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, 316 insertions, 16 deletions

diff --git a/src/core/NEON/kernels/NEDepthwiseConvolutionLayerNativeKernel.cpp b/src/core/NEON/kernels/NEDepthwiseConvolutionLayerNativeKernel.cpp
index c9d4e9be50..a0d45afd2a 100644
--- a/src/core/NEON/kernels/NEDepthwiseConvolutionLayerNativeKernel.cpp
+++ b/src/core/NEON/kernels/NEDepthwiseConvolutionLayerNativeKernel.cpp
@@ -24,19 +24,30 @@
 #include "arm_compute/core/NEON/kernels/NEDepthwiseConvolutionLayerNativeKernel.h"
 
 #include "arm_compute/core/AccessWindowStatic.h"
+#include "arm_compute/core/CPP/Validate.h"
 #include "arm_compute/core/NEON/wrapper/traits.h"
 #include "arm_compute/core/NEON/wrapper/wrapper.h"
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
-
-#include "support/ToolchainSupport.h"
+#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
+#include "src/core/NEON/kernels/convolution/depthwise/impl_qa8_qa8.hpp"
 
 namespace arm_compute
 {
 namespace
 {
+void pad_vectors(std::vector<int> &mult, std::vector<int> &shift, int vec_size)
+{
+    ARM_COMPUTE_ERROR_ON(mult.size() != shift.size());
+    while(mult.size() % vec_size != 0)
+    {
+        mult.push_back(0);
+        shift.push_back(0);
+    }
+}
+
 template <typename T, int S, bool has_biases>
-void depthwise_loop_multiplier1(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info,
-                                const Size2D &dilation, const Window &window)
+void depthwise_loop_multiplier1_fp(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info,
+                                   const Size2D &dilation, const Window &window)
 {
     using VectorType = typename wrapper::traits::neon_vector<T, S>::type;
     using TagType    = typename wrapper::traits::neon_vector<T, S>::tag_type;
@@ -108,8 +119,8 @@ void depthwise_loop_multiplier1(const ITensor *input, const ITensor *weights, co
 }
 
 template <typename T, bool has_biases>
-void depthwise_loop_generic(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info,
-                            const Size2D &dilation, unsigned int depth_multiplier, const Window &window)
+void depthwise_loop_generic_fp(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info,
+                               const Size2D &dilation, unsigned int depth_multiplier, const Window &window)
 {
     const size_t input_stride_y   = input->info()->strides_in_bytes().y();
     const size_t input_stride_z   = input->info()->strides_in_bytes().z();
@@ -191,21 +202,243 @@ void depthwise_loop_generic(const ITensor *input, const ITensor *weights, const
     input_it, weights_it, biases_it, output_it);
 }
 
