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Fix compilation issues from codegen

master
agibsonccc 2021-03-13 11:00:11 +09:00
parent df1be4a116
commit 185e7f554f
12 changed files with 117 additions and 21 deletions
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2
contrib/codegen-tools/codegen/generate.sh
View File

@ -7,5 +7,5 @@ if test "$#" -eq 0; then
echo "Usage example 2 (all namespaces): ./generate.sh all"
else
mvn clean package -DskipTests
java -cp target/codegen-1.0.0-SNAPSHOT-shaded.jar org.nd4j.codegen.cli.CLI -dir ../../ -namespaces "$@"
java -cp target/codegen-1.0.0-SNAPSHOT-shaded.jar org.nd4j.codegen.cli.CLI -dir ../../../ -namespaces "$@"
fi

2
contrib/codegen-tools/codegen/pom.xml
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@ -102,7 +102,7 @@
<dependency>
<groupId>org.nd4j</groupId>
<artifactId>nd4j-api</artifactId>
<version>1.0.0-SNAPSHOT</version>
<version>${project.version}</version>
</dependency>
</dependencies>

18
contrib/codegen-tools/codegen/src/main/ops/org/nd4j/codegen/ops/SDBaseOps.kt
View File

@ -29,7 +29,6 @@ import org.nd4j.codegen.api.doc.DocScope
import org.nd4j.codegen.dsl.*
import org.nd4j.codegen.api.DataType.*
import org.nd4j.codegen.mixins.*
import org.nd4j.linalg.api.buffer.DataType
import java.lang.Boolean.FALSE
fun SDBaseOps() = Namespace("BaseOps"){
@ -594,7 +593,7 @@ fun SDBaseOps() = Namespace("BaseOps"){
legacy = true
Input(NUMERIC, "x") { description = "Input variable" }
Arg(BOOL, "keepDims") { description = "If true: keep the dimensions that are reduced on (as size 1). False: remove the reduction dimensions"
; defaultValue=FALSE }
; defaultValue=FALSE }
Arg(INT, "dimensions") { count = AtLeast(0); description = "Dimensions to reduce over. If dimensions are not specified, full array reduction is performed" }
Output(NUMERIC, "output"){ description = "Reduced array of rank (input rank - num dimensions)" }
Doc(Language.ANY, DocScope.ALL){
@ -773,6 +772,19 @@ fun SDBaseOps() = Namespace("BaseOps"){
useMixin(keepDimsDoc)
}
Op("split") {
javaPackage = "org.nd4j.linalg.api.ops.impl.shape"
javaOpClass = "Split"
Input(NUMERIC,"input") {description = "Input to split"}
Arg(INT, "numSplit") { description = "Number of splits" }
Arg(INT, "splitDim") { description = "The dimension to split on" }
Doc(Language.ANY, DocScope.ALL){
"""
Split a value in to a list of ndarrays.
""".trimIndent()
}
}
Op("oneHot") {
javaPackage = "org.nd4j.linalg.api.ops.impl.shape"
Input(NUMERIC, "indices") { description = "Indices - value 0 to depth-1" }
@ -780,7 +792,7 @@ fun SDBaseOps() = Namespace("BaseOps"){
Arg(INT, "axis") { description = "" }
Arg(NUMERIC, "on") { description = "" }
Arg(NUMERIC, "off") { description = "" }
Arg(DATA_TYPE, "dataType") { description = "Output data type"; defaultValue = DataType.FLOAT }
Arg(DATA_TYPE, "dataType") { description = "Output data type"; defaultValue = org.nd4j.linalg.api.buffer.DataType.FLOAT }
Output(NUMERIC, "output"){ description = "Output variable" }
Doc(Language.ANY, DocScope.ALL){

1
deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/e2e/KerasModelEndToEndTest.java
View File

