The NMODL Transpiler =================== The NMODL Transpiler is a code generation engine for the **N**\ EURON **MOD**\ eling **L**\ anguage (`NMODL <../../nmodl/language.html>`__). It is designed with modern compiler and code generation techniques to: - Provide **modular tools** for parsing, analysing and transforming NMODL - Provide **easy to use**, high level Python API - Generate **optimised code** for modern compute architectures including CPUs, GPUs - **Flexibility** to implement new simulator backends - Support for **full** NMODL specification About NMODL ----------- Simulators like NEURON use NMODL as a domain specific language (DSL) to describe a wide range of membrane and intracellular submodels. Here is an example of exponential synapse specified in NMODL: .. code:: NEURON { POINT_PROCESS ExpSyn RANGE tau, e, i NONSPECIFIC_CURRENT i } UNITS { (nA) = (nanoamp) (mV) = (millivolt) (uS) = (microsiemens) } PARAMETER { tau = 0.1 (ms) <1e-9,1e9> e = 0 (mV) } ASSIGNED { v (mV) i (nA) } STATE { g (uS) } INITIAL { g = 0 } BREAKPOINT { SOLVE state METHOD cnexp i = g*(v - e) } DERIVATIVE state { g' = -g/tau } NET_RECEIVE(weight (uS)) { g = g + weight } Installation ------------ See `here `__ for detailed instructions of building the NMODL transpiler from source (as part of NEURON). Try NMODL --------------------- The NMODL transpiler is distributed as part of NEURON. Once NEURON is installed, you can try the NMODL Python API discussed later in this README as: :: $ python3 Python 3.6.8 (default, Apr 8 2019, 18:17:52) >>> from neuron.nmodl import dsl >>> examples = dsl.list_examples() >>> nmodl_string = dsl.load_example(examples[-1]) ... You can open and run all example notebooks provided in the ``docs/nmodl/transpiler/notebooks`` folder. You can also create new notebooks in ``my_notebooks``, which will be stored in a subfolder ``notebooks`` at your current working directory. Using the Python API -------------------- Once the NMODL transpiler is installed, you can use the Python parsing API to load NMOD file as: .. code:: python from neuron.nmodl import dsl examples = dsl.list_examples() nmodl_string = dsl.load_example(examples[-1]) driver = dsl.NmodlDriver() modast = driver.parse_string(nmodl_string) The ``parse_file`` API returns Abstract Syntax Tree (`AST `__) representation of input NMODL file. One can look at the AST by converting to JSON form as: .. code:: python >>> print (dsl.to_json(modast)) { "Program": [ { "NeuronBlock": [ { "StatementBlock": [ { "Suffix": [ { "Name": [ { "String": [ { "name": "POINT_PROCESS" } ... Every key in the JSON form represent a node in the AST. You can also use visualization API to look at the details of AST as: :: from neuron.nmodl import ast ast.view(modast) which will open AST view in web browser: .. figure:: https://user-images.githubusercontent.com/666852/57329449-12c9a400-7114-11e9-8da5-0042590044ec.gif :alt: ast_viz Vizualisation of the AST in the NMODL transpiler The central *Program* node represents the whole MOD file and each of it’s children represent the block in the input NMODL file. Note that this requires X-forwarding if you are using the Docker image. Once the AST is created, one can use exisiting visitors to perform various analysis/optimisations. One can also easily write his own custom visitor using Python Visitor API. See `Python API tutorial `__ for details. The NMODL Transpiler also allows us to transform the AST representation back to NMODL form as: .. code:: python >>> print (dsl.to_nmodl(modast)) NEURON { POINT_PROCESS ExpSyn RANGE tau, e, i NONSPECIFIC_CURRENT i } UNITS { (nA) = (nanoamp) (mV) = (millivolt) (uS) = (microsiemens) } PARAMETER { tau = 0.1 (ms) <1e-09,1000000000> e = 0 (mV) } ... High Level Analysis and Code Generation --------------------------------------- The NMODL transpiler provides rich model introspection and analysis capabilities using `various visitors <../../doxygen/group__visitor__classes.html>`__. Here is an example of theoretical performance characterisation of channels and synapses from rat neocortical column microcircuit `published in 2015 `__: .. figure:: https://user-images.githubusercontent.com/666852/57336711-2cc0b200-7127-11e9-8053-8f662e2ec191.png :alt: nmodl-perf-stats Performance results of the NMODL transpiler To understand how you can write your own introspection and analysis tool, see `this