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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_JIT_GEMM_CONVOLUTION_HPP
#define CPU_JIT_GEMM_CONVOLUTION_HPP
#include "c_types_map.hpp"
#include "memory_tracking.hpp"
#include "gemm_convolution_utils.hpp"
#include "gemm/gemm.hpp"
#include "ref_eltwise.hpp"
#include "cpu_convolution_pd.hpp"
namespace mkldnn {
namespace impl {
namespace cpu {
struct gemm_convolution_fwd_t: public cpu_primitive_t {
struct pd_t: public cpu_convolution_fwd_pd_t {
pd_t(engine_t *engine,
const convolution_desc_t *adesc, const primitive_attr_t *attr,
const typename pd_t::base_class *hint_fwd_pd)
: cpu_convolution_fwd_pd_t(engine, adesc, attr, hint_fwd_pd)
, jcp_() {}
DECLARE_COMMON_PD_T(GEMM_IMPL_STR, gemm_convolution_fwd_t);
status_t init() {
bool ok = true
&& is_fwd()
&& set_default_alg_kind(alg_kind::convolution_direct)
&& expect_data_types(data_type::f32, data_type::f32,
data_type::f32, data_type::f32, data_type::f32)
&& !has_zero_dim_memory()
&& set_default_formats_common(dat_tag(), wei_tag(), dat_tag())
&& post_ops_ok()
&& memory_desc_matches_tag(*src_md(), dat_tag())
&& memory_desc_matches_tag(*dst_md(), dat_tag())
&& memory_desc_matches_tag(*weights_md(), wei_tag());
if (!ok) return status::unimplemented;
auto scratchpad = scratchpad_registry().registrar();
return jit_gemm_convolution_utils::init_conf(jcp_, scratchpad,
*desc(), src_md(), weights_md(0), dst_md(),
mkldnn_get_max_threads());
}
jit_gemm_conv_conf_t jcp_;
protected:
format_tag_t dat_tag() const {
using namespace format_tag;
return utils::pick(ndims() - 3, ncw, nchw, ncdhw);
}
format_tag_t wei_tag() const {
using namespace format_tag;
return with_groups()
? utils::pick(ndims() - 3, goiw, goihw, goidhw)
: utils::pick(ndims() - 3, oiw, oihw, oidhw);
}
bool post_ops_ok() const {
auto const &po = attr()->post_ops_;
auto is_eltwise = [&](int idx)
{ return po.entry_[idx].is_eltwise(); };
auto is_sum = [&](int idx) { return po.entry_[idx].is_sum(); };
switch (po.len_) {
case 0: return true; // no post_ops
case 1: return is_eltwise(0) || is_sum(0); // sum OR eltwise
case 2: return is_sum(0) && is_eltwise(1); // sum -> eltwise
default: return false;
}
return false;
}
};
gemm_convolution_fwd_t(const pd_t *apd)
: cpu_primitive_t(apd, true)
, eltwise_(nullptr)
{
const auto &post_ops = pd()->attr()->post_ops_;
const data_t one = 1.0, zero = 0.0;
beta_ = post_ops.find(primitive_kind::sum) >= 0 ? one : zero;
const int entry_idx = post_ops.find(primitive_kind::eltwise);
if (entry_idx != -1) eltwise_ = new ref_eltwise_scalar_fwd_t(
post_ops.entry_[entry_idx].eltwise);
}
~gemm_convolution_fwd_t() { delete eltwise_; }
typedef typename prec_traits<data_type::f32>::type data_t;
virtual status_t execute(const exec_ctx_t &ctx) const override {
execute_forward(ctx);
return status::success;
}
private:
void execute_forward(const exec_ctx_t &ctx) const;
const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
data_t beta_;
ref_eltwise_scalar_fwd_t* eltwise_;
};
struct gemm_convolution_bwd_data_t: public cpu_primitive_t {
struct pd_t: public cpu_convolution_bwd_data_pd_t {
pd_t(engine_t *engine,
const convolution_desc_t *adesc, const primitive_attr_t *attr,
const convolution_fwd_pd_t *hint_fwd_pd)
: cpu_convolution_bwd_data_pd_t(engine, adesc, attr, hint_fwd_pd)
, jcp_() {}
DECLARE_COMMON_PD_T(GEMM_IMPL_STR, gemm_convolution_bwd_data_t);
