1 files changed, 604 insertions, 0 deletions
diff --git a/src/core/NEON/kernels/arm_conv/depthwise/depthwise_depthfirst_multiplier.hpp b/src/core/NEON/kernels/arm_conv/depthwise/depthwise_depthfirst_multiplier.hpp
new file mode 100644
index 0000000000..b93caa2aaa
--- /dev/null
+++ b/src/core/NEON/kernels/arm_conv/depthwise/depthwise_depthfirst_multiplier.hpp
@@ -0,0 +1,604 @@
+/*
+ * Copyright (c) 2021-2023 Arm Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+
+#pragma once
+
+#include "depthwise_depthfirst.hpp"
+#include "interleaves/generic_quantized_dot_product.hpp"
+
+#include <limits>
+
+namespace arm_conv {
+namespace depthwise {
+
+template <typename TInput, typename TWeight, typename TOutput, typename TAccum>
+class DepthfirstMultiplierStrategy : public DepthwiseDepthfirstStrategyCommon<TInput, TWeight, TOutput, TAccum, Nothing>
+{
+  using Parent = DepthwiseDepthfirstStrategyCommon<TInput, TWeight, TOutput, TAccum, Nothing>;
+
+  protected:
+  virtual interleaves::PackingArguments get_packing_args(const DepthwiseArgs &args) const
+  {
+    return interleaves::PackingArguments(
+      args.kernel_rows, args.kernel_cols, sizeof(TWeight),
+      true, sizeof(TAccum), this->uses_premultiply(),
+      this->get_vl_type(),
+      sizeof(TAccum), 1,
+      [args] (unsigned int pos, unsigned int &x, unsigned int &y) -> bool
+      {
+        if (pos < args.kernel_rows * args.kernel_cols)
+        {
+          y = pos % args.kernel_cols;
+          x = pos / args.kernel_cols;
+          return true;
+        }
+        return false;
+      }
+    );
+  }
+  
+  bool uses_premultiply() const override {
+    return false;
+  }
+
+  public:
+  using Parent::Parent;
+
+  size_t get_storage_size(const DepthwiseArgs &args) const override
+  {
+    return interleaves::get_storage_size_generic(this->get_packing_args(args), args);
+  }
+
+  void pack_parameters(const DepthwiseArgs &args, void *buffer, const void *biases, const Nothing &, const void *weights, size_t ld_weight_col, size_t ld_weight_row) const override
+  {
+    interleaves::pack_parameters_generic(
+      this->get_packing_args(args), args,
+      buffer, biases, weights, ld_weight_col, ld_weight_row
+    );
+  }
+
+  using KernelType = std::function<void(
+    const TInput *const *,  // Input pointers
+    TOutput *const *,  // Output pointers
+    const void *,  // Ravelled bias, weights, and quantization parameters
+    unsigned int,  // # output channels
+    TAccum, TAccum  // Min and max activation clamps
+  )>;
+  virtual KernelType get_kernel(void) const = 0;
+};
+
+
+template <typename TInput, typename TWeight, typename TOutput>
+class DepthfirstMultiplierStrategy<TInput, TWeight, TOutput, int32_t> : public DepthwiseDepthfirstStrategyCommon<TInput, TWeight, TOutput, int32_t, arm_gemm::Requantize32>
+{
+  using Parent = DepthwiseDepthfirstStrategyCommon<TInput, TWeight, TOutput, int32_t, arm_gemm::Requantize32>;
+
+  public:
+  using Parent::Parent;
+
+  size_t get_storage_size(const DepthwiseArgs &args) const override
+  {
+    return interleaves::quantized::get_storage_size(args, this->get_vl_type(), this->get_accumulator_depth_vl());
+  }
+
+  void pack_parameters(const DepthwiseArgs &args, void *buffer, const void *biases, const arm_gemm::Requantize32 &qp, const void *weights, size_t ld_weight_col, size_t ld_weight_row) const override
+  {
+    interleaves::quantized::pack_parameters<TWeight>(
+      buffer, reinterpret_cast<const int32_t *>(biases),
+      reinterpret_cast<const TWeight *>(weights), ld_weight_col, ld_weight_row,
+      args, qp, this->get_vl_type(), this->get_accumulator_depth_vl()
+    );
+  }
+
