inference-engine/thirdparty/mkl-dnn/src/cpu/jit_uni_x8s8s32x_convolution.hpp

   1 /*******************************************************************************
   2 * Copyright 2018-2019 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_CONVOLUTION_HPP
  18 #define CPU_JIT_UNI_X8S8S32X_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_x8s8s32x_conv_kernel.hpp"
  26 #include "jit_generator.hpp"
  27 #include "mkldnn_thread.hpp"
  28 #include "jit_uni_depthwise.hpp"
  29
  30 namespace mkldnn {
  31 namespace impl {
  32 namespace cpu {
  33
  34 template <cpu_isa_t isa, impl::data_type_t src_type, impl::data_type_t dst_type>
  35 struct _jit_uni_x8s8s32x_convolution_fwd_t: public cpu_primitive_t {
  36     struct pd_t: public cpu_convolution_fwd_pd_t {
  37         pd_t(engine_t *engine, const convolution_desc_t *adesc,
  38                 const primitive_attr_t *attr,
  39                 const typename pd_t::base_class *hint_fwd_pd)
  40             : cpu_convolution_fwd_pd_t(engine, adesc, attr, hint_fwd_pd)
  41             , jcp_(), jcp_dw_() {}
  42
  43         DECLARE_COMMON_PD_T(
  44                 JIT_IMPL_NAME_HELPER("jit:", isa, ""),
  45                 _jit_uni_x8s8s32x_convolution_fwd_t<isa, src_type, dst_type>);
  46
  47         virtual status_t init() override {
  48             using namespace prop_kind;
  49             assert(this->engine()->kind() == engine_kind::cpu);
  50             bool ok = true
  51                 && utils::one_of(this->desc()->prop_kind, forward_training,
  52                         forward_inference)
  53                 && this->desc()->alg_kind == alg_kind::convolution_direct
  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                 && this->desc()->src_desc.data_type == src_type
  59                 && this->desc()->dst_desc.data_type == dst_type;
  60             if (!ok) return status::unimplemented;
  61
  62             status_t sts = jit_uni_x8s8s32x_conv_fwd_kernel<isa>::init_conf(jcp_, *this->desc(),
  63                     this->src_pd_, this->weights_pd_,
  64                     this->dst_pd_, this->bias_pd_, *this->attr());
  65             if (sts != status::success) return sts;
  66
  67             if (jcp_.with_dw_conv) {
  68                 status_t sts_dw = jit_uni_dw_conv_row_f32<isa>::init_conf(jcp_, jcp_dw_, *this->attr());
  69                 if (sts_dw != status::success) return sts_dw;
  70             }
  71
  72             auto scratchpad = scratchpad_registry().registrar();
  73             jit_uni_x8s8s32x_conv_fwd_kernel<isa>::init_scratchpad(scratchpad, jcp_, jcp_dw_, *this->attr());
  74
  75             return status::success;
  76         }
  77
  78         jit_conv_conf_t jcp_;
  79         jit_conv_conf_t jcp_dw_;
  80     };
  81
  82     _jit_uni_x8s8s32x_convolution_fwd_t(const pd_t *apd,
  83             const input_vector &inputs, const output_vector &outputs)
  84         : cpu_primitive_t(apd, inputs, outputs) {
  85         kernel_ = new jit_uni_x8s8s32x_conv_fwd_kernel<isa>(pd()->jcp_, pd()->jcp_dw_, *pd()->attr());
  86
  87         if (pd()->jcp_.with_dw_conv) {
  88             kernel_dw_ = new jit_uni_dw_conv_row_f32<isa>(pd()->jcp_dw_, *pd()->attr(), pd()->jcp_dw_.oc);
  89         }
  90     }
  91
  92     ~_jit_uni_x8s8s32x_convolution_fwd_t() {
  93         delete kernel_;
  94
  95         if (pd()->jcp_.with_dw_conv) {
  96             delete kernel_dw_;
  97         }
  98     };
  99
 100     typedef typename prec_traits<data_type::u8>::type src_data_t;
 101     typedef typename prec_traits<data_type::s8>::type wei_data_t;
 102     typedef typename prec_traits<data_type::f32>::type bia_data_t;
 103     typedef typename prec_traits<dst_type>::type dst_data_t;
 104
 105     virtual void execute(event_t *e) const {
 106         if (pd()->jcp_.with_dw_conv)
 107             execute_forward_with_dw_conv();
 108         else
 109             execute_forward();
 110
 111         e->set_state(event_t::ready);
 112     }
 113
 114 private:
 115     void execute_forward() const;
 116     void execute_forward_with_dw_conv() const;
 117     const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
 118     jit_uni_x8s8s32x_conv_fwd_kernel<isa> *kernel_;
 119     jit_uni_dw_conv_row_f32<isa> *kernel_dw_;
 120 };
 121
 122 template <impl::data_type_t src_type, impl::data_type_t dst_type>
 123 using jit_avx2_x8s8s32x_convolution_fwd_t = _jit_uni_x8s8s32x_convolution_fwd_t<avx2, src_type, dst_type>;
 124
 125 template <impl::data_type_t src_type, impl::data_type_t dst_type>
 126 using jit_sse42_x8s8s32x_convolution_fwd_t = _jit_uni_x8s8s32x_convolution_fwd_t<sse42, src_type, dst_type>;
 127
 128 }
 129 }
 130 }
 131
 132 #endif