text: /*******************************************************************************
  * Copyright 2016-2018 Intel Corporation
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
  * You may obtain a copy of the License at
  *
  *     http://www.apache.org/licenses/LICENSE-2.0
  *
  * Unless required by applicable law or agreed to in writing, software
  * distributed under the License is distributed on an "AS IS" BASIS,
  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  * See the License for the specific language governing permissions and
  * limitations under the License.
  *******************************************************************************/
  
  #include <assert.h>
  #include "mkldnn.h"
  
  #include "c_types_map.hpp"
  #include "type_helpers.hpp"
  #include "utils.hpp"
  
  using namespace mkldnn::impl;
  using namespace mkldnn::impl::utils;
  using namespace mkldnn::impl::status;
  using namespace mkldnn::impl::prop_kind;
  using namespace mkldnn::impl::alg_kind;
  using namespace mkldnn::impl::types;
  
  namespace mkldnn {
  namespace impl {
  status_t conv_desc_init(convolution_desc_t *conv_desc,
          prop_kind_t prop_kind, alg_kind_t alg_kind,
          const memory_desc_t *src_desc, const memory_desc_t *weights_desc,
          const memory_desc_t *bias_desc, const memory_desc_t *dst_desc,
          const dims_t strides, const dims_t dilates,
          const dims_t padding_l, const dims_t padding_r,
          padding_kind_t padding_kind) {
      bool args_ok = true
          && !any_null(conv_desc, src_desc, weights_desc, dst_desc, strides,
                  padding_l)
          && one_of(alg_kind, convolution_auto, convolution_direct, convolution_winograd)
          && one_of(padding_kind, padding_kind::padding_zero);
      if (!args_ok) return invalid_arguments;
  
      if (padding_r == nullptr) padding_r = padding_l;
  
      auto cd = convolution_desc_t();
      cd.primitive_kind = primitive_kind::convolution;
      cd.prop_kind = prop_kind;
      cd.alg_kind = alg_kind;
  
      cd.diff_src_desc = cd.src_desc = zero_md();
      cd.diff_dst_desc = cd.dst_desc = zero_md();
      cd.diff_weights_desc = cd.weights_desc = zero_md();
      cd.diff_bias_desc = cd.bias_desc = zero_md();
  
      const bool is_fwd = one_of(prop_kind, forward_training, forward_inference);
      const bool with_bias =
          bias_desc && bias_desc->format_kind != format_kind::undef;
      const bool with_groups = weights_desc->ndims == src_desc->ndims + 1;
  
      (prop_kind == backward_data ? cd.diff_src_desc : cd.src_desc) = *src_desc;
      (is_fwd ? cd.dst_desc : cd.diff_dst_desc)  = *dst_desc;
      (prop_kind == backward_weights ? cd.diff_weights_desc : cd.weights_desc) =
          *weights_desc;
      if (with_bias)
          (prop_kind == backward_weights ? cd.diff_bias_desc : cd.bias_desc) =
              *bias_desc;
  
      int sp_dims = src_desc->ndims - 2;
      utils::array_copy(cd.strides, strides, sp_dims);
      utils::array_copy(cd.padding[0], padding_l, sp_dims);
      utils::array_copy(cd.padding[1], padding_r, sp_dims);
      if (dilates)
          utils::array_copy(cd.dilates, dilates, sp_dims);
      else
          utils::array_set(cd.dilates, 0, sp_dims);
  
      cd.padding_kind = padding_kind;
      cd.accum_data_type = types::default_accum_data_type(src_desc->data_type,
              weights_desc->data_type, dst_desc->data_type, prop_kind);
  
      const int g = with_groups ? weights_desc->dims[0] : 1;
      const int bias_dim = prop_kind == backward_data
          ? src_desc->dims[1]
          : dst_desc->dims[1];
  
      bool consistency = true
          && memory_desc_wrapper(weights_desc).nelems()
          && src_desc->ndims == dst_desc->ndims
          && utils::one_of(src_desc->ndims, 3, 4, 5)
          && utils::one_of(weights_desc->ndims, src_desc->ndims,
                  src_desc->ndims + 1)
          && (with_bias ? bias_desc->ndims == 1 : true)
          && (with_bias ? bias_desc->dims[0] == bias_dim : true)
          && src_desc->dims[0] == dst_desc->dims[0]
          && src_desc->dims[1] == g * weights_desc->dims[with_groups + 1]
          && dst_desc->dims[1] == g * weights_desc->dims[with_groups + 0];
      for (int i = 2; i < src_desc->ndims; ++i)
      {
          int src = src_desc->dims[i];
          int ker = weights_desc->dims[with_groups + i];
          int dil = cd.dilates[i - 2];
          int pad_l = padding_l[i - 2];
          int pad_r = padding_r[i - 2];
          int str = strides[i - 2];
          int dst = dst_desc->dims[i];
          int ker_range = 1 + (ker - 1) * (dil + 1);
  
          if (str < 1) return invalid_arguments;
          consistency = consistency
              && dil >= 0
              && pad_l >= 0
              && pad_r + str > 0
              && (src - ker_range + pad_l + pad_r) / str + 1 == dst;
      }
      if (!consistency) return invalid_arguments;
  
