/******************************************************************************* * 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 #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::types; namespace { status_t ip_desc_init(inner_product_desc_t *ip_desc, prop_kind_t prop_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) { bool args_ok = !any_null(ip_desc, src_desc, weights_desc, dst_desc); if (!args_ok) return invalid_arguments; auto id = inner_product_desc_t(); id.primitive_kind = primitive_kind::inner_product; id.prop_kind = prop_kind; id.diff_src_desc = id.src_desc = zero_md(); id.diff_dst_desc = id.dst_desc = zero_md(); id.diff_weights_desc = id.weights_desc = zero_md(); id.diff_bias_desc = id.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; (prop_kind == backward_data ? id.diff_src_desc : id.src_desc) = *src_desc; (is_fwd ? id.dst_desc : id.diff_dst_desc) = *dst_desc; (prop_kind == backward_weights ? id.diff_weights_desc : id.weights_desc) = *weights_desc; if (with_bias) (prop_kind == backward_weights ? id.diff_bias_desc : id.bias_desc) = *bias_desc; id.accum_data_type = types::default_accum_data_type(src_desc->data_type, weights_desc->data_type, dst_desc->data_type, prop_kind); bool consistency = true && memory_desc_wrapper(weights_desc).nelems() && one_of(src_desc->ndims, 2, 3, 4, 5) && dst_desc->ndims == 2 && weights_desc->ndims == src_desc->ndims && (with_bias ? bias_desc->ndims == 1 : true) && (with_bias ? bias_desc->dims[0] == dst_desc->dims[1] : true) && src_desc->dims[0] == dst_desc->dims[0] && array_cmp(&src_desc->dims[1], &weights_desc->dims[1], src_desc->ndims - 1) && dst_desc->dims[1] == weights_desc->dims[0]; if (!consistency) return invalid_arguments; *ip_desc = id; return success; } } status_t mkldnn_inner_product_forward_desc_init(inner_product_desc_t *ip_desc, prop_kind_t prop_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) { if (!one_of(prop_kind, forward_training, forward_inference)) return invalid_arguments; return ip_desc_init(ip_desc, prop_kind, src_desc, weights_desc, bias_desc, dst_desc); } status_t mkldnn_inner_product_backward_data_desc_init( inner_product_desc_t *ip_desc, const memory_desc_t *diff_src_desc, const memory_desc_t *weights_desc, const memory_desc_t *diff_dst_desc) { return ip_desc_init(ip_desc, backward_data, diff_src_desc, weights_desc, nullptr, diff_dst_desc); } status_t mkldnn_inner_product_backward_weights_desc_init( inner_product_desc_t *ip_desc, 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) { return ip_desc_init(ip_desc, backward_weights, src_desc, diff_weights_desc, diff_bias_desc, diff_dst_desc); } // vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s