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diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/nchw_pooling.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/nchw_pooling.cpp
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--- a/thirdparty/oidn/mkl-dnn/src/cpu/nchw_pooling.cpp
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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 <assert.h>
-#include <math.h>
-
-#include "c_types_map.hpp"
-#include "type_helpers.hpp"
-#include "math_utils.hpp"
-#include "mkldnn_thread.hpp"
-#include "nstl.hpp"
-
-#include "nchw_pooling.hpp"
-
-namespace mkldnn {
-namespace impl {
-namespace cpu {
-
-template <impl::data_type_t data_type>
-void nchw_pooling_fwd_t<data_type>::execute_forward(
- const exec_ctx_t &ctx) const {
- using namespace alg_kind;
-
- auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
- auto dst = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DST);
- auto ws = CTX_OUT_MEM(unsigned char *, MKLDNN_ARG_WORKSPACE);
-
- const memory_desc_wrapper ws_d(pd()->workspace_md());
- const data_type_t ws_dt = ws ? ws_d.data_type() : data_type::undef;
-
- const int MB = pd()->MB();
- const int C = pd()->C();
- const int OD = pd()->OD();
- const int OH = pd()->OH();
- const int OW = pd()->OW();
- const int ID = pd()->ID();
- const int IH = pd()->IH();
- const int IW = pd()->IW();
- const int KD = pd()->KD();
- const int KH = pd()->KH();
- const int KW = pd()->KW();
- const int SD = pd()->KSD();
- const int SH = pd()->KSH();
- const int SW = pd()->KSW();
- const int padF = pd()->padFront();
- const int padT = pd()->padT();
- const int padL = pd()->padL();
-
- auto alg = pd()->desc()->alg_kind;
-
- auto apply_offset = [=](int index, int offset) {
- return (index > offset) ? index - offset : 0;
- };
-
- auto set_ws = [=](int mb, int c, int od, int oh, int ow, int value) {
- if (ws) {
- assert(ws_dt == data_type::u8 || ws_dt == data_type::s32);
- size_t ws_offset
- = (size_t)OW * OH * OD * C * mb
- + (size_t)OW * OH * OD * c
- + (size_t)OW * OH * od
- + (size_t)OW * oh
- + (size_t)ow;
- if (ws_dt == data_type::u8) {
- assert(0 <= value && value <= 255);
- ws[ws_offset] = value;
- } else
- reinterpret_cast<int *>(ws)[ws_offset] = value;
- }
- };
-
- auto ker_max = [=](data_t *d, int mb, int c, int od, int oh, int ow) {
- for (int kd = 0; kd < KD; ++kd) {
- for (int kh = 0; kh < KH; ++kh) {
- for (int kw = 0; kw < KW; ++kw) {
- const int id = od * SD - padF + kd;
- const int ih = oh * SH - padT + kh;
- const int iw = ow * SW - padL + kw;
-
- if (id < 0 || id >= ID) continue;
- if (ih < 0 || ih >= IH) continue;
- if (iw < 0 || iw >= IW) continue;
-
- auto src_offset
- = (size_t)IW * IH * ID * C * mb
- + (size_t)IW * IH * ID * c
- + (size_t)IW * IH * id
- + (size_t)IW * ih
- + (size_t)iw;
- auto s = src[src_offset];
- if (s > d[0]) {
- d[0] = s;
- set_ws(mb, c, od, oh, ow, kd*KH*KW + kh*KW + kw);
- }
- }
- }
- }
- };
-
- auto ker_avg = [=](data_t *d, int mb, int c, int od, int oh, int ow) {
- auto id_start = apply_offset(od*SD, padF);
- auto ih_start = apply_offset(oh*SH, padT);
- auto iw_start = apply_offset(ow*SW, padL);
- auto id_end = nstl::min(od*SD - padF + KD, ID);
- auto ih_end = nstl::min(oh*SH - padT + KH, IH);
- auto iw_end = nstl::min(ow*SW - padL + KW, IW);
-
- auto num_summands = (alg == pooling_avg_include_padding) ? KD*KW*KH
