Merge pull request #4 from KonduitAI/revert-3-shugeo_div_no_nan
Revert "Implement divide_no_nan op."master
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2381c51480
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@ -77,8 +77,7 @@
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(27, LogicalOr) ,\
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(28, LogicalXor) ,\
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(29, LogicalNot) ,\
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(30, LogicalAnd), \
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(31, DivideNoNan)
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(30, LogicalAnd)
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// these ops return same data type as input
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#define TRANSFORM_SAME_OPS \
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@ -244,8 +243,8 @@
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(42, LstmClip), \
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(43, TruncateMod) ,\
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(44, SquaredReverseSubtract) ,\
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(45, ReversePow), \
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(46, DivideNoNan)
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(45, ReversePow)
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@ -379,8 +378,7 @@
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(34, AMaxPairwise), \
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(35, AMinPairwise) ,\
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(36, TruncateMod), \
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(37, ReplaceNans), \
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(38, DivideNoNan)
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(37, ReplaceNans)
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@ -46,7 +46,6 @@ namespace nd4j {
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static BroadcastOpsTuple Add();
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static BroadcastOpsTuple Assign();
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static BroadcastOpsTuple Divide();
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static BroadcastOpsTuple DivideNoNan();
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static BroadcastOpsTuple Multiply();
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static BroadcastOpsTuple Subtract();
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};
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@ -156,9 +156,6 @@ namespace nd4j {
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DECLARE_CUSTOM_OP(divide_bp, 3, 2, false, 0, 0);
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#endif
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#if NOT_EXCLUDED(OP_divide_no_nan)
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DECLARE_BROADCASTABLE_OP(divide_no_nan, 0, 0);
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#endif
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/**
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* This is one of auto-broadcastable operations. It accepts 2 operands, and operation is applied based on their shapes:
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* 1) if shapes are equal that's pairwise operation, result will have the same shape.
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@ -37,10 +37,6 @@ namespace nd4j {
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return custom(nd4j::scalar::Divide, nd4j::pairwise::Divide, nd4j::broadcast::Divide);
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}
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BroadcastOpsTuple BroadcastOpsTuple::DivideNoNan() {
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return custom(nd4j::scalar::DivideNoNan, nd4j::pairwise::DivideNoNan, nd4j::broadcast::DivideNoNan);
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}
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BroadcastOpsTuple BroadcastOpsTuple::Multiply() {
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return custom(nd4j::scalar::Multiply, nd4j::pairwise::Multiply, nd4j::broadcast::Multiply);
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}
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@ -360,34 +360,6 @@ namespace simdOps {
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}
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};
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template <typename X, typename Y, typename Z>
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class DivideNoNan {
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public:
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op_def static Z op(X d1, Y d2) {
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if (d2 == (Y)0) return (Z)0;
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return static_cast<Z>(d1 / d2);
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}
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op_def static Z op(X d1, Y d2, Z *params) {
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if (d2 == (Y)0) return (Z)0;
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return static_cast<Z>(d1 / d2);
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}
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op_def static Z op(X d1) {
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return static_cast<Z>(d1);
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}
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// op for MetaOps
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op_def static Z op(X d1, Y *params) {
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if (params[0] == (Y)0) return (Z)0;
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return static_cast<Z>(d1 / params[0]);
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}
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op_def static X startingValue() {
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return static_cast<X>(1);
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}
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};
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template <typename X, typename Y, typename Z>
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class SafeDivide {
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public:
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@ -1194,43 +1194,6 @@ TEST_F(DeclarableOpsTests1, BroadcastDivideTest_1) {
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delete res;
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}
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//////////////////////////////////////////////////////////////////////
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TEST_F(DeclarableOpsTests1, BroadcastDivideTest_2) {
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auto x = NDArrayFactory::create<float>('c', {3, 4, 5, 1});
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auto y = NDArrayFactory::create<float>('c', {1, 6});
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auto exp = NDArrayFactory::create<float>('c', {3, 4, 5, 6});
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x.assign(6);
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y.assign(2);
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exp.assign(3);
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nd4j::ops::divide_no_nan div;
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auto res = div.execute({&x, &y}, {}, {});
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ASSERT_EQ(res->status(), ND4J_STATUS_OK);
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ASSERT_TRUE(res->at(0)->equalsTo(exp));
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delete res;
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}
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//////////////////////////////////////////////////////////////////////
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TEST_F(DeclarableOpsTests1, BroadcastDivideTest_3) {
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auto x = NDArrayFactory::create<float>({6,6,6,6,6});
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auto y = NDArrayFactory::create<float>({3,3,0,3,3});
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auto exp = NDArrayFactory::create<float>({2, 2, 0, 2, 2});
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nd4j::ops::divide_no_nan div;
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auto res = div.execute({&x, &y}, {}, {});
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ASSERT_EQ(res->status(), ND4J_STATUS_OK);
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ASSERT_TRUE(res->at(0)->equalsTo(exp));
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delete res;
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}
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//////////////////////////////////////////////////////////////////////
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TEST_F(DeclarableOpsTests1, BroadcastReverseDivideTest_1) {
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