, total_batches, 1));
+            in_slice.set(3, Window::Dimension(0, batch_size, 1));
             do
             {
                 // Set inputs