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.
/*
* ******************************************************************************
* *
* *
* * 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.
* *
* * 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.
* *
* * SPDX-License-Identifier: Apache-2.0
* *****************************************************************************
*/
package org.nd4j.linalg.api.ops.executioner;
import lombok.NonNull;
import org.bytedeco.javacpp.Pointer;
import org.nd4j.linalg.api.buffer.DataBuffer;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ndarray.INDArrayStatistics;
import org.nd4j.linalg.api.ops.*;
import org.nd4j.linalg.api.ops.aggregates.Aggregate;
import org.nd4j.linalg.api.ops.aggregates.Batch;
import org.nd4j.linalg.api.ops.impl.scatter.ScatterUpdate;
import org.nd4j.linalg.api.ops.impl.summarystats.Variance;
import org.nd4j.linalg.api.rng.Random;
import org.nd4j.linalg.api.shape.LongShapeDescriptor;
import org.nd4j.linalg.api.shape.TadPack;
import org.nd4j.linalg.cache.TADManager;
import org.nd4j.linalg.profiler.ProfilerConfig;
import java.util.List;
import java.util.Map;
import java.util.Properties;
public interface OpExecutioner {
// in case of adding new executioner - list it here
enum ExecutionerType {
NATIVE_CPU,
CUDA
}
enum ProfilingMode {
DISABLED,
NAN_PANIC,
INF_PANIC,
ANY_PANIC,
OPERATIONS,
METHODS,
ALL,
SCOPE_PANIC,
BANDWIDTH,
}
/**
* This method returns true if verbose mode is enabled, false otherwise
* @return
*/
boolean isVerbose();
/**
* This method returns true if debug mode is enabled, false otherwise
* @return
*/
boolean isDebug();
/**
* This method returns type for this executioner instance
* @return
*/
ExecutionerType type();
/**
* This method returns opName of the last invoked op
*
* @return
*/
String getLastOp();
/**
* Execute the operation
*
* @param op the operation to execute
*/
INDArray exec(Op op);
/**
* Execute the operation
*
* @param op the operation to execute
*/
INDArray exec(Op op, OpContext opContext);
/**Execute a TransformOp and return the result
* @param op the operation to execute
*/
TransformOp execAndReturn(TransformOp op);
/**
* Execute and return the result from an accumulation
*
* @param op the operation to execute
* @return the accumulated result
*/
ReduceOp execAndReturn(ReduceOp op);
/**
* Execute and return the result from an accumulation
*
* @param op the operation to execute
* @return the accumulated result
*/
Variance execAndReturn(Variance op);
/**Execute and return the result from an index accumulation
* @param op the index accumulation operation to execute
* @return the accumulated index
*/
IndexAccumulation execAndReturn(IndexAccumulation op);
/**Execute and return the result from a scalar op
*
* @param op the operation to execute
* @return the accumulated result
*/
ScalarOp execAndReturn(ScalarOp op);
/** Execute and return the result from a vector op
* @param op*/
BroadcastOp execAndReturn(BroadcastOp op);
/**
* Execute a reduceOp, possibly along one or more dimensions
* @param reduceOp the reduceOp
* @return the reduceOp op
*/
INDArray exec(ReduceOp reduceOp);
/**
* Execute a broadcast op, possibly along one or more dimensions
* @param broadcast the accumulation
* @return the broadcast op
*/
INDArray exec(BroadcastOp broadcast);
/**
* Execute ScalarOp
* @param broadcast
* @return
*/
INDArray exec(ScalarOp broadcast);
/**
* Execute an variance accumulation op, possibly along one or more dimensions
* @param accumulation the accumulation
* @return the accmulation op
*/
INDArray exec(Variance accumulation);
/** Execute an index accumulation along one or more dimensions
* @param indexAccum the index accumulation operation
* @return result
*/
INDArray exec(IndexAccumulation indexAccum);
/**
*
* Execute and return a result
* ndarray from the given op
* @param op the operation to execute
* @return the result from the operation
*/
Op execAndReturn(Op op);
/**
* Execute MetaOp
*
* @param op
*/
void exec(MetaOp op);
/**
* Execute GridOp
* @param op
*/
void exec(GridOp op);
/**
*
* @param op
*/
void exec(Aggregate op);
/**
* This method executes previously built batch
*
* @param batch
*/
void exec(Batch batch);
/**
* This method takes arbitrary sized list of aggregates,
* and packs them into batches
*
* @param batch
*/
void exec(List batch);
/**
* This method executes specified RandomOp using default RNG available via Nd4j.getRandom()
*
* @param op
*/
INDArray exec(RandomOp op);
/**
* This method executes specific RandomOp against specified RNG
*
* @param op
* @param rng
*/
INDArray exec(RandomOp op, Random rng);
/**
* This method return set of key/value and
* key/key/value objects,
* describing current environment
*
* @return
*/
Properties getEnvironmentInformation();
/**
* This method specifies desired profiling mode
*
* @param mode
*/
@Deprecated
void setProfilingMode(ProfilingMode mode);
/**
* This method stores specified configuration.
