Remove unused code in arm_conv/depthwise/

* Removed header files in arm_conv/depthwise

* Resolves MLCE-990

Change-Id: Iacddd80e2d83ff0fbafb817014f90c5bc80dab3c
Signed-off-by: Pablo Marquez Tello <pablo.tello@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/8946
Reviewed-by: Andrew Mundy <Andrew.mundy@arm.com>
Reviewed-by: Viet-Hoa Do <viet-hoa.do@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Benchmark: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>

1 files changed, 0 insertions, 412 deletions

diff --git a/src/core/NEON/kernels/arm_conv/depthwise/depthwise_depthfirst_quantized.hpp b/src/core/NEON/kernels/arm_conv/depthwise/depthwise_depthfirst_quantized.hpp
deleted file mode 100644
index f97569e958..0000000000
--- a/src/core/NEON/kernels/arm_conv/depthwise/depthwise_depthfirst_quantized.hpp
+++ /dev/null
@@ -1,412 +0,0 @@
-/*
- * Copyright (c) 2021 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 "src/core/NEON/kernels/arm_gemm/utils.hpp"
-
-#ifdef CYCLE_PROFILING
-#include "profiler.hpp"
-#endif
-
-namespace arm_conv {
-namespace depthwise {
-
-namespace
-{
-
-// We have two sets of quantized kernels; those which use the dot-product
-// instructions and which require the biases and quantisation parameters to be
-// ravelled into weights/parameter array, and those which use the MLAL
-// instructions and which consume separate bias and quantisation parameter
-// arrays. The following code adapts these two sets of kernels to use the same
-// API - allowing the same driver loop to call them both.
-
-template <typename TIn, typename TWeight, typename TOut>
-using UnravelledKernFn = std::function<void(unsigned int, const TIn *const *, const TWeight *, const int32_t *, const arm_gemm::Requantize32 &, const int32_t *, const int32_t *, TOut *const *)>;
-
-template <typename TIn, typename TOut>
-using RavelledKernFn = std::function<void(const TIn *const *, TOut *const *, const void *, uint64_t, const arm_gemm::Requantize32 &)>;
-
-template <typename TIn, typename TWeight, typename TOut>
-const UnravelledKernFn<TIn, TWeight, TOut> get_unified_kernel(const UnravelledKernFn<TIn, TWeight, TOut> &f) { return f; }
-
-template <typename TIn, typename TWeight, typename TOut>
-const UnravelledKernFn<TIn, TWeight, TOut> get_unified_kernel(const RavelledKernFn<TIn, TOut> &f)
-{
-  return [f] (const unsigned int n_channels,
-              const TIn *const *const inptrs,
-              const TWeight *const weights,
-              const int32_t *,  // Bias (ravelled)
-              const arm_gemm::Requantize32 &qp,
-              const int32_t *,  // Requantisation muls (ravelled)
-              const int32_t *,  // Requantisation shifts (ravelled)
-              TOut *const *const outptrs) {
-    return f(inptrs, outptrs, weights, n_channels, qp);
-  };
-}
-
-template <typename T>
-using UnravelledPackingFn = std::function<void(unsigned int, void *, const T *, size_t, size_t)>;
-
-template <typename T>
-using RavelledPackingFn = std::function<void(unsigned int, void *, const int32_t *, const T *, const arm_gemm::Requantize32 &, size_t, size_t)>;
-
-template <typename T>
-const RavelledPackingFn<T> get_unified_packer(const UnravelledPackingFn<T> &f)
-{
-  return [f] (const unsigned int n_channels,
-              void *buffer,
-              const int32_t *,  // Bias
-              const T *weights,
-              const arm_gemm::Requantize32 &,
-              size_t ld_weight_col,
-              size_t ld_weight_row)
-  {
-    return f(n_channels, buffer, weights, ld_weight_col, ld_weight_row);
-  };
-}
-
-template <typename T>
-const RavelledPackingFn<T> get_unified_packer(const RavelledPackingFn<T> &f) { return f; }
-
-template <typename T>
-constexpr bool requires_unravelled_bias_and_quant_params(const UnravelledPackingFn<T> &) { return true; }
-
-template <typename T>
-constexpr bool requires_unravelled_bias_and_quant_params(const RavelledPackingFn<T> &) { return false; }
-
-template <class strategy>
-constexpr bool strategy_requires_unravelled_bias_and_quant_params(void)
-{
-  return requires_unravelled_bias_and_quant_params<typename strategy::weight_type>(strategy::pack_parameters);
-}
-
-}
-
-template <class strategy>
-class DepthwiseDepthfirstQuantized :
-  public DepthwiseCommon<typename strategy::input_type,
-                         typename strategy::weight_type,
-                         typename strategy::return_type>
-{
-  using TInput = typename strategy::input_type;
-  using TWeight = typename strategy::weight_type;
-  using TOutput = typename strategy::return_type;
-  using TAccum = typename strategy::bias_type;
-
-  arm_gemm::Requantize32 m_qp;
-
-  size_t sizeof_input_buffer(unsigned int n_channels) const
-  {