+template <typename T, typename TW, int S, bool has_biases, bool is_per_channel>
+void depthwise_loop_multiplier1_quantized(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info,
+                                          const Size2D &dilation, std::vector<int> output_multiplier, std::vector<int> output_shift, const Window &window)
+{
+    using VectorType = typename wrapper::traits::neon_vector<T, S>::type;
+    using TagType    = typename wrapper::traits::neon_vector<T, S>::tag_type;
+
+    const size_t input_stride_y   = input->info()->strides_in_bytes().y();
+    const size_t input_stride_z   = input->info()->strides_in_bytes().z();
+    const size_t input_max_offset = input->info()->strides_in_bytes().z() * input->info()->dimension(2) - (input->info()->padding().bottom + input->info()->padding().top) *
+                                    input->info()->strides_in_bytes().y();
+    const size_t weights_width    = weights->info()->dimension(1);
+    const size_t weights_height   = weights->info()->dimension(2);
+    const size_t weights_stride_y = weights->info()->strides_in_bytes().y();
+    const size_t weights_stride_z = weights->info()->strides_in_bytes().z();
+    const size_t conv_stride_x    = conv_info.stride().first;
+    const size_t conv_stride_y    = conv_info.stride().second;
+    const size_t conv_pad_left    = conv_info.pad_left();
+    const size_t conv_pad_top     = conv_info.pad_top();
+
+    const int32_t input_qoffset   = input->info()->quantization_info().uniform().offset;
+    const int32_t weights_qoffset = weights->info()->quantization_info().uniform().offset;
+    const int32_t output_qoffset  = output->info()->quantization_info().uniform().offset;
+    const int32_t k_offset        = weights_width * weights_height * input_qoffset * weights_qoffset;
+
+    Window win_input = window;
+    win_input.set(Window::DimY, Window::Dimension(0, 0, 0));
+    win_input.set(Window::DimZ, Window::Dimension(0, 0, 0));
+
+    Window win_weights = win_input;
+    win_weights.set(3, Window::Dimension(0, 0, 0));
+
+    Iterator input_it(input, win_input);
+    Iterator weights_it(weights, win_weights);
+    Iterator output_it(output, window);
+    Iterator biases_it{};
+
+    if(has_biases)
+    {
+        biases_it = Iterator(biases, win_weights);
+    }
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        std::vector<int32_t> acc(S, 0);
+        std::vector<int32_t> in_sum(S, 0);
+        std::vector<int32_t> we_sum(S, 0);
+
+        const int input_y      = id.y() * conv_stride_x - conv_pad_left;
+        const int input_z      = id.z() * conv_stride_y - conv_pad_top;
+        int       input_offset = input_y * input_stride_y + input_z * input_stride_z;
+
+        auto weights_ptr = weights_it.ptr();
+        for(size_t h = 0; h < weights_height; ++h)
+        {
+            int offs = input_offset;
+            for(size_t w = 0; w < weights_width; ++w)
+            {
+                const auto input_vals   = wrapper::vload(reinterpret_cast<T *>(input_it.ptr() + std::min(static_cast<size_t>(offs), input_max_offset)));
+                const auto weights_vals = wrapper::vload(reinterpret_cast<TW *>(weights_ptr + w * weights_stride_y));
+
+                for(int i = 0; i < S; ++i)
+                {
+                    acc.at(i) += input_vals[i] * weights_vals[i];
+                    in_sum.at(i) += input_vals[i];
+                    we_sum.at(i) += weights_vals[i];
+                }
+
+                offs += dilation.x() * input_stride_y;
+            }
+
+            weights_ptr += weights_stride_z;
+            input_offset += dilation.y() * input_stride_z;
+        }
+
+        VectorType out_vals = wrapper::vdup_n(static_cast<T>(0), TagType{});
+        for(int i = 0; i < S; ++i)
+        {
+            acc.at(i) -= in_sum.at(i) * weights_qoffset;
+            acc.at(i) -= we_sum.at(i) * input_qoffset;
+            acc.at(i) += k_offset;
+
+            if(has_biases)
+            {
+                acc.at(i) += *reinterpret_cast<int32_t *>(biases_it.ptr() + i * sizeof(int32_t));
+            }
+
+            acc.at(i)   = rounding_divide_by_exp2(saturating_doubling_high_mul(acc.at(i), output_multiplier.at(id.x() + i)), output_shift.at(id.x() + i)) + output_qoffset;
+            out_vals[i] = static_cast<T>(utility::clamp<int32_t, uint8_t>(acc.at(i)));
+        }
+
+        wrapper::vstore(reinterpret_cast<T *>(output_it.ptr()), out_vals);
+    },
+    input_it, weights_it, biases_it, output_it);
+}
+
+template <typename T, typename TW, bool has_biases, bool is_per_channel>
+void depthwise_loop_generic_quantized(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info,
+                                      const Size2D &dilation, unsigned int depth_multiplier, std::vector<int> output_multiplier, std::vector<int> output_shift, const Window &window)
+{
+    const size_t input_stride_y   = input->info()->strides_in_bytes().y();
+    const size_t input_stride_z   = input->info()->strides_in_bytes().z();
+    const size_t input_max_offset = input->info()->strides_in_bytes().z() * input->info()->dimension(2) - (input->info()->padding().bottom + input->info()->padding().top) *
+                                    input->info()->strides_in_bytes().y();
+    const size_t weights_width    = weights->info()->dimension(1);
