COMPMID-2708 NEDepthwiseConvolution Generic: support for QUANT8_PER_CHANNEL_SYMM

COMPMID-2470 Implement a new and generic depthwise convolution for NEON QASYMM8 NHWC
COMPMID-2477 Enable FP16 data type for the new generic convolution on NEON for NHWC
COMPMID-2625 Remove old implementation files for the generic NEDepthwiseConvolution

Change-Id: I8f6deda4fc69dd7e472fba3228b1ed5dad172f3e
Signed-off-by: Giorgio Arena <giorgio.arena@arm.com>
Reviewed-on: https://review.mlplatform.org/c/2094
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>

1 files changed, 48 insertions, 256 deletions

diff --git a/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp b/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp
index fbdee84474..76ae1fba3a 100644
--- a/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp
+++ b/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp
@@ -23,15 +23,10 @@
  */
 #include "arm_compute/runtime/NEON/functions/NEDepthwiseConvolutionLayer.h"
 
-#include "arm_compute/core/Helpers.h"
-#include "arm_compute/core/ITensor.h"
-#include "arm_compute/core/PixelValue.h"
+#include "arm_compute/core/utils/misc/InfoHelpers.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"
-
-#include "arm_compute/core/utils/misc/InfoHelpers.h"
 
 using namespace arm_compute::misc;
 using namespace arm_compute::misc::shape_calculator;
@@ -701,10 +696,8 @@ void NEDepthwiseConvolutionLayerOptimized::prepare()
 }
 
 NEDepthwiseConvolutionLayer::NEDepthwiseConvolutionLayer()
-    : _im2col_kernel(), _weights_reshape_kernel(), _v2mm_kernel(), _depthwise_conv_kernel(), _vector_to_tensor_kernel(), _output_stage_kernel(), _fill_border(), _v2mm_input_fill_border(),
-      _v2mm_weights_fill_border(), _permute_input(), _permute_weights(), _permute_output(), _activationlayer_function(), _input_reshaped(), _weights_reshaped(), _v2mm_output(), _output_reshaped(),
-      _permuted_input(), _permuted_weights(), _permuted_output(), _is_prepared(false), _is_quantized(false), _is_nhwc(false), _is_activationlayer_enabled(false), _is_optimized(false),
-      _original_weights(nullptr)
+    : _depthwise_conv_kernel(), _fill_border(), _permute_input(), _permute_weights(), _permute_output(), _activationlayer_function(), _permuted_input(), _permuted_weights(), _permuted_output(),
+      _is_prepared(false), _is_nchw(false), _is_activationlayer_enabled(false), _original_weights(nullptr)
 {
 }
 
@@ -712,143 +705,45 @@ void NEDepthwiseConvolutionLayer::configure(ITensor *input, const ITensor *weigh
                                             unsigned int depth_multiplier, const ActivationLayerInfo &act_info, const Size2D &dilation)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
-    // Perform validation step
     ARM_COMPUTE_ERROR_THROW_ON(NEDepthwiseConvolutionLayer::validate(input->info(), weights->info(), (biases == nullptr) ? nullptr : biases->info(),
                                                                      output->info(), conv_info, depth_multiplier, act_info, dilation));
 
-    _is_nhwc      = input->info()->data_layout() == DataLayout::NHWC;
-    _is_optimized = _is_nhwc && input->info()->data_type() == DataType::F32;
+    _is_nchw     = input->info()->data_layout() == DataLayout::NCHW;
+    _is_prepared = !_is_nchw;
 