+  using KernelType = std::function<void(
+    const TInput *const *,  // Input pointers
+    TOutput *const *,  // Output pointers
+    const void *,  // Ravelled bias, weights, and quantization parameters
+    unsigned int,  // # output channels
+    const arm_gemm::Requantize32 &
+  )>;
+  virtual KernelType get_kernel(void) const = 0;
+};
+
+
+template <typename TInput, typename TWeight, typename TOutput, typename TAccum>
+class GenericDepthfirstMultiplierKernelStrategy
+{
+  const arm_gemm::VLType m_vl_type;
+  const unsigned int m_output_rows, m_output_cols;
+
+  public:
+  GenericDepthfirstMultiplierKernelStrategy(unsigned int output_rows, unsigned int output_cols, arm_gemm::VLType vl_type)
+  : m_vl_type(vl_type), m_output_rows(output_rows), m_output_cols(output_cols)
+  {
+  }
+
+  virtual ~GenericDepthfirstMultiplierKernelStrategy() = default;
+
+  arm_gemm::VLType get_vl_type(void) const { return m_vl_type; }
+  unsigned int get_output_rows(void) const { return m_output_rows; }
+  unsigned int get_output_cols(void) const { return m_output_cols; }
+
+  using KernelType = std::function<void(
+    const TInput *const *,  // Input pointers
+    TOutput *const *,  // Output pointers
+    const TWeight *,  // Ravelled weight parameters
+    const TAccum *,  // Bias,
+    unsigned int, unsigned int,  // Number of kernel points, number of output channels
+    TAccum, TAccum  // Activation minimum and maximum
+  )>;
+  virtual KernelType get_kernel(void) const = 0;
+};
+
+template <typename TInput, typename TWeight, typename TOutput>
+class GenericDepthfirstMultiplierKernelStrategy<TInput, TWeight, TOutput, int32_t>
+{
+  const arm_gemm::VLType m_vl_type;
+  const unsigned int m_output_rows, m_output_cols;
+
+  public:
+  GenericDepthfirstMultiplierKernelStrategy(unsigned int output_rows, unsigned int output_cols, arm_gemm::VLType vl_type)
+  : m_vl_type(vl_type), m_output_rows(output_rows), m_output_cols(output_cols)
+  {
+  }
+
+  virtual ~GenericDepthfirstMultiplierKernelStrategy() = default;
+
+  arm_gemm::VLType get_vl_type(void) const { return m_vl_type; }
+  unsigned int get_output_rows(void) const { return m_output_rows; }
+  unsigned int get_output_cols(void) const { return m_output_cols; }
+
+  using KernelType = std::function<void(
+    const TInput *const *,  // Input pointers
+    TOutput *const *,  // Output pointers
+    const TWeight *,  // Ravelled weight parameters
+    const int32_t *,  // Bias,
+    unsigned int, unsigned int,  // Number of kernel points, number of output channels
+    const int32_t *, const int32_t *, const int32_t *,  // Per-channel left-shifts, multipliers, right-shifts (need to account for start channel)
+    const arm_gemm::Requantize32 &
+  )>;
+  virtual KernelType get_kernel(void) const = 0;
+};
+
+template <typename TInput,
+          typename TWeight=TInput,
+          typename TOutput=TInput,
+          typename TAccum=typename DefaultTAccum<TInput>::Type,
+          typename OutputStage=typename DefaultOutputStage<TOutput>::Type>
+class GenericDepthfirstMultiplierStrategy : public DepthwiseDepthfirstStrategyCommon<TInput, TWeight, TOutput, TAccum, OutputStage>
+{
+  using KernelStrategyType = GenericDepthfirstMultiplierKernelStrategy<TInput, TWeight, TOutput, TAccum>;
+  std::unique_ptr<KernelStrategyType> m_kern;
+
+  protected:
+  virtual interleaves::PackingArguments get_packing_args(const DepthwiseArgs &args) const
+  {
+    return interleaves::PackingArguments(
+      args.kernel_rows, args.kernel_cols, sizeof(TWeight),
+      false, sizeof(TAccum), this->uses_premultiply(),
+      this->get_vl_type(),
+      sizeof(TAccum), 1,
+      [args] (unsigned int pos, unsigned int &x, unsigned int &y) -> bool
