Remove calculate output shape from java side (#151)

* remove some unneeded java-side output shape calculations

Signed-off-by: Ryan Nett <rnett@skymind.io>

* delete Broadcast

Signed-off-by: Ryan Nett <rnett@skymind.io>

* delete Linear and Module,

Signed-off-by: Ryan Nett <rnett@skymind.io>

* update Identity, HashCode, and NoOp

Signed-off-by: Ryan Nett <rnett@skymind.io>

* removed Cast java-side shape function, added tests and SDVariable.isEmpty

Signed-off-by: Ryan Nett <rnett@skymind.io>

* ignoring test w/ issues on master

Signed-off-by: Ryan Nett <rnett@skymind.io>

* noop needs more work, fixed BaseArithmeticBackprop and BaseDynamicTransform ops

merge in master for c++ build fix

Signed-off-by: Ryan Nett <rnett@skymind.io>

* fix EqualTo

Signed-off-by: Ryan Nett <rnett@skymind.io>

* fix other cond ops

Signed-off-by: Ryan Nett <rnett@skymind.io>

* "fake" ops calculateOutputShape() throws exception

Signed-off-by: Ryan Nett <rnett@skymind.io>

* use c++ shape calc for Linspace

Signed-off-by: Ryan Nett <rnett@skymind.io>

* fix exception message, move most to BaseCompatOp

Signed-off-by: Ryan Nett <rnett@skymind.io>

* remove SDVariable.isEmpty

Signed-off-by: Ryan Nett <rnett@skymind.io>

* remove commented out code

Signed-off-by: Ryan Nett <rnett@skymind.io>

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/functions/DifferentialFunctionFactory.java

@@ -163,7 +163,6 @@ import org.nd4j.linalg.api.ops.impl.scatter.ScatterMin;
 import org.nd4j.linalg.api.ops.impl.scatter.ScatterMul;
 import org.nd4j.linalg.api.ops.impl.scatter.ScatterSub;
 import org.nd4j.linalg.api.ops.impl.scatter.ScatterUpdate;
-import org.nd4j.linalg.api.ops.impl.shape.Broadcast;
 import org.nd4j.linalg.api.ops.impl.shape.Concat;
 import org.nd4j.linalg.api.ops.impl.shape.ConfusionMatrix;
 import org.nd4j.linalg.api.ops.impl.shape.Cross;
@@ -1450,14 +1449,6 @@ public class DifferentialFunctionFactory {
         return new MatrixInverse(sameDiff(), in, false).outputVariable();
     }
 
-    public SDVariable broadcast(SDVariable iX, int... shape) {
-        return broadcast(iX, ArrayUtil.toLongArray(shape));
-    }
-
-    public SDVariable broadcast(SDVariable iX, long... shape) {
-        return new Broadcast(sameDiff(), iX, shape).outputVariable();
-    }
-
     public SDVariable onehot(SDVariable indices, int depth, int axis, double on, double off, DataType dataType) {
         return new OneHot(sameDiff(), indices, depth, axis, on, off, dataType).outputVariable();
     }

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/SDVariable.java

@@ -111,9 +111,6 @@ public class SDVariable implements Serializable {
         return variableType == VariableType.CONSTANT;
     }
 
-
-
-
     /**
      * Allocate and return a  new array
      * based on the vertex id and weight initialization.

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/imports/converters/ImportClassMapping.java

@@ -98,7 +98,6 @@ public class ImportClassMapping {
             org.nd4j.linalg.api.ops.impl.indexaccum.custom.ArgMax.class,
             org.nd4j.linalg.api.ops.impl.indexaccum.custom.ArgMin.class,
             org.nd4j.linalg.api.ops.impl.layers.ExternalErrorsFunction.class,
-            org.nd4j.linalg.api.ops.impl.layers.Linear.class,
             org.nd4j.linalg.api.ops.impl.layers.convolution.AvgPooling2D.class,
             org.nd4j.linalg.api.ops.impl.layers.convolution.AvgPooling3D.class,
             org.nd4j.linalg.api.ops.impl.layers.convolution.BatchNorm.class,
@@ -267,7 +266,6 @@ public class ImportClassMapping {
             org.nd4j.linalg.api.ops.impl.scatter.ScatterSub.class,
             org.nd4j.linalg.api.ops.impl.scatter.ScatterUpdate.class,
             org.nd4j.linalg.api.ops.impl.shape.ApplyGradientDescent.class,
-            org.nd4j.linalg.api.ops.impl.shape.Broadcast.class,
             org.nd4j.linalg.api.ops.impl.shape.BroadcastDynamicShape.class,
             org.nd4j.linalg.api.ops.impl.shape.Concat.class,
             org.nd4j.linalg.api.ops.impl.shape.ConfusionMatrix.class,