-    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
-  {
-    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;
-  }
-
-  size_t sizeof_bias_buffer(unsigned int n_channels) const
-  {
-    if (strategy_requires_unravelled_bias_and_quant_params<strategy>())
-    {
-      return (m_qp.bias == nullptr) ? sizeof(TAccum) * n_channels : 0;
-    }
-
-    return 0;
-  }
-
-  size_t sizeof_requant_mul_buffer(unsigned int n_channels) const
-  {
-    if (strategy_requires_unravelled_bias_and_quant_params<strategy>())
-    {
-      return m_qp.per_channel_requant ? 0 : sizeof(int32_t) * n_channels;
-    }
-
-    return 0;
-  }
-
-  size_t sizeof_requant_shift_buffer(unsigned int n_channels) const
-  {
-    if (strategy_requires_unravelled_bias_and_quant_params<strategy>())
-    {
-      return m_qp.per_channel_requant ? 0 : sizeof(int32_t) * n_channels;
-    }
-
-    return 0;
-  }
-
-  public:
-  DepthwiseDepthfirstQuantized(const DepthwiseArgs &args, const arm_gemm::Requantize32 &qp)
-    : DepthwiseCommon<TInput, TWeight, TOutput>(args), m_qp(qp)
-  {
-  }
-
-  DepthwiseDepthfirstQuantized(DepthwiseDepthfirstQuantized &) = delete;
-  DepthwiseDepthfirstQuantized &operator=(DepthwiseDepthfirstQuantized &) = delete;
-
-  size_t get_storage_size(void) const override
-  {
-    return strategy::get_packed_size(this->m_args);
-  }
-
-  void pack_parameters(void *buffer, const void *const bias, const void *weights, size_t ld_weight_col, size_t ld_weight_row) override
-  {
-    if (strategy_requires_unravelled_bias_and_quant_params<strategy>())
-    {
-      m_qp.bias = static_cast<const int32_t *>(bias);
-    }
-
-    get_unified_packer<TWeight>(strategy::pack_parameters)(
-      this->m_args.input_channels,
-      buffer,
-      static_cast<const int32_t *>(bias),
-      reinterpret_cast<const TWeight *>(weights),
-      m_qp,
-      ld_weight_col,
-      ld_weight_row
-    );
-  }
-
-  size_t get_working_size(const unsigned int n_threads, const unsigned int n_channels) const override
-  {
-    const unsigned int n_output_channels = n_channels * this->m_args.channel_multiplier;
-    return n_threads * (
-      sizeof_output_buffer(n_output_channels) +
-      sizeof_input_buffer(n_channels) +
-      sizeof_bias_buffer(n_channels) +
-      sizeof_requant_mul_buffer(n_channels) +
-      sizeof_requant_shift_buffer(n_channels)
-    );
-  }
-
-  using DepthwiseCommon<typename strategy::input_type, typename strategy::weight_type, typename strategy::return_type>::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 *_working_space,
-    const unsigned int thread_id,
-    const unsigned int n_threads
-  ) const override
-  {
-    strategy strat(this->m_args.cpu_info);
-#ifdef CYCLE_PROFILING
-    arm_gemm::profiler prof;
-#endif
-    // Get a unified API for the kernel function
-    auto kernel = get_unified_kernel<TInput, TWeight, TOutput>(strat.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);
-
-    // Create an array for the input pointers
-    const TInput * _inptr_array[strategy::input_rows * strategy::input_cols];
-    const TInput **const inptr_array = _inptr_array;
-
-    // Create an array for the output pointers
-    TOutput * _outptr_array[strategy::output_rows * strategy::output_cols];
-    TOutput **const outptr_array = _outptr_array;
-
-    // Allocate portions of the working space
-    uint8_t *working_space = static_cast<uint8_t *>(_working_space) + get_working_size(thread_id, input_channels);
-
-    TOutput *const output_buffer = reinterpret_cast<TOutput *>(working_space);
-    working_space += sizeof_output_buffer(input_channels * this->m_args.channel_multiplier);
-
-    TInput *const input_buffer = reinterpret_cast<TInput *>(working_space);
-    working_space += sizeof_input_buffer(input_channels);
-
-    const int32_t *const bias_ptr = (m_qp.bias == nullptr) ? reinterpret_cast<int32_t *>(working_space)
-                                                           : m_qp.bias;
-    working_space += sizeof_bias_buffer(input_channels * this->m_args.channel_multiplier);
-
-    const int32_t *const requant_mul_vec = !m_qp.per_channel_requant ? reinterpret_cast<int32_t *>(working_space)
-                                                                     : m_qp.per_channel_muls;
-    working_space += sizeof_requant_mul_buffer(input_channels * this->m_args.channel_multiplier);
-
-    const int32_t *const requant_shift_vec = !m_qp.per_channel_requant ? reinterpret_cast<int32_t *>(working_space)
-                                                                       : m_qp.per_channel_right_shifts;
-
-    if (strategy_requires_unravelled_bias_and_quant_params<strategy>())
-    {
-      // Initialise the bias buffer
-      if (m_qp.bias == nullptr)
-      {