tutorial `__. Once analysis and optimization passes are performed, the NMODL transpiler can generate optimised code for modern compute architectures including CPUs (Intel, AMD, ARM) and GPUs (NVIDIA, AMD) platforms. For example, C++, OpenACC and OpenMP backends are implemented and one can choose these backends on command line as: :: $ nmodl expsyn.mod sympy --analytic To know more about code generation backends, `see here `__. NMODL transpiler provides number of options (for code generation, optimization passes and ODE solver) which can be listed as: :: $ nmodl -H NMODL : Source-to-Source Code Generation transpiler [version] Usage: /path/<>/nmodl [OPTIONS] file... [SUBCOMMAND] Positionals: file TEXT:FILE ... REQUIRED One or more MOD files to process Options: -h,--help Print this help message and exit -H,--help-all Print this help message including all sub-commands --verbose=info Verbose logger output (trace, debug, info, warning, error, critical, off) -o,--output TEXT=. Directory for backend code output --scratch TEXT=tmp Directory for intermediate code output --units TEXT=/path/<>/nrnunits.lib Directory of units lib file Subcommands: host HOST/CPU code backends Options: --c C/C++ backend (true) acc Accelerator code backends Options: --oacc C/C++ backend with OpenACC (false) sympy SymPy based analysis and optimizations Options: --analytic Solve ODEs using SymPy analytic integration (false) --pade Pade approximation in SymPy analytic integration (false) --cse CSE (Common Subexpression Elimination) in SymPy analytic integration (false) --conductance Add CONDUCTANCE keyword in BREAKPOINT (false) passes Analyse/Optimization passes Options: --inline Perform inlining at NMODL level (false) --unroll Perform loop unroll at NMODL level (false) --const-folding Perform constant folding at NMODL level (false) --localize Convert RANGE variables to LOCAL (false) --global-to-range Convert GLOBAL variables to RANGE (false) --localize-verbatim Convert RANGE variables to LOCAL even if verbatim block exist (false) --local-rename Rename LOCAL variable if variable of same name exist in global scope (false) --verbatim-inline Inline even if verbatim block exist (false) --verbatim-rename Rename variables in verbatim block (true) --json-ast Write AST to JSON file (false) --nmodl-ast Write AST to NMODL file (false) --json-perf Write performance statistics to JSON file (false) --show-symtab Write symbol table to stdout (false) codegen Code generation options Options: --layout TEXT:{aos,soa}=soa Memory layout for code generation --datatype TEXT:{float,double}=soa Data type for floating point variables --force Force code generation even if there is any incompatibility --only-check-compatibility Check compatibility and return without generating code --opt-ionvar-copy Optimize copies of ion variables (false) Documentation ------------- We are working on user documentation, you can find the current version as part of the NEURON readthedocs page: - `Documentation `__ Citation -------- If you would like to know more about the the NMODL transpiler, see following paper: - Pramod Kumbhar, Omar Awile, Liam Keegan, Jorge Alonso, James King, Michael Hines and Felix Schürmann. 2019. An optimizing multi-platform source-to-source compiler transpiler for the NEURON MODeling Language. In Eprint : `arXiv:1905.02241 `__ Support / Contribuition ----------------------- If you see any issue, feel free to `raise a ticket `__. If you would like to improve this transpiler, see `open issues `__ and `contribution guidelines `__. Examples / Benchmarks --------------------- The benchmarks used to test the performance and parsing capabilities of NMODL transpiler are currently being migrated to GitHub. These benchmarks will be published soon in following repositories: - `NMODL Benchmark `__ - `NMODL Database `__ Funding & Acknowledgment ------------------------ The development of this software was supported by funding to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL), from the Swiss government’s ETH Board of the Swiss Federal Institutes of Technology. In addition, the development was supported by funding from the National Institutes of Health (NIH) under the Grant Number R01NS11613 (Yale University) and the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 785907 (Human Brain Project SGA2). 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