Update Neon™ depthwise kernel

- Reduce duplication and simplify overall structure.
- Improve multi-threaded performance by sharing more data
  in lower-level caches.

Partially Resolves: COMPMID-5054
Signed-off-by: Ramy Elgammal <ramy.elgammal@arm.com>
Change-Id: Iac747f39b21c540122fa75218762631c4d787911
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/7449
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Andrew Mundy
Reviewed-by: Sheri Zhang <sheri.zhang@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>

1 files changed, 221 insertions, 299 deletions

diff --git a/src/core/NEON/kernels/arm_conv/depthwise/depthwise_depthfirst_generic.hpp b/src/core/NEON/kernels/arm_conv/depthwise/depthwise_depthfirst_generic.hpp
index f04f7751db..9f53f7cc6f 100644
--- a/src/core/NEON/kernels/arm_conv/depthwise/depthwise_depthfirst_generic.hpp
+++ b/src/core/NEON/kernels/arm_conv/depthwise/depthwise_depthfirst_generic.hpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2021 Arm Limited.
+ * Copyright (c) 2021-2022 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -24,355 +24,277 @@
 
 #pragma once
 
-#include "src/core/NEON/kernels/arm_gemm/utils.hpp"
-
-#ifdef CYCLE_PROFILING
-#include "profiler.hpp"
-#endif
-
-#include <limits>
+#include "depthwise_depthfirst.hpp"
 
 namespace arm_conv {
 namespace depthwise {
 
-template <class Strategy, unsigned OutputRows, unsigned int OutputCols>
-class DepthwiseDepthfirstGenericBase :
-  public DepthwiseCommon<typename Strategy::input_type,
-                         typename Strategy::weight_type,
-                         typename Strategy::return_type>
+template <typename TInput, typename TOutput, typename TAccum>
+struct GenericDepthfirstKernelStrategyFunctionType
 {
-  protected:
+  using KernelType = std::function<void(const TInput *const *const, TOutput *const *const, const void *, const void *, const unsigned int, const unsigned int, const TAccum, const TAccum)>;
+};
 
-  using TInput = typename Strategy::input_type;
-  using TWeight = typename Strategy::weight_type;
-  using TOutput = typename Strategy::return_type;
-  using TAccum = typename Strategy::bias_type;
+template <typename TInput, typename TOutput>
+struct GenericDepthfirstKernelStrategyFunctionType<TInput, TOutput, int32_t>
+{
+  using KernelType = std::function<void(const TInput *const *const, TOutput *const *const, const void *, const arm_gemm::Requantize32 &, unsigned int, unsigned int)>;
+};
 
-  size_t sizeof_input_ptr_array(void) const
-  {
-    return sizeof(TInput *) * this->m_args.kernel_rows * this->m_args.kernel_cols * Strategy::n_output_points;
-  }
+template <typename TInput, typename TWeight, typename TOutput, typename TAccum>
+class GenericDepthfirstKernelStrategy
+{
+  unsigned int m_n_output_points;
+  arm_gemm::VLType m_vl_type;
+  unsigned int m_accumulator_depth_vl;
 
-  size_t sizeof_input_buffer(unsigned int n_channels) const
+  public:
+  GenericDepthfirstKernelStrategy(unsigned int n_output_points, arm_gemm::VLType vl_type, unsigned int accumulator_depth_vl=1)
+  : m_n_output_points(n_output_points), m_vl_type(vl_type), m_accumulator_depth_vl(accumulator_depth_vl)
   {
-    const unsigned int vl = arm_gemm::utils::get_vector_length<TInput>(Strategy::vl_type);
-    const auto rounded_channels = arm_gemm::roundup(n_channels, vl);
-    return sizeof(TInput) * rounded_channels;
   }
 
