426 lines
18 KiB
C++
426 lines
18 KiB
C++
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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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// Created by raver119 on 16.10.2017.
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//
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#include <ops/declarable/LegacyRandomOp.h>
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#include <helpers/RandomLauncher.h>
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#include <NativeOpExecutioner.h>
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#include <NDArrayFactory.h>
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#include <Status.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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LegacyRandomOp::LegacyRandomOp() : LegacyOp::LegacyOp(1) {
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// just a no-op
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}
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LegacyRandomOp::LegacyRandomOp(int opNum) : LegacyOp::LegacyOp(1, opNum) {
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// just a no-op
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}
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LegacyOp* LegacyRandomOp::clone() {
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return new LegacyRandomOp(this->_opNum);
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}
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template <typename T>
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Nd4jStatus LegacyRandomOp::validateAndExecute_(Context &block) {
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auto input = INPUT_VARIABLE(0);
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int opNum = block.opNum() < 0 ? this->_opNum : block.opNum();
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/*
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(0, randomOps::UniformDistribution) ,\
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(1, randomOps::DropOut) ,\
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(2, randomOps::DropOutInverted) ,\
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(3, randomOps::ProbablisticMerge) ,\
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(4, randomOps::Linspace) ,\
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(5, randomOps::Choice) ,\
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(6, randomOps::GaussianDistribution) ,\
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(7, randomOps::BernoulliDistribution) ,\
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(8, randomOps::BinomialDistribution),\
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(9, randomOps::BinomialDistributionEx),\
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(10, randomOps::LogNormalDistribution) ,\
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(11, randomOps::TruncatedNormalDistribution) ,\
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(12, randomOps::AlphaDropOut)
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*/
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switch(opNum) {
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case nd4j::random::UniformDistribution: {
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// uniform distribution
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T from, to;
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if (block.width() > 2) {
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auto arg1 = INPUT_VARIABLE(1);
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auto arg2 = INPUT_VARIABLE(2);
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REQUIRE_TRUE(arg1->isScalar(), 0, "Uniform: Second argument must be scalar");
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REQUIRE_TRUE(arg2->isScalar(), 0, "Uniform: Third argument must be scalar");
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from = arg1->e<T>(0);
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to = arg2->e<T>(0);
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} else if (block.getTArguments()->size() == 2) {
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from = T_ARG(0);
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to = T_ARG(1);
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} else {
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REQUIRE_TRUE(false, 0, "Uniform requires either TArgs or 3 arguments to be present");
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}
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auto z = OUTPUT_VARIABLE(0); //NDArrayFactory::create_<T>('c', shape, block.getWorkspace());
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RandomLauncher::fillUniform(block.randomGenerator(), z, from, to);
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// FIXME:
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//OVERWRITE_RESULT(z);
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}
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break;
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case nd4j::random::DropOut: {
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auto z = OUTPUT_VARIABLE(0);
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T prob;
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if (block.width() > 1) {
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auto arg = INPUT_VARIABLE(1);
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REQUIRE_TRUE(arg->isScalar(), 0, "DropOut: Second argument must be scalar");
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prob = arg->e<T>(0);
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} else if (block.getTArguments()->size() > 0) {
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prob = T_ARG(0);
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} else {
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REQUIRE_TRUE(false, 0, "DropOut requires either TArgs or second argument to be present");
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}
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if (!block.isInplace())
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z->assign(input);
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RandomLauncher::applyDropOut(block.randomGenerator(), z, prob);
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}
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break;
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case nd4j::random::DropOutInverted: {
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auto z = OUTPUT_VARIABLE(0);
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nd4j::ops::dropout op;
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return op.execute(&block);
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}
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break;
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case nd4j::random::GaussianDistribution: {
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// gaussian distribution
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T mean, stdev;
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if (block.width() > 2) {
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auto arg1 = INPUT_VARIABLE(1);
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auto arg2 = INPUT_VARIABLE(2);
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REQUIRE_TRUE(arg1->isScalar(), 0, "Gaussian: Second argument must be scalar");
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REQUIRE_TRUE(arg2->isScalar(), 0, "Gaussian: Third argument must be scalar");
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mean = arg1->e<T>(0);
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stdev = arg2->e<T>(0);
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} else if (block.getTArguments()->size() == 2) {
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mean = T_ARG(0);
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stdev = T_ARG(1);
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} else {
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REQUIRE_TRUE(false, 0, "Gaussian requires either TArgs or 3 arguments to be present");
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}
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REQUIRE_TRUE(input->isVector(), 0, "Gaussian requires pure shape as first argument");
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std::vector<Nd4jLong> shape(input->lengthOf());
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for (int e = 0; e < input->lengthOf(); e++)
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shape[e] = input->e<Nd4jLong>(e);
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auto z = OUTPUT_VARIABLE(0);//NDArrayFactory::create_<T>('c', shape, block.getWorkspace());
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RandomLauncher::fillGaussian(block.randomGenerator(), z, mean, stdev);
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// FIXME: !!
