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#include "caffe2/opt/device.h"
#include "caffe2/core/logging.h"
#include "nomnigraph/Graph/Algorithms.h"
using namespace nom;
using namespace nom::repr;
std::vector<NNGraph::EdgeRef> getInputEdges(
const NNGraph::SubgraphType& sg,
const NNGraph& g) {
std::vector<NNGraph::EdgeRef> inputTensorEdges;
for (const auto& node : sg.getNodes()) {
NOM_REQUIRE_OR_CONT(nn::is<NeuralNetOperator>(node));
NOM_REQUIRE_OR_CONT(nn::hasInputs(node));
// Check if tensor's parents are in the sg
for (const auto& input : nn::getInputs(node)) {
NOM_REQUIRE_OR_CONT(
!nn::hasProducer(input) || !sg.hasNode(nn::getProducer(input)));
inputTensorEdges.emplace_back(g.getEdge(input, node));
}
}
return inputTensorEdges;
}
std::vector<NNGraph::EdgeRef> getOutputEdges(
const NNGraph::SubgraphType& sg,
const NNGraph& g) {
std::vector<NNGraph::EdgeRef> outputTensorEdges;
for (const auto& node : sg.getNodes()) {
NOM_REQUIRE_OR_CONT(nn::is<NeuralNetOperator>(node));
for (const auto& output : nn::getOutputs(node)) {
auto consumers = nn::getConsumers(output);
for (const auto& consumer : consumers) {
NOM_REQUIRE_OR_CONT(!sg.hasNode(consumer));
outputTensorEdges.emplace_back(g.getEdge(node, output));
}
NOM_REQUIRE_OR_CONT(consumers.size() == 0);
outputTensorEdges.emplace_back(g.getEdge(node, output));
}
}
return outputTensorEdges;
}
namespace caffe2 {
namespace opt {
void insertCopies(
NNModule* nn,
std::function<bool(NNGraph::NodeRef)> supported,
std::function<NNGraph::NodeRef(NNGraph&)> copyToFn,
std::function<NNGraph::NodeRef(NNGraph&)> copyFromFn) {
auto matches = nom::algorithm::binaryMatch(&nn->dataFlow, supported);
// We're doing a lot of inplace mutation so this is necessary.
std::set<NNGraph::EdgeRef> changedEdges;
for (const auto& match : matches) {
for (const auto& edge : getInputEdges(match, nn->dataFlow)) {
NOM_REQUIRE_OR_CONT(changedEdges.count(edge) == 0);
auto input = edge->tail();
NNGraph::NodeRef newInput = nullptr;
// First we check if there already is a copyNode that we can reuse.
auto copyNode = copyToFn(nn->dataFlow);
auto copyOp = nn::get<NeuralNetOperator>(copyNode);
// Rectify redudancies.
for (const auto& consumer : nn::getConsumers(input)) {
auto consumerOp = nn::get<NeuralNetOperator>(consumer);
// We already have a copy node, let's reuse it.
if (consumerOp->getKind() == copyOp->getKind()) {
nn->dataFlow.deleteNode(copyNode);
copyNode = consumer;
newInput = nn::getOutputs(copyNode).front();
break;
}
}
// Second, we may have found the out-edge of a previous match.
auto copyFromNode = copyFromFn(nn->dataFlow);
auto copyFromOp = nn::get<NeuralNetOperator>(copyFromNode);
do {
// NOLINTNEXTLINE(bugprone-terminating-continue)
NOM_REQUIRE_OR_CONT(nn::hasProducer(input));
const auto& producer = nn::getProducer(input);
const auto& producerOp = nn::get<NeuralNetOperator>(producer);
// NOLINTNEXTLINE(bugprone-terminating-continue)
NOM_REQUIRE_OR_CONT(producerOp->getKind() == copyFromOp->getKind());
// NOLINTNEXTLINE(bugprone-terminating-continue)
NOM_REQUIRE_OR_CONT(nn::hasInputs(producer));
auto oldInputs = nn::getInputs(producer);
// NOLINTNEXTLINE(bugprone-terminating-continue)
NOM_REQUIRE_OR_CONT(oldInputs.size() == 1);
nn->dataFlow.deleteNode(copyNode);
newInput = oldInputs.front();
} while (false);
nn->dataFlow.deleteNode(copyFromNode);
// Third, we may have to insert a copy operation
// if the above checks failed.
if (!newInput) {
auto data = nn::get<NeuralNetData>(input);
newInput = nn->dataFlow.createNode(
std::make_unique<repr::Tensor>(data->getName() + "_opencl_0"));
nn->dataFlow.createEdge(input, copyNode);
nn->dataFlow.createEdge(copyNode, newInput);
}
// Finally, swap our input node to reflect a tensor already
// on the device.
input->removeOutEdge(edge);
edge->setTail(newInput);
newInput->addOutEdge(edge);
changedEdges.insert(edge);
}
for (const auto& edge : getOutputEdges(match, nn->dataFlow)) {
NOM_REQUIRE_OR_CONT(changedEdges.count(edge) == 0);
auto output = edge->head();
auto copyNode = copyFromFn(nn->dataFlow);
auto data = nn::get<NeuralNetData>(output);
auto newOutput = nn->dataFlow.createNode(
std::make_unique<repr::Tensor>(data->getName() + "_opencl_0"));
output->removeInEdge(edge);
edge->setHead(newOutput);
changedEdges.insert(edge);
nn->dataFlow.createEdge(newOutput, copyNode);
nn->dataFlow.createEdge(copyNode, output);
// We may have broken some consumers that are actually in the match.
for (auto consumer : nn::getConsumers(output)) {
if (match.getNodes().count(consumer)) {
auto brokenEdge = nn->dataFlow.getEdge(output, consumer);
output->removeOutEdge(brokenEdge);
brokenEdge->setTail(newOutput);
newOutput->addOutEdge(brokenEdge);
}
}
}
}
}
} // namespace opt
} // namespace caffe2
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