-  size_t sizeof_output_buffer(unsigned int n_channels) const
+  virtual ~GenericDepthfirstKernelStrategy() = default;
+
+  virtual arm_gemm::VLType get_vl_type() const { return m_vl_type; }
+  virtual unsigned int get_accumulator_depth_vl() const { return m_accumulator_depth_vl; }
+  virtual unsigned int get_n_output_points() const { return m_n_output_points; }
+
+  using KernelType = typename GenericDepthfirstKernelStrategyFunctionType<TInput, TOutput, TAccum>::KernelType;
+  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 GenericDepthfirstStrategy : public DepthwiseDepthfirstStrategyCommon<TInput, TWeight, TOutput, TAccum, OutputStage>
+{
+  protected:
+  using KernelStrategyType = GenericDepthfirstKernelStrategy<TInput, TWeight, TOutput, TAccum>;
+  std::unique_ptr<KernelStrategyType> m_strategy;
+
+  public:
+  GenericDepthfirstStrategy(
+    KernelStrategyType *strat, unsigned int n_output_rows, unsigned int n_output_cols,
+    const DepthwiseArgs &args
+  )
+  : DepthwiseDepthfirstStrategyCommon<TInput, TWeight, TOutput, TAccum, OutputStage>(
+      n_output_rows, n_output_cols,
+      args.kernel_rows, args.kernel_cols,
+      args.stride_rows, args.stride_cols
+    ),
+    m_strategy(strat)
   {
-    const unsigned int vl = arm_gemm::utils::get_vector_length<TOutput>(Strategy::vl_type);
-    const auto rounded_channels = arm_gemm::roundup(n_channels, vl);
-    return sizeof(TOutput) * rounded_channels;
   }
 
-  unsigned int input_rows(void) const
+  GenericDepthfirstStrategy(GenericDepthfirstStrategy &) = delete;
+  GenericDepthfirstStrategy operator=(GenericDepthfirstStrategy &) = delete;
+
+  arm_gemm::VLType get_vl_type(void) const override { return m_strategy->get_vl_type(); }
+  unsigned int get_accumulator_depth_vl(void) const override { return m_strategy->get_accumulator_depth_vl(); }
+
+  size_t get_storage_size(const DepthwiseArgs &args) const override
   {
-    return this->m_args.kernel_rows + (OutputRows - 1)*this->m_args.stride_rows;
+    interleaves::PackingArguments packing_args(
+      this->get_kernel_rows(), this->get_kernel_cols(), sizeof(TWeight),
+      false, sizeof(TAccum),  // Don't pack the bias
+      this->get_vl_type(), sizeof(TAccum), this->get_accumulator_depth_vl(),
+      [this] (unsigned int idx, unsigned int &x, unsigned int &y) -> bool
+      { return this->get_kernel_packing_point(idx, x, y); }
+    );
+    return interleaves::get_storage_size_generic(packing_args, args);
   }
 
-  unsigned int input_cols(void) const
+  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
   {
-    return this->m_args.kernel_cols + (OutputCols - 1)*this->m_args.stride_cols;
+    interleaves::PackingArguments packing_args(
+      this->get_kernel_rows(), this->get_kernel_cols(), sizeof(TWeight),
+      false, sizeof(TAccum),  // Don't pack the bias
+      this->get_vl_type(), sizeof(TAccum), this->get_accumulator_depth_vl(),
+      [this] (unsigned int idx, unsigned int &x, unsigned int &y) -> bool
+      { return this->get_kernel_packing_point(idx, x, y); }
+    );
+    interleaves::pack_parameters_generic(
+      packing_args, args, buffer, biases, weights, ld_weight_col, ld_weight_row);
   }
 
