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)
410 lines
16 KiB
C++
410 lines
16 KiB
C++
/*******************************************************************************
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* Copyright 2016-2018 Intel Corporation
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*******************************************************************************/
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#include "c_types_map.hpp"
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#include "mkldnn_thread.hpp"
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#include "type_helpers.hpp"
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#include "utils.hpp"
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#include "jit_avx2_convolution.hpp"
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namespace mkldnn {
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namespace impl {
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namespace cpu {
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using namespace mkldnn::impl::status;
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using namespace mkldnn::impl::memory_tracking::names;
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using namespace mkldnn::impl::utils;
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#define src_blk_off(f, n, c, d, h, w) \
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(pd()->ndims() == 3) \
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? (f).blk_off(n, c, w) \
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: (pd()->ndims() == 4) \
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? (f).blk_off(n, c, h, w) \
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: (f).blk_off(n, c, d, h, w)
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#define wht_blk_off_(f, g, ...) \
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pd()->with_groups() ? (f).blk_off(g, __VA_ARGS__) : (f).blk_off(__VA_ARGS__)
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#define wht_blk_off(f, g, oc, ic, kd, kh, kw) \
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(pd()->ndims() == 3) \
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? wht_blk_off_(f, g, oc, ic, kw) \
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: (pd()->ndims() == 4) \
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? wht_blk_off_(f, g, oc, ic, kh, kw) \
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: wht_blk_off_(f, g, oc, ic, kd, kh, kw)
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void jit_avx2_convolution_fwd_t::execute_forward(const exec_ctx_t &ctx) const {
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auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
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auto weights = CTX_IN_MEM(const data_t *, MKLDNN_ARG_WEIGHTS);
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auto bias = CTX_IN_MEM(const data_t *, MKLDNN_ARG_BIAS);
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auto dst = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DST);
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const memory_desc_wrapper src_d(pd()->src_md());
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const memory_desc_wrapper dst_d(pd()->dst_md());
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const memory_desc_wrapper weights_d(pd()->weights_md(0));
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const memory_desc_wrapper bias_d(pd()->weights_md(1));
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const auto &jcp = kernel_->jcp;
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int ocb_work = div_up(jcp.nb_oc, jcp.nb_oc_blocking);
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const size_t work_amount = jcp.mb * jcp.ngroups * ocb_work * jcp.od
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* jcp.oh;
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auto ker = [&](const int ithr, const int nthr) {
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size_t start{0}, end{0};
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balance211(work_amount, nthr, ithr, start, end);
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int icbb = 0;
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while (icbb < jcp.nb_ic) {
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int icb_step = jcp.nb_ic_blocking;
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int icb_step_rem = jcp.nb_ic - icbb;
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if (icb_step_rem < jcp.nb_ic_blocking_max)
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icb_step = icb_step_rem;
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size_t n{0}, g{0}, ocbb{0}, oh{0}, od{0};
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nd_iterator_init(start, n, jcp.mb, g, jcp.ngroups, ocbb, ocb_work,
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od, jcp.od, oh, jcp.oh);
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for (size_t iwork = start; iwork < end; ++iwork) {
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int ocb = ocbb * jcp.nb_oc_blocking;
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int ocb_num = jcp.nb_oc_blocking;
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for (int icb = icbb; icb < icbb + icb_step; ++icb) {
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auto par_conv = jit_conv_call_s();
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const int ij = oh * jcp.stride_h;
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const int i_t_overflow = nstl::max(0, jcp.t_pad - ij);
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const int i_b_overflow = nstl::max(jcp.ih, ij
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+ (jcp.kh-1) * (jcp.dilate_h+1) - jcp.t_pad+1) - jcp.ih;
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const int dj = od * jcp.stride_d;
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const int d_t_overflow = nstl::max(0, jcp.f_pad - dj);
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const int d_b_overflow = nstl::max(jcp.id, dj
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+ (jcp.kd-1) * (jcp.dilate_d+1) - jcp.f_pad+1) - jcp.id;
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const size_t _oc = g * jcp.nb_oc + ocb;
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const size_t _ic = g * jcp.nb_ic * jcp.nonblk_group_off + icb;
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const int ih = nstl::max(ij - jcp.t_pad
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+ div_up(i_t_overflow,
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(jcp.dilate_h+1)) * (jcp.dilate_h + 1), 0);
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const int id = nstl::max(dj - jcp.f_pad
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+ div_up(d_t_overflow,
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(jcp.dilate_d+1)) * (jcp.dilate_d + 1), 0);
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par_conv.src = &src[src_blk_off(src_d, n,
