From bf96fa872d618f82e05398d91430fcbcdf61c855 Mon Sep 17 00:00:00 2001 From: ngpaladi <ngpaladi@users.noreply.github.com> Date: Tue, 22 Dec 2020 18:23:26 -0500 Subject: [PATCH 1/7] Sphinx doc (#1) * Sphinx Documentation Initial Update * Update workflow for sphinx-docs test branch * Update to build of temporary branch * Prepare to move to master --- .github/workflows/build-sphinx.yml | 33 +++ .gitignore | 1 + sphinx-docs/Makefile | 20 ++ sphinx-docs/api/configuration.rst | 215 +++++++++++++++++++ sphinx-docs/api/hls-model.rst | 102 +++++++++ sphinx-docs/api/profiling.rst | 60 ++++++ sphinx-docs/command.rst | 132 ++++++++++++ sphinx-docs/concepts.rst | 52 +++++ sphinx-docs/conf.py | 89 ++++++++ sphinx-docs/img/activations.png | Bin 0 -> 47691 bytes sphinx-docs/img/hls4ml_logo.png | Bin 0 -> 26295 bytes sphinx-docs/img/hls4ml_logo_navbar.png | Bin 0 -> 10910 bytes sphinx-docs/img/logo.jpg | Bin 0 -> 64144 bytes sphinx-docs/img/logo.png | Bin 0 -> 23360 bytes sphinx-docs/img/nn_map_paper_fig_2.png | Bin 0 -> 98357 bytes sphinx-docs/img/overview.jpg | Bin 0 -> 202059 bytes sphinx-docs/img/overview.pdf | Bin 0 -> 642072 bytes sphinx-docs/img/reuse_factor_paper_fig_8.png | Bin 0 -> 82654 bytes sphinx-docs/img/weights.png | Bin 0 -> 67048 bytes sphinx-docs/index.rst | 41 ++++ sphinx-docs/reference.rst | 53 +++++ sphinx-docs/release_notes.rst | 69 ++++++ sphinx-docs/setup.rst | 177 +++++++++++++++ sphinx-docs/status.rst | 65 ++++++ 24 files changed, 1109 insertions(+) create mode 100644 .github/workflows/build-sphinx.yml create mode 100644 sphinx-docs/Makefile create mode 100644 sphinx-docs/api/configuration.rst create mode 100644 sphinx-docs/api/hls-model.rst create mode 100644 sphinx-docs/api/profiling.rst create mode 100644 sphinx-docs/command.rst create mode 100644 sphinx-docs/concepts.rst create mode 100644 sphinx-docs/conf.py create mode 100644 sphinx-docs/img/activations.png create mode 100644 sphinx-docs/img/hls4ml_logo.png create mode 100644 sphinx-docs/img/hls4ml_logo_navbar.png create mode 100644 sphinx-docs/img/logo.jpg create mode 100644 sphinx-docs/img/logo.png create mode 100644 sphinx-docs/img/nn_map_paper_fig_2.png create mode 100644 sphinx-docs/img/overview.jpg create mode 100644 sphinx-docs/img/overview.pdf create mode 100644 sphinx-docs/img/reuse_factor_paper_fig_8.png create mode 100644 sphinx-docs/img/weights.png create mode 100644 sphinx-docs/index.rst create mode 100644 sphinx-docs/reference.rst create mode 100644 sphinx-docs/release_notes.rst create mode 100644 sphinx-docs/setup.rst create mode 100644 sphinx-docs/status.rst diff --git a/.github/workflows/build-sphinx.yml b/.github/workflows/build-sphinx.yml new file mode 100644 index 000000000..e46d0a863 --- /dev/null +++ b/.github/workflows/build-sphinx.yml @@ -0,0 +1,33 @@ +name: build-sphinx +on: + push: + branches: + - master + +jobs: + build: + + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v1 + - uses: ammaraskar/sphinx-action@master + with: + docs-folder: "sphinx-docs/" + pre-build-command: "pip install sphinx-rtd-theme" + - name: Commit Documentation Changes + run: | + git clone https://github.com/fastmachinelearning/hls4ml.git --branch gh-pages --single-branch gh-pages + cp -r sphinx-docs/_build/html/* gh-pages/ + cd gh-pages + touch .nojekyll + git config --local user.email "action@github.com" + git config --local user.name "GitHub Action" + git add . + git commit -m "Update Sphinx Documentation" -a || true + - name: Push Documentation Changes + uses: ad-m/github-push-action@master + with: + branch: gh-pages + directory: gh-pages + github_token: ${{ secrets.GITHUB_TOKEN }} \ No newline at end of file diff --git a/.gitignore b/.gitignore index 9c0ec2704..19ebfc5bf 100644 --- a/.gitignore +++ b/.gitignore @@ -8,3 +8,4 @@ vivado_prj .vscode my-hls-test *.tar.gz +sphinx-docs/_build \ No newline at end of file diff --git a/sphinx-docs/Makefile b/sphinx-docs/Makefile new file mode 100644 index 000000000..d4bb2cbb9 --- /dev/null +++ b/sphinx-docs/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line, and also +# from the environment for the first two. +SPHINXOPTS ?= +SPHINXBUILD ?