-  void execute_tiles(
-    std::function<void(const TInput *const *, TOutput *const *)> tile_fn,
-    std::function<void(TInput *, unsigned int)> initialise_input_buffer,
-    const unsigned int batches,
-    const unsigned int input_height,
-    const unsigned int input_width,
-    const unsigned int input_channels,
-    const PaddingValues &padding,
-    const void *const _input,
-    const size_t ld_input_col,
-    const size_t ld_input_row,
-    const size_t ld_input_batch,
-    const unsigned int output_height,
-    const unsigned int output_width,
-    void *const _output,
-    const size_t ld_output_col,
-    const size_t ld_output_row,
-    const size_t ld_output_batch,
-    void *const _working_space,
-    const unsigned int thread_id,
-    const unsigned int n_threads
-  ) const
+  const typename KernelStrategyType::KernelType get_kernel() const { return m_strategy->get_kernel(); }
+};
+
+// Use a templated function to marshal arguments when executing the kernel.
+template <typename OutputStage> struct DepthwiseDepthfirstGenericKernelCall;
+
+template <>
+struct DepthwiseDepthfirstGenericKernelCall<Nothing>
+{
+  template <typename StratType, typename WorkspaceType, typename TAccum>
+  static void execute(
+    const StratType *strat, const WorkspaceType *ws, const Nothing &,
+    const TAccum *bias, const void *params,
+    const unsigned int n_kernel_points, const unsigned int n_output_channels
+  )
   {
-    static_assert(OutputRows * OutputCols <= Strategy::n_output_points,
-                  "Too many output points for kernel.");
-
-    // Determine what portion of the work to do.
-    const unsigned int n_rows_per_thread = arm_gemm::iceildiv(output_height, n_threads);
-    const int start_out_height = std::min(thread_id * n_rows_per_thread, output_height);
-    const int end_out_height = std::min(start_out_height + n_rows_per_thread, output_height);
-
-    // Cast input and output pointers into the right types
-    const TInput *const inptr = static_cast<const TInput *>(_input);
-    TOutput *const outptr = static_cast<TOutput *>(_output);
-
-    // Allocate portions of the working space
-    uint8_t *const working_space = static_cast<uint8_t *>(_working_space) + this->get_working_size(thread_id, input_channels);
-    const TInput **const inptr_array = reinterpret_cast<const TInput **>(working_space);
-    TOutput *const output_buffer = reinterpret_cast<TOutput *>(working_space + this->sizeof_input_ptr_array());
-    TInput *const input_buffer = reinterpret_cast<TInput *>(working_space + this->sizeof_input_ptr_array() + this->sizeof_output_buffer(input_channels * this->m_args.channel_multiplier));
-
-    // Create an array for the output pointers
-    TOutput * _outptr_array[Strategy::n_output_points];
-    TOutput **const outptr_array = _outptr_array;
-
-    // Initialise the input buffer
-    initialise_input_buffer(input_buffer, input_channels);
-
-    // For each output tile, construct the requisite set of pointers and call
-    // into the kernel.
-    for (unsigned int batch = 0; batch < batches; batch++)
-    {
-      // Get batch pointers
-      const auto inptr_batch = inptr + batch * ld_input_batch;
-      const auto outptr_batch = outptr + batch * ld_output_batch;
-
-      for (int start_out_i = start_out_height;
-           start_out_i < end_out_height;
-           start_out_i += static_cast<int>(OutputRows))
-      {
-        const int end_out_i = std::min(start_out_i + OutputRows,
-                                       output_height);
-
-        for (int start_out_j = 0;
-             start_out_j < static_cast<int>(output_width);
-             start_out_j += static_cast<int>(OutputCols))
-        {
