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-rw-r--r--thirdparty/oidn/mkl-dnn/src/cpu/gemm_convolution.cpp307
1 files changed, 0 insertions, 307 deletions
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/gemm_convolution.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/gemm_convolution.cpp
deleted file mode 100644
index 604a728b47..0000000000
--- a/thirdparty/oidn/mkl-dnn/src/cpu/gemm_convolution.cpp
+++ /dev/null
@@ -1,307 +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 "mkldnn_types.h"
-
-#include "c_types_map.hpp"
-#include "gemm_convolution.hpp"
-#include "utils.hpp"
-#include "type_helpers.hpp"
-#include "mkldnn_thread.hpp"
-#include "ref_eltwise.hpp"
-
-namespace mkldnn {
-namespace impl {
-namespace cpu {
-
-using namespace mkldnn::impl::status;
-using namespace mkldnn::impl::memory_tracking::names;
-using namespace mkldnn::impl::utils;
-
-void gemm_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);
-
- auto col = scratchpad(ctx).get<data_t>(key_conv_gemm_col);
-
- const jit_gemm_conv_conf_t &jcp = this->pd()->jcp_;
-
- const int M = jcp.os * jcp.od;
- const size_t src_step = jcp.ic * jcp.ih * jcp.iw * jcp.id;
- const size_t dst_step = jcp.oc * M;
- const size_t weights_g_size = jcp.ic * jcp.oc * jcp.ks;
-
- assert(IMPLICATION(
- jcp.id != 1, jcp.oh_block == jcp.oh && jcp.ow_block == jcp.ow));
- assert(IMPLICATION(jcp.ow_block != jcp.ow, jcp.oh_block == 1));
-
- const int K = jcp.ic * jcp.ks;
- const int N = jcp.oc;
-
- if (jcp.im2col_sz && jcp.id != 1)
- parallel_nd(jcp.im2col_sz * jcp.nthr,
- [&](ptrdiff_t i) { col[i] = (data_t)0; });
-
- const int nb_oh = div_up(jcp.oh, jcp.oh_block);
- const int nb_ow = div_up(jcp.ow, jcp.ow_block);
- const size_t work_amount = jcp.ngroups * jcp.mb * jcp.od * nb_oh * nb_ow;
- parallel(jcp.nthr, [&](const int ithr, const int nthr) {
- data_t *_col = col + (ptrdiff_t)ithr * jcp.im2col_sz;
-
- int g{ 0 }, n{ 0 }, od{ 0 }, ohb{ 0 }, owb{ 0 };
- size_t start = 0, end = 0;
-
- balance211(work_amount, nthr, ithr, start, end);
- nd_iterator_init(start, g, jcp.ngroups, n, jcp.mb, od, jcp.od, ohb,
- nb_oh, owb, nb_ow);
- for (size_t iwork = start; iwork < end; ++iwork) {
- int oh = ohb * jcp.oh_block;
- int ow = owb * jcp.ow_block;
- const data_t *_src = src + (n * jcp.ngroups + g) * src_step;
- const data_t *_weights = weights + g * weights_g_size;
- data_t *_dst_im = dst + (n * jcp.ngroups + g) * dst_step;
- const int h_step = nstl::min(jcp.oh_block, jcp.oh - oh);
- const int w_step = nstl::min(jcp.ow_block, jcp.ow - ow);
- if (jcp.im2col_sz) {
- if (jcp.id == 1)
- jit_gemm_convolution_utils::im2col(
- jcp, _src, _col, oh, h_step, ow, w_step);
- else
- jit_gemm_convolution_utils::im2col_3d(jcp, _src, _col, od);
- }
-
- const data_t one = 1.0;
-
- const int m = h_step * w_step;
- const int LDA = jcp.im2col_sz ? m : M;
- data_t *_dst = _dst_im + od * jcp.os + oh * jcp.ow + ow;
-
- extended_sgemm("N", "N", &m, &N, &K, &one,
- jcp.im2col_sz ? _col : _src + od * m, &LDA, _weights, &K,
- &this->beta_, _dst, &M);
-
- data_t *d = _dst;
- if (eltwise_) {
- // fast branch for ReLU case
- if (eltwise_->alg_ == alg_kind::eltwise_relu) {
