diff --git a/.gitignore b/.gitignore
index 7e18c635..2cf8e222 100644
--- a/.gitignore
+++ b/.gitignore
@@ -276,3 +276,9 @@ Thumbs.db #thumbnail cache on Windows
/examples/MDF/RNN/iaf_example.net.nml
/examples/MDF/RNN/iaf_example__lems.xml
/examples/MDF/RNN/iaf_example_lems_definitions.xml
+/examples/PyTorch/simple_pytorch_to_mdf
+/examples/PyTorch/simple_pytorch_to_mdf_torchviz
+/examples/PyTorch/Translated_mlp_pure_mdf.json
+/examples/PyTorch/simple_pytorch_to_mdf.1
+/examples/MDF/NLP/TestNLP_3
+/examples/MDF/NLP/TestNLP
diff --git a/README.md b/README.md
index 7782fd22..c9598558 100644
--- a/README.md
+++ b/README.md
@@ -7,24 +7,26 @@
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
# ModECI Model Description Format (MDF)
-[**Click here for full documentation**](https://mdf.readthedocs.io)
+[**Click here for the full MDF documentation**](https://mdf.readthedocs.io)
-**Note: MDF is still in development! See the [open issues related to the specification](https://github.com/ModECI/MDF/issues?q=is%3Aissue+is%3Aopen+label%3Aspecification) or go [here](http://modeci.org/#aboutPage) to get in contact regarding MDF.**
+**Note: MDF is still in development! See the [open issues related to the specification](https://github.com/ModECI/MDF/issues?q=is%3Aissue+is%3Aopen+label%3Aspecification) or go [here](http://modeci.org/#contactPage) to get in contact regarding MDF.**
*The MDF format was first proposed following a meeting organised at Princeton in July 2019 by Russ Poldrack of the Center for Reproducible Neuroscience (CRN) at Stanford and the [Brain Imaging Data Standard (BIDS)](https://bids.neuroimaging.io/) initiative. For more on the previous work in this area, see [here](https://github.com/OpenSourceBrain/PsyNeuLinkShowcase/tree/master/BIDS-MDF).*
## Overview
-MDF is intended to be an open source, community-supported standard and associated library of tools for expressing computational models in a form that allows them to be exchanged between diverse programming languages and execution environments. It consists of a specification for expressing models in serialized formats (currently equivalent JSON and YAML representations are supported, though others such as HDF5 are planned) and a set of Python tools for implementing a model described using MDF. The serialized formats can be used when importing a model into a supported target environment to execute it; and, conversely, when exporting a model built in a supported environment so that it can be re-used in other environments.
+MDF is an open source, community-supported standard and associated library of tools for expressing computational models in a form that allows them to be exchanged between diverse programming languages and execution environments. The overarching aim is to provide a common format for models across computational neuroscience, cognitive science and machine learning.
-The MDF Python API can be used to create or load an MDF model for inspection and validation. It also includes a basic [execution engine](https://github.com/ModECI/MDF/blob/main/src/modeci_mdf/execution_engine.py) for simulating models in the format. However, this is not intended as a general-purpose simulation environment, nor is MDF intended as a programming language. Rather, the primary purpose of the Python API is to facilitate and validate the exchange of models between existing environments that serve different communities. Accordingly, these Python tools include bi-directional support for importing to and exporting from widely-used programming environments in a range of disciplines, and for easily extending these to other environments.
+It consists of a specification for expressing models in serialized formats (currently JSON, YAML and BSON representations are supported, though others such as HDF5 are planned) and a set of Python tools for implementing a model described using MDF. The serialized formats can be used when importing a model into a supported target environment to execute it; and, conversely, when exporting a model built in a supported environment so that it can be re-used in other environments.
+
+The MDF Python API can be used to create or load an MDF model for inspection and validation. It also includes a basic [execution engine](https://mdf.readthedocs.io/en/latest/api/_autosummary/modeci_mdf.execution_engine.html#module-modeci_mdf.execution_engine) for simulating models in the format. However, this is not intended to provide a efficient, general-purpose simulation environment, nor is MDF intended as a programming language. Rather, the primary purpose of the Python API is to facilitate and validate the exchange of models between existing environments that serve different communities. Accordingly, these Python tools include bi-directional support for importing to and exporting from widely-used programming environments in a range of disciplines, and for easily extending these to other environments.
## Development
-The implementation and dissemination of the MDF language and associated tools is being carried out by the [Model Exchange and Convergence Initiative (ModECI)](http://modeci.org/), which has been supported by the [NSF Convergence Accelerator Program](https://www.nsf.gov/od/oia/convergence-accelerator/) (Track D: AI-Driven Innovation via Data and Model Sharing), as a publicly accessible [open-source project](https://github.com/ModECI/MDF). The initial design has been informed by a series of workshops involving developers of key software environments and other stakeholders in machine learning, cognitive science and neuroscience. Future workshops will address broadening of support to other domains in basic and applied science and technology development (e.g., population biology, medical informatics, structural and environmental monitoring, and complex systems control). Environments for which support is currently being developed include [PyTorch](https://pytorch.org), [ONNX](http://onnx.ai), [WebGME](https://webgme.org), [NeuroML](https://neuroml.org), [PsyNeuLink](http://www.psyneuln.deptcpanel.princeton.edu), and [ACT-R](http://act-r.psy.cmu.edu).
+The implementation and dissemination of the MDF language and associated tools is being carried out by the [Model Exchange and Convergence Initiative (ModECI)](http://modeci.org/), which has been supported by the [NSF Convergence Accelerator Program](https://www.nsf.gov/od/oia/convergence-accelerator/) (Track D: AI-Driven Innovation via Data and Model Sharing), as a publicly accessible [open-source project](https://github.com/ModECI/MDF). The initial design has been informed by a [series of workshops](http://modeci.org/#communityPage) involving developers of key software environments and other stakeholders in machine learning, cognitive science and neuroscience. Future workshops will address broadening of support to other domains in basic and applied science and technology development (e.g., population biology, medical informatics, structural and environmental monitoring, and complex systems control). Environments for which support is currently being developed include [PyTorch](https://pytorch.org), [ONNX](http://onnx.ai), [WebGME](https://webgme.org), [NeuroML](https://neuroml.org), [PsyNeuLink](http://www.psyneuln.deptcpanel.princeton.edu), and [ACT-R](http://act-r.psy.cmu.edu).
-
+
Fig 1: Some of the current and planned formats which MDF will interact with. Click on the image for more information.
@@ -34,25 +36,25 @@ Successful interfacing of MDF to existing disciplinary standards (such as [ONNX]
### The core elements of the MDF standard
-**[Models](https://github.com/ModECI/MDF/blob/main/docs/README.md#model)** The highest level construct in MDF is a model that consists of one or more **graphs** and model attributes. The former describe the operational features of the model (its structure and execution), while the latter provide additional information (metadata) useful for executing, evaluating, testing or visualizing it.
+**[Models](https://mdf.readthedocs.io/en/latest/api/Specification.html#model)** The highest level construct in MDF is a model that consists of one or more **graphs** and model attributes. The former describe the operational features of the model (its structure and execution), while the latter provide additional information (metadata) useful for executing, evaluating, testing or visualizing it.
-**[Graphs](https://github.com/ModECI/MDF/blob/main/docs/README.md#graph)** A graph specifies the structure and process flow of a **model**. The most fundamental element of a graph is a **node**, which specifies some unit of computation in terms of its **parameters** and **functions**. Nodes are connected to other nodes via directed **edges**, which, in the absence of additional conditions, define the computational flow of the model.
+**[Graphs](https://mdf.readthedocs.io/en/latest/api/Specification.html#graph)** A graph specifies the structure and process flow of a **model**. The most fundamental element of a graph is a **node**, which specifies some unit of computation in terms of its **parameters** and **functions**. Nodes are connected to other nodes via directed **edges**, which, in the absence of additional **conditions**, define the computational flow of the model.
-**[Nodes](https://github.com/ModECI/MDF/blob/main/docs/README.md#node)** These define the core elements of computation in a **graph**, that receive and transmit information via their **input and output ports**. In general, ports represent points of contact between a **node** and the **edges**that connect it to other nodes.
+**[Nodes](https://mdf.readthedocs.io/en/latest/api/Specification.html#node)** These define the core elements of computation in a **graph**, that receive and transmit information via their **input and output ports**. In general, ports represent points of contact between a **node** and the **edges**that connect it to other nodes.
-**[Output Ports](https://github.com/ModECI/MDF/blob/main/docs/README.md#outputport)** An output port is the starting point of the data transmission process. After processing the information in a **node**, an output port is used to begin the transmission of information to the next **node** through **edges**.
+**[Output Ports](https://mdf.readthedocs.io/en/latest/api/Specification.html#outputport)** An output port is the starting point of the data transmission process. After processing the information in a **node**, an output port is used to begin the transmission of information to the next **node** through **edges**.
-**[Edges](https://github.com/ModECI/MDF/blob/main/docs/README.md#edge)** These transmit information from the **output port** of one **node** to the **input port** of another, collectively defining a **graph’s** topography. Edges may contain weights that can operate on the information they carry.
+**[Edges](https://mdf.readthedocs.io/en/latest/api/Specification.html#edge)** These transmit information from the **output port** of one **node** to the **input port** of another, collectively defining a **graph’s** topography. Edges may contain weights that can operate on the information they carry.
-**[Input Ports](https://github.com/ModECI/MDF/blob/main/docs/README.md#inputport)** An input port is the endpoint of the data transmission process. It receives the information transmitted through an **edge** and inputs it to the next **node** for further processing.
+**[Input Ports](https://mdf.readthedocs.io/en/latest/api/Specification.html#inputport)** An input port is the endpoint of the data transmission process. It receives the information transmitted through an **edge** and inputs it to the next **node** for further processing.
-**[Conditions](https://github.com/ModECI/MDF/blob/main/docs/README.md#condition)** These are a core and distinctive element of the MDF specification, that complement other computational graph-based formats by providing a high-level set of descriptors for specifying conditional execution of **nodes**. This allows models with relatively complex execution requirements (e.g., containing cycles, branches, and/or temporal dependencies) to be expressed as **graphs** in a sufficiently abstract form that facilities exchange among high-level modeling environments without requiring that they be “lowered” to and then recovered from more elaborated procedural descriptions.
+**[Conditions](https://mdf.readthedocs.io/en/latest/api/Specification.html#condition)** These are a core and distinctive element of the MDF specification, that complement other computational graph-based formats by providing a high-level set of descriptors for specifying conditional execution of **nodes**. This allows models with relatively complex execution requirements (e.g., containing cycles, branches, and/or temporal dependencies) to be expressed as **graphs** in a sufficiently abstract form that facilities exchange among high-level modeling environments without requiring that they be “lowered” to and then recovered from more elaborated procedural descriptions.
-**[Parameters](https://github.com/ModECI/MDF/blob/main/docs/README.md#node)** Attributes that determine the configuration and operation of **nodes** and **edges**, such as function parameters and weight matrices, can be defined in the MDF using parameters. In the case of parameters specifying large data structures (e.g., weight-matrices), arrays in widely used formats (e.g. numpy arrays) can be used, and serialisation in portable binary formats (e.g. HDF5) will be supported. Parameters can either be fixed values, which doen't change when the **node** is executed, or can change over time (stateful parameters).
+**[Parameters](https://mdf.readthedocs.io/en/latest/api/Specification.html#parameter)** Attributes that determine the configuration and operation of **nodes** and **edges**, can be defined in the MDF using parameters. In the case of parameters specifying large data structures (e.g., weight-matrices), arrays in widely used formats (e.g. numpy arrays, TensorFlow tensors) can be used, and serialisation in portable binary formats (e.g. BSON) is supported. Parameters can either be fixed values, which don't change when the **node** is executed, or can change over time (stateful parameters).
-**[Functions](https://github.com/ModECI/MDF/blob/main/docs/README.md#function)** A single value which is evaluated as a function of values on **input ports** and other functions and **parameters**.
+**[Functions](https://mdf.readthedocs.io/en/latest/api/Specification.html#function)** A single value which is evaluated as a function of values on **input ports** and other functions and **parameters**. A key distinction with **parameters** is that a function is always stateless.
-**[Model metadata](https://github.com/ModECI/MDF/blob/main/docs/README.md#model)** There is the ability to add “metadata” to the **model**, **graph**, **nodes** and many of their sub elements which provide additional information about that element. While the metadata should not be essential to the mathematical description of the behavior/structure of the element, it could be useful for human interpretability of its function/purpose, or used when it is mapped to a specific application for simulation/visualization. Metadata can be added to the top level model to specify contact information, citations, acknowledgements, pointers to sample data and benchmark results, and environments in which the specified model was originally implemented and any that have been validated to support its execution.
+**[Model metadata](https://mdf.readthedocs.io/en/latest/api/Specification.html#model)** There is the ability to add “metadata” to the **model**, **graph**, **nodes** and many of their sub elements which provide additional information about that element. While the metadata should not be essential to the mathematical description of the behavior/structure of the element, it could be useful for human interpretability of its function/purpose, or used when it is mapped to a specific application for simulation/visualization. Metadata can be added to the top level model to specify contact information, citations, acknowledgements, pointers to sample data and benchmark results, and environments in which the specified model was originally implemented and any that have been validated to support its execution.
Fig 2: A simple graph with 3 nodes and 2 edges expressed in MDF.
@@ -65,30 +67,13 @@ Successful interfacing of MDF to existing disciplinary standards (such as [ONNX]
### Requirements
Requires Python >= 3.7
-### Installation steps
-To install the MDF package and run it locally:
-
-1. Clone this repository
-```
-git clone https://github.com/ModECI/MDF.git
-```
-2. Change to the directory
-```
-cd MDF
-```
-3. Create a virtual environment (e.g. called `mdf-env`)
+### Quick start
```
-pip install virtualenv
-virtualenv mdf-env
-```
-4. Activate the virtual environment
-```
-source mdf-env/bin/activate
-```
-5. Install the package
-```
-pip install .
+pip install modeci-mdf
```
+For more detailed installation instructions see [here](https://mdf.readthedocs.io/en/latest/api/Installation.html).
+
+For guidelines on contributing to the development of MDF, see [here](https://github.com/ModECI/MDF/blob/main/CONTRIBUTING.md).
## Examples
diff --git a/docs/MDF_specification.json b/docs/MDF_specification.json
index e5eaeabf..3f34cf1e 100644
--- a/docs/MDF_specification.json
+++ b/docs/MDF_specification.json
@@ -34,7 +34,7 @@
}
},
"Graph": {
- "definition": "A directed graph consisting of Node(s) connected via Edge(s)",
+ "definition": "A directed graph consisting of :class:`~Node`s (with :class:`~Parameter`s and :class:`~Function`s evaluated internally) connected via :class:`~Edge`s.",
"allowed_parameters": {
"metadata": {
"type": "Union[Any, NoneType]",
diff --git a/docs/MDF_specification.yaml b/docs/MDF_specification.yaml
index 45e489cc..fa3abd7c 100644
--- a/docs/MDF_specification.yaml
+++ b/docs/MDF_specification.yaml
@@ -29,7 +29,8 @@ specification:
type: Graph
description: The collection of graphs that make up the MDF model.
Graph:
- definition: A directed graph consisting of Node(s) connected via Edge(s)
+ definition: A directed graph consisting of :class:`~Node`s (with :class:`~Parameter`s
+ and :class:`~Function`s evaluated internally) connected via :class:`~Edge`s.
allowed_parameters:
metadata:
type: Union[Any, NoneType]
diff --git a/docs/README.md b/docs/README.md
index d5e8bc0a..c63a7739 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -54,7 +54,7 @@ The top level construct in MDF is Model, which may contain multiple Nodes (with Parameters and Functions evaluated internally) connected via Edges.
### Allowed parameters
diff --git a/docs/generate.py b/docs/generate.py
index 3e2f217a..9e115182 100644
--- a/docs/generate.py
+++ b/docs/generate.py
@@ -4,9 +4,27 @@
import json
import types
import yaml
+import shutil
-mod = Model(id="Simple")
+shutil.copy("../README.md", "sphinx/source/api/Introduction.md")
+for ex in [
+ "ACT-R",
+ "MDF",
+ "NeuroML",
+ "ONNX",
+ "PsyNeuLink",
+ "PyTorch",
+ "Quantum",
+ "WebGME",
+]:
+ shutil.copy(
+ "../examples/%s/README.md" % ex,
+ f"sphinx/source/api/export_format/{ex}/{ex}.md",
+ )
+
+
+mod = Model(id="Simple")
doc = mod.generate_documentation(format="markdown")
diff --git a/docs/sphinx/source/api/Export.md b/docs/sphinx/source/api/Export.md
index a287d0da..4a5717c2 100644
--- a/docs/sphinx/source/api/Export.md
+++ b/docs/sphinx/source/api/Export.md
@@ -1,5 +1,6 @@
-## Supported export Models
-This is the list of currently supported models which MDF supports
+# Supported export and import formats
+
+This is the list of currently supported export/import formats which MDF supports
- ACT-R [README](https://github.com/ModECI/MDF/tree/main/examples/ACT-R#readme)
- NeuroML [README](https://github.com/ModECI/MDF/tree/main/examples/NeuroML#readme)
diff --git a/docs/sphinx/source/api/Installation.md b/docs/sphinx/source/api/Installation.md
index d30912c5..cc5dc154 100644
--- a/docs/sphinx/source/api/Installation.md
+++ b/docs/sphinx/source/api/Installation.md
@@ -4,12 +4,45 @@
Python (>=3.7)
-## Installation
+## Quick start
```
pip install modeci_mdf
```
-## Additionally
+
+## Installation from source
+To install the MDF package from source and run it locally:
+
+### 1) Clone this repository
+```
+git clone https://github.com/ModECI/MDF.git
+```
+### 2) Change to the directory
+```
+cd MDF
+```
+### 3) Create a virtual environment (e.g. called `mdf-env`)
+```
+pip install virtualenv
+virtualenv mdf-env
+```
+### 4) Activate the virtual environment
+```
+source mdf-env/bin/activate
+```
+### 5) Install the package
+```
+pip install .