-          const int end_out_j = std::min(start_out_j + OutputCols,
-                                         output_width);
-
-          // Fill the pointer arrays with pointers to the input/output buffers.
-          for (auto index = 0u;
-               index < (Strategy::n_output_points * this->m_args.kernel_rows * this->m_args.kernel_cols);
-               index++)
-          {
-            inptr_array[index] = input_buffer;
-          }
-          for (auto index = 0u; index < Strategy::n_output_points; index++)
-          {
-            outptr_array[index] = output_buffer;
-          }
-
-          // Construct the pointer arrays together. Note that the input pointer
-          // array is striped. Since the array has already been filled with
-          // pointers to the padding array we merely fill in the valid points
-          // as we get to them.
-          unsigned int output_index = 0;
-          auto outptr_row = outptr_batch + start_out_i * ld_output_row + start_out_j * ld_output_col;
-          for (auto out_i = start_out_i; out_i < end_out_i; out_i++)
-          {
-            auto outptr_col = outptr_row;
-
-            // Compute the padding for this row of tiles.
-            const int start_in_i = out_i * this->m_args.stride_rows - padding.top;
-            const int end_in_i = start_in_i + this->m_args.kernel_rows;
-            const auto pad_top = static_cast<unsigned int>(std::max<int>(0, 0 - start_in_i));
-            const auto pad_bottom = static_cast<unsigned int>(std::max<int>(0, end_in_i - input_height));
-            const unsigned int valid_rows = this->m_args.kernel_rows - pad_top - pad_bottom;
-
-            for (auto out_j = start_out_j; out_j < end_out_j; out_j++, output_index++)
-            {
-              // Compute the output pointer.
-              outptr_array[output_index] = outptr_col;
-              outptr_col += ld_output_col;
-
-              // Compute the padding for this tile.
-              const int start_in_j = out_j * this->m_args.stride_cols - padding.left;
-              const int end_in_j = start_in_j + this->m_args.kernel_cols;
-              const auto pad_left = static_cast<unsigned int>(std::max<int>(0, 0 - start_in_j));
-              const auto pad_right = static_cast<unsigned int>(std::max<int>(0, end_in_j - input_width));
-              const unsigned int valid_cols = this->m_args.kernel_cols - pad_left - pad_right;
-
-              // Hence compute the input pointers.
-              auto input_index = output_index + Strategy::n_output_points * (pad_top * this->m_args.kernel_cols + pad_left);
-              auto inptr_row = inptr_batch + (start_in_i + pad_top) * ld_input_row + (start_in_j + pad_left) * ld_input_col;
-              for (auto in_i = 0u; in_i < valid_rows; in_i++)
-              {
-                auto inptr_col = inptr_row;
-                auto input_index_col = input_index;
-
-                for (auto in_j = 0u; in_j < valid_cols; in_j++)
-                {
-                  inptr_array[input_index_col] = inptr_col;
-                  inptr_col += ld_input_col;
-                  input_index_col += Strategy::n_output_points;
-                }
-
-                inptr_row += ld_input_row;
-                input_index += Strategy::n_output_points * this->m_args.kernel_cols;
-              }
-            }
-
-            outptr_row += ld_output_row;
-          }
-
-          tile_fn(inptr_array, outptr_array);
-        }
-      }
-    }
+    strat->get_kernel()(
+      ws->inptr_array,
+      ws->outptr_array,
+      params, bias,
+      n_kernel_points, n_output_channels,
+      ws->activation_min, ws->activation_max
+    );
   }
+};
 
