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Revert "Implement divide_no_nan op."

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raver119 2019-10-03 20:25:52 +03:00 committed by GitHub
parent bbfa41cbc8
commit cff26f13c5
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6 changed files with 4 additions and 79 deletions
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10
libnd4j/include/loops/legacy_ops.h
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@ -77,8 +77,7 @@
(27, LogicalOr) ,\
(28, LogicalXor) ,\
(29, LogicalNot) ,\
(30, LogicalAnd), \
(31, DivideNoNan)
(30, LogicalAnd)
// these ops return same data type as input
#define TRANSFORM_SAME_OPS \
@ -244,8 +243,8 @@
(42, LstmClip), \
(43, TruncateMod) ,\
(44, SquaredReverseSubtract) ,\
(45, ReversePow), \
(46, DivideNoNan)
(45, ReversePow)
@ -379,8 +378,7 @@
(34, AMaxPairwise), \
(35, AMinPairwise) ,\
(36, TruncateMod), \
(37, ReplaceNans), \
(38, DivideNoNan)
(37, ReplaceNans)

1
libnd4j/include/ops/BroadcastOpsTuple.h
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@ -46,7 +46,6 @@ namespace nd4j {
static BroadcastOpsTuple Add();
static BroadcastOpsTuple Assign();
static BroadcastOpsTuple Divide();
static BroadcastOpsTuple DivideNoNan();
static BroadcastOpsTuple Multiply();
static BroadcastOpsTuple Subtract();
};

3
libnd4j/include/ops/declarable/headers/broadcastable.h
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@ -156,9 +156,6 @@ namespace nd4j {
DECLARE_CUSTOM_OP(divide_bp, 3, 2, false, 0, 0);
#endif
#if NOT_EXCLUDED(OP_divide_no_nan)
DECLARE_BROADCASTABLE_OP(divide_no_nan, 0, 0);
#endif
/**
* This is one of auto-broadcastable operations. It accepts 2 operands, and operation is applied based on their shapes:
* 1) if shapes are equal that's pairwise operation, result will have the same shape.

4
libnd4j/include/ops/impl/BroadcastOpsTuple.cpp
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@ -37,10 +37,6 @@ namespace nd4j {
return custom(nd4j::scalar::Divide, nd4j::pairwise::Divide, nd4j::broadcast::Divide);
}
BroadcastOpsTuple BroadcastOpsTuple::DivideNoNan() {
return custom(nd4j::scalar::DivideNoNan, nd4j::pairwise::DivideNoNan, nd4j::broadcast::DivideNoNan);
}
BroadcastOpsTuple BroadcastOpsTuple::Multiply() {
return custom(nd4j::scalar::Multiply, nd4j::pairwise::Multiply, nd4j::broadcast::Multiply);
}

28
libnd4j/include/ops/ops.h
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@ -360,34 +360,6 @@ namespace simdOps {
}
};
template <typename X, typename Y, typename Z>
class DivideNoNan {
public:
op_def static Z op(X d1, Y d2) {
if (d2 == (Y)0) return (Z)0;
return static_cast<Z>(d1 / d2);
}
op_def static Z op(X d1, Y d2, Z *params) {
if (d2 == (Y)0) return (Z)0;
return static_cast<Z>(d1 / d2);
}
op_def static Z op(X d1) {
return static_cast<Z>(d1);
}
// op for MetaOps
op_def static Z op(X d1, Y *params) {
if (params[0] == (Y)0) return (Z)0;
return static_cast<Z>(d1 / params[0]);
}
op_def static X startingValue() {
return static_cast<X>(1);
}
};
template <typename X, typename Y, typename Z>
class SafeDivide {
public:

37
libnd4j/tests_cpu/layers_tests/DeclarableOpsTests1.cpp
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@ -1194,43 +1194,6 @@ TEST_F(DeclarableOpsTests1, BroadcastDivideTest_1) {
delete res;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, BroadcastDivideTest_2) {
auto x = NDArrayFactory::create<float>('c', {3, 4, 5, 1});
auto y = NDArrayFactory::create<float>('c', {1, 6});
auto exp = NDArrayFactory::create<float>('c', {3, 4, 5, 6});
x.assign(6);
y.assign(2);
exp.assign(3);
nd4j::ops::divide_no_nan div;
auto res = div.execute({&x, &y}, {}, {});
ASSERT_EQ(res->status(), ND4J_STATUS_OK);
ASSERT_TRUE(res->at(0)->equalsTo(exp));
delete res;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, BroadcastDivideTest_3) {
auto x = NDArrayFactory::create<float>({6,6,6,6,6});
auto y = NDArrayFactory::create<float>({3,3,0,3,3});
auto exp = NDArrayFactory::create<float>({2, 2, 0, 2, 2});
nd4j::ops::divide_no_nan div;
auto res = div.execute({&x, &y}, {}, {});
ASSERT_EQ(res->status(), ND4J_STATUS_OK);
ASSERT_TRUE(res->at(0)->equalsTo(exp));
delete res;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, BroadcastReverseDivideTest_1) {