COMPMID-801: NHWC support in CLIm2Col.

And extended tests coverage adding kernel shapes 3x1, 1x5 and 7x7

Change-Id: Ia7c1d4da2368d5f5fbc1a41187f4ac1aca5f150f
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/127727
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>

1 files changed, 179 insertions, 94 deletions

diff --git a/src/core/CL/kernels/CLIm2ColKernel.cpp b/src/core/CL/kernels/CLIm2ColKernel.cpp
index 53a4dca9a3..00d9fcb0e0 100644
--- a/src/core/CL/kernels/CLIm2ColKernel.cpp
+++ b/src/core/CL/kernels/CLIm2ColKernel.cpp
@@ -31,7 +31,10 @@
 #include "arm_compute/core/Error.h"
 #include "arm_compute/core/Helpers.h"
 #include "arm_compute/core/Size2D.h"
+#include "arm_compute/core/TensorInfo.h"
 #include "arm_compute/core/Types.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "support/ToolchainSupport.h"
 
 #include <cmath>
@@ -48,6 +51,7 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, b
     ARM_COMPUTE_RETURN_ERROR_ON(input->data_type() == DataType::QASYMM8 && has_bias);
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output);
     ARM_COMPUTE_RETURN_ERROR_ON((dilation.x() < 1) || (dilation.y() < 1));
+    ARM_COMPUTE_RETURN_ERROR_ON(input->data_layout() == DataLayout::UNKNOWN);
 
     // Checks performed when output is configured
     if(output->total_size() != 0)
@@ -58,63 +62,63 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, b
 
     return Status{};
 }
-} // namespace
 
-CLIm2ColKernel::CLIm2ColKernel()
-    : _input(nullptr), _output(nullptr), _conv_info(), _convolved_dims(), _num_elems_processed_per_iteration(1), _run_func(nullptr), _kernel_dims()
+inline bool run_im2col_reduced(ITensorInfo *input, ITensorInfo *output, const PadStrideInfo &conv_info)
 {
-}
-
-void CLIm2ColKernel::configure(const ICLTensor *input, ICLTensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, const Size2D &dilation)
-{
-    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
-
-    // Perform validation step
-    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), has_bias, dilation));
-
-    _input       = input;
-    _output      = output;
-    _conv_info   = conv_info;
-    _kernel_dims = kernel_dims;
-
-    const DataType  data_type  = input->info()->data_type();
-    const GPUTarget gpu_target = get_target();
-
-    // Create kernel
-    CLBuildOptions build_opts;
-    build_opts.add_option(("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type)));
-    build_opts.add_option("-DELEMENT_SIZE=" + support::cpp11::to_string(input->info()->element_size()));
-    build_opts.add_option_if(has_bias, "-DHAS_BIAS");
-    build_opts.add_option_if(is_data_type_fixed_point(data_type), "-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input->info()->fixed_point_position()));
-
     int stride_x = 0;
     int stride_y = 0;
 
     std::tie(stride_x, stride_y) = conv_info.stride();
 
-    const bool run_img2col_reduced = (output->info()->dimension(0) == (input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2))) && (TensorShape::num_max_dimensions >= 4)
-                                     && (std::equal(input->info()->tensor_shape().cbegin() + 3,
-                                                    input->info()->tensor_shape().cend(),
-                                                    output->info()->tensor_shape().cbegin() + 1))
-                                     && ((stride_x == 1) && (stride_y == 1) && !conv_info.has_padding());
+    return (output->dimension(0) == (input->dimension(0) * input->dimension(1) * input->dimension(2))) && (TensorShape::num_max_dimensions >= 4)
+           && (std::equal(input->tensor_shape().cbegin() + 3,
+                          input->tensor_shape().cend(),
+                          output->tensor_shape().cbegin() + 1))
+           && ((stride_x == 1) && (stride_y == 1) && !conv_info.has_padding());
+}
 
-    bool is_optimized_path = false;
+} // namespace
 
-    _num_elems_processed_per_iteration = 1;
+CLIm2ColKernel::CLIm2ColKernel()
+    : _input(nullptr), _output(nullptr), _conv_info(), _convolved_dims(), _num_elems_processed_per_iteration(1), _run_func(nullptr), _kernel_dims()
+{
+}
 