+    const size_t weights_height   = weights->info()->dimension(2);
+    const size_t weights_stride_y = weights->info()->strides_in_bytes().y();
+    const size_t weights_stride_z = weights->info()->strides_in_bytes().z();
+    const size_t conv_stride_x    = conv_info.stride().first;
+    const size_t conv_stride_y    = conv_info.stride().second;
+    const size_t conv_pad_left    = conv_info.pad_left();
+    const size_t conv_pad_top     = conv_info.pad_top();
+
+    const int32_t input_qoffset   = input->info()->quantization_info().uniform().offset;
+    const int32_t weights_qoffset = weights->info()->quantization_info().uniform().offset;
+    const int32_t output_qoffset  = output->info()->quantization_info().uniform().offset;
+    const int32_t k_offset        = weights_width * weights_height * input_qoffset * weights_qoffset;
+
+    Window win_input = window;
+    win_input.set(Window::DimY, Window::Dimension(0, 0, 0));
+    win_input.set(Window::DimZ, Window::Dimension(0, 0, 0));
+
+    Window win_weights = win_input;
+    win_weights.set(3, Window::Dimension(0, 0, 0));
+
+    win_input.set_dimension_step(Window::DimX, 1);
+
+    Iterator input_it(input, win_input);
+    Iterator weights_it(weights, win_weights);
+    Iterator output_it(output, window);
+    Iterator biases_it{};
+
+    if(has_biases)
+    {
+        biases_it = Iterator(biases, win_weights);
+    }
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        std::vector<int32_t> acc(depth_multiplier, 0);
+        std::vector<int32_t> we_sum(depth_multiplier, 0);
+        int32_t              in_sum = 0;
+
+        const int input_y      = id.y() * conv_stride_x - conv_pad_left;
+        const int input_z      = id.z() * conv_stride_y - conv_pad_top;
+        int       input_offset = input_y * input_stride_y + input_z * input_stride_z;
+
+        auto weights_ptr = weights_it.ptr();
+        for(size_t h = 0; h < weights_height; ++h)
+        {
+            int offs = input_offset;
+            for(size_t w = 0; w < weights_width; ++w)
+            {
+                const auto input_val = *(reinterpret_cast<T *>(input_it.ptr() + std::min(static_cast<size_t>(offs), input_max_offset)));
+
+                for(size_t m = 0; m < depth_multiplier; ++m)
+                {
+                    const auto weights_val = *(reinterpret_cast<TW *>(weights_ptr + m * sizeof(T) + w * weights_stride_y));
+                    acc.at(m) += input_val * weights_val;
+
+                    we_sum.at(m) += weights_val;
+                }
+
+                offs += dilation.x() * input_stride_y;
+                in_sum += input_val;
+            }
+
+            weights_ptr += weights_stride_z;
+            input_offset += dilation.y() * input_stride_z;
+        }
+
+        for(size_t m = 0; m < depth_multiplier; ++m)
+        {
+            acc.at(m) -= in_sum * weights_qoffset;
+            acc.at(m) -= we_sum.at(m) * input_qoffset;
+            acc.at(m) += k_offset;
+
+            if(has_biases)
+            {
+                const auto biases_val = *(reinterpret_cast<int32_t *>(biases_it.ptr() + m * sizeof(int32_t)));
+
+                int32_t out_val = acc.at(m) + biases_val;
+                out_val         = rounding_divide_by_exp2(saturating_doubling_high_mul(out_val, output_multiplier.at(id.x() + m)),
+                                                          output_shift.at(id.x() + m))
+                                  + output_qoffset;
+                *(reinterpret_cast<T *>(output_it.ptr() + m * sizeof(T))) = static_cast<T>(utility::clamp<int32_t, uint8_t>(out_val));
+            }
+            else
+            {
+                int32_t out_val = rounding_divide_by_exp2(saturating_doubling_high_mul(acc.at(m), output_multiplier.at(id.x() + m)),
+                                                          output_shift.at(id.x() + m))
+                                  + output_qoffset;
+                *(reinterpret_cast<T *>(output_it.ptr() + m * sizeof(T))) = static_cast<T>(utility::clamp<int32_t, uint8_t>(out_val));
+            }
+        }
+    },
+    input_it, weights_it, biases_it, output_it);
+}
+
 Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier,
                           const Size2D &dilation)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
-    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output);
+    ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input);
+    ARM_COMPUTE_RETURN_ERROR_ON(input->data_layout() == DataLayout::UNKNOWN);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
     ARM_COMPUTE_RETURN_ERROR_ON(depth_multiplier == 0);
+    ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(1) + (weights->dimension(1) - 1) * (dilation.x() - 1) > input->dimension(1) + conv_info.pad_left() + conv_info.pad_right());
+    ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(2) + (weights->dimension(2) - 1) * (dilation.y() - 1) > input->dimension(2) + conv_info.pad_top() + conv_info.pad_bottom());
     ARM_COMPUTE_RETURN_ERROR_ON((input->dimension(0) * depth_multiplier) != weights->dimension(0));
     ARM_COMPUTE_RETURN_ERROR_ON((dilation.x() < 1) || (dilation.y() < 1));
     ARM_COMPUTE_RETURN_ERROR_ON((conv_info.stride().first < 1) || (conv_info.stride().second < 1));
 
