Null arrays fix (#208)

* don't skip null arrays

Signed-off-by: raver119 <raver119@gmail.com>

* one test tweak

Signed-off-by: raver119 <raver119@gmail.com>

libnd4j/include/ops/declarable/DeclarableOp.h

@@ -171,7 +171,7 @@ namespace nd4j {
 
             Nd4jStatus execute(const std::vector<NDArray*> &inputs, const std::vector<NDArray*> &outputs);
 
-            template <class T>
+            template <class T, typename = std::enable_if<DataTypeUtils::scalarTypesForExecution<T>::value>>
             Nd4jStatus execute(const std::vector<NDArray*> &inputs, const std::vector<NDArray*> &outputs, std::initializer_list<T> tArgs);
 
             Nd4jStatus execute(const std::vector<NDArray*> &inputs, const std::vector<NDArray*> &outputs, const std::vector<double> &tArgs, const std::vector<Nd4jLong> &iArgs, const std::vector<bool> &bArgs = std::vector<bool>(), const std::vector<nd4j::DataType> &dArgs = std::vector<nd4j::DataType>(), bool isInplace = false);
@@ -179,7 +179,7 @@ namespace nd4j {
 
             nd4j::ResultSet* evaluate(const std::vector<NDArray*> &inputs);
 
-            template <class T>
+            template <class T, typename = std::enable_if<DataTypeUtils::scalarTypesForExecution<T>::value>>
             nd4j::ResultSet* evaluate(const std::vector<NDArray*> &inputs, std::initializer_list<T> args);
 
             nd4j::ResultSet* evaluate(const std::vector<NDArray*> &inputs, const std::vector<double> &tArgs, const std::vector<Nd4jLong> &iArgs, const std::vector<bool> &bArgs = std::vector<bool>(), const std::vector<nd4j::DataType> &dArgs = std::vector<nd4j::DataType>(), bool isInplace = false);

libnd4j/include/ops/declarable/generic/nn/convo/conv1d.cpp

@@ -222,7 +222,7 @@ CUSTOM_OP_IMPL(conv1d_bp, 3, 2, false, 0, 5) {
     auto gradWReshaped   = gradW  ->reshape(gradW->ordering(),  {1, weights->sizeAt(0), weights->sizeAt(1), weights->sizeAt(2)});    // [kW, iC, oC] -> [1, kW, iC, oC]
 
     nd4j::ops::conv2d_bp conv2dBP;
-    const Nd4jStatus status = conv2dBP.execute({&inputReshaped, &weightsReshaped, bias, &gradOReshaped}, {&gradIReshaped, &gradWReshaped, gradB}, {}, {1,kW,  1,sW,  0,pW,  1,dW,  paddingMode,  !isNCW}, {});
+    auto status = conv2dBP.execute({&inputReshaped, &weightsReshaped, bias, &gradOReshaped}, {&gradIReshaped, &gradWReshaped, gradB}, {}, {1,kW,  1,sW,  0,pW,  1,dW,  paddingMode,  !isNCW}, {});
     if (status != ND4J_STATUS_OK)
         return status;
 

libnd4j/include/ops/declarable/impl/DeclarableOp.cpp

@@ -165,10 +165,7 @@ namespace nd4j {
                 // we build list of input shapes
                 if (ctx.isFastPath()) {
                     for (const auto p:ctx.fastpath_in()) {
-                        if (p == nullptr)
+                        inSha.push_back(p == nullptr ? nullptr : p->getShapeInfo());
-                            continue;
-
-                        inSha.push_back(p->getShapeInfo());
                     }
                 } else {
                     for (auto p: *ctx.inputs()) {
@@ -184,6 +181,11 @@ namespace nd4j {
                     }
                 }
 
+                // if we override shape function, we'll return size of fastPath
+                if (ctx.isFastPath() && ctx.shapeFunctionOverride()) {
+                    return (int) ctx.fastpath_out().size();
+                }
+
                 // optionally saving input time
                 if (Environment::getInstance()->isProfiling() && node != nullptr) {
                     inputEnd = std::chrono::system_clock::now();
@@ -193,11 +195,6 @@ namespace nd4j {
                     shapeStart = std::chrono::system_clock::now();
                 }
 
-                // if we override shape function, we'll return size of fastPath
-                if (ctx.isFastPath() && ctx.shapeFunctionOverride()) {
-                    return (int) ctx.fastpath_out().size();
-                }
-
                 auto outSha = this->calculateOutputShape(&inSha, ctx);
                 results = outSha->size();
 
@@ -870,16 +867,10 @@ namespace nd4j {
             Context ctx(1);
 
             for (int e = 0; e < inputs.size(); e++) {
-                if (inputs[e] == nullptr)
-                    break;
-
                 ctx.setInputArray(e, inputs[e]);
             }
 
             for (int e = 0; e < outputs.size(); e++) {
-                if (outputs[e] == nullptr)
-                    break;
-
                 ctx.setOutputArray(e, outputs[e]);
             }
 

libnd4j/tests_cpu/layers_tests/DeclarableOpsTests19.cpp

@@ -37,4 +37,33 @@ public:
         printf("\n");
         fflush(stdout);
     }
-};
+};
+
+TEST_F(DeclarableOpsTests19, test_conv1d_bp_1) {
+    /*
+    DynamicCustomOp op = DynamicCustomOp.builder("conv1d_bp")
+            .addInputs(
+                    Nd4j.create(DataType.FLOAT, 2,2,12),
+                    Nd4j.create(DataType.FLOAT, 3,2,3),
+                    Nd4j.create(DataType.FLOAT, 2,3,6)
+            )
+            .addOutputs(
+                    Nd4j.create(DataType.FLOAT, 2,2,12),
+                    Nd4j.create(DataType.FLOAT, 3,2,3))
+            .addIntegerArguments(3,2,0,1,2,0)
+            .build();
+
+    Nd4j.exec(op);
+     */
+
+    auto t = NDArrayFactory::create<float>('c', {2, 2, 12});
+    auto u = NDArrayFactory::create<float>('c', {3, 2, 3});
+    auto v = NDArrayFactory::create<float>('c', {2, 3, 6});
+
+    nd4j::ops::conv1d_bp op;
+    auto result = op.evaluate({&t, &u, &v}, {3, 2, 0, 1, 2,0});
+    ASSERT_EQ(Status::OK(), result->status());
+
+
+    delete result;
+}

nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/rng/RandomTests.java

@@ -810,9 +810,11 @@ public class RandomTests extends BaseNd4jTest {
             threads[x].start();
         }
 
-        for (int x = 0; x < threads.length; x++) {
+        // we want all threads finished before comparing arrays
+        for (int x = 0; x < threads.length; x++)
             threads[x].join();
 
+        for (int x = 0; x < threads.length; x++) {
             assertNotEquals(null, list.get(x));
 
             if (x > 0) {