-    std::string kernel_name;
-    if(!run_img2col_reduced)
+std::string
+CLIm2ColKernel::configure_window(const ICLTensor *input, ICLTensor *output, const Size2D &kernel_dims,
+                                 const Size2D &dilation, const PadStrideInfo &conv_info, CLBuildOptions &build_opts)
+{
+    std::string      kernel_name;
+    bool             is_optimized_path = false;
+    const bool       reduced           = run_im2col_reduced(input->info(), output->info(), conv_info);
+    const DataType   data_type         = input->info()->data_type();
+    const bool       squared_im2col    = kernel_dims.width == kernel_dims.height;
+    const DataLayout data_layout       = input->info()->data_layout();
+
+    if(!reduced)
     {
         // Default kernel name
-        kernel_name = "im2col_generic_dchw";
+        if(data_layout == DataLayout::NCHW)
+        {
+            kernel_name = "im2col_generic_dchw";
+        }
+        else
+        {
+            kernel_name = "im2col_generic_nhwc";
+        }
 
-        _convolved_dims = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1),
-                                            kernel_dims.width, kernel_dims.height,
-                                            conv_info, dilation);
+        const unsigned int width_idx     = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
+        const unsigned int height_idx    = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
+        const unsigned int channel_idx   = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL);
+        const unsigned int input_width   = input->info()->dimension(width_idx);
+        const unsigned int input_height  = input->info()->dimension(height_idx);
+        const unsigned int input_channel = input->info()->dimension(channel_idx);
+
+        _convolved_dims = scaled_dimensions(input_width, input_height, kernel_dims.width, kernel_dims.height, conv_info, dilation);
 
         build_opts.add_option("-DKERNEL_WIDTH=" + support::cpp11::to_string(kernel_dims.width));
         build_opts.add_option("-DKERNEL_HEIGHT=" + support::cpp11::to_string(kernel_dims.height));
-        build_opts.add_option("-DKERNEL_DEPTH=" + support::cpp11::to_string(input->info()->dimension(2)));
+        build_opts.add_option("-DKERNEL_DEPTH=" + support::cpp11::to_string(input_channel));
         build_opts.add_option("-DCONVOLVED_WIDTH=" + support::cpp11::to_string(_convolved_dims.first));
         build_opts.add_option("-DCONVOLVED_HEIGHT=" + support::cpp11::to_string(_convolved_dims.second));
         build_opts.add_option("-DSTRIDE_X=" + support::cpp11::to_string(conv_info.stride().first));
@@ -123,14 +127,12 @@ void CLIm2ColKernel::configure(const ICLTensor *input, ICLTensor *output, const
         build_opts.add_option("-DPAD_TOP=" + support::cpp11::to_string(conv_info.pad_top()));
         build_opts.add_option("-DPAD_RIGHT=" + support::cpp11::to_string(conv_info.pad_right()));
         build_opts.add_option("-DPAD_BOTTOM=" + support::cpp11::to_string(conv_info.pad_bottom()));
-        build_opts.add_option("-DSRC_WIDTH=" + support::cpp11::to_string(input->info()->dimension(0)));
-        build_opts.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(input->info()->dimension(1)));
+        build_opts.add_option("-DSRC_WIDTH=" + support::cpp11::to_string(input_width));
+        build_opts.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(input_height));
         build_opts.add_option("-DDILATION_X=" + support::cpp11::to_string(dilation.x()));
         build_opts.add_option("-DDILATION_Y=" + support::cpp11::to_string(dilation.y()));
         build_opts.add_option_if_else(is_data_type_quantized(data_type), "-DPAD_VALUE=" + support::cpp11::to_string(input->info()->quantization_info().offset), "-DPAD_VALUE=0");
 
