/*******************************************************************************
* 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 saudet
// @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(maxpool2d, ENGINE_CPU) {
auto input = INPUT_VARIABLE(0);
REQUIRE_TRUE(input->rankOf() == 4, 0, "Input should have rank of 4, but got %i instead",
input->rankOf());
// 0,1 - kernel Height/Width; 2,3 - stride Height/Width; 4,5 - pad Height/Width; 6,7 - dilation Height/Width; 8 - same mode;
auto argI = *(block.getIArguments());
auto output = OUTPUT_VARIABLE(0);
const auto kH = INT_ARG(0);
const auto kW = INT_ARG(1);
const auto sH = INT_ARG(2);
const auto sW = INT_ARG(3);
int pH = INT_ARG(4);
int pW = INT_ARG(5);
const auto dH = INT_ARG(6);
const auto dW = INT_ARG(7);
const auto isSameMode = static_cast<bool>(INT_ARG(8));
REQUIRE_TRUE(dH != 0 && dW != 0, 0, "AVGPOOL2D op: dilation must not be zero, but got instead {%i, %i}",
dH, dW);
int oH = 0;
int oW = 0;
int isNCHW = block.getIArguments()->size() > 10 ? !INT_ARG(10) : 1; // INT_ARG(10): 0-NCHW, 1-NHWC
const int iH = static_cast<int>(isNCHW ? input->sizeAt(2) : input->sizeAt(1));
const int iW = static_cast<int>(isNCHW ? input->sizeAt(3) : input->sizeAt(2));
if (!isNCHW) {
input = new NDArray(
input->permute({0, 3, 1, 2})); // [bS, iH, iW, iC] -> [bS, iC, iH, iW]
output = new NDArray(
output->permute({0, 3, 1, 2})); // [bS, oH, oW, iC] -> [bS, iC, oH, oW]
}
ConvolutionUtils::calcOutSizePool2D(oH, oW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode);
if (isSameMode)
ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW);
const int bS = input->sizeAt(0);
const int iC = input->sizeAt(1);
const int oC = output->sizeAt(1);
auto poolingMode = PoolingType::MAX_POOL;
int extraParam0 = 1;
dnnl_memory_desc_t empty;
dnnl::memory::desc pool_src_md(empty), pool_dst_md(empty);
dnnl::memory::desc user_src_md(empty), user_dst_md(empty);
dnnl::memory::dims pool_strides, pool_kernel, pool_padding, pool_padding_r;
dnnl::algorithm algorithm;
mkldnnUtils::getMKLDNNMemoryDescPool2d(kH, kW, sH, sW, pH, pW, dH, dW, poolingMode, extraParam0,
true,
bS, iC, iH, iW, oC, oH, oW, input, nullptr, output,
algorithm,
&pool_src_md, nullptr, &pool_dst_md, &user_src_md, nullptr,
&user_dst_md,
pool_strides, pool_kernel, pool_padding, pool_padding_r);
auto pool_desc = pooling_forward::desc(prop_kind::forward_inference, algorithm, pool_src_md,
pool_dst_md,
pool_strides, pool_kernel, pool_padding, pool_padding_r);
auto engine = mkldnnUtils::getEngine(LaunchContext::defaultContext()->engine());
auto pool_prim_desc = pooling_forward::primitive_desc(pool_desc, engine);
auto user_src_memory = dnnl::memory(user_src_md, engine, input->buffer());
auto user_dst_memory = dnnl::memory(user_dst_md, engine, output->buffer());
auto pool_src_memory = user_src_memory;
dnnl::stream stream(engine);
if (pool_prim_desc.src_desc() != user_src_memory.get_desc()) {
pool_src_memory = dnnl::memory(pool_prim_desc.src_desc(), engine);
reorder(user_src_memory, pool_src_memory).execute(stream, user_src_memory, pool_src_memory);
}
auto pool_dst_memory = user_dst_memory;
if (pool_prim_desc.dst_desc() != user_dst_memory.get_desc()) {
pool_dst_memory = dnnl::memory(pool_prim_desc.dst_desc(), engine);
}
pooling_forward(pool_prim_desc).execute(stream, {{DNNL_ARG_SRC, pool_src_memory},
{DNNL_ARG_DST, pool_dst_memory}});
if (pool_prim_desc.dst_desc() != user_dst_memory.get_desc()) {
reorder(pool_dst_memory, user_dst_memory).execute(stream, pool_dst_memory, user_dst_memory);
}
stream.wait();
if (!isNCHW) {
delete input;
delete output;
}
return Status::OK();
}
//////////////////////////////////////////////////////////////////////////
PLATFORM_CHECK(maxpool2d, ENGINE_CPU) {
auto input = INPUT_VARIABLE(0);
auto output = OUTPUT_VARIABLE(0);
return block.isUseMKLDNN() && nd4j::MKLDNNStream::isSupported({input, output});
}
//////////////////////////////////////////////////////////////////////////