+    if(is_data_type_quantized_per_channel(weights->data_type()))
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QSYMM8_PER_CHANNEL);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+        ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(0) != weights->quantization_info().scale().size());
+    }
+    else
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output);
+    }
+
     if(biases != nullptr)
     {
         ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
         ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(0));
+
+        if(is_data_type_quantized_asymmetric(input->data_type()))
+        {
+            ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32);
+        }
+        else
+        {
+            ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases);
+        }
     }
 
     if(output->total_size() != 0)
@@ -216,7 +449,6 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights,
 
     return Status{};
 }
-} // namespace
 
 std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *weights, ITensorInfo *biases,
                                                         ITensorInfo *output, const PadStrideInfo &conv_info,
@@ -226,7 +458,7 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITen
     const TensorShape output_shape = misc::shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier, dilation);
 
     // Output auto inizialitation if not yet initialized
-    auto_init_if_empty(*output, input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(output_shape));
+    auto_init_if_empty(*output, input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(output_shape).set_quantization_info(output->quantization_info()));
 
     // Configure kernel window (generic)
     const unsigned int num_elems_read_per_iteration    = (depth_multiplier == 1) ? 8 / element_size_from_data_type(input->data_type()) : 1;
@@ -253,9 +485,10 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITen
     Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
     return std::make_pair(err, win);
 }
+} // namespace
 
 NEDepthwiseConvolutionLayerNativeKernel::NEDepthwiseConvolutionLayerNativeKernel()
-    : _func(), _border_size(0), _input(), _weights(), _biases(), _output(), _conv_info(), _depth_multiplier(1), _dilation()
+    : _func(), _border_size(0), _input(), _weights(), _biases(), _output(), _conv_info(), _depth_multiplier(1), _dilation(), _output_multiplier(), _output_shift()
 {
 }
 
@@ -279,10 +512,56 @@ void NEDepthwiseConvolutionLayerNativeKernel::configure(const ITensor *input, co
     _border_size      = BorderSize(_conv_info.pad_left(), 0, std::max(std::max(conv_info.pad_right(), conv_info.pad_bottom()), conv_info.pad_top()), 0);
     _dilation         = dilation;
 