- parallel_nd(jcp.oc, [&](const int oc) {
- data_t b = jcp.with_bias ? bias[g * jcp.oc + oc] : 0;
- data_t *d_ = d + oc * M;
- PRAGMA_OMP_SIMD()
- for (int oS = 0; oS < m; ++oS) {
- d_[oS] += b;
- if (d_[oS] < 0) d_[oS] *= eltwise_->alpha_;
- }
- });
- } else {
- parallel_nd(jcp.oc, [&](const int oc) {
- data_t b = jcp.with_bias ? bias[g * jcp.oc + oc] : 0;
- data_t *d_ = d + oc * M;
- PRAGMA_OMP_SIMD()
- for (int oS = 0; oS < m; ++oS) {
- d_[oS] += b;
- d_[oS] = eltwise_->compute_scalar(d_[oS]);
- }
- });
- }
- } else if (jcp.with_bias) {
- parallel_nd(jcp.oc, [&](const int oc) {
- data_t b = bias[g * jcp.oc + oc];
- data_t *d_ = d + oc * M;
- PRAGMA_OMP_SIMD()
- for (int oS = 0; oS < m; ++oS) {
- d_[oS] += b;
- }
- });
- }
- nd_iterator_step(g, jcp.ngroups, n, jcp.mb, od, jcp.od, ohb, nb_oh,
- owb, nb_ow);
- }
- });
-}
-
-void gemm_convolution_bwd_data_t::execute_backward_data(
- const exec_ctx_t &ctx) const {
- auto diff_dst = CTX_IN_MEM(const data_t *, MKLDNN_ARG_DIFF_DST);
- auto weights = CTX_IN_MEM(const data_t *, MKLDNN_ARG_WEIGHTS);
- auto diff_src = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_SRC);
-
- auto col = scratchpad(ctx).get<data_t>(key_conv_gemm_col);
-
- const jit_gemm_conv_conf_t &jcp = this->pd()->jcp_;
-
- const int M = jcp.os * jcp.od;
- const size_t src_step = jcp.ic * jcp.ih * jcp.iw * jcp.id;
- const size_t dst_step = jcp.oc * M;
- const size_t weights_g_size = jcp.ic * jcp.oc * jcp.ks;
-
- const int m = jcp.os;
- const int K = jcp.oc;
- const int N = jcp.ic * jcp.ks;
- const int LDC = jcp.im2col_sz ? m : M;
-
- const size_t work_amount = (size_t)jcp.ngroups * jcp.mb;
-
- if (jcp.id > 1) {
- const ptrdiff_t diff_src_sz = (ptrdiff_t)(work_amount * src_step);
- parallel_nd(diff_src_sz, [&](ptrdiff_t i) { diff_src[i] = (data_t)0; });
- }
-
- parallel(jcp.nthr, [&](const int ithr, const int nthr) {
- data_t *_col = col + (ptrdiff_t)ithr * jcp.im2col_sz;
-
- int g{0}, n{0};
- size_t start = 0, end = 0;
- balance211(work_amount, nthr, ithr, start, end);
- nd_iterator_init(start, g, jcp.ngroups, n, jcp.mb);
- for (size_t iwork = start; iwork < end; ++iwork) {
-
- data_t *_diff_src = diff_src + (n * jcp.ngroups + g)*src_step;
- const data_t *_weights = weights + g * weights_g_size;
- for (int od = 0; od < jcp.od; ++od) {
- const data_t *_diff_dst = diff_dst + (n * jcp.ngroups + g)
- *dst_step + od * m;
-
- const data_t zero = 0.0, one = 1.0;
- extended_sgemm("N", "T", &m, &N, &K, &one, _diff_dst, &M,
- _weights, &N, &zero,
- jcp.im2col_sz ? _col:_diff_src + od * m, &LDC);
-
- if (jcp.im2col_sz) {
- if (jcp.id == 1)
- jit_gemm_convolution_utils::col2im(jcp, _col,
- _diff_src);
- else
- jit_gemm_convolution_utils::col2im_3d(jcp, _col,
- _diff_src, od);
- }
- }
- nd_iterator_step(g, jcp.ngroups, n, jcp.mb);
- }
- });
-}
-
-void gemm_convolution_bwd_weights_t::execute_backward_weights(
- const exec_ctx_t &ctx) const {
- auto diff_dst = CTX_IN_MEM(const data_t *, MKLDNN_ARG_DIFF_DST);
- auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
- auto diff_weights = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_WEIGHTS);
- auto diff_bias = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_BIAS);