-    if(!_is_optimized)
+    ITensor       *input_to_use   = input;
+    const ITensor *weights_to_use = weights;
+    ITensor       *output_to_use  = output;
+    if(_is_nchw)
     {
-        ITensor       *input_to_use   = input;
-        const ITensor *weights_to_use = weights;
-        ITensor       *output_to_use  = output;
-
-        if(_is_nhwc)
-        {
-            _permute_input.configure(input, &_permuted_input, PermutationVector(1U, 2U, 0U));
-            _permuted_input.info()->set_data_layout(DataLayout::NCHW);
-            input_to_use = &_permuted_input;
-
-            _permute_weights.configure(weights, &_permuted_weights, PermutationVector(1U, 2U, 0U));
-            _permuted_weights.info()->set_data_layout(DataLayout::NCHW);
-            weights_to_use = &_permuted_weights;
-        }
-
-        const size_t weights_w = weights_to_use->info()->dimension(0);
-        const size_t weights_h = weights_to_use->info()->dimension(1);
-        const size_t weights_z = weights_to_use->info()->dimension(2);
-
-        _is_quantized     = is_data_type_quantized_asymmetric(input->info()->data_type());
-        _is_prepared      = false;
-        _original_weights = weights_to_use;
-
-        // Should bias be appended ?
-        bool append_bias = (biases != nullptr) && !_is_quantized;
-
-        // Calculate output shape
-        TensorShape output_shape = shape_calculator::compute_depthwise_convolution_shape(*input->info(), *weights->info(), conv_info, depth_multiplier, dilation);
-
-        // Output auto inizialitation if not yet initialized
-        auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape));
-        ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->info()->tensor_shape(), output_shape);
-
-        if(_is_nhwc)
-        {
-            permute(output_shape, PermutationVector(1U, 2U, 0U));
-            _permuted_output.allocator()->init(output->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(output_shape));
-            _permuted_output.info()->set_data_layout(DataLayout::NCHW);
-            _permuted_output.info()->set_quantization_info(output->info()->quantization_info());
-            output_to_use = &_permuted_output;
-        }
-
-        // Output width and height
-        const unsigned int conv_w = output_shape.x();
-        const unsigned int conv_h = output_shape.y();
-
-        // Set up intermediate tensors
-        const size_t patch_size = weights_w * weights_h + (append_bias ? 1 : 0);
-        const size_t conv_size  = conv_w * conv_h;
-
-        // Im2Col configuration
-        TensorShape shape_im2col = input_to_use->info()->tensor_shape();
-        shape_im2col.set(0, patch_size);
-        shape_im2col.set(1, conv_size);
-        shape_im2col.set(2, weights_z);
-        _input_reshaped.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col).set_data_layout(DataLayout::NCHW));
-        _im2col_kernel.configure(input_to_use, &_input_reshaped, Size2D(weights_w, weights_h), conv_info, append_bias, depth_multiplier, dilation);
-
-        // Weights reshape configuration
-        const TensorShape shape_weights_reshape(patch_size, weights_z);
-        _weights_reshaped.allocator()->init(weights->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_weights_reshape).set_data_layout(DataLayout::NCHW));
-        _weights_reshape_kernel.configure(weights_to_use, &_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_to_use->info()->tensor_shape();
-        shape_v2mm_out.set(0, conv_size * weights_z);
-        shape_v2mm_out.set(1, 1);
-        shape_v2mm_out.set(2, 1);
-        _v2mm_output.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_data_type(v2mm_dt).set_tensor_shape(shape_v2mm_out).set_data_layout(DataLayout::NCHW));
-        _v2mm_kernel.configure(&_input_reshaped, &_weights_reshaped, &_v2mm_output);
-        _output_reshaped.allocator()->init(_v2mm_output.info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(output_shape));
-        _vector_to_tensor_kernel.configure(&_v2mm_output, (_is_quantized) ? &_output_reshaped : output_to_use, conv_w, conv_h);
-
-        // Output staged configuration
-        if(_is_quantized)
-        {
-            const UniformQuantizationInfo iq_info = input->info()->quantization_info().uniform();
-            const UniformQuantizationInfo wq_info = weights->info()->quantization_info().uniform();
-            const UniformQuantizationInfo oq_info = output->info()->quantization_info().uniform();
-
-            float multiplier = (iq_info.scale * wq_info.scale) / oq_info.scale;
-            int   output_multiplier;
-            int   output_shift;
-            quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
-            _output_stage_kernel.configure(&_output_reshaped, biases, output_to_use, output_multiplier, output_shift, oq_info.offset);
-            _output_reshaped.allocator()->allocate();
-        }
-
-        if(_is_nhwc)
-        {
-            _permute_output.configure(&_permuted_output, output, PermutationVector(2U, 0U, 1U));
+        _permute_input.configure(input, &_permuted_input, PermutationVector(2U, 0U, 1U));
+        _permuted_input.info()->set_data_layout(DataLayout::NHWC);
+        input_to_use = &_permuted_input;
 
