https://github.com/bluebrain/nmodl

Code Generation Framework For NEURON MODeling Language

https://github.com/bluebrain/nmodl

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Code Generation Framework For NEURON MODeling Language

README.rst

The NMODL Framework
===================

WARNING
_______

**NMODL has been fully integrated into the NEURON repository.**
There will be no further development efforts on NMODL as an independent project.

All future development will happen at:
`https://github.com/neuronsimulator/nrn `_.

---------------------

The NMODL Framework is a code generation engine for **N**\ EURON
**MOD**\ eling **L**\ anguage
(`NMODL `__).
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
`INSTALL.rst `__
for detailed instructions to build the NMODL from source.

Try NMODL with Docker
---------------------

To quickly test the NMODL Framework’s analysis capabilities we provide a
`docker `__ image, which includes the NMODL
Framework python library and a fully functional Jupyter notebook
environment. After installing
`docker `__ and
`docker-compose `__ you can
pull and run the NMODL image from your terminal.

To try Python interface directly from CLI, you can run docker image as:

::

   docker run -it --entrypoint=/bin/sh bluebrain/nmodl

And try NMODL Python API discussed later in this README as:

::

   $ python3
   Python 3.6.8 (default, Apr  8 2019, 18:17:52)
   >>> from nmodl import dsl
   >>> import os
   >>> examples = dsl.list_examples()
   >>> nmodl_string = dsl.load_example(examples[-1])
   ...

To try Jupyter notebooks you can download docker compose file and run it
as:

.. code:: sh

   wget "https://raw.githubusercontent.com/BlueBrain/nmodl/master/docker/docker-compose.yml"
   DUID=$(id -u) DGID=$(id -g) HOSTNAME=$(hostname) docker-compose up

If all goes well you should see at the end status messages similar to
these:

::

   [I 09:49:53.923 NotebookApp] The Jupyter Notebook is running at:
   [I 09:49:53.923 NotebookApp] http://(4c8edabe52e1 or 127.0.0.1):8888/?token=a7902983bad430a11935
   [I 09:49:53.923 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
       To access the notebook, open this file in a browser:
           file:///root/.local/share/jupyter/runtime/nbserver-1-open.html
       Or copy and paste one of these URLs:
           http://(4c8edabe52e1 or 127.0.0.1):8888/?token=a7902983bad430a11935

Based on the example above you should then open your browser and
navigate to the URL
``http://127.0.0.1:8888/?token=a7902983bad430a11935``.

You can open and run all example notebooks provided in the ``examples``
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 Framework is installed, you can use the Python parsing
API to load NMOD file as:

.. code:: python

   from 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 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 Framework

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 Framework 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 Framework provides rich model introspection and analysis
capabilities using `various
visitors `__.
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 Framework

To understand how you can write your own introspection and analysis
tool, see `this
tutorial `__.

Once analysis and optimization passes are performed, the NMODL Framework
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 Framework 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 Framework [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 current drafts of :

-  `User Documentation `__
-  `Developer / API
   Documentation `__

Citation
--------

If you would like to know more about the the NMODL Framework, see
the following paper:

- Pramod Kumbhar, Omar Awile, Liam Keegan, Jorge Blanco Alonso, James King,
  Michael Hines, and Felix Schürmann. 2020. An optimizing multi-platform
  source-to-source compiler framework for the NEURON MODeling Language.
  In *Computational Science – ICCS 2020*, Springer, Cham, 45–58.
  DOI: `10.1007/978-3-030-50371-0_4 `__

Some additional details are covered in the pre-print:

-  Pramod Kumbhar, Omar Awile, Liam Keegan, Jorge Alonso, James King,
   Michael Hines and Felix Schürmann. 2019. An optimizing multi-platform
   source-to-source compiler framework 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 framework, see `open
issues `__ and `contribution
guidelines `__.

Examples / Benchmarks
---------------------

The benchmarks used to test the performance and parsing capabilities of
NMODL Framework 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).

Copyright © 2017-2024 Blue Brain Project, EPFL

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