-
- auto col = scratchpad(ctx).get<data_t>(key_conv_gemm_col);
- auto wei_reduction = scratchpad(ctx).get<data_t>(key_conv_wei_reduction);
-
- const jit_gemm_conv_conf_t &jcp = this->pd()->jcp_;
-
- const int K = jcp.os * jcp.od;
- const size_t src_step = jcp.ic * jcp.ih * jcp.iw * jcp.id;
- const size_t dst_step = jcp.oc * K;
- const size_t weights_g_size = jcp.ic * jcp.oc * jcp.ks;
-
- const int k = jcp.os;
- const int N = jcp.oc;
- const int M = jcp.ic * jcp.ks;
- const int LDA = jcp.im2col_sz ? k : K;
-
- parallel_nd(jcp.im2col_sz * jcp.nthr,
- [&](ptrdiff_t i) { col[i] = (data_t)0; });
-
- parallel(jcp.nthr, [&](const int ithr, const int nthr) {
- int ithr_g, nthr_g, ithr_mb, nthr_mb;
- size_t g_start{0}, g_end{0}, mb_start{0}, mb_end{0};
-
- const int mb_for_balance = jcp.need_wei_reduction ? jcp.mb : 1;
- jit_gemm_convolution_utils::bwd_weights_balance(ithr, nthr, jcp.ngroups,
- mb_for_balance, ithr_g, nthr_g, ithr_mb, nthr_mb);
-
- assert(IMPLICATION(!jcp.need_wei_reduction, nthr_mb == 1));
- const int need_reduction = nthr_mb != 1;
-
- if (ithr_g != -1 && ithr_mb != -1) {
- balance211((size_t)jcp.ngroups, nthr_g, ithr_g, g_start, g_end);
- balance211((size_t)jcp.mb, nthr_mb, ithr_mb, mb_start, mb_end);
-
- assert(IMPLICATION((g_end - g_start) > 1, need_reduction == 0));
-
- data_t *_col = col + (ptrdiff_t)ithr * jcp.im2col_sz;
- data_t *weights_reduce_base = wei_reduction
- + ithr_g * nthr_mb * weights_g_size;
- data_t *weights_reduce = weights_reduce_base
- + ithr_mb * weights_g_size;
-
- for (size_t g = g_start; g < g_end; ++g) {
- data_t *_diff_weights = need_reduction
- ? weights_reduce : (diff_weights + g * weights_g_size);
- for (size_t mb = mb_start; mb < mb_end; ++mb) {
- const data_t *_src = src + (mb*jcp.ngroups+g)*src_step;
- for (int od = 0; od < jcp.od; ++od) {
- const data_t *_diff_dst = diff_dst
- + (mb*jcp.ngroups+g)*dst_step + od * k;
-
- if (jcp.im2col_sz) {
- if (jcp.id == 1)
- jit_gemm_convolution_utils::im2col(
- jcp, _src, _col, 0, jcp.oh, 0, jcp.ow);
- else
- jit_gemm_convolution_utils::im2col_3d(jcp, _src,
- _col, od);
- }
-
- const data_t zero = 0.0, one = 1.0;
- extended_sgemm(
- "T", "N", &M, &N, &k, &one,
- jcp.im2col_sz ? _col : _src + od * k,
- &LDA, _diff_dst, &K,
- mb == mb_start && od == 0 ? &zero : &one,
- _diff_weights, &M);
- }
- }
- }
- if (need_reduction) {
- mkldnn_thr_barrier();
- data_t *weights_base = diff_weights + g_start * weights_g_size;
- jit_gemm_convolution_utils::bwd_weights_reduction_par(
- ithr_mb, nthr_mb, jcp, weights_reduce_base, weights_base);
- }
- } else
- if (need_reduction) { mkldnn_thr_barrier(); }
- });
-
- if (jcp.with_bias) {
- parallel_nd(jcp.ngroups, jcp.oc, [&](int g, int oc) {
- data_t db = 0;
- size_t offset_ = (size_t)g * dst_step + (size_t)oc * K;
- for (int mb = 0; mb < jcp.mb; ++mb)
- {
- size_t offset = offset_ + (size_t)mb * jcp.ngroups * dst_step;
- for (int od = 0; od < jcp.od; ++od)
- for (int oh = 0; oh < jcp.oh; ++oh)
- PRAGMA_OMP_SIMD(reduction(+:db))
- for (int ow = 0; ow < jcp.ow; ++ow) {
- db += diff_dst[offset];
- offset++;
- }
- }
- diff_bias[g*jcp.oc+oc] = db;
- });
- }
-}
-
-}
-}
-}