+      {
+        if (pos < args.kernel_rows * args.kernel_cols)
+        {
+          y = pos % args.kernel_cols;
+          x = pos / args.kernel_cols;
+          return true;
+        }
+        return false;
+      }
+    );
+  }
+  
+  bool uses_premultiply() const override {
+    return false;
+  }
+
+  public:
+  GenericDepthfirstMultiplierStrategy(KernelStrategyType *kern, const DepthwiseArgs &args)
+  : DepthwiseDepthfirstStrategyCommon<TInput, TWeight, TOutput, TAccum, OutputStage>(
+      kern->get_output_rows(), kern->get_output_cols(),
+      args.kernel_rows, args.kernel_cols,
+      args.stride_rows, args.stride_cols
+    ),
+    m_kern(kern)
+  {
+  };
+
+  arm_gemm::VLType get_vl_type(void) const override { return m_kern->get_vl_type(); }
+  const typename KernelStrategyType::KernelType get_kernel(void) const { return m_kern->get_kernel(); }
+
+  size_t get_storage_size(const DepthwiseArgs &args) const override
+  {
+    return interleaves::get_storage_size_generic(this->get_packing_args(args), args);
+  }
+
+  void pack_parameters(const DepthwiseArgs &args, void *buffer, const void *biases, const OutputStage &, const void *weights, size_t ld_weight_col, size_t ld_weight_row) const override
+  {
+    interleaves::pack_parameters_generic(
+      this->get_packing_args(args), args,
+      buffer, biases, weights, ld_weight_col, ld_weight_row
+    );
+  }
+};
+
+// Specialise elements of the wrapper based on the type of kernel.
+namespace depthfirst_multiplier {
+
+/* Working space element which contains a pointer for each row of input, a row
+ * of padding, and a space which can be used to construct an NCHW-ordered patch
+ * of input.
+ */
+template <typename T, bool IsGeneric=false, typename OutputStage=Nothing>
+class InputPatchElement
+{
+  public:
+  struct Workspace
+  {
+    constexpr static bool InputPatchIsGeneric = IsGeneric;
+    const T **input_rows;
+    T *input_padding;
+    T *input_patch;
+  };
+
+  static size_t get_element_size(const WorkspaceArgs<IDepthfirstStrategy, OutputStage> &args)
+  {
+    return sizeof_input_rows(args) + sizeof_input_padding(args) + sizeof_input_patch(args);
+  }
+
+  template <class WorkspaceType>
+  static void *initialise(WorkspaceType *ws, void *buffer, const WorkspaceArgs<IDepthfirstStrategy, OutputStage> &args)
+  {
+    auto buffer_bytes = reinterpret_cast<char *>(buffer);
+
+    ws->input_rows = reinterpret_cast<const T **>(buffer_bytes);
+    buffer_bytes += sizeof_input_rows(args);
+
+    ws->input_padding = reinterpret_cast<T*>(buffer_bytes);
+    buffer_bytes += sizeof_input_padding(args);
+
+    ws->input_patch = reinterpret_cast<T*>(buffer_bytes);
+    buffer_bytes += sizeof_input_patch(args);
+
+    // Initialise the padding
+    memset(ws->input_padding,
+           get_input_buffer_fill_value(args.output_stage),
+           sizeof_input_padding(args));
+
+    return buffer_bytes;
+  }
+
+  protected:
+  static size_t sizeof_input_rows(const WorkspaceArgs<IDepthfirstStrategy, OutputStage> &args)
+  {
+    if (IsGeneric)
+    {
+      return sizeof(T *) * args.strategy->get_output_rows() * args.depthwise_args.kernel_rows * args.depthwise_args.kernel_cols;
+    }
+    else
+    {
+      return sizeof(T *) * args.strategy->get_input_rows();
+    }
+  }
+
+  static size_t sizeof_input_padding(const WorkspaceArgs<IDepthfirstStrategy, OutputStage> &args)
+  {
+    // Round-up the number of columns to be a whole number of QUADS
+    auto input_cols = arm_gemm::roundup<size_t>(args.strategy->get_input_cols(), 16 / sizeof(T));
+    return sizeof(T) * input_cols;
+  }
+
