1bea8e1eac
-Added LocalVector (needed it) -Added stb_rect_pack (It's pretty cool, we could probably use it for other stuff too) -Fixes and changes all around the place -Added library for 128 bits fixed point (required for Delaunay3D)
92 lines
2.9 KiB
C++
92 lines
2.9 KiB
C++
/*******************************************************************************
|
|
* 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_thread.hpp"
|
|
|
|
#include "simple_sum.hpp"
|
|
|
|
namespace mkldnn {
|
|
namespace impl {
|
|
namespace cpu {
|
|
|
|
template <data_type_t data_type>
|
|
status_t simple_sum_t<data_type>::execute(const exec_ctx_t &ctx) const {
|
|
auto output = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DST);
|
|
|
|
const memory_desc_wrapper o_d(pd()->dst_md());
|
|
output += o_d.blk_off(0);
|
|
|
|
const int num_arrs = pd()->n_inputs();
|
|
const data_t *input_ptrs[max_num_arrs];
|
|
const size_t nelems = o_d.nelems();
|
|
|
|
for (int a = 0; a < num_arrs; ++a) {
|
|
const memory_desc_wrapper i_d(pd()->src_md(a));
|
|
input_ptrs[a] = CTX_IN_MEM(const data_t *, MKLDNN_ARG_MULTIPLE_SRC + a)
|
|
+ i_d.blk_off(0);
|
|
}
|
|
|
|
const size_t block_size = 16 * 1024 / sizeof(data_type);
|
|
const size_t blocks_number = nelems / block_size;
|
|
const size_t tail = nelems % block_size;
|
|
|
|
const auto scales = pd()->scales();
|
|
parallel(0, [&](const int ithr, const int nthr) {
|
|
size_t start{0}, end{0};
|
|
balance211(blocks_number, nthr, ithr, start, end);
|
|
|
|
for (size_t nb = start; nb < end; ++nb) {
|
|
size_t start_e = nb * block_size;
|
|
size_t end_e = start_e + block_size;
|
|
|
|
PRAGMA_OMP_SIMD()
|
|
for (size_t e = start_e; e < end_e; e++) {
|
|
output[e] = data_t(scales[0] * input_ptrs[0][e]);
|
|
}
|
|
for (int a = 1; a < num_arrs; a++) {
|
|
PRAGMA_OMP_SIMD()
|
|
for (size_t e = start_e; e < end_e; e++) {
|
|
output[e] += data_t(scales[a] * input_ptrs[a][e]);
|
|
}
|
|
}
|
|
}
|
|
|
|
if (tail != 0 && ithr == nthr - 1) {
|
|
size_t start_e = nelems - tail;
|
|
size_t end_e = nelems;
|
|
|
|
PRAGMA_OMP_SIMD()
|
|
for (size_t e = start_e; e < end_e; e++) {
|
|
output[e] = data_t(scales[0] * input_ptrs[0][e]);
|
|
}
|
|
for (int a = 1; a < num_arrs; a++) {
|
|
PRAGMA_OMP_SIMD()
|
|
for (size_t e = start_e; e < end_e; e++) {
|
|
output[e] += data_t(scales[a] * input_ptrs[a][e]);
|
|
}
|
|
}
|
|
}
|
|
});
|
|
|
|
return status::success;
|
|
}
|
|
|
|
template struct simple_sum_t<data_type::f32>;
|
|
|
|
}
|
|
}
|
|
}
|