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

   1 /*******************************************************************************
   2 * Copyright 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_JIT_UNI_PLANAR_CONVOLUTION_HPP
  18 #define CPU_JIT_UNI_PLANAR_CONVOLUTION_HPP
  19
  20 #include "c_types_map.hpp"
  21 #include "cpu_convolution_pd.hpp"
  22 #include "cpu_engine.hpp"
  23 #include "cpu_reducer.hpp"
  24 #include "jit_primitive_conf.hpp"
  25 #include "jit_uni_planar_conv_kernel_f32.hpp"
  26 #include "mkldnn_thread.hpp"
  27 #include "jit_uni_depthwise.hpp"
  28
  29 namespace mkldnn {
  30 namespace impl {
  31 namespace cpu {
  32
  33 template <cpu_isa_t isa>
  34 struct _jit_uni_planar_convolution_fwd_t: public cpu_primitive_t {
  35     struct pd_t: public cpu_convolution_fwd_pd_t {
  36         pd_t(engine_t *engine, const convolution_desc_t *adesc,
  37                 const primitive_attr_t *attr,
  38                 const typename pd_t::base_class *hint_fwd_pd)
  39             : cpu_convolution_fwd_pd_t(engine, adesc, attr, hint_fwd_pd)
  40             , jcp_() {}
  41
  42         DECLARE_COMMON_PD_T(
  43                 JIT_IMPL_NAME_HELPER("jit_planar:", isa, ""),
  44                 _jit_uni_planar_convolution_fwd_t<isa>);
  45
  46         virtual status_t init() override {
  47             using namespace prop_kind;
  48             assert(this->engine()->kind() == engine_kind::cpu);
  49             bool ok = true
  50                 && this->set_default_params() == status::success
  51                 && utils::one_of(this->desc()->prop_kind, forward_training,
  52                         forward_inference)
  53                 && this->desc()->alg_kind == alg_kind::convolution_direct
  54                 && !this->has_zero_dim_memory()
  55                 && utils::everyone_is(data_type::f32,
  56                         this->desc()->src_desc.data_type,
  57                         this->desc()->weights_desc.data_type,
  58                         this->desc()->dst_desc.data_type)
  59                 && IMPLICATION(this->with_bias(),
  60                         data_type::f32 == this->desc()->bias_desc.data_type);
  61             if (!ok) return status::unimplemented;
  62
  63             status_t sts = jit_uni_planar_conv_fwd_kernel_f32<isa>::init_conf(jcp_, *this->desc(),
  64                     *this->src_pd_.desc(), *this->weights_pd_.desc(),
  65                     *this->dst_pd_.desc(), *this->attr());
  66
  67             return sts;
  68         }
  69
  70         jit_conv_conf_t jcp_;
  71
  72     protected:
  73         virtual status_t set_default_params() override {
  74             using namespace memory_format;
  75
  76             if (this->src_pd_.desc()->format == any)
  77                 CHECK(this->src_pd_.set_format(this->ndims() == 4 ? nchw : ncdhw));
  78             if (this->dst_pd_.desc()->format == any)
  79                 CHECK(this->dst_pd_.set_format(this->ndims() == 4 ? nchw : ncdhw));
  80             if (this->weights_pd_.desc()->format == any)
  81                 CHECK(this->weights_pd_.set_format(this->ndims() == 4 ? oihw : oidhw));
  82             if (this->bias_pd_.desc()->format == any)
  83                 CHECK(this->bias_pd_.set_format(x));
  84             return status::success;
  85         }
  86     };
  87
  88     _jit_uni_planar_convolution_fwd_t(const pd_t *apd,
  89                                       const input_vector &inputs, const output_vector &outputs)
  90         : cpu_primitive_t(apd, inputs, outputs) {
  91         kernel_ = new jit_uni_planar_conv_fwd_kernel_f32<isa>(pd()->jcp_, *pd()->attr());
  92     }
  93
  94     ~_jit_uni_planar_convolution_fwd_t() {
  95         delete kernel_;
  96     };
  97
  98     typedef typename prec_traits<data_type::f32>::type data_t;
  99
 100     virtual void execute(event_t *e) const {
 101         execute_forward();
 102         e->set_state(event_t::ready);
 103     }
 104
 105 private:
 106     void execute_forward() const;
 107
 108     const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
 109     jit_uni_planar_conv_fwd_kernel_f32<isa> *kernel_;
 110 };
 111
 112 using jit_avx512_common_planar_convolution_fwd_t = _jit_uni_planar_convolution_fwd_t<avx512_common>;
 113 using jit_avx2_planar_convolution_fwd_t = _jit_uni_planar_convolution_fwd_t<avx2>;
 114
 115 }
 116 }
 117 }
 118
 119 #endif