+  static size_t sizeof_input_patch(const WorkspaceArgs<IDepthfirstStrategy, OutputStage> &args)
+  {
+    if (IsGeneric)
+    {
+      // Round-up the number of columns to be a whole number of QUADS
+      auto output_cols = arm_gemm::roundup<size_t>(args.strategy->get_output_cols(), 16 / sizeof(T));
+      const auto kernel_points = args.depthwise_args.kernel_rows * args.depthwise_args.kernel_cols;
+      return sizeof(T) * kernel_points * args.strategy->get_output_rows() * output_cols;
+    }
+    else
+    {
+      // Round-up the number of columns to be a whole number of QUADS
+      auto input_cols = arm_gemm::roundup<size_t>(args.strategy->get_input_cols(), 16 / sizeof(T));
+      return sizeof(T) * args.strategy->get_input_rows() * input_cols;
+    }
+  }
+};
+
+template <bool IsGeneric, typename TInput, typename TWeight, typename TOutput, typename TAccum, typename OutputStage>
+struct StrategyType
+{
+  using Type = DepthfirstMultiplierStrategy<TInput, TWeight, TOutput, TAccum>;
+
+  template <typename WorkspaceType>
+  static void execute(
+    const DepthwiseArgs &args, const WorkspaceType *ws, const Type *strat,
+    const OutputStage &, const unsigned int,
+    const void *parameters, const void *
+  )
+  {
+    strat->get_kernel()(
+      ws->input_rows,
+      ws->outptr_array,
+      parameters, args.channel_multiplier,
+      ws->activation_min, ws->activation_max
+    );
+  }
+};
+
+template <typename TInput, typename TWeight, typename TOutput, typename TAccum, typename OutputStage>
+struct StrategyType<true, TInput, TWeight, TOutput, TAccum, OutputStage>
+{
+  using Type = GenericDepthfirstMultiplierStrategy<TInput, TWeight, TOutput, TAccum, OutputStage>;
+
+  template <typename WorkspaceType>
+  static void execute(
+    const DepthwiseArgs &args, const WorkspaceType *ws, const Type *strat,
+    const OutputStage &, const unsigned int start_output_channel,
+    const void *parameters, const void *bias
+  )
+  {
+    strat->get_kernel()(
+      ws->input_rows, ws->outptr_array,
+      reinterpret_cast<const TWeight *>(parameters),
+      bias == nullptr ? nullptr : reinterpret_cast<const TAccum *>(bias) + start_output_channel,
+      strat->get_kernel_rows() * strat->get_kernel_cols(),
+      args.channel_multiplier,
+      ws->activation_min, ws->activation_max
+    );
+  }
+};
+
+template <typename TInput, typename TWeight, typename TOutput>
+struct StrategyType<false, TInput, TWeight, TOutput, int32_t, arm_gemm::Requantize32>
+{
+  using Type = DepthfirstMultiplierStrategy<TInput, TWeight, TOutput, int32_t>;
+
+  template <typename WorkspaceType>
+  static void execute(
+    const DepthwiseArgs &args, const WorkspaceType *ws, const Type *strat,
+    const arm_gemm::Requantize32 &qp, const unsigned int,
+    const void *parameters, const void *
+  )
+  {
+    strat->get_kernel()(
+      ws->input_rows,
+      ws->outptr_array,
+      parameters, args.channel_multiplier,
+      qp
+    );
+  }
+};
+
+template <typename TInput, typename TWeight, typename TOutput>
+struct StrategyType<true, TInput, TWeight, TOutput, int32_t, arm_gemm::Requantize32>
+{
+  using Type = GenericDepthfirstMultiplierStrategy<TInput, TWeight, TOutput, int32_t, arm_gemm::Requantize32>;
+
+  template <typename WorkspaceType>
+  static void execute(
+    const DepthwiseArgs &args, const WorkspaceType *ws, const Type *strat,
+    const arm_gemm::Requantize32 &qp, const unsigned int start_output_channel,
+    const void *parameters, const void *
+  )
+  {
+    auto get_ptr = [start_output_channel] (const int32_t *ptr) -> const int32_t *
+    {
+      return ptr == nullptr ? nullptr : ptr + start_output_channel;
+    };
+
+    strat->get_kernel()(
