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/*******************************************************************************
* Copyright 2016-2018 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_REF_SOFTMAX_FWD_HPP
#define CPU_REF_SOFTMAX_FWD_HPP
#include <assert.h>
#include "c_types_map.hpp"
#include "cpu_softmax_pd.hpp"
#include "cpu_engine.hpp"
#include "type_helpers.hpp"
#include "utils.hpp"
namespace mkldnn {
namespace impl {
namespace cpu {
template <impl::data_type_t data_type>
struct ref_softmax_fwd_t: public cpu_primitive_t {
struct pd_t: public cpu_softmax_fwd_pd_t {
pd_t(engine_t *engine, const softmax_desc_t *adesc,
const primitive_attr_t *attr,
const softmax_fwd_pd_t *hint_fwd_pd)
: cpu_softmax_fwd_pd_t(engine, adesc, attr, hint_fwd_pd) {}
DECLARE_COMMON_PD_T("ref:any", ref_softmax_fwd_t);
virtual status_t init() override {
using namespace prop_kind;
assert(engine()->kind() == engine_kind::cpu);
bool ok = true
&& utils::one_of(desc()->prop_kind, forward_inference,
forward_training)
&& data_pd_.desc()->data_type == data_type
&& attr()->has_default_values();
if (!ok) return status::unimplemented;
return status::success;
}
};
ref_softmax_fwd_t(const pd_t *pd, const input_vector &inputs,
const output_vector &outputs)
: cpu_primitive_t(&conf_, inputs, outputs), conf_(*pd), ws_(nullptr) {
auto ndims = conf_.desc()->data_desc.ndims;
auto dims = conf_.desc()->data_desc.dims;
auto axis = conf_.desc()->softmax_axis;
outer_size_ = utils::array_product(dims, axis);
channels_ = dims[axis];
inner_size_ = utils::array_product(dims + axis + 1, ndims - axis - 1);
val_max_ = val_denom_ = 0;
if (inner_size_ > 1) {
ws_ = new data_t[2*inner_size_];
max_ = &ws_[0];
denom_ = &ws_[inner_size_];
} else {
max_ = &val_max_;
denom_ = &val_denom_;
}
const memory_desc_wrapper data_d(conf_.src_pd());
use_dense_ = inner_size_ == 1 && data_d.is_dense()
&& data_d.blocking_desc().block_dims[axis] == 1
&& data_d.blocking_desc().strides[0][axis] == 1;
}
~ref_softmax_fwd_t() { if (ws_) delete [] ws_; }
typedef typename prec_traits<data_type>::type data_t;
virtual void execute(event_t *e) {
if (use_dense_) execute_forward_dense();
else execute_forward_generic();
e->set_state(event_t::ready);
}
private:
void execute_forward_dense();
void execute_forward_generic();
void _max(int n, const data_t *x, data_t *max_data);
void _sub(int n, data_t alpha, const data_t *x, data_t *y);
void _exp(int n, const data_t *a, data_t *r);
void _sum(int n, const data_t *x, data_t *sum_data);
void _scal(int n, data_t alpha, data_t *x);
pd_t conf_;
bool use_dense_;
int outer_size_, channels_, inner_size_;
data_t val_max_, val_denom_;
data_t *ws_, *max_, *denom_;
};
template <impl::data_type_t data_type>
struct ref_softmax_bwd_t: public cpu_primitive_t {
struct pd_t: public cpu_softmax_bwd_pd_t {
pd_t(engine_t *engine, const softmax_desc_t *adesc,
const primitive_attr_t *attr,
const softmax_fwd_pd_t *hint_fwd_pd)
: cpu_softmax_bwd_pd_t(engine, adesc, attr, hint_fwd_pd) {}
DECLARE_COMMON_PD_T("ref:any", ref_softmax_bwd_t);
virtual status_t init() override {
using namespace prop_kind;
assert(engine()->kind() == engine_kind::cpu);
bool ok = true
&& utils::one_of(desc()->prop_kind, backward_data)
&& diff_src_pd_.desc()->data_type == data_type
&& diff_dst_pd_.desc()->data_type == data_type
&& attr()->has_default_values();
if (!ok) return status::unimplemented;
return status::success;
}
};
ref_softmax_bwd_t(const pd_t *pd, const input_vector &inputs,
const output_vector &outputs)
: cpu_primitive_t(&conf_, inputs, outputs), conf_(*pd) {
auto dims = conf_.desc()->diff_desc.dims;
auto axis = conf_.desc()->softmax_axis;
auto ndims = conf_.desc()->diff_desc.ndims;
outer_size_ = utils::array_product(dims, axis);
channels_ = dims[axis];
inner_size_ = utils::array_product(dims + axis + 1, ndims - axis - 1);
// Diff desc as well as data desc whould be checked
const memory_desc_wrapper data_d(conf_.dst_pd());
const memory_desc_wrapper diff_d(conf_.diff_dst_pd());
use_dense_ = true
&& inner_size_ == 1
&& diff_d == data_d
&& diff_d.is_dense()
&& diff_d.blocking_desc().block_dims[axis] == 1
&& diff_d.blocking_desc().strides[0][axis] == 1;
}
~ref_softmax_bwd_t() {}
typedef typename prec_traits<data_type>::type data_t;
virtual void execute(event_t *e) {
if (use_dense_) execute_backward_dense();
else execute_backward_generic();
e->set_state(event_t::ready);
}
private:
void execute_backward_dense();
void execute_backward_generic();
pd_t conf_;
bool use_dense_;
int outer_size_, channels_, inner_size_;
data_t val_max_, val_denom_;
data_t *max_, *denom_;
};
}
}
}
#endif
// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
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