145 lines
6.5 KiB
C++
145 lines
6.5 KiB
C++
/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://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, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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//
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// @author saudet
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// @author raver119@gmail.com
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//
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#include <ops/declarable/PlatformHelper.h>
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#include <ops/declarable/OpRegistrator.h>
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#include <platform_boilerplate.h>
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#include <helpers/MKLDNNStream.h>
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#include "mkldnnUtils.h"
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#include <ops/declarable/helpers/convolutions.h>
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using namespace dnnl;
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namespace nd4j {
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namespace ops {
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namespace platforms {
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PLATFORM_IMPL(maxpool2d, ENGINE_CPU) {
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auto input = INPUT_VARIABLE(0);
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REQUIRE_TRUE(input->rankOf() == 4, 0, "Input should have rank of 4, but got %i instead",
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input->rankOf());
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// 0,1 - kernel Height/Width; 2,3 - stride Height/Width; 4,5 - pad Height/Width; 6,7 - dilation Height/Width; 8 - same mode;
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auto argI = *(block.getIArguments());
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auto output = OUTPUT_VARIABLE(0);
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const auto kH = INT_ARG(0);
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const auto kW = INT_ARG(1);
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const auto sH = INT_ARG(2);
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const auto sW = INT_ARG(3);
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int pH = INT_ARG(4);
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int pW = INT_ARG(5);
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const auto dH = INT_ARG(6);
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const auto dW = INT_ARG(7);
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const auto isSameMode = static_cast<bool>(INT_ARG(8));
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REQUIRE_TRUE(dH != 0 && dW != 0, 0, "AVGPOOL2D op: dilation must not be zero, but got instead {%i, %i}",
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dH, dW);
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int oH = 0;
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int oW = 0;
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int isNCHW = block.getIArguments()->size() > 10 ? !INT_ARG(10) : 1; // INT_ARG(10): 0-NCHW, 1-NHWC
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const int iH = static_cast<int>(isNCHW ? input->sizeAt(2) : input->sizeAt(1));
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const int iW = static_cast<int>(isNCHW ? input->sizeAt(3) : input->sizeAt(2));
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if (!isNCHW) {
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input = new NDArray(
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input->permute({0, 3, 1, 2})); // [bS, iH, iW, iC] -> [bS, iC, iH, iW]
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output = new NDArray(
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output->permute({0, 3, 1, 2})); // [bS, oH, oW, iC] -> [bS, iC, oH, oW]
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}
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ConvolutionUtils::calcOutSizePool2D(oH, oW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode);
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if (isSameMode)
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ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW);
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const int bS = input->sizeAt(0);
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const int iC = input->sizeAt(1);
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const int oC = output->sizeAt(1);
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auto poolingMode = PoolingType::MAX_POOL;
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int extraParam0 = 1;
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dnnl_memory_desc_t empty;
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dnnl::memory::desc pool_src_md(empty), pool_dst_md(empty);
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dnnl::memory::desc user_src_md(empty), user_dst_md(empty);
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dnnl::memory::dims pool_strides, pool_kernel, pool_padding, pool_padding_r;
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dnnl::algorithm algorithm;
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mkldnnUtils::getMKLDNNMemoryDescPool2d(kH, kW, sH, sW, pH, pW, dH, dW, poolingMode, extraParam0,
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true,
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bS, iC, iH, iW, oC, oH, oW, input, nullptr, output,
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algorithm,
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&pool_src_md, nullptr, &pool_dst_md, &user_src_md, nullptr,
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&user_dst_md,
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pool_strides, pool_kernel, pool_padding, pool_padding_r);
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auto pool_desc = pooling_forward::desc(prop_kind::forward_inference, algorithm, pool_src_md,
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pool_dst_md,
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pool_strides, pool_kernel, pool_padding, pool_padding_r);
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auto engine = mkldnnUtils::getEngine(LaunchContext::defaultContext()->engine());
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auto pool_prim_desc = pooling_forward::primitive_desc(pool_desc, engine);
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auto user_src_memory = dnnl::memory(user_src_md, engine, input->buffer());
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auto user_dst_memory = dnnl::memory(user_dst_md, engine, output->buffer());
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auto pool_src_memory = user_src_memory;
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dnnl::stream stream(engine);
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if (pool_prim_desc.src_desc() != user_src_memory.get_desc()) {
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pool_src_memory = dnnl::memory(pool_prim_desc.src_desc(), engine);
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reorder(user_src_memory, pool_src_memory).execute(stream, user_src_memory, pool_src_memory);
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}
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auto pool_dst_memory = user_dst_memory;
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if (pool_prim_desc.dst_desc() != user_dst_memory.get_desc()) {
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pool_dst_memory = dnnl::memory(pool_prim_desc.dst_desc(), engine);
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}
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pooling_forward(pool_prim_desc).execute(stream, {{DNNL_ARG_SRC, pool_src_memory},
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{DNNL_ARG_DST, pool_dst_memory}});
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if (pool_prim_desc.dst_desc() != user_dst_memory.get_desc()) {
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reorder(pool_dst_memory, user_dst_memory).execute(stream, pool_dst_memory, user_dst_memory);
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}
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stream.wait();
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if (!isNCHW) {
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delete input;
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delete output;
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}
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return Status::OK();
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}
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PLATFORM_CHECK(maxpool2d, ENGINE_CPU) {
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auto input = INPUT_VARIABLE(0);
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auto output = OUTPUT_VARIABLE(0);
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return block.isUseMKLDNN() && nd4j::MKLDNNStream::isSupported({input, output});
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}
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}
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}
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} |