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103 lines
3.6 KiB
103 lines
3.6 KiB
8 years ago
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#include "hl_base.h"
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#include "PadOp.h"
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namespace paddle {
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__global__ void KePad(real* outputs, const real* inputs,
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int inC, int inH, int inW,
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int padc, int padh, int padw,
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int outC, int outH, int outW, int nthreads) {
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const int idx = threadIdx.x + blockIdx.x * blockDim.x;
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if (idx < nthreads) {
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const int w = idx % inW;
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const int h = (idx / inW) % inH;
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const int c = (idx / inW / inH) % inC;
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const int n = idx / inW / inH / inC;
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const int off = ((n * outC + c + padc) * outH + h + padh) * outW + padw + w;
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outputs[off] = inputs[idx];
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}
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}
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template <>
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void Pad<DEVICE_TYPE_GPU>(real* outputs,
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const real* inputs,
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const int num,
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const int inC,
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const int inH,
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const int inW,
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const int padc0,
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const int padc1,
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const int padh0,
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const int padh1,
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const int padw0,
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const int padw1) {
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size_t nth = num * inC * inH * inW;
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int blockSize = 1024;
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int gridSize = (nth + 1024 - 1) / 1024;
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int outC = inC + padc0 + padc1;
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int outH = inH + padh0 + padh1;
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int outW = inW + padw0 + padw1;
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KePad<<<gridSize, blockSize, 0, STREAM_DEFAULT>>>
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(outputs, inputs, inC, inH, inW, padc0, padh0, padw0,
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outC, outH, outW, nth);
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CHECK_SYNC("Pad");
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}
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__global__ void KePadDiff(real* inGrad, const real* outGrad,
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int inC, int inH, int inW,
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int padc, int padh, int padw,
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int outC, int outH, int outW, int nthreads) {
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const int idx = threadIdx.x + blockIdx.x * blockDim.x;
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if (idx < nthreads) {
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const int w = idx % inW;
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const int h = (idx / inW) % inH;
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const int c = (idx / inW / inH) % inC;
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const int n = idx / inW / inH / inC;
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const int off = ((n * outC + c + padc) * outH + h + padh) * outW + padw + w;
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inGrad[idx] += outGrad[off];
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}
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}
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template <>
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void PadGrad<DEVICE_TYPE_GPU>(real* inGrad,
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const real* outGrad,
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const int num,
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const int inC,
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const int inH,
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const int inW,
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const int padc0,
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const int padc1,
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const int padh0,
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const int padh1,
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const int padw0,
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const int padw1) {
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int nth = num * inC * inH * inW;
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int blockSize = 1024;
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int gridSize = (nth + 1024 - 1) / 1024;
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int outC = inC + padc0 + padc1;
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int outH = inH + padh0 + padh1;
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int outW = inW + padw0 + padw1;
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KePadDiff <<<gridSize, blockSize, 0, STREAM_DEFAULT>>>
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(inGrad, outGrad, inC, inH, inW, padc0, padh0, padw0,
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outC, outH, outW, nth);
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CHECK_SYNC("PadGrad");
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}
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} // namespace paddle
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