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/*******************************************************************************
* 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::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
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