+      ws->input_rows, ws->outptr_array,
+      reinterpret_cast<const TWeight *>(parameters),
+      get_ptr(qp.bias),
+      strat->get_kernel_rows() * strat->get_kernel_cols(),
+      args.channel_multiplier,
+      get_ptr(qp.per_channel_left_shifts),
+      get_ptr(qp.per_channel_muls),
+      get_ptr(qp.per_channel_right_shifts),
+      qp
+    );
+  }
+};
+
+template <bool IsGeneric> struct PrepareInputSample;
+
+template <> struct PrepareInputSample<false>
+{
+  template <typename WorkspaceType, typename StrategyType, typename T>
+  static void execute(
+    const DepthwiseArgs &, WorkspaceType *ws, const StrategyType *strat,
+    T *base_ptr, size_t ld_row, size_t ld_col,
+    const unsigned int input_pad_top, const unsigned int valid_rows,
+    const unsigned int input_pad_left, const unsigned int valid_cols
+  )
+  {
+    fill_nchw_patch_array(
+      ws->input_rows, ws->input_patch, strat->get_input_rows(), strat->get_input_cols(),
+      base_ptr, ld_row, ld_col,
+      ws->input_padding,
+      input_pad_top, valid_rows,
+      input_pad_left, valid_cols
+    );
+  }
+};
+
+template <> struct PrepareInputSample<true>
+{
+  template <typename WorkspaceType, typename StrategyType, typename T>
+  static void execute(
+    const DepthwiseArgs &args, WorkspaceType *ws, const StrategyType *strat,
+    T *base_ptr, size_t ld_row, size_t ld_col,
+    const unsigned int input_pad_top, const unsigned int valid_rows,
+    const unsigned int input_pad_left, const unsigned int valid_cols
+  )
+  {
+    fill_patch_array_generic_kernel(
+      ws->input_rows, ws->input_patch,
+      strat->get_output_rows(), strat->get_output_cols(),
+      args.kernel_rows, args.kernel_cols,
+      args.stride_rows, args.stride_cols,
+      base_ptr, ld_row, ld_col,
+      ws->input_padding,
+      input_pad_top, valid_rows,
+      input_pad_left, valid_cols
+    );
+  }
+};
+
+}  // namespace depthfirst_multiplier
+
+template <typename TInput,
+          typename TWeight=TInput,
+          typename TOutput=TInput,
+          typename TAccum=typename DefaultTAccum<TInput>::Type,
+          bool is_generic=false,
+          typename OutputStage=typename DefaultOutputStage<TOutput>::Type>
+class DepthwiseDepthfirstMultiplier : public DepthfirstDriver<TInput, TWeight, TOutput>
+{
+  protected:
+  using StratType = typename depthfirst_multiplier::StrategyType<is_generic, TInput, TWeight, TOutput, TAccum, OutputStage>::Type;
+  using WorkspaceManager = Workspace<
+    OutputArrayElement<TOutput>,
+    depthfirst_multiplier::InputPatchElement<TInput, is_generic, OutputStage>,
+    ActivationsElement<TOutput, OutputStage>
+  >;
+  using WorkingSpace = typename WorkspaceManager::WorkspaceType;
+
+  OutputStage m_os;  // Copy of the output parameters
+  const void *m_bias = nullptr;  // Copy of the bias (should we need it)
+
+  bool uses_premultiply() const override {
+    return false;
+  }
+
+  public:
+  DepthwiseDepthfirstMultiplier(StratType *const strat, const DepthwiseArgs &args, const OutputStage &os = {})
+  : DepthfirstDriver<TInput, TWeight, TOutput>(strat, args), m_os(os)
+  {
+  }
+
+  DepthwiseDepthfirstMultiplier(DepthwiseDepthfirstMultiplier &) = delete;
+  DepthwiseDepthfirstMultiplier &operator=(DepthwiseDepthfirstMultiplier &) = delete;
+
+  size_t get_storage_size(void) const override
+  {
+    return reinterpret_cast<const StratType *>(this->m_strat.get())
+      ->get_storage_size(this->m_args);
+  }
+
+  void pack_parameters(void *buffer, const void *biases, const void *weights, size_t ld_weight_col, size_t ld_weight_row) override
+  {
+    reinterpret_cast<const StratType *>(this->m_strat.get())