-  public:
-  DepthwiseDepthfirstGenericBase(const DepthwiseArgs &args) : DepthwiseCommon<TInput, TWeight, TOutput>(args)
+template <>
+struct DepthwiseDepthfirstGenericKernelCall<arm_gemm::Requantize32>
+{
+  template <typename StratType, typename WorkspaceType>
+  static void execute(
+    const StratType *strat, const WorkspaceType *ws, const arm_gemm::Requantize32 &qp,
+    const int32_t *, const void *params,
+    const unsigned int n_kernel_points, const unsigned int n_output_channels
+  )
   {
+    strat->get_kernel()(
+      ws->inptr_array,
+      ws->outptr_array,
+      params, qp,
+      n_kernel_points, n_output_channels
+    );
   }
+};
 
-  DepthwiseDepthfirstGenericBase(DepthwiseDepthfirstGenericBase &) = delete;
-  DepthwiseDepthfirstGenericBase &operator=(DepthwiseDepthfirstGenericBase &) = delete;
 
-  size_t get_storage_size(void) const override
+/* Workspace Element for an array of input pointers as consumed by the
+ * "Generic" depthwise kernels.
+ */
+template <typename T>
+class GenericInputArrayElement
+{
+  public:
+  struct Workspace
   {
-    const unsigned int vl = arm_gemm::utils::get_vector_length<TAccum>(Strategy::vl_type);
-    const auto rounded_channels = arm_gemm::roundup(this->m_args.input_channels, vl);
-    return (this->m_args.kernel_rows * this->m_args.kernel_cols) * rounded_channels * sizeof(TWeight);
-  }
+    const T **inptr_array;
+  };
 
-  void pack_parameters(void *_buffer, const void *, const void *_weights, size_t ld_weight_col, size_t ld_weight_row) override
+  template <class OutputStage>
+  static size_t get_element_size(const WorkspaceArgs<IDepthfirstStrategy, OutputStage> &args)
   {
-    // Cast the pointers
-    TWeight *buffer = static_cast<TWeight *>(_buffer);
-    const TWeight *const weights = static_cast<const TWeight *>(_weights);
-
-    const unsigned int vl = arm_gemm::utils::get_vector_length<TAccum>(Strategy::vl_type);
-    ld_weight_col = (ld_weight_col == 0) ? this->m_args.input_channels : ld_weight_col;
-    ld_weight_row = (ld_weight_row == 0) ? this->m_args.kernel_cols * ld_weight_col : ld_weight_row;
-
-    for (unsigned int n = 0; n < this->m_args.input_channels; n += vl)
-    {
-      const unsigned int todo = std::min(vl, this->m_args.input_channels - n);
-
-      // Copy each of the weights in turn
-      auto weights_row = weights + n;
-      for (unsigned int i = 0; i < this->m_args.kernel_rows; i++)
-      {
-        auto weights_col = weights_row;
-
-        for (unsigned int j = 0; j < this->m_args.kernel_cols; j++)
-        {
-          for (unsigned int m = 0; m < todo; m++)
-          {
-            buffer[m] = weights_col[m];
-          }
-          buffer += vl;
-
-          weights_col += ld_weight_col;
-        }
-
-        weights_row += ld_weight_row;
-      }
-    }
+    const auto kernel_points = args.depthwise_args.kernel_rows * args.depthwise_args.kernel_cols;
+    return sizeof(T **) * args.strategy->get_input_rows() * args.strategy->get_input_cols() * kernel_points;
   }
 
-  size_t get_working_size(const unsigned int n_threads, const unsigned int n_channels) const override
+  template <class WorkspaceType, class OutputStage>
+  static void *initialise(WorkspaceType *ws, void *buffer, const WorkspaceArgs<IDepthfirstStrategy, OutputStage> &args)
   {
-    const unsigned int n_output_channels = n_channels * this->m_args.channel_multiplier;
-    return n_threads * (sizeof_input_ptr_array() +
-                        sizeof_output_buffer(n_output_channels) +
-                        sizeof_input_buffer(n_channels));
+    ws->inptr_array = reinterpret_cast<const T**>(buffer);
+    return reinterpret_cast<char *>(buffer) + get_element_size(args);
   }
 };
 