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//OVERWRITE_RESULT(z);
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}
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break;
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case nd4j::random::BernoulliDistribution: {
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// bernoulli distribution
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T prob;
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if (block.width() > 1) {
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auto arg1 = INPUT_VARIABLE(1);
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REQUIRE_TRUE(arg1->isScalar(), 0, "Bernoulli: Second argument must be scalar");
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prob = arg1->e<T>(0);
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} else if (block.getTArguments()->size() > 0) {
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prob = T_ARG(0);
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} else {
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REQUIRE_TRUE(false, 0, "Bernoulli requires either 1 TArg or 2 arguments to be present");
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}
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REQUIRE_TRUE(input->isVector(), 0, "Bernoulli requires pure shape as first argument");
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std::vector<Nd4jLong> shape(input->lengthOf());
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for (int e = 0; e < input->lengthOf(); e++)
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shape[e] = input->e<Nd4jLong>(e);
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auto z = OUTPUT_VARIABLE(0); // NDArrayFactory::create_<T>('c', shape, block.getWorkspace());
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RandomLauncher::fillBernoulli(block.randomGenerator(), z, prob);
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// FIXME:
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//OVERWRITE_RESULT(z);
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}
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break;
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case nd4j::random::BinomialDistributionEx: {
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// BinomialEx distribution
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T prob;
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int trials;
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if (block.width() > 2) {
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auto arg1 = INPUT_VARIABLE(1);
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auto arg2 = INPUT_VARIABLE(2);
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REQUIRE_TRUE(arg1->isScalar(), 0, "Binomial: Second argument must be scalar");
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REQUIRE_TRUE(arg2->isScalar(), 0, "Binomial: Third argument must be scalar");
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trials = arg1->e<int>(0);
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prob = arg2->e<T>(0);
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} else if (block.getTArguments()->size() == 1 && block.getIArguments()->size() == 1) {
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trials = INT_ARG(0);
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prob = T_ARG(0);
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} else {
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REQUIRE_TRUE(false, 0, "Binomial requires either TArgs/IArgs or 3 arguments to be present");
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}
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REQUIRE_TRUE(input->isVector(), 0, "Binomial requires pure shape as first argument");
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std::vector<Nd4jLong> shape(input->lengthOf());
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for (int e = 0; e < input->lengthOf(); e++)
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shape[e] = input->e<Nd4jLong>(e);
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auto z = OUTPUT_VARIABLE(0);//NDArrayFactory::create_<T>('c', shape, block.getWorkspace());
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RandomLauncher::fillBinomial(block.randomGenerator(), z, trials, prob);
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// FIXME: !!!
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//OVERWRITE_RESULT(z);
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}
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break;
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case nd4j::random::LogNormalDistribution: {
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// lognorm distribution
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T mean, stdev;
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if (block.width() > 2) {
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auto arg1 = INPUT_VARIABLE(1);
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auto arg2 = INPUT_VARIABLE(2);
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REQUIRE_TRUE(arg1->isScalar(), 0, "LogNormal: Second argument must be scalar");
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REQUIRE_TRUE(arg2->isScalar(), 0, "LogNormal: Third argument must be scalar");
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mean = arg1->e<T>(0);
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stdev = arg2->e<T>(0);
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} else if (block.getTArguments()->size() == 2) {
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mean = T_ARG(0);
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stdev = T_ARG(1);
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} else {
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REQUIRE_TRUE(false, 0, "LogNormal requires either TArgs or 3 arguments to be present");
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}
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REQUIRE_TRUE(input->isVector(), 0, "LogNormal requires pure shape as first argument");
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std::vector<Nd4jLong> shape(input->lengthOf());
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for (int e = 0; e < input->lengthOf(); e++)
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shape[e] = input->e<Nd4jLong>(e);
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auto z = OUTPUT_VARIABLE(0);//NDArrayFactory::create_<T>('c', shape, block.getWorkspace());
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RandomLauncher::fillLogNormal(block.randomGenerator(), z, mean, stdev);
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// FIXME: !!