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/BaseModule.java

@@ -1,63 +0,0 @@
-/*******************************************************************************
- * 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
- ******************************************************************************/
-
-package org.nd4j.linalg.api.ops;
-
-import lombok.NoArgsConstructor;
-import org.nd4j.autodiff.samediff.SDVariable;
-import org.nd4j.autodiff.samediff.SameDiff;
-import org.nd4j.linalg.api.ndarray.INDArray;
-
-import java.util.ArrayList;
-import java.util.List;
-
-/**
- * Abstract base class for {@link Module}
- * that handles Dynamic ops and handles nesting.
- *
- * This is a logical unit for defining layers
- * very similar to pytorch's modules, or tensorflow's layers.
- *
- * @author Adam Gibson
- */
-@NoArgsConstructor
-public abstract class BaseModule extends DynamicCustomOp implements Module {
-    private List<Module> modules = new ArrayList<>();
-
-    public BaseModule(String opName, INDArray[] inputs, INDArray[] outputs, List<Double> tArguments, List<Integer> iArguments, List<Module> modules) {
-        super(opName, inputs, outputs, tArguments, iArguments);
-        this.modules = modules;
-    }
-
-    public BaseModule(String opName, SameDiff sameDiff, SDVariable[] args, boolean inPlace, List<Module> modules) {
-        super(opName, sameDiff, args, inPlace);
-        this.modules = modules;
-    }
-
-    @Override
-    public Module[] subModules() {
-        return modules.toArray(new Module[modules.size()]);
-    }
-
-    @Override
-    public void addModule(Module module) {
-        modules.add(module);
-    }
-
-
-
-
-}

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/Module.java

@@ -1,51 +0,0 @@
-/*******************************************************************************
- * 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
- ******************************************************************************/
-
-package org.nd4j.linalg.api.ops;
-
-import org.nd4j.autodiff.samediff.SDVariable;
-import org.nd4j.linalg.api.ndarray.INDArray;
-
-/**
- * A Module is a {@link CustomOp}
- * with varying input arguments
- * and automatically calculated outputs
- * defined at a higher level than c++.
- *
- * A Module is meant to be a way of implementing custom operations
- * in straight java/nd4j.
- */
-public interface Module extends CustomOp {
-
-    /**
-     *
-     * @param inputs
-     */
-    void exec(INDArray... inputs);
-
-
-    Module[] subModules();
-
-
-    void addModule(Module module);
-
-
-    void execSameDiff(SDVariable... input);
-
-
-
-
-}

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/NoOp.java

@@ -80,6 +80,7 @@ public class NoOp extends DynamicCustomOp {
         return 1;
     }
 
+
     @Override
     public List<LongShapeDescriptor> calculateOutputShape(){
         if(inputArguments != null && !inputArguments.isEmpty()){

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/controlflow/compat/BaseCompatOp.java

@@ -16,6 +16,7 @@
 
 package org.nd4j.linalg.api.ops.impl.controlflow.compat;
 
+import java.util.List;
 import lombok.NonNull;
 import lombok.val;
 import org.nd4j.autodiff.samediff.SDVariable;
@@ -25,6 +26,7 @@ import org.nd4j.imports.descriptors.properties.AttributeAdapter;
 import org.nd4j.imports.descriptors.properties.PropertyMapping;
 import org.nd4j.imports.graphmapper.tf.TFGraphMapper;
 import org.nd4j.linalg.api.ops.DynamicCustomOp;
+import org.nd4j.linalg.api.shape.LongShapeDescriptor;
 import org.tensorflow.framework.AttrValue;
 import org.tensorflow.framework.GraphDef;
 import org.tensorflow.framework.NodeDef;
@@ -89,4 +91,9 @@ public abstract class BaseCompatOp extends DynamicCustomOp {
     public Map<String, Map<String, AttributeAdapter>> attributeAdaptersForFunction() {
         return super.attributeAdaptersForFunction();
     }
+
+    @Override
+    public List<LongShapeDescriptor> calculateOutputShape() {
+        throw new UnsupportedOperationException("calculateOutputShape() is not supported for control flow ops.");
+    }
 }

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/controlflow/compat/Enter.java

@@ -68,15 +68,6 @@ public class Enter extends BaseCompatOp {
         return OP_NAME;
     }
 
-    @Override
-    public List<LongShapeDescriptor> calculateOutputShape() {
-        if(arg().getArr() != null) {
-            return Collections.singletonList(LongShapeDescriptor.fromShape(arg().getShape(), arg().getArr().dataType()));
-        }
-        else
-            return Collections.emptyList();
-    }
-
     @Override
     public SDVariable[] outputVariables() {
         return super.outputVariables();

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/controlflow/compat/Exit.java

@@ -56,15 +56,6 @@ public class Exit extends BaseCompatOp {
         return OP_NAME;
     }
 
-    @Override
-    public List<LongShapeDescriptor> calculateOutputShape() {
-        if(arg().getArr() != null) {
-            return Collections.singletonList(arg().getShapeDescriptor());
-        }
-        else
-            return Collections.emptyList();
-    }
-
     @Override
     public SDVariable[] outputVariables() {
         return super.outputVariables();

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/controlflow/compat/LoopCond.java

@@ -37,15 +37,6 @@ public class LoopCond extends BaseCompatOp {
         return "loop_cond";
     }
 
-    @Override
-    public List<LongShapeDescriptor> calculateOutputShape() {
-        if(arg().getArr() != null) {
-            return Collections.singletonList(LongShapeDescriptor.fromShape(arg().getShape(), arg().dataType()));
-        }
-        else
-            return Collections.emptyList();
-    }
-
     @Override
     public SDVariable[] outputVariables() {
         return super.outputVariables();

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/controlflow/compat/Merge.java

