/******************************************************************************* * Copyright 2018 Intel Corporation * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *******************************************************************************/ #ifndef CPU_NCSP_BATCH_NORMALIZATION_HPP #define CPU_NCSP_BATCH_NORMALIZATION_HPP #include <assert.h> #include "c_types_map.hpp" #include "cpu_batch_normalization_pd.hpp" #include "cpu_engine.hpp" #include "type_helpers.hpp" #include "utils.hpp" namespace mkldnn { namespace impl { namespace cpu { struct ncsp_batch_normalization_fwd_t : public cpu_primitive_t { struct pd_t : public cpu_batch_normalization_fwd_pd_t { pd_t(engine_t *engine, const batch_normalization_desc_t *adesc, const primitive_attr_t *attr, const batch_normalization_fwd_pd_t *hint_fwd_pd) : cpu_batch_normalization_fwd_pd_t( engine, adesc, attr, hint_fwd_pd) {} DECLARE_COMMON_PD_T("ncsp_bnorm:any", ncsp_batch_normalization_fwd_t); virtual status_t init() override { using namespace prop_kind; using namespace data_type; assert(engine()->kind() == engine_kind::cpu); bool ok = true && is_fwd() && !has_zero_dim_memory() && desc()->data_desc.data_type == f32 && utils::implication(use_scaleshift(), desc()->data_scaleshift_desc.data_type == f32) && utils::one_of(data_pd_.desc()->format, memory_format::nchw, memory_format::ncdhw, memory_format::nc) && (attr()->has_default_values() || this->with_relu_post_op()); if (!ok) return status::unimplemented; if (is_training() && fuse_bn_relu()) { bn_init_default_ws(this, this->workspace_pd_, 8); } if (stats_is_src() || is_training()) { memory_desc_t stats_d; dims_t stats_dims = { C() }; mkldnn_memory_desc_init(&stats_d, 1, stats_dims, data_type::f32, memory_format::x); mean_pd_ = cpu_memory_t::pd_t(engine_, &stats_d); variance_pd_ = cpu_memory_t::pd_t(engine_, &stats_d); } return success; } }; typedef typename prec_traits<data_type::f32>::type data_t; ncsp_batch_normalization_fwd_t(const pd_t *pd, const input_vector &inputs, const output_vector &outputs); ~ncsp_batch_normalization_fwd_t(); virtual void execute(event_t *e) { execute_forward(); e->set_state(event_t::ready); } private: data_t *stats_reduction_, *tmp_mean_, *tmp_variance_; void execute_forward(); pd_t conf_; }; struct ncsp_batch_normalization_bwd_t : public cpu_primitive_t { struct pd_t : public cpu_batch_normalization_bwd_pd_t { pd_t(engine_t *engine, const batch_normalization_desc_t *adesc, const primitive_attr_t *attr, const batch_normalization_fwd_pd_t *hint_fwd_pd) : cpu_batch_normalization_bwd_pd_t( engine, adesc, attr, hint_fwd_pd) {} DECLARE_COMMON_PD_T("ncsp_bnorm:any", ncsp_batch_normalization_bwd_t); virtual status_t init() override { using namespace prop_kind; using namespace data_type; assert(engine()->kind() == engine_kind::cpu); bool ok = true && is_bwd() && !has_zero_dim_memory() && desc()->data_desc.data_type == f32 && utils::implication(use_scaleshift(), desc()->data_scaleshift_desc.data_type == f32) && utils::one_of(data_pd_.desc()->format, memory_format::nchw, memory_format::ncdhw, memory_format::nc) && attr()->has_default_values(); if (!ok) return status::unimplemented; if (fuse_bn_relu()) { bn_init_default_ws(this, this->workspace_pd_, 8); const size_t this_ws_sz = memory_desc_wrapper(this->workspace_pd()).size(); bool ws_ok = true && hint_fwd_pd_->workspace_pd() && memory_desc_wrapper(hint_fwd_pd_->workspace_pd()) .size() == this_ws_sz; if (!ws_ok) return status::unimplemented; } return success; } }; typedef typename prec_traits<data_type::f32>::type data_t; ncsp_batch_normalization_bwd_t(const pd_t *pd, const input_vector &inputs, const output_vector &outputs); ~ncsp_batch_normalization_bwd_t(); virtual void execute(event_t *e) { execute_backward(); e->set_state(event_t::ready); } private: void execute_backward(); pd_t conf_; data_t *stats_reduction_, *tmp_diff_scaleshift_; }; } } } #endif // vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s