@ -77,7 +77,6 @@ import static org.junit.Assert.*;
* @author dave@skymind.io, Max Pumperla
*/
@Slf4j
@Ignore
public class KerasModelEndToEndTest extends BaseDL4JTest {
private static final String GROUP_ATTR_INPUTS = "inputs";
private static final String GROUP_ATTR_OUTPUTS = "outputs";

1
deeplearning4j/pom.xml
View File

@ -197,7 +197,6 @@
<directory>deeplearning4j-modelimport</directory>
<directory>deeplearning4j-modelexport-solr</directory>
<directory>deeplearning4j-zoo</directory>
<directory>deeplearning4j-nearestneighbors-parent</directory>
</directories>
</configuration>
</plugin>

26
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDBaseOps.java
View File

@ -3799,6 +3799,32 @@ public class SDBaseOps {
return sd.updateVariableNameAndReference(out, name);
}
/**
* Split a value in to a list of ndarrays.<br>
*
* @param input Input to split (NUMERIC type)
* @param numSplit Number of splits
* @param splitDim The dimension to split on
*/
public SDVariable[] split(SDVariable input, int numSplit, int splitDim) {
SDValidation.validateNumerical("split", "input", input);
return new org.nd4j.linalg.api.ops.impl.shape.Split(sd,input, numSplit, splitDim).outputVariables();
}
/**
* Split a value in to a list of ndarrays.<br>
*
* @param names names May be null. Arrays of names for the output variables.
* @param input Input to split (NUMERIC type)
* @param numSplit Number of splits
* @param splitDim The dimension to split on
*/
public SDVariable[] split(String[] names, SDVariable input, int numSplit, int splitDim) {
SDValidation.validateNumerical("split", "input", input);
SDVariable[] out = new org.nd4j.linalg.api.ops.impl.shape.Split(sd,input, numSplit, splitDim).outputVariables();
return sd.updateVariableNamesAndReferences(out, names);
}
/**
* Squared L2 norm: see norm2(String, SDVariable, boolean, int...)<br>
*

12
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/ir/TensorNamespace.java
View File

