diff --git a/Source/1BitSGD b/Source/1BitSGD index 26475afc2..6535b0876 160000 --- a/Source/1BitSGD +++ b/Source/1BitSGD @@ -1 +1 @@ -Subproject commit 26475afc2945db5be61494dfbb542ba058f9b862 +Subproject commit 6535b08760744c890a88e4c934352ae7fb6b6e30 diff --git a/Source/CNTKv2LibraryDll/API/CNTKLibrary.h b/Source/CNTKv2LibraryDll/API/CNTKLibrary.h index c43a93fcb..dd555aacb 100644 --- a/Source/CNTKv2LibraryDll/API/CNTKLibrary.h +++ b/Source/CNTKv2LibraryDll/API/CNTKLibrary.h @@ -3623,6 +3623,9 @@ namespace CNTK // Optionally overridable method to restore state pertaining this distributed training method from a previous checkpoint CNTK_API virtual void RestoreFromCheckpoint(const Dictionary& checkpoint) = 0; + // Return the distributed communicator used in the distributed trainer + CNTK_API virtual DistributedCommunicatorPtr GetCommunicator() = 0; + virtual ~DistributedTrainer() {} }; diff --git a/Source/CNTKv2LibraryDll/DataParallelDistributedTrainer.h b/Source/CNTKv2LibraryDll/DataParallelDistributedTrainer.h index 19cc02b1b..1382bfb42 100644 --- a/Source/CNTKv2LibraryDll/DataParallelDistributedTrainer.h +++ b/Source/CNTKv2LibraryDll/DataParallelDistributedTrainer.h @@ -30,6 +30,11 @@ namespace CNTK void RestoreFromCheckpoint(const Dictionary& checkpoint) override; private: + DistributedCommunicatorPtr GetCommunicator() override + { + return m_communicator; + } + DistributedCommunicatorPtr m_communicator; bool m_useAsyncBufferedParameterUpdate; }; diff --git a/Source/CNTKv2LibraryDll/Trainer.cpp b/Source/CNTKv2LibraryDll/Trainer.cpp index d5dd5ab5a..8b231ceb7 100644 --- a/Source/CNTKv2LibraryDll/Trainer.cpp +++ b/Source/CNTKv2LibraryDll/Trainer.cpp @@ -223,22 +223,43 @@ namespace CNTK void Trainer::SaveCheckpoint(const std::wstring& modelFilePath, bool usinglegacyModelFormat) { - m_combinedTrainingFunction->SaveModel(modelFilePath, usinglegacyModelFormat); - - vector learnerStates; - - for (const auto& learner : m_parameterLearners) + bool shouldSave = true; + if (m_distributedTrainer != nullptr) { - // TODO: add DictionaryValue(T&&) - learnerStates.push_back(DictionaryValue(learner->Serialize())); + // all workers need to sync up before saving model to avoid write-after-read hazard + // i.e. one worker is in the middle of reading a checkpoint while another overwrites + m_distributedTrainer->GetCommunicator()->Barrier(); + + // for distributed training, only save checkpoint at worker 0 + shouldSave = m_distributedTrainer->GetCommunicator()->CurrentWorker().IsMain(); } + + if (shouldSave) + { + m_combinedTrainingFunction->SaveModel(modelFilePath, usinglegacyModelFormat); + + vector learnerStates; + + for (const auto& learner : m_parameterLearners) + { + // TODO: add DictionaryValue(T&&) + learnerStates.push_back(DictionaryValue(learner->Serialize())); + } - std::wstring trainerStateCheckpointFilePath = GetTrainerStateCheckpointFilePath(modelFilePath); - auto ckpStream = GetFstream(trainerStateCheckpointFilePath, false); - // TODO: this will create an extra copy of all leaner states, - // add DictionaryValue ctor that takes an rvalue! - *ckpStream << DictionaryValue(learnerStates); - ckpStream->flush(); + std::wstring trainerStateCheckpointFilePath = GetTrainerStateCheckpointFilePath(modelFilePath); + auto ckpStream = GetFstream(trainerStateCheckpointFilePath, false); + // TODO: this will create an extra copy of all leaner states, + // add DictionaryValue ctor that takes an rvalue! + *ckpStream << DictionaryValue(learnerStates); + ckpStream->flush(); + } + + if (m_distributedTrainer != nullptr) + { + // all workers need to sync up after saving model to avoid read-after-write hazard + // i.e. one worker is in the middle of write while another tries to read + m_distributedTrainer->GetCommunicator()->Barrier(); + } } void Trainer::RestoreFromCheckpoint(const std::wstring& modelFilePath) diff --git a/Source/CNTKv2LibraryDll/dllmain.cpp b/Source/CNTKv2LibraryDll/dllmain.cpp index b0383b54d..4f3b098bf 100644 --- a/Source/CNTKv2LibraryDll/dllmain.cpp +++ b/Source/CNTKv2LibraryDll/dllmain.cpp @@ -49,7 +49,8 @@ BOOL APIENTRY DllMain(HMODULE /*hModule*/, } break; case DLL_PROCESS_DETACH: - _CrtSetReportHook2(_CRT_RPTHOOK_REMOVE, HandleDebugAssert); + // DLL_PROCESS_DETACH may have race condition with code page unload + //_CrtSetReportHook2(_CRT_RPTHOOK_REMOVE, HandleDebugAssert); break; #else case DLL_PROCESS_ATTACH: diff --git a/Tests/UnitTests/V2LibraryDistributionTests/Main.cpp b/Tests/UnitTests/V2LibraryDistributionTests/Main.cpp index 950804012..493040da8 100644 --- a/Tests/UnitTests/V2LibraryDistributionTests/Main.cpp +++ b/Tests/UnitTests/V2LibraryDistributionTests/Main.cpp @@ -57,15 +57,15 @@ void TrainSimpleDistributedFeedForwardClassifer(const DeviceDescriptor& device, std::unordered_map> inputMeansAndInvStdDevs = { { featureStreamInfo, { nullptr, nullptr } } }; ComputeInputPerDimMeansAndInvStdDevs(minibatchSource, inputMeansAndInvStdDevs); - auto nonLinearity = std::bind(Sigmoid, _1, L""); + auto nonLinearity = std::bind(Sigmoid, _1, L"Sigmoid"); auto input = InputVariable({ inputDim }, DataType::Float, L"features"); auto normalizedinput = PerDimMeanVarianceNormalize(input, inputMeansAndInvStdDevs[featureStreamInfo].first, inputMeansAndInvStdDevs[featureStreamInfo].second); - auto classifierOutput = FullyConnectedDNNLayer(normalizedinput, hiddenLayerDim, device, nonLinearity); + auto classifierOutput = FullyConnectedDNNLayer(normalizedinput, hiddenLayerDim, device, nonLinearity, std::wstring(L"FullyConnectedInput") ); for (size_t i = 1; i < numHiddenLayers; ++i) - classifierOutput = FullyConnectedDNNLayer(classifierOutput, hiddenLayerDim, device, nonLinearity); + classifierOutput = FullyConnectedDNNLayer(classifierOutput, hiddenLayerDim, device, nonLinearity, std::wstring(L"FullyConnectedHidden")); - auto outputTimesParam = Parameter(NDArrayView::RandomUniform({ numOutputClasses, hiddenLayerDim }, -0.05, 0.05, 1, device)); - auto outputBiasParam = Parameter(NDArrayView::RandomUniform({ numOutputClasses }, -0.05, 0.05, 1, device)); + auto outputTimesParam = Parameter(NDArrayView::RandomUniform({ numOutputClasses, hiddenLayerDim }, -0.05, 0.05, 1, device), L"outputTimesParam"); + auto outputBiasParam = Parameter(NDArrayView::RandomUniform({ numOutputClasses }, -0.05, 0.05, 1, device), L"outputBiasParam"); classifierOutput = Plus(outputBiasParam, Times(outputTimesParam, classifierOutput), L"classifierOutput"); auto labels = InputVariable({ numOutputClasses }, DataType::Float, L"labels"); diff --git a/Tests/UnitTests/V2LibraryTests/Common.h b/Tests/UnitTests/V2LibraryTests/Common.h index 5f19d7d98..427b57202 100644 --- a/Tests/UnitTests/V2LibraryTests/Common.h +++ b/Tests/UnitTests/V2LibraryTests/Common.h @@ -157,16 +157,16 @@ inline CNTK::FunctionPtr FullyConnectedLinearLayer(CNTK::Variable input, size_t assert(input.Shape().Rank() == 1); size_t inputDim = input.Shape()[0]; - auto timesParam = CNTK::Parameter({ outputDim, inputDim }, CNTK::DataType::Float, CNTK::GlorotUniformInitializer(), device); - auto timesFunction = CNTK::Times(timesParam, input); + auto timesParam = CNTK::Parameter({ outputDim, inputDim }, CNTK::DataType::Float, CNTK::GlorotUniformInitializer(), device, L"timesParam"); + auto timesFunction = CNTK::Times(timesParam, input, L"times"); - auto plusParam = CNTK::Parameter({ outputDim }, 0.0f, device); + auto plusParam = CNTK::Parameter({ outputDim }, 0.0f, device, L"plusParam"); return CNTK::Plus(plusParam, timesFunction, outputName); } -inline CNTK::FunctionPtr FullyConnectedDNNLayer(CNTK::Variable input, size_t outputDim, const CNTK::DeviceDescriptor& device, const std::function& nonLinearity) +inline CNTK::FunctionPtr