      *conv_desc = cd;
      return success;
  }
  }
  }
  
  status_t mkldnn_convolution_forward_desc_init(convolution_desc_t *conv_desc,
          prop_kind_t prop_kind, alg_kind_t alg_kind,
          const memory_desc_t *src_desc, const memory_desc_t *weights_desc,
          const memory_desc_t *bias_desc, const memory_desc_t *dst_desc,
          const dims_t strides, const dims_t padding_l, const dims_t padding_r,
          padding_kind_t padding_kind) {
      if (!one_of(prop_kind, forward_training, forward_inference))
          return invalid_arguments;
      return mkldnn::impl::conv_desc_init(conv_desc, prop_kind, alg_kind, src_desc,
              weights_desc, bias_desc, dst_desc, strides, nullptr,
              padding_l, padding_r, padding_kind);
  }
  
  status_t mkldnn_dilated_convolution_forward_desc_init(
          convolution_desc_t *conv_desc, prop_kind_t prop_kind,
          alg_kind_t alg_kind, const memory_desc_t *src_desc,
          const memory_desc_t *weights_desc, const memory_desc_t *bias_desc,
          const memory_desc_t *dst_desc, const dims_t strides,
          const dims_t dilates, const dims_t padding_l,
          const dims_t padding_r, padding_kind_t padding_kind) {
      if (!one_of(prop_kind, forward_training, forward_inference))
          return invalid_arguments;
      return mkldnn::impl::conv_desc_init(conv_desc, prop_kind, alg_kind, src_desc,
              weights_desc, bias_desc, dst_desc, strides, dilates,
              padding_l, padding_r, padding_kind);
  }
  
  status_t mkldnn_convolution_backward_data_desc_init(
          convolution_desc_t *conv_desc, alg_kind_t alg_kind,
          const memory_desc_t *diff_src_desc, const memory_desc_t *weights_desc,
          const memory_desc_t *diff_dst_desc, const dims_t strides,
          const dims_t padding_l, const dims_t padding_r,
          padding_kind_t padding_kind) {
      return mkldnn::impl::conv_desc_init(conv_desc, backward_data, alg_kind, diff_src_desc,
              weights_desc, nullptr, diff_dst_desc, strides, nullptr,
              padding_l, padding_r, padding_kind);
  }
  
  status_t mkldnn_dilated_convolution_backward_data_desc_init(
          convolution_desc_t *conv_desc, alg_kind_t alg_kind,
          const memory_desc_t *diff_src_desc, const memory_desc_t *weights_desc,
          const memory_desc_t *diff_dst_desc, const dims_t strides,
          const dims_t dilates, const dims_t padding_l, const dims_t padding_r,
          padding_kind_t padding_kind) {
      return mkldnn::impl::conv_desc_init(conv_desc, backward_data, alg_kind, diff_src_desc,
              weights_desc, nullptr, diff_dst_desc, strides, dilates,
              padding_l, padding_r, padding_kind);
  }
  
  status_t mkldnn_convolution_backward_weights_desc_init(
          convolution_desc_t *conv_desc, alg_kind_t alg_kind,
          const memory_desc_t *src_desc, const memory_desc_t *diff_weights_desc,
          const memory_desc_t *diff_bias_desc,
          const memory_desc_t *diff_dst_desc, const dims_t strides,
          const dims_t padding_l, const dims_t padding_r,
          padding_kind_t padding_kind) {
      return mkldnn::impl::conv_desc_init(conv_desc, backward_weights, alg_kind, src_desc,
              diff_weights_desc, diff_bias_desc, diff_dst_desc, strides,
              nullptr, padding_l, padding_r, padding_kind);
  }
  
  status_t mkldnn_dilated_convolution_backward_weights_desc_init(
          convolution_desc_t *conv_desc, alg_kind_t alg_kind,
          const memory_desc_t *src_desc, const memory_desc_t *diff_weights_desc,
          const memory_desc_t *diff_bias_desc,
          const memory_desc_t *diff_dst_desc, const dims_t strides,
          const dims_t dilates, const dims_t padding_l, const dims_t padding_r,
          padding_kind_t padding_kind) {
      return mkldnn::impl::conv_desc_init(conv_desc, backward_weights, alg_kind, src_desc,
              diff_weights_desc, diff_bias_desc, diff_dst_desc, strides,
              dilates, padding_l, padding_r, padding_kind);
  }
  
  // vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
  