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>
diff --git a/src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp b/src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp
index 40a8601..bc2f1ed 100644
--- a/src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp
+++ b/src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp
@@ -42,72 +42,18 @@
using namespace arm_compute::detail;
using namespace arm_compute::misc::shape_calculator;
-NEDepthwiseConvolutionLayer3x3Kernel::NEDepthwiseConvolutionLayer3x3Kernel()
- : _border_size(0), _input(), _output(), _weights(), _conv_info()
+namespace
{
-}
-
-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::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 @@
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 @@
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;
+ 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);
+ 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 @@
}
};
+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)
+{
+ const unsigned int conv_stride_x = std::get<0>(conv_info.stride());
+ switch(conv_stride_x)
+ {
+ case 1:
+ convolver_3x3<T1, T2, 1>::convolve(window, num_elems_written_per_iteration, input, weights, output, conv_info);
+ break;
+ case 2:
+ convolver_3x3<T1, T2, 2>::convolve(window, num_elems_written_per_iteration, input, weights, output, conv_info);
+ break;
+ case 3:
+ 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);
- const unsigned int conv_stride_x = _conv_info.stride().first;
- const unsigned int num_elems_written_per_iteration = 16 >> conv_stride_x;
-
- switch(conv_stride_x)
+ switch(_input->info()->data_type())
{
- case 1:
- convolver_3x3<1>::convolve(window, num_elems_written_per_iteration, _input, _weights, _output, _conv_info);
+ case DataType::F32:
+ convolve_3x3<float, float>(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);
- break;
- case 3:
- convolver_3x3<3>::convolve(window, num_elems_written_per_iteration, _input, _weights, _output, _conv_info);
+ 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");