-    switch(_input->info()->data_type())
+    if(is_data_type_quantized(_input->info()->data_type()))
+    {
+        const auto input_scale  = input->info()->quantization_info().uniform().scale;
+        const auto output_scale = output->info()->quantization_info().uniform().scale;
+
+        auto weights_scale = weights->info()->quantization_info().scale();
+        if(!is_data_type_quantized_per_channel(_weights->info()->data_type()))
+        {
+            for(size_t i = 1; i < _weights->info()->dimension(0); ++i)
+            {
+                weights_scale.push_back(weights_scale.front());
+            }
+        }
+
+        for(size_t i = 0; i < weights_scale.size(); ++i)
+        {
+            int         out_mult   = 0;
+            int         out_shift  = 0;
+            const float multiplier = input_scale * weights_scale.at(i) / output_scale;
+            ARM_COMPUTE_ERROR_ON(multiplier > 1.f);
+            arm_compute::quantization::calculate_quantized_multiplier_less_than_one(multiplier, &out_mult, &out_shift);
+
+            _output_multiplier.push_back(out_mult);
+            _output_shift.push_back(out_shift);
+        }
+    }
+
+    switch(_weights->info()->data_type())
     {
+        case DataType::QASYMM8:
+            _func = (biases != nullptr) ? &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise<uint8_t, uint8_t, 8, true, false> :
+                    &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise<uint8_t, uint8_t, 8, false, false>;
+            pad_vectors(_output_multiplier, _output_shift, 8);
+            break;
+        case DataType::QSYMM8_PER_CHANNEL:
+            _func = (biases != nullptr) ? &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise<uint8_t, int8_t, 8, true, true> :
+                    &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise<uint8_t, int8_t, 8, false, true>;
+            pad_vectors(_output_multiplier, _output_shift, 8);
+            break;
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+        case DataType::F16:
+            _func = (biases != nullptr) ? &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise<float16_t, float16_t, 4, true, false> :
+                    &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise<float16_t, float16_t, 4, false, false>;
+            pad_vectors(_output_multiplier, _output_shift, 4);
+            break;
+#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
         case DataType::F32:
-            _func = (biases != nullptr) ? &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise<float, 2, true> : &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise<float, 2, false>;
+            _func = (biases != nullptr) ? &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise<float, float, 2, true, false> :
+                    &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise<float, float, 2, false, false>;
+            pad_vectors(_output_multiplier, _output_shift, 2);
             break;
         default:
             ARM_COMPUTE_ERROR("Data type not supported");
@@ -314,7 +593,28 @@ void NEDepthwiseConvolutionLayerNativeKernel::run(const Window &window, const Th
     (this->*_func)(window);
 }
 
-template <typename T, int S, bool has_biases>
+template < typename T, typename TW, int S, bool has_biases, bool is_per_channel, typename std::enable_if < std::is_same<T, float>::value
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+                                                                                                           || std::is_same<T, float16_t>::value
+#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+                                                                                                           ,
+                                                                                                           int >::type >
+void NEDepthwiseConvolutionLayerNativeKernel::run_depthwise(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+
+    if(_depth_multiplier == 1)
+    {
+        depthwise_loop_multiplier1_fp<T, S, has_biases>(_input, _weights, _biases, _output, _conv_info, _dilation, window);
+    }
+    else
+    {
+        depthwise_loop_generic_fp<T, has_biases>(_input, _weights, _biases, _output, _conv_info, _dilation, _depth_multiplier, window);
+    }
+}
+
+template <typename T, typename TW, int S, bool has_biases, bool is_per_channel, typename std::enable_if<std::is_same<T, uint8_t>::value, int>::type>
 void NEDepthwiseConvolutionLayerNativeKernel::run_depthwise(const Window &window)
 {
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
@@ -322,11 +622,11 @@ void NEDepthwiseConvolutionLayerNativeKernel::run_depthwise(const Window &window
 
     if(_depth_multiplier == 1)
     {
-        depthwise_loop_multiplier1<T, S, has_biases>(_input, _weights, _biases, _output, _conv_info, _dilation, window);
+        depthwise_loop_multiplier1_quantized<T, TW, S, has_biases, is_per_channel>(_input, _weights, _biases, _output, _conv_info, _dilation, _output_multiplier, _output_shift, window);
     }
     else
     {
-        depthwise_loop_generic<T, has_biases>(_input, _weights, _biases, _output, _conv_info, _dilation, _depth_multiplier, window);
+        depthwise_loop_generic_quantized<T, TW, has_biases, is_per_channel>(_input, _weights, _biases, _output, _conv_info, _dilation, _depth_multiplier, _output_multiplier, _output_shift, window);
     }
 }
 } // namespace arm_compute