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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_1x1_convolution.hpp"
#include "utils.hpp"
#include "mkldnn_thread.hpp"
#include "type_helpers.hpp"
namespace mkldnn {
namespace impl {
namespace cpu {
#define data_blk_off(f, n, c, h, w) \
((ndims == 3) \
? (f).blk_off(n, c, w) \
: (f).blk_off(n, c, h, w))
using namespace mkldnn::impl::status;
using namespace mkldnn::impl::utils;
void jit_sse42_1x1_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 auto &jcp = kernel_->jcp;
const int ndims = src_d.ndims();
const int work_amount = jcp.mb * jcp.ngroups * jcp.nb_bcast;
parallel(0, [&](const int ithr, const int nthr) {
// TODO (Roma): remove this restriction
assert(jcp.stride_w == 1 && jcp.stride_h == 1);
auto par_conv = jit_1x1_conv_call_s();
const int nb_oc = jcp.nb_load;
const int nb_ic = jcp.nb_reduce;
const int nb_ic_blocking = jcp.nb_reduce_blocking;
const int os_block = jcp.bcast_block;
int start{0}, end{0};
balance211(work_amount, nthr, ithr, start, end);
int iwork = start;
while (iwork < end) {
int n{0}, g{0}, osb{0};
nd_iterator_init(iwork, n, jcp.mb, g, jcp.ngroups, osb,
jcp.nb_bcast);
const int bcast_step_rem = jcp.nb_bcast - osb;
int bcast_step = bcast_step_rem <= jcp.nb_bcast_blocking_max
? bcast_step_rem : jcp.nb_bcast_blocking;
bcast_step = nstl::min<int>(bcast_step, end - iwork);
const int os = osb * os_block;
const int ow = os % jcp.ow;
const int oh = os / jcp.ow;
const int iw = nstl::max<int>(ow * jcp.stride_w - jcp.l_pad, 0);
const int ih = nstl::max<int>(oh * jcp.stride_h - jcp.t_pad, 0);
par_conv.bcast_dim = this_block_size(os, jcp.os,
bcast_step * os_block);
int ocb = 0;
while (ocb < jcp.nb_load) {
const int load_step_rem = jcp.nb_load - ocb;
const int load_step = load_step_rem < jcp.nb_load_blocking_max
? load_step_rem : jcp.nb_load_blocking;
const size_t _ocb = g * nb_oc + ocb;
par_conv.load_dim = this_block_size(ocb * jcp.oc_block, jcp.oc,
load_step * jcp.oc_block);
const size_t dst_off = data_blk_off(dst_d, n, _ocb, oh, ow);
par_conv.output_data = &dst[dst_off];
par_conv.bias_data = &bias[_ocb * jcp.oc_block];
for (int icb = 0; icb < nb_ic; icb += nb_ic_blocking) {
par_conv.first_last_flag = 0
| (icb == 0) * FLAG_REDUCE_FIRST
| (icb + nb_ic_blocking >= nb_ic) * FLAG_REDUCE_LAST;
par_conv.reduce_dim = this_block_size(icb * jcp.ic_block,
jcp.ic, nb_ic_blocking * jcp.ic_block);
const size_t _icb = g * nb_ic + icb;
const size_t src_off = data_blk_off(src_d, n, _icb, ih, iw);
par_conv.bcast_data = &src[src_off];
par_conv.load_data = &weights[pd()->with_groups()
? weights_d.blk_off(g, ocb, icb)
: weights_d.blk_off(ocb, icb)];
kernel_->jit_ker(&par_conv);
}
ocb += load_step;
}
iwork += bcast_step;
}
});
if (pd()->wants_zero_pad_dst())
ctx.memory(MKLDNN_ARG_DST)->zero_pad();
}
}
}
}
|