-            _permuted_input.allocator()->allocate();
-            _permuted_weights.allocator()->allocate();
-            _permuted_output.allocator()->allocate();
-        }
+        _permute_weights.configure(weights, &_permuted_weights, PermutationVector(2U, 0U, 1U));
+        _permuted_weights.info()->set_data_layout(DataLayout::NHWC);
+        weights_to_use = &_permuted_weights;
 
-        // Fill borders on inputs
-        PixelValue zero_in(static_cast<int32_t>(0));
-        PixelValue zero_w(static_cast<int32_t>(0));
-        if(_is_quantized)
-        {
-            zero_in = PixelValue(static_cast<int32_t>(input->info()->quantization_info().uniform().offset));
-            zero_w  = PixelValue(static_cast<int32_t>(weights->info()->quantization_info().uniform().offset));
-        }
-        BorderSize border_size = _v2mm_kernel.border_size();
-        _v2mm_input_fill_border.configure(&_input_reshaped, border_size, BorderMode::CONSTANT, zero_in);
+        _permuted_output.allocator()->init(output->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(TensorShape()));
+        output_to_use = &_permuted_output;
+    }
+    _original_weights = weights_to_use;
 
-        border_size.bottom = 0;
-        _v2mm_weights_fill_border.configure(&_weights_reshaped, border_size, BorderMode::CONSTANT, zero_w);
+    _depthwise_conv_kernel.configure(input_to_use, weights_to_use, biases, output_to_use, conv_info, depth_multiplier, dilation);
+    _fill_border.configure(input_to_use, _depthwise_conv_kernel.border_size(), BorderMode::CONSTANT, PixelValue(static_cast<uint64_t>(0), input->info()->data_type(), input->info()->quantization_info()));
 
-        // Allocate intermediate tensors
-        _input_reshaped.allocator()->allocate();
-        _v2mm_output.allocator()->allocate();
-    }
-    else
+    if(_is_nchw)
     {
-        // Configure kernel
-        _depthwise_conv_kernel.configure(input, weights, biases, output, conv_info, depth_multiplier, dilation);
+        _permute_output.configure(&_permuted_output, output, PermutationVector(1U, 2U, 0U));
+        _permuted_output.info()->set_data_layout(DataLayout::NHWC);
 
-        // Fill input borders
-        _fill_border.configure(input, _depthwise_conv_kernel.border_size(), BorderMode::CONSTANT, PixelValue(static_cast<uint64_t>(0), input->info()->data_type()));
+        _permuted_input.allocator()->allocate();
+        _permuted_weights.allocator()->allocate();
+        _permuted_output.allocator()->allocate();
     }
 
     //Configure Activation Layer
     _is_activationlayer_enabled = act_info.enabled();
-
     if(_is_activationlayer_enabled)
     {
         _activationlayer_function.configure(output, nullptr, act_info);
@@ -859,103 +754,24 @@ Status NEDepthwiseConvolutionLayer::validate(const ITensorInfo *input, const ITe
                                              unsigned int depth_multiplier, const ActivationLayerInfo &act_info, const Size2D &dilation)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
-    ARM_COMPUTE_RETURN_ERROR_ON(input->data_layout() == DataLayout::UNKNOWN);
-    ARM_COMPUTE_RETURN_ERROR_ON(dilation.x() < 1 || dilation.y() < 1);
-
-    const unsigned int width_idx   = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH);
-    const unsigned int height_idx  = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT);
-    const unsigned int channel_idx = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::CHANNEL);
-
-    ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(width_idx) + (weights->dimension(width_idx) - 1) * (dilation.x() - 1) > input->dimension(width_idx) + conv_info.pad_left() + conv_info.pad_right());
-    ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(height_idx) + (weights->dimension(height_idx) - 1) * (dilation.y() - 1) > input->dimension(height_idx) + conv_info.pad_top() + conv_info.pad_bottom());
-    ARM_COMPUTE_RETURN_ERROR_ON((input->dimension(channel_idx) * depth_multiplier) != weights->dimension(channel_idx));
-
-    if(input->data_layout() != DataLayout::NHWC || input->data_type() != DataType::F32)
+    if(input->data_layout() == DataLayout::NCHW)
     {
-        // Clone output to use auto init
-        auto output_clone = output->clone();
-
-        const ITensorInfo *input_to_use   = input;
-        const ITensorInfo *weights_to_use = weights;
-        const ITensorInfo *output_to_use  = output_clone.get();
-
         TensorShape permuted_input_shape   = input->tensor_shape();
         TensorShape permuted_weights_shape = weights->tensor_shape();
-        TensorInfo  permuted_input;
-        TensorInfo  permuted_weights;
-
-        if(input->data_layout() == DataLayout::NHWC)
-        {
-            permute(permuted_input_shape, PermutationVector(1U, 2U, 0U));
-            permute(permuted_weights_shape, PermutationVector(1U, 2U, 0U));
-
-            permuted_input   = TensorInfo(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(permuted_input_shape).set_data_layout(DataLayout::NCHW));
-            permuted_weights = TensorInfo(weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(permuted_weights_shape).set_data_layout(DataLayout::NCHW));
-
-            input_to_use   = &permuted_input;
-            weights_to_use = &permuted_weights;
-        }
+        TensorShape permuted_output_shape  = misc::shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier, dilation);
+        permute(permuted_input_shape, PermutationVector(2U, 0U, 1U));
+        permute(permuted_weights_shape, PermutationVector(2U, 0U, 1U));
+        permute(permuted_output_shape, PermutationVector(2U, 0U, 1U));
 