-        for (unsigned int c = 0; c < input_channels * this->m_args.channel_multiplier; c++)
-        {
-          const_cast<int32_t *>(bias_ptr)[c] = 0;
-        }
-      }
-
-      // Initialise the requantisation parameters
-      if (!m_qp.per_channel_requant)
-      {
-        for (unsigned int c = 0; c < input_channels * this->m_args.channel_multiplier; c++)
-        {
-          const_cast<int32_t *>(requant_mul_vec)[c] = m_qp.per_layer_mul;
-          const_cast<int32_t *>(requant_shift_vec)[c] = m_qp.per_layer_right_shift;
-        }
-      }
-    }
-
-    // Initialise the input buffer
-    for (unsigned int c = 0; c < input_channels; c++)
-    {
-      input_buffer[c] = static_cast<TInput>(m_qp.a_offset);
-    }
-
-    // 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>(strategy::output_rows))
-      {
-        const int end_out_i = start_out_i + strategy::output_rows;
-        const int start_in_i = start_out_i * strategy::stride_rows - padding.top;
-        const int end_in_i = start_in_i + strategy::input_rows;
-
-        // Compute top/bottom padding
-        const auto pad_top = static_cast<unsigned int>(-std::min(start_in_i, 0));
-        const auto pad_bottom = static_cast<unsigned int>(-std::min(static_cast<int>(input_height) - end_in_i, 0));
-        const unsigned int valid_output_rows = std::min(
-          end_out_i - start_out_i,
-          static_cast<int>(output_height) - start_out_i
-        );
-
-        // Fill the input pointer array with padding values
-        for (auto index = 0u; index < strategy::input_rows * strategy::input_cols; index++)
-        {
-          inptr_array[index] = input_buffer;
-        }
-
-        for (int start_out_j = 0; start_out_j < static_cast<int>(output_width);)
-        {
-          const int start_in_j = start_out_j * strategy::stride_cols - this->m_args.padding.left;
-          const int pad_left = -std::min(0, start_in_j);
-
-          const int end_out_j = start_out_j + strategy::output_cols;
-          const int end_in_j = start_in_j + strategy::input_cols;
-
-          const auto pad_right = static_cast<unsigned int>(-std::min(static_cast<int>(input_width) - end_in_j, 0));
-          const unsigned int valid_output_cols = std::min(
-            end_out_j - start_out_j,
-            static_cast<int>(output_width) - start_out_j
-          );
-
-          // Construct the input pointer array - fill the array with pointers to
-          // the input buffer and then fill in the required values.
-          for (auto i = pad_top; i < strategy::input_rows - pad_bottom; i++)
-          {
-            // Can skip over the left padding because we will have either the
-            // same or less than the previous tile.
-            unsigned int j = pad_left;
-            const TInput *colptr = inptr_batch + (start_in_i + i) * ld_input_row + (start_in_j + j) * ld_input_col;
-            const TInput **ptrs = inptr_array + i * strategy::input_cols + j;
-            for (; j < strategy::input_cols - pad_right; j++)
-            {
-              *(ptrs++) = colptr;
-              colptr += ld_input_col;
-            }
-            for (; j < strategy::input_cols; j++)
-            {
-              *(ptrs++) = input_buffer;
-            }
-          }
-
-          // Construct the output pointer array.
-          TOutput **outptr_pos = outptr_array;
-          for (auto i = 0u; i < valid_output_rows; i++)
-          {
-            unsigned int j = 0u;
-            TOutput *colptr = outptr_batch + (start_out_i + i) * ld_output_row + start_out_j * ld_output_col;
-            for (; j < valid_output_cols; j++)
-            {
-              *(outptr_pos++) = colptr;
-               colptr += ld_output_col;
-            }
-            for (; j < strategy::output_cols; j++)
-            {
-              *(outptr_pos++) = output_buffer;
-            }
-          }
-          for (auto i = valid_output_rows; i < strategy::output_rows; i++)
-          {
-            for (auto j = 0u; j < strategy::output_cols; j++)
-            {
-              *(outptr_pos++) = output_buffer;
-            }
-          }
-
-          start_out_j += strategy::output_cols;
-
-#ifdef CYCLE_PROFILING
-          // TODO Work number
-          auto p = prof.ScopedProfiler(PROFILE_KERNEL, (unsigned long)(strategy::output_rows * strategy::output_cols * this->m_args.kernel_rows * this->m_args.kernel_cols));
-#endif
-          kernel(
-            this->m_args.input_channels,
-            inptr_array,
-            reinterpret_cast<const TWeight *>(parameters),
-            bias_ptr, m_qp, requant_mul_vec, requant_shift_vec,
-            outptr_array
-          );
-        }
-      }
-    }
-  }
-};
-
-}  // namespace depthwise
-}  // namespace arm_conv