= sphinx-build +SOURCEDIR = . +BUILDDIR = _build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/sphinx-docs/api/configuration.rst b/sphinx-docs/api/configuration.rst new file mode 100644 index 000000000..300a3570e --- /dev/null +++ b/sphinx-docs/api/configuration.rst @@ -0,0 +1,215 @@ +============= +Configuration +============= + + + +We currently support two ways of setting hls4ml's model configuration. This page documents both methods' usage. + + +.. contents:: \ + + +**NOTE:** + + +* + One important part of hls4ml to remember is that the user is responsible for the format of the inputs. There is no automatic formatting or normalization so this must be done in the training. + +* + For developers, you might also want to checkout this section: `Detailed configuration in converted hls codes <#detailed-configuration-in-converted-hls-codes>`_. + +---- + +1. Python API +============= + +Using hls4ml, you can quickly generate a simple configuration dictionary from a keras model: + +.. code-block:: python + + import hls4ml + config = hls4ml.utils.config_from_keras_model(model, granularity='model') + +For more advanced and detailed configuration, you can also set them through the created dictionary. For example, to change the reuse factor: + +.. code-block:: python + + config['Model']['ReuseFactor'] = 2 + +Or to set the precision of a specific layer's weight: + +.. code-block:: python + + config['LayerName']['fc1']['Precision']['weight'] = 'ap_fixed<8,4>' + +To better understand how the configuration hierachy works, refer to the next section for more details. + +---- + +2. YAML Configuration file +========================== + +2.1 Top Level Configuration +--------------------------- + +Configuration files are YAML files in hls4ml (\ ``*.yml``\ ). An example configuration file is `here <https://github.com/hls-fpga-machine-learning/example-models/blob/master/keras-config.yml>`_. + +It looks like this: + +.. code-block:: yaml + + KerasJson: keras/KERAS_3layer.json + KerasH5: keras/KERAS_3layer_weights.h5 #You can also use h5 file from Keras's model.save() without supplying json file. + InputData: keras/KERAS_3layer_input_features.dat + OutputPredictions: keras/KERAS_3layer_predictions.dat + OutputDir: my-hls-test + ProjectName: myproject + XilinxPart: xcku115-flvb2104-2-i + ClockPeriod: 5 + + IOType: io_parallel # options: io_serial/io_parallel + HLSConfig: + Model: + Precision: ap_fixed<16,6> + ReuseFactor: 1 + Strategy: Latency + LayerType: + Dense: + ReuseFactor: 2 + Strategy: Resource + Compression: True + +There are a number of configuration options that you have. Let's go through them. You have basic setup parameters: + + +* **KerasJson/KerasH5**\ : for Keras, the model architecture and weights are stored in a ``json`` and ``h5`` file. The path to those files are required here. + We also support keras model's file obtained just from ``model.save()``. In this case you can just supply the ``h5`` file in ``KerasH5:`` field. +* **InputData/OutputPredictions**\ : path to your input/predictions of the model. If none is supplied, then hls4ml will create aritificial data for simulation. The data used above in the example can be found `here <https://cernbox.cern.ch/index.php/s/2LTJVVwCYFfkg59>`_. We also support ``npy`` data files. We welcome suggestions on more input data types to support. +* **OutputDir**\ : the output directory where you want your HLS project to appear +* **ProjectName**\ : the name of the HLS project IP that is produced +* **XilinxPart**\ : the particular FPGA part number that you are considering, here it's a Xilinx Virtex-7 FPGA +* **ClockPeriod**\ : the clock period, in ns, at which your algorithm runs + Then you have some optimization parameters for how your algorithm runs: +* **IOType**\ : your options are ``io_parallel`` or ``io_serial`` where this really defines if you are pipelining your algorithm or not +* **ReuseFactor**\ : in the case that you are pipelining, this defines the pipeline interval or initiation interval +* **Strategy**\ : Optimization strategy on FPGA, either "Latency" or "Resource". If none is supplied then hl4ml uses "Latency" as default. Note that a reuse factor larger than 1 should be specified when using "resource" strategy. An example of using larger reuse factor can be found `here. <https://github.com/hls-fpga-machine-learning/models/tree/master/keras/KERAS_dense>`_ +* **Precision**\ : this defines the precsion of your inputs, outputs, weights and biases. It is denoted by ``ap_fixed<X,Y>``\ , where ``Y`` is the number of bits representing the signed number above the binary point (i.e. the integer part), and ``X`` is the total number of bits. + Additionally, integers in fixed precision data type (\ ``ap_int<N>``\ , where ``N`` is a bit-size from 1 to 1024) can also be used. You have a chance to further configure this more finely with per-layer configuration described below. + +2.2 Per-Layer Configuration +--------------------------- + +In the hls4ml configuration file, it is possible to specify the model *Precision* and *ReuseFactor* with finer granularity. + +Under the ``HLSConfig`` heading, these can be set for the ``Model``\ , per ``LayerType``\ , per ``LayerName``\ , and for named variables within the layer (for precision only). The most basic configuration may look like this: + +.. code-block:: yaml + + HLSConfig: + Model: + Precision: ap_fixed<16,6> + ReuseFactor: 1 + +This configuration use ``ap_fixed<16,6>`` for every variable and a ReuseFactor of 1 throughout. + +Specify all ``Dense`` layers to use a different precision like this: + +.. code-block:: yaml + + HLSConfig: + Model: + Precision: ap_fixed<16,6> + ReuseFactor: 1 + LayerType: + Dense: + Precision: ap_fixed<14,5> + +In this case, all variables in any ``Dense`` layers will be represented with ``ap_fixed<14,5>`` while any other layer types will use ``ap_fixed<16,6>``. + +A specific layer can be targeted like this: + +.. code-block:: yaml + + HLSConfig: + Model: + Precision: ap_fixed<16,6> + ReuseFactor: 16 + LayerName: + dense1: + Precision: + weight: ap_fixed<14,2> + bias: ap_fixed<14,4> + result: ap_fixed<16,6> + ReuseFactor: 12 + Strategy: Resource + +In this case, the default model configuration will use ``ap_fixed<16,6>`` and a ``ReuseFactor`` of 16. The layer named ``dense1`` (defined in the user provided model architecture file) will instead use different precision for the ``weight``\ , ``bias``\ , and ``result`` (output) variables, a ``ReuseFactor`` of 12, and the ``Resource`` strategy (while the model default is ``Latency`` strategy. + +More than one layer can have a configuration specified, e.g.: + +.. code-block:: yaml + + HLSConfig: + Model: + ... + LayerName: + dense1: + ... + batchnormalization1: + ... + dense2: + ... + +For more information on the optimization parameters and what they mean, you can visit the :doc:`Concepts <../concepts>` chapter. + +---- + +Detailed Configuration in Converted HLS Code +============================================ + +**NOTE**\ : this section is developer-oriented. + +After you create your project, you have the opportunity to do more configuration if you so choose. + +In your project, the file ``<OutputDir>/firmware/<ProjectName>.cpp`` is your top level file. It has the network architecture constructed for you. An example is `here <https://github.com/hls-fpga-machine-learning/models/blob/master/HLS_projects/KERAS-1layer-hls/firmware/myproject.cpp>`_ and the important snippet is: + +.. code-block:: cpp + + layer2_t layer2_out[N_LAYER_2]; + #pragma HLS ARRAY_PARTITION variable=layer2_out complete dim=0 + nnet::dense_latency<input_t, layer2_t, config2>(input_1, layer2_out, w2, b2); + + layer3_t layer3_out[N_LAYER_2]; + #pragma HLS ARRAY_PARTITION variable=layer3_out complete dim=0 + nnet::relu<layer2_t, layer3_t, relu_config3>(layer2_out, layer3_out); + + layer4_t layer4_out[N_LAYER_4]; + #pragma HLS ARRAY_PARTITION variable=layer4_out complete dim=0 + nnet::dense_latency<layer3_t, layer4_t, config4>(layer3_out, layer4_out, w4, b4); + + nnet::sigmoid<layer4_t, result_t, sigmoid_config5>(layer4_out, layer5_out); + +You can see, for the simple 1-layer DNN, the computation (\ ``nnet::dense_latency``\ ) and activation (\ ``nnet::relu``\ /\ ``nnet::sigmoid``\ ) caluclation for each layer. For each layer, it has its own additional configuration parameters, e.g. ``config2``. + +In your project, the file ``<OutputDir>/firmware/parameters.h`` stores all the configuration options for each neural network library. +An example is `here <https://github.com/hls-fpga-machine-learning/models/blob/master/HLS_projects/KERAS-1layer-hls/firmware/parameters.h>`_. So for example, the detailed configuration options for an example DNN layer is: + +.. code-block:: cpp + + //hls-fpga-machine-learning insert layer-config + struct config2 : nnet::dense_config { + static const unsigned n_in = N_INPUT_1_1; + static const unsigned n_out = N_LAYER_2; + static const unsigned io_type = nnet::io_parallel; + static const unsigned reuse_factor = 1; + static const unsigned n_zeros = 0; + static const unsigned n_nonzeros = 320; + static const bool store_weights_in_bram = false; + typedef ap_fixed<16,6> accum_t; + typedef model_default_t bias_t; + typedef model_default_t weight_t; + typedef ap_uint<1> index_t; + }; + +It is at this stage that a user can even further configure their network HLS implementation in finer detail. diff --git a/sphinx-docs/api/hls-model.rst b/sphinx-docs/api/hls-model.rst new file mode 100644 index 000000000..91f6b013e --- /dev/null +++ b/sphinx-docs/api/hls-model.rst @@ -0,0 +1,102 @@ +================ +HLS Model Class +================ + +This page documents our hls_model class usage. You can generate generate an hls model object from a keras model through ``hls4ml``'s API: + +.. code-block:: python + + import hls4ml + + # Generate a simple configuration from keras model + config = hls4ml.utils.config_from_keras_model(keras_model, granularity='name') + + # Convert to an hls model + hls_model = hls4ml.converters.convert_from_keras_model(keras_model, hls_config=config, output_dir='test_prj') + +After that, you can use several methods in that object. Here is a list of all the methods: + + +* :ref:`write <write-method>` +* :ref:`compile <compile-method>` +* :ref:`predict <predict-method>` +* :ref:`build <build-method>` +* :ref:`trace <trace-method>` + +Similar functionalities are also supported through command line interface. If you prefer using them, please refer to Command Help section. + +---- + +.. _write-method: + +``write`` method +==================== + +Write your keras model as a hls project to ``hls_model``\ 's ``output_dir``\ : + +.. code-block:: python + + hls_model.write() + +---- + +.. _compile-method: + +``compile`` method +====================== + +Compile your hls project. + +.. code-block:: python + + hls_model.compile() + +---- + +.. _predict-method: + +``predict`` method +====================== + +Similar to ``keras``\ 's predict API, you can get the predictions of ``hls_model`` just by supplying an input ``numpy`` array: + +.. code-block:: python + + # Suppose that you already have input array X + # Note that you have to do hls_model.compile() before using predict + + y = hls_model.predict(X) + +This is similar to doing ``csim`` simulation, but you can get your prediction results much faster. It's very helpful when you want to quickly prototype different configurations for your model. + +---- + +.. _build-method: + +``build`` method +==================== + +.. code-block:: python + + hls_model.build() + + #You can also read the report of the build + hls4ml.report.read_vivado_report('hls4ml_prj') + +---- + +.. _trace-method: + +``trace`` method +==================== + +The trace method is an advanced version of the ``predict`` method. It's used to trace individual outputs from each layer of the hls_model. This is useful for debugging and setting the appropriate configuration. + +**Return:** A dictionary where the keys are the names of the layers, and its values are the layers's outputs. + +.. code-block:: python + + predict_ouputs, trace_outputs = hls_model.trace(X) + + #We also support a similar function for keras + keras_trace = hls4ml.model.profiling.get_ymodel_keras(keras_model, X) diff --git a/sphinx-docs/api/profiling.rst b/sphinx-docs/api/profiling.rst new file mode 100644 index 000000000..5b70962c5 --- /dev/null +++ b/sphinx-docs/api/profiling.rst @@ -0,0 +1,60 @@ +========= +Profiling +========= + +In the ``hls4ml`` configuration file, it is possible to specify the model ``Precision`` and ``ReuseFactor`` with fine granularity. + +Using a low precision can help reduce the FPGA resource usage of a model, but may result in loss of model performance if chosen inappropriately. The profiling tools in ``hls4ml`` help you to decide the appropriate model precision. + +Profiling uses some extra dependencies, to install these, run ``pip install hls4ml[profiling]``. The profiling tools are provided as a ``Python`` module which you can use. + +Three types of objects can be provided: **a Keras model object**\ , **test data**\ , and an **HLSModel object**. +You will need