- : (id_end - id_start)*(ih_end - ih_start)*(iw_end - iw_start);
-
- for (int id = id_start; id < id_end; ++id) {
- for (int ih = ih_start; ih < ih_end; ++ih) {
- for (int iw = iw_start; iw < iw_end; ++iw) {
- auto src_offset
- = (size_t)IW * IH * ID * C * mb
- + (size_t)IW * IH * ID * c
- + (size_t)IW * IH * id
- + (size_t)IW * ih
- + (size_t)iw;
- d[0] += src[src_offset];
- }
- }
- }
-
- d[0] = math::out_round<data_t>((float)d[0] / num_summands);
- };
-
-
- if (pd()->desc()->alg_kind == pooling_max) {
- parallel_nd(MB, C, OD, OH, OW,
- [&](int mb, int c, int od, int oh, int ow) {
- size_t dst_offset
- = (size_t)OW * OH * OD * C * mb
- + (size_t)OW * OH * OD * c
- + (size_t)OW * OH * od
- + (size_t)OW * oh
- + (size_t)ow;
- data_t *d = &dst[dst_offset];
- d[0] = nstl::numeric_limits<data_t>::lowest();
- set_ws(mb, c, od, oh, ow, 0);
- ker_max(d, mb, c, od, oh, ow);
- });
- } else {
- parallel_nd(MB, C, OD, OH, OW,
- [&](int mb, int c, int od, int oh, int ow) {
- size_t dst_offset
- = (size_t)OW * OH * OD * C * mb
- + (size_t)OW * OH * OD * c
- + (size_t)OW * OH * od
- + (size_t)OW * oh
- + (size_t)ow;
- data_t *d = &dst[dst_offset];
- d[0] = 0;
- ker_avg(d, mb, c, od, oh, ow);
- });
- }
-}
-
-template <impl::data_type_t data_type>
-void nchw_pooling_bwd_t<data_type>::execute_backward(
- const exec_ctx_t &ctx) const {
- using namespace alg_kind;
-
- auto diff_dst = CTX_IN_MEM(const data_t *, MKLDNN_ARG_DIFF_DST);
- auto ws = CTX_IN_MEM(const unsigned char *, MKLDNN_ARG_WORKSPACE);
- auto diff_src = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_SRC);
-
- const memory_desc_wrapper ws_d(pd()->workspace_md());
-
- const int MB = pd()->MB();
- const int C = pd()->C();
- const int OD = pd()->OD();
- const int OH = pd()->OH();
- const int OW = pd()->OW();
- const int ID = pd()->ID();
- const int IH = pd()->IH();
- const int IW = pd()->IW();
- const int KD = pd()->KD();
- const int KH = pd()->KH();
- const int KW = pd()->KW();
- const int SD = pd()->KSD();
- const int SH = pd()->KSH();
- const int SW = pd()->KSW();
- const int padF = pd()->padFront();
- const int padT = pd()->padT();
- const int padL = pd()->padL();
-
- const bool is_3d = pd()->desc()->diff_src_desc.ndims == 5;
-
- auto alg = pd()->desc()->alg_kind;
-
- auto apply_offset = [=](int index, int offset) {
- return (index > offset) ? index - offset : 0;
- };
-
- auto ker_zero = [=](int mb, int c) {
- size_t diff_src_offset = (size_t)mb*C*ID*IH*IW + (size_t)c*ID*IH*IW;
- for (int id = 0; id < ID; ++id) {
- for (int ih = 0; ih < IH; ++ih) {
- for (int iw = 0; iw < IW; ++iw) {
- diff_src[diff_src_offset++] = 0;
- }
- }
- }
- };
-
- auto ker_max = [=](const data_t *d, int mb, int c, int od, int oh, int ow) {
- auto b_c = ws_d.blocking_desc().inner_nblks == 0
- ? 1 : ws_d.blocking_desc().inner_blks[0];
- auto ws_offset = is_3d
- ? ws_d.blk_off(mb, c / b_c, od, oh, ow) + c % b_c
- : ws_d.blk_off(mb, c / b_c, oh, ow) + c % b_c;
-
- const int index = ws_d.data_type() == data_type::u8
- ? (int)ws[ws_offset] : ((const int *)ws)[ws_offset];
- const int kw = index % KW;
- const int kh = (index / KW) % KH;
- const int kd = (index / KW) / KH;
-
- const int id = od * SD - padF + kd;
- const int ih = oh * SH - padT + kh;
- const int iw = ow * SW - padL + kw;
-
- // If padding area could fit the kernel,
- // then input displacement would be out of bounds.