-        const bool         is_quantized = is_data_type_quantized_asymmetric(input->data_type());
-        const bool         append_bias  = (biases != nullptr) && !is_quantized;
-        TensorShape        output_shape = shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier, dilation);
-        const size_t       weights_w    = weights_to_use->dimension(0);
-        const size_t       weights_h    = weights_to_use->dimension(1);
-        const size_t       weights_z    = weights_to_use->dimension(2);
-        const unsigned int conv_w       = output_shape[width_idx];
-        const unsigned int conv_h       = output_shape[height_idx];
-        const size_t       patch_size   = weights_w * weights_h + (append_bias ? 1 : 0);
-        const size_t       conv_size    = conv_w * conv_h;
-
-        // Output auto inizialitation if not yet initialized
-        auto_init_if_empty(*output_clone, input->clone()->set_tensor_shape(output_shape));
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape);
-
-        TensorInfo permuted_output;
-        if(input->data_layout() == DataLayout::NHWC)
-        {
-            permute(output_shape, PermutationVector(1U, 2U, 0U));
-            permuted_output = TensorInfo(output_clone->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(output_shape).set_data_layout(DataLayout::NCHW));
-            output_to_use   = &permuted_output;
-        }
+        const TensorInfo permuted_input   = TensorInfo(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(permuted_input_shape).set_data_layout(DataLayout::NHWC));
+        const TensorInfo permuted_weights = TensorInfo(weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(permuted_weights_shape).set_data_layout(DataLayout::NHWC));
+        const TensorInfo permuted_output  = TensorInfo(output->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(permuted_output_shape).set_data_layout(DataLayout::NCHW));
 