+```
+
+Alternatively, to install MDF plus all of the modules required for the export/import interfaces (e.g. PsyNeuLink, NeuroML):
+
+```
+pip install .[all]
+```
+
+
+## Additional dependencies
To generate generate Graph images in MDF you require Graphviz which uses dot.
diff --git a/docs/sphinx/source/api/Introduction.md b/docs/sphinx/source/api/Introduction.md
index 7782fd22..c9598558 100644
--- a/docs/sphinx/source/api/Introduction.md
+++ b/docs/sphinx/source/api/Introduction.md
@@ -7,24 +7,26 @@
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
# ModECI Model Description Format (MDF)
-[**Click here for full documentation**](https://mdf.readthedocs.io)
+[**Click here for the full MDF documentation**](https://mdf.readthedocs.io)
-**Note: MDF is still in development! See the [open issues related to the specification](https://github.com/ModECI/MDF/issues?q=is%3Aissue+is%3Aopen+label%3Aspecification) or go [here](http://modeci.org/#aboutPage) to get in contact regarding MDF.**
+**Note: MDF is still in development! See the [open issues related to the specification](https://github.com/ModECI/MDF/issues?q=is%3Aissue+is%3Aopen+label%3Aspecification) or go [here](http://modeci.org/#contactPage) to get in contact regarding MDF.**
*The MDF format was first proposed following a meeting organised at Princeton in July 2019 by Russ Poldrack of the Center for Reproducible Neuroscience (CRN) at Stanford and the [Brain Imaging Data Standard (BIDS)](https://bids.neuroimaging.io/) initiative. For more on the previous work in this area, see [here](https://github.com/OpenSourceBrain/PsyNeuLinkShowcase/tree/master/BIDS-MDF).*
## Overview
-MDF is intended to be an open source, community-supported standard and associated library of tools for expressing computational models in a form that allows them to be exchanged between diverse programming languages and execution environments. It consists of a specification for expressing models in serialized formats (currently equivalent JSON and YAML representations are supported, though others such as HDF5 are planned) and a set of Python tools for implementing a model described using MDF. The serialized formats can be used when importing a model into a supported target environment to execute it; and, conversely, when exporting a model built in a supported environment so that it can be re-used in other environments.
+MDF is an open source, community-supported standard and associated library of tools for expressing computational models in a form that allows them to be exchanged between diverse programming languages and execution environments. The overarching aim is to provide a common format for models across computational neuroscience, cognitive science and machine learning.
-The MDF Python API can be used to create or load an MDF model for inspection and validation. It also includes a basic [execution engine](https://github.com/ModECI/MDF/blob/main/src/modeci_mdf/execution_engine.py) for simulating models in the format. However, this is not intended as a general-purpose simulation environment, nor is MDF intended as a programming language. Rather, the primary purpose of the Python API is to facilitate and validate the exchange of models between existing environments that serve different communities. Accordingly, these Python tools include bi-directional support for importing to and exporting from widely-used programming environments in a range of disciplines, and for easily extending these to other environments.
+It consists of a specification for expressing models in serialized formats (currently JSON, YAML and BSON representations are supported, though others such as HDF5 are planned) and a set of Python tools for implementing a model described using MDF. The serialized formats can be used when importing a model into a supported target environment to execute it; and, conversely, when exporting a model built in a supported environment so that it can be re-used in other environments.
+
+The MDF Python API can be used to create or load an MDF model for inspection and validation. It also includes a basic [execution engine](https://mdf.readthedocs.io/en/latest/api/_autosummary/modeci_mdf.execution_engine.html#module-modeci_mdf.execution_engine) for simulating models in the format. However, this is not intended to provide a efficient, general-purpose simulation environment, nor is MDF intended as a programming language. Rather, the primary purpose of the Python API is to facilitate and validate the exchange of models between existing environments that serve different communities. Accordingly, these Python tools include bi-directional support for importing to and exporting from widely-used programming environments in a range of disciplines, and for easily extending these to other environments.
## Development
-The implementation and dissemination of the MDF language and associated tools is being carried out by the [Model Exchange and Convergence Initiative (ModECI)](http://modeci.org/), which has been supported by the [NSF Convergence Accelerator Program](https://www.nsf.gov/od/oia/convergence-accelerator/) (Track D: AI-Driven Innovation via Data and Model Sharing), as a publicly accessible [open-source project](https://github.com/ModECI/MDF). The initial design has been informed by a series of workshops involving developers of key software environments and other stakeholders in machine learning, cognitive science and neuroscience. Future workshops will address broadening of support to other domains in basic and applied science and technology development (e.g., population biology, medical informatics, structural and environmental monitoring, and complex systems control). Environments for which support is currently being developed include [PyTorch](https://pytorch.org), [ONNX](http://onnx.ai), [WebGME](https://webgme.org), [NeuroML](https://neuroml.org), [PsyNeuLink](http://www.psyneuln.deptcpanel.princeton.edu), and [ACT-R](http://act-r.psy.cmu.edu).
+The implementation and dissemination of the MDF language and associated tools is being carried out by the [Model Exchange and Convergence Initiative (ModECI)](http://modeci.org/), which has been supported by the [NSF Convergence Accelerator Program](https://www.nsf.gov/od/oia/convergence-accelerator/) (Track D: AI-Driven Innovation via Data and Model Sharing), as a publicly accessible [open-source project](https://github.com/ModECI/MDF). The initial design has been informed by a [series of workshops](http://modeci.org/#communityPage) involving developers of key software environments and other stakeholders in machine learning, cognitive science and neuroscience. Future workshops will address broadening of support to other domains in basic and applied science and technology development (e.g., population biology, medical informatics, structural and environmental monitoring, and complex systems control). Environments for which support is currently being developed include [PyTorch](https://pytorch.org), [ONNX](http://onnx.ai), [WebGME](https://webgme.org), [NeuroML](https://neuroml.org), [PsyNeuLink](http://www.psyneuln.deptcpanel.princeton.edu), and [ACT-R](http://act-r.psy.cmu.edu).
-
+
Fig 1: Some of the current and planned formats which MDF will interact with. Click on the image for more information.
@@ -34,25 +36,25 @@ Successful interfacing of MDF to existing disciplinary standards (such as [ONNX]
### The core elements of the MDF standard
-**[Models](https://github.com/ModECI/MDF/blob/main/docs/README.md#model)** The highest level construct in MDF is a model that consists of one or more **graphs** and model attributes. The former describe the operational features of the model (its structure and execution), while the latter provide additional information (metadata) useful for executing, evaluating, testing or visualizing it.
+**[Models](https://mdf.readthedocs.io/en/latest/api/Specification.html#model)** The highest level construct in MDF is a model that consists of one or more **graphs** and model attributes. The former describe the operational features of the model (its structure and execution), while the latter provide additional information (metadata) useful for executing, evaluating, testing or visualizing it.
-**[Graphs](https://github.com/ModECI/MDF/blob/main/docs/README.md#graph)** A graph specifies the structure and process flow of a **model**. The most fundamental element of a graph is a **node**, which specifies some unit of computation in terms of its **parameters** and **functions**. Nodes are connected to other nodes via directed **edges**, which, in the absence of additional conditions, define the computational flow of the model.
+**[Graphs](https://mdf.readthedocs.io/en/latest/api/Specification.html#graph)** A graph specifies the structure and process flow of a **model**. The most fundamental element of a graph is a **node**, which specifies some unit of computation in terms of its **parameters** and **functions**. Nodes are connected to other nodes via directed **edges**, which, in the absence of additional **conditions**, define the computational flow of the model.
-**[Nodes](https://github.com/ModECI/MDF/blob/main/docs/README.md#node)** These define the core elements of computation in a **graph**, that receive and transmit information via their **input and output ports**. In general, ports represent points of contact between a **node** and the **edges**that connect it to other nodes.
+**[Nodes](https://mdf.readthedocs.io/en/latest/api/Specification.html#node)** These define the core elements of computation in a **graph**, that receive and transmit information via their **input and output ports**. In general, ports represent points of contact between a **node** and the **edges**that connect it to other nodes.
-**[Output Ports](https://github.com/ModECI/MDF/blob/main/docs/README.md#outputport)** An output port is the starting point of the data transmission process. After processing the information in a **node**, an output port is used to begin the transmission of information to the next **node** through **edges**.
+**[Output Ports](https://mdf.readthedocs.io/en/latest/api/Specification.html#outputport)** An output port is the starting point of the data transmission process. After processing the information in a **node**, an output port is used to begin the transmission of information to the next **node** through **edges**.
-**[Edges](https://github.com/ModECI/MDF/blob/main/docs/README.md#edge)** These transmit information from the **output port** of one **node** to the **input port** of another, collectively defining a **graph’s** topography. Edges may contain weights that can operate on the information they carry.
+**[Edges](https://mdf.readthedocs.io/en/latest/api/Specification.html#edge)** These transmit information from the **output port** of one **node** to the **input port** of another, collectively defining a **graph’s** topography. Edges may contain weights that can operate on the information they carry.
-**[Input Ports](https://github.com/ModECI/MDF/blob/main/docs/README.md#inputport)** An input port is the endpoint of the data transmission process. It receives the information transmitted through an **edge** and inputs it to the next **node** for further processing.
+**[Input Ports](https://mdf.readthedocs.io/en/latest/api/Specification.html#inputport)** An input port is the endpoint of the data transmission process. It receives the information transmitted through an **edge** and inputs it to the next **node** for further processing.
-**[Conditions](https://github.com/ModECI/MDF/blob/main/docs/README.md#condition)** These are a core and distinctive element of the MDF specification, that complement other computational graph-based formats by providing a high-level set of descriptors for specifying conditional execution of **nodes**. This allows models with relatively complex execution requirements (e.g., containing cycles, branches, and/or temporal dependencies) to be expressed as **graphs** in a sufficiently abstract form that facilities exchange among high-level modeling environments without requiring that they be “lowered” to and then recovered from more elaborated procedural descriptions.
+**[Conditions](https://mdf.readthedocs.io/en/latest/api/Specification.html#condition)** These are a core and distinctive element of the MDF specification, that complement other computational graph-based formats by providing a high-level set of descriptors for specifying conditional execution of **nodes**. This allows models with relatively complex execution requirements (e.g., containing cycles, branches, and/or temporal dependencies) to be expressed as **graphs** in a sufficiently abstract form that facilities exchange among high-level modeling environments without requiring that they be “lowered” to and then recovered from more elaborated procedural descriptions.
-**[Parameters](https://github.com/ModECI/MDF/blob/main/docs/README.md#node)** Attributes that determine the configuration and operation of **nodes** and **edges**, such as function parameters and weight matrices, can be defined in the MDF using parameters. In the case of parameters specifying large data structures (e.g., weight-matrices), arrays in widely used formats (e.g. numpy arrays) can be used, and serialisation in portable binary formats (e.g. HDF5) will be supported. Parameters can either be fixed values, which doen't change when the **node** is executed, or can change over time (stateful parameters).
+**[Parameters](https://mdf.readthedocs.io/en/latest/api/Specification.html#parameter)** Attributes that determine the configuration and operation of **nodes** and **edges**, can be defined in the MDF using parameters. In the case of parameters specifying large data structures (e.g., weight-matrices), arrays in widely used formats (e.g. numpy arrays, TensorFlow tensors) can be used, and serialisation in portable binary formats (e.g. BSON) is supported. Parameters can either be fixed values, which don't change when the **node** is executed, or can change over time (stateful parameters).
-**[Functions](https://github.com/ModECI/MDF/blob/main/docs/README.md#function)** A single value which is evaluated as a function of values on **input ports** and other functions and **parameters**.
+**[Functions](https://mdf.readthedocs.io/en/latest/api/Specification.html#function)** A single value which is evaluated as a function of values on **input ports** and other functions and **parameters**. A key distinction with **parameters** is that a function is always stateless.
-**[Model metadata](https://github.com/ModECI/MDF/blob/main/docs/README.md#model)** There is the ability to add “metadata” to the **model**, **graph**, **nodes** and many of their sub elements which provide additional information about that element. While the metadata should not be essential to the mathematical description of the behavior/structure of the element, it could be useful for human interpretability of its function/purpose, or used when it is mapped to a specific application for simulation/visualization. Metadata can be added to the top level model to specify contact information, citations, acknowledgements, pointers to sample data and benchmark results, and environments in which the specified model was originally implemented and any that have been validated to support its execution.
+**[Model metadata](https://mdf.readthedocs.io/en/latest/api/Specification.html#model)** There is the ability to add “metadata” to the **model**, **graph**, **nodes** and many of their sub elements which provide additional information about that element. While the metadata should not be essential to the mathematical description of the behavior/structure of the element, it could be useful for human interpretability of its function/purpose, or used when it is mapped to a specific application for simulation/visualization. Metadata can be added to the top level model to specify contact information, citations, acknowledgements, pointers to sample data and benchmark results, and environments in which the specified model was originally implemented and any that have been validated to support its execution.
Fig 2: A simple graph with 3 nodes and 2 edges expressed in MDF.
@@ -65,30 +67,13 @@ Successful interfacing of MDF to existing disciplinary standards (such as [ONNX]
### Requirements
Requires Python >= 3.7
-### Installation steps
-To install the MDF package and run it locally:
-
-1. Clone this repository
-```
-git clone https://github.com/ModECI/MDF.git
-```
-2. Change to the directory
-```
-cd MDF
-```
-3. Create a virtual environment (e.g. called `mdf-env`)
+### Quick start
```
-pip install virtualenv
-virtualenv mdf-env
-```
-4. Activate the virtual environment
-```
-source mdf-env/bin/activate
-```
-5. Install the package
-```
-pip install .
+pip install modeci-mdf
```
+For more detailed installation instructions see [here](https://mdf.readthedocs.io/en/latest/api/Installation.html).
+
+For guidelines on contributing to the development of MDF, see [here](https://github.com/ModECI/MDF/blob/main/CONTRIBUTING.md).
## Examples
diff --git a/docs/sphinx/source/api/Specification.rst b/docs/sphinx/source/api/Specification.rst
index 4335b2d3..2a75de52 100644
--- a/docs/sphinx/source/api/Specification.rst
+++ b/docs/sphinx/source/api/Specification.rst
@@ -32,7 +32,7 @@ Allowed child Data Type Description
=====
Graph
=====
-A directed graph consisting of Node(s) connected via Edge(s)
+A directed graph consisting of `Nodes <#node>`__ (with `Parameters <#parameter>`__ and `Functions <#function>`__ evaluated internally) connected via `Edges <#edge>`__.
**Allowed parameters**
diff --git a/docs/sphinx/source/api/export_format/ACT-R/ACT-R.md b/docs/sphinx/source/api/export_format/ACT-R/ACT-R.md
index c123b70e..21b24928 100644
--- a/docs/sphinx/source/api/export_format/ACT-R/ACT-R.md
+++ b/docs/sphinx/source/api/export_format/ACT-R/ACT-R.md
@@ -14,7 +14,7 @@ The below graph represents the basic structure of all ACT-R models in MDF:
There are also more detailed graphs [count.png](count.png) and
[addition.png](addition.png) for each example.
-### Count Model
+## Count Model
[ACT-R](count.lisp) | [JSON](count.json) | [YAML](count.yaml) | [Python Script](count.py) | [Graph](count.png)
@@ -39,7 +39,7 @@ The script can use any values specified in [count.lisp](count.lisp), so the
model can be modified and run multiple times in order to test different values.
The [count](count.png) graph represents this example.
-### Addition Model
+## Addition Model
[ACT-R](addition.lisp) | [JSON](addition.json) | [YAML](addition.yaml) | [Python Script](addition.py) | [Graph](addition.png)
diff --git a/docs/sphinx/source/api/export_format/MDF/MDF.md b/docs/sphinx/source/api/export_format/MDF/MDF.md
index 25f3d98c..beca901a 100644
--- a/docs/sphinx/source/api/export_format/MDF/MDF.md
+++ b/docs/sphinx/source/api/export_format/MDF/MDF.md
@@ -8,19 +8,21 @@ Examples of Python, JSON and YAML files to illustrate the structure and usage of
[Python source](simple.py) | [JSON](Simple.json) | [YAML](Simple.yaml)
-A simple example with 2 [Nodes](../../docs/README.md#node) connected by an [Edge](../../docs/README.md#edge):
+A simple example with 2 [Nodes](https://mdf.readthedocs.io/en/latest/api/Specification.html#node) connected by an [Edge](https://mdf.readthedocs.io/en/latest/api/Specification.html#edge):
![simple](images/simple.png)
-With more detail on [Nodes](../../docs/README.md#node) (showing [Input Ports](../../docs/README.md#inputport) (green), [Parameters](../../docs/README.md#parameter) (blue) and [Output Ports](../../docs/README.md#output_port)) (red) and [Edges](../../docs/README.md#edge):
+With more detail on [Nodes](https://mdf.readthedocs.io/en/latest/api/Specification.html#node) (showing [Input Ports](https://mdf.readthedocs.io/en/latest/api/Specification.html#inputport) (green), [Parameters](https://mdf.readthedocs.io/en/latest/api/Specification.html#parameter) (blue) and [Output Ports](https://mdf.readthedocs.io/en/latest/api/Specification.html#output_port)) (red) and [Edges](https://mdf.readthedocs.io/en/latest/api/Specification.html#edge):
+
![simple_3](images/simple_3.png)
+
## ABCD
[Python source](abcd.py) | [JSON](ABCD.json) | [YAML](ABCD.yaml)
-Another simple example with more [Nodes](../../docs/README.md#node).
+Another simple example with more [Nodes](https://mdf.readthedocs.io/en/latest/api/Specification.html#node).
![abcd](images/abcd.png) ![abcd_3](images/abcd_3.png)
@@ -28,7 +30,7 @@ Another simple example with more [Nodes](../../docs/README.md#node).
[Python source](arrays.py) | [JSON](Arrays.json) | [YAML](Arrays.yaml)
-An example using arrays for [Parameters](../../docs/README.md#parameter) and weights on [Edges](../../docs/README.md#edge).
+An example using arrays for [Parameters](https://mdf.readthedocs.io/en/latest/api/Specification.html#parameter) and weights on [Edges](https://mdf.readthedocs.io/en/latest/api/Specification.html#edge).
![arrays](images/arrays.png)
@@ -36,7 +38,7 @@ An example using arrays for [Parameters](../../docs/README.md#parameter) and wei
[Python source](states.py) | [JSON](States.json) | [YAML](States.yaml)
-An example with [Nodes](../../docs/README.md#node) containing persistent [States](../../docs/README.md#state).
