diff options
Diffstat (limited to 'thirdparty/oidn/mkl-dnn/src/cpu/ref_convolution.cpp')
| -rw-r--r-- | thirdparty/oidn/mkl-dnn/src/cpu/ref_convolution.cpp | 395 |
1 files changed, 0 insertions, 395 deletions
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/ref_convolution.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/ref_convolution.cpp deleted file mode 100644 index c0a979c4cf..0000000000 --- a/thirdparty/oidn/mkl-dnn/src/cpu/ref_convolution.cpp +++ /dev/null @@ -1,395 +0,0 @@ -/******************************************************************************* -* 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. -*******************************************************************************/ - -#include "c_types_map.hpp" -#include "math_utils.hpp" -#include "mkldnn_thread.hpp" -#include "mkldnn_traits.hpp" -#include "type_helpers.hpp" - -#include "ref_convolution.hpp" - -namespace mkldnn { -namespace impl { -namespace cpu { - -using math::saturate; -using math::get_bias; - -template <data_type_t src_type, data_type_t wei_type, - data_type_t dst_type, data_type_t acc_type> -void ref_convolution_fwd_t<src_type, wei_type, dst_type, acc_type>:: -execute_forward(const exec_ctx_t &ctx) const { - auto src = CTX_IN_MEM(const src_data_t *, MKLDNN_ARG_SRC); - auto weights = CTX_IN_MEM(const wei_data_t *, MKLDNN_ARG_WEIGHTS); - auto bias = CTX_IN_MEM(const char *, MKLDNN_ARG_BIAS); - auto dst = CTX_OUT_MEM(dst_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 bool with_groups = pd()->with_groups(); - - const int G = pd()->G(); - const int MB = pd()->MB(); - 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 OC = pd()->OC() / G; - const int IC = pd()->IC() / G; - const int KD = pd()->KD(); - const int KH = pd()->KH(); - const int KW = pd()->KW(); - - const int KSD = pd()->KSD(); - const int KSH = pd()->KSH(); - const int KSW = pd()->KSW(); - - const int KDD = pd()->KDD(); - const int KDH = pd()->KDH(); - const int KDW = pd()->KDW(); - - const int padFront = pd()->padFront(); - const int padT = pd()->padT(); - const int padL = pd()->padL(); - - const bool with_relu = 0; // TODO: change if support post_ops - const float nslope = 0.f; - - const int ndims = pd()->desc()->src_desc.ndims; - - auto ker = [=](int g, int mb, int oc, int od, int oh, - int ow) { - acc_data_t d = 0; - for (int ic = 0; ic < IC; ++ic) - 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 * KSD - padFront + kd * (1 + KDD); - const int ih = oh * KSH - padT + kh * (1 + KDH); - const int iw = ow * KSW - padL + kw * (1 + KDW); - - if (id < 0 || id >= ID) continue; - if (ih < 0 || ih >= IH) continue; - if (iw < 0 || iw >= IW) continue; - - if (ndims == 5) - d += (acc_data_t)src[src_d.off(mb, g*IC + ic, id, ih, iw)] - * (with_groups - ? weights[weights_d.off(g, oc, ic, kd, kh, kw)] - : weights[weights_d.off(oc, ic, kd, kh, kw)]); - else if (ndims == 4) - d += (acc_data_t)src[src_d.off(mb, g*IC + ic, ih, iw)] - * (with_groups - ? weights[weights_d.off(g, oc, ic, kh, kw)] - : weights[weights_d.off(oc, ic, kh, kw)]); - else if (ndims == 3) - d += (acc_data_t)src[src_d.off(mb, g*IC + ic, iw)] - * (with_groups - ? weights[weights_d.off(g, oc, ic, kw)] - : weights[weights_d.off(oc, ic, kw)]); - else - assert(false); - - } - return d; - }; - - parallel_nd(G, MB, OC, OD, OH, OW, - [&](int g, int mb, int oc, int od, int oh, int ow) { - float a = bias - ? get_bias(bias, bias_d.off(g * OC + oc), - pd()->desc()->bias_desc.data_type) - : 0; - a += ker(g, mb, oc, od, oh, ow); - if (with_relu && a < 0) - a = a * nslope; - if (ndims == 5) - dst[dst_d.off(mb, g*OC + oc, od, oh, ow)] = saturate<dst_data_t>(a); - else if (ndims == 4) - dst[dst_d.off(mb, g*OC + oc, oh, ow)] = saturate<dst_data_t>(a); - else if (ndims == 3) - dst[dst_d.off(mb, g*OC + oc, ow)] = saturate<dst_data_t>(a); - else - assert(false); - }); -} - -template <data_type_t diff_src_type, data_type_t wei_type, - data_type_t diff_dst_type, data_type_t acc_type> -void ref_convolution_bwd_data_t<diff_src_type, wei_type, diff_dst_type, - acc_type>::execute_backward_data(const exec_ctx_t &ctx) const { - auto diff_dst = CTX_IN_MEM(const diff_dst_data_t *, MKLDNN_ARG_DIFF_DST); - auto weights = CTX_IN_MEM(const wei_data_t *, MKLDNN_ARG_WEIGHTS); - auto bias = CTX_IN_MEM(const char *, MKLDNN_ARG_BIAS); - auto diff_src = CTX_OUT_MEM(diff_src_data_t *, MKLDNN_ARG_DIFF_SRC); - - const memory_desc_wrapper diff_dst_d(pd()->diff_dst_md()); - const memory_desc_wrapper diff_src_d(pd()->diff_src_md()); - const memory_desc_wrapper weights_d(pd()->weights_md(0)); - const memory_desc_wrapper bias_d(pd()->weights_md(1)); - - const bool with_groups = pd()->with_groups(); - - const int G = pd()->G(); - const int MB = pd()->MB(); - 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 OC = pd()->OC() / G; - const int IC = pd()->IC() / G; - const int KD = pd()->KD(); - const int KH = pd()->KH(); - const int KW = pd()->KW(); - - const int KSD = pd()->KSD(); - const int KSH = pd()->KSH(); - const int KSW = pd()->KSW(); - - const int KDD = pd()->KDD(); - const int KDH = pd()->KDH(); - const int KDW = pd()->KDW(); - - const int padFront = pd()->padFront(); - const int padT = pd()->padT(); - const int padL = pd()->padL(); - - const int ndims = pd()->desc()->diff_src_desc.ndims; - - auto ker = [=](int g, int mb, int ic, int id, int ih, - int iw) { - acc_data_t d = 0; - for (int oc = 0; oc < OC; ++oc) - for (int kd = 0; kd < KD; ++kd) - for (int kh = 0; kh < KH; ++kh) - for (int kw = 0; kw < KW; ++kw) { - if (iw + padL < kw * (1 + KDW) - || ih + padT < kh * (1 + KDH) - || id + padFront < kd * (1 + KDD)) - continue; - int ow = iw - kw * (1 + KDW) + padL; - int oh = ih - kh * (1 + KDH) + padT; - int od = id - kd * (1 + KDD) + padFront; - if (ow % KSW != 0 || oh % KSH != 0 || od % KSD != 0) - continue; - - ow /= KSW; - oh /= KSH; - od /= KSD; - - if (od < OD && oh < OH && ow < OW) { - if (ndims == 5) - d += (acc_data_t)diff_dst[diff_dst_d.off(mb, g*OC - + oc, od, oh, ow)] * (with_groups - ? weights[weights_d.off(g, oc, ic, kd, kh, kw)] - : weights[weights_d.off(oc, ic, kd, kh, kw)]); - else if (ndims == 4) - d += (acc_data_t)diff_dst[diff_dst_d.off(mb, g*OC - + oc, oh, ow)] * (with_groups - ? weights[weights_d.off(g, oc, ic, kh, kw)] - : weights[weights_d.off(oc, ic, kh, kw)]); - else if (ndims == 3) - d += (acc_data_t)diff_dst[diff_dst_d.off(mb, g*OC - + oc, ow)] * (with_groups - ? weights[weights_d.off(g, oc, ic, kw)] - : weights[weights_d.off(oc, ic, kw)]); - else - assert(false); - } - } - return d; - }; - - parallel_nd(G, MB, IC, ID, IH, IW, - [&](int g, int mb, int ic, int id, int ih, int iw) { - auto ds_idx = (ndims == 5) - ? diff_src_d.off(mb, g*IC + ic, id, ih, iw) - : (ndims == 4) - ? diff_src_d.off(mb, g*IC + ic, ih, iw) - : diff_src_d.off(mb, g*IC + ic, iw); - float a = bias - ? get_bias(bias, bias_d.off(g * IC + ic), - pd()->desc()->bias_desc.data_type) - : 0; - a += ker(g, mb, ic, id, ih, iw); - diff_src[ds_idx] = saturate<diff_src_data_t>(a); - }); -} - -template <data_type_t src_type, data_type_t diff_wei_type, - data_type_t diff_dst_type, data_type_t acc_type> -void ref_convolution_bwd_weights_t<src_type, diff_wei_type, diff_dst_type, - acc_type>::execute_backward_weights(const exec_ctx_t &ctx) const { - auto diff_dst = CTX_IN_MEM(const diff_dst_data_t *, MKLDNN_ARG_DIFF_DST); - auto src = CTX_IN_MEM(const src_data_t *, MKLDNN_ARG_SRC); - auto diff_weights = CTX_OUT_MEM(diff_wei_data_t *, MKLDNN_ARG_DIFF_WEIGHTS); - auto diff_bias = CTX_OUT_MEM(diff_wei_data_t *, MKLDNN_ARG_DIFF_BIAS); - - const memory_desc_wrapper src_d(pd()->src_md()); - const memory_desc_wrapper diff_dst_d(pd()->diff_dst_md()); - const memory_desc_wrapper diff_weights_d(pd()->diff_weights_md(0)); - const memory_desc_wrapper diff_bias_d(pd()->diff_weights_md(1)); - - const bool with_groups = pd()->with_groups(); - - const int G = pd()->G(); - const int MB = pd()->MB(); - 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 OC = pd()->OC() / G; - const int IC = pd()->IC() / G; - const int KD = pd()->KD(); - const int KH = pd()->KH(); - const int KW = pd()->KW(); - - const int KSD = pd()->KSD(); - const int KSH = pd()->KSH(); - const int KSW = pd()->KSW(); - - const int KDD = pd()->KDD(); - const int KDH = pd()->KDH(); - const int KDW = pd()->KDW(); - - const int padFront = pd()->padFront(); - const int padT = pd()->padT(); - const int padL = pd()->padL(); - - const int ndims = pd()->desc()->src_desc.ndims; - -auto ker = [=](acc_data_t &d, int g, int oc, int ic, int kd, int kh, int kw) { - for (int mb = 0; mb < MB; ++mb) - for (int od = 0; od < OD; ++od) - for (int oh = 0; oh < OH; ++oh) - for (int ow = 0; ow < OW; ++ow) { - if (ow*KSW + kw * (1 + KDW) < padL - || oh*KSH + kh * (1 + KDH) < padT - || od*KSD + kd * (1 + KDD) < padFront - || ow*KSW + kw * (1 + KDW) >= IW + padL - || oh*KSH + kh * (1 + KDH) >= IH + padT - || od*KSD + kd * (1 + KDD) >= ID + padFront) - continue; - - int id = od*KSD - padFront + kd * (1 + KDD); - int ih = oh*KSH - padT + kh * (1 + KDH); - int iw = ow*KSW - padL + kw * (1 + KDW); - if (ndims == 5) - d += (acc_data_t)diff_dst[diff_dst_d.off(mb, g*OC + oc, od, - oh, ow)] * src[src_d.off(mb, g*IC + ic, id, ih, iw)]; - else if (ndims == 4) - d += (acc_data_t)diff_dst[diff_dst_d.off(mb, g*OC + oc, oh, ow)] - * src[src_d.off(mb, g*IC + ic, ih, iw)]; - else if (ndims == 3) - d += (acc_data_t)diff_dst[diff_dst_d.off(mb, g*OC + oc, ow)] - * src[src_d.off(mb, g*IC + ic, iw)]; - else - assert(false); - } - }; - - auto ker_bias = [=](acc_data_t &d, int g, int oc) { - for (int mb = 0; mb < MB; ++mb) - for (int od = 0; od < OD; ++od) - for (int oh = 0; oh < OH; ++oh) - for (int ow = 0; ow < OW; ++ow) { - if (ndims == 5) - d += (acc_data_t)diff_dst[diff_dst_d.off(mb, g*OC + oc, od, oh, - ow)]; - else if (ndims == 4) - d += (acc_data_t)diff_dst[diff_dst_d.off(mb, g*OC + oc, oh, - ow)]; - else if (ndims == 3) - d += (acc_data_t)diff_dst[diff_dst_d.off(mb, g*OC + oc, ow)]; - else - assert(false); - } - }; - - parallel_nd(G, OC, [&](int g, int oc) { - if (diff_bias) { - // XXX: loss of precision when bias is a float... - acc_data_t db = 0; - ker_bias(db, g, oc); - diff_bias[diff_bias_d.off(g*OC+oc)] - = saturate<diff_wei_data_t>(db); - } - - for (int ic = 0; ic < IC; ++ic) - for (int kd = 0; kd < KD; ++kd) - for (int kh = 0; kh < KH; ++kh) - for (int kw = 0; kw < KW; ++kw) { - acc_data_t dw = 0; - ker(dw, g, oc, ic, kd, kh, kw); - - if (ndims == 5) { - auto idx = with_groups - ? diff_weights_d.off(g, oc, ic, kd, kh, kw) - : diff_weights_d.off(oc, ic, kd, kh, kw); - diff_weights[idx] = saturate<diff_wei_data_t>(dw); - } else if (ndims == 4) { - auto idx = with_groups - ? diff_weights_d.off(g, oc, ic, kh, kw) - : diff_weights_d.off(oc, ic, kh, kw); - diff_weights[idx] = saturate<diff_wei_data_t>(dw); - } else if (ndims == 3) { - auto idx = with_groups - ? diff_weights_d.off(g, oc, ic, kw) - : diff_weights_d.off(oc, ic, kw); - diff_weights[idx] = saturate<diff_wei_data_t>(dw); - } else { - assert(false); - } - } - }); -} - -using namespace data_type; - -template struct ref_convolution_fwd_t<f32>; - -template struct ref_convolution_fwd_t<u8, s8, f32, s32>; -template struct ref_convolution_fwd_t<u8, s8, s32, s32>; -template struct ref_convolution_fwd_t<u8, s8, s8, s32>; -template struct ref_convolution_fwd_t<u8, s8, u8, s32>; - -template struct ref_convolution_bwd_data_t<f32, f32, f32, f32>; - -template struct ref_convolution_bwd_data_t<f32, s8, u8, s32>; -template struct ref_convolution_bwd_data_t<s32, s8, u8, s32>; -template struct ref_convolution_bwd_data_t<s8, s8, u8, s32>; -template struct ref_convolution_bwd_data_t<u8, s8, u8, s32>; - -template struct ref_convolution_bwd_weights_t<f32, f32, f32, f32>; - -} -} -} - -// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s |