Python utilities used by Deep Procedural Intelligence
Project description
DPU Utilities
This contains a set of utilities used across projects of the DPU team.
Python
Stored in the python subdirectory, published as the dpu-utils package.
Installation
pip install dpu-utils
Overview of Utilities:
Generic Utilities:
dpu_utils.utils.RichPatha convenient way of using both paths and Azure paths in your code.dpu_utils.utils.*Iteratoriterator wrappers that can parallelize their iteration in other threads/processes.dpu_utils.utils.{load,save}_json[l]_gzconvenience methods for loading .json[l].gz from the filesystem.dpu_utils.utils.git_tag_runthat tags the current working directory git the state of the code.dpu_utils.utils.run_and_debugwhen an exception happens, start a debug session. Usually a wrapper of__main__.dpu_utils.utils.ChunkWriterthat helps writing chunks to the output.
TensorFlow Utilities:
dpu_utils.tfutils.GradRatioLoggingOptimizera wrapper around optimizers that logs the ratios of grad norms to parameter norms.dpu_utils.tfutils.unsorted_segment_logsumexpdpu_utils.tfutils.unsorted_segment_log_softmaxdpu_utils.tfutils.TFVariableSaversave TF variables in an object that can be pickled.
General Machine Learning Utilities:
dpu_utils.mlutils.CharTensorizerfor character-level tensorization.dpu_utils.mlutils.Vocabularya str to int vocabulary for machine learning models
TensorFlow Models:
dpu_utils.tfmodels.SparseGGNNa sparse GGNN implementation.dpu_utils.tfmodels.AsyncGGNNan asynchronous GGNN implementation.
Code-related Utilities
dpu_utils.codeutils.split_identifier_into_parts()split identifiers into subtokens on CamelCase and snake_case.dpu_utils.codeutils.{Lattice, CSharpLattice}represent lattices and some useful operations in Python.dpu_utils.codeutils.get_language_keywords()that retrieves the keyword tokens for many programming languages.dpu_utils.codeutils.deduplication.DuplicateDetectorthat detects duplicates in codebases.
Command-line tools
Approximate Duplicate Code Detection
You can use the deduplicationcli command to detect duplicates in pre-processed source code, by invoking
deduplicationcli DATA_PATH OUT_JSON
where DATA_PATH is a file containing tokenized .jsonl.gz files and OUT_JSON is the target output file.
For more options look at --help.
An exact (but usually slower) version of this can be found here along with code to tokenize Java, C#, Python and JavaScript into the relevant formats.
Tests
Run the unit tests
python setup.py test
Generate code coverage reports
# pip install coverage
coverage run --source dpu_utils/ setup.py test && \
coverage html
The resulting HTML file will be in htmlcov/index.html.
.NET
Stored in the dotnet subdirectory.
Generic Utilities:
Microsoft.Research.DPU.Utils.RichPath: a convenient way of using both paths and Azure paths in your code.
Code-related Utilities:
Microsoft.Research.DPU.CSharpSourceGraphExtraction: infrastructure to extract Program Graphs from C# projects.
Contributing
This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.
When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.
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