+An example with [Nodes](https://mdf.readthedocs.io/en/latest/api/Specification.html#node) containing persistent [States](https://mdf.readthedocs.io/en/latest/api/Specification.html#state).
![states](images/states.png)
@@ -45,7 +47,7 @@ An example with [Nodes](../../docs/README.md#node) containing persistent [States
[Python source](abc_conditions.py) | [JSON](abc_conditions.json) | [YAML](abc_conditions.yaml)
-A simple 3 [Nodes](../../docs/README.md#node) graph with scheduling [Conditions](../../docs/README.md#condition). For more examples of conditions see [here](conditions/README.md).
+A simple 3 [Nodes](https://mdf.readthedocs.io/en/latest/api/Specification.html#node) graph with scheduling [Conditions](https://mdf.readthedocs.io/en/latest/api/Specification.html#condition). For more examples of conditions see [here](conditions/README.md).
![abc_conditions](images/abc_conditions.png)
@@ -53,7 +55,7 @@ A simple 3 [Nodes](../../docs/README.md#node) graph with scheduling [Conditions]
[Python source](params_funcs.py) | [JSON](ParametersFunctions.json) | [YAML](ParametersFunctions.yaml)
-A simple [Node](../../docs/README.md#node) with a number of different types of [Parameters](../../docs/README.md#parameter) (in blue; fixed and **stateful**) and [Functions](../../docs/README.md#function) (in purple; can be built in or ONNX based).
+A simple [Node](https://mdf.readthedocs.io/en/latest/api/Specification.html#node) with a number of different types of [Parameters](https://mdf.readthedocs.io/en/latest/api/Specification.html#parameter) (in blue; fixed and **stateful**) and [Functions](https://mdf.readthedocs.io/en/latest/api/Specification.html#function) (in purple; can be built in or ONNX based).
![params_funcs](images/params_funcs.png)
diff --git a/docs/sphinx/source/api/export_format/ONNX/ONNX.md b/docs/sphinx/source/api/export_format/ONNX/ONNX.md
index f2cbcafd..137b1f59 100644
--- a/docs/sphinx/source/api/export_format/ONNX/ONNX.md
+++ b/docs/sphinx/source/api/export_format/ONNX/ONNX.md
@@ -31,6 +31,8 @@ This is an example of a PyTorch model with 2 nodes. First, the [script](simple_a
[Python source](simple_abc.py) | [JSON](abc.json) | [YAML](abc.yaml)
+**Note: Example still in development!**
+
This is an example of a PyTorch model that is implemented in `onnx_mdf/examples/simple_abc.py`. The model code
is very simple:
@@ -72,23 +74,14 @@ inner `B` module has a loop construct.
![ABC](abc.png)
-It is exported to ONNX via a combination of tracing and scripting. The resulting ONNX model looks something
-like this:
-
-![ABC ONNX IR](examples/abc_ir.png)
-
-This ONNX IR representation can be converted to MDF. To run this full example, execute this command:
-
-```
-$ simple-abc-example
-```
-
-You can see the converted MDF models in [JSON](examples/abc-mdf.json) and [YAML](examples/abc-mdf.yml):
+It is exported to ONNX via a combination of tracing and scripting.
### ABCD Branching Conditional Model
[Python source](simple_abcd.py) | [JSON](abcd.json) | [YAML](abcd.yaml)
+**Note: Example still in development!**
+
This is an example of a PyTorch model that have four components (A, B, C, D). We loop over the whole
model 10 iterations. A is executed only on the first iteration, B is executed every iteration, C is
executed every 5 times B is executed, and D is executed every 10 times B is executed. A, B, C, and D are
@@ -153,13 +146,3 @@ class ABCD(torch.nn.Module):
The ONNX IR representation of this model is shown below. The small computation sub-graphs
contained in the if and else body attributes are not shown. These are either a simple multiplication and addition or
an identity.
-
-![ABCD ONNX IR](examples/abcd/abcd_ir.svg)
-
-To run this example, execute the following command:
-
-```
-$ simple-abcd-example
-```
-
-You can see the converted MDF models in [JSON](examples/abcd/abc-mdf.json) and [YAML](examples/abcd/abcd-mdf.yml):
diff --git a/docs/sphinx/source/api/export_format/PyTorch/PyTorch.md b/docs/sphinx/source/api/export_format/PyTorch/PyTorch.md
index c04d8f1e..3556b459 100644
--- a/docs/sphinx/source/api/export_format/PyTorch/PyTorch.md
+++ b/docs/sphinx/source/api/export_format/PyTorch/PyTorch.md
@@ -5,8 +5,9 @@
## MDF to PyTorch
-To perform an MDF to PyTorch conversion, provide an MDF model as an input to the `mdf_to_pytorch` function
-which is available in [exporter.py](/src/modeci_mdf/interfaces/pytorch/exporter.py). The output of `mdf_to_pytorch` is a PyTorch model.
+To export an MDF model to PyTorch, provide an MDF model as an input to the [mdf_to_pytorch()](https://mdf.readthedocs.io/en/latest/api/_autosummary/modeci_mdf.interfaces.pytorch.exporter.mdf_to_pytorch.html#modeci_mdf.interfaces.pytorch.exporter.mdf_to_pytorch) function.
+
+The output of `mdf_to_pytorch` is a PyTorch model.
```
mdf_to_pytorch(
@@ -16,19 +17,29 @@ mdf_to_pytorch(
model_input: input file name
)
```
-Returns a dictionary where **key** = **model name**, **value** = **pytorch model object**
-### Implementation
+It returns a dictionary where `key` = `model name` and `value` = `PyTorch model object`.
+
+A test script demonstrating conversion of MDF model to PyTorch is at [MDF_to_PyTorch.py](/examples/PyTorch/MDF_PyTorch/MDF_to_PyTorch.py). This converts multiple MDF models to their respective PyTorch models. The converted models are available in folder: [MDF_PyTorch](/examples/PyTorch/MDF_PyTorch).
+
+### Examples
Below are some working examples of this functionality.
-1. One of sample MDF examples [ABCD.json](../MDF/ABCD.json) is converted to PyTorch [ABCD_pytorch.py](MDF_PyTorch/ABCD_pytorch.py).
-The PyTorch model is further converted to ONNX [ABCD.onnx](MDF_PyTorch/ABCD.onnx) and the results are compared in all three environments.
+#### 1) Simple ABCD example
+
+We convert one of the sample MDF examples [ABCD.json](../MDF/ABCD.json):
+
+![ABCD](../MDF/images/abcd.png)
+
+This is converted to PyTorch and can be seen here: [ABCD_pytorch.py](MDF_PyTorch/ABCD_pytorch.py).
+
+The PyTorch model is further converted to ONNX [ABCD.onnx](MDF_PyTorch/ABCD.onnx). An image of the contents of the ONNX model (visualized using [NETRON](https://netron.app/)) is below.
![ABCD.svg](ABCD.svg)
-2. Multi-Layer Perceptron MDF to PyTorch Conversion:
+#### 2) Multi-Layer Perceptron MDF to PyTorch Conversion:
To run an example where a simple Multi-Layer Perceptron (MLP) created using the MDF specification and executed using sample digit-recognition data, run:
@@ -36,27 +47,15 @@ The PyTorch model is further converted to ONNX [ABCD.onnx](MDF_PyTorch/ABCD.onnx
python mlp_pure_mdf.py
```
- A graph of the network can be created with `python mlp_pure_mdf.py -graph`:
-
- **MDF graph**
+A graph of the network can be created with `python mlp_pure_mdf.py -graph`:
![mlp_pure_mdf.png](mlp_pure_mdf.png)
- The network can be run against images from the MNIST database with: `python mlp_pure_mdf.py -run`, and produce 98% accuracy. The image below shows the results of 300 images:
+The network can be run against images from the MNIST database with: `python mlp_pure_mdf.py -run`, and produce 98% accuracy. The image below shows the results of 300 images:
![mlp_pure_mdf.results.png](mlp_pure_mdf.results.png)
- Conversion to PyTorch: TODO...
-
-The demo to convert an MDF model to PyTorch is at [MDF_to_PyTorch.py](/examples/PyTorch/MDF_PyTorch/MDF_to_PyTorch.py). This converts all the available MDF models to their respective Pytorch Models.
-Any model created using the MDF specification is translated to a PyTorch model, run:
-
-```bash
-python MDF_to_PyTorch
-```
-
-**NOTE:** The converted models are available in folder: [MDF_PyTorch](/examples/PyTorch/MDF_PyTorch).
## PyTorch to MDF
@@ -67,8 +66,8 @@ can be translated to TorchScript (via `torch.jit.script` or `torch.jit.trace`) s
then be able to be converted to their MDF representation automatically. Below are
several working examples of this functionality.
-To perform an PyTorch to MDF conversion, provide a PyTorch model as an input to the `pytorch_to_mdf` function
-which is available in [importer.py](/src/modeci_mdf/interfaces/pytorch/importer.py). The output of `pytorch_to_mdf` is an MDF model.
+To perform an PyTorch to MDF conversion, provide a PyTorch model as an input to the [pytorch_to_mdf()](https://mdf.readthedocs.io/en/latest/api/_autosummary/modeci_mdf.interfaces.pytorch.importer.pytorch_to_mdf.html#modeci_mdf.interfaces.pytorch.importer.pytorch_to_mdf) function
+which is available in [importer.py](/src/modeci_mdf/interfaces/pytorch/importer.py). The output of `pytorch_to_mdf()` is an MDF model.
```
pytorch_to_mdf(
@@ -80,14 +79,13 @@ pytorch_to_mdf(
use only ATEN ops.
)
```
-Returns a translated MDF model
+Returns a translated MDF model.
-### Implementation
+### Examples of usage
-Below are some working examples of this functionality.
+#### 1) Simple PyTorch To MDF
-1. Simple Pytorch To MDF:
- This is a simple fully-connected neural network model example consisting of input image of 224 * 224 * 3 and resulting in two classes as the output
+This is a simple fully-connected neural network model example consisting of input image of 224 * 224 * 3 and resulting in two classes as the output
To run an example of converting a PyTorch model written in PyTorch to its MDF representation simply run:
```bash
@@ -102,26 +100,32 @@ Below are some working examples of this functionality.
![simple_pytorch_to_mdf.svg](simple_pytorch_to_mdf.svg)
- **NOTE**: This command will run the netron python server on the local host where we can export the graph as svg/png
+ **NOTE**: This command will run the NETRON python server on the local host where we can export the graph as svg/png
The graph representation of the MDF model can be generated with:
```bash
python simple_pytorch_to_mdf.py -graph
```
- ![simple_pytorch_to_mdf.png](simple_pytorch_to_mdf.png)
+Graphical export from MDF level 1:
- To Visualize the PyTorch model
+![simple_pytorch_to_mdf.1.png](simple_pytorch_to_mdf.1.png)
+
+Graphical export from MDF level 3:
+
+![simple_pytorch_to_mdf.png](simple_pytorch_to_mdf.png)
+
+To visualize the PyTorch model:
```bash
python simple_pytorch_to_mdf.py -graph-torch
```
- ![simple_pytorch_to_mdf_torchviz.png](simple_pytorch_to_mdf_torchviz.png)
+![simple_pytorch_to_mdf_torchviz.png](simple_pytorch_to_mdf_torchviz.png)
- The MDF for this model is the written to [simple_pytorch_to_mdf.json](simple_pytorch_to_mdf.json). The model is then executed
- via the MDF scheduler and the results are compared to the native execution in PyTorch.
+The MDF for this model is the written to [simple_pytorch_to_mdf.json](simple_pytorch_to_mdf.json). The model is then executed
+via the MDF scheduler and the results are compared to the native execution in PyTorch.
-2. Inception Blocks Model:
+#### 2) Inception Blocks Model
![inception.svg](inception.svg)
To run an example of converting a PyTorch InceptionV3 like model written in PyTorch to its MDF representation simply run:
diff --git a/docs/sphinx/source/index.rst b/docs/sphinx/source/index.rst
index db149c0c..df404dfd 100644
--- a/docs/sphinx/source/index.rst
+++ b/docs/sphinx/source/index.rst
@@ -11,13 +11,16 @@
:width: 15%
:target: http://modeci.org
-|logo| ModECI_MDF
-====================
+|logo| ModECI Model Description Format (MDF)
+============================================
MDF is an open source, community-supported standard and associated library
of tools for expressing computational models in a form that allows them to be exchanged
-between diverse programming languages and execution environments. It consists of a
-specification for expressing models in serialized form (currently JSON or YAML
+between diverse programming languages and execution environments. The overarching aim is
+to provide a common format for models across **computational neuroscience, cognitive science and machine learning**.
+
+It consists of a
+specification for expressing models in serialized form (currently JSON, YAML or BSON
representations, though others such as HDF5 are planned) and a set of Python
tools for implementing a model described using MDF. The serialized formats can be used
when importing a model into a supported target environment to execute it; and,
diff --git a/examples/ACT-R/README.md b/examples/ACT-R/README.md
index c123b70e..21b24928 100644
--- a/examples/ACT-R/README.md
+++ b/examples/ACT-R/README.md
@@ -14,7 +14,7 @@ The below graph represents the basic structure of all ACT-R models in MDF:
There are also more detailed graphs [count.png](count.png) and
[addition.png](addition.png) for each example.
-### Count Model
+## Count Model
[ACT-R](count.lisp) | [JSON](count.json) | [YAML](count.yaml) | [Python Script](count.py) | [Graph](count.png)
@@ -39,7 +39,7 @@ The script can use any values specified in [count.lisp](count.lisp), so the
model can be modified and run multiple times in order to test different values.
The [count](count.png) graph represents this example.
-### Addition Model
+## Addition Model
[ACT-R](addition.lisp) | [JSON](addition.json) | [YAML](addition.yaml) | [Python Script](addition.py) | [Graph](addition.png)
diff --git a/examples/MDF/README.md b/examples/MDF/README.md
index 25f3d98c..beca901a 100644
--- a/examples/MDF/README.md
+++ b/examples/MDF/README.md
@@ -8,19 +8,21 @@ Examples of Python, JSON and YAML files to illustrate the structure and usage of
[Python source](simple.py) | [JSON](Simple.json) | [YAML](Simple.yaml)
-A simple example with 2 [Nodes](../../docs/README.md#node) connected by an [Edge](../../docs/README.md#edge):
+A simple example with 2 [Nodes](https://mdf.readthedocs.io/en/latest/api/Specification.html#node) connected by an [Edge](https://mdf.readthedocs.io/en/latest/api/Specification.html#edge):
![simple](images/simple.png)
-With more detail on [Nodes](../../docs/README.md#node) (showing [Input Ports](../../docs/README.md#inputport) (green), [Parameters](../../docs/README.md#parameter) (blue) and [Output Ports](../../docs/README.md#output_port)) (red) and [Edges](../../docs/README.md#edge):
+With more detail on [Nodes](https://mdf.readthedocs.io/en/latest/api/Specification.html#node) (showing [Input Ports](https://mdf.readthedocs.io/en/latest/api/Specification.html#inputport) (green), [Parameters](https://mdf.readthedocs.io/en/latest/api/Specification.html#parameter) (blue) and [Output Ports](https://mdf.readthedocs.io/en/latest/api/Specification.html#output_port)) (red) and [Edges](https://mdf.readthedocs.io/en/latest/api/Specification.html#edge):
+
![simple_3](images/simple_3.png)
+
## ABCD
[Python source](abcd.py) | [JSON](ABCD.json) | [YAML](ABCD.yaml)
-Another simple example with more [Nodes](../../docs/README.md#node).
+Another simple example with more [Nodes](https://mdf.readthedocs.io/en/latest/api/Specification.html#node).
![abcd](images/abcd.png) ![abcd_3](images/abcd_3.png)
@@ -28,7 +30,7 @@ Another simple example with more [Nodes](../../docs/README.md#node).
[Python source](arrays.py) | [JSON](Arrays.json) | [YAML](Arrays.yaml)
-An example using arrays for [Parameters](../../docs/README.md#parameter) and weights on [Edges](../../docs/README.md#edge).
+An example using arrays for [Parameters](https://mdf.readthedocs.io/en/latest/api/Specification.html#parameter) and weights on [Edges](https://mdf.readthedocs.io/en/latest/api/Specification.html#edge).
![arrays](images/arrays.png)
@@ -36,7 +38,7 @@ An example using arrays for [Parameters](../../docs/README.md#parameter) and wei
[Python source](states.py) | [JSON](States.json) | [YAML](States.yaml)
-An example with [Nodes](../../docs/README.md#node) containing persistent [States](../../docs/README.md#state).
+An example with [Nodes](https://mdf.readthedocs.io/en/latest/api/Specification.html#node) containing persistent [States](https://mdf.readthedocs.io/en/latest/api/Specification.html#state).
![states](images/states.png)
@@ -45,7 +47,7 @@ An example with [Nodes](../../docs/README.md#node) containing persistent [States
[Python source](abc_conditions.py) | [JSON](abc_conditions.json) | [YAML](abc_conditions.yaml)
-A simple 3 [Nodes](../../docs/README.md#node) graph with scheduling [Conditions](../../docs/README.md#condition). For more examples of conditions see [here](conditions/README.md).
+A simple 3 [Nodes](https://mdf.readthedocs.io/en/latest/api/Specification.html#node) graph with scheduling [Conditions](https://mdf.readthedocs.io/en/latest/api/Specification.html#condition). For more examples of conditions see [here](conditions/README.md).
![abc_conditions](images/abc_conditions.png)
@@ -53,7 +55,7 @@ A simple 3 [Nodes](../../docs/README.md#node) graph with scheduling [Conditions]
[Python source](params_funcs.py) | [JSON](ParametersFunctions.json) | [YAML](ParametersFunctions.yaml)
-A simple [Node](../../docs/README.md#node) with a number of different types of [Parameters](../../docs/README.md#parameter) (in blue; fixed and **stateful**) and [Functions](../../docs/README.md#function) (in purple; can be built in or ONNX based).
+A simple [Node](https://mdf.readthedocs.io/en/latest/api/Specification.html#node) with a number of different types of [Parameters](https://mdf.readthedocs.io/en/latest/api/Specification.html#parameter) (in blue; fixed and **stateful**) and [Functions](https://mdf.readthedocs.io/en/latest/api/Specification.html#function) (in purple; can be built in or ONNX based).
