COMPMID-791: Adds support of QASYMM8 in NEDepthwiseConvolution3x3

Change-Id: I1a9ed6c3420ddf8978aeaad48d9915333b006b49
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/116374
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>

1 files changed, 125 insertions, 88 deletions

diff --git a/src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp b/src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp
index 40a8601aaa..bc2f1ed266 100644
--- a/src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp
+++ b/src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp
@@ -42,72 +42,18 @@ using namespace arm_compute;
 using namespace arm_compute::detail;
 using namespace arm_compute::misc::shape_calculator;
 
-NEDepthwiseConvolutionLayer3x3Kernel::NEDepthwiseConvolutionLayer3x3Kernel()
-    : _border_size(0), _input(), _output(), _weights(), _conv_info()
-{
-}
-
-BorderSize NEDepthwiseConvolutionLayer3x3Kernel::border_size() const
+namespace
 {
-    return _border_size;
-}
-
-void NEDepthwiseConvolutionLayer3x3Kernel::configure(const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info)
-{
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
-    ARM_COMPUTE_ERROR_ON(weights->info()->dimension(0) != 3 || weights->info()->dimension(1) != 3);
-
-    // Get convolved dimensions
-    const TensorShape output_shape = compute_depthwise_convolution_shape(*input->info(), *weights->info(), conv_info);
-
-    // Output auto inizialitation if not yet initialized
-    auto_init_if_empty(*output->info(),
-                       output_shape,
-                       1,
-                       input->info()->data_type(),
-                       input->info()->fixed_point_position(),
-                       input->info()->quantization_info());
-
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->info()->tensor_shape(), output_shape);
-
-    _input                           = input;
-    _output                          = output;
-    _weights                         = weights;
-    _conv_info                       = conv_info;
-    const unsigned int conv_stride_x = conv_info.stride().first;
-    const unsigned int conv_stride_y = conv_info.stride().second;
-    const unsigned int conv_pad_left = conv_info.pad_left();
-    const unsigned int conv_pad_top  = conv_info.pad_top();
-
-    ARM_COMPUTE_ERROR_ON(conv_stride_x < 1 || conv_stride_x > 3);
-
-    const unsigned int num_elems_written_per_iteration = 16 >> conv_stride_x;
-    _border_size                                       = BorderSize(conv_pad_top, conv_info.pad_right(), conv_info.pad_bottom(), conv_pad_left);
-
-    // Configure kernel window
-    Window win = calculate_max_window(*output->info(), Steps(num_elems_written_per_iteration));
-
-    const unsigned int num_x_steps               = (output_shape.x() + num_elems_written_per_iteration - 1) / num_elems_written_per_iteration;
-    const int          input_num_elems_processed = get_input_num_elems_processed(num_elems_written_per_iteration, conv_stride_x);
-
-    AccessWindowStatic input_access(input->info(), -conv_pad_left, -conv_pad_top, (num_x_steps - 1) * input_num_elems_processed + 12, conv_stride_y * (output_shape.y() - 1) + 2);
-    AccessWindowStatic weights_access(weights->info(), 0, 0, weights->info()->dimension(0), weights->info()->dimension(1));
-    AccessWindowStatic output_access(output->info(), 0, 0, num_x_steps * num_elems_written_per_iteration, output_shape.y());
-
-    update_window_and_padding(win, input_access, weights_access, output_access);
-    output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape()));
-
-    INEKernel::configure(win);
-}
-
-template <unsigned int stridex>
+template <typename T1, typename T2, unsigned int stridex>
 class convolver_3x3
 {
 public:
     static void convolve(const Window &window, unsigned int num_elems_written_per_iteration,
                          const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info)
     {
+        const int input_offset   = -input->info()->quantization_info().offset;
+        const int weights_offset = -weights->info()->quantization_info().offset;
+
         const int          input_stride_x  = input->info()->strides_in_bytes().x();
         const int          input_stride_y  = input->info()->strides_in_bytes().y();
         const int          output_stride_y = output->info()->strides_in_bytes().y();
@@ -117,8 +63,8 @@ public:
         const int          output_h        = output->info()->dimension(1);
         const int          delta_input     = get_input_num_elems_processed<stridex>(num_elems_written_per_iteration);
         const unsigned int conv_stride_y   = std::get<1>(conv_info.stride());
-        const unsigned int conv_pad_x      = std::get<0>(conv_info.pad());
-        const unsigned int conv_pad_y      = std::get<1>(conv_info.pad());
+        const unsigned int conv_pad_x      = conv_info.pad_left();
+        const unsigned int conv_pad_y      = conv_info.pad_top();
 
