virtualx-engine/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx2_convolution.cpp
Juan Linietsky 1bea8e1eac New lightmapper
-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)
2020-05-10 15:59:09 -03:00

410 lines
16 KiB
C++

/*******************************************************************************
* 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 "mkldnn_thread.hpp"
#include "type_helpers.hpp"
#include "utils.hpp"
#include "jit_avx2_convolution.hpp"
namespace mkldnn {
namespace impl {
namespace cpu {
using namespace mkldnn::impl::status;
using namespace mkldnn::impl::memory_tracking::names;
using namespace mkldnn::impl::utils;
#define src_blk_off(f, n, c, d, h, w) \
(pd()->ndims() == 3) \
? (f).blk_off(n, c, w) \
: (pd()->ndims() == 4) \
? (f).blk_off(n, c, h, w) \
: (f).blk_off(n, c, d, h, w)
#define wht_blk_off_(f, g, ...) \
pd()->with_groups() ? (f).blk_off(g, __VA_ARGS__) : (f).blk_off(__VA_ARGS__)
#define wht_blk_off(f, g, oc, ic, kd, kh, kw) \
(pd()->ndims() == 3) \
? wht_blk_off_(f, g, oc, ic, kw) \
: (pd()->ndims() == 4) \
? wht_blk_off_(f, g, oc, ic, kh, kw) \
: wht_blk_off_(f, g, oc, ic, kd, kh, kw)
void jit_avx2_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);
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 auto &jcp = kernel_->jcp;
int ocb_work = div_up(jcp.nb_oc, jcp.nb_oc_blocking);
const size_t work_amount = jcp.mb * jcp.ngroups * ocb_work * jcp.od
* jcp.oh;
auto ker = [&](const int ithr, const int nthr) {
size_t start{0}, end{0};
balance211(work_amount, nthr, ithr, start, end);
int icbb = 0;
while (icbb < jcp.nb_ic) {
int icb_step = jcp.nb_ic_blocking;
int icb_step_rem = jcp.nb_ic - icbb;
if (icb_step_rem < jcp.nb_ic_blocking_max)
icb_step = icb_step_rem;
size_t n{0}, g{0}, ocbb{0}, oh{0}, od{0};
nd_iterator_init(start, n, jcp.mb, g, jcp.ngroups, ocbb, ocb_work,
od, jcp.od, oh, jcp.oh);
for (size_t iwork = start; iwork < end; ++iwork) {
int ocb = ocbb * jcp.nb_oc_blocking;
int ocb_num = jcp.nb_oc_blocking;
for (int icb = icbb; icb < icbb + icb_step; ++icb) {
auto par_conv = jit_conv_call_s();
const int ij = oh * jcp.stride_h;
const int i_t_overflow = nstl::max(0, jcp.t_pad - ij);
const int i_b_overflow = nstl::max(jcp.ih, ij
+ (jcp.kh-1) * (jcp.dilate_h+1) - jcp.t_pad+1) - jcp.ih;
const int dj = od * jcp.stride_d;
const int d_t_overflow = nstl::max(0, jcp.f_pad - dj);
const int d_b_overflow = nstl::max(jcp.id, dj
+ (jcp.kd-1) * (jcp.dilate_d+1) - jcp.f_pad+1) - jcp.id;
const size_t _oc = g * jcp.nb_oc + ocb;
const size_t _ic = g * jcp.nb_ic * jcp.nonblk_group_off + icb;
const int ih = nstl::max(ij - jcp.t_pad
+ div_up(i_t_overflow,
(jcp.dilate_h+1)) * (jcp.dilate_h + 1), 0);
const int id = nstl::max(dj - jcp.f_pad
+ div_up(d_t_overflow,
(jcp.dilate_d+1)) * (jcp.dilate_d + 1), 0);
par_conv.src = &src[src_blk_off(src_d, n,
jcp.ic == 3 ? 0 : _ic, id, ih, 0)];
par_conv.dst = &dst[src_blk_off(dst_d, n, _oc, od, oh, 0)];
const int wh = div_up(i_t_overflow, (jcp.dilate_h + 1));
const int wd = div_up(d_t_overflow, (jcp.dilate_d + 1));
par_conv.filt = &weights[wht_blk_off(weights_d, g, ocb,
jcp.ic == 3 ? 0 : icb, wd, wh, 0)];
if (icb == 0) {
if (bias)
par_conv.bias =
&bias[bias_d.blk_off(_oc * jcp.oc_block)];
par_conv.flags |= FLAG_IC_FIRST;
}
if (jcp.with_eltwise && icb + 1 == jcp.nb_ic) {
par_conv.flags |= FLAG_IC_LAST;
}
par_conv.oc_blocks =
nstl::min(ocb + ocb_num, jcp.nb_oc) - ocb;
par_conv.kw_padding = 0;
const int kh_padding = jcp.kh
- div_up(i_t_overflow, (jcp.dilate_h + 1))
- div_up(i_b_overflow, (jcp.dilate_h + 1));
par_conv.kh_padding = nstl::max(0, kh_padding);
const int kd_padding = jcp.kd
- div_up(d_t_overflow, (jcp.dilate_d + 1))