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jcp.ic == 3 ? 0 : _ic, id, ih, 0)];
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par_conv.dst = &dst[src_blk_off(dst_d, n, _oc, od, oh, 0)];
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const int wh = div_up(i_t_overflow, (jcp.dilate_h + 1));
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const int wd = div_up(d_t_overflow, (jcp.dilate_d + 1));
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par_conv.filt = &weights[wht_blk_off(weights_d, g, ocb,
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jcp.ic == 3 ? 0 : icb, wd, wh, 0)];
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if (icb == 0) {
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if (bias)
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par_conv.bias =
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&bias[bias_d.blk_off(_oc * jcp.oc_block)];
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par_conv.flags |= FLAG_IC_FIRST;
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}
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if (jcp.with_eltwise && icb + 1 == jcp.nb_ic) {
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par_conv.flags |= FLAG_IC_LAST;
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}
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par_conv.oc_blocks =
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nstl::min(ocb + ocb_num, jcp.nb_oc) - ocb;
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par_conv.kw_padding = 0;
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const int kh_padding = jcp.kh
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- div_up(i_t_overflow, (jcp.dilate_h + 1))
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- div_up(i_b_overflow, (jcp.dilate_h + 1));
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par_conv.kh_padding = nstl::max(0, kh_padding);
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const int kd_padding = jcp.kd
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- div_up(d_t_overflow, (jcp.dilate_d + 1))
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- div_up(d_b_overflow, (jcp.dilate_d + 1));
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par_conv.kd_padding = nstl::max(0, kd_padding);
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kernel_->jit_ker(&par_conv);
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}
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nd_iterator_step(n, jcp.mb, g, jcp.ngroups, ocbb, ocb_work,
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od, jcp.od, oh, jcp.oh);
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}
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icbb += icb_step;
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}
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};
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if (pd()->wants_padded_bias()) {
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auto padded_bias = scratchpad(ctx).get<data_t>(key_conv_padded_bias);
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utils::array_copy(padded_bias, bias, jcp.oc_without_padding);
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utils::array_set(padded_bias + jcp.oc_without_padding, 0.f,
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jcp.oc - jcp.oc_without_padding);
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bias = padded_bias;
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}
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parallel(0, ker);
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if (pd()->wants_zero_pad_dst())
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ctx.memory(MKLDNN_ARG_DST)->zero_pad();
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}
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void jit_avx2_convolution_bwd_data_t::execute_backward_data(
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const exec_ctx_t &ctx) const {
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auto diff_dst = CTX_IN_MEM(const data_t *, MKLDNN_ARG_DIFF_DST);
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auto weights = CTX_IN_MEM(const data_t *, MKLDNN_ARG_WEIGHTS);
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auto diff_src = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_SRC);
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const memory_desc_wrapper diff_dst_d(pd()->diff_dst_md());
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const memory_desc_wrapper diff_src_d(pd()->diff_src_md());
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const memory_desc_wrapper weights_d(pd()->weights_md(0));
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const auto &jcp = kernel_->jcp;
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int icb_work = jcp.nb_ic / jcp.nb_ic_blocking;
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int ih_block_size = jcp.ih;
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int num_ih_blocks = utils::div_up(jcp.ih, ih_block_size);
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size_t work_amount = jcp.mb * jcp.ngroups * icb_work * num_ih_blocks;
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if (work_amount < (size_t)2 * mkldnn_get_max_threads()) {
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ih_block_size = 1;
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num_ih_blocks = utils::div_up(jcp.ih, ih_block_size);
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work_amount *= num_ih_blocks;
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}
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auto ker = [&](const int ithr, const int nthr) {
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size_t start{0}, end{0};
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balance211(work_amount, nthr, ithr, start, end);
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size_t n{0}, g{0}, icbb{0}, ihb{0};
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nd_iterator_init(start, n, jcp.mb, g, jcp.ngroups, icbb, icb_work,
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ihb, num_ih_blocks);
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for (size_t iwork = start; iwork < end; ++iwork) {
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for (int oc = 0; oc < jcp.nb_oc; oc += jcp.nb_oc_blocking)
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for (int id = 0; id < jcp.id; ++id) {
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auto par_conv = jit_conv_call_s();
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const int idp = jcp.id + 2 * jcp.f_pad;
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const int d_t_overflow = nstl::max(0,
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jcp.kd - 1 - id - jcp.f_pad);
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const int back_pad = idp - jcp.id - jcp.f_pad;
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const int d_b_overflow = nstl::max(0,