![params_funcs](images/params_funcs.png)
diff --git a/examples/MDF/RNN/IaF.run.png b/examples/MDF/RNN/IaF.run.png
index f4c70b02..f30ba282 100644
Binary files a/examples/MDF/RNN/IaF.run.png and b/examples/MDF/RNN/IaF.run.png differ
diff --git a/examples/MDF/RNN/RNN.run.png b/examples/MDF/RNN/RNN.run.png
index 68935fff..aaf403f6 100644
Binary files a/examples/MDF/RNN/RNN.run.png and b/examples/MDF/RNN/RNN.run.png differ
diff --git a/examples/MDF/abcd.py b/examples/MDF/abcd.py
index 119ce63e..32d20c1f 100644
--- a/examples/MDF/abcd.py
+++ b/examples/MDF/abcd.py
@@ -132,6 +132,7 @@ def main():
level=1,
filename_root="abcd",
only_warn_on_fail=True, # Makes sure test of this doesn't fail on Windows on GitHub Actions
+ is_horizontal=True,
)
mod.to_graph_image(
engine="dot",
diff --git a/examples/MDF/images/abcd.png b/examples/MDF/images/abcd.png
index 5e66ac8c..b56f328a 100644
Binary files a/examples/MDF/images/abcd.png and b/examples/MDF/images/abcd.png differ
diff --git a/examples/MDF/images/simple.png b/examples/MDF/images/simple.png
index 88fe7f3a..81921af9 100644
Binary files a/examples/MDF/images/simple.png and b/examples/MDF/images/simple.png differ
diff --git a/examples/NeuroML/Izh_run.png b/examples/NeuroML/Izh_run.png
index e9a3d02b..882f7e34 100644
Binary files a/examples/NeuroML/Izh_run.png and b/examples/NeuroML/Izh_run.png differ
diff --git a/examples/NeuroML/MDFFNrun.png b/examples/NeuroML/MDFFNrun.png
index a5d0b409..7e024140 100644
Binary files a/examples/NeuroML/MDFFNrun.png and b/examples/NeuroML/MDFFNrun.png differ
diff --git a/examples/ONNX/README.md b/examples/ONNX/README.md
index f2cbcafd..137b1f59 100644
--- a/examples/ONNX/README.md
+++ b/examples/ONNX/README.md
@@ -31,6 +31,8 @@ This is an example of a PyTorch model with 2 nodes. First, the [script](simple_a
[Python source](simple_abc.py) | [JSON](abc.json) | [YAML](abc.yaml)
+**Note: Example still in development!**
+
This is an example of a PyTorch model that is implemented in `onnx_mdf/examples/simple_abc.py`. The model code
is very simple:
@@ -72,23 +74,14 @@ inner `B` module has a loop construct.
![ABC](abc.png)
-It is exported to ONNX via a combination of tracing and scripting. The resulting ONNX model looks something
-like this:
-
-![ABC ONNX IR](examples/abc_ir.png)
-
-This ONNX IR representation can be converted to MDF. To run this full example, execute this command:
-
-```
-$ simple-abc-example
-```
-
-You can see the converted MDF models in [JSON](examples/abc-mdf.json) and [YAML](examples/abc-mdf.yml):
+It is exported to ONNX via a combination of tracing and scripting.
### ABCD Branching Conditional Model
[Python source](simple_abcd.py) | [JSON](abcd.json) | [YAML](abcd.yaml)
+**Note: Example still in development!**
+
This is an example of a PyTorch model that have four components (A, B, C, D). We loop over the whole
model 10 iterations. A is executed only on the first iteration, B is executed every iteration, C is
executed every 5 times B is executed, and D is executed every 10 times B is executed. A, B, C, and D are
@@ -153,13 +146,3 @@ class ABCD(torch.nn.Module):
The ONNX IR representation of this model is shown below. The small computation sub-graphs
contained in the if and else body attributes are not shown. These are either a simple multiplication and addition or
an identity.
-
-![ABCD ONNX IR](examples/abcd/abcd_ir.svg)
-
-To run this example, execute the following command:
-
-```
-$ simple-abcd-example
-```
-
-You can see the converted MDF models in [JSON](examples/abcd/abc-mdf.json) and [YAML](examples/abcd/abcd-mdf.yml):
diff --git a/examples/PyTorch/MDF_PyTorch/ABCD.onnx b/examples/PyTorch/MDF_PyTorch/ABCD.onnx
index d697c708..920d4cee 100644
Binary files a/examples/PyTorch/MDF_PyTorch/ABCD.onnx and b/examples/PyTorch/MDF_PyTorch/ABCD.onnx differ
diff --git a/examples/PyTorch/MDF_PyTorch/ABCD_pytorch.py b/examples/PyTorch/MDF_PyTorch/ABCD_pytorch.py
index c0df4bd2..4d423c8f 100644
--- a/examples/PyTorch/MDF_PyTorch/ABCD_pytorch.py
+++ b/examples/PyTorch/MDF_PyTorch/ABCD_pytorch.py
@@ -1,4 +1,9 @@
-"Converted examples from MDF models version(mdf.s) to PyTorch/ONNX"
+"""
+This script has been generated by modeci_mdf v0.4.2.
+It is an export of a MDF model (mdf.s - MDF stateful, i.e. full MDF allowing stateful parameters) to PyTorch
+
+"""
+
import torch
import torch.nn as nn
import onnx
@@ -161,16 +166,17 @@ def forward(self, input):
)
output = model(dummy_input)
torch.onnx.export(
- model,
- dummy_input,
- "ABCD.onnx",
- verbose=True,
- input_names=[],
- opset_version=9,
+ model, dummy_input, "ABCD.onnx", verbose=True, input_names=[], opset_version=9
)
onnx_model = onnx.load("ABCD.onnx")
onnx.checker.check_model(onnx_model)
sess = rt.InferenceSession("ABCD.onnx")
-res = sess.run(None, {sess.get_inputs()[0].name: dummy_input.numpy()})
+res = sess.run(
+ None,
+ {sess.get_inputs()[0].name: dummy_input.numpy()}
+ if len(sess.get_inputs()) > 0
+ else {},
+)
+
if __name__ == "__main__":
print("Exported to PyTorch and ONNX")
diff --git a/examples/PyTorch/MDF_PyTorch/Arrays.onnx b/examples/PyTorch/MDF_PyTorch/Arrays.onnx
index ba625a57..e25dae1b 100644
Binary files a/examples/PyTorch/MDF_PyTorch/Arrays.onnx and b/examples/PyTorch/MDF_PyTorch/Arrays.onnx differ
diff --git a/examples/PyTorch/MDF_PyTorch/Arrays_pytorch.py b/examples/PyTorch/MDF_PyTorch/Arrays_pytorch.py
index f029669c..59df398c 100644
--- a/examples/PyTorch/MDF_PyTorch/Arrays_pytorch.py
+++ b/examples/PyTorch/MDF_PyTorch/Arrays_pytorch.py
@@ -1,4 +1,9 @@
-"Converted examples from MDF models version(mdf.s) to PyTorch/ONNX"
+"""
+This script has been generated by modeci_mdf v0.4.2.
+It is an export of a MDF model (mdf.s - MDF stateful, i.e. full MDF allowing stateful parameters) to PyTorch
+
+"""
+
import torch
import torch.nn as nn
import onnx
@@ -77,20 +82,17 @@ def forward(self, input):
)
output = model(dummy_input)
torch.onnx.export(
- model,
- dummy_input,
- "Arrays.onnx",
- verbose=True,
- input_names=[],
- opset_version=9,
+ model, dummy_input, "Arrays.onnx", verbose=True, input_names=[], opset_version=9
)
onnx_model = onnx.load("Arrays.onnx")
onnx.checker.check_model(onnx_model)
sess = rt.InferenceSession("Arrays.onnx")
-# res = sess.run(None, {sess.get_inputs()[0].name: dummy_input.numpy()})
res = sess.run(
- None, {}
-) # The input to the graph gets optimized out. No need to pass. Example should probably use input.
+ None,
+ {sess.get_inputs()[0].name: dummy_input.numpy()}
+ if len(sess.get_inputs()) > 0
+ else {},
+)
if __name__ == "__main__":
print("Exported to PyTorch and ONNX")
diff --git a/examples/PyTorch/MDF_PyTorch/MDF_to_PyTorch.py b/examples/PyTorch/MDF_PyTorch/MDF_to_PyTorch.py
index 527af8a1..47497000 100644
--- a/examples/PyTorch/MDF_PyTorch/MDF_to_PyTorch.py
+++ b/examples/PyTorch/MDF_PyTorch/MDF_to_PyTorch.py
@@ -1,27 +1,55 @@
from pathlib import Path
import os
+import sys
from modeci_mdf.utils import load_mdf
from modeci_mdf.interfaces.pytorch.exporter import mdf_to_pytorch
-# sample_examples=["examples/MDF/translation/Translated_Arrays.json", "examples/MDF/translation/Translated_Simple.json",
-# "examples/MDF/translation/Translated_ABCD.json", "examples/MDF/ABCD.json", "examples/MDF/Arrays.json"
-# "examples/MDF/Simple.json"]
+
def main(filename):
base_path = Path(__file__).parent
file_path = str((base_path / "../../.." / filename).resolve())
+
+ print("Converting MDF model in %s to PyTorch" % file_path)
+
model_input = file_path.replace(os.sep, "/")
mdf_model = load_mdf(model_input)
+
if "Translated" in model_input:
pytorch_model = mdf_to_pytorch(
- mdf_model, model_input, eval_models=False, version="mdf.0"
+ mdf_model,
+ model_input,
+ eval_models=False,
+ version="mdf.0", # (MDF "zero" - a simplified form of MDF)
)
else:
pytorch_model = mdf_to_pytorch(
- mdf_model, model_input, eval_models=False, version="mdf.s"
+ mdf_model,
+ model_input,
+ eval_models=False,
+ version="mdf.s", # (MDF "stateful" - full MDF allowing stateful parameters)
)
if __name__ == "__main__":
- filename = "examples/MDF/Simple.json"
- main(filename)
+
+ sample_examples = [
+ "examples/MDF/Simple.json",
+ "examples/MDF/ABCD.json",
+ "examples/MDF/Arrays.json",
+ "examples/MDF/translation/Translated_Arrays.json",
+ "examples/MDF/translation/Translated_Simple.json",
+ "examples/MDF/translation/Translated_ABCD.json",
+ ]
+
+ if "-all" in sys.argv:
+ for ex in sample_examples:
+ main(ex)
+
+ elif "-test" in sys.argv:
+ for ex in sample_examples:
+ if not "Translated" in ex:
+ main(ex)
+ else:
+ filename = "examples/MDF/Simple.json"
+ main(filename)
diff --git a/examples/PyTorch/MDF_PyTorch/Simple.onnx b/examples/PyTorch/MDF_PyTorch/Simple.onnx
index b7e13ac5..ae6a1ac8 100644
Binary files a/examples/PyTorch/MDF_PyTorch/Simple.onnx and b/examples/PyTorch/MDF_PyTorch/Simple.onnx differ
diff --git a/examples/PyTorch/MDF_PyTorch/Simple_pytorch.py b/examples/PyTorch/MDF_PyTorch/Simple_pytorch.py
index 8957e2cb..40f215a0 100644
--- a/examples/PyTorch/MDF_PyTorch/Simple_pytorch.py
+++ b/examples/PyTorch/MDF_PyTorch/Simple_pytorch.py
@@ -1,4 +1,9 @@
-"Converted examples from MDF models version(mdf.s) to PyTorch/ONNX"
+"""
+This script has been generated by modeci_mdf v0.4.2.
+It is an export of a MDF model (mdf.s - MDF stateful, i.e. full MDF allowing stateful parameters) to PyTorch
+
+"""
+
import torch
import torch.nn as nn
import onnx
@@ -80,16 +85,17 @@ def forward(self, input):
)
output = model(dummy_input)
torch.onnx.export(
- model,
- dummy_input,
- "Simple.onnx",
- verbose=True,
- input_names=[],
- opset_version=9,
+ model, dummy_input, "Simple.onnx", verbose=True, input_names=[], opset_version=9
)
onnx_model = onnx.load("Simple.onnx")
onnx.checker.check_model(onnx_model)
sess = rt.InferenceSession("Simple.onnx")
-res = sess.run(None, {sess.get_inputs()[0].name: dummy_input.numpy()})
+res = sess.run(
+ None,
+ {sess.get_inputs()[0].name: dummy_input.numpy()}
+ if len(sess.get_inputs()) > 0
+ else {},
+)
+
if __name__ == "__main__":
print("Exported to PyTorch and ONNX")
diff --git a/examples/PyTorch/README.md b/examples/PyTorch/README.md
index c04d8f1e..3556b459 100644
--- a/examples/PyTorch/README.md
+++ b/examples/PyTorch/README.md
@@ -5,8 +5,9 @@
## MDF to PyTorch
-To perform an MDF to PyTorch conversion, provide an MDF model as an input to the `mdf_to_pytorch` function
-which is available in [exporter.py](/src/modeci_mdf/interfaces/pytorch/exporter.py). The output of `mdf_to_pytorch` is a PyTorch model.
+To export an MDF model to PyTorch, provide an MDF model as an input to the [mdf_to_pytorch()](https://mdf.readthedocs.io/en/latest/api/_autosummary/modeci_mdf.interfaces.pytorch.exporter.mdf_to_pytorch.html#modeci_mdf.interfaces.pytorch.exporter.mdf_to_pytorch) function.
+
+The output of `mdf_to_pytorch` is a PyTorch model.
```
mdf_to_pytorch(
@@ -16,19 +17,29 @@ mdf_to_pytorch(
model_input: input file name
)
```
-Returns a dictionary where **key** = **model name**, **value** = **pytorch model object**
-### Implementation
+It returns a dictionary where `key` = `model name` and `value` = `PyTorch model object`.
+
+A test script demonstrating conversion of MDF model to PyTorch is at [MDF_to_PyTorch.py](/examples/PyTorch/MDF_PyTorch/MDF_to_PyTorch.py). This converts multiple MDF models to their respective PyTorch models. The converted models are available in folder: [MDF_PyTorch](/examples/PyTorch/MDF_PyTorch).
+
+### Examples
Below are some working examples of this functionality.
-1. One of sample MDF examples [ABCD.json](../MDF/ABCD.json) is converted to PyTorch [ABCD_pytorch.py](MDF_PyTorch/ABCD_pytorch.py).
-The PyTorch model is further converted to ONNX [ABCD.onnx](MDF_PyTorch/ABCD.onnx) and the results are compared in all three environments.
+#### 1) Simple ABCD example
+
+We convert one of the sample MDF examples [ABCD.json](../MDF/ABCD.json):
+
+![ABCD](../MDF/images/abcd.png)
+
+This is converted to PyTorch and can be seen here: [ABCD_pytorch.py](MDF_PyTorch/ABCD_pytorch.py).
+
+The PyTorch model is further converted to ONNX [ABCD.onnx](MDF_PyTorch/ABCD.onnx). An image of the contents of the ONNX model (visualized using [NETRON](https://netron.app/)) is below.
![ABCD.svg](ABCD.svg)
-2. Multi-Layer Perceptron MDF to PyTorch Conversion:
+#### 2) Multi-Layer Perceptron MDF to PyTorch Conversion:
To run an example where a simple Multi-Layer Perceptron (MLP) created using the MDF specification and executed using sample digit-recognition data, run:
@@ -36,27 +47,15 @@ The PyTorch model is further converted to ONNX [ABCD.onnx](MDF_PyTorch/ABCD.onnx
python mlp_pure_mdf.py
```
- A graph of the network can be created with `python mlp_pure_mdf.py -graph`:
-
- **MDF graph**
+A graph of the network can be created with `python mlp_pure_mdf.py -graph`:
![mlp_pure_mdf.png](mlp_pure_mdf.png)
- The network can be run against images from the MNIST database with: `python mlp_pure_mdf.py -run`, and produce 98% accuracy. The image below shows the results of 300 images:
+The network can be run against images from the MNIST database with: `python mlp_pure_mdf.py -run`, and produce 98% accuracy. The image below shows the results of 300 images:
![mlp_pure_mdf.results.png](mlp_pure_mdf.results.png)
- Conversion to PyTorch: TODO...
-
-The demo to convert an MDF model to PyTorch is at [MDF_to_PyTorch.py](/examples/PyTorch/MDF_PyTorch/MDF_to_PyTorch.py). This converts all the available MDF models to their respective Pytorch Models.
-Any model created using the MDF specification is translated to a PyTorch model, run:
-
-```bash
-python MDF_to_PyTorch
-```
-
-**NOTE:** The converted models are available in folder: [MDF_PyTorch](/examples/PyTorch/MDF_PyTorch).
## PyTorch to MDF
@@ -67,8 +66,8 @@ can be translated to TorchScript (via `torch.jit.script` or `torch.jit.trace`) s
then be able to be converted to their MDF representation automatically. Below are
several working examples of this functionality.
-To perform an PyTorch to MDF conversion, provide a PyTorch model as an input to the `pytorch_to_mdf` function
-which is available in [importer.py](/src/modeci_mdf/interfaces/pytorch/importer.py). The output of `pytorch_to_mdf` is an MDF model.
+To perform an PyTorch to MDF conversion, provide a PyTorch model as an input to the [pytorch_to_mdf()](https://mdf.readthedocs.io/en/latest/api/_autosummary/modeci_mdf.interfaces.pytorch.importer.pytorch_to_mdf.html#modeci_mdf.interfaces.pytorch.importer.pytorch_to_mdf) function
+which is available in [importer.py](/src/modeci_mdf/interfaces/pytorch/importer.py). The output of `pytorch_to_mdf()` is an MDF model.
```
pytorch_to_mdf(
@@ -80,14 +79,13 @@ pytorch_to_mdf(
use only ATEN ops.
)
```
-Returns a translated MDF model
+Returns a translated MDF model.
-### Implementation
+### Examples of usage
-Below are some working examples of this functionality.
+#### 1) Simple PyTorch To MDF
-1. Simple Pytorch To MDF:
- This is a simple fully-connected neural network model example consisting of input image of 224 * 224 * 3 and resulting in two classes as the output
+This is a simple fully-connected neural network model example consisting of input image of 224 * 224 * 3 and resulting in two classes as the output
To run an example of converting a PyTorch model written in PyTorch to its MDF representation simply run:
```bash
@@ -102,26 +100,32 @@ Below are some working examples of this functionality.