*
* @param config
*/
void setProfilingConfig(ProfilerConfig config);
/**
* Ths method returns current profiling
*
* @return
*/
@Deprecated
ProfilingMode getProfilingMode();
/**
* This method returns TADManager instance used for this OpExecutioner
*
* @return
*/
TADManager getTADManager();
/**
* This method prints out environmental information returned by getEnvironmentInformation() method
*/
void printEnvironmentInformation();
/**
* This method ensures all operations that supposed to be executed at this moment, are executed.
*/
void push();
/**
* This method ensures all operations that supposed to be executed at this moment, are executed and finished.
*/
void commit();
/**
* This method encodes array as thresholds, updating input array at the same time
*
* @param input
* @return encoded array is returned
*/
INDArray thresholdEncode(INDArray input, double threshold);
/**
* This method encodes array as thresholds, updating input array at the same time
*
* @param input
* @return encoded array is returned
*/
INDArray thresholdEncode(INDArray input, double threshold, Integer boundary);
/**
* This method decodes thresholds array, and puts it into target array
*
* @param encoded
* @param target
* @return target is returned
*/
INDArray thresholdDecode(INDArray encoded, INDArray target);
/**
* This method returns number of elements affected by encoder
* @param indArray
* @param target
* @param threshold
* @return
*/
long bitmapEncode(INDArray indArray, INDArray target, double threshold);
/**
*
* @param indArray
* @param threshold
* @return
*/
INDArray bitmapEncode(INDArray indArray, double threshold);
/**
*
* @param encoded
* @param target
* @return
*/
INDArray bitmapDecode(INDArray encoded, INDArray target);
/**
* This method returns names of all custom operations available in current backend, and their number of input/output arguments
* @return
*/
Map getCustomOperations();
/**
* This method executes given CustomOp
*
* PLEASE NOTE: You're responsible for input/output validation
* @param op
*/
CustomOp execAndReturn(CustomOp op);
INDArray[] exec(CustomOp op);
/**
* This method executes op with given context
* @param op
* @param context
* @return method returns output arrays defined within context
*/
INDArray[] exec(CustomOp op, OpContext context);
List calculateOutputShape(CustomOp op);
List calculateOutputShape(CustomOp op, OpContext opContext);
/**
* Equivalent to calli
*/
INDArray[] allocateOutputArrays(CustomOp op);
void enableDebugMode(boolean reallyEnable);
void enableVerboseMode(boolean reallyEnable);
boolean isExperimentalMode();
void registerGraph(long id, Pointer graph);
Map executeGraph(long id, Map map, Map reverseMap);
void forgetGraph(long id);
/**
* This method allows to set desired number of elements per thread, for performance optimization purposes.
* I.e. if array contains 2048 elements, and threshold is set to 1024, 2 threads will be used for given op execution.
*
* Default value: 1024
*
* @param threshold
*/
void setElementsThreshold(int threshold);
/**
* This method allows to set desired number of sub-arrays per thread, for performance optimization purposes.
* I.e. if matrix has shape of 64 x 128, and threshold is set to 8, each thread will be processing 8 sub-arrays (sure, if you have 8 core cpu).
* If your cpu has, say, 4, cores, only 4 threads will be spawned, and each will process 16 sub-arrays
*
* Default value: 8
* @param threshold
*/
void setTadThreshold(int threshold);
/**
* This method extracts String from Utf8Buffer
* @param buffer
* @param index
* @return
*/
String getString(DataBuffer buffer, long index);
/**
* Temporary hook
* @param op
* @param array
* @param indices
* @param updates
* @param axis
*/
@Deprecated
void scatterUpdate(ScatterUpdate.UpdateOp op, @NonNull INDArray array, @NonNull INDArray indices, @NonNull INDArray updates, int[] axis);
/**
* This method returns OpContext which can be used (and reused) to execute custom ops
* @return
*/
OpContext buildContext();
/**
*
* @param array
*/
INDArrayStatistics inspectArray(INDArray array);
/**
* This method returns shapeInfo DataBuffer
*
* @param shape
* @param stride
* @param elementWiseStride
* @param order
* @param dtype
* @return
*/
DataBuffer createShapeInfo(long[] shape, long[] stride, long elementWiseStride, char order, DataType dtype, boolean empty);
DataBuffer createShapeInfo(long[] shape, long[] stride, long elementWiseStride, char order, DataType dtype, long extra);
/**
* This method returns host/device tad buffers
*/
TadPack tadShapeInfoAndOffsets(INDArray array, int[] dimension);
/**
* This method returns constant buffer for the given jvm array
* @param values
* @return
*/
DataBuffer createConstantBuffer(long[] values, DataType desiredType);
DataBuffer createConstantBuffer(int[] values, DataType desiredType);
DataBuffer createConstantBuffer(float[] values, DataType desiredType);
DataBuffer createConstantBuffer(double[] values, DataType desiredType);
String runLightBenchmarkSuit(boolean printOut);
String runFullBenchmarkSuit(boolean printOut);
}