-        const bool squared_im2col = kernel_dims.width == kernel_dims.height;
-
         if(dilation == Size2D(1U, 1U))
         {
             if(squared_im2col && !is_data_type_fixed_point(data_type))
@@ -153,12 +155,31 @@ void CLIm2ColKernel::configure(const ICLTensor *input, ICLTensor *output, const
                         _lws_hint                          = cl::NDRange(1, 1, 8);
                         _num_elems_processed_per_iteration = 1;
                         is_optimized_path                  = true;
-                        kernel_name                        = "im2col3x3_dchw";
+                        switch(data_layout)
+                        {
+                            case DataLayout::NCHW:
+                                kernel_name = "im2col3x3_dchw";
+                                break;
+                            case DataLayout::NHWC:
+                                kernel_name = "im2col3x3_nhwc";
+                                break;
+                            default:
+                                ARM_COMPUTE_ERROR("Not supported.");
+                                break;
+                        }
                         break;
                     case 5:
                         _num_elems_processed_per_iteration = 1;
                         is_optimized_path                  = true;
-                        kernel_name                        = "im2col5x5_dchw";
+                        switch(data_layout)
+                        {
+                            case DataLayout::NCHW:
+                                kernel_name = "im2col5x5_dchw";
+                                break;
+                            default:
+                                // using generic_nhwc
+                                break;
+                        }
                         break;
                     case 11:
                         // Optimized im2col11x11 if pad_x = pad_y = 0
@@ -177,28 +198,34 @@ void CLIm2ColKernel::configure(const ICLTensor *input, ICLTensor *output, const
             else if(kernel_dims.width > 1 && !conv_info.has_padding())
             {
                 _num_elems_processed_per_iteration = 1;
-                kernel_name                        = "im2col_generic_padx0_pady0_dchw";
-
-                // Optimized im2col is performed using one or more vector operations with the specified vector size
-                // and a remainder. For example, for 5x5 convolutions, im2col is performed using vectors of size 4
-                // and scalars; for 7x7 convolutions, using vectors of size 4 and vectors of size 3.
-                // Using the vector size of 4 is always safe since OpenCL supports vectors of size 2 and 3.
-                // Using the vector size of 8, however, may be faster.
-                size_t vector_size = 4;
-                // For 2x2 convolutions, use vectors of size 2. (For 3x3 convolutions, im2col_kernel3x3_padx0_pady0
-                // is used instead.)
-                if(kernel_dims.width < vector_size)
-                {
-                    vector_size = kernel_dims.width;
-                }
-                // Vector size optimized for the 11x11 AlexNet convolution on Bifrost.
-                if(gpu_target_is_in(gpu_target, GPUTarget::G71, GPUTarget::G72, GPUTarget::G51, GPUTarget::G51BIG, GPUTarget::G51LIT, GPUTarget::TNOX) && kernel_dims.width == 11)
+                is_optimized_path                  = false;
+
+                if(data_layout == DataLayout::NCHW)
                 {
-                    vector_size = 8;
+                    kernel_name = "im2col_generic_padx0_pady0_dchw";
+
+                    // Optimized im2col is performed using one or more vector operations with the specified vector size
+                    // and a remainder. For example, for 5x5 convolutions, im2col is performed using vectors of size 4
+                    // and scalars; for 7x7 convolutions, using vectors of size 4 and vectors of size 3.
+                    // Using the vector size of 4 is always safe since OpenCL supports vectors of size 2 and 3.
+                    // Using the vector size of 8, however, may be faster.
+                    size_t vector_size = 4;
+                    // For 2x2 convolutions, use vectors of size 2. (For 3x3 convolutions, im2col_kernel3x3_padx0_pady0
+                    // is used instead.)
+                    if(kernel_dims.width < vector_size)
+                    {
+                        vector_size = kernel_dims.width;
+                    }
+                    // Vector size optimized for the 11x11 AlexNet convolution on Bifrost.
+                    const GPUTarget gpu_target = get_target();
+                    if(gpu_target_is_in(gpu_target, GPUTarget::G71, GPUTarget::G72, GPUTarget::G51, GPUTarget::G51BIG, GPUTarget::G51LIT, GPUTarget::TNOX) && kernel_dims.width == 11)
+                    {
+                        vector_size = 8;
+                    }
+                    const size_t width_mod_vector_size = kernel_dims.width % vector_size;
+                    build_opts.add_option("-DVECTOR_SIZE=" + support::cpp11::to_string(vector_size));
+                    build_opts.add_option("-DWIDTH_MOD_VECTOR_SIZE=" + support::cpp11::to_string(width_mod_vector_size));
                 }
-                const size_t width_mod_vector_size = kernel_dims.width % vector_size;
-                build_opts.add_option("-DVECTOR_SIZE=" + support::cpp11::to_string(vector_size));
-                build_opts.add_option("-DWIDTH_MOD_VECTOR_SIZE=" + support::cpp11::to_string(width_mod_vector_size));
             }
         }
         _run_func = &CLIm2ColKernel::run_generic;
@@ -209,27 +236,37 @@ void CLIm2ColKernel::configure(const ICLTensor *input, ICLTensor *output, const
         kernel_name                        = "im2col_reduced_dchw";
         _run_func                          = &CLIm2ColKernel::run_reduced;
     }
-
-    // Create kernel
-    _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options()));
-
     // Configure kernel window
     Window win;
     if(is_optimized_path)
     {
-        win = calculate_max_window(*input->info(),
-                                   Steps(_num_elems_processed_per_iteration),
-                                   false,
-                                   BorderSize(conv_info.pad_top(), conv_info.pad_right(), conv_info.pad_bottom(), conv_info.pad_left()));
-
-        const int x = -conv_info.pad_left();
-        const int y = -conv_info.pad_top();
-        const int w = kernel_dims.width * _num_elems_processed_per_iteration;
-        const int h = kernel_dims.height;
-
-        AccessWindowRectangle input_access(input->info(), x, y, w, h);
-
-        update_window_and_padding(win, input_access);
+        if(data_layout == DataLayout::NHWC)
+        {
+            win = calculate_max_window(*input->info(),
+                                       Steps(_num_elems_processed_per_iteration),
+                                       false,
+                                       BorderSize(conv_info.pad_top(), conv_info.pad_right(), conv_info.pad_bottom(), conv_info.pad_left()));
+            const int             x = -conv_info.pad_left();
+            const int             y = -conv_info.pad_top();
+            const int             h = kernel_dims.width * _num_elems_processed_per_iteration;
+            const int             w = 1;
+            AccessWindowRectangle input_access(input->info(), x, y, w, h);
+            update_window_and_padding(win, input_access);
+        }
+        else
+        {
+            win = calculate_max_window(*input->info(),
+                                       Steps(_num_elems_processed_per_iteration),
+                                       false,
+                                       BorderSize(conv_info.pad_top(), conv_info.pad_right(), conv_info.pad_bottom(), conv_info.pad_left()));
+
+            const int             x = -conv_info.pad_left();
+            const int             y = -conv_info.pad_top();
+            const int             w = kernel_dims.width * _num_elems_processed_per_iteration;
+            const int             h = kernel_dims.height;
+            AccessWindowRectangle input_access(input->info(), x, y, w, h);
+            update_window_and_padding(win, input_access);
+        }
     }
     else
     {
@@ -239,13 +276,41 @@ void CLIm2ColKernel::configure(const ICLTensor *input, ICLTensor *output, const
     }
 