![simple_pytorch_to_mdf.svg](simple_pytorch_to_mdf.svg)
- **NOTE**: This command will run the netron python server on the local host where we can export the graph as svg/png
+ **NOTE**: This command will run the NETRON python server on the local host where we can export the graph as svg/png
The graph representation of the MDF model can be generated with:
```bash
python simple_pytorch_to_mdf.py -graph
```
- ![simple_pytorch_to_mdf.png](simple_pytorch_to_mdf.png)
+Graphical export from MDF level 1:
- To Visualize the PyTorch model
+![simple_pytorch_to_mdf.1.png](simple_pytorch_to_mdf.1.png)
+
+Graphical export from MDF level 3:
+
+![simple_pytorch_to_mdf.png](simple_pytorch_to_mdf.png)
+
+To visualize the PyTorch model:
```bash
python simple_pytorch_to_mdf.py -graph-torch
```
- ![simple_pytorch_to_mdf_torchviz.png](simple_pytorch_to_mdf_torchviz.png)
+![simple_pytorch_to_mdf_torchviz.png](simple_pytorch_to_mdf_torchviz.png)
- The MDF for this model is the written to [simple_pytorch_to_mdf.json](simple_pytorch_to_mdf.json). The model is then executed
- via the MDF scheduler and the results are compared to the native execution in PyTorch.
+The MDF for this model is the written to [simple_pytorch_to_mdf.json](simple_pytorch_to_mdf.json). The model is then executed
+via the MDF scheduler and the results are compared to the native execution in PyTorch.
-2. Inception Blocks Model:
+#### 2) Inception Blocks Model
![inception.svg](inception.svg)
To run an example of converting a PyTorch InceptionV3 like model written in PyTorch to its MDF representation simply run:
diff --git a/examples/PyTorch/Translated_mlp_pure_mdf.json b/examples/PyTorch/Translated_mlp_pure_mdf.json
deleted file mode 100644
index e95cc707..00000000
--- a/examples/PyTorch/Translated_mlp_pure_mdf.json
+++ /dev/null
@@ -1,44297 +0,0 @@
-{
- "mlp_pure_mdf": {
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- "generating_application": "Python modeci-mdf v0.3.1",
- "graphs": {
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- "B": "weight"
- }
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- "sum": {
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- "slope": 1,
- "intercept": "bias"
- }
- }
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- }
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- "edges": {
- "edge_1": {
- "sender": "mlp_input_layer",
- "receiver": "mlp_relu_1",
- "sender_port": "out_port",
- "receiver_port": "in_port"
- },
- "edge_2": {
- "sender": "mlp_relu_1",
- "receiver": "mlp_hidden_layer_with_relu",
- "sender_port": "out_port",
- "receiver_port": "in_port"
- },
- "edge_3": {
- "sender": "mlp_hidden_layer_with_relu",
- "receiver": "mlp_output_layer",
- "sender_port": "out_port",
- "receiver_port": "in_port"
- }
- }
- }
- }
- }
-}
diff --git a/examples/PyTorch/ddm.json b/examples/PyTorch/ddm.json
index ae6b69e1..fafae65e 100644
--- a/examples/PyTorch/ddm.json
+++ b/examples/PyTorch/ddm.json
@@ -1,11 +1,11 @@
{
"function": {
- "format": "ModECI MDF v0.2",
- "generating_application": "Python modeci-mdf v0.2.1",
+ "format": "ModECI MDF v0.4",
+ "generating_application": "Python modeci-mdf v0.4.2",
"graphs": {
"functionGraph": {
"nodes": {
- "Constant_7": {
+ "Constant_6": {
"parameters": {
"value": {
"value": 9223372036854775807
@@ -16,12 +16,12 @@
}
},
"output_ports": {
- "_7": {
+ "_6": {
"value": "onnx::Constant_1"
}
}
},
- "Constant_8": {
+ "Constant_7": {
"parameters": {
"value": {
"value": 0.0
@@ -32,15 +32,14 @@
}
},
"output_ports": {
- "_8": {
+ "_7": {
"value": "onnx::Constant_1"
}
}
},
- "Abs_9": {
+ "Abs_8": {
"input_ports": {
"input1": {
- "shape": "(?)",
"type": "Tensor"
}
},
@@ -53,19 +52,17 @@
}
},
"output_ports": {
- "_9": {
+ "_8": {
"value": "onnx::Abs_1"
}
}
},
- "Less_10": {
+ "Less_9": {
"input_ports": {
- "_9": {
- "shape": "(?)",
+ "_8": {
"type": "Tensor"
},
"input4": {
- "shape": "(?)",
"type": "Tensor"
}
},
@@ -73,21 +70,20 @@
"onnx::Less_1": {
"function": "onnx::Less",
"args": {
- "A": "_9",
+ "A": "_8",
"B": "input4"
}
}
},
"output_ports": {
- "_10": {
+ "_9": {
"value": "onnx::Less_1"
}
}
},
- "Cast_11": {
+ "Cast_10": {
"input_ports": {
- "_10": {
- "shape": "(?)",
+ "_9": {
"type": "Tensor"
}
},
@@ -98,26 +94,24 @@
"onnx::Cast_1": {
"function": "onnx::Cast",
"args": {
- "input": "_10"
+ "input": "_9"
}
}
},
"output_ports": {
- "_11": {
+ "_10": {
"value": "onnx::Cast_1"
}
}
},
- "LoopSubgraphLoop_12_13": {
+ "LoopSubgraphLoop_11_12": {
"nodes": {
- "Mul_18": {
+ "Mul_17": {
"input_ports": {
"input2": {
- "shape": "(?)",
"type": "Tensor"
},
"input6": {
- "shape": "(?)",
"type": "Tensor"
}
},
@@ -131,47 +125,82 @@
}
},
"output_ports": {
- "_18": {
+ "_17": {
"value": "onnx::Mul_1"
}
}
},
- "ATen_19": {
+ "RandomNormalLike_18": {
"input_ports": {
+ "_17": {
+ "type": "Tensor"
+ }
+ },
+ "parameters": {
+ "onnx::RandomNormalLike_1": {
+ "function": "onnx::RandomNormalLike",
+ "args": {
+ "input": "_17"
+ }
+ }
+ },
+ "output_ports": {
"_18": {
- "shape": "(?)",
+ "value": "onnx::RandomNormalLike_1"
+ }
+ }
+ },
+ "Mul_19": {
+ "input_ports": {
+ "input5": {
"type": "Tensor"
},
- "input5": {
- "shape": "(?)",
+ "_18": {
"type": "Tensor"
}
},
"parameters": {
- "arg2": {
- "value": null
- },
- "aten::ATen_1": {
- "function": "aten::ATen",
+ "onnx::Mul_1": {
+ "function": "onnx::Mul",
"args": {
- "arguments": [
- "arg0",
- "arg1",
- "arg2"
- ]
+ "A": "input5",
+ "B": "_18"
}
}
},
"output_ports": {
"_19": {
- "value": "aten::ATen_1"
+ "value": "onnx::Mul_1"
+ }
+ }
+ },
+ "Add_20": {
+ "input_ports": {
+ "_19": {
+ "type": "Tensor"
+ },
+ "_17": {
+ "type": "Tensor"
+ }
+ },
+ "parameters": {
+ "onnx::Add_1": {
+ "function": "onnx::Add",
+ "args": {
+ "A": "_19",
+ "B": "_17"
+ }
+ }
+ },
+ "output_ports": {
+ "_20": {
+ "value": "onnx::Add_1"
}
}
},
- "Sqrt_20": {
+ "Sqrt_21": {
"input_ports": {
"input6": {
- "shape": "(?)",
"type": "Tensor"
}
},
@@ -184,19 +213,17 @@
}
},
"output_ports": {
- "_20": {
+ "_21": {
"value": "onnx::Sqrt_1"
}
}
},
- "Mul_21": {
+ "Mul_22": {
"input_ports": {
- "_19": {
- "shape": "(?)",
+ "_20": {
"type": "Tensor"
},
- "_20": {
- "shape": "(?)",
+ "_21": {
"type": "Tensor"
}
},
@@ -204,25 +231,23 @@
"onnx::Mul_1": {
"function": "onnx::Mul",
"args": {
- "A": "_19",
- "B": "_20"
+ "A": "_20",
+ "B": "_21"
}
}
},
"output_ports": {
- "_21": {
+ "_22": {
"value": "onnx::Mul_1"
}
}
},
- "Add_22": {
+ "Add_23": {
"input_ports": {
- "_17": {
- "shape": "(?)",
+ "_16": {
"type": "Tensor"
},
- "_21": {
- "shape": "(?)",
+ "_22": {
"type": "Tensor"
}
},
@@ -230,18 +255,18 @@
"onnx::Add_1": {
"function": "onnx::Add",
"args": {
- "A": "_17",
- "B": "_21"
+ "A": "_16",
+ "B": "_22"
}
}
},
"output_ports": {
- "_22": {
+ "_23": {
"value": "onnx::Add_1"
}
}
},
- "Constant_23": {
+ "Constant_24": {
"parameters": {
"value": {
"value": 1.0
@@ -252,19 +277,17 @@
}
},
"output_ports": {
- "_23": {
+ "_24": {
"value": "onnx::Constant_1"
}
}
},
- "Add_24": {
+ "Add_25": {
"input_ports": {
- "_16": {
- "shape": "(?)",
+ "_15": {
"type": "Tensor"
},
- "_23": {
- "shape": "(?)",
+ "_24": {
"type": "Tensor"
}
},
@@ -272,21 +295,20 @@
"onnx::Add_1": {
"function": "onnx::Add",
"args": {
- "A": "_16",
- "B": "_23"
+ "A": "_15",
+ "B": "_24"
}
}
},
"output_ports": {
- "_24": {
+ "_25": {
"value": "onnx::Add_1"
}
}
},
- "Abs_25": {
+ "Abs_26": {
"input_ports": {
- "_22": {
- "shape": "(?)",
+ "_23": {
"type": "Tensor"
}
},
@@ -294,24 +316,22 @@
"onnx::Abs_1": {
"function": "onnx::Abs",
"args": {
- "X": "_22"
+ "X": "_23"
}
}
},
"output_ports": {
- "_25": {
+ "_26": {
"value": "onnx::Abs_1"
}
}
},
- "Less_26": {
+ "Less_27": {
"input_ports": {
- "_25": {
- "shape": "(?)",
+ "_26": {
"type": "Tensor"
},
"input4": {
- "shape": "(?)",
"type": "Tensor"
}
},
@@ -319,21 +339,20 @@
"onnx::Less_1": {
"function": "onnx::Less",
"args": {
- "A": "_25",
+ "A": "_26",
"B": "input4"
}
}
},
"output_ports": {
- "_26": {
+ "_27": {
"value": "onnx::Less_1"
}
}
},
- "Cast_27": {
+ "Cast_28": {
"input_ports": {
- "_26": {
- "shape": "(?)",
+ "_27": {
"type": "Tensor"
}
},
@@ -344,76 +363,92 @@
"onnx::Cast_1": {
"function": "onnx::Cast",
"args": {
- "input": "_26"
+ "input": "_27"
}
}
},
"output_ports": {
- "_27": {
+ "_28": {
"value": "onnx::Cast_1"
}
}
}
},
"edges": {
- "Mul_18_ATen_19": {
- "sender": "Mul_18",
- "receiver": "ATen_19",
+ "Mul_17_RandomNormalLike_18": {
+ "sender": "Mul_17",
+ "receiver": "RandomNormalLike_18",
+ "sender_port": "_17",
+ "receiver_port": "_17"
+ },
+ "Mul_17_Add_20": {
+ "sender": "Mul_17",
+ "receiver": "Add_20",
+ "sender_port": "_17",
+ "receiver_port": "_17"
+ },
+ "RandomNormalLike_18_Mul_19": {
+ "sender": "RandomNormalLike_18",
+ "receiver": "Mul_19",
"sender_port": "_18",
"receiver_port": "_18"
},
- "ATen_19_Mul_21": {
- "sender": "ATen_19",
- "receiver": "Mul_21",
+ "Mul_19_Add_20": {
+ "sender": "Mul_19",
+ "receiver": "Add_20",
"sender_port": "_19",
"receiver_port": "_19"
},
- "Sqrt_20_Mul_21": {
- "sender": "Sqrt_20",
- "receiver": "Mul_21",
+ "Add_20_Mul_22": {
+ "sender": "Add_20",
+ "receiver": "Mul_22",
"sender_port": "_20",
"receiver_port": "_20"
},
- "Mul_21_Add_22": {
- "sender": "Mul_21",
- "receiver": "Add_22",
+ "Sqrt_21_Mul_22": {
+ "sender": "Sqrt_21",
+ "receiver": "Mul_22",
"sender_port": "_21",
"receiver_port": "_21"
},
- "Add_22_Abs_25": {
- "sender": "Add_22",
- "receiver": "Abs_25",
+ "Mul_22_Add_23": {
+ "sender": "Mul_22",
+ "receiver": "Add_23",
"sender_port": "_22",
"receiver_port": "_22"
},
- "Constant_23_Add_24": {
- "sender": "Constant_23",
- "receiver": "Add_24",
+ "Add_23_Abs_26": {
+ "sender": "Add_23",
+ "receiver": "Abs_26",
"sender_port": "_23",
"receiver_port": "_23"
},
- "Abs_25_Less_26": {
- "sender": "Abs_25",
- "receiver": "Less_26",
- "sender_port": "_25",
- "receiver_port": "_25"
+ "Constant_24_Add_25": {
+ "sender": "Constant_24",
+ "receiver": "Add_25",
+ "sender_port": "_24",
+ "receiver_port": "_24"
},
- "Less_26_Cast_27": {
- "sender": "Less_26",
- "receiver": "Cast_27",
+ "Abs_26_Less_27": {
+ "sender": "Abs_26",
+ "receiver": "Less_27",
"sender_port": "_26",
"receiver_port": "_26"
+ },
+ "Less_27_Cast_28": {
+ "sender": "Less_27",
+ "receiver": "Cast_28",
+ "sender_port": "_27",
+ "receiver_port": "_27"
}
}
},
- "Mul_28": {
+ "Mul_29": {
"input_ports": {
- "_12": {
- "shape": "(?)",
+ "_11": {
"type": "Tensor"
},
"input6": {
- "shape": "(?)",
"type": "Tensor"
}
},
@@ -421,25 +456,23 @@
"onnx::Mul_1": {
"function": "onnx::Mul",
"args": {
- "A": "_12",
+ "A": "_11",
"B": "input6"
}
}
},
"output_ports": {
- "_28": {
+ "_29": {
"value": "onnx::Mul_1"
}
}
},
- "Add_29": {
+ "Add_30": {
"input_ports": {
"input3": {
- "shape": "(?)",
"type": "Tensor"
},
- "_28": {
- "shape": "(?)",
+ "_29": {
"type": "Tensor"
}
},
@@ -448,24 +481,22 @@
"function": "onnx::Add",
"args": {
"A": "input3",
- "B": "_28"
+ "B": "_29"
}
}
},
"output_ports": {
- "_29": {
+ "_30": {
"value": "onnx::Add_1"
}
}
},
- "GreaterOrEqual_30": {
+ "GreaterOrEqual_31": {
"input_ports": {
- "_13": {
- "shape": "(?)",
+ "_12": {
"type": "Tensor"
},
"input4": {
- "shape": "(?)",
"type": "Tensor"
}
},
@@ -473,57 +504,58 @@
"onnx::GreaterOrEqual_1": {
"function": "onnx::GreaterOrEqual",
"args": {
- "A": "_13",
+ "A": "_12",
"B": "input4"
}
}
},
"output_ports": {
- "_30": {
+ "_31": {
"value": "onnx::GreaterOrEqual_1"
}
}
}
},
"edges": {
- "Constant_7_Loop_12_13": {
+ "Constant_6_Loop_11_12": {
+ "sender": "Constant_6",
+ "receiver": "Loop_11_12",
+ "sender_port": "_6",
+ "receiver_port": "_6"
+ },
+ "Constant_7_Loop_11_12": {
"sender": "Constant_7",
- "receiver": "Loop_12_13",
+ "receiver": "Loop_11_12",
"sender_port": "_7",
"receiver_port": "_7"
},
- "Constant_8_Loop_12_13": {
- "sender": "Constant_8",
- "receiver": "Loop_12_13",
+ "Abs_8_Less_9": {
+ "sender": "Abs_8",
+ "receiver": "Less_9",
"sender_port": "_8",
"receiver_port": "_8"
},
- "Abs_9_Less_10": {
- "sender": "Abs_9",
- "receiver": "Less_10",
+ "Less_9_Cast_10": {
+ "sender": "Less_9",
+ "receiver": "Cast_10",
"sender_port": "_9",
"receiver_port": "_9"
},
- "Less_10_Cast_11": {
- "sender": "Less_10",
- "receiver": "Cast_11",
+ "Cast_10_Loop_11_12": {
+ "sender": "Cast_10",
+ "receiver": "Loop_11_12",
"sender_port": "_10",
"receiver_port": "_10"
},
- "Cast_11_Loop_12_13": {
- "sender": "Cast_11",
- "receiver": "Loop_12_13",
- "sender_port": "_11",
- "receiver_port": "_11"
- },
- "Mul_28_Add_29": {
- "sender": "Mul_28",
- "receiver": "Add_29",
- "sender_port": "_28",
- "receiver_port": "_28"
+ "Mul_29_Add_30": {
+ "sender": "Mul_29",
+ "receiver": "Add_30",
+ "sender_port": "_29",
+ "receiver_port": "_29"
}
}
}
- }
+ },
+ "onnx_opset_version": 9
}
}
diff --git a/examples/PyTorch/ddm.png b/examples/PyTorch/ddm.png
index 9f8763fe..8381778b 100644
Binary files a/examples/PyTorch/ddm.png and b/examples/PyTorch/ddm.png differ
diff --git a/examples/PyTorch/inception.json b/examples/PyTorch/inception.json
index b089df14..7fb24753 100644
--- a/examples/PyTorch/inception.json
+++ b/examples/PyTorch/inception.json
@@ -1,7 +1,7 @@
{
"InceptionBlocks": {
"format": "ModECI MDF v0.4",
- "generating_application": "Python modeci-mdf v0.4.0",
+ "generating_application": "Python modeci-mdf v0.4.2",
"graphs": {
"InceptionBlocksGraph": {
"nodes": {
diff --git a/examples/PyTorch/inception.png b/examples/PyTorch/inception.png
index 2a6a4a07..71f856a5 100644
Binary files a/examples/PyTorch/inception.png and b/examples/PyTorch/inception.png differ
diff --git a/examples/PyTorch/inception.py b/examples/PyTorch/inception.py
index 8c3df546..fb10a6c7 100644
--- a/examples/PyTorch/inception.py
+++ b/examples/PyTorch/inception.py
@@ -310,6 +310,7 @@ def main():
level=1,
filename_root="inception",
only_warn_on_fail=True, # Makes sure test of this doesn't fail on Windows on GitHub Actions