-template <class Strategy, unsigned OutputRows, unsigned int OutputCols>
-class DepthwiseDepthfirstGeneric : public DepthwiseDepthfirstGenericBase<Strategy, OutputRows, OutputCols>
+template <typename TInput, typename TWeight=TInput, typename TOutput=TInput,
+          typename TAccum=typename DefaultTAccum<TInput>::Type,
+          typename OutputStage=typename DefaultOutputStage<TOutput>::Type>
+class DepthwiseDepthfirstGeneric : public DepthwiseDepthfirstCommon<TInput, TWeight, TOutput, TAccum, OutputStage>
 {
-  using Parent = DepthwiseDepthfirstGenericBase<Strategy, OutputRows, OutputCols>;
-  using TInput = typename Parent::TInput;
-  using TWeight = typename Parent::TWeight;
-  using TAccum = typename Parent::TAccum;
-  using TOutput = typename Parent::TOutput;
-
+  using StratType = GenericDepthfirstStrategy<TInput, TWeight, TOutput, TAccum, OutputStage>;
+  using Parent = DepthwiseDepthfirstCommon<TInput, TWeight, TOutput, TAccum, OutputStage>;
+  using WorkspaceManager = Workspace<
+    OutputArrayElement<TOutput>,
+    GenericInputArrayElement<TInput>,
+    InputBufferElement<TInput>,
+    ActivationsElement<TAccum, OutputStage>
+  >;
+  using WorkingSpace = typename WorkspaceManager::WorkspaceType;
   const TAccum *m_bias = nullptr;
 
   public:
-  DepthwiseDepthfirstGeneric(const DepthwiseArgs &args) : Parent(args)
+  DepthwiseDepthfirstGeneric(StratType *const strat, const DepthwiseArgs &args, const OutputStage &os={})
+  : Parent(strat, args, os)
   {
   }
 
   DepthwiseDepthfirstGeneric(DepthwiseDepthfirstGeneric &) = delete;
   DepthwiseDepthfirstGeneric &operator=(DepthwiseDepthfirstGeneric &) = delete;
 
-  void pack_parameters(void *buffer, const void *bias, const void *weights, size_t ld_weight_col, size_t ld_weight_row) override
+  void pack_parameters(
+    void *buffer, const void *biases,
+    const void *weights, size_t ld_weight_col, size_t ld_weight_row
+  ) override
+  {
+    Parent::pack_parameters(buffer, biases, weights, ld_weight_col, ld_weight_row);
+    m_bias = reinterpret_cast<const TAccum *>(biases);  // Get a copy of the biases
+    depthwise_depthfirst::stash_bias(this->get_output_stage(), m_bias);
+  }
+
+  size_t get_working_size_per_thread(const unsigned int n_input_channels) const override
+  {
+    DepthwiseArgs args(this->m_args);
+    args.input_channels = n_input_channels;
+    return WorkspaceManager::get_sizeof_workspace(WorkspaceArgs<IDepthfirstStrategy, OutputStage>(this->m_strat.get(), args, this->get_output_stage()));
+  }
+
+  void initialise_working_space(void *buffer, unsigned int n_input_channels) const override
   {
-    m_bias = static_cast<const TAccum *>(bias);
-    Parent::pack_parameters(buffer, bias, weights, ld_weight_col, ld_weight_row);
+    DepthwiseArgs args(this->m_args);
+    args.input_channels = n_input_channels;
+    return WorkspaceManager::initialise(buffer, WorkspaceArgs<IDepthfirstStrategy, OutputStage>(this->m_strat.get(), args, this->get_output_stage()));
   }
 