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//OVERWRITE_RESULT(z);
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}
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break;
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case nd4j::random::TruncatedNormalDistribution: {
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// truncated norm distribution
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T mean, stdev;
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if (block.width() > 2) {
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auto arg1 = INPUT_VARIABLE(1);
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auto arg2 = INPUT_VARIABLE(2);
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REQUIRE_TRUE(arg1->isScalar(), 0, "TruncatedNormal: Second argument must be scalar");
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REQUIRE_TRUE(arg2->isScalar(), 0, "TruncatedNormal: Third argument must be scalar");
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mean = arg1->e<T>(0);
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stdev = arg2->e<T>(0);
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} else if (block.getTArguments()->size() == 2) {
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mean = T_ARG(0);
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stdev = T_ARG(1);
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} else {
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REQUIRE_TRUE(false, 0, "TruncatedNormal requires either TArgs or 3 arguments to be present");
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}
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REQUIRE_TRUE(input->isVector(), 0, "TruncatedNormal requires pure shape as first argument");
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std::vector<Nd4jLong> shape(input->lengthOf());
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for (int e = 0; e < input->lengthOf(); e++)
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shape[e] = input->e<Nd4jLong>(e);
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auto z = OUTPUT_VARIABLE(0); // NDArrayFactory::create_<T>('c', shape, block.getWorkspace());
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RandomLauncher::fillTruncatedNormal(block.randomGenerator(), z, mean, stdev);
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// FIXME: !!!
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//OVERWRITE_RESULT(z);
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}
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break;
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case nd4j::random::AlphaDropOut: {
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auto z = OUTPUT_VARIABLE(0);
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T prob, a, b, pa;
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if (block.width() > 4) {
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auto arg1 = INPUT_VARIABLE(1);
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auto arg2 = INPUT_VARIABLE(2);
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auto arg3 = INPUT_VARIABLE(3);
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auto arg4 = INPUT_VARIABLE(4);
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REQUIRE_TRUE(arg1->isScalar(), 0, "AlphaDropOut: Second argument must be scalar");
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REQUIRE_TRUE(arg2->isScalar(), 0, "AlphaDropOut: Third argument must be scalar");
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REQUIRE_TRUE(arg3->isScalar(), 0, "AlphaDropOut: Fourth argument must be scalar");
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REQUIRE_TRUE(arg4->isScalar(), 0, "AlphaDropOut: Fifth argument must be scalar");
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prob = arg1->e<T>(0);
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a = arg2->e<T>(0);
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b = arg3->e<T>(0);
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pa = arg4->e<T>(0);
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} else if (block.getTArguments()->size() == 4) {
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prob = T_ARG(0);
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a = T_ARG(1);
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b = T_ARG(2);
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pa = T_ARG(3);
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} else {
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REQUIRE_TRUE(false, 0, "AlphaDropOut requires either TArgs or 5 arguments to be present");
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}
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if (!block.isInplace())
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z->assign(input);
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RandomLauncher::applyAlphaDropOut(block.randomGenerator(), z, prob, a, b, pa);
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}
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break;
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case nd4j::random::Linspace: {
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auto z = OUTPUT_VARIABLE(0);
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auto start = INPUT_VARIABLE(0);
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auto finish = INPUT_VARIABLE(1);
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auto numOfElements = INPUT_VARIABLE(2);
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z->linspace(start->e<double>(0), (finish->e<double>(0) - start->e<double>(0)) / (numOfElements->e<Nd4jLong>(0) - 1.));
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}
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break;
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default: {
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nd4j_printf("Unknown random op requested: [%i]\n", opNum);
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return ND4J_STATUS_KERNEL_FAILURE;
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}
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}
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return Status::OK();
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}
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Nd4jStatus LegacyRandomOp::validateAndExecute(Context &block) {
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// REQUIRE_TRUE(block.getRNG() != nullptr, 0, "RNG should be provided for LegacyRandomOp, but got NULL instead at node_%i", block.nodeId())
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auto z = OUTPUT_VARIABLE(0);
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BUILD_SINGLE_SELECTOR(z->dataType(), return validateAndExecute_, (block), FLOAT_TYPES);
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}
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/**
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* For transform operations, output shape always equals to input shape. With just a few exclusions, like im2col and col2im.
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* But these ops already have CustomOp implementations.