@ -5411,14 +5411,12 @@ public final class TensorNamespace {
* Serializations can either use one of the fields above, or use this
* raw bytes field. The only exception is the string case, where one is
* required to store the content in the repeated bytes string_data field.
*
* When this raw_data field is used to store tensor value, elements MUST
* be stored in as fixed-width, little-endian order.
* Floating-point data types MUST be stored in IEEE 754 format.
* Complex64 elements must be written as two consecutive FLOAT values, real component first.
* Complex128 elements must be written as two consecutive DOUBLE values, real component first.
* Boolean type MUST be written one byte per tensor element (00000001 for true, 00000000 for false).
*
* Note: the advantage of specific field rather than the raw_data field is
* that in some cases (e.g. int data), protobuf does a better packing via
* variable length storage, and may lead to smaller binary footprint.
@ -5657,7 +5655,6 @@ public final class TensorNamespace {
/**
* <pre>
* Tensors
*
* A serialized tensor value.
* </pre>
*
@ -7013,14 +7010,12 @@ public final class TensorNamespace {
* Serializations can either use one of the fields above, or use this
* raw bytes field. The only exception is the string case, where one is
* required to store the content in the repeated bytes string_data field.
*
* When this raw_data field is used to store tensor value, elements MUST
* be stored in as fixed-width, little-endian order.
* Floating-point data types MUST be stored in IEEE 754 format.
* Complex64 elements must be written as two consecutive FLOAT values, real component first.
* Complex128 elements must be written as two consecutive DOUBLE values, real component first.
* Boolean type MUST be written one byte per tensor element (00000001 for true, 00000000 for false).
*
* Note: the advantage of specific field rather than the raw_data field is
* that in some cases (e.g. int data), protobuf does a better packing via
* variable length storage, and may lead to smaller binary footprint.
@ -7771,7 +7766,6 @@ public final class TensorNamespace {
/**
* <pre>
* Tensors
*
* A serialized tensor value.
* </pre>
*
@ -9086,14 +9080,12 @@ public final class TensorNamespace {
* Serializations can either use one of the fields above, or use this
* raw bytes field. The only exception is the string case, where one is
* required to store the content in the repeated bytes string_data field.
*
* When this raw_data field is used to store tensor value, elements MUST
* be stored in as fixed-width, little-endian order.
* Floating-point data types MUST be stored in IEEE 754 format.
* Complex64 elements must be written as two consecutive FLOAT values, real component first.
* Complex128 elements must be written as two consecutive DOUBLE values, real component first.
* Boolean type MUST be written one byte per tensor element (00000001 for true, 00000000 for false).
*
* Note: the advantage of specific field rather than the raw_data field is
* that in some cases (e.g. int data), protobuf does a better packing via
* variable length storage, and may lead to smaller binary footprint.
@ -9110,14 +9102,12 @@ public final class TensorNamespace {
* Serializations can either use one of the fields above, or use this
* raw bytes field. The only exception is the string case, where one is
* required to store the content in the repeated bytes string_data field.
*
* When this raw_data field is used to store tensor value, elements MUST
* be stored in as fixed-width, little-endian order.
* Floating-point data types MUST be stored in IEEE 754 format.
* Complex64 elements must be written as two consecutive FLOAT values, real component first.
* Complex128 elements must be written as two consecutive DOUBLE values, real component first.
* Boolean type MUST be written one byte per tensor element (00000001 for true, 00000000 for false).
*
* Note: the advantage of specific field rather than the raw_data field is
* that in some cases (e.g. int data), protobuf does a better packing via
* variable length storage, and may lead to smaller binary footprint.
@ -9140,14 +9130,12 @@ public final class TensorNamespace {
* Serializations can either use one of the fields above, or use this
* raw bytes field. The only exception is the string case, where one is
* required to store the content in the repeated bytes string_data field.
*
* When this raw_data field is used to store tensor value, elements MUST
* be stored in as fixed-width, little-endian order.
* Floating-point data types MUST be stored in IEEE 754 format.
* Complex64 elements must be written as two consecutive FLOAT values, real component first.
* Complex128 elements must be written as two consecutive DOUBLE values, real component first.
* Boolean type MUST be written one byte per tensor element (00000001 for true, 00000000 for false).
*
* Note: the advantage of specific field rather than the raw_data field is
* that in some cases (e.g. int data), protobuf does a better packing via
* variable length storage, and may lead to smaller binary footprint.

5
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/shape/Split.java
View File

@ -58,6 +58,11 @@ public class Split extends DynamicCustomOp {
super(null, new INDArray[]{in}, wrapOrNull(out), null, (List<Integer>)null);
}
public Split(INDArray input, int numSplit, int splitDim) {
super(null,input,null,Collections.emptyList(),new int[0]);
addIArgument(numSplit,splitDim);
}
@Override
public String opName() {

12
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDBase.java
View File

@ -1800,6 +1800,18 @@ public class NDBase {
return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.shape.Slice(input, begin, size))[0];
}
/**
* Split a value in to a list of ndarrays.<br>
*
* @param input Input to split (NUMERIC type)
* @param numSplit Number of splits
* @param splitDim The dimension to split on
*/
public INDArray[] split(INDArray input, int numSplit, int splitDim) {
NDValidation.validateNumerical("split", "input", input);
return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.shape.Split(input, numSplit, splitDim));
}
/**
* Squared L2 norm: see norm2(String, SDVariable, boolean, int...)<br>
*

55
nd4j/nd4j-backends/nd4j-backend-impls/nd4j-native/src/main/java/org/nd4j/nativeblas/Nd4jCpu.java
View File

