Many resources are needed to download a project. Please understand that we have to compensate our server costs. Thank you in advance. Project price only 1 $
You can buy this project and download/modify it how often you want.
/*******************************************************************************
* Copyright (c) 2015-2019 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
******************************************************************************/
package org.nd4j.linalg.api.ops.impl.transforms.custom;
import lombok.NoArgsConstructor;
import lombok.NonNull;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.base.Preconditions;
import org.nd4j.imports.NoOpNameFoundException;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import java.util.Arrays;
import java.util.Collections;
import java.util.List;
/**
* Composed op: g*standarize(x) + b
*
* Bias is optional, and can be set as null
*
* @author Paul Dubs
*/
@NoArgsConstructor
public class LayerNorm extends DynamicCustomOp {
private boolean noBias = false;
private boolean channelsFirst;
public LayerNorm(@NonNull SameDiff sameDiff, @NonNull SDVariable input, @NonNull SDVariable gain, SDVariable bias, boolean channelsFirst, int... dimensions) {
super(null, sameDiff, wrapFilterNull(input, gain, bias), false);
this.noBias = bias == null;
this.channelsFirst = channelsFirst;
setDimensions(dimensions);
}
public LayerNorm(SameDiff sameDiff, SDVariable input, SDVariable gain, boolean channelsFirst, int... dimensions) {
this(sameDiff, input, gain, null, channelsFirst, dimensions);
}
public LayerNorm(INDArray input, INDArray gain, INDArray bias, INDArray result, boolean channelsFirst, int... dimensions) {
super("layer_norm", wrapFilterNull(input, gain, bias), wrapOrNull(result));
this.noBias = bias == null;
this.channelsFirst = channelsFirst;
setDimensions(dimensions);
}
public LayerNorm(@NonNull INDArray input, @NonNull INDArray gain, boolean channelsFirst, int... dimensions) {
this(input, gain, null, channelsFirst, dimensions);
}
public LayerNorm(INDArray input, INDArray gain, INDArray result, boolean channelsFirst, int... dimensions) {
this(input, gain, null, result, channelsFirst, dimensions);
}
@Override
public void setDimensions(int[] dimensions) {
Preconditions.checkArgument(dimensions != null, "LayerNorm: You have to provide dimensions");
Preconditions.checkArgument(dimensions.length > 0, "LayerNorm: You have to provide dimensions");
this.dimensions = dimensions;
this.iArguments.clear();
addIArgument(dimensions);
this.bArguments.clear();
this.bArguments.add(channelsFirst);
}
@Override
public String opName() {
return "layer_norm";
}
@Override
public String tensorflowName() {
throw new NoOpNameFoundException("No tensorflow name found for shape " + opName());
}
@Override
public String onnxName() {
throw new NoOpNameFoundException("No onnx name found for shape " + opName());
}
@Override
public List doDiff(List gradient) {
SDVariable[] ret;
if(noBias){
ret = f().layerNormBp(arg(0), arg(1), gradient.get(0), channelsFirst, dimensions);
}else{
ret = f().layerNormBp(arg(0), arg(1), arg(2), gradient.get(0), channelsFirst, dimensions);
}
return Arrays.asList(ret);
}
@Override
public List calculateOutputDataTypes(List dataTypes){
Preconditions.checkState(dataTypes != null && dataTypes.size() >= 2 && dataTypes.size() <= 3, "Expected exactly 2 or 3 input datatypes, got %s", dataTypes);
DataType first = dataTypes.get(0);
for (DataType dataType : dataTypes) {
Preconditions.checkState(dataType.isFPType(), "Input %s datatype must be a floating point type, got datypes %s", dataTypes);
Preconditions.checkState(first == dataType, "All datatypes must be same type, got input datatypes %s", dataTypes);
}
return Collections.singletonList(first);
}
@Override
public int numOutputArguments() {
return noBias ? 2 : 3;
}
}