From 5769657d611f6763d8d4b047700c75b8ed0dd066 Mon Sep 17 00:00:00 2001
From: Franck Dary <franck.dary@lis-lab.fr>
Date: Mon, 1 Mar 2021 14:49:45 +0100
Subject: [PATCH] Trying to load gpu tansor onto cpu mem
---
decoder/src/Beam.cpp | 2 +-
reading_machine/src/Classifier.cpp | 12 +++---------
torch_modules/src/NumericColumnModule.cpp | 2 +-
trainer/src/Trainer.cpp | 4 ++--
4 files changed, 7 insertions(+), 13 deletions(-)
diff --git a/decoder/src/Beam.cpp b/decoder/src/Beam.cpp
index 32e7360..1b0aafa 100644
--- a/decoder/src/Beam.cpp
+++ b/decoder/src/Beam.cpp
@@ -48,7 +48,7 @@ void Beam::update(ReadingMachine & machine, bool debug)
elements[index].config.setAppliableTransitions(appliableTransitions);
auto context = classifier.getNN()->extractContext(elements[index].config).back();
- auto neuralInput = torch::from_blob(context.data(), {(long)context.size()}, torch::kLong).clone().to(NeuralNetworkImpl::device);
+ auto neuralInput = torch::from_blob(context.data(), {(long)context.size()}, torch::TensorOptions(torch::kLong).device(NeuralNetworkImpl::device));
auto prediction = classifier.isRegression() ? classifier.getNN()(neuralInput).squeeze(0) : torch::softmax(classifier.getNN()(neuralInput).squeeze(0), 0);
float entropy = classifier.isRegression() ? 0.0 : NeuralNetworkImpl::entropy(prediction);
diff --git a/reading_machine/src/Classifier.cpp b/reading_machine/src/Classifier.cpp
index aa11dc9..b5e929b 100644
--- a/reading_machine/src/Classifier.cpp
+++ b/reading_machine/src/Classifier.cpp
@@ -81,17 +81,15 @@ Classifier::Classifier(const std::string & name, std::filesystem::path path, std
getNN()->loadDicts(path);
getNN()->registerEmbeddings();
- getNN()->to(torch::kCPU);
-
if (!train)
{
- torch::load(getNN(), getBestFilename());
+ torch::load(getNN(), getBestFilename(), NeuralNetworkImpl::device);
getNN()->registerEmbeddings();
getNN()->to(NeuralNetworkImpl::device);
}
else if (std::filesystem::exists(getLastFilename()))
{
- torch::load(getNN(), getLastFilename());
+ torch::load(getNN(), getLastFilename(), NeuralNetworkImpl::device);
getNN()->to(NeuralNetworkImpl::device);
resetOptimizer();
loadOptimizer();
@@ -185,7 +183,7 @@ void Classifier::loadOptimizer()
{
auto optimizerPath = std::filesystem::path(fmt::format("{}/{}_optimizer.pt", path.string(), name));
if (std::filesystem::exists(optimizerPath))
- torch::load(*optimizer, optimizerPath);
+ torch::load(*optimizer, optimizerPath, NeuralNetworkImpl::device);
}
void Classifier::saveOptimizer()
@@ -273,16 +271,12 @@ std::string Classifier::getLastFilename() const
void Classifier::saveBest()
{
- getNN()->to(torch::kCPU);
torch::save(getNN(), getBestFilename());
- getNN()->to(NeuralNetworkImpl::device);
}
void Classifier::saveLast()
{
- getNN()->to(torch::kCPU);
torch::save(getNN(), getLastFilename());
- getNN()->to(NeuralNetworkImpl::device);
saveOptimizer();
}
diff --git a/torch_modules/src/NumericColumnModule.cpp b/torch_modules/src/NumericColumnModule.cpp
index 4899d59..15d2b19 100644
--- a/torch_modules/src/NumericColumnModule.cpp
+++ b/torch_modules/src/NumericColumnModule.cpp
@@ -46,7 +46,7 @@ NumericColumnModuleImpl::NumericColumnModuleImpl(std::string name, const std::st
torch::Tensor NumericColumnModuleImpl::forward(torch::Tensor input)
{
auto context = input.narrow(1, firstInputIndex, getInputSize());
- auto values = torch::from_blob(context.data_ptr(), context.sizes(), context.strides(), torch::TensorOptions(torch::kDouble).requires_grad(false).device(NeuralNetworkImpl::device)).to(torch::kFloat).unsqueeze(-1).clone();
+ auto values = torch::from_blob(context.data_ptr(), context.sizes(), context.strides(), torch::TensorOptions(torch::kDouble).requires_grad(false).device(NeuralNetworkImpl::device)).to(torch::kFloat).unsqueeze(-1);
return myModule->forward(values).reshape({input.size(0), -1});
}
diff --git a/trainer/src/Trainer.cpp b/trainer/src/Trainer.cpp
index d315742..a85fff0 100644
--- a/trainer/src/Trainer.cpp
+++ b/trainer/src/Trainer.cpp
@@ -93,7 +93,7 @@ void Trainer::extractExamples(std::vector<SubConfig> & configs, bool debug, std:
if (dynamicOracle and util::choiceWithProbability(1.0) and config.getState() != "tokenizer" and config.getState() != "segmenter")
{
auto & classifier = *machine.getClassifier(config.getState());
- auto neuralInput = torch::from_blob(context.data(), {(long)context.size()}, torch::kLong).clone().to(NeuralNetworkImpl::device);
+ auto neuralInput = torch::from_blob(context.data(), {(long)context.size()}, torch::TensorOptions(torch::kLong).device(NeuralNetworkImpl::device));
auto prediction = classifier.isRegression() ? classifier.getNN()(neuralInput).squeeze(0) : torch::softmax(classifier.getNN()(neuralInput).squeeze(0), 0);
entropy = NeuralNetworkImpl::entropy(prediction);
@@ -291,7 +291,7 @@ void Trainer::Examples::saveIfNeeded(const std::string & state, std::filesystem:
int nbClasses = classes[0].size(0);
- auto tensorToSave = torch::cat({torch::stack(contexts), torch::stack(classes)}, 1).to(torch::kCPU);
+ auto tensorToSave = torch::cat({torch::stack(contexts), torch::stack(classes)}, 1);
auto filename = fmt::format("{}-{}_{}-{}.{}.{}.tensor", state, nbClasses, lastSavedIndex, currentExampleIndex-1, epoch, dynamicOracle);
torch::save(tensorToSave, dir/filename);
lastSavedIndex = currentExampleIndex;
--
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