+ is_horizontal=True,
)
diff --git a/examples/PyTorch/leaky_competing_accumulator.yaml b/examples/PyTorch/leaky_competing_accumulator.yaml
deleted file mode 100644
index a5a31952..00000000
--- a/examples/PyTorch/leaky_competing_accumulator.yaml
+++ /dev/null
@@ -1,79 +0,0 @@
-LeakyCompetingAccumulator:
- format: ModECI MDF v0.1
- generating_application: Python modeci-mdf v0.1.2
-
- graphs:
- lca_2:
- nodes:
- input_layer:
- input_ports:
- input:
- shape: (2,)
- output_ports:
- out_port:
- value: input
-
- accumulator_layer:
- parameters:
- leak: 0.2
- competition: 0.4
- self_excitation: 0.0
- noise_std: 1.0
- time_step_size: 0.001
- starting_value: 0.0
-
- input_ports:
- input:
- shape: (2,)
-
- prev_activity:
- shape: (2,)
- initializer: [starting_value, starting_value]
-
- functions:
- lca_integrate_timestep:
- function: lca_timestep
- # dx[i] = input[i] - (leak-self_excitation)*activity[i] - np.sum(competition*np.delete(activity, i)))
- # dW[i] = np.random.normal(loc=0.0, scale=noise_std*sqrt(time_step_size))
- # output[i] = activity[i] + dx[i] + dW[i]
- # output[i] = max(0.0, output[i])
- # return output
- args:
- input: input
- activity: prev_activity
-
- output_ports:
- output:
- value: lca_timestep
- shape: (2,)
-
- edges:
- input_to_accumulator:
- sender: input_layer
- sender_port: out_port
- receiver: accumulator_layer
- receiver_port: input
-
- recurrent_activity:
- sender: accumulator_layer
- sender_port: output
- receiver: accumulator_layer
- receiver_port: prev_activity
-
- conditions:
- node_specific:
- accumulator_layer:
- type: or
- args:
- - type: TimeInterval
- args: []
- kwargs:
- end: 3000
- interval: 1
- unit: ms
- - type: Threshold
- args: []
- kwargs:
- parameter: output[i]
- threshold: 0.8
- direction: ">="
diff --git a/examples/PyTorch/leaky_competing_accumulator2.yaml b/examples/PyTorch/leaky_competing_accumulator2.yaml
deleted file mode 100644
index eaf70d5f..00000000
--- a/examples/PyTorch/leaky_competing_accumulator2.yaml
+++ /dev/null
@@ -1,157 +0,0 @@
-LeakyCompetingAccumulator:
- format: ModECI MDF v0.1
- generating_application: Python modeci-mdf v0.1.2
-
- graphs:
- lca_2:
- nodes:
- input_node1:
- input_ports:
- input:
- shape: (1,)
- output_ports:
- out_port:
- value: input
-
- input_node2:
- input_ports:
- input:
- shape: (1,)
- output_ports:
- out_port:
- value: input
-
- accumulator_node1:
- parameters:
- leak: 0.2
- competition: 0.4
- self_excitation: 0.0
- noise_std: 1.0
- time_step_size: 0.001
- starting_value: 0.0
-
- input_ports:
- input:
- shape: (1,)
-
- prev_activity:
- shape: (1,)
- initializer: starting_value
-
- comp_activity:
- shape: (1,)
- initializer: starting_value
-
- functions:
- lca_integrate_timestep:
- function: lca_timestep
- # dx = input - (leak-self_excitation)*activity - competition*comp_activity
- # dW = np.random.normal(loc=0.0, scale=noise_std*sqrt(time_step_size))
- # output = activity + dx + dW
- # output = max(0.0, output)
- # return output
- args:
- input: input
- activity: prev_activity
- comp_activity: comp_activity
-
- output_ports:
- output:
- value: lca_timestep
- shape: (1,)
-
- accumulator_node2:
- parameters:
- leak: 0.2
- competition: 0.4
- self_excitation: 0.0
- noise_std: 1.0
- time_step_size: 0.001
- starting_value: 0.0
-
- input_ports:
- input:
- shape: (1,)
-
- prev_activity:
- shape: (1,)
- initializer: starting_value
-
- comp_activity:
- shape: (1,)
- initializer: starting_value
-
- functions:
- lca_integrate_timestep:
- function: lca_timestep
- # dx = input - (leak-self_excitation)*activity - competition*comp_activity
- # dW = np.random.normal(loc=0.0, scale=noise_std*sqrt(time_step_size))
- # output = activity + dx + dW
- # output = max(0.0, output)
- # return output
- args:
- input: input
- activity: prev_activity
- comp_activity: comp_activity
-
- output_ports:
- output:
- value: lca_timestep
- shape: (1,)
-
- edges:
- input1_to_accumulator1:
- sender: input_node1
- sender_port: out_port
- receiver: accumulator_node1
- receiver_port: input
-
- input2_to_accumulator2:
- sender: input_node2
- sender_port: out_port
- receiver: accumulator_node2
- receiver_port: input
-
- recurrent_activity1:
- sender: accumulator_node1
- sender_port: output
- receiver: accumulator_node1
- receiver_port: prev_activity
-
- recurrent_activity2:
- sender: accumulator_node2
- sender_port: output
- receiver: accumulator_node2
- receiver_port: prev_activity
-
- comp_activity1:
- sender: accumulator_node1
- sender_port: output
- receiver: accumulator_node2
- receiver_port: comp_activity
-
- comp_activity2:
- sender: accumulator_node3
- sender_port: output
- receiver: accumulator_node1
- receiver_port: comp_activity
-
-
-
- conditions:
- node_specific:
- accumulator_node1:
- type: or
- args:
- - type: TimeInterval
- args: []
- kwargs:
- end: 3000
- interval: 1
- unit: ms
- - type: Threshold
- args: []
- kwargs:
- parameter: output[i]
- threshold: 0.8
- direction: ">="
diff --git a/examples/PyTorch/mlp.py.old b/examples/PyTorch/mlp.py.old
deleted file mode 100644
index cab2a30c..00000000
--- a/examples/PyTorch/mlp.py.old
+++ /dev/null
@@ -1,46 +0,0 @@
-import os
-import torch
-import numpy as np
-import torch.nn
-import sys
-from modeci_mdf.interfaces.pytorch.exporter import mdf_to_pytorch
-
-
-# Load Model
-script_dir = os.path.dirname(sys.argv[0])
-mdf_name = "mlp_classifier.json"
-
-file_path = os.path.join(os.getcwd(), script_dir, mdf_name)
-
-models = mdf_to_pytorch(file_path, eval_models=True)
-model = models["mlp_classifier"]
-model.eval()
-
-# Iterate on training data, feed forward and log accuracy
-
-imgs_path = os.path.join(
- os.getcwd(), os.path.dirname(sys.argv[0]), "example_data", "imgs.npy"
-)
-labels_path = os.path.join(
- os.getcwd(), os.path.dirname(sys.argv[0]), "example_data", "labels.npy"
-)
-
-imgs = np.load(imgs_path)
-labels = np.load(labels_path)
-
-matches = 0
-for i in range(len(imgs)):
- img = torch.Tensor(imgs[i, :, :]).view(-1, 14 * 14)
- target = labels[i]
- prediction = model(img)
- match = target == int(prediction)
- if match:
- matches += 1
- print(
- "Image %i: target: %s, prediction: %s, match: %s"
- % (i, target, prediction, match)
- )
-
-accuracy = (100.0 * matches) / len(imgs)
-print("Matches: %i/%i, accuracy: %s%%" % (matches, len(imgs), accuracy))
-assert accuracy > 97
diff --git a/examples/PyTorch/mlp_classifier.json b/examples/PyTorch/mlp_classifier.json
deleted file mode 100644
index 5c0adc12..00000000
--- a/examples/PyTorch/mlp_classifier.json
+++ /dev/null
@@ -1,289 +0,0 @@
-{
- "mlp_classifier": {
- "graphs": {
- "mlp_classifier": {
- "nodes": {
-
- "mlp_input_matmul": {
- "parameters": {
- "weight": {
- "value": "weights.mlp_classifier.graphs.mlp_classifier.nodes.mlp_input_layer.parameters.weight"
- }
- },
- "functions":{
- "Linear_1": {
- "function": "MatMul",
- "args":{
- "A":"in_1",
- "B":"weight"
- }
- }
- },
- "input_ports": {
- "in_1": {
- "shape": [196]
- }
- },
- "output_ports": {
- "out_1": {
- "shape": [128],
- "value":"Linear_1"
- }
- }
- },
- "mlp_input_add": {
- "parameters": {
- "bias": {
- "value": "weights.mlp_classifier.graphs.mlp_classifier.nodes.mlp_input_layer.parameters.bias"
- }
- },
- "functions":{
- "Bias_Add_1": {
- "function": "Add",
- "args":{
- "A":"in_1",
- "B":"bias"
- }
- }
- },
- "input_ports": {
- "in_1": {
- "shape": [128]
- }
- },
- "output_ports": {
- "out_1": {
- "shape": [128],
- "value":"Bias_Add_1"
- }
- }
- },
- "mlp_input_relu": {
- "functions": {
- "Relu_1": {
- "function": "relu",
- "args": {
- "A": "in_1"
- }
- }
- },
- "input_ports": {
- "in_1": {
- "shape": [128]
- }
- },
- "output_ports": {
- "out_1": {
- "shape": [128],
- "value":"Relu_1"
- }
- }
- },
-
- "mlp_hidden_matmul": {
- "parameters": {
- "weight": {
- "value": "weights.mlp_classifier.graphs.mlp_classifier.nodes.mlp_hidden_layer_with_relu.parameters.weight"
- }
- },
- "functions":{
- "Linear_2": {
- "function": "MatMul",
- "args":{
- "A":"in_1",
- "B":"weight"
- }
- }
- },
- "input_ports": {
- "in_1": {
- "shape": [128]
- }
- },
- "output_ports": {
- "out_1": {
- "shape": [128],
- "value":"Linear_2"
- }
- }
- },
- "mlp_hidden_add": {
- "parameters": {
- "bias": {
- "value": "weights.mlp_classifier.graphs.mlp_classifier.nodes.mlp_hidden_layer_with_relu.parameters.bias"
- }
- },
- "functions":{
- "Bias_Add_2": {
- "function": "Add",
- "args":{
- "A":"in_1",
- "B":"bias"
- }
- }
- },
- "input_ports": {
- "in_1": {
- "shape": [128]
- }
- },
- "output_ports": {
- "out_1": {
- "shape": [128],
- "value":"Bias_Add_2"
- }
- }
- },
- "mlp_hidden_relu": {
- "functions": {
- "Relu_2": {
- "function": "relu",
- "args": {
- "A": "in_1"
- }
- }
- },
- "input_ports": {
- "in_1": {
- "shape": [128]
- }
- },
- "output_ports": {
- "out_1": {
- "shape": [128],
- "value":"Relu_2"
- }
- }
- },
-
- "mlp_output_matmul": {
- "parameters": {
- "weight": {
- "value": "weights.mlp_classifier.graphs.mlp_classifier.nodes.mlp_output_layer.parameters.weight"
- }
- },
- "functions":{
- "Linear_3": {
- "function": "MatMul",
- "args": {
- "A": "in_1",
- "B": "weight"
- }
- }
- },
- "input_ports": {
- "in_1": {
- "shape": [128]
- }
- },
- "output_ports": {
- "out_1": {
- "shape": [10],
- "value":"Linear_3"
- }
- }
- },
- "mlp_output_add": {
- "parameters": {
- "bias": {
- "value": "weights.mlp_classifier.graphs.mlp_classifier.nodes.mlp_output_layer.parameters.bias"
- }
- },
- "functions":{
- "Bias_Add_3": {
- "function": "Add",
- "args": {
- "A": "in_1",
- "B": "bias"
- }
- }
- },
- "input_ports": {
- "in_1": {
- "shape": [10]
- }
- },
- "output_ports": {
- "out_1": {
- "shape": [10],
- "value":"Bias_Add_3"
- }
- }
- },
-
- "argmax_1": {
- "functions": {
- "argmax_1": {
- "function": "argmax",
- "args": {
- "A": "in_1"
- }
- }
- },
- "input_ports": {
- "in_1": {
- "shape": [10]
- }
- },
- "output_ports": {
- "out_1": {
- "shape": [1],
- "value":"argmax_1"
- }
- }
- }
- },
- "edges": {
- "edge1": {
- "sender": "mlp_input_matmul",
- "receiver": "mlp_input_add",
- "sender_port": "out_1",
- "receiver_port": "in_1"
- },
- "edge2": {
- "sender": "mlp_input_add",
- "receiver": "mlp_input_relu",
- "sender_port": "out_1",
- "receiver_port": "in_1"
- },
- "edge3": {
- "sender": "mlp_input_relu",
- "receiver": "mlp_hidden_matmul",
- "sender_port": "out_1",
- "receiver_port": "in_1"
- },
- "edge4": {
- "sender": "mlp_hidden_matmul",
- "receiver": "mlp_hidden_add",
- "sender_port": "out_1",
- "receiver_port": "in_1"
- },
- "edge5": {
- "sender": "mlp_hidden_add",
- "receiver": "mlp_hidden_relu",
- "sender_port": "out_1",
- "receiver_port": "in_1"
- },
- "edge6": {
- "sender": "mlp_hidden_relu",
- "receiver": "mlp_output_matmul",
- "sender_port": "out_1",
- "receiver_port": "in_1"
- },
- "edge7": {
- "sender": "mlp_output_matmul",
- "receiver": "mlp_output_add",
- "sender_port": "out_1",
- "receiver_port": "in_1"
- },
- "edge8": {
- "sender": "mlp_output_add",
- "receiver": "argmax_1",
- "sender_port": "out_1",
- "receiver_port": "in_1"
- }
- }
- }
- }
- }
-}
diff --git a/examples/PyTorch/mlp_pure_mdf.json b/examples/PyTorch/mlp_pure_mdf.json
index 12ad4e16..ccb8b3c0 100644
--- a/examples/PyTorch/mlp_pure_mdf.json
+++ b/examples/PyTorch/mlp_pure_mdf.json
@@ -1,7 +1,7 @@
{
"mlp_pure_mdf": {
- "format": "ModECI MDF v0.3",
- "generating_application": "Python modeci-mdf v0.3.3",
+ "format": "ModECI MDF v0.4",
+ "generating_application": "Python modeci-mdf v0.4.2",
"graphs": {
"mlp_pure_mdf": {
"nodes": {
diff --git a/examples/PyTorch/mlp_pure_mdf.png b/examples/PyTorch/mlp_pure_mdf.png
index 42b894e0..4ead2edb 100644
Binary files a/examples/PyTorch/mlp_pure_mdf.png and b/examples/PyTorch/mlp_pure_mdf.png differ
diff --git a/examples/PyTorch/mlp_pure_mdf.py b/examples/PyTorch/mlp_pure_mdf.py
index 4dec5a3f..a660bfa2 100644
--- a/examples/PyTorch/mlp_pure_mdf.py
+++ b/examples/PyTorch/mlp_pure_mdf.py
@@ -162,10 +162,16 @@ def main():
mod_graph = mod.graphs[0]
- new_file = mod.to_yaml_file("%s.yaml" % mod.id)
- new_file = mod.to_json_file("%s.json" % mod.id)
+ yaml_file = mod.to_yaml_file("%s.yaml" % mod.id)
+ json_file = mod.to_json_file("%s.json" % mod.id)
- # mdf_to_graphviz(mod_graph,view_on_render=not test_all, level=3)
+ if "-mdf_to_pytorch" in sys.argv:
+ print("Exporting model to pure PyTorch")
+ from modeci_mdf.interfaces.pytorch import mdf_to_pytorch
+
+ pytorch_model = mdf_to_pytorch(
+ mod, yaml_file, eval_models=False, version="mdf.s"
+ )
from modelspec.utils import FORMAT_NUMPY, FORMAT_TENSORFLOW
@@ -189,10 +195,6 @@ def main():
out = _val_info(eg.enodes[n].evaluable_outputs["out_port"].curr_value)
print(f"Final output value of node {n}:\t {out}")
- from modeci_mdf.interfaces.pytorch import mdf_to_pytorch
-
- ###pytorch_model = mdf_to_pytorch(mod, ".", eval_models=False, version="mdf.s")
-
if "-graph" in sys.argv:
mod.to_graph_image(
engine="dot",
diff --git a/examples/PyTorch/mlp_pure_mdf.results.png b/examples/PyTorch/mlp_pure_mdf.results.png
index afcd6b34..25000a72 100644
Binary files a/examples/PyTorch/mlp_pure_mdf.results.png and b/examples/PyTorch/mlp_pure_mdf.results.png differ
diff --git a/examples/PyTorch/mlp_pure_mdf.yaml b/examples/PyTorch/mlp_pure_mdf.yaml
index 4bc5099e..872a282f 100644
--- a/examples/PyTorch/mlp_pure_mdf.yaml
+++ b/examples/PyTorch/mlp_pure_mdf.yaml
@@ -1,6 +1,6 @@
mlp_pure_mdf:
- format: ModECI MDF v0.3
- generating_application: Python modeci-mdf v0.3.3
+ format: ModECI MDF v0.4
+ generating_application: Python modeci-mdf v0.4.2
graphs:
mlp_pure_mdf:
nodes:
diff --git a/examples/PyTorch/regenerate.sh b/examples/PyTorch/regenerate.sh
new file mode 100755
index 00000000..bb1bbf50
--- /dev/null
+++ b/examples/PyTorch/regenerate.sh
@@ -0,0 +1,17 @@
+#!/bin/bash
+set -ex
+
+cd MDF_PyTorch
+python MDF_to_PyTorch.py -test
+
+cd ..
