Merge pull request #5 from KonduitAI/shugeo_divnonan_full
Added implementation for divide_no_nan op and tests.master
raver119
GitHub
commit
b8f2a83a5a
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@ -77,7 +77,8 @@
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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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(30, LogicalAnd), \
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(31, DivideNoNan)
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// these ops return same data type as input
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#define TRANSFORM_SAME_OPS \
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@ -243,8 +244,8 @@
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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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(45, ReversePow), \
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(46, DivideNoNan)
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@ -378,7 +379,8 @@
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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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(37, ReplaceNans), \
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(38, DivideNoNan)
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@ -46,6 +46,7 @@ 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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@ -0,0 +1,57 @@
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/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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//
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// @author George A. Shulinok <sgazeos@gmail.com>
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//
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#include <op_boilerplate.h>
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#if NOT_EXCLUDED(OP_divide_no_nan)
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#include <ops/declarable/generic/helpers/BroadcastHelper.h>
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#include <ops/declarable/CustomOperations.h>
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namespace nd4j {
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namespace ops {
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BROADCASTABLE_OP_IMPL(divide_no_nan, 0, 0) {
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auto x = INPUT_VARIABLE(0);
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auto y = INPUT_VARIABLE(1);
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auto z = OUTPUT_VARIABLE(0);
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BROADCAST_CHECK_EMPTY(x,y,z);
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REQUIRE_TRUE(!y->isB(), 0, "DIVIDE_NO_NAN OP: you can't divide by bool array!");
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auto tZ = BroadcastHelper::broadcastApply(BroadcastOpsTuple::DivideNoNan(), x, y, z);
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if (tZ == nullptr)
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return ND4J_STATUS_KERNEL_FAILURE;
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else if (tZ != z) {
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OVERWRITE_RESULT(tZ);
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}
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return Status::OK();
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}
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DECLARE_SYN(Div, divide);
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DECLARE_TYPES(divide_no_nan) {
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getOpDescriptor()
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->setAllowedInputTypes(0, DataType::ANY)
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->setAllowedInputTypes(1, DataType::ANY)
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->setAllowedOutputTypes(0, DataType::INHERIT);
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}
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}
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}
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#endif
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@ -156,6 +156,18 @@ 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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/**
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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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* 2) if shape X is scalar and shape Y is array - result will have shape equal to Y.
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* 3) if shape X is array and shape Y is scalar - result will have shape equal to X.
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* 4) if shape X and Y are both arrays, but shapes aren't equal - result shape will be broadcast result.
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*
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* This operation returns Z = Divide(X, Y) with exception, 0 if Y = 0
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*/
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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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@ -37,6 +37,10 @@ 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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@ -360,6 +360,34 @@ 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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@ -1194,6 +1194,41 @@ 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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