PLATFORM_IMPL(maxpool2d_bp, ENGINE_CPU) {
auto input = INPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
auto gradO = INPUT_VARIABLE(1); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW), epsilon_next
auto gradI = OUTPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW), epsilon
int kH = INT_ARG(0); // filter(kernel) height
int kW = INT_ARG(1); // filter(kernel) width
int sH = INT_ARG(2); // strides height
int sW = INT_ARG(3); // strides width
int pH = INT_ARG(4); // paddings height
int pW = INT_ARG(5); // paddings width
int dH = INT_ARG(6); // dilations height
int dW = INT_ARG(7); // dilations width
int isSameMode = INT_ARG(8); // 0-VALID, 1-SAME
int extraParam0 = INT_ARG(9);
int isNCHW =
block.getIArguments()->size() > 10 ? !INT_ARG(10) : 1; // INT_ARG(10): 0-NCHW, 1-NHWC
REQUIRE_TRUE(input->rankOf() == 4, 0,
"AVGPOOL2D_BP op: input should have rank of 4, but got %i instead", input->rankOf());
REQUIRE_TRUE(dH != 0 && dW != 0, 0,
"AVGPOOL2D_BP op: dilation must not be zero, but got instead {%i, %i}", dH, dW);
int bS, iC, iH, iW, oC, oH, oW; // batch size, input channels, input height/width, output channels, output height/width;
int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes
ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC,
indIiH, indWiC, indWoC, indWkH, indOoH);
std::string expectedGradOShape = ShapeUtils::shapeAsString(
ShapeUtils::composeShapeUsingDimsAndIdx({bS, iC, oH, oW, 0, indIOioC, indIiH, indIiH + 1}));
std::string expectedGradIShape = ShapeUtils::shapeAsString(
ShapeUtils::composeShapeUsingDimsAndIdx({bS, iC, iH, iW, 0, indIOioC, indIiH, indIiH + 1}));
REQUIRE_TRUE(expectedGradOShape == ShapeUtils::shapeAsString(gradO), 0,
"AVGPOOL2D_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,
"AVGPOOL2D_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 (!isNCHW) {
input = new NDArray(input->permute(
{0, 3, 1, 2})); // [bS, iH, iW, iC] -> [bS, iC, iH, iW]
gradI = new NDArray(gradI->permute(
{0, 3, 1, 2})); // [bS, iH, iW, iC] -> [bS, iC, iH, iW]
gradO = new NDArray(gradO->permute(
{0, 3, 1, 2})); // [bS, oH, oW, iC] -> [bS, iC, oH, oW]
}
if (isSameMode) // SAME
ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW);
auto poolingMode = PoolingType::MAX_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::getMKLDNNMemoryDescPool2d(kH, kW, sH, sW, pH, pW, dH, dW, poolingMode, extraParam0,
true,
bS, iC, iH, iW, oC, 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);
// input is sometimes null, so we can't rely on pool_src_md being valid
auto pool_desc = pooling_forward::desc(prop_kind::forward, algorithm,
input->buffer() != nullptr ? pool_src_md : pool_diff_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_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);
}
auto user_src_memory = dnnl::memory(user_src_md, engine, input->buffer());
auto pool_src_memory = user_src_memory;
if (pool_prim_desc.src_desc() != user_src_memory.get_desc()) {
pool_src_memory = dnnl::memory(pool_prim_desc.src_desc(), engine);
reorder(user_src_memory, pool_src_memory).execute(stream, user_src_memory, pool_src_memory);
}
auto pool_dst_memory = dnnl::memory(pool_prim_desc.dst_desc(), engine);
auto pool_workspace_memory = dnnl::memory(pool_prim_desc.workspace_desc(), engine);
pooling_forward(pool_prim_desc).execute(stream, {{DNNL_ARG_SRC, pool_src_memory},
{DNNL_ARG_DST, pool_dst_memory},
{DNNL_ARG_WORKSPACE, pool_workspace_memory}});
// probably wrong, fix that
pooling_backward(poolB_prim_desc).execute(stream, {{DNNL_ARG_DIFF_DST, poolB_dst_memory},
{DNNL_ARG_WORKSPACE, pool_workspace_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 (!isNCHW) {
delete input;
delete gradI;
delete gradO;
}
return Status::OK();
}
PLATFORM_CHECK(maxpool2d_bp, ENGINE_CPU) {
auto input = INPUT_VARIABLE(0);
auto output = OUTPUT_VARIABLE(0);
return block.isUseMKLDNN() && nd4j::MKLDNNStream::isSupported({input, output});
}
}
}
}