-        // Im2Col configuration
-        TensorShape shape_im2col = input_to_use->tensor_shape();
-        shape_im2col.set(0, patch_size);
-        shape_im2col.set(1, conv_size);
-        shape_im2col.set(2, weights_z);
-        TensorInfo input_reshaped(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col).set_data_layout(DataLayout::NCHW));
-        ARM_COMPUTE_RETURN_ON_ERROR(NEDepthwiseIm2ColKernel::validate(input_to_use, &input_reshaped, Size2D(weights_w, weights_h), conv_info, append_bias, depth_multiplier, dilation));
-
-        // Weights reshape configuration
-        const TensorShape shape_weights_reshape(patch_size, weights_z);
-        TensorInfo        weights_reshaped(weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_weights_reshape).set_data_layout(DataLayout::NCHW));
-        ARM_COMPUTE_RETURN_ON_ERROR(NEDepthwiseWeightsReshapeKernel::validate(weights_to_use, &weights_reshaped, append_bias ? biases : nullptr));
-
-        // GEMV configuration
-        DataType    v2mm_dt        = (input->data_type() == DataType::QASYMM8) ? DataType::S32 : input->data_type();
-        TensorShape shape_v2mm_out = input_to_use->tensor_shape();
-        shape_v2mm_out.set(0, conv_size * weights_z);
-        shape_v2mm_out.set(1, 1);
-        shape_v2mm_out.set(2, 1);
-        TensorInfo v2mm_output(input->clone()->set_is_resizable(true).reset_padding().set_data_type(v2mm_dt).set_tensor_shape(shape_v2mm_out).set_data_layout(DataLayout::NCHW));
-        ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMMatrixVectorMultiplyKernel::validate(&input_reshaped, &weights_reshaped, &v2mm_output));
-
-        TensorInfo output_reshaped(v2mm_output.clone()->set_is_resizable(true).reset_padding().set_tensor_shape(output_to_use->tensor_shape()));
-        ARM_COMPUTE_RETURN_ON_ERROR(NEDepthwiseVectorToTensorKernel::validate(&v2mm_output, (is_quantized) ? &output_reshaped : output_to_use, conv_w, conv_h));
+        ARM_COMPUTE_RETURN_ON_ERROR(NEPermute::validate(input, &permuted_input, PermutationVector(2U, 0U, 1U)));
+        ARM_COMPUTE_RETURN_ON_ERROR(NEPermute::validate(weights, &permuted_weights, PermutationVector(2U, 0U, 1U)));
+        ARM_COMPUTE_RETURN_ON_ERROR(NEPermute::validate(&permuted_output, output, PermutationVector(1U, 2U, 0U)));
 
-        if(is_quantized)
-        {
-            const UniformQuantizationInfo iq_info = input->quantization_info().uniform();
-            const UniformQuantizationInfo wq_info = weights->quantization_info().uniform();
-            const UniformQuantizationInfo oq_info = output->quantization_info().uniform();
-
-            float multiplier = (iq_info.scale * wq_info.scale) / oq_info.scale;
-            int   output_multiplier;
-            int   output_shift;
-            ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift));
-            ARM_COMPUTE_RETURN_ON_ERROR(NEDirectConvolutionLayerOutputStageKernel::validate(&output_reshaped, biases, output_to_use, output_multiplier, output_shift, oq_info.offset));
-        }
+        ARM_COMPUTE_RETURN_ON_ERROR(NEDepthwiseConvolutionLayerNativeKernel::validate(&permuted_input, &permuted_weights, biases, &permuted_output, conv_info, depth_multiplier, dilation));
     }
     else
     {
@@ -973,33 +789,18 @@ Status NEDepthwiseConvolutionLayer::validate(const ITensorInfo *input, const ITe
 
 void NEDepthwiseConvolutionLayer::run()
 {
-    if(!_is_optimized)
+    if(_is_nchw)
     {
         prepare();
+        _permute_input.run();
+    }
 
-        if(_is_nhwc)
-        {
-            _permute_input.run();
-        }
-
-        NEScheduler::get().schedule(&_im2col_kernel, Window::DimX);
-        NEScheduler::get().schedule(&_v2mm_input_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);
-        }
+    NEScheduler::get().schedule(&_fill_border, Window::DimX);
+    NEScheduler::get().schedule(&_depthwise_conv_kernel, Window::DimY);
 
-        if(_is_nhwc)
-        {
-            _permute_output.run();
-        }
-    }
-    else
+    if(_is_nchw)
     {
-        NEScheduler::get().schedule(&_fill_border, Window::DimX);
-        NEScheduler::get().schedule(&_depthwise_conv_kernel, Window::DimY);
+        _permute_output.run();
     }
 
     if(_is_activationlayer_enabled)
@@ -1010,21 +811,12 @@ void NEDepthwiseConvolutionLayer::run()
 
 void NEDepthwiseConvolutionLayer::prepare()
 {
-    if(!_is_prepared && !_is_optimized)
+    if(!_is_prepared)
     {
         ARM_COMPUTE_ERROR_ON(!_original_weights->is_used());
 
-        if(_is_nhwc)
-        {
-            _permute_weights.run();
-        }
-
-        // Run reshape and mark original weights as unused
-        _weights_reshaped.allocator()->allocate();
-        NEScheduler::get().schedule(&_weights_reshape_kernel, Window::DimX);
-        NEScheduler::get().schedule(&_v2mm_weights_fill_border, Window::DimX);
+        _permute_weights.run();
         _original_weights->mark_as_unused();
-
         _is_prepared = true;
     }
 }