- div_up(d_b_overflow, (jcp.dilate_d + 1));
par_conv.kd_padding = nstl::max(0, kd_padding);
kernel_->jit_ker(&par_conv);
}
nd_iterator_step(n, jcp.mb, g, jcp.ngroups, ocbb, ocb_work,
od, jcp.od, oh, jcp.oh);
}
icbb += icb_step;
}
};
if (pd()->wants_padded_bias()) {
auto padded_bias = scratchpad(ctx).get<data_t>(key_conv_padded_bias);
utils::array_copy(padded_bias, bias, jcp.oc_without_padding);
utils::array_set(padded_bias + jcp.oc_without_padding, 0.f,
jcp.oc - jcp.oc_without_padding);
bias = padded_bias;
}
parallel(0, ker);
if (pd()->wants_zero_pad_dst())
ctx.memory(MKLDNN_ARG_DST)->zero_pad();
}
void jit_avx2_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);
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 auto &jcp = kernel_->jcp;
int icb_work = jcp.nb_ic / jcp.nb_ic_blocking;
int ih_block_size = jcp.ih;
int num_ih_blocks = utils::div_up(jcp.ih, ih_block_size);
size_t work_amount = jcp.mb * jcp.ngroups * icb_work * num_ih_blocks;
if (work_amount < (size_t)2 * mkldnn_get_max_threads()) {
ih_block_size = 1;
num_ih_blocks = utils::div_up(jcp.ih, ih_block_size);
work_amount *= num_ih_blocks;
}
auto ker = [&](const int ithr, const int nthr) {
size_t start{0}, end{0};
balance211(work_amount, nthr, ithr, start, end);
size_t n{0}, g{0}, icbb{0}, ihb{0};
nd_iterator_init(start, n, jcp.mb, g, jcp.ngroups, icbb, icb_work,
ihb, num_ih_blocks);
for (size_t iwork = start; iwork < end; ++iwork) {
for (int oc = 0; oc < jcp.nb_oc; oc += jcp.nb_oc_blocking)
for (int id = 0; id < jcp.id; ++id) {
auto par_conv = jit_conv_call_s();
const int idp = jcp.id + 2 * jcp.f_pad;
const int d_t_overflow = nstl::max(0,
jcp.kd - 1 - id - jcp.f_pad);
const int back_pad = idp - jcp.id - jcp.f_pad;
const int d_b_overflow = nstl::max(0,
jcp.kd - 1 - (jcp.id - 1 - id) - back_pad);
const int od = id + jcp.f_pad - d_b_overflow;
int ih_start = ihb * ih_block_size;
int ih_end = nstl::min(jcp.ih, ih_start + ih_block_size);
for (int ih = ih_start; ih < ih_end; ++ih) {
const int i_t_overflow = nstl::max(0, (jcp.kh - 1
- ih - jcp.t_pad) / jcp.stride_h);
const int i_b_overflow = nstl::max(0, (jcp.kh - jcp.ih
+ ih - jcp.b_pad) / jcp.stride_h);
int overflow_kh_hi = jcp.kh - 1 - abs((jcp.ih - 1
+ jcp.b_pad - ih) % jcp.stride_h);
int overflow_kh_lo = (ih + jcp.t_pad) % jcp.stride_h;
par_conv.kd_padding = jcp.kd - d_t_overflow - d_b_overflow;
par_conv.kh_padding = (overflow_kh_hi - overflow_kh_lo)
/ jcp.stride_h + 1 - i_t_overflow - i_b_overflow;
par_conv.kw_padding = 0;
const int k_lo = overflow_kh_lo
+ i_b_overflow * jcp.stride_h;
const int oh = (ih + jcp.t_pad - k_lo) / jcp.stride_h;
par_conv.src = &diff_src[src_blk_off(diff_src_d, n,
/*jcp.ic == 3 ? 0 :*/
g * jcp.nb_ic + jcp.nb_ic_blocking * icbb, id, ih, 0)];
par_conv.dst = &diff_dst[src_blk_off(diff_dst_d,
n, g * jcp.nb_oc + oc, od, oh, 0)];
par_conv.filt = &weights[wht_blk_off(weights_d, g, oc,
jcp.ic == 3 ? 0 : jcp.nb_ic_blocking * icbb,
d_b_overflow, k_lo, 0)];
par_conv.src_prf = nullptr;
par_conv.dst_prf = nullptr;
par_conv.filt_prf = nullptr;
par_conv.channel = oc;
par_conv.ch_blocks = nstl::min(jcp.nb_oc - oc,
jcp.nb_oc_blocking);
kernel_->jit_ker(&par_conv);
}
}
nd_iterator_step(n, jcp.mb, g, jcp.ngroups, icbb, icb_work, ihb,
num_ih_blocks);
}
};
parallel(0, ker);
}
void jit_avx2_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_in = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_BIAS);
auto scratchpad = this->scratchpad(ctx);
data_t *diff_bias = pd()->wants_padded_bias()
? scratchpad.get<data_t>(key_conv_padded_bias) : diff_bias_in;
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 auto &jcp = kernel_->jcp;