         // setup output window for the iterator
         Window window_out = window;
@@ -141,29 +87,31 @@ public:
 
         execute_window_loop(window_out, [&](const Coordinates & id)
         {
-            const uint8_t *input_ptr = in.ptr() - conv_pad_x * input_stride_x - conv_pad_y * input_stride_y;
-            int            ih        = 0;
-            int            oh        = 0;
-
-            const uint8_t      *ptr_weights_base = weights_ptr + id.z() * kernel_stride_z;
-            const auto          ptr_weights_r0   = reinterpret_cast<const float *>(ptr_weights_base);
-            const auto          ptr_weights_r1   = reinterpret_cast<const float *>(ptr_weights_base + kernel_stride_y);
-            const auto          ptr_weights_r2   = reinterpret_cast<const float *>(ptr_weights_base + kernel_stride_y * 2);
-            const float32x4x3_t vw_r0            = load_matrix_row(ptr_weights_r0);
-            const float32x4x3_t vw_r1            = load_matrix_row(ptr_weights_r1);
-            const float32x4x3_t vw_r2            = load_matrix_row(ptr_weights_r2);
+            int ih = 0;
+            int oh = 0;
+
+            const uint8_t *input_ptr        = in.ptr() - conv_pad_x * input_stride_x - conv_pad_y * input_stride_y;
+            const uint8_t *ptr_weights_base = weights_ptr + id.z() * kernel_stride_z;
+
+            const auto ptr_weights_r0 = reinterpret_cast<const T1 *>(ptr_weights_base);
+            const auto ptr_weights_r1 = reinterpret_cast<const T1 *>(ptr_weights_base + kernel_stride_y);
+            const auto ptr_weights_r2 = reinterpret_cast<const T1 *>(ptr_weights_base + kernel_stride_y * 2);
+            const auto vw_r0          = load_matrix_row(ptr_weights_r0, weights_offset);
+            const auto vw_r1          = load_matrix_row(ptr_weights_r1, weights_offset);
+            const auto vw_r2          = load_matrix_row(ptr_weights_r2, weights_offset);
 
             for(ih = 0, oh = 0; oh < output_h; ++oh, ih += conv_stride_y)
             {
-                auto in_top = reinterpret_cast<const float *>(input_ptr + (ih + 0) * input_stride_y);
-                auto in_mid = reinterpret_cast<const float *>(input_ptr + (ih + 1) * input_stride_y);
-                auto in_low = reinterpret_cast<const float *>(input_ptr + (ih + 2) * input_stride_y);
-                auto p_out  = reinterpret_cast<float *>(out.ptr() + oh * output_stride_y);
+                auto in_top = reinterpret_cast<const T1 *>(input_ptr + (ih + 0) * input_stride_y);
+                auto in_mid = reinterpret_cast<const T1 *>(input_ptr + (ih + 1) * input_stride_y);
+                auto in_low = reinterpret_cast<const T1 *>(input_ptr + (ih + 2) * input_stride_y);
+                auto p_out  = reinterpret_cast<T2 *>(out.ptr() + oh * output_stride_y);
 
                 for(int ow = 0; ow < output_w; ow += num_elems_written_per_iteration,
-                    in_top += delta_input, in_mid += delta_input, in_low += delta_input, p_out += num_elems_written_per_iteration)
+                    in_top += delta_input, in_mid += delta_input, in_low += delta_input,
+                    p_out += num_elems_written_per_iteration)
                 {
-                    auto vres = convolve_3x3<stridex>(in_top, in_mid, in_low, vw_r0, vw_r1, vw_r2, 0);
+                    auto vres = convolve_3x3<stridex>(in_top, in_mid, in_low, vw_r0, vw_r1, vw_r2, 0, input_offset);
                     store_results<stridex>(p_out, vres);
                 }
             }
@@ -172,24 +120,113 @@ public:
     }
 };
 
