inference-engine/thirdparty/mkl-dnn/src/cpu/jit_avx512_core_x8s8s32x_convolution.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_JIT_AVX512_CORE_X8S8S32X_CONVOLUTION_HPP
  18 #define CPU_JIT_AVX512_CORE_X8S8S32X_CONVOLUTION_HPP
  19
  20 #include "c_types_map.hpp"
  21 #include "cpu_convolution_pd.hpp"
  22 #include "cpu_engine.hpp"
  23 #include "jit_transpose_src_utils.hpp"
  24 #include "cpu_reducer.hpp"
  25 #include "cpu_barrier.hpp"
  26
  27 #include "jit_avx512_core_x8s8s32x_conv_kernel.hpp"
  28
  29 namespace mkldnn {
  30 namespace impl {
  31 namespace cpu {
  32
  33 template <bool with_relu, impl::data_type_t src_type, impl::data_type_t dst_type>
  34 struct _jit_avx512_core_x8s8s32x_convolution_fwd_t : public cpu_primitive_t {
  35     struct pd_t : public _cpu_convolution_fwd_pd_t<with_relu> {
  36         pd_t(engine_t *engine, const typename pd_t::base_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<with_relu>(engine, adesc, attr,
  40                     hint_fwd_pd)
  41             , jcp_()
  42         {
  43         }
  44         DECLARE_COMMON_PD_T(
  45                 JIT_IMPL_NAME_HELPER("jit_int8:", avx512_core, ""),
  46                 _jit_avx512_core_x8s8s32x_convolution_fwd_t<with_relu, src_type,
  47                 dst_type>);
  48
  49         virtual status_t init() override
  50         {
  51             using namespace prop_kind;
  52             assert(this->engine()->kind() == engine_kind::cpu);
  53             bool ok = true
  54                     && utils::one_of(this->cdesc_().prop_kind, forward_training,
  55                                forward_inference)
  56                     && this->cdesc_().alg_kind == alg_kind::convolution_direct
  57                     && !this->has_zero_dim_memory()
  58                     && this->cdesc_().src_desc.data_type == src_type
  59                     && this->cdesc_().dst_desc.data_type == dst_type
  60                     && IMPLICATION(this->with_bias(), utils::one_of(
  61                             this->cdesc_().bias_desc.data_type, data_type::f32,
  62                             data_type::s32, data_type::s8, data_type::u8))
  63                     && this->cdesc_().accum_data_type == data_type::s32;
  64             if (!ok)
  65                 return status::unimplemented;
  66
  67             return jit_avx512_core_x8s8s32x_fwd_kernel::init_conf(
  68                     jcp_, this->cdesc_(), this->src_pd_, this->weights_pd_,
  69                     this->dst_pd_,this->bias_pd_, *this->attr(),
  70                     mkldnn_get_max_threads(),
  71                     with_relu, this->negative_slope());
  72         }
  73
  74         jit_conv_conf_t jcp_;
  75     };
  76
  77     _jit_avx512_core_x8s8s32x_convolution_fwd_t(const pd_t *pd,
  78             const input_vector &inputs, const output_vector &outputs)
  79         : cpu_primitive_t(&conf_, inputs, outputs), conf_(*pd)
  80         , local_scales_(nullptr)
  81     {
  82         kernel_ = new jit_avx512_core_x8s8s32x_fwd_kernel(conf_.jcp_,
  83                     *conf_.attr());
  84         if (conf_.jcp_.signed_input && conf_.jcp_.ver != ver_vnni) {
  85             size_t scales_size = (conf_.attr()->output_scales_.count_ == 1)
  86                 ? 16
  87                 : conf_.attr()->output_scales_.count_;
  88             local_scales_ = (float *)malloc(sizeof(float) * scales_size, 64);
  89             for (size_t i = 0; i < scales_size; i++) {
  90                 local_scales_[i] = conf_.attr()->output_scales_.scales_[i] *
  91                                         (1.f / conf_.jcp_.wei_adj_scale);
  92             }
  93         }
  94     }
  95
  96     ~_jit_avx512_core_x8s8s32x_convolution_fwd_t() {
  97         delete kernel_;
  98         if (local_scales_) free(local_scales_);
  99     };
 100
 101     typedef typename prec_traits<src_type>::type src_data_t;
 102     typedef typename prec_traits<data_type::s8>::type wei_data_t;
 103     typedef typename prec_traits<dst_type>::type dst_data_t;
 104
 105     virtual void execute(event_t *e)
 106     {
 107         execute_forward();
 108         e->set_state(event_t::ready);
 109     }
 110
 111 private:
 112     void execute_forward();
 113     pd_t conf_;
 114     jit_avx512_core_x8s8s32x_fwd_kernel *kernel_;
 115     float *local_scales_;
 116 };
 117
 118 template <impl::data_type_t src_type, impl::data_type_t dst_type>
 119 using jit_avx512_core_x8s8s32x_convolution_fwd_t =
 120     _jit_avx512_core_x8s8s32x_convolution_fwd_t<false, src_type, dst_type>;
 121
 122 template <impl::data_type_t src_type, impl::data_type_t dst_type>
 123 using jit_avx512_core_x8s8s32x_convolution_relu_t =
 124     _jit_avx512_core_x8s8s32x_convolution_fwd_t<true, src_type, dst_type>;
 125
 126 }
 127 }
 128 }
 129
 130 #endif
 131
 132 // vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s