@ -25870,6 +25870,61 @@ public static final double TAD_THRESHOLD = TAD_THRESHOLD();
}
// #endif
/**
* Implementation of CTC loss function
*
* Input arrays:
* 0: labels - labels NDArray {BATCH_LEN, MAX_TARGET_LEN}, type integer
* 1: logits - logits NDArray {BATCH_LEN, FRAME_LEN, CLASS_LEN }. log softmax of rnn output. It should include a blank label as well, type float
* 2: targetLabelLengths - Length of label sequence in labels NDArray {BATCH_LEN}, type integer
* 3: logitsLengths - Length of input sequence in logits NDArray {BATCH_LEN}, type integer
*
*
* Input integer arguments:
* 0: blank index - index of the blank label in logits
*
* Output array:
* 0: loss values, type float. NDArray {BATCH_LEN} negative log probabilities of loss
*/
// #if NOT_EXCLUDED(OP_ctc_loss)
@Namespace("sd::ops") public static class ctc_loss extends DeclarableCustomOp {
static { Loader.load(); }
/** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */
public ctc_loss(Pointer p) { super(p); }
/** Native array allocator. Access with {@link Pointer#position(long)}. */
public ctc_loss(long size) { super((Pointer)null); allocateArray(size); }
private native void allocateArray(long size);
@Override public ctc_loss position(long position) {
return (ctc_loss)super.position(position);
}
@Override public ctc_loss getPointer(long i) {
return new ctc_loss((Pointer)this).position(position + i);
}
public ctc_loss() { super((Pointer)null); allocate(); }
private native void allocate();
public native ShapeList calculateOutputShape(ShapeList inputShape, @ByRef Context block);
}
@Namespace("sd::ops") public static class ctc_loss_grad extends DeclarableCustomOp {
static { Loader.load(); }
/** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */
public ctc_loss_grad(Pointer p) { super(p); }
/** Native array allocator. Access with {@link Pointer#position(long)}. */
public ctc_loss_grad(long size) { super((Pointer)null); allocateArray(size); }
private native void allocateArray(long size);
@Override public ctc_loss_grad position(long position) {
return (ctc_loss_grad)super.position(position);
}
@Override public ctc_loss_grad getPointer(long i) {
return new ctc_loss_grad((Pointer)this).position(position + i);
}
public ctc_loss_grad() { super((Pointer)null); allocate(); }
private native void allocate();
public native ShapeList calculateOutputShape(ShapeList inputShape, @ByRef Context block);
}
// #endif

2
nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/opvalidation/TransformOpValidation.java
View File

@ -2123,7 +2123,7 @@ public class TransformOpValidation extends BaseOpValidation {
//TODO: Methods failed ResizeLanczos5, ResizeMitchelcubic, ResizeArea
for (ImageResizeMethod method : ImageResizeMethod.values()) {
if (method==ImageResizeMethod.ResizeLanczos5 || method==ImageResizeMethod.ResizeArea || method==ImageResizeMethod.ResizeMitchellcubic)
if (method==ImageResizeMethod.ResizeLanczos5 || method==ImageResizeMethod.ResizeArea || method == ImageResizeMethod.ResizeMitchelcubic)
{continue;}
log.info("Trying {}", method);

2
nd4j/samediff-import/samediff-import-onnx/src/test/kotlin/org/nd4j/samediff/frameworkimport/onnx/processing/GroupConvPreProcessingRule.kt
View File

@ -69,7 +69,7 @@ class GroupConvPreProcessingRule: PreImportHook {
val listOfFunctions = ArrayList<DifferentialFunction>()
val weights = sd.getVariable(op.inputsToOp[1])
//for onnx, this is the number of ops
val split = sd.split(op.name + "_split",weights,numSizeSplits.toInt(),1)
val split = sd.split(listOf(op.name + "_split").toTypedArray(),weights,numSizeSplits.toInt(),1)
val resultMap = HashMap<String,List<SDVariable>>()
/**
* NOTE: Need to look in to how to wire up inputs and outputs properly.