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*
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*/
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ShapeList *LegacyRandomOp::calculateOutputShape(ShapeList *inputShape, nd4j::graph::Context &block) {
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auto inShape = inputShape->at(0);
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auto xType = ArrayOptions::dataType(inShape);
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Nd4jLong *newShape;
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if (DataTypeUtils::isR(xType)) {
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COPY_SHAPE(inShape, newShape);
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return SHAPELIST(newShape);
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} else if (DataTypeUtils::isZ(xType)) {
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auto zShapeArr = INPUT_VARIABLE(0);
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auto zShapeVector = zShapeArr->asVectorT<Nd4jLong>();
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auto dtype = block.dataType();
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||
|
|
|
||
|
|
newShape = ConstantShapeHelper::getInstance()->createShapeInfo(dtype, 'c', zShapeVector);
|
||
|
|
return SHAPELIST(newShape);
|
||
|
|
} else
|
||
|
|
throw std::runtime_error("LegacyRandomOp: Unknown input data type!");
|
||
|
|
}
|
||
|
|
|
||
|
|
Nd4jStatus LegacyRandomOp::execute(Context* block) {
|
||
|
|
return DeclarableOp::execute(block);
|
||
|
|
}
|
||
|
|
|
||
|
|
nd4j::ResultSet* LegacyRandomOp::execute(nd4j::graph::RandomGenerator& rng, std::initializer_list<NDArray*> inputs, std::initializer_list<double> tArgs, std::initializer_list<int> iArgs, bool isInplace) {
|
||
|
|
std::vector<NDArray*> ins(inputs);
|
||
|
|
std::vector<double> tas(tArgs);
|
||
|
|
std::vector<int> ias(iArgs);
|
||
|
|
return this->execute(rng, ins, tas, ias, isInplace);
|
||
|
|
}
|
||
|
|
|
||
|
|
nd4j::ResultSet* LegacyRandomOp::execute(nd4j::graph::RandomGenerator& rng, std::vector<NDArray*>& inputs, std::vector<double>& tArgs, std::vector<int>& iArgs, bool isInplace) {
|
||
|
|
VariableSpace variableSpace;
|
||
|
|
auto arrayList = new ResultSet();
|
||
|
|
//ResultSet arrayList;
|
||
|
|
|
||
|
|
if (isInplace)
|
||
|
|
arrayList->setNonRemovable();
|
||
|
|
|
||
|
|
int cnt = -1;
|
||
|
|
std::vector<int> in;
|
||
|
|
for (auto v: inputs) {
|
||
|
|
if (v == nullptr)
|
||
|
|
continue;
|
||
|
|
|
||
|
|
auto var = new Variable(v);
|
||
|
|
var->markRemovable(false);
|
||
|
|
in.push_back(cnt);
|
||
|
|
variableSpace.putVariable(cnt--, var);
|
||
|
|
}
|
||
|
|
|
||
|
|
Context block(1, &variableSpace, false);
|
||
|
|
// FIX ME: implement setRng method
|
||
|
|
block.setRng(rng);
|
||
|
|
block.fillInputs(in);
|
||
|
|
block.markInplace(isInplace);
|
||
|
|
|
||
|
|
for (int e = 0; e < tArgs.size(); e++)
|
||
|
|
block.getTArguments()->emplace_back(tArgs.at(e));
|
||
|
|
|
||
|
|
|
||
|
|
for (int e = 0; e < iArgs.size(); e++)
|
||
|
|
block.getIArguments()->emplace_back(iArgs.at(e));
|
||
|
|
|
||
|
|
Nd4jStatus status = this->execute(&block);
|
||
|
|
arrayList->setStatus(status);
|
||
|
|
if (status != ND4J_STATUS_OK)
|
||
|
|
return arrayList;
|
||
|
|
|
||
|
|
|
||
|
|
for (int e = 0; e < DataTypeUtils::max<int>(); e++) {
|
||
|
|
std::pair<int,int> pair(1, e);
|
||
|
|
if (variableSpace.hasVariable(pair)) {
|
||
|
|
auto var = variableSpace.getVariable(pair);
|
||
|
|
auto arr = var->getNDArray();
|
||
|
|
if (!arr->isAttached()) {
|
||
|
|
var->markRemovable(false);
|
||
|
|
arrayList->push_back(arr);
|
||
|
|
} else {
|
||
|
|
arrayList->push_back(arr->detach());
|
||
|
|
}
|
||
|
|
} else
|
||
|
|
break;
|
||
|
|
}
|
||
|
|
|
||
|
|
return arrayList;
|
||
|
|
}
|
||
|
|
|
||
|
|
BUILD_SINGLE_TEMPLATE(template Nd4jStatus LegacyRandomOp::validateAndExecute_, (Context&), FLOAT_TYPES);
|
||
|
|
}
|
||
|
|
}
|