@@ -64,15 +64,6 @@ public class Merge extends BaseCompatOp {
         return 60L;
     }
 
-    @Override
-    public List<LongShapeDescriptor> calculateOutputShape() {
-        if(arg().getArr() != null) {
-            return Collections.singletonList(arg().getShapeDescriptor());
-        }
-        else
-            return Collections.emptyList();
-    }
-
     @Override
     public SDVariable[] outputVariables() {
         return super.outputVariables();

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/controlflow/compat/NextIteration.java

@@ -53,15 +53,6 @@ public class NextIteration extends BaseCompatOp {
         return OP_NAME;
     }
 
-    @Override
-    public List<LongShapeDescriptor> calculateOutputShape() {
-        if(arg().getArr() != null) {
-            return Collections.singletonList(LongShapeDescriptor.fromShape(arg().getShape(), arg().dataType()));
-        }
-        else
-            return Collections.emptyList();
-    }
-
     @Override
     public SDVariable[] outputVariables() {
         return super.outputVariables();

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/controlflow/compat/Switch.java

@@ -63,18 +63,6 @@ public class Switch extends BaseCompatOp {
         return OP_NAME;
     }
 
-    @Override
-    public List<LongShapeDescriptor> calculateOutputShape() {
-        if(args()[0].getArr() != null) {
-            val arg0 = args()[0];
-            val arr0 = arg0.getArr();
-            val dtype = arr0.dataType();
-            return Arrays.asList(LongShapeDescriptor.fromShape(arg0.getShape(), dtype),LongShapeDescriptor.fromShape(arg0.getShape(), dtype));
-        }
-        else
-            return Collections.emptyList();
-    }
-
     @Override
     public SDVariable[] outputVariables() {
         return super.outputVariables();

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/Linear.java

@@ -1,198 +0,0 @@
-/*******************************************************************************
- * 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
- ******************************************************************************/
-
-package org.nd4j.linalg.api.ops.impl.layers;
-
-import lombok.Builder;
-import lombok.NoArgsConstructor;
-import lombok.val;
-import onnx.Onnx;
-import org.nd4j.autodiff.functions.DifferentialFunction;
-import org.nd4j.autodiff.samediff.SDVariable;
-import org.nd4j.autodiff.samediff.SameDiff;
-import org.nd4j.autodiff.samediff.VariableType;
-import org.nd4j.imports.NoOpNameFoundException;
-import org.nd4j.linalg.api.blas.params.MMulTranspose;
-import org.nd4j.linalg.api.ndarray.INDArray;
-import org.nd4j.linalg.api.ops.BaseModule;
-import org.nd4j.linalg.api.ops.Module;
-import org.nd4j.linalg.api.ops.impl.reduce.Mmul;
-import org.nd4j.linalg.api.shape.LongShapeDescriptor;
-import org.nd4j.linalg.api.shape.Shape;
-import org.nd4j.linalg.factory.Nd4j;
-import org.nd4j.weightinit.WeightInitScheme;
-import org.nd4j.weightinit.impl.ZeroInitScheme;
-import org.tensorflow.framework.AttrValue;
-import org.tensorflow.framework.GraphDef;
-import org.tensorflow.framework.NodeDef;
-
-import java.util.ArrayList;
-import java.util.List;
-import java.util.Map;
-
-/**
- * Linear:
- * a * bT
- *
- * @author Adam Gibson
- */
-@NoArgsConstructor
-public class Linear extends BaseModule {
-    private DifferentialFunction forward;
-    private int nIn,nOut;
-    private WeightInitScheme weightInitScheme,biasWeightInitScheme;
-
-    @Builder(builderMethodName = "execBuilder")
-    public Linear(int nIn,
-                  int nOut,
-                  WeightInitScheme weightInitScheme,
-                  WeightInitScheme biasWeightInitScheme) {
-        super(null,
-                getParams(nIn,nOut,weightInitScheme,biasWeightInitScheme),
-                new INDArray[]{},
-                new ArrayList<Double>(), new ArrayList<Integer>(), new ArrayList<Module>());
-        this.weightInitScheme = weightInitScheme;
-        this.biasWeightInitScheme = biasWeightInitScheme;
-        this.nIn = nIn;
-        this.nOut = nOut;
-    }
-
-    @Builder(builderMethodName = "sameDiffBuilder")
-    public Linear(SameDiff sameDiff,
-                  int nIn,
-                  int nOut,
-                  WeightInitScheme weightInitScheme,
-                  WeightInitScheme biasWeightInitScheme) {
-        super(null, sameDiff, null, false, new ArrayList<Module>());
-        this.weightInitScheme = weightInitScheme;
-        this.biasWeightInitScheme = biasWeightInitScheme;
-
-        this.nIn = nIn;
-        this.nOut = nOut;
-
-    }
-
-    @Override
-    public String opName() {
-        return "linear";
-    }
-
-    @Override
-    public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String, AttrValue> attributesForNode, GraphDef graph) {
-
-    }
-
-    @Override
-    public void initFromOnnx(Onnx.NodeProto node, SameDiff initWith, Map<String, Onnx.AttributeProto> attributesForNode, Onnx.GraphProto graph) {
-
-    }
-
-    @Override
-    public List<SDVariable> doDiff(List<SDVariable> f1) {
-        execSameDiff();
-        return forward.doDiff(f1);
-    }
-
-    @Override
-    public List<LongShapeDescriptor> calculateOutputShape() {
-        List<LongShapeDescriptor> ret = new ArrayList<>();
-        ret.add(LongShapeDescriptor.fromShape(Shape.getMatrixMultiplyShape(inputArguments()[0].shape(),new long[]{nOut,nIn}), inputArguments()[1].dataType()));
-
-        ret.add(LongShapeDescriptor.fromShape(Shape.getMatrixMultiplyShape(inputArguments()[0].shape(),inputArguments()[1].transpose().shape()), inputArguments()[1].dataType()));
-        if(biasWeightInitScheme != null) {
-            ret.add(LongShapeDescriptor.fromShape(new long[]{nOut,1}, inputArguments()[1].dataType()));
-        }
-        return ret;
-    }
-
-    @Override
-    public String onnxName() {
-        throw new NoOpNameFoundException("No onnx op opName found for " +  opName());
-    }
-
-    @Override
-    public String tensorflowName() {
-        throw new NoOpNameFoundException("No tensorflow op opName found for " +  opName());
-    }
-
-
-
-    @Override
-    public void exec(INDArray... inputs) {
-        val inputArguments = inputArguments();
-        if(inputArguments == null || inputArguments.length < 1) {
-            throw new IllegalStateException("No arguments found.");
-        }
-
-        INDArray weights = inputArguments[0];
-        INDArray right = inputArguments[1];
-
-        val outputArguments = outputArguments();
-
-        if(outputArguments == null || outputArguments.length < 1) {
-            if(inputArguments.length == 1)
-              addOutputArgument(inputs[0].mmul(weights.transpose()));
-            else
-                addOutputArgument(inputs[0].mmul(weights.transpose()).addiColumnVector(right));
-
-        }
-        else {
-            inputs[0].mmul(weights.transpose(),outputArguments[0]);
-        }
-
-    }
-
-    @Override
-    public void execSameDiff(SDVariable... input) {
-        val args = args();
-        if(args == null || args.length == 0) {
-            throw new IllegalStateException("No arguments found");
-        }
-
-        if(forward == null) {
-            //bias needs to be added yet
-            if(args.length > 1) {
-                /*
-                forward =  f().add(new Mmul(sameDiff, input[0],args()[0],
-                        MMulTranspose.builder()
-                                .transposeA(false)
-                                .transposeB(true)
-                                .build()).outputVariables()[0],args()[1]);
-             */
-            } else {
-                forward = new Mmul(sameDiff, input[0],args()[0],
-                        MMulTranspose.builder().transposeA(false).transposeB(true).build());
-            }
-
-            this.outputVariables = forward.outputVariables();
-        }
-
-
-    }
-
-    private static INDArray[] getParams(int nIn,
-                                        int nOut,
-                                        WeightInitScheme paramsScheme,
-                                        WeightInitScheme biasInitScheme) {
-        if(biasInitScheme != null) {
-            return new INDArray[] {paramsScheme.create(Nd4j.defaultFloatingPointType(), new long[]{nOut,nIn}),biasInitScheme.create(Nd4j.defaultFloatingPointType(), new long[]{nOut,1})};
-        }
-        else {
-            return new INDArray[] {paramsScheme.create(Nd4j.defaultFloatingPointType(), new long[]{nOut,nIn})};
-
-        }
-    }
-}