status_t init() {
bool ok = true
&& desc()->prop_kind == prop_kind::backward_data
&& set_default_alg_kind(alg_kind::convolution_direct)
&& expect_data_types(data_type::f32, data_type::f32,
data_type::undef, data_type::f32, data_type::f32)
&& !has_zero_dim_memory()
&& set_default_formats_common(dat_tag(), wei_tag(), dat_tag())
&& memory_desc_matches_tag(*diff_src_md(), dat_tag())
&& memory_desc_matches_tag(*diff_dst_md(), dat_tag())
&& memory_desc_matches_tag(*weights_md(), wei_tag());
if (!ok) return status::unimplemented;
auto scratchpad = scratchpad_registry().registrar();
return jit_gemm_convolution_utils::init_conf(jcp_, scratchpad,
*desc(), diff_src_md(), weights_md(0), diff_dst_md(),
mkldnn_get_max_threads());
}
jit_gemm_conv_conf_t jcp_;
protected:
format_tag_t dat_tag() const {
using namespace format_tag;
return utils::pick(ndims() - 3, ncw, nchw, ncdhw);
}
format_tag_t wei_tag() const {
using namespace format_tag;
return with_groups()
? utils::pick(ndims() - 3, goiw, goihw, goidhw)
: utils::pick(ndims() - 3, oiw, oihw, oidhw);
}
};
gemm_convolution_bwd_data_t(const pd_t *apd)
: cpu_primitive_t(apd, true) {}
typedef typename prec_traits<data_type::f32>::type data_t;
virtual status_t execute(const exec_ctx_t &ctx) const override {
execute_backward_data(ctx);
return status::success;
}
private:
void execute_backward_data(const exec_ctx_t &ctx) const;
const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
};
struct gemm_convolution_bwd_weights_t: public cpu_primitive_t {
struct pd_t: public cpu_convolution_bwd_weights_pd_t {
pd_t(engine_t *engine,
const convolution_desc_t *adesc,
const primitive_attr_t *attr,
const convolution_fwd_pd_t *hint_fwd_pd)
: cpu_convolution_bwd_weights_pd_t(engine, adesc, attr, hint_fwd_pd)
, jcp_() {}
DECLARE_COMMON_PD_T(GEMM_IMPL_STR, gemm_convolution_bwd_weights_t);
status_t init() {
bool ok = true
&& desc()->prop_kind == prop_kind::backward_weights
&& set_default_alg_kind(alg_kind::convolution_direct)
&& expect_data_types(data_type::f32, data_type::f32,
data_type::f32, data_type::f32, data_type::f32)
&& !has_zero_dim_memory()
&& set_default_formats_common(dat_tag(), wei_tag(), dat_tag())
&& memory_desc_matches_tag(*src_md(), dat_tag())
&& memory_desc_matches_tag(*diff_dst_md(), dat_tag())
&& memory_desc_matches_tag(*diff_weights_md(), wei_tag());
if (!ok) return status::unimplemented;
auto scratchpad = scratchpad_registry().registrar();
return jit_gemm_convolution_utils::init_conf(jcp_, scratchpad,
*desc(), src_md(), diff_weights_md(0), diff_dst_md(),
mkldnn_get_max_threads());
}
jit_gemm_conv_conf_t jcp_;
protected:
format_tag_t dat_tag() const {
using namespace format_tag;
return utils::pick(ndims() - 3, ncw, nchw, ncdhw);
}
format_tag_t wei_tag() const {
using namespace format_tag;
return with_groups()
? utils::pick(ndims() - 3, goiw, goihw, goidhw)
: utils::pick(ndims() - 3, oiw, oihw, oidhw);
}
};
gemm_convolution_bwd_weights_t(const pd_t *apd)
: cpu_primitive_t(apd, true) {}
typedef typename prec_traits<data_type::f32>::type data_t;
virtual status_t execute(const exec_ctx_t &ctx) const override {
execute_backward_weights(ctx);
return status::success;
}
private:
void execute_backward_weights(const exec_ctx_t &ctx) const;
const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
};
}
}
}
#endif
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