FullyConnectedDNNLayer(CNTK::Variable input, size_t outputDim, const CNTK::DeviceDescriptor& device, const std::function& nonLinearity, const std::wstring& outputName = L"") { - return nonLinearity(FullyConnectedLinearLayer(input, outputDim, device)); + return nonLinearity(FullyConnectedLinearLayer(input, outputDim, device, outputName)); } inline CNTK::FunctionPtr FullyConnectedFeedForwardClassifierNet(CNTK::Variable input, diff --git a/Tests/UnitTests/V2LibraryTests/NDArrayViewTests.cpp b/Tests/UnitTests/V2LibraryTests/NDArrayViewTests.cpp index 8c55fbe70..17087ddab 100644 --- a/Tests/UnitTests/V2LibraryTests/NDArrayViewTests.cpp +++ b/Tests/UnitTests/V2LibraryTests/NDArrayViewTests.cpp @@ -109,7 +109,6 @@ void TestNDArrayView(size_t numAxes, const DeviceDescriptor& device) // Test readonliness auto errorMsg = "Was incorrectly able to get a writable buffer pointer from a readonly view"; - // Should not be able to get the WritableDataBuffer for a read-only view VerifyException([&aliasView]() { ElementType* aliasViewBuffer = aliasView->WritableDataBuffer(); diff --git a/bindings/python/tutorials/CNTK_103B_MNIST_FeedForwardNetwork.ipynb b/bindings/python/tutorials/CNTK_103B_MNIST_FeedForwardNetwork.ipynb index 7446b7da8..dd0a32414 100644 --- a/bindings/python/tutorials/CNTK_103B_MNIST_FeedForwardNetwork.ipynb +++ b/bindings/python/tutorials/CNTK_103B_MNIST_FeedForwardNetwork.ipynb @@ -92,7 +92,7 @@ "source": [ "### Input and Labels\n", "\n", - "In this tutorial we are generating synthetic data using `numpy` library. In real world problems, one would use a reader, that would read feature values (`features`: *age* and *tumor size*) corresponding to each obeservation (patient). Note, each observation can reside in a higher dimension space (when more features are available) and will be represented as a tensor in CNTK. More advanced tutorials shall introduce the handling of high dimensional data." + "In this tutorial we are using the MNIST data you have downloaded using CNTK_103A_MNIST_DataLoader notebook. The dataset has 60,000 training images and 10,000 test images with each image being 28 x 28 pixels. Thus the number of features is equal to 784 (= 28 x 28 pixels), 1 per pixel. The variable `num_output_classes` is set to 10 corresponding to the number of digits (0-9) in the dataset." ] }, { @@ -169,7 +169,7 @@ "source": [ "Network input and output: \n", "- **input** variable (a key CNTK concept): \n", - ">An **input** variable is a container in which we fill different observations (data point or sample, equivalent to a blue/red dot in our example) during model learning (a.k.a.training) and model evaluation (a.k.a testing). Thus, the shape of the `input_variable` must match the shape of the data that will be provided. For example, when data are images each of height 10 pixels and width 5 pixels, the input feature dimension will be two (representing image height and width). Similarly, in our examples the dimensions are age and tumor size, thus `input_dim` = 2). More on data and their dimensions to appear in separate tutorials.\n", + ">An **input** variable is a container in which we fill different observations in this case image pixels during model learning (a.k.a.training) and model evaluation (a.k.a testing). Thus, the shape of the `input_variable` must match the shape of the data that will be provided. For example, when data are images each of height 10 pixels and width 5 pixels, the input feature dimension will be 50 (representing the total number of image pixels). More on data and their dimensions to appear in separate tutorials.\n", "\n", "\n", "**Question** What is the input dimension of your chosen model? This is fundamental to our understanding of variables in a network or model representation in CNTK.\n" @@ -241,7 +241,7 @@ "source": [ "`z` will be used to represent the output of a network.