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org.deeplearning4j.nn.layers.mkldnn.MKLDNNConvHelper Maven / Gradle / Ivy
/*
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* * 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.
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* * See the NOTICE file distributed with this work for additional
* * information regarding copyright ownership.
* * 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.
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* * SPDX-License-Identifier: Apache-2.0
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package org.deeplearning4j.nn.layers.mkldnn;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.ConvolutionMode;
import org.deeplearning4j.nn.conf.layers.ConvolutionLayer;
import org.deeplearning4j.nn.gradient.DefaultGradient;
import org.deeplearning4j.nn.gradient.Gradient;
import org.deeplearning4j.nn.layers.convolution.ConvolutionHelper;
import org.deeplearning4j.nn.params.ConvolutionParamInitializer;
import org.deeplearning4j.nn.workspace.ArrayType;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
import org.deeplearning4j.util.ConvolutionUtils;
import org.nd4j.linalg.activations.IActivation;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.OpContext;
import org.nd4j.linalg.api.ops.impl.layers.convolution.Conv2D;
import org.nd4j.linalg.api.ops.impl.layers.convolution.Conv2DDerivative;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.common.primitives.Pair;
import org.nd4j.common.util.ArrayUtil;
import java.util.Collections;
import java.util.Map;
public class MKLDNNConvHelper implements ConvolutionHelper {
protected OpContext context;
protected OpContext contextBwd;
public MKLDNNConvHelper(DataType dataType) {
}
@Override
public boolean checkSupported() {
return BaseMKLDNNHelper.mklDnnEnabled();
}
@Override
public Pair backpropGradient(INDArray input, INDArray weights, INDArray bias, INDArray delta, int[] kernel, int[] strides, int[] pad,
INDArray biasGradView, INDArray weightGradView, IActivation afn, ConvolutionLayer.AlgoMode mode,
ConvolutionLayer.BwdFilterAlgo bwdFilterAlgo, ConvolutionLayer.BwdDataAlgo bwdDataAlgo, ConvolutionMode convolutionMode,
int[] dilation, CNN2DFormat format, LayerWorkspaceMgr workspaceMgr) {
if(input.dataType() != DataType.FLOAT || weights.dataType() != DataType.FLOAT)
return null; //MKL-DNN only supports floating point dtype
int hDim = 2;
int wDim = 3;
if(format == CNN2DFormat.NHWC){
hDim = 1;
wDim = 2;
}
if (convolutionMode == ConvolutionMode.Same) {
pad = ConvolutionUtils.getSameModeTopLeftPadding(new int[]{(int)delta.size(hDim), (int)delta.size(wDim)}, new int[] {(int) input.size(hDim), (int) input.size(wDim)},
kernel, strides, dilation);
}
if(contextBwd == null){
contextBwd = Nd4j.getExecutioner().buildContext();
contextBwd.setIArguments(kernel[0], kernel[1],
strides[0], strides[1],
pad[0], pad[1],
dilation[0], dilation[1],
ArrayUtil.fromBoolean(convolutionMode == ConvolutionMode.Same),
format == CNN2DFormat.NCHW ? 0 : 1, //0=NCHW, 1=NHWC
1 //Weight format: 1 - [oC, iC, kH, kW]
);
};
INDArray gradAtInput = workspaceMgr.createUninitialized(ArrayType.ACTIVATION_GRAD, input.dataType(), input.shape());
INDArray[] inputsArr = biasGradView == null ? new INDArray[]{input, weights, delta} : new INDArray[]{input, weights, bias, delta};
INDArray[] outputArr = biasGradView == null ? new INDArray[]{gradAtInput, weightGradView} : new INDArray[]{gradAtInput, weightGradView, biasGradView};
contextBwd.purge();
for( int i=0; i(g, gradAtInput);
}
@Override
public INDArray preOutput(INDArray input, INDArray weights, INDArray bias, int[] kernel, int[] strides, int[] pad,
ConvolutionLayer.AlgoMode mode, ConvolutionLayer.FwdAlgo fwdAlgo, ConvolutionMode convolutionMode,
int[] dilation, CNN2DFormat format, LayerWorkspaceMgr workspaceMgr) {
if(input.dataType() != DataType.FLOAT || weights.dataType() != DataType.FLOAT)
return null; //MKL-DNN only supports floating point dtype
int hDim = 2;
int wDim = 3;
if(format == CNN2DFormat.NHWC){
hDim = 1;
wDim = 2;
}
int inH = (int)input.size(hDim);
int inW = (int)input.size(wDim);
int[] outSize;
if (convolutionMode == ConvolutionMode.Same) {
outSize = ConvolutionUtils.getOutputSize(input, kernel, strides, null, convolutionMode, dilation, format); //Also performs validation
pad = ConvolutionUtils.getSameModeTopLeftPadding(outSize, new int[] {inH, inW}, kernel, strides, dilation);
} else {
outSize = ConvolutionUtils.getOutputSize(input, kernel, strides, pad, convolutionMode, dilation, format); //Also performs validation
}
if(context == null ){
context = Nd4j.getExecutioner().buildContext();
context.setIArguments(kernel[0], kernel[1],
strides[0], strides[1],
pad[0], pad[1],
dilation[0], dilation[1],
ArrayUtil.fromBoolean(convolutionMode == ConvolutionMode.Same),
format == CNN2DFormat.NCHW ? 0 : 1, //0=NCHW, 1=NHWC
1 //Weight format: 1 - [oC, iC, kH, kW]
);
};
int outDepth = (int) weights.size(0);
long[] outShape = (format == CNN2DFormat.NCHW) ? new long[]{input.size(0), outDepth, outSize[0], outSize[1]} : new long[]{input.size(0), outSize[0], outSize[1], outDepth};
INDArray out = workspaceMgr.createUninitialized(ArrayType.ACTIVATIONS, input.dataType(), outShape);
INDArray[] inputsArr = bias == null ? new INDArray[]{input, weights} : new INDArray[]{input, weights, bias};
context.purge();
for( int i = 0; i < inputsArr.length; i++) {
context.setInputArray(i, inputsArr[i]);
}
context.setOutputArray(0, out);
Conv2D op = new Conv2D();
Nd4j.exec(op, context);
return out;
}
@Override
public INDArray activate(INDArray z, IActivation afn, boolean training) {
return afn.getActivation(z, training);
}
@Override
public Map helperMemoryUse() {
return Collections.emptyMap();
}
}