/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://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.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
//
// @author raver119@gmail.com
//
#include <ops/declarable/PlatformHelper.h>
#include <ops/declarable/OpRegistrator.h>
#include <platform_boilerplate.h>
#include <helpers/MKLDNNStream.h>
#include "mkldnnUtils.h"
#include <ops/declarable/helpers/convolutions.h>
using namespace dnnl;
namespace nd4j {
namespace ops {
namespace platforms {
PLATFORM_IMPL(avgpool3dnew_bp, ENGINE_CPU) {
auto input = INPUT_VARIABLE(
0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW)
auto gradO = INPUT_VARIABLE(
1); // [bS, oD, oH, oW, oC] (NDHWC) or [bS, oC, oD, oH, oW] (NCDHW), epsilon_next
auto gradI = OUTPUT_VARIABLE(
0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW), epsilon
const int kD = INT_ARG(0); // filter(kernel) depth
const int kH = INT_ARG(1); // filter(kernel) height
const int kW = INT_ARG(2); // filter(kernel) width
const int sD = INT_ARG(3); // strides depth
const int sH = INT_ARG(4); // strides height
const int sW = INT_ARG(5); // strides width
int pD = INT_ARG(6); // paddings depth
int pH = INT_ARG(7); // paddings height
int pW = INT_ARG(8); // paddings width
const int dD = INT_ARG(9); // dilations depth
const int dH = INT_ARG(10); // dilations height
const int dW = INT_ARG(11); // dilations width
const int isSameMode = INT_ARG(12); // 1-SAME, 0-VALID
int extraParam0 = INT_ARG(13); // unnecessary for max case, required only for avg and pnorm cases
int isNCDHW = block.getIArguments()->size() > 14 ? !INT_ARG(14) : 1; // 1-NDHWC, 0-NCDHW
REQUIRE_TRUE(input->rankOf() == 5, 0,
"MAXPOOL3D_BP op: input should have rank of 5, but got %i instead", input->rankOf());
REQUIRE_TRUE(dD != 0 && dH != 0 && dW != 0, 0,
"MAXPOOL3DNEW op: dilation must not be zero, but got instead {%i, %i, %i}", dD, dH, dW);
int bS, iC, iD, iH, iW, oC, oD, oH, oW; // batch size, input channels, input depth/height/width, output channels, output depth/height/width;
int indIOioC, indIOioD, indWoC, indWiC, indWkD; // corresponding indexes
ConvolutionUtils::getSizesAndIndexesConv3d(isNCDHW, *input, *gradO, bS, iC, iD, iH, iW, oC, oD, oH, oW,
indIOioC, indIOioD, indWiC, indWoC, indWkD);
std::string expectedGradOShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx(
{bS, iC, oD, oH, oW, 0, indIOioC, indIOioD, indIOioD + 1, indIOioD + 2}));
std::string expectedGradIShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx(
{bS, iC, iD, iH, iW, 0, indIOioC, indIOioD, indIOioD + 1, indIOioD + 2}));
REQUIRE_TRUE(expectedGradOShape == ShapeUtils::shapeAsString(gradO), 0,
"MAXPOOL3D_BP op: wrong shape of output's gradients array (next epsilon), expected is %s, but got %s instead !",
expectedGradOShape.c_str(), ShapeUtils::shapeAsString(gradO).c_str());
REQUIRE_TRUE(expectedGradIShape == ShapeUtils::shapeAsString(gradI), 0,
"MAXPOOL3D_BP op: wrong shape of input's gradients array (epsilon), expected is %s, but got %s instead !",
expectedGradIShape.c_str(), ShapeUtils::shapeAsString(gradI).c_str());