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/loss/L2Loss.java

@@ -38,11 +38,6 @@ public class L2Loss extends DynamicCustomOp {
         super(sameDiff, new SDVariable[]{var});
     }
 
-    @Override
-    public List<LongShapeDescriptor> calculateOutputShape() {
-        return Collections.singletonList(LongShapeDescriptor.fromShape(new long[0], Nd4j.defaultFloatingPointType()));
-    }
-
     @Override
     public String opName() {
         return "l2_loss";

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/reduce/HashCode.java

@@ -47,11 +47,6 @@ public class HashCode extends DynamicCustomOp {
         this.outputArguments.add(result);
     }
 
-    @Override
-    public List<LongShapeDescriptor> calculateOutputShape() {
-        return Collections.singletonList(LongShapeDescriptor.fromShape(new long[0], DataType.LONG));
-    }
-
     @Override
     public String opName() {
         return "hashcode";

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/shape/Broadcast.java

@@ -1,78 +0,0 @@
-/*******************************************************************************
- * 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
- ******************************************************************************/
-
-package org.nd4j.linalg.api.ops.impl.shape;
-
-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.ops.DynamicCustomOp;
-import org.nd4j.linalg.api.shape.LongShapeDescriptor;
-
-import java.util.Arrays;
-import java.util.Collections;
-import java.util.List;
-
-/**
- * Broadcast function
- *
- * @author Adam Gibson
- */
-public class Broadcast extends DynamicCustomOp {
-    private long[] shape;
-    public Broadcast(SameDiff sameDiff,SDVariable iX, long[] shape) {
-        super(null,sameDiff,new SDVariable[]{iX});
-        this.shape = shape;
-    }
-
-
-    public Broadcast() {}
-
-
-    @Override
-    public List<LongShapeDescriptor> calculateOutputShape() {
-        return Arrays.asList(LongShapeDescriptor.fromShape(shape, larg().dataType()));
-    }
-
-    @Override
-    public String opName() {
-        return "broadcast";
-    }
-
-
-
-    @Override
-    public List<SDVariable> doDiff(List<SDVariable> i_v) {
-        throw new UnsupportedOperationException();
-    }
-
-    @Override
-    public String onnxName() {
-        throw new NoOpNameFoundException("No onnx op opName found for " +  opName());
-    }
-
-    @Override
-    public String tensorflowName() {
-        throw new NoOpNameFoundException("No tensorflow op opName found for " +  opName());
-    }
-
-    @Override
-    public List<DataType> calculateOutputDataTypes(List<DataType> dataTypes){
-        return Collections.singletonList(dataTypes.get(0));
-    }
-}