\n", "\n", - "We introduced sigmoid function in CNTK 102, im this tutorial you should try different activation functions. You may choose to do this right away and take a peek into the performance later in the tutorial or run the preset tutorial and then choose to perform the suggested activity.\n", + "We introduced sigmoid function in CNTK 102, in this tutorial you should try different activation functions. You may choose to do this right away and take a peek into the performance later in the tutorial or run the preset tutorial and then choose to perform the suggested activity.\n", "\n", "\n", "** Suggested Activity **\n", @@ -434,24 +434,24 @@ "output_type": "stream", "text": [ "Minibatch: 0, Loss: 2.3835, Error: 93.75%\n", - "Minibatch: 500, Loss: 0.2551, Error: 7.81%\n", - "Minibatch: 1000, Loss: 0.0403, Error: 0.00%\n", - "Minibatch: 1500, Loss: 0.1603, Error: 4.69%\n", - "Minibatch: 2000, Loss: 0.0520, Error: 1.56%\n", - "Minibatch: 2500, Loss: 0.0643, Error: 1.56%\n", - "Minibatch: 3000, Loss: 0.0481, Error: 3.12%\n", - "Minibatch: 3500, Loss: 0.0170, Error: 0.00%\n", - "Minibatch: 4000, Loss: 0.0144, Error: 0.00%\n", - "Minibatch: 4500, Loss: 0.0413, Error: 1.56%\n", - "Minibatch: 5000, Loss: 0.0178, Error: 0.00%\n", - "Minibatch: 5500, Loss: 0.0283, Error: 1.56%\n", - "Minibatch: 6000, Loss: 0.0403, Error: 1.56%\n", - "Minibatch: 6500, Loss: 0.0306, Error: 1.56%\n", - "Minibatch: 7000, Loss: 0.0017, Error: 0.00%\n", - "Minibatch: 7500, Loss: 0.0012, Error: 0.00%\n", + "Minibatch: 500, Loss: 0.2581, Error: 6.25%\n", + "Minibatch: 1000, Loss: 0.0369, Error: 0.00%\n", + "Minibatch: 1500, Loss: 0.1706, Error: 6.25%\n", + "Minibatch: 2000, Loss: 0.0523, Error: 1.56%\n", + "Minibatch: 2500, Loss: 0.0672, Error: 1.56%\n", + "Minibatch: 3000, Loss: 0.0417, Error: 1.56%\n", + "Minibatch: 3500, Loss: 0.0226, Error: 0.00%\n", + "Minibatch: 4000, Loss: 0.0117, Error: 0.00%\n", + "Minibatch: 4500, Loss: 0.0493, Error: 1.56%\n", + "Minibatch: 5000, Loss: 0.0186, Error: 0.00%\n", + "Minibatch: 5500, Loss: 0.0302, Error: 1.56%\n", + "Minibatch: 6000, Loss: 0.0122, Error: 0.00%\n", + "Minibatch: 6500, Loss: 0.0358, Error: 1.56%\n", + "Minibatch: 7000, Loss: 0.0020, Error: 0.00%\n", + "Minibatch: 7500, Loss: 0.0018, Error: 0.00%\n", "Minibatch: 8000, Loss: 0.0010, Error: 0.00%\n", "Minibatch: 8500, Loss: 0.0006, Error: 0.00%\n", - "Minibatch: 9000, Loss: 0.0043, Error: 0.00%\n" + "Minibatch: 9000, Loss: 0.0034, Error: 0.00%\n" ] } ], @@ -494,9 +494,9 @@ "outputs": [ { "data": { - "image/png": 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ENDqC5vGBD6SxJkss0ehIFhg+HK69FlZfPXXnzZ7d6IjMzPoOJycNMHcubLop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\n", 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OrPAOOihdQhk6tNaRfGDoULj8cvj8552cmJn1tELclXigiYCddoJNNoGvfS1N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\nn5MuX70J/B5YsWyb0aRfyXN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Do2pVo0fDU0/B22/3diRmZmZ9Vk3JSURMB54CFmhOOC1u9Oj011coNjMza5p6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\nzMzMWoqTEzMzM2sp/w+kiJAQ9+6xngAAAABJRU5ErkJggg==\n", 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M4eyzz+7sMOrG9WleXaku4Po0s65UF+ha9Xnuuef4xje+AR10JefOHtgKfHKht3VZMJ3w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\nFsg11ii/j9mzF3k+FqrPZZf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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -595,7 +595,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Average test error: 1.67%\n" + "Average test error: 1.66%\n" ] } ], @@ -726,7 +726,7 @@ "data": { "image/png": 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u4QUr3nXeYDO3u7H3mDVd7XhhSm5hiRgukvCI8SaqWAgYIWIJm/0+TvB5Io4a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\nWqXlcpkWi0VaLBZXe8lxCkK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