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/*******************************************************************************
* Copyright 2017-2018 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.
*******************************************************************************/
#include "mkldnn_types.h"
#include "c_types_map.hpp"
#include "jit_sse42_convolution.hpp"
#include "mkldnn_thread.hpp"
namespace mkldnn {
namespace impl {
namespace cpu {
using namespace mkldnn::impl::status;
using namespace mkldnn::impl::utils;
#define src_blk_off(f, n, c, h, w) \
(pd()->ndims() == 3) \
? (f).blk_off(n, c, w) \
: (f).blk_off(n, c, h, w)
#define wht_blk_off_(f, g, ...) \
pd()->with_groups() \
? (f).blk_off(g, __VA_ARGS__) \
: (f).blk_off(__VA_ARGS__)
#define wht_blk_off(f, g, oc, ic, kh, kw) \
pd()->ndims() == 3 \
? wht_blk_off_(f, g, oc, ic, kw) \
: wht_blk_off_(f, g, oc, ic, kh, kw)
void jit_sse42_convolution_fwd_t::execute_forward(
const exec_ctx_t &ctx) const {
auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
auto weights = CTX_IN_MEM(const data_t *, MKLDNN_ARG_WEIGHTS);
auto bias = CTX_IN_MEM(const data_t *, MKLDNN_ARG_BIAS);
auto dst = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DST);
const memory_desc_wrapper src_d(pd()->src_md());
const memory_desc_wrapper dst_d(pd()->dst_md());
const memory_desc_wrapper weights_d(pd()->weights_md(0));
const memory_desc_wrapper bias_d(pd()->weights_md(1));
const auto &jcp = kernel_->jcp;
int ocb_work = div_up(jcp.nb_oc, jcp.nb_oc_blocking);
const size_t work_amount = jcp.mb * jcp.ngroups * ocb_work * jcp.oh;
parallel(0, [&](const int ithr, const int nthr) {
size_t start{ 0 }, end{ 0 };
balance211(work_amount, nthr, ithr, start, end);
int icbb = 0;
while (icbb < jcp.nb_ic) {
int icb_step = jcp.nb_ic_blocking;
int icb_step_rem = jcp.nb_ic - icbb;
if (icb_step_rem < jcp.nb_ic_blocking_max)
icb_step = icb_step_rem;
size_t n{0}, g{0}, ocbb{0}, oh{0};
nd_iterator_init(start, n, jcp.mb, g, jcp.ngroups, ocbb, ocb_work,
oh, jcp.oh);
for (size_t iwork = start; iwork < end; ++iwork) {
int ocb = ocbb * jcp.nb_oc_blocking;
int ocb_num = jcp.nb_oc_blocking;
for (int icb = icbb; icb < icbb + icb_step; ++icb) {
auto par_conv = jit_conv_call_s();
const int ij = oh * jcp.stride_h;
const int i_t_overflow = nstl::max(0, jcp.t_pad - ij);
const int i_b_overflow = nstl::max(jcp.ih, ij
+ (jcp.kh-1) * (jcp.dilate_h+1) - jcp.t_pad+1) - jcp.ih;
const size_t _oc = g * jcp.nb_oc + ocb;
const size_t _ic = g * jcp.nb_ic + icb;
const int ih = nstl::max(ij - jcp.t_pad
+ div_up(i_t_overflow,
(jcp.dilate_h+1)) * (jcp.dilate_h + 1), 0);
par_conv.src = &src[src_blk_off(src_d, n,
jcp.ic == 3 ? 0 : _ic, ih, 0)];
par_conv.dst = &dst[src_blk_off(dst_d, n, _oc, oh, 0)];
const int wh = div_up(i_t_overflow, (jcp.dilate_h + 1));
par_conv.filt = &weights[wht_blk_off(weights_d, g, ocb,
jcp.ic == 3 ? 0 : icb, wh, 0)];
if (icb == 0) {
if (bias)
par_conv.bias =
&bias[bias_d.blk_off(_oc * jcp.oc_block)];
par_conv.flags |= FLAG_IC_FIRST;
}
if (jcp.with_eltwise && icb + 1 == jcp.nb_ic) {
par_conv.flags |= FLAG_IC_LAST;
}
par_conv.oc_blocks =
nstl::min(ocb + ocb_num, jcp.nb_oc) - ocb;
par_conv.kw_padding = 0;
const int kh_padding = jcp.kh
- div_up(i_t_overflow, (jcp.dilate_h + 1))
- div_up(i_b_overflow, (jcp.dilate_h + 1));
par_conv.kh_padding = nstl::max(0, kh_padding);
kernel_->jit_ker(&par_conv);
}
nd_iterator_step(n, jcp.mb, g, jcp.ngroups, ocbb, ocb_work,
oh, jcp.oh);
}
icbb += icb_step;
}
});
if (pd()->wants_zero_pad_dst())
ctx.memory(MKLDNN_ARG_DST)->zero_pad();
}
}
}
}
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