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/shape/Linspace.java

@@ -82,12 +82,4 @@ public class Linspace extends DynamicCustomOp {
     public List<SDVariable> doDiff(List<SDVariable> gradients){
         return Arrays.asList(f().zerosLike(arg(0)), f().zerosLike(arg(1)), f().zerosLike(arg(2)));
     }
-
-    @Override
-    public List<LongShapeDescriptor> calculateOutputShape(){
-        INDArray l = arg(2).getArr();
-        if(l == null)
-            return Collections.emptyList();
-        return Collections.singletonList(LongShapeDescriptor.fromShape(new long[]{l.getLong(0)}, dataType));
-    }
 }

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/shape/Rank.java

@@ -85,14 +85,6 @@ public class Rank extends DynamicCustomOp {
         return "Rank";
     }
 
-    @Override
-    public List<LongShapeDescriptor> calculateOutputShape() {
-        List<LongShapeDescriptor> ret = new ArrayList<>();
-        ret.add(LongShapeDescriptor.fromShape(new long[]{}, DataType.INT));
-        return ret;
-    }
-
-
     @Override
     public List<SDVariable> doDiff(List<SDVariable> i_v) {
         return Collections.singletonList(sameDiff.zerosLike(arg()));

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/shape/Unstack.java

@@ -152,17 +152,4 @@ public class Unstack extends DynamicCustomOp {
         return out;
     }
 
-    @Override
-    public List<LongShapeDescriptor> calculateOutputShape(){
-        //TEMPORARY workaround for: https://github.com/deeplearning4j/deeplearning4j/issues/7093
-        if(inputArguments.size() == 1 && inputArguments.get(0).rank() == 1){
-            INDArray arr = inputArguments.get(0);
-            Preconditions.checkState(jaxis == 0, "Can only unstack along dimension 0 for rank 1 arrays, got axis %s for array %ndShape", jaxis, arr);
-            LongShapeDescriptor lsd = LongShapeDescriptor.fromShape(new long[0], arr.dataType());
-            List<LongShapeDescriptor> out = Arrays.asList(ArrayUtil.nTimes((int)arr.length(), lsd, LongShapeDescriptor.class));
-            return out;
-        }
-        return super.calculateOutputShape();
-    }
-
 }

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/shape/tensorops/BaseTensorOp.java

@@ -32,7 +32,7 @@ import java.util.Collections;
 import java.util.List;
 import java.util.Map;
 
-public abstract  class BaseTensorOp extends DynamicCustomOp {
+public abstract class BaseTensorOp extends DynamicCustomOp {
 
     public BaseTensorOp(String name, SameDiff sameDiff, SDVariable[] args){
         super(name, sameDiff, args);
@@ -78,8 +78,7 @@ public abstract  class BaseTensorOp extends DynamicCustomOp {
 
     @Override
     public List<LongShapeDescriptor> calculateOutputShape() {
-        //Not used/not required
+        throw new UnsupportedOperationException("calculateOutputShape() is not supported for tensor ops.");
-        return Collections.emptyList();
     }
 
     @Override

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/shape/tensorops/TensorArraySize.java

@@ -41,12 +41,6 @@ public class TensorArraySize extends BaseTensorOp {
       return "tensorarraysizev3";
    }
 
-   @Override
-   public List<LongShapeDescriptor> calculateOutputShape() {
-      // output is scalar only
-      return Collections.singletonList(LongShapeDescriptor.fromShape(new long[]{}, DataType.LONG));
-   }
-
    @Override
    public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String, AttrValue> attributesForNode, GraphDef graph) {
       super.initFromTensorFlow(nodeDef, initWith, attributesForNode, graph);

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/BaseDynamicTransformOp.java

@@ -46,68 +46,6 @@ public abstract class BaseDynamicTransformOp extends DynamicCustomOp {
         super(null, inputs, outputs);
     }
 