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jcp.kd - 1 - (jcp.id - 1 - id) - back_pad);
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const int od = id + jcp.f_pad - d_b_overflow;
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int ih_start = ihb * ih_block_size;
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int ih_end = nstl::min(jcp.ih, ih_start + ih_block_size);
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for (int ih = ih_start; ih < ih_end; ++ih) {
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const int i_t_overflow = nstl::max(0, (jcp.kh - 1
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- ih - jcp.t_pad) / jcp.stride_h);
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const int i_b_overflow = nstl::max(0, (jcp.kh - jcp.ih
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+ ih - jcp.b_pad) / jcp.stride_h);
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int overflow_kh_hi = jcp.kh - 1 - abs((jcp.ih - 1
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+ jcp.b_pad - ih) % jcp.stride_h);
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int overflow_kh_lo = (ih + jcp.t_pad) % jcp.stride_h;
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par_conv.kd_padding = jcp.kd - d_t_overflow - d_b_overflow;
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par_conv.kh_padding = (overflow_kh_hi - overflow_kh_lo)
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/ jcp.stride_h + 1 - i_t_overflow - i_b_overflow;
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par_conv.kw_padding = 0;
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const int k_lo = overflow_kh_lo
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+ i_b_overflow * jcp.stride_h;
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const int oh = (ih + jcp.t_pad - k_lo) / jcp.stride_h;
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par_conv.src = &diff_src[src_blk_off(diff_src_d, n,
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/*jcp.ic == 3 ? 0 :*/
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g * jcp.nb_ic + jcp.nb_ic_blocking * icbb, id, ih, 0)];
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par_conv.dst = &diff_dst[src_blk_off(diff_dst_d,
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n, g * jcp.nb_oc + oc, od, oh, 0)];
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par_conv.filt = &weights[wht_blk_off(weights_d, g, oc,
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jcp.ic == 3 ? 0 : jcp.nb_ic_blocking * icbb,
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d_b_overflow, k_lo, 0)];
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par_conv.src_prf = nullptr;
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par_conv.dst_prf = nullptr;
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par_conv.filt_prf = nullptr;
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par_conv.channel = oc;
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par_conv.ch_blocks = nstl::min(jcp.nb_oc - oc,
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jcp.nb_oc_blocking);
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kernel_->jit_ker(&par_conv);
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}
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}
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nd_iterator_step(n, jcp.mb, g, jcp.ngroups, icbb, icb_work, ihb,
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num_ih_blocks);
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}
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};
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parallel(0, ker);
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}
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void jit_avx2_convolution_bwd_weights_t::execute_backward_weights(
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const exec_ctx_t &ctx) const {
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auto diff_dst = CTX_IN_MEM(const data_t *, MKLDNN_ARG_DIFF_DST);
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auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
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auto diff_weights = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_WEIGHTS);
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auto diff_bias_in = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_BIAS);
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auto scratchpad = this->scratchpad(ctx);
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data_t *diff_bias = pd()->wants_padded_bias()
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? scratchpad.get<data_t>(key_conv_padded_bias) : diff_bias_in;
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const memory_desc_wrapper src_d(pd()->src_md());
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const memory_desc_wrapper diff_dst_d(pd()->diff_dst_md());
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const memory_desc_wrapper diff_weights_d(pd()->diff_weights_md(0));
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const auto &jcp = kernel_->jcp;
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auto reducer_bia_scratchpad = memory_tracking::grantor_t(scratchpad,
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prefix_reducer_bia);
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auto rb = this->reducer_bias_;
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rb->init(reducer_bia_scratchpad);
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auto reducer_wei_scratchpad = memory_tracking::grantor_t(scratchpad,
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prefix_reducer_wei);
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auto rw = this->reducer_weights_;
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rw->init(reducer_wei_scratchpad);
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auto ker = [&](int ithr, int nthr) {
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assert(nthr == rw->balancer().nthr_);
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const int w_job_start = rw->balancer().ithr_job_off(ithr);
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const int w_njobs = rw->balancer().ithr_njobs(ithr);
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if (w_njobs == 0) return;
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/* reduction dimension */
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int img_od_start{0}, img_od_end{0}, img{0}, od_s{0};
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balance211(jcp.mb * jcp.od, rw->balancer().nthr_per_group_,
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rw->balancer().id_in_group(ithr), img_od_start, img_od_end);
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int img_start = img_od_start, img_end = img_od_end;
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nd_iterator_init(img_start, img, jcp.mb, od_s, jcp.od);
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const int img_first = img;
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/* jobs */