-void NEDepthwiseConvolutionLayer3x3Kernel::run(const Window &window, const ThreadInfo &info)
+template <typename T1, typename T2>
+inline void convolve_3x3(const Window &window, unsigned int num_elems_written_per_iteration,
+                         const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info)
 {
-    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
-    ARM_COMPUTE_UNUSED(info);
-
-    const unsigned int conv_stride_x                   = _conv_info.stride().first;
-    const unsigned int num_elems_written_per_iteration = 16 >> conv_stride_x;
-
+    const unsigned int conv_stride_x = std::get<0>(conv_info.stride());
     switch(conv_stride_x)
     {
         case 1:
-            convolver_3x3<1>::convolve(window, num_elems_written_per_iteration, _input, _weights, _output, _conv_info);
+            convolver_3x3<T1, T2, 1>::convolve(window, num_elems_written_per_iteration, input, weights, output, conv_info);
             break;
         case 2:
-            convolver_3x3<2>::convolve(window, num_elems_written_per_iteration, _input, _weights, _output, _conv_info);
+            convolver_3x3<T1, T2, 2>::convolve(window, num_elems_written_per_iteration, input, weights, output, conv_info);
             break;
         case 3:
-            convolver_3x3<3>::convolve(window, num_elems_written_per_iteration, _input, _weights, _output, _conv_info);
+            convolver_3x3<T1, T2, 3>::convolve(window, num_elems_written_per_iteration, input, weights, output, conv_info);
+            break;
+        default:
+            ARM_COMPUTE_ERROR("Not implemented");
+    }
+}
+} // namespace
+
+NEDepthwiseConvolutionLayer3x3Kernel::NEDepthwiseConvolutionLayer3x3Kernel()
+    : _border_size(0), _input(), _output(), _weights(), _conv_info(), _num_elems_written_per_iteration(0)
+{
+}
+
+BorderSize NEDepthwiseConvolutionLayer3x3Kernel::border_size() const
+{
+    return _border_size;
+}
+
+void NEDepthwiseConvolutionLayer3x3Kernel::configure(const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
+    ARM_COMPUTE_ERROR_ON(weights->info()->dimension(0) != 3 || weights->info()->dimension(1) != 3);
+
+    // Get convolved dimensions
+    const TensorShape output_shape = compute_depthwise_convolution_shape(*input->info(), *weights->info(), conv_info);
+    const DataType    output_dt    = (input->info()->data_type() == DataType::QASYMM8) ? DataType::S32 : input->info()->data_type();
+
+    // Output auto inizialitation if not yet initialized
+    auto_init_if_empty(*output->info(),
+                       input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(output_shape).set_data_type(output_dt));
+
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->info()->tensor_shape(), output_shape);
+
+    _input                           = input;
+    _output                          = output;
+    _weights                         = weights;
+    _conv_info                       = conv_info;
+    const unsigned int conv_stride_x = conv_info.stride().first;
+    const unsigned int conv_stride_y = conv_info.stride().second;
+    const unsigned int conv_pad_left = conv_info.pad_left();
+    const unsigned int conv_pad_top  = conv_info.pad_top();
+
+    ARM_COMPUTE_ERROR_ON(conv_stride_x < 1 || conv_stride_x > 3);
+
+    unsigned int num_elems_read_per_iteration = 0;
+    switch(input->info()->data_type())
+    {
+        case DataType::QASYMM8:
+            num_elems_read_per_iteration     = 16;
+            _num_elems_written_per_iteration = 16 >> conv_stride_x;
+            break;
+        case DataType::F32:
+            num_elems_read_per_iteration     = 12;
+            _num_elems_written_per_iteration = 16 >> conv_stride_x;
+            break;
+        default:
+            ARM_COMPUTE_ERROR("Data type not supported.");
+    }
+    _border_size = BorderSize(conv_pad_top, conv_info.pad_right(), conv_info.pad_bottom(), conv_pad_left);
+
+    // Configure kernel window
+    Window win = calculate_max_window(*output->info(), Steps(_num_elems_written_per_iteration));
+
+    const unsigned int num_x_steps               = (output_shape.x() + _num_elems_written_per_iteration - 1) / _num_elems_written_per_iteration;
+    const int          input_num_elems_processed = get_input_num_elems_processed(_num_elems_written_per_iteration, conv_stride_x);
+
+    AccessWindowStatic input_access(input->info(),
+                                    -conv_pad_left,
+                                    -conv_pad_top,
+                                    (num_x_steps - 1) * input_num_elems_processed + num_elems_read_per_iteration,
+                                    conv_stride_y * (output_shape.y() - 1) + 2);
+    AccessWindowStatic weights_access(weights->info(), 0, 0, weights->info()->dimension(0), weights->info()->dimension(1));
+    AccessWindowStatic output_access(output->info(), 0, 0, num_x_steps * _num_elems_written_per_iteration, output_shape.y());
+
+    update_window_and_padding(win, input_access, weights_access, output_access);
+    output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape()));
+
+    INEKernel::configure(win);
+}
+
+void NEDepthwiseConvolutionLayer3x3Kernel::run(const Window &window, const ThreadInfo &info)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_UNUSED(info);
+
+    switch(_input->info()->data_type())
+    {
+        case DataType::F32:
+            convolve_3x3<float, float>(window, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info);
+            break;
+        case DataType::QASYMM8:
+            convolve_3x3<uint8_t, int32_t>(window, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info);
             break;
         default:
             ARM_COMPUTE_ERROR("Not implemented");