auto reducer_bia_scratchpad = memory_tracking::grantor_t(scratchpad,
prefix_reducer_bia);
auto rb = this->reducer_bias_;
rb->init(reducer_bia_scratchpad);
auto reducer_wei_scratchpad = memory_tracking::grantor_t(scratchpad,
prefix_reducer_wei);
auto rw = this->reducer_weights_;
rw->init(reducer_wei_scratchpad);
auto ker = [&](int ithr, int nthr) {
assert(nthr == rw->balancer().nthr_);
const int w_job_start = rw->balancer().ithr_job_off(ithr);
const int w_njobs = rw->balancer().ithr_njobs(ithr);
if (w_njobs == 0) return;
/* reduction dimension */
int img_od_start{0}, img_od_end{0}, img{0}, od_s{0};
balance211(jcp.mb * jcp.od, rw->balancer().nthr_per_group_,
rw->balancer().id_in_group(ithr), img_od_start, img_od_end);
int img_start = img_od_start, img_end = img_od_end;
nd_iterator_init(img_start, img, jcp.mb, od_s, jcp.od);
const int img_first = img;
/* jobs */
int g_start{0}, ocb_start{0}, icb_start{0};
nd_iterator_init(w_job_start, g_start, jcp.ngroups, ocb_start,
jcp.nb_oc, icb_start, jcp.nb_ic);
while (img_start < img_end) {
int g = g_start, ocb = ocb_start, icb = icb_start;
const int work_rem = img_end - img_start;
const int od_e = od_s + work_rem > jcp.od ? jcp.od : od_s + work_rem;
const int id_s = od_s * jcp.stride_d;
const int idp = jcp.id + jcp.f_pad + jcp.back_pad;
if (id_s < idp - jcp.back_pad - jcp.kd + 1)
for (int w_job_loc = 0; w_job_loc < w_njobs; ++w_job_loc) {
const size_t _oc = g * jcp.nb_oc + ocb;
const size_t _ic = g * jcp.nb_ic + icb;
/* TODO: put dw <-- 0 in kernel */
if (img == img_first)
array_set(rw->get_local_ptr(ithr, diff_weights,
reducer_wei_scratchpad) +
w_job_loc * rw->balancer().job_size_, 0,
rw->balancer().job_size_);
for (int od = od_s; od < od_e; ++od) {
const int id = od * jcp.stride_d;
if (id >= jcp.id - jcp.back_pad - jcp.kd + 1) break;
auto par_conv = jit_conv_call_s();
par_conv.src = &src[src_blk_off(src_d, img, _ic, id, 0, 0)];
par_conv.dst =
&diff_dst[src_blk_off(diff_dst_d, img, _oc, od, 0, 0)];
par_conv.filt = rw->get_local_ptr(ithr, diff_weights,
reducer_wei_scratchpad) +
w_job_loc * rw->balancer().job_size_;
kernel_->jit_ker(&par_conv);
}
nd_iterator_step(g, jcp.ngroups, ocb, jcp.nb_oc, icb,
jcp.nb_ic);
}
nd_iterator_jump(img_start, img_end, img, jcp.mb, od_s, jcp.od);
}
rw->reduce(ithr, diff_weights, reducer_wei_scratchpad);
};
auto ker_bias = [&](int ithr, int nthr) {
assert(nthr == rb->balancer().nthr_);
const int b_job_start = rb->balancer().ithr_job_off(ithr);
const int b_njobs = rb->balancer().ithr_njobs(ithr);
if (b_njobs == 0) return;
/* reduction dimension */
int img_start{0}, img_end{0};
balance211(jcp.mb, rb->balancer().nthr_per_group_,
rb->balancer().id_in_group(ithr), img_start, img_end);
/* jobs */
int g_start{0}, ocb_start{0};
nd_iterator_init(b_job_start, g_start, jcp.ngroups, ocb_start,
jcp.nb_oc);
for (int img = img_start; img < img_end; ++img) {
int g = g_start, ocb = ocb_start;
for (int b_job_loc = 0; b_job_loc < b_njobs; ++b_job_loc) {
const size_t _oc = g * jcp.nb_oc + ocb;
const data_t *d_dst = &diff_dst[diff_dst_d.blk_off(img, _oc)];
data_t *d_bias = rb->get_local_ptr(ithr, diff_bias,
reducer_bia_scratchpad) +
b_job_loc * rb->balancer().job_size_;
if (img == img_start)
for (int o = 0; o < 8; ++o)
d_bias[o] = 0.;
for (int dhw = 0; dhw < jcp.od * jcp.oh * jcp.ow; ++dhw) {
PRAGMA_OMP_SIMD()
for (int o = 0; o < 8; ++o)
d_bias[o] += d_dst[o];
d_dst += 8;
}
nd_iterator_step(g, jcp.ngroups, ocb, jcp.nb_oc);
}
}
rb->reduce(ithr, diff_bias, reducer_bia_scratchpad);
};
parallel(0, [&](const int ithr, const int nthr) {
ker(ithr, nthr);
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];
}
}
}
}
}
// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s