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int g_start{0}, ocb_start{0}, icb_start{0};
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nd_iterator_init(w_job_start, g_start, jcp.ngroups, ocb_start,
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jcp.nb_oc, icb_start, jcp.nb_ic);
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while (img_start < img_end) {
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int g = g_start, ocb = ocb_start, icb = icb_start;
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const int work_rem = img_end - img_start;
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const int od_e = od_s + work_rem > jcp.od ? jcp.od : od_s + work_rem;
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const int id_s = od_s * jcp.stride_d;
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const int idp = jcp.id + jcp.f_pad + jcp.back_pad;
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if (id_s < idp - jcp.back_pad - jcp.kd + 1)
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for (int w_job_loc = 0; w_job_loc < w_njobs; ++w_job_loc) {
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const size_t _oc = g * jcp.nb_oc + ocb;
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const size_t _ic = g * jcp.nb_ic + icb;
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/* TODO: put dw <-- 0 in kernel */
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if (img == img_first)
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array_set(rw->get_local_ptr(ithr, diff_weights,
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reducer_wei_scratchpad) +
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w_job_loc * rw->balancer().job_size_, 0,
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rw->balancer().job_size_);
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for (int od = od_s; od < od_e; ++od) {
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const int id = od * jcp.stride_d;
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if (id >= jcp.id - jcp.back_pad - jcp.kd + 1) break;
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auto par_conv = jit_conv_call_s();
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par_conv.src = &src[src_blk_off(src_d, img, _ic, id, 0, 0)];
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par_conv.dst =
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&diff_dst[src_blk_off(diff_dst_d, img, _oc, od, 0, 0)];
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par_conv.filt = rw->get_local_ptr(ithr, diff_weights,
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reducer_wei_scratchpad) +
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w_job_loc * rw->balancer().job_size_;
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kernel_->jit_ker(&par_conv);
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}
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nd_iterator_step(g, jcp.ngroups, ocb, jcp.nb_oc, icb,
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jcp.nb_ic);
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}
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nd_iterator_jump(img_start, img_end, img, jcp.mb, od_s, jcp.od);
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}
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rw->reduce(ithr, diff_weights, reducer_wei_scratchpad);
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};
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auto ker_bias = [&](int ithr, int nthr) {
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assert(nthr == rb->balancer().nthr_);
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const int b_job_start = rb->balancer().ithr_job_off(ithr);
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const int b_njobs = rb->balancer().ithr_njobs(ithr);
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if (b_njobs == 0) return;
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/* reduction dimension */
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int img_start{0}, img_end{0};
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balance211(jcp.mb, rb->balancer().nthr_per_group_,
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rb->balancer().id_in_group(ithr), img_start, img_end);
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/* jobs */
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int g_start{0}, ocb_start{0};
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nd_iterator_init(b_job_start, g_start, jcp.ngroups, ocb_start,
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jcp.nb_oc);
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for (int img = img_start; img < img_end; ++img) {
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int g = g_start, ocb = ocb_start;
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for (int b_job_loc = 0; b_job_loc < b_njobs; ++b_job_loc) {
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const size_t _oc = g * jcp.nb_oc + ocb;
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const data_t *d_dst = &diff_dst[diff_dst_d.blk_off(img, _oc)];
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data_t *d_bias = rb->get_local_ptr(ithr, diff_bias,
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reducer_bia_scratchpad) +
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b_job_loc * rb->balancer().job_size_;
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if (img == img_start)
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for (int o = 0; o < 8; ++o)
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d_bias[o] = 0.;
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for (int dhw = 0; dhw < jcp.od * jcp.oh * jcp.ow; ++dhw) {
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PRAGMA_OMP_SIMD()
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for (int o = 0; o < 8; ++o)
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d_bias[o] += d_dst[o];
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d_dst += 8;
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}
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nd_iterator_step(g, jcp.ngroups, ocb, jcp.nb_oc);
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}
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}
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rb->reduce(ithr, diff_bias, reducer_bia_scratchpad);
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};
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parallel(0, [&](const int ithr, const int nthr) {
|
|
ker(ithr, nthr);
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|
if (pd()->with_bias())
|
|
ker_bias(ithr, nthr);
|
|
});
|
|
|
|
/* TODO: put this in ker_bias */
|
|
if (pd()->wants_padded_bias()) {
|
|
assert(jcp.ngroups == 1);
|
|
for (int oc = 0; oc < jcp.oc_without_padding; ++oc)
|
|
diff_bias_in[oc] = diff_bias[oc];
|
|
}
|
|
}
|
|
|
|
}
|
|
}
|
|
}
|
|
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|
// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
|