     output->info()->set_valid_region(ValidRegion(Coordinates(), output->info()->tensor_shape()));
-    if(!run_img2col_reduced)
+    if(!reduced)
     {
         // set the Z dimension's step same size as the whole dimension so that one can't split across the Z dimension
         win.set_dimension_step(Window::DimZ, win[Window::DimZ].end() - win[Window::DimZ].start());
     }
-
     ICLKernel::configure(win);
+    return kernel_name;
+}
+
+void CLIm2ColKernel::configure(const ICLTensor *input, ICLTensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, const Size2D &dilation)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
+    ARM_COMPUTE_ERROR_ON(input->info()->data_layout() == DataLayout::UNKNOWN);
+    ARM_COMPUTE_ERROR_ON_MSG(output->info()->data_layout() != DataLayout::NCHW, "Special case Im2Col output layout is NCHW");
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), has_bias, dilation));
+
+    _input       = input;
+    _output      = output;
+    _kernel_dims = kernel_dims;
+    _conv_info   = conv_info;
+
+    const DataType data_type = input->info()->data_type();
+
+    // Create kernel
+    CLBuildOptions build_opts;
+    build_opts.add_option(("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type)));
+    build_opts.add_option("-DELEMENT_SIZE=" + support::cpp11::to_string(input->info()->element_size()));
+    build_opts.add_option_if(has_bias, "-DHAS_BIAS");
+    build_opts.add_option_if(is_data_type_fixed_point(data_type), "-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input->info()->fixed_point_position()));
+
+    _num_elems_processed_per_iteration = 1;
+
+    const std::string kernel_name = configure_window(input, output, kernel_dims, dilation, conv_info, build_opts);
+    // Create kernel
+    _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options()));
 