-  using DepthwiseDepthfirstGenericBase<Strategy, OutputRows, OutputCols>::execute;
-  void execute(
-    const unsigned int batches,
-    const unsigned int input_height,
-    const unsigned int input_width,
-    const unsigned int input_channels,
-    const PaddingValues &padding,
-    const void *const _input,
-    const size_t ld_input_col,
-    const size_t ld_input_row,
-    const size_t ld_input_batch,
-    const void *const parameters,
-    const unsigned int output_height,
-    const unsigned int output_width,
-    void *const _output,
-    const size_t ld_output_col,
-    const size_t ld_output_row,
-    const size_t ld_output_batch,
-    void *const _working_space,
-    const unsigned int thread_id,
-    const unsigned int n_threads
+  protected:
+  void compute_tile_padded(
+    unsigned int output_i, unsigned int output_j,
+    unsigned int channel_start, unsigned int channel_end,
+    const TensorSpec<const TInput *> &input,
+    const TensorSpec<TOutput *> &output,
+    const void *parameters,
+    void *working_space_raw
   ) const override
   {
-    Strategy strat(this->m_args.cpu_info);
-#ifdef CYCLE_PROFILING
-    arm_gemm::profiler prof;
-#endif
-
-    // Compute activation values
-    TAccum activation_min, activation_max;
-    std::tie(activation_min, activation_max) = get_default_activation_values<TAccum>();
-
-    switch (this->m_args.activation.type)
-    {
-      case arm_gemm::Activation::Type::BoundedReLU:
-        activation_max = static_cast<TAccum>(this->m_args.activation.param1);
-        // Fall through
-      case arm_gemm::Activation::Type::ReLU:
-        activation_min = static_cast<TAccum>(0);
-        break;
-      default:
-        break;
-    }
-
-    // Create a function to initialise the input buffer
-    const auto initialise_input_buffer = [] (TInput *const buffer, const unsigned int n) {
-      std::memset(buffer, 0, n * sizeof(TInput));
-    };
-
-    // Create a function to execute a tile of work
-    const auto tile_fn = [&] (const TInput *const *const inptrs, TOutput *const * const outptrs) {
-#ifdef CYCLE_PROFILING
-      auto p = prof.ScopedProfiler(
-        PROFILE_KERNEL,
-        (unsigned long) (OutputRows * OutputCols * this->m_args.kernel_rows* this->m_args.kernel_cols)
-      );
-#endif
-      strat.kernel(inptrs, outptrs, parameters, m_bias,
-                   this->m_args.kernel_rows * this->m_args.kernel_cols,
-                   this->m_args.input_channels, activation_min, activation_max);
-    };
-
-    // Call into a parent utility function to do the actual work.
-    Parent::execute_tiles(
-      tile_fn, initialise_input_buffer,
-      batches, input_height, input_width, input_channels, padding,
-      _input, ld_input_col, ld_input_row, ld_input_batch,
-      output_height, output_width,
-      _output, ld_output_col, ld_output_row, ld_output_batch,
-      _working_space, thread_id, n_threads
+    // Get the working space
+    WorkingSpace *ws = reinterpret_cast<WorkingSpace *>(working_space_raw);
+
+    const int ii = static_cast<int>(output_i * this->m_args.stride_rows) - this->m_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 * this->m_args.stride_cols) - this->m_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);
+
+    fill_pointer_array_generic_kernel<const TInput>(
+      ws->inptr_array,
+      this->m_strat->get_output_rows(), this->m_strat->get_output_cols(),
+      this->m_args.kernel_rows, this->m_args.kernel_cols,
+      this->m_args.stride_rows, this->m_args.stride_cols,
+      input.base + input_i*input.ld_row + input_j*input.ld_col + channel_start,
+      input.ld_row, input.ld_col,
+      ws->input_buffer,
+      input_pad_top, this->m_args.input_rows - input_i,
+      input_pad_left, this->m_args.input_cols - input_j
+    );
+
+    // Compute the output pointer array
+    fill_pointer_array<TOutput>(
+      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 + channel_start,
+      output.ld_row, output.ld_col,
+      ws->output_buffer,
+      0, this->m_args.output_rows - output_i, // Top padding, # valid rows
+      0, this->m_args.output_cols - output_j  // Left padding, # valid columns
+    );
+
+    // Execute the kernel
+    DepthwiseDepthfirstGenericKernelCall<OutputStage>::execute(
+      reinterpret_cast<const StratType *>(this->m_strat.get()), ws,
+      this->get_output_stage(), m_bias, parameters,
+      this->m_args.kernel_rows * this->m_args.kernel_cols,
+      channel_end - channel_start
     );
   }
 };