COMPMID-791: Generic Depthwise Convolution Layer NEON QASYMM8

Change-Id: I33cf54e68f6c097ac58b6f16c3f9a720978f09cd
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/117289
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Michalis Spyrou <michalis.spyrou@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>

1 files changed, 54 insertions, 16 deletions

diff --git a/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp b/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp
index 2d08b45210..1af0b18933 100644
--- a/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp
+++ b/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp
@@ -26,11 +26,13 @@
 #include "arm_compute/core/Helpers.h"
 #include "arm_compute/core/ITensor.h"
 #include "arm_compute/core/PixelValue.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "arm_compute/core/utils/quantization/AsymmHelpers.h"
 #include "arm_compute/runtime/NEON/NEScheduler.h"
 #include "support/ToolchainSupport.h"
 
 using namespace arm_compute;
+using namespace arm_compute::misc;
 
 NEDepthwiseConvolutionLayer3x3::NEDepthwiseConvolutionLayer3x3()
     : _kernel(), _output_stage_kernel(), _border_handler(), _accumulator(), _has_bias(false), _is_quantized(false)
@@ -90,13 +92,14 @@ void NEDepthwiseConvolutionLayer3x3::run()
 }
 
 NEDepthwiseConvolutionLayer::NEDepthwiseConvolutionLayer()
-    : _im2col_kernel(), _weights_reshape_kernel(), _v2mm_kernel(), _vector_to_tensor_kernel(), _input_reshaped(), _weights_reshaped(), _v2mm_output()
+    : _im2col_kernel(), _weights_reshape_kernel(), _v2mm_kernel(), _vector_to_tensor_kernel(), _output_stage_kernel(), _v2mm_input_fill_border(), _v2mm_weights_fill_border(), _input_reshaped(),
+      _weights_reshaped(), _v2mm_output(), _output_reshaped(), _is_quantized(false)
 {
 }
 
 void NEDepthwiseConvolutionLayer::configure(ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info)
 {
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
+    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(input->info()->dimension(2) != weights->info()->dimension(2));
 
@@ -104,14 +107,20 @@ void NEDepthwiseConvolutionLayer::configure(ITensor *input, const ITensor *weigh
     const size_t weights_h = weights->info()->dimension(1);
     const size_t weights_z = weights->info()->dimension(2);
 
-    bool has_bias = (biases != nullptr);
+    _is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type());
+
+    // Should bias be appended ?
+    bool append_bias = (biases != nullptr) && !_is_quantized;
 
-    unsigned int conv_w = 0;
-    unsigned int conv_h = 0;
-    std::tie(conv_w, conv_h) = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1), weights_w, weights_h, conv_info);
+    // Calculate output shape
+    TensorShape dwc_output_shape = shape_calculator::compute_depthwise_convolution_shape(*input->info(), *weights->info(), conv_info);
+
+    // Output width and height
+    const unsigned int conv_w = dwc_output_shape.x();
+    const unsigned int conv_h = dwc_output_shape.y();
 
     // Set up intermediate tensors
-    const size_t patch_size = weights_w * weights_h + ((has_bias) ? 1 : 0);
+    const size_t patch_size = weights_w * weights_h + (append_bias ? 1 : 0);
     const size_t conv_size  = conv_w * conv_h;
 
     // Im2Col configuration
@@ -119,25 +128,48 @@ void NEDepthwiseConvolutionLayer::configure(ITensor *input, const ITensor *weigh
     shape_im2col.set(0, patch_size);
     shape_im2col.set(1, conv_size);
     shape_im2col.set(2, weights_z);
-    const TensorInfo info_im2col(shape_im2col, 1, input->info()->data_type(), input->info()->fixed_point_position());
-    _input_reshaped.allocator()->init(info_im2col);
-    _im2col_kernel.configure(input, &_input_reshaped, Size2D(weights_w, weights_h), conv_info, has_bias);
+    _input_reshaped.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col));
+    _im2col_kernel.configure(input, &_input_reshaped, Size2D(weights_w, weights_h), conv_info, append_bias);
 
