The NMODL Transpiler

The NMODL Transpiler is a code generation engine for the NEURON MODeling Language (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:

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:

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:

>>> 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:

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:

>>> 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. Here is an example of theoretical performance characterisation of channels and synapses from rat neocortical column microcircuit published in 2015:

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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