inference-engine/thirdparty/mkl-dnn/src/cpu/ref_batch_normalization.hpp

   1 /*******************************************************************************
   2 * Copyright 2016-2018 Intel Corporation
   3 *
   4 * Licensed under the Apache License, Version 2.0 (the "License");
   5 * you may not use this file except in compliance with the License.
   6 * You may obtain a copy of the License at
   7 *
   8 *     http://www.apache.org/licenses/LICENSE-2.0
   9 *
  10 * Unless required by applicable law or agreed to in writing, software
  11 * distributed under the License is distributed on an "AS IS" BASIS,
  12 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  13 * See the License for the specific language governing permissions and
  14 * limitations under the License.
  15 *******************************************************************************/
  16
  17 #ifndef CPU_REF_BATCH_NORMALIZATION_FWD_HPP
  18 #define CPU_REF_BATCH_NORMALIZATION_FWD_HPP
  19
  20 #include <assert.h>
  21
  22 #include "c_types_map.hpp"
  23 #include "cpu_batch_normalization_pd.hpp"
  24 #include "cpu_engine.hpp"
  25 #include "type_helpers.hpp"
  26 #include "utils.hpp"
  27
  28 namespace mkldnn {
  29 namespace impl {
  30 namespace cpu {
  31
  32 template <impl::data_type_t data_type>
  33 struct ref_batch_normalization_fwd_t: public cpu_primitive_t {
  34     struct pd_t: public cpu_batch_normalization_fwd_pd_t {
  35         pd_t(engine_t *engine, const batch_normalization_desc_t *adesc,
  36                 const primitive_attr_t *attr,
  37                 const batch_normalization_fwd_pd_t *hint_fwd_pd)
  38             : cpu_batch_normalization_fwd_pd_t(engine, adesc, attr,
  39                     hint_fwd_pd) {}
  40
  41         DECLARE_COMMON_PD_T("ref:any", ref_batch_normalization_fwd_t);
  42
  43         virtual status_t init() override {
  44             using namespace prop_kind;
  45             assert(engine()->kind() == engine_kind::cpu);
  46             bool ok = true
  47                 && utils::one_of(desc()->prop_kind, forward_training,
  48                         forward_inference)
  49                 && utils::everyone_is(data_type, desc()->data_desc.data_type,
  50                         desc()->data_scaleshift_desc.data_type)
  51                 && (attr()->has_default_values() || this->with_relu_post_op());
  52             if (!ok) return status::unimplemented;
  53
  54             if (stats_is_src() || is_training()) {
  55                 memory_desc_t stats_d;
  56                 dims_t stats_dims = { C() };
  57                 mkldnn_memory_desc_init(&stats_d, 1, stats_dims, data_type,
  58                         memory_format::x);
  59                 mean_pd_ = cpu_memory_t::pd_t(engine_, &stats_d);
  60                 variance_pd_ = cpu_memory_t::pd_t(engine_, &stats_d);
  61             }
  62
  63             if (is_training() && fuse_bn_relu())
  64                 bn_init_default_ws(this, this->workspace_pd_, 8);
  65
  66             return status::success;
  67         }
  68     };
  69
  70     ref_batch_normalization_fwd_t(const pd_t *apd, const input_vector &inputs,
  71             const output_vector &outputs)
  72         : cpu_primitive_t(apd, inputs, outputs) {}
  73     typedef typename prec_traits<data_type>::type data_t;
  74
  75     virtual void execute(event_t *e) const {
  76         execute_forward();
  77         e->set_state(event_t::ready);
  78     }
  79
  80 private:
  81     void execute_forward() const;
  82     const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
  83 };
  84
  85 template <impl::data_type_t data_type>
  86 struct ref_batch_normalization_bwd_t: public cpu_primitive_t {
  87     struct pd_t: public cpu_batch_normalization_bwd_pd_t {
  88         pd_t(engine_t *engine, const batch_normalization_desc_t *adesc,
  89                 const primitive_attr_t *attr,
  90                 const batch_normalization_fwd_pd_t *hint_fwd_pd)
  91             : cpu_batch_normalization_bwd_pd_t(engine, adesc, attr,
  92                     hint_fwd_pd) {}
  93
  94         DECLARE_COMMON_PD_T("ref:any", ref_batch_normalization_bwd_t);
  95
  96         virtual status_t init() override {
  97             using namespace prop_kind;
  98             assert(engine()->kind() == engine_kind::cpu);
  99             bool ok = true
 100                 && utils::one_of(desc()->prop_kind, backward, backward_data)
 101                 && utils::everyone_is(data_type, desc()->data_desc.data_type,
 102                         desc()->diff_data_desc.data_type,
 103                         desc()->data_desc.data_type,
 104                         desc()->data_scaleshift_desc.data_type)
 105                 && attr()->has_default_values()
 106                 && hint_fwd_pd_ != nullptr;
 107             if (!ok) return status::unimplemented;
 108
 109             if (fuse_bn_relu()) {
 110                 bn_init_default_ws(this, this->workspace_pd_, 8);
 111                 const size_t this_ws_sz
 112                     = memory_desc_wrapper(this->workspace_pd()).size();
 113
 114                 bool ws_ok = true
 115                     && hint_fwd_pd_->workspace_pd()
 116                     && memory_desc_wrapper(hint_fwd_pd_->workspace_pd()).size()
 117                             == this_ws_sz;
 118                 if (!ws_ok)
 119                     return status::unimplemented;
 120             }
 121
 122             bool stats_ok = true
 123                 && hint_fwd_pd_->mean_pd()->desc()->ndims == 1
 124                 && hint_fwd_pd_->mean_pd()->desc()->format == memory_format::x
 125                 && hint_fwd_pd_->mean_pd()->desc()->data_type == data_type
 126                 && hint_fwd_pd_->variance_pd()->desc()->ndims == 1
 127                 && hint_fwd_pd_->variance_pd()->desc()->format == memory_format::x
 128                 && hint_fwd_pd_->variance_pd()->desc()->data_type == data_type;
 129             if (!stats_ok) return status::unimplemented;
 130
 131             return status::success;
 132         }
 133     };
 134
 135     ref_batch_normalization_bwd_t(const pd_t *apd, const input_vector &inputs,
 136             const output_vector &outputs)
 137         : cpu_primitive_t(apd, inputs, outputs) {}
 138     typedef typename prec_traits<data_type>::type data_t;
 139
 140     virtual void execute(event_t *e) const {
 141         execute_backward();
 142         e->set_state(event_t::ready);
 143     }
 144
 145 private:
 146     void execute_backward() const;
 147     const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
 148 };
 149
 150 }
 151 }
 152 }
 153
 154 #endif
 155
 156 // vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s