     // Weights reshape configuration
     const TensorShape shape_weights_reshape(patch_size, weights_z);
-    const TensorInfo  info_weights_reshape(shape_weights_reshape, 1, weights->info()->data_type(), weights->info()->fixed_point_position());
-    _weights_reshaped.allocator()->init(info_weights_reshape);
-    _weights_reshape_kernel.configure(weights, &_weights_reshaped, biases);
+    _weights_reshaped.allocator()->init(weights->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_weights_reshape));
+    _weights_reshape_kernel.configure(weights, &_weights_reshaped, append_bias ? biases : nullptr);
 
     // GEMV configuration
+    DataType    v2mm_dt        = (input->info()->data_type() == DataType::QASYMM8) ? DataType::S32 : input->info()->data_type();
     TensorShape shape_v2mm_out = input->info()->tensor_shape();
     shape_v2mm_out.set(0, conv_size * weights_z);
     shape_v2mm_out.set(1, 1);
     shape_v2mm_out.set(2, 1);
-    const TensorInfo info_v2mm_out(shape_v2mm_out, 1, input->info()->data_type(), input->info()->fixed_point_position());
-    _v2mm_output.allocator()->init(info_v2mm_out);
+    _v2mm_output.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_data_type(v2mm_dt).set_tensor_shape(shape_v2mm_out));
     _v2mm_kernel.configure(&_input_reshaped, &_weights_reshaped, &_v2mm_output);
-    _vector_to_tensor_kernel.configure(&_v2mm_output, output, conv_w, conv_h);
+    _output_reshaped.allocator()->init(_v2mm_output.info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(dwc_output_shape));
+    _vector_to_tensor_kernel.configure(&_v2mm_output, (_is_quantized) ? &_output_reshaped : output, conv_w, conv_h);
+
+    // Output staged configuration
+    if(_is_quantized)
+    {
+        float multiplier = input->info()->quantization_info().scale * weights->info()->quantization_info().scale / output->info()->quantization_info().scale;
+        int   output_multiplier, output_shift;
+        quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
+        _output_stage_kernel.configure(&_output_reshaped, biases, output, output_multiplier, output_shift, output->info()->quantization_info().offset);
+        _output_reshaped.allocator()->allocate();
+    }
+
+    // Fill borders on inputs
+    PixelValue zero_in(0);
+    PixelValue zero_w(0);
+    if(_is_quantized)
+    {
+        zero_in = PixelValue(static_cast<int32_t>(input->info()->quantization_info().offset));
+        zero_w  = PixelValue(static_cast<int32_t>(weights->info()->quantization_info().offset));
+    }
+    BorderSize border_size = _v2mm_kernel.border_size();
+    _v2mm_input_fill_border.configure(&_input_reshaped, border_size, BorderMode::CONSTANT, zero_in);
+
+    border_size.bottom = 0;
+    _v2mm_weights_fill_border.configure(&_weights_reshaped, border_size, BorderMode::CONSTANT, zero_w);
 
     // Allocate intermediate tensors
     _input_reshaped.allocator()->allocate();
@@ -149,6 +181,12 @@ void NEDepthwiseConvolutionLayer::run()
 {
     NEScheduler::get().schedule(&_im2col_kernel, Window::DimX);
     NEScheduler::get().schedule(&_weights_reshape_kernel, Window::DimX);
+    NEScheduler::get().schedule(&_v2mm_input_fill_border, Window::DimX);
+    NEScheduler::get().schedule(&_v2mm_weights_fill_border, Window::DimX);
     NEScheduler::get().schedule(&_v2mm_kernel, Window::DimX);
     NEScheduler::get().schedule(&_vector_to_tensor_kernel, Window::DimX);
+    if(_is_quantized)
+    {
+        NEScheduler::get().schedule(&_output_stage_kernel, Window::DimX);
+    }
 }
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