+      ->pack_parameters(this->m_args, buffer, biases, m_os, weights, ld_weight_col, ld_weight_row);
+    m_bias = biases;
+    depthwise_depthfirst::stash_bias(m_os, biases);
+  }
+
+  size_t get_working_size_per_thread() const override
+  {
+    DepthwiseArgs args(this->m_args);
+    return WorkspaceManager::get_sizeof_workspace(WorkspaceArgs<IDepthfirstStrategy, OutputStage>(this->m_strat.get(), args, m_os));
+  }
+
+  void initialise_working_space(void *buffer) const override
+  {
+    DepthwiseArgs args(this->m_args);
+    return WorkspaceManager::initialise(buffer, WorkspaceArgs<IDepthfirstStrategy, OutputStage>(this->m_strat.get(), args, m_os));
+  }
+
+  void compute_tile_padded(
+    const DepthwiseArgs &args,
+    unsigned int output_i, unsigned int output_j,
+    unsigned int output_channel_start, unsigned int output_channel_end,
+    const TensorSpec<const TInput *> &input,
+    const TensorSpec<TOutput *> &output,
+    const void *parameters,
+    void *working_space_raw
+  ) const override
+  {
+    // Get the working space
+    auto ws = reinterpret_cast<WorkingSpace *>(working_space_raw);
+
+    const int ii = static_cast<int>(output_i * args.stride_rows) - args.padding.top;
+    const auto input_pad_top = static_cast<unsigned int>(ii < 0 ? -ii : 0);
+    const auto input_i = static_cast<unsigned int>(ii < 0 ? 0 : ii);
+
+    const int ij = static_cast<int>(output_j * args.stride_cols) - args.padding.left;
+    const auto input_pad_left = static_cast<unsigned int>(ij < 0 ? -ij : 0);
+    const auto input_j = static_cast<unsigned int>(ij < 0 ? 0 : ij);
+
+    // Compute the output pointer array. We'll update this array after every
+    // invocation of the kernel.
+    fill_pointer_array(
+      ws->outptr_array, this->m_strat->get_output_rows(), this->m_strat->get_output_cols(),
+      output.base + output_i*output.ld_row + output_j*output.ld_col + output_channel_start,
+      output.ld_row, output.ld_col,
+      ws->output_buffer,
+      0, args.output_rows - output_i, // Top padding, # valid rows
+      0, args.output_cols - output_j  // Left padding, # valid columns
+    );
+
+    // Compute the parameter stride
+    DepthwiseArgs single_iter(args);
+    single_iter.input_channels = 1;
+    const size_t parameter_stride = reinterpret_cast<const StratType *>(this->m_strat.get())
+      ->get_storage_size(single_iter);
+
+    for (; output_channel_start < output_channel_end;
+         output_channel_start += args.channel_multiplier)
+    {
+      // Compute the input pointer array
+      const auto input_channel = output_channel_start / args.channel_multiplier;
+
+      // Construct the input patch
+      depthfirst_multiplier::PrepareInputSample<is_generic>::execute(
+        args, ws, this->m_strat.get(),
+        input.base + input_channel + input_i*input.ld_row + input_j*input.ld_col, input.ld_row, input.ld_col,
+        input_pad_top, args.input_rows - input_i,
+        input_pad_left, args.input_cols - input_j
+      );
+
+      // Execute the kernel
+      depthfirst_multiplier::StrategyType<is_generic, TInput, TWeight, TOutput, TAccum, OutputStage>::execute(
+        args, ws, reinterpret_cast<const StratType *>(this->m_strat.get()), m_os, output_channel_start,
+        parameters, m_bias
+      );
+
+      // Update the output pointers
+      for (unsigned int n = 0; n < this->m_strat->get_output_rows() * this->m_strat->get_output_cols(); n++)
+      {
+        ws->outptr_array[n] += args.channel_multiplier;
+      }
+
+      // Progress the parameters
+      parameters = reinterpret_cast<const char *>(parameters) + parameter_stride;
+    }
+  }
+};
+
+}  // namespace depthwise
+}  // namespace arm_conv