     // Set config_id for enabling LWS tuning
     _config_id = kernel_name;
@@ -277,23 +342,43 @@ void CLIm2ColKernel::run_generic(const Window &window, cl::CommandQueue &queue)
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
     ARM_COMPUTE_ERROR_ON_MISMATCHING_WINDOWS(ICLKernel::window(), window);
 
+    const DataLayout   data_layout = _input->info()->data_layout();
+    const unsigned int width_idx   = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
+    const unsigned int height_idx  = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
+
     // Get initial windows
     Window window_collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ);
     // Change the Z dimension's step back to 1
     window_collapsed.set_dimension_step(Window::DimZ, 1);
 
-    Window slice     = window_collapsed.first_slice_window_3D();
-    Window slice_in  = window_collapsed.first_slice_window_3D();
-    Window slice_out = window_collapsed.first_slice_window_3D();
+    const Window first_slice_3d = window_collapsed.first_slice_window_3D();
+
+    Window slice     = first_slice_3d;
+    Window slice_in  = first_slice_3d;
+    Window slice_out = first_slice_3d;
 
-    // Setup slice if stride_x != 0 or stride_y != 0
-    if(_convolved_dims.first != _input->info()->dimension(0) || _convolved_dims.second != _input->info()->dimension(1))
+    const bool out_dim_not_same_input_dim = _convolved_dims.first != _input->info()->dimension(width_idx) || _convolved_dims.second != _input->info()->dimension(height_idx);
+
+    // Setup slice if convolved dims are not the same as input dims
+    if(out_dim_not_same_input_dim)
     {
         // If the stride_x or stride_y are not 1, the output tensor of matrix multiply (Convolved tensor) will not
         // have the same shape of the im2col input tensor
         // In this case we need to re-compute the window using the shape of the tensor after matrix multiply (convolved_dims)
-        slice.set(Window::DimX, Window::Dimension(0, static_cast<int>(_convolved_dims.first), 1));
-        slice.set(Window::DimY, Window::Dimension(0, static_cast<int>(_convolved_dims.second), 1));
+        slice.set(width_idx, Window::Dimension(0, static_cast<int>(_convolved_dims.first), 1));
+        if(data_layout == DataLayout::NHWC)
+        {
+            // if layout is NHWC, we need to multiply convolved_dims.height by the number of batches as for this
+            // format we collapsed HEIGHT and all subsequent dimensions (batches) together. This is necessary to ensure
+            // global_id(2) values are in the correct range.
+            const Window tmp_win     = window.collapse_if_possible(ICLKernel::window(), 3);
+            const int    num_batches = tmp_win[3].end();
+            slice.set(height_idx, Window::Dimension(0, static_cast<int>(_convolved_dims.second) * num_batches, 1));
+        }
+        else
+        {
+            slice.set(height_idx, Window::Dimension(0, static_cast<int>(_convolved_dims.second), 1));
+        }
     }
 
     // Setup input slice
@@ -304,7 +389,7 @@ void CLIm2ColKernel::run_generic(const Window &window, cl::CommandQueue &queue)
 
     // Setup output slice
     slice_out.set(Window::DimX, Window::Dimension(0, _output->info()->dimension(0), _kernel_dims.area()));
-    slice_out.set(Window::DimY, Window::Dimension(0, _output->info()->dimension(1), 1));
+    slice_out.set(Window::DimY, Window::Dimension(0, _output->info()->dimension(1), _output->info()->dimension(1)));
     slice_out.set(Window::DimZ, Window::Dimension(0, 1, 1));
 
     do