if (!isNCDHW) {
input = new NDArray(input->permute(
{0, 4, 1, 2, 3})); // [bS, iD, iH, iW, iC] -> [bS, iC, iD, iH, iW]
gradI = new NDArray(gradI->permute(
{0, 4, 1, 2, 3})); // [bS, iD, iH, iW, iC] -> [bS, iC, iD, iH, iW]
gradO = new NDArray(gradO->permute(
{0, 4, 1, 2, 3})); // [bS, oD, oH, oW, iC] -> [bS, iC, oD, oH, oW]
}
if (isSameMode) // SAME
ConvolutionUtils::calcPadding3D(pD, pH, pW, oD, oH, oW, iD, iH, iW, kD, kH, kW, sD, sH, sW, dD, dH, dW);
auto poolingMode = PoolingType::AVG_POOL;
dnnl_memory_desc_t empty;
dnnl::memory::desc pool_src_md(empty), pool_diff_src_md(empty), pool_dst_md(empty);
dnnl::memory::desc user_src_md(empty), user_diff_src_md(empty), user_dst_md(empty);
dnnl::memory::dims pool_strides, pool_kernel, pool_padding, pool_padding_r;
dnnl::algorithm algorithm;
mkldnnUtils::getMKLDNNMemoryDescPool3d(kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, poolingMode,
extraParam0, true,
bS, iC, iD, iH, iW, oC, oD, oH, oW, input, gradI, gradO,
algorithm,
&pool_src_md, &pool_diff_src_md, &pool_dst_md, &user_src_md,
&user_diff_src_md, &user_dst_md,
pool_strides, pool_kernel, pool_padding, pool_padding_r);
if (input->buffer() == nullptr) {
pool_src_md = pool_diff_src_md;
user_src_md = user_diff_src_md;
}
auto pool_desc = pooling_forward::desc(prop_kind::forward, algorithm, pool_src_md, pool_dst_md,
pool_strides, pool_kernel, pool_padding, pool_padding_r);
auto engine = mkldnnUtils::getEngine(LaunchContext::defaultContext()->engine());
dnnl::stream stream(engine);
auto pool_prim_desc = pooling_forward::primitive_desc(pool_desc, engine);
auto poolB_desc = pooling_backward::desc(algorithm, pool_diff_src_md, pool_dst_md, pool_strides,
pool_kernel, pool_padding, pool_padding_r);
auto poolB_prim_desc = pooling_backward::primitive_desc(poolB_desc, engine, pool_prim_desc);
auto userB_src_memory = dnnl::memory(user_diff_src_md, engine, gradI->buffer());
auto userB_dst_memory = dnnl::memory(user_dst_md, engine, gradO->buffer());
auto poolB_src_memory = userB_src_memory;
if (poolB_prim_desc.diff_src_desc() != userB_src_memory.get_desc()) {
poolB_src_memory = dnnl::memory(poolB_prim_desc.diff_src_desc(), engine);
}
auto poolB_dst_memory = userB_dst_memory;
if (poolB_prim_desc.diff_dst_desc() != userB_dst_memory.get_desc()) {
poolB_dst_memory = dnnl::memory(poolB_prim_desc.diff_dst_desc(), engine);
reorder(userB_dst_memory, poolB_dst_memory).execute(stream, userB_dst_memory, poolB_dst_memory);
}
pooling_backward(poolB_prim_desc).execute(stream, {{DNNL_ARG_DIFF_DST, poolB_dst_memory},
{DNNL_ARG_DIFF_SRC, poolB_src_memory}});
if (poolB_prim_desc.diff_src_desc() != userB_src_memory.get_desc()) {
reorder(poolB_src_memory, userB_src_memory).execute(stream, poolB_src_memory, userB_src_memory);
}
stream.wait();
if (!isNCDHW) {
delete input;
delete gradI;
delete gradO;
}
return Status::OK();
}
PLATFORM_CHECK(avgpool3dnew_bp, ENGINE_CPU) {
auto input = INPUT_VARIABLE(0);
auto output = OUTPUT_VARIABLE(0);
return block.isUseMKLDNN() && nd4j::MKLDNNStream::isSupported({input, output});
}
}
}
}