- // No need to back propagate there as padding is
- // virtual in pooling_max case.
- if (id < 0 || id >= ID)
- return;
- if (ih < 0 || ih >= IH)
- return;
- if (iw < 0 || iw >= IW)
- return;
-
- size_t diff_src_offset =
- (size_t)mb*C*ID*IH*IW + (size_t)c*ID*IH*IW + (size_t)id*IH*IW
- + (size_t)ih*IW + (size_t)iw;
- diff_src[diff_src_offset] += d[0];
- };
-
- auto ker_avg = [=](const data_t *d, int mb, int c, int od, int oh, int ow) {
- auto id_start = apply_offset(od*SD, padF);
- auto ih_start = apply_offset(oh*SH, padT);
- auto iw_start = apply_offset(ow*SW, padL);
- auto id_end = nstl::min(od*SD - padF + KD, ID);
- auto ih_end = nstl::min(oh*SH - padT + KH, IH);
- auto iw_end = nstl::min(ow*SW - padL + KW, IW);
-
- size_t num_summands = (alg == pooling_avg_include_padding)
- ? (size_t)KW*KH*KD
- : (size_t)(id_end - id_start)*(ih_end - ih_start)
- *(iw_end - iw_start);
-
- for (int id = id_start; id < id_end; ++id) {
- for (int ih = ih_start; ih < ih_end; ++ih) {
- for (int iw = iw_start; iw < iw_end; ++iw) {
- size_t diff_src_offset = (size_t)mb*C*ID*IH*IW
- + (size_t)c*ID*IH*IW + (size_t)id*IH*IW
- + (size_t)ih*IW + (size_t)iw;
- diff_src[diff_src_offset] += d[0] / num_summands;
- }
- }
- }
- };
-
- if (pd()->desc()->alg_kind == pooling_max) {
- parallel_nd(MB, C, [&](int mb, int c) {
- size_t diff_dst_offset = (size_t)mb*C*OD*OH*OW
- + (size_t)c*OD*OH*OW;
- ker_zero(mb, c);
- for (int od = 0; od < OD; ++od) {
- for (int oh = 0; oh < OH; ++oh) {
- for (int ow = 0; ow < OW; ++ow) {
- const data_t *d = &diff_dst[diff_dst_offset++];
- ker_max(d, mb, c, od, oh, ow);
- }
- }
- }
- });
- } else {
- parallel_nd(MB, C, [&](int mb, int c) {
- size_t diff_dst_offset = (size_t)mb*C*OD*OH*OW
- + (size_t)c*OD*OH*OW;
- ker_zero(mb, c);
- for (int od = 0; od < OD; ++od) {
- for (int oh = 0; oh < OH; ++oh) {
- for (int ow = 0; ow < OW; ++ow) {
- const data_t *d = &diff_dst[diff_dst_offset++];
- ker_avg(d, mb, c, od, oh, ow);
- }
- }
- }
- });
- }
-}
-
-template struct nchw_pooling_fwd_t<data_type::f32>;
-template struct nchw_pooling_bwd_t<data_type::f32>;
-
-}
-}
-}
-
-// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s