+
+python mlp_pure_mdf.py -graph
+
+
+python simple_pytorch_to_mdf.py -graph -graph-torch
+
+
+python inception.py -graph
+
+python pytorch_ddm.py -graph
diff --git a/examples/PyTorch/simple_pytorch_to_mdf b/examples/PyTorch/simple_pytorch_to_mdf
deleted file mode 100644
index a2fc4f5b..00000000
--- a/examples/PyTorch/simple_pytorch_to_mdf
+++ /dev/null
@@ -1,28 +0,0 @@
-digraph SimpleNetGraph {
- node [color="#444444" fontcolor="#444444" penwidth=1 shape=box style=rounded]
- Constant_9 [label=<>]
- node [color="#444444" fontcolor="#444444" penwidth=1 shape=box style=rounded]
- Reshape_10 [label=<>]
- node [color="#444444" fontcolor="#444444" penwidth=1 shape=box style=rounded]
- Gemm_11 [label=<>]
- node [color="#444444" fontcolor="#444444" penwidth=1 shape=box style=rounded]
- Relu_12 [label=<>]
- node [color="#444444" fontcolor="#444444" penwidth=1 shape=box style=rounded]
- Gemm_13 [label=<>]
- node [color="#444444" fontcolor="#444444" penwidth=1 shape=box style=rounded]
- Relu_14 [label=<>]
- node [color="#444444" fontcolor="#444444" penwidth=1 shape=box style=rounded]
- Gemm_15 [label=<>]
- node [color="#444444" fontcolor="#444444" penwidth=1 shape=box style=rounded]
- Relu_16 [label=<>]
- node [color="#444444" fontcolor="#444444" penwidth=1 shape=box style=rounded]
- Gemm_17 [label=<>]
- Constant_9 -> Reshape_10 [label="" arrowhead=empty]
- Reshape_10 -> Gemm_11 [label="" arrowhead=empty]
- Gemm_11 -> Relu_12 [label="" arrowhead=empty]
- Relu_12 -> Gemm_13 [label="" arrowhead=empty]
- Gemm_13 -> Relu_14 [label="" arrowhead=empty]
- Relu_14 -> Gemm_15 [label="" arrowhead=empty]
- Gemm_15 -> Relu_16 [label="" arrowhead=empty]
- Relu_16 -> Gemm_17 [label="" arrowhead=empty]
-}
diff --git a/examples/PyTorch/simple_pytorch_to_mdf.1.png b/examples/PyTorch/simple_pytorch_to_mdf.1.png
new file mode 100644
index 00000000..447361c1
Binary files /dev/null and b/examples/PyTorch/simple_pytorch_to_mdf.1.png differ
diff --git a/examples/PyTorch/simple_pytorch_to_mdf.json b/examples/PyTorch/simple_pytorch_to_mdf.json
index a7bf8c44..fcd5e5ff 100644
--- a/examples/PyTorch/simple_pytorch_to_mdf.json
+++ b/examples/PyTorch/simple_pytorch_to_mdf.json
@@ -1,7 +1,7 @@
{
"SimpleNet": {
"format": "ModECI MDF v0.4",
- "generating_application": "Python modeci-mdf v0.4.1",
+ "generating_application": "Python modeci-mdf v0.4.2",
"graphs": {
"SimpleNetGraph": {
"nodes": {
diff --git a/examples/PyTorch/simple_pytorch_to_mdf.png b/examples/PyTorch/simple_pytorch_to_mdf.png
index d858e407..8f0c60fb 100644
Binary files a/examples/PyTorch/simple_pytorch_to_mdf.png and b/examples/PyTorch/simple_pytorch_to_mdf.png differ
diff --git a/examples/PyTorch/simple_pytorch_to_mdf.py b/examples/PyTorch/simple_pytorch_to_mdf.py
index c3af18a2..3e85243a 100644
--- a/examples/PyTorch/simple_pytorch_to_mdf.py
+++ b/examples/PyTorch/simple_pytorch_to_mdf.py
@@ -95,6 +95,15 @@ def main():
# export to mdf graph
if "-graph" in sys.argv:
+ mdf_model.to_graph_image(
+ engine="dot",
+ output_format="png",
+ view_on_render=False,
+ level=1,
+ filename_root="simple_pytorch_to_mdf.1",
+ only_warn_on_fail=True, # Makes sure test of this doesn't fail on Windows on GitHub Actions
+ is_horizontal=True,
+ )
mdf_model.to_graph_image(
engine="dot",
output_format="png",
diff --git a/examples/PyTorch/simple_pytorch_to_mdf_torchviz.png b/examples/PyTorch/simple_pytorch_to_mdf_torchviz.png
index ea57e27a..60995f0d 100644
Binary files a/examples/PyTorch/simple_pytorch_to_mdf_torchviz.png and b/examples/PyTorch/simple_pytorch_to_mdf_torchviz.png differ
diff --git a/examples/README.md b/examples/README.md
index e1f49745..1dd3d2d3 100644
--- a/examples/README.md
+++ b/examples/README.md
@@ -1,7 +1,7 @@
-
-
# Examples of MDF files and conversions to target environments
+
+
- [MDF files](MDF): A number of Python scripts using the MDF Python API, as well as the JSON and YAML files.
- [NeuroML](NeuroML): Examples of interactions between NeuroML and MDF.
- [ONNX](ONNX): Examples of interactions between MDF and ONNX.
diff --git a/examples/SimpleExample.ipynb b/examples/SimpleExample.ipynb
index 658388ab..97342c1c 100644
--- a/examples/SimpleExample.ipynb
+++ b/examples/SimpleExample.ipynb
@@ -18,7 +18,7 @@
"\n",
"## 1. Create a [Model](https://mdf.readthedocs.io/en/latest/api/Specification.html#model) containing a [Graph](https://mdf.readthedocs.io/en/latest/api/Specification.html#graph) \n",
"\n",
- "A Model is the top-level construct in MDF,it contains Graph objects.\n"
+ "A Model is the top-level construct in MDF, it contains Graph objects.\n"
]
},
{
@@ -33,6 +33,7 @@
"# Create a model containing one graph\n",
"mod = Model(id=\"AB\")\n",
"mod_graph = Graph(id=\"ab_example\")\n",
+ "\n",
"#append the Graph object to the Model\n",
"mod.graphs.append(mod_graph)\n"
]
@@ -44,7 +45,9 @@
"source": [
"\n",
"### 1.1 Create the first [Node](https://mdf.readthedocs.io/en/latest/api/Specification.html#node)\n",
- "A Node defines the computational flow of a Model. It receives and transmits information via Input and Output ports."
+ "A Node defines the computational flow of a Model. It receives and transmits information via [Input](https://mdf.readthedocs.io/en/latest/api/Specification.html#inputport) and [Output ports](https://mdf.readthedocs.io/en/latest/api/Specification.html#outputport), and can have internal [parameters](https://mdf.readthedocs.io/en/latest/api/Specification.html#parameter). \n",
+ "\n",
+ "The metadata does not influence the evaluation of the node, but is useful for adding information related to how the model element should be handled by different applications (e.g. for visualization) or defining the purpose/provenance of the model."
]
},
{
@@ -132,7 +135,7 @@
"metadata": {},
"source": [
"### 1.3 Create an [Edge](https://mdf.readthedocs.io/en/latest/api/Specification.html#edge) between the [Nodes](https://mdf.readthedocs.io/en/latest/api/Specification.html#node)\n",
- "Use the [Simple Connect](https://mdf.readthedocs.io/en/latest/api/_autosummary/modeci_mdf.utils.simple_connect.html?highlight=simple%20connect#modeci-mdf-utils-simple-connect) method to do this."
+ "Use the [simple_connect()](https://mdf.readthedocs.io/en/latest/api/_autosummary/modeci_mdf.utils.simple_connect.html?highlight=simple%20connect#modeci-mdf-utils-simple-connect) utility method to add an edge between the nodes."
]
},
{
@@ -255,7 +258,7 @@
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
""
]
@@ -271,7 +274,8 @@
" output_format=\"png\",\n",
" view_on_render=False,\n",
" level=3,\n",
- " filename_root=\"AB\"\n",
+ " filename_root=\"AB\",\n",
+ " is_horizontal=True\n",
" )\n",
"\n",
"from IPython.display import Image\n",
@@ -329,7 +333,9 @@
"id": "d3d3ef42",
"metadata": {},
"source": [
- "### 3.2 Change a value to a Numpy array & execute the graph again"
+ "### 3.2 Change a value to a Numpy array & execute the graph again\n",
+ "\n",
+ "An important point about MDF is that the parameters etc. don't just have to be scalar/float values, but can be arrays, such as a Numpy array."
]
},
{
@@ -415,10 +421,10 @@
"mod_graph.nodes.append(a_node)\n",
"\n",
"#https://mdf.readthedocs.io/en/latest/api/Specification.html#parameter\n",
- "#Add a stateless parameter - this is a parameter whose value is not influenced by a previous parameter\n",
+ "#Add a stateless parameter - this is a parameter which will evaluate to the same value every execution of the node (for the same external inputs)\n",
"a_node.parameters.append(Parameter(id=\"increment\", value=1.0))\n",
"\n",
- "#Add a stateful parameter - this is a parameter whose value is influenced by a previous parameter\n",
+ "#Add a stateful parameter - this is a parameter whose value is influenced by a previous value of a parameter, possibly itself\n",
"p1 = Parameter(\n",
" id=\"count\",\n",
" value=\"count + increment\",\n",
@@ -439,7 +445,9 @@
"id": "4a72cd13",
"metadata": {},
"source": [
- "### 4.2. Generate a graph image from the model "
+ "### 4.2. Generate a graph image from the model \n",
+ "\n",
+ "A graphical representation of the new node is created. Note the stateful parameter name is in **bold**. "
]
},
{
@@ -459,7 +467,7 @@
},
{
"data": {
- "image/png": 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\n",
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\n",
"text/plain": [
""
]
@@ -480,7 +488,7 @@
" )\n",
"\n",
"from IPython.display import Image\n",
- "Image(filename=\"states.png\")"
+ "Image(filename=\"states.png\")\n"
]
},
{
@@ -543,20 +551,18 @@
"Output of A: 5\n",
"===== Evaluating at t = 4 ======\n",
"Evaluating graph: state_example, root nodes: ['A'], with array format numpy\n",
- "Output of A: 6\n",
- "===== Evaluating at t = 5 ======\n",
- "Evaluating graph: state_example, root nodes: ['A'], with array format numpy\n",
- "Output of A: 7\n"
+ "Output of A: 6\n"
]
}
],
"source": [
- "#Evaluating the Graph 5 times using a time series & changing time increment by 1\n",
+ "#Evaluating the Graph 5 more times using a time series & changing time increment by 1 \n",
+ "# (so count is 6 at the end, as it already has value 1)\n",
"dt = 1\n",
"duration = 5\n",
"t = 0\n",
"times = []\n",
- "while t <= duration:\n",
+ "while t < duration:\n",
" times.append(t)\n",
" print(\"===== Evaluating at t = %s ======\" % (t))\n",
" if t == 0:\n",
@@ -575,7 +581,7 @@
"source": [
"# 5. Conditions in MDF \n",
"In MDF, Conditions are a set of descriptors which specify & determine how many times individual [Nodes](https://mdf.readthedocs.io/en/latest/api/Specification.html#node) are allowed to execute.\n",
- "To learn more about Conditions check : [Graph-Scheduler](https://kmantel.github.io/graph-scheduler/Condition.html)"
+ "To learn more about Conditions see [here](https://github.com/ModECI/MDF/blob/main/examples/MDF/conditions/README.md) and also [Graph-Scheduler](https://kmantel.github.io/graph-scheduler/Condition.html)"
]
},
{
@@ -596,7 +602,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "state_example:\n",
+ "conditions_example:\n",
" nodes:\n",
" A:\n",
" parameters:\n",
@@ -622,6 +628,8 @@
"#import the necessary package to use Conditions in MDF\n",
"import graph_scheduler\n",
"\n",
+ "mod_graph.id = 'conditions_example'\n",
+ "\n",
"b_node = Node(id=\"B\")\n",
"mod_graph.nodes.append(b_node)\n",
"\n",
@@ -653,8 +661,9 @@
"source": [
"#This condition means that A is always executed\n",
"cond_a = Condition(type=\"Always\")\n",
+ "\n",
"#This condition means that B will execute when A has run 3 times\n",
- "cond_b = Condition(type=\"EveryNCalls\", dependencies=a_node.id, n=3) "
+ "cond_b = Condition(type=\"EveryNCalls\", dependencies=a_node.id, n=3) \n"
]
},
{
@@ -667,7 +676,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "state_example:\n",
+ "conditions_example:\n",
" nodes:\n",
" A:\n",
" parameters:\n",
@@ -711,34 +720,58 @@
},
{
"cell_type": "markdown",
- "id": "722471db",
"metadata": {},
"source": [
- "### 5.3 Execute the Graph\n",
- "The Graph is converted into an [EvaluableGraph](https://mdf.readthedocs.io/en/latest/api/_autosummary/modeci_mdf.execution_engine.EvaluableGraph.html#modeci_mdf.execution_engine.EvaluableGraph),which can be used to evaluate the current state of the graph's parameters. \n"
+ "### 5.3. Generate a graph image from the model "
]
},
{
"cell_type": "code",
"execution_count": 16,
- "id": "7a7b18c8",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Graph state_example with 2 nodes and 0 edges\n",
- "\n",
- "Node(metadata=None, id='A', input_ports=[], functions=[], parameters=[Parameter(metadata=None, id='increment', value=1.0, default_initial_value=None, time_derivative=None, function=None, args=None, conditions=[]), Parameter(metadata=None, id='count', value='count + increment', default_initial_value=None, time_derivative=None, function=None, args=None, conditions=[])], output_ports=[OutputPort(metadata=None, id='output', value='count', shape=None, type=None)])\n",
- "Node(metadata=None, id='B', input_ports=[], functions=[], parameters=[Parameter(metadata=None, id='param_B', value='param_B + 1', default_initial_value=None, time_derivative=None, function=None, args=None, conditions=[])], output_ports=[OutputPort(metadata=None, id='output', value='param_B', shape=None, type=None)])\n"
+ "Converting MDF graph: conditions_example to graphviz (level: 2, format: png)\n",
+ " Node: A\n",
+ " Node: B\n",
+ "Written graph image to: conditions.png\n"
]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
}
],
"source": [
- "from modeci_mdf.utils import print_summary\n",
- "#import the print summary helper function which summarizes the Graph\n",
- "print_summary(mod_graph)"
+ "mod.to_graph_image(\n",
+ " engine=\"dot\",\n",
+ " output_format=\"png\",\n",
+ " view_on_render=False,\n",
+ " level=2,\n",
+ " filename_root=\"conditions\"\n",
+ " )\n",
+ "\n",
+ "from IPython.display import Image\n",
+ "Image(filename=\"conditions.png\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "722471db",
+ "metadata": {},
+ "source": [
+ "### 5.4 Execute the Graph\n",
+ "The Graph is converted into an [EvaluableGraph](https://mdf.readthedocs.io/en/latest/api/_autosummary/modeci_mdf.execution_engine.EvaluableGraph.html#modeci_mdf.execution_engine.EvaluableGraph),which can be used to evaluate the current state of the graph's parameters. \n"
]
},
{
@@ -752,80 +785,9 @@
"output_type": "stream",
"text": [
"\n",
- "Init graph: state_example\n",
- "\n",
- " Init node: A\n",
- "all_params_to_check: [Parameter(metadata=None, id='increment', value=1.0, default_initial_value=None, time_derivative=None, function=None, args=None, conditions=[]), Parameter(metadata=None, id='count', value='count + increment', default_initial_value=None, time_derivative=None, function=None, args=None, conditions=[])]\n",
- " Checking whether parameter: increment with args: None, value: 1.0 () is sufficiently determined by known vars ['constant', 'increment', 'count']\n",
- " Are all of [] in ['constant', 'increment', 'count', 'increment']? [], i.e. True\n",
- " Checking whether parameter: count with args: None, value: count + increment () is sufficiently determined by known vars ['constant', 'increment', 'count', 'increment']\n",
- " Are all of ['count', 'increment'] in ['constant', 'increment', 'count', 'increment', 'count']? [True, True], i.e. True\n",
- "\n",
- " Init node: B\n",
- "all_params_to_check: [Parameter(metadata=None, id='param_B', value='param_B + 1', default_initial_value=None, time_derivative=None, function=None, args=None, conditions=[])]\n",
- " Checking whether parameter: param_B with args: None, value: param_B + 1 () is sufficiently determined by known vars ['constant', 'param_B']\n",
- " Are all of ['param_B'] in ['constant', 'param_B', 'param_B']? [True], i.e. True\n",
- "Evaluating graph: state_example, root nodes: ['A', 'B'], with array format numpy\n",
- " node-based conditions\n",
- " A: Always()\n",
- " B: EveryNCalls(Node(metadata=None, id='A', input_ports=[], functions=[], parameters=[Parameter(metadata=None, id='increment', value=1.0, default_initial_value=None, time_derivative=None, function=None, args=None, conditions=[]), Parameter(metadata=None, id='count', value='count + increment', default_initial_value=None, time_derivative=None, function=None, args=None, conditions=[])], output_ports=[OutputPort(metadata=None, id='output', value='count', shape=None, type=None)]), 3)\n",
- " termination conditions\n",
- " TimeScale.ENVIRONMENT_SEQUENCE: Never()\n",
- " TimeScale.ENVIRONMENT_STATE_UPDATE: AllHaveRun()\n",
- "> Evaluating time step: Time(environment_sequence: 0, environment_state_update: 0, consideration_set_execution: 0)\n",
- "\n",
- " ---------------\n",
- " Evaluating Node: A with ['increment', 'count']\n",
- " Initial eval of = 1 \n",
- " Initial eval of \n",
- " Evaluating Parameter: increment = 1.0 (stateful: False) with [increment=1, count=0] \n",
- " Evaluated Parameter: increment = 1.0 (stateful: False) with [increment=1, count=0] \n",