to initialise these objects by using a trained model, loading a model from a file, and loading your data. The Keras model and data each need to be in the format that would normally allow you to run, e.g. ``model.predict(X)``. + +.. code-block:: python + + from hls4ml.model.profiling import numerical + from hls4ml.converters import keras_to_hls + import matplotlib.pyplot as plt + import yaml + + # pseudo code: + model = load_model() + X = load_data() + + # real code: + # load your hls4ml .yml config file with yaml + with open("keras-config.yml", 'r') as ymlfile: + config = yaml.load(ymlfile) + + hls_model = keras_to_hls(config) + + # produce an activation profile (ap) + # and weights profile (wp) + ap, wp = numerical(keras_model=model, hls_model = hls_model, X=X) + plt.show() + +Calling the ``hls4ml.model.profiling.numerical`` method with these three objects provided will produce two figures as below: + +.. image:: ../img/activations.png + :width: 45% +.. image:: ../img/weights.png + :width: 45% + +Different plots styles are available with the ``plot`` keyword argument. Valid options are ``boxplot`` (default), ``histogram``\ , ``violinplot``. In the default boxplot style, each variable in the neural network is evaluated using the given test data and the distribution of (non-zero) values is shown with a box and whisker diagram. + +When different combinations of the input objects are given, different plots will be produced: + +1) Only Keras model: only the weights profile plot will be produced, the activation profile will be ``None``. No grey boxes representing the data types will be shown. + +2) Only HLSModel (or HLSModel and Keras model): only the weights profile plot will be produced, with grey boxes indicating the data types from the HLSModel. + +3) Keras model and data (\ ``X``\ ): both the weights profile and activation profile will be produced. No grey boxes representing the data types will be shown. + +4) Keras model, HLSModel, and data: both weights and activation profiles are produced, with grey boxes indicating the data types from the HLSModel. + +Each box shows the median and quartiles of the distribution. The grey shaded boxes show the range which can be represented with the hls4ml config file used. + +As a starting point, a good configuration would at least cover the box and whisker for each variable with the grey box. Make sure the box and whisker is contained to the right by using sufficient integer bits to avoid overflow. It might be that more precision is needed (grey boxes extend further to the left) to achieve satisfactory performance. In some cases, it is safe to barely cover the values and still achieve good accuracy. + +To establish whether the configuration gives good performance, run C Simulation with test data and compare the results to your model evaluated on the CPU with floating point. diff --git a/sphinx-docs/command.rst b/sphinx-docs/command.rst new file mode 100644 index 000000000..18ea92be1 --- /dev/null +++ b/sphinx-docs/command.rst @@ -0,0 +1,132 @@ +======================== +Command Line Interface +======================== + +This page documents all the commands that ``hls4ml`` supports. + +---- + +Overview +========= + +To start you can just type in ``hls4ml -h`` or ``hls4ml --help`` in your command line, a messege will show up like below: + +.. code-block:: + + usage: hls4ml [-h] [--version] {config,convert,build,report} ... + + HLS4ML - Machine learning inference in FPGAs + + positional arguments: + {config,convert,build,report} + config Create a conversion configuration file + convert Convert Keras or ONNX model to HLS + build Build generated HLS project + report Show synthesis report of an HLS project + + optional arguments: + -h, --help show this help message and exit + --version show program's version number and exit + +To get help about any particular ``command``\ , you can just do: + +.. code-block:: + + hls4ml command -h + +For example, to get help about the ``config`` command, you can just type the followings: + +.. code-block:: + + hls4ml config -h + +---- + +hls4ml config +============== + +.. code-block:: + + hls4ml config [-h] [-m MODEL] [-w WEIGHTS] [-o OUTPUT] + +This creates a conversion configuration file. Visit Configuration section of the :doc:`Setup <setup>` page for more details on how to write