-
-    @Override
-    public List<LongShapeDescriptor> calculateOutputShape() {
-        long[] firstArgShape;
-        long[] secondArgShape;
-        DataType dtypeZ;
-
-        if(numInputArguments() == 2){
-            return super.calculateOutputShape();    //Use c++ shape calc, which also accounts for empty broadcast cases, etc
-//            firstArgShape = inputArguments.get(0).shape();
-//            secondArgShape = inputArguments.get(1).shape();
-//            dtypeZ = Shape.pickPairwiseDataType(inputArguments.get(0).dataType(), inputArguments.get(1).dataType());
-        } else {
-            val args = args();
-            if (args.length < 2) {
-                if (args[0] == null || (inputArguments.isEmpty() && args[0].getShape() == null)) {
-                    return Collections.emptyList();
-                }
-                DataType dtypeX = !inputArguments.isEmpty() ? inputArguments.get(0).dataType() : args[0].dataType();
-                long[] shape = !inputArguments.isEmpty() ? inputArguments.get(0).shape() : args[0].getShape();
-
-                return Collections.singletonList(LongShapeDescriptor.fromShape(shape, dtypeX));
-            }
-
-            if(inputArguments.size() == 2 && inputArguments.get(0) != null && inputArguments.get(1) != null){
-                firstArgShape = inputArguments.get(0).shape();
-                secondArgShape = inputArguments.get(1).shape();
-            } else {
-                firstArgShape = args[0].getShape();
-                secondArgShape = args[1].getShape();
-            }
-            if (args[0] == null || args[0].getShape() == null) {
-                return Collections.emptyList();
-            }
-
-            if (args[1] == null || args[1].getShape() == null) {
-                return Collections.emptyList();
-            }
-
-            // detecting datatype based on both args
-            val dtypeX = inputArguments.size() > 0 ? inputArguments.get(0).dataType() : args[0].dataType();
-            val dtypeY = inputArguments.size() > 1 ? inputArguments.get(1).dataType() : args[1].dataType();
-            dtypeZ = Shape.pickPairwiseDataType(dtypeX, dtypeY);
-        }
-
-
-
-        if(Arrays.equals(firstArgShape, secondArgShape)){
-            try {
-                return Collections.singletonList(LongShapeDescriptor.fromShape(firstArgShape, dtypeZ));
-            } catch (Throwable e) {
-                throw new RuntimeException("calculateOutputShape() failed for [" + this.opName() + "]", e);
-            }
-        } else {
-            //Handle broadcast shape: [1,4]+[3,1] = [3,4]
-            Shape.assertBroadcastable(firstArgShape, secondArgShape, this.getClass());
-            val outShape = Shape.broadcastOutputShape(firstArgShape, secondArgShape);
-
-            return Collections.singletonList(LongShapeDescriptor.fromShape(outShape, dtypeZ));
-        }
-    }
-
     @Override
     public List<DataType> calculateOutputDataTypes(List<DataType> dataTypes){
         Preconditions.checkState(dataTypes != null && dataTypes.size() == 2, "Expected exactly 2 input datatypes for %s, got input %s", getClass(), dataTypes);

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/custom/EqualTo.java

@@ -71,18 +71,6 @@ public class EqualTo extends BaseDynamicTransformOp {
         return Arrays.asList(sameDiff.zerosLike(args()[0]), sameDiff.zerosLike(args()[1]));
     }
 
-    @Override
-    public List<LongShapeDescriptor> calculateOutputShape() {
-        if (args() == null)
-            return Collections.emptyList();
-
-        if (inputArguments.size() == 0)
-            return Collections.emptyList();
-
-
-        return Nd4j.getExecutioner().calculateOutputShape(this);
-    }
-
     @Override
     public List<DataType> calculateOutputDataTypes(List<DataType> dataTypes){
         Preconditions.checkState(dataTypes != null && dataTypes.size() == 2, "Expected exactly 2 input datatypes for %s, got %s", getClass(), dataTypes);

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/custom/GreaterThan.java

@@ -73,18 +73,6 @@ public class GreaterThan extends BaseDynamicTransformOp {
         return Arrays.asList(sameDiff.zerosLike(args()[0]), sameDiff.zerosLike(args()[1]));
     }
 
-    @Override
-    public List<LongShapeDescriptor> calculateOutputShape() {
-        if (args() == null)
-            return Collections.emptyList();
-
-        if (inputArguments.size() == 0)
-            return Collections.emptyList();
-
-
-        return Nd4j.getExecutioner().calculateOutputShape(this);
-    }
-
     @Override
     public List<DataType> calculateOutputDataTypes(List<DataType> dataTypes){
         Preconditions.checkState(dataTypes != null && dataTypes.size() == 2, "Expected exactly 2 input datatypes for %s, got %s", getClass(), dataTypes);

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/custom/GreaterThanOrEqual.java

@@ -76,18 +76,6 @@ public class GreaterThanOrEqual extends BaseDynamicTransformOp {
         return Arrays.asList(sameDiff.zerosLike(args()[0]), sameDiff.zerosLike(args()[1]));
     }
 
-    @Override
-    public List<LongShapeDescriptor> calculateOutputShape() {
-        if (args() == null)
-            return Collections.emptyList();
-
-        if (inputArguments.size() == 0)
-            return Collections.emptyList();
-
-
-        return Nd4j.getExecutioner().calculateOutputShape(this);
-    }
-
     @Override
     public List<DataType> calculateOutputDataTypes(List<DataType> dataTypes){
         Preconditions.checkState(dataTypes != null && dataTypes.size() == 2, "Expected exactly 2 input datatypes for %s, got %s", getClass(), dataTypes);

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/custom/LessThan.java

@@ -73,18 +73,6 @@ public class LessThan extends BaseDynamicTransformOp {
         return Arrays.asList(sameDiff.zerosLike(args()[0]), sameDiff.zerosLike(args()[1]));
     }
 