- " =\t1\n",
- " Evaluating Parameter: count = count + increment (stateful: True) with [increment=1, count=0] \n",
- " Evaluated Parameter: count = count + increment (stateful: True) with [increment=1, count=0] \n",
- " =\t1\n",
- " Evaluating OutputPort(metadata=None, id='output', value='count', shape=None, type=None) with [increment=1, count=1] \n",
- " Evaluated OutputPort(metadata=None, id='output', value='count', shape=None, type=None) with [increment=1, count=1] \n",
- " =\t1\n",
- "> Evaluating time step: Time(environment_sequence: 0, environment_state_update: 0, consideration_set_execution: 0)\n",
- "\n",
- " ---------------\n",
- " Evaluating Node: A with ['increment', 'count']\n",
- " Evaluating Parameter: increment = 1.0 (stateful: False) with [increment=1, count=1] \n",
- " Evaluated Parameter: increment = 1.0 (stateful: False) with [increment=1, count=1] \n",
- " =\t1\n",
- " Evaluating Parameter: count = count + increment (stateful: True) with [increment=1, count=1] \n",
- " Evaluated Parameter: count = count + increment (stateful: True) with [increment=1, count=1] \n",
- " =\t2\n",
- " Evaluating OutputPort(metadata=None, id='output', value='count', shape=None, type=None) with [increment=1, count=2] \n",
- " Evaluated OutputPort(metadata=None, id='output', value='count', shape=None, type=None) with [increment=1, count=2] \n",
- " =\t2\n",
- "> Evaluating time step: Time(environment_sequence: 0, environment_state_update: 0, consideration_set_execution: 0)\n",
- "\n",
- " ---------------\n",
- " Evaluating Node: A with ['increment', 'count']\n",
- " Evaluating Parameter: increment = 1.0 (stateful: False) with [increment=1, count=2] \n",
- " Evaluated Parameter: increment = 1.0 (stateful: False) with [increment=1, count=2] \n",
- " =\t1\n",
- " Evaluating Parameter: count = count + increment (stateful: True) with [increment=1, count=2] \n",
- " Evaluated Parameter: count = count + increment (stateful: True) with [increment=1, count=2] \n",
- " =\t3\n",
- " Evaluating OutputPort(metadata=None, id='output', value='count', shape=None, type=None) with [increment=1, count=3] \n",
- " Evaluated OutputPort(metadata=None, id='output', value='count', shape=None, type=None) with [increment=1, count=3] \n",
- " =\t3\n",
- "\n",
- " ---------------\n",
- " Evaluating Node: B with ['param_B']\n",
- " Initial eval of \n",
- " Evaluating Parameter: param_B = param_B + 1 (stateful: True) with [param_B=0] \n",
- " Evaluated Parameter: param_B = param_B + 1 (stateful: True) with [param_B=0] \n",
- " =\t1\n",
- " Evaluating OutputPort(metadata=None, id='output', value='param_B', shape=None, type=None) with [param_B=1] \n",
- " Evaluated OutputPort(metadata=None, id='output', value='param_B', shape=None, type=None) with [param_B=1] \n",
- " =\t1\n",
- "> Order of execution of nodes: ['A', 'A', 'A', 'B']\n",
- "\n",
- " Trial terminated\n",
+ "Init graph: conditions_example\n",
+ "Evaluating graph: conditions_example, root nodes: ['A', 'B'], with array format numpy\n",
+ "Output of A: 3\n",
"Output of B: 1\n"
]
}
@@ -834,67 +796,30 @@
"\n",
"from modeci_mdf.execution_engine import EvaluableGraph\n",
"\n",
- "eg = EvaluableGraph(mod_graph, verbose=True)\n",
+ "eg = EvaluableGraph(mod_graph, verbose=False)\n",
"#Calling the evaluate method will print out the order of the execution of Nodes per the set conditions\n",
"#Expected to be ('A','A','A','B')\n",
"eg.evaluate()\n",
+ "\n",
+ "#The Output of A expected to be 3 as it executes 3 times\n",
+ "print('Output of A: %s'%eg.enodes[\"A\"].evaluable_outputs[\"output\"].curr_value) \n",
+ "\n",
"#The Output of B expected to be 1 as it executes only once\n",
"print('Output of B: %s'%eg.enodes[\"B\"].evaluable_outputs[\"output\"].curr_value) "
]
},
- {
- "cell_type": "markdown",
- "id": "015238da",
- "metadata": {},
- "source": [
- "### 5.4. Generate a graph image from the model "
- ]
- },
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": null,
"id": "145d9c13",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Converting MDF graph: state_example to graphviz (level: 3, format: png)\n",
- " Node: A\n",
- " Node: B\n",
- "Written graph image to: conditions.png\n"
- ]
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "execution_count": 18,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "mod.to_graph_image(\n",
- " engine=\"dot\",\n",
- " output_format=\"png\",\n",
- " view_on_render=False,\n",
- " level=3,\n",
- " filename_root=\"conditions\"\n",
- " )\n",
- "\n",
- "from IPython.display import Image\n",
- "Image(filename=\"conditions.png\")"
- ]
+ "outputs": [],
+ "source": []
}
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3 (ipykernel)",
+ "display_name": "Python 3",
"language": "python",
"name": "python3"
},
@@ -908,7 +833,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.5"
+ "version": "3.8.10"
}
},
"nbformat": 4,
diff --git a/src/modeci_mdf/execution_engine.py b/src/modeci_mdf/execution_engine.py
index 188d0078..e2abde9c 100644
--- a/src/modeci_mdf/execution_engine.py
+++ b/src/modeci_mdf/execution_engine.py
@@ -379,11 +379,12 @@ def evaluate(
class EvaluableParameter:
"""
- Evaluates the current value of a :class:`~modeci_mdf.mdf.Parameter` during MDF graph execution.
+ Evaluates the current value of a :class:`~modeci_mdf.mdf.Parameter` during the MDF graph execution.
Args:
parameter: The parameter to evaluate during execution.
verbose: Whether to print output of parameter calculations.
+
"""
DEFAULT_INIT_VALUE = 0 # Temporary!
@@ -424,6 +425,7 @@ def get_current_value(
Returns:
The evaluated value of the parameter.
+
"""
# FIXME: Shouldn't this just call self.evaluate, seems like there is redundant code here?
if self.curr_value is None:
@@ -481,6 +483,7 @@ def evaluate(
Returns:
The current value of the parameter.
+
"""
if self.verbose:
print(
@@ -753,7 +756,12 @@ def __init__(self, node: Node, verbose: Optional[bool] = False):
all_known_vars.append(p.id)
# params_init[s] = s.curr_value"""
+ # TODO: the below checks for evaluability of functions and
+ # parameters using known variables are very similar and could be
+ # simplified with a function
all_funcs = [f for f in node.functions]
+ num_funcs_remaining = {f.id: None for f in node.functions}
+ func_missing_vars = {f.id: [] for f in node.functions}
# Order the functions into the correct sequence
while len(all_funcs) > 0:
@@ -774,6 +782,9 @@ def __init__(self, node: Node, verbose: Optional[bool] = False):
)
all_present = [v in all_known_vars for v in all_req_vars]
+ func_missing_vars[f.id] = {
+ v for v in all_req_vars if v not in all_known_vars
+ }
if verbose:
print(
@@ -788,15 +799,30 @@ def __init__(self, node: Node, verbose: Optional[bool] = False):
# params_init, array_format=FORMAT_DEFAULT
# )
else:
- if len(all_funcs) == 0:
- raise Exception(
- "Error! Could not evaluate function: %s with args %s using known vars %s"
- % (f.id, f.args, all_known_vars)
+ # track the number of remaining functions each time f
+ # is examined. If it's the same as last time, we know
+ # every function was examined and nothing changed, so
+ # we can stop because otherwise it will just infinitely
+ # loop
+ if num_funcs_remaining[f.id] == len(all_funcs):
+ func_missing_vars = {
+ f: ", ".join(v) for f, v in func_missing_vars.items()
+ }
+ raise ValueError(
+ "Error! Could not evaluate functions using known vars. The following vars are missing:\n\t"
+ + "\n\t".join(
+ f"{f}: {v}"
+ for f, v in func_missing_vars.items()
+ if len(v) > 0
+ )
)
else:
+ num_funcs_remaining[f.id] = len(all_funcs)
all_funcs.append(f)
all_params_to_check = [p for p in node.parameters]
+ num_params_remaining = {p.id: None for p in node.parameters}
+ param_missing_vars = {f.id: [] for f in node.parameters}
if self.verbose:
print("all_params_to_check: %s" % all_params_to_check)
@@ -825,6 +851,9 @@ def __init__(self, node: Node, verbose: Optional[bool] = False):
all_known_vars_plus_this = all_known_vars + [p.id]
all_present = [v in all_known_vars_plus_this for v in all_req_vars]
+ param_missing_vars[p.id] = {
+ v for v in all_req_vars if v not in all_known_vars
+ }
if verbose:
print(
@@ -842,12 +871,20 @@ def __init__(self, node: Node, verbose: Optional[bool] = False):
all_known_vars.append(p.id)
else:
- if len(all_params_to_check) == 0:
- raise Exception(
- "Error! Could not evaluate parameter: %s with args %s using known vars %s"
- % (p.id, p.args, all_known_vars_plus_this)
+ if num_params_remaining[p.id] == len(all_params_to_check):
+ param_missing_vars = {
+ p: ", ".join(v) for p, v in param_missing_vars.items()
+ }
+ raise ValueError(
+ "Error! Could not evaluate parameters using known vars. The following vars are missing:\n\t"
+ + "\n\t".join(
+ f"{p}: {v}"
+ for p, v in param_missing_vars.items()
+ if len(v) > 0
+ )
)
else:
+ num_params_remaining[p.id] = len(all_params_to_check)
all_params_to_check.append(p) # Add back to end of list...
for op in node.output_ports:
diff --git a/src/modeci_mdf/interfaces/graphviz/exporter.py b/src/modeci_mdf/interfaces/graphviz/exporter.py
index 228fa983..0f9bf6ba 100644
--- a/src/modeci_mdf/interfaces/graphviz/exporter.py
+++ b/src/modeci_mdf/interfaces/graphviz/exporter.py
@@ -142,6 +142,7 @@ def mdf_to_graphviz(
view_on_render=False,
level=LEVEL_2,
filename_root=None,
+ is_horizontal=False,
):
DEFAULT_POP_SHAPE = "ellipse"
@@ -156,6 +157,9 @@ def mdf_to_graphviz(
filename="%s.gv" % mdf_graph.id if not filename_root else filename_root,
engine=engine,
format=output_format,
+ graph_attr={"rankdir": "LR"}
+ if is_horizontal
+ else None, # to make the graph horizontal
)
# graph termination condition(s) added globally
global_term_cond_present = False
@@ -463,6 +467,8 @@ def mdf_to_graphviz(
example = sys.argv[1]
view = NO_VIEW not in sys.argv
+ is_horizontal = "-horizontal" in sys.argv
+
model = load_mdf(example)
mod_graph = model.graphs[0]
@@ -474,5 +480,9 @@ def mdf_to_graphviz(
print("------------------")
# nmllite_file = example.replace('.json','.nmllite.json')
mdf_to_graphviz(
- mod_graph, engine=engines["d"], view_on_render=view, level=int(sys.argv[2])
+ mod_graph,
+ engine=engines["d"],
+ view_on_render=view,
+ level=int(sys.argv[2]),
+ is_horizontal=is_horizontal,
)
diff --git a/src/modeci_mdf/interfaces/pytorch/exporter.py b/src/modeci_mdf/interfaces/pytorch/exporter.py
index 62994545..2abe39d0 100644
--- a/src/modeci_mdf/interfaces/pytorch/exporter.py
+++ b/src/modeci_mdf/interfaces/pytorch/exporter.py
@@ -16,9 +16,11 @@
from modeci_mdf.functions.standard import mdf_functions
from modeci_mdf.utils import load_mdf
from modeci_mdf.execution_engine import EvaluableGraph
+from modeci_mdf import __version__
-not_self = set()
+# Todo: remove these global variables!!
+# not_self = set()
param_set = set()
graph_input = []
@@ -42,6 +44,8 @@ def get_module_declaration_text(
"""
declaration_text = ("\n\nclass {}(nn.Module):").format(name)
+ not_self = set()
+
input_ports = node_dict["input_ports"]
functions = node_dict["functions"]
parameters = node_dict["parameters"]
@@ -188,6 +192,7 @@ def get_module_declaration_text(
for function in functions:
declaration_text += f"\n\t\t{function.id}="
func_dic = function.function
+ exp = ""
for i in func_dic:
if i in mdf_functions:
exp = mdf_functions[i]["expression_string"]
@@ -298,17 +303,28 @@ def build_script(
"""
model_id = model_id1 + ".onnx"
script = ""
+ base_info = (
+ "'''\nThis script has been generated by modeci_mdf v"
+ + __version__
+ + ".\nIt is an export of a MDF model"
+ )
if version == "mdf.s":
- s1 = "Converted examples from MDF models version(mdf.s) to PyTorch/ONNX"
- script += f'"{s1}"'
+ s1 = (
+ base_info
+ + " (mdf.s - MDF stateful, i.e. full MDF allowing stateful parameters) to PyTorch\n"
+ )
+ script += s1
elif version == "mdf.0":
- s2 = "Converted examples from MDF models version(mdf.0) to PyTorch/ONNX"
- script += f'"{s2}"'
+ s2 = base_info + " (mdf.0 - MDF zero, a simplified form of MDF) to PyTorch\n"
+ script += s2
+
+ script += "\n'''\n"
+
imports_string = (
"\nimport torch"
"\nimport torch.nn as nn\nimport onnx\nimport onnxruntime as rt\nfrom math import *"
)
- print(script)
+ # print(script)
# Declarations string
modules_declaration_text = ""
@@ -353,7 +369,7 @@ def build_script(
if len(nodes) == 1:
script += "f\nmodel={nodes[0].id}"
return script
- script += "\nmodel = Model("
+ script += "\n\nmodel = Model("
for node in nodes:
script += f"{node.id}={node.id}(),"
script += ")"
@@ -367,9 +383,9 @@ def build_script(
script += f"\nonnx_model = onnx.load('{model_id}')"
script += "\nonnx.checker.check_model(onnx_model)"
script += f"\nsess = rt.InferenceSession('{model_id}')"
- script += "\nres = sess.run(None, {sess.get_inputs()[0].name: dummy_input.numpy()})"
+ script += "\nres = sess.run(None, {sess.get_inputs()[0].name: dummy_input.numpy()} if len(sess.get_inputs())>0 else {})"
x, y = "__main__", "Exported to PyTorch and ONNX"
- script += f"\nif __name__ == '{x}':"
+ script += f"\n\nif __name__ == '{x}':"
script += f"\n\tprint('{y}')"
return script
@@ -441,7 +457,7 @@ def _script_to_model(script: str, model_id1: str, version: str, model_input: str
"""
import importlib.util
- print("version", version)
+ # print("version", version)
if version == "mdf.s":
path_list = model_input.split("/")[:-2] + ["PyTorch/MDF_PyTorch/"]
out_filename = "/".join(path_list)
@@ -481,6 +497,8 @@ def mdf_to_pytorch(
)
scripts = _generate_scripts_from_model(mdf_model, version)
models = {}
+ param_set.clear()
+ graph_input.clear()
for script_name, script in scripts.items():
model = _script_to_model(script, mdf_model.id, version, model_input)
diff --git a/src/modeci_mdf/mdf.py b/src/modeci_mdf/mdf.py
index 7e3188f7..42684f52 100644
--- a/src/modeci_mdf/mdf.py
+++ b/src/modeci_mdf/mdf.py
@@ -200,7 +200,7 @@ def is_stateful(self) -> bool:
"""
Is the parameter stateful?
- A parameter is considered stateful if it has a :code:`time_derivative`, :code:`defualt_initial_value`, or it's
+ A parameter is considered stateful if it has a :code:`time_derivative`, :code:`default_initial_value`, or its
id is referenced in its value expression.
Returns:
@@ -423,7 +423,7 @@ def _parse_cattr_unstructure(cls, o):
@modelspec.define(eq=False)
class Graph(MdfBase):
r"""
- A directed graph consisting of Node(s) connected via Edge(s)
+ A directed graph consisting of :class:`~Node`s (with :class:`~Parameter`s and :class:`~Function`s evaluated internally) connected via :class:`~Edge`s.
Attributes:
id: A unique identifier for this Graph
@@ -526,6 +526,7 @@ def to_graph_image(
level: int = 2,
filename_root: Optional[str] = None,
only_warn_on_fail: bool = False,
+ is_horizontal: bool = False,
):
"""Convert MDF graph to an image (png or svg) using the Graphviz export
@@ -547,10 +548,14 @@ def to_graph_image(
view_on_render=view_on_render,
level=level,
filename_root=filename_root,
+ is_horizontal=is_horizontal,
)
except Exception as e:
if only_warn_on_fail:
+ import traceback
+
+ print(traceback.format_exc())
print(
"Failure to generate image! Ensure Graphviz executables (dot etc.) are installed on native system. Error: \n%s"
% e
diff --git a/test_all.sh b/test_all.sh
index 0fbfa27f..851b0377 100755
--- a/test_all.sh
+++ b/test_all.sh
@@ -31,11 +31,11 @@ python -m modeci_mdf.interfaces.neuroml.exporter States.json -run
## Test exporting to graphs via GraphViz
-python -m modeci_mdf.interfaces.graphviz.exporter Simple.json 1 -noview
+python -m modeci_mdf.interfaces.graphviz.exporter Simple.json 1 -noview -horizontal
mv simple_example.gv.png images/simple.png
python -m modeci_mdf.interfaces.graphviz.exporter Simple.json 3 -noview
mv simple_example.gv.png images/simple_3.png
-python -m modeci_mdf.interfaces.graphviz.exporter ABCD.json 1 -noview
+python -m modeci_mdf.interfaces.graphviz.exporter ABCD.json 1 -noview -horizontal
mv abcd_example.gv.png images/abcd.png
python -m modeci_mdf.interfaces.graphviz.exporter ABCD.json 3 -noview
mv abcd_example.gv.png images/abcd_3.png