inference-engine/thirdparty/mkl-dnn/src/cpu/jit_uni_x8s8s32x_dw_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_X8S8S32X_DW_CONVOLUTION_HPP
  18 #define CPU_JIT_UNI_X8S8S32X_DW_CONVOLUTION_HPP
  19
  20 #include "c_types_map.hpp"
  21 #include "cpu_convolution_pd.hpp"
  22 #include "cpu_engine.hpp"
  23 #include "jit_primitive_conf.hpp"
  24 #include "jit_generator.hpp"
  25 #include "jit_uni_x8s8s32x_dw_conv_kernel.hpp"
  26
  27 namespace mkldnn {
  28 namespace impl {
  29 namespace cpu {
  30
  31 template <cpu_isa_t isa, impl::data_type_t src_type, impl::data_type_t dst_type>
  32 struct _jit_uni_x8s8s32x_dw_convolution_fwd_t: public cpu_primitive_t {
  33     struct pd_t: public cpu_convolution_fwd_pd_t {
  34         pd_t(engine_t *engine, const convolution_desc_t *adesc,
  35                 const primitive_attr_t *attr,
  36                 const typename pd_t::base_class *hint_fwd_pd)
  37             : cpu_convolution_fwd_pd_t(engine, adesc, attr,
  38                 hint_fwd_pd)
  39             , jcp_() {}
  40
  41         DECLARE_COMMON_PD_T(
  42                 JIT_IMPL_NAME_HELPER("jit_dw:", isa, ""),
  43                 _jit_uni_x8s8s32x_dw_convolution_fwd_t<isa, src_type, dst_type>);
  44
  45         virtual status_t init() override {
  46             using namespace prop_kind;
  47             assert(this->engine()->kind() == engine_kind::cpu);
  48             bool ok = true
  49                 && this->set_default_params() == status::success
  50                 && utils::one_of(this->desc()->prop_kind, forward_training,
  51                         forward_inference)
  52                 && this->desc()->alg_kind == alg_kind::convolution_direct
  53                 && this->desc()->dst_desc.data_type == dst_type
  54                 && IMPLICATION(this->with_bias(), utils::one_of(
  55                     this->desc()->bias_desc.data_type, data_type::f32,
  56                     data_type::s32, data_type::s8, data_type::u8))
  57                 && this->desc()->accum_data_type == data_type::s32;
  58             if (!ok) return status::unimplemented;
  59
  60             return jit_uni_x8s8s32x_dw_conv_fwd_kernel<isa>::init_conf(jcp_,
  61                         *this->desc(),
  62                         *this->src_pd_.desc(), *this->weights_pd_.desc(),
  63                         *this->dst_pd_.desc(), *this->bias_pd_.desc(),
  64                         *this->attr());
  65         }
  66
  67         jit_conv_conf_t jcp_;
  68
  69     protected:
  70         virtual status_t set_default_params() override {
  71             using namespace memory_format;
  72             auto desired_act_fmt = nhwc;
  73             auto desired_wei_fmt = isa == avx512_common ? Goihw16g : Goihw8g;
  74
  75             if (this->src_pd_.desc()->format == any)
  76                 CHECK(this->src_pd_.set_format(desired_act_fmt));
  77             if (this->dst_pd_.desc()->format == any)
  78                 CHECK(this->dst_pd_.set_format(desired_act_fmt));
  79             if (this->weights_pd_.desc()->format == any)
  80                 CHECK(this->weights_pd_.set_format(desired_wei_fmt));
  81             if (this->bias_pd_.desc()->format == any)
  82                 CHECK(this->bias_pd_.set_format(x));
  83             return status::success;
  84         }
  85     };
  86
  87     _jit_uni_x8s8s32x_dw_convolution_fwd_t(const pd_t *apd,
  88             const input_vector &inputs, const output_vector &outputs)
  89         : cpu_primitive_t(apd, inputs, outputs)
  90     {
  91         kernel_ = new jit_uni_x8s8s32x_dw_conv_fwd_kernel<isa>(pd()->jcp_, *pd()->attr());
  92     }
  93
  94     ~_jit_uni_x8s8s32x_dw_convolution_fwd_t() { delete kernel_; };
  95
  96     typedef typename prec_traits<data_type::u8>::type src_data_t;
  97     typedef typename prec_traits<data_type::s8>::type wei_data_t;
  98     typedef typename prec_traits<dst_type>::type dst_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     const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
 108     jit_uni_x8s8s32x_dw_conv_fwd_kernel<isa> *kernel_;
 109 };
 110
 111 template <impl::data_type_t src_type, impl::data_type_t dst_type>
 112 using jit_avx2_x8s8s32x_dw_convolution_fwd_t = _jit_uni_x8s8s32x_dw_convolution_fwd_t<avx2, src_type, dst_type>;
 113 template <impl::data_type_t src_type, impl::data_type_t dst_type>
 114 using jit_sse42_x8s8s32x_dw_convolution_fwd_t = _jit_uni_x8s8s32x_dw_convolution_fwd_t<sse42, src_type, dst_type>;
 115
 116 }
 117 }
 118 }
 119
 120 #endif