-    @Override
-    public List<LongShapeDescriptor> calculateOutputShape() {
-        if (args() == null)
-            return Collections.emptyList();
-
-        if (inputArguments.size() == 0)
-            return Collections.emptyList();
-
-
-        return Nd4j.getExecutioner().calculateOutputShape(this);
-    }
-
     @Override
     public List<DataType> calculateOutputDataTypes(List<DataType> dataTypes){
         Preconditions.checkState(dataTypes != null && dataTypes.size() == 2, "Expected exactly 2 input datatypes for %s, got %s", getClass(), dataTypes);

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/custom/LessThanOrEqual.java

@@ -71,18 +71,6 @@ public class LessThanOrEqual extends BaseDynamicTransformOp {
         return Arrays.asList(sameDiff.zerosLike(args()[0]), sameDiff.zerosLike(args()[1]));
     }
 
-    @Override
-    public List<LongShapeDescriptor> calculateOutputShape() {
-        if (args() == null)
-            return Collections.emptyList();
-
-        if (inputArguments.size() == 0)
-            return Collections.emptyList();
-
-
-        return Nd4j.getExecutioner().calculateOutputShape(this);
-    }
-
     @Override
     public List<DataType> calculateOutputDataTypes(List<DataType> dataTypes){
         Preconditions.checkState(dataTypes != null && dataTypes.size() == 2, "Expected exactly 2 input datatypes for %s, got %s", getClass(), dataTypes);

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/custom/NotEqualTo.java

@@ -72,18 +72,6 @@ public class NotEqualTo extends BaseDynamicTransformOp {
         return Arrays.asList(sameDiff.zerosLike(args()[0]), sameDiff.zerosLike(args()[1]));
     }
 
-    @Override
-    public List<LongShapeDescriptor> calculateOutputShape() {
-        if (args() == null)
-            return Collections.emptyList();
-
-        if (inputArguments.size() == 0)
-            return Collections.emptyList();
-
-
-        return Nd4j.getExecutioner().calculateOutputShape(this);
-    }
-
     @Override
     public List<DataType> calculateOutputDataTypes(List<DataType> dataTypes){
         Preconditions.checkState(dataTypes != null && dataTypes.size() == 2, "Expected exactly 2 input datatypes for %s, got %s", getClass(), dataTypes);

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/dtype/Cast.java

@@ -125,41 +125,6 @@ public class Cast extends BaseDynamicTransformOp {
         return ret;
     }
 
-    @Override
-    public List<LongShapeDescriptor> calculateOutputShape() {
-        if(inputArguments.size() > 0){
-            long[] s = inputArguments.get(0).shape();
-            LongShapeDescriptor lsd = LongShapeDescriptor.fromShape(s, typeDst);
-            if(inputArguments.get(0).isEmpty()){
-                long e = lsd.getExtras();
-                e = ArrayOptionsHelper.setOptionBit(e, ArrayType.EMPTY);
-                lsd.setExtras(e);
-            }
-            return Collections.singletonList(lsd);
-        }
-
-        if (arg() != null && (arg().getArr() != null || arg().getShape() != null)) {
-            if (arg().getArr() != null) {
-                long[] s = arg().getArr().shape();
-                LongShapeDescriptor lsd = LongShapeDescriptor.fromShape(s, typeDst);
-                if(inputArguments.size() > 0 && inputArguments.get(0) != null && inputArguments.get(0).isEmpty()){
-                    long e = lsd.getExtras();
-                    e = ArrayOptionsHelper.setOptionBit(e, ArrayType.EMPTY);
-                    lsd.setExtras(e);
-                }
-                return Collections.singletonList(lsd);
-            } else {
-                long[] s = arg().getShape();
-                if(Shape.isPlaceholderShape(s)){
-                    return Collections.emptyList();
-                }
-                return Collections.singletonList(LongShapeDescriptor.fromShape(s, typeDst));
-            }
-        }
-
-        return Collections.emptyList();
-    }
-
     @Override
     public void setValueFor(Field target, Object value) {
         //This is a hack around a property mapping issue - TF datatype DT_DOUBLE return attribute.getType() of DT_DOUBLE which doesn't make sense

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/gradient/GradientBackwardsMarker.java

@@ -77,7 +77,7 @@ public class GradientBackwardsMarker extends DynamicCustomOp {
 
     @Override
     public List<LongShapeDescriptor> calculateOutputShape() {
-        return Collections.singletonList(LongShapeDescriptor.fromShape(new long[0], DataType.FLOAT));
+        throw new UnsupportedOperationException("calculateOutputShape() is not supported for control flow ops.");
     }
 
     @Override

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/pairwise/arithmetic/bp/BaseArithmeticBackpropOp.java

@@ -46,11 +46,6 @@ public abstract class BaseArithmeticBackpropOp extends BaseDynamicTransformOp {
         throw new UnsupportedOperationException("Not supported");
     }
 
-    @Override
-    public List<LongShapeDescriptor> calculateOutputShape(){
-        return Nd4j.getExecutioner().calculateOutputShape(this);
-    }
-
     @Override
     public List<DataType> calculateOutputDataTypes(List<DataType> dataTypes){
         Preconditions.checkState(dataTypes != null && dataTypes.size() == 3, "Expected exactly 3 input datatypes for %s, got input %s", getClass(), dataTypes);

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/same/Identity.java

@@ -79,11 +79,4 @@ public class Identity extends BaseDynamicTransformOp {
         return dataTypes;
     }
 
-    @Override
-    public List<LongShapeDescriptor> calculateOutputShape() {
-        if(inputArguments == null || inputArguments.isEmpty())
-            return Collections.emptyList();
-        return Collections.singletonList(inputArguments.get(0).shapeDescriptor());
-    }
-
 }

nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/opvalidation/ShapeOpValidation.java

@@ -1619,21 +1619,6 @@ public class ShapeOpValidation extends BaseOpValidation {
         assertEquals(expected, result.eval());
     }
 
-    @Test
-    public void testBroadcast() {
-        OpValidationSuite.ignoreFailing();
-        SameDiff sd = SameDiff.create();
-        SDVariable in = sd.var("in", Nd4j.rand(3, 4));
-        SDVariable broadcast = sd.f().broadcast(in, 3, 4, 5);
-
-        INDArray out = sd.execAndEndResult();
-        assertArrayEquals(new long[]{3, 4, 5}, out.shape());
-
-        for (int i = 0; i < 5; i++) {
-            assertEquals(in.getArr(), out.get(all(), all(), point(i)));
-        }
-    }
-
 
     @Test
     public void testSlice2d() {

nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/samediff/SameDiffTests.java

@@ -53,7 +53,6 @@ import org.nd4j.linalg.api.buffer.DataType;
 import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.api.ops.DynamicCustomOp;
 import org.nd4j.linalg.api.ops.impl.layers.ExternalErrorsFunction;
-import org.nd4j.linalg.api.ops.impl.layers.Linear;
 import org.nd4j.linalg.api.ops.impl.layers.convolution.config.Conv2DConfig;
 import org.nd4j.linalg.api.ops.impl.layers.convolution.config.LocalResponseNormalizationConfig;
 import org.nd4j.linalg.api.ops.impl.reduce3.ManhattanDistance;
@@ -522,39 +521,6 @@ public class SameDiffTests extends BaseNd4jTest {
     }
 
 
-    @Test
-    public void testLinearModule() {
-        int nIn = 5;
-        Linear linear = Linear.execBuilder()
-                .nIn(nIn)
-                .nOut(4)
-                .weightInitScheme(new UniformInitScheme('f', nIn))
-                .biasWeightInitScheme(new ZeroInitScheme('f'))
-                .build();
-        linear.exec(Nd4j.linspace(1, 20, 20).reshape(4, 5));
-        assertEquals(1, linear.numOutputArguments());
-
-    }
-
-
-    @Test
-    public void testLinearModule2() {
-        Linear linear = Linear.execBuilder()
-                .nIn(3)
-                .nOut(2)
-                .weightInitScheme(new OneInitScheme('f'))
-                .biasWeightInitScheme(new ZeroInitScheme('f'))
-                .build();
-        linear.exec(Nd4j.linspace(1, 6, 6).reshape(2, 3));
-        INDArray assertion = Nd4j.create(new double[][]{
-                {6, 6},
-                {15, 15}
-        });
-        assertEquals(assertion, linear.outputArguments()[0]);
-
-    }
-
-
     @Test
     public void testDefineFunctionArrayExistence() {
         SameDiff sameDiff = SameDiff.create();
@@ -3577,4 +3543,24 @@ public class SameDiffTests extends BaseNd4jTest {
 
         assertEquals(e, mod.eval());
     }
+
+    @Test
+    public void castShapeTest1(){
+        SameDiff sd = SameDiff.create();
+        SDVariable x = sd.constant(Nd4j.createFromArray(1, 2, 3, 4));
+        SDVariable casted = x.castTo(DataType.FLOAT);
+
+        assertEquals(casted.dataType(), DataType.FLOAT);
+    }
+
+    @Test
+    @Ignore // casted shape is null
+    public void castShapeTestEmpty(){
+        SameDiff sd = SameDiff.create();
+        SDVariable x = sd.constant(Nd4j.empty(DataType.INT));
+        SDVariable casted = x.castTo(DataType.FLOAT);
+
+        assertEquals(casted.dataType(), DataType.FLOAT);
+        assertTrue(casted.getShapeDescriptor().isEmpty());
+    }
 }

nd4s/src/test/scala/org/nd4s/samediff/SameDiffTest.scala

@@ -15,7 +15,7 @@ import org.nd4j.linalg.api.blas.params.MMulTranspose
 import org.nd4j.linalg.api.buffer.DataType
 import org.nd4j.linalg.api.ndarray.INDArray
 import org.nd4j.linalg.api.ops.DynamicCustomOp
-import org.nd4j.linalg.api.ops.impl.layers.{ ExternalErrorsFunction, Linear }
+import org.nd4j.linalg.api.ops.impl.layers.ExternalErrorsFunction
 import org.nd4j.linalg.api.ops.impl.layers.convolution.config.{ Conv2DConfig, LocalResponseNormalizationConfig }
 import org.nd4j.linalg.api.ops.impl.reduce3.ManhattanDistance
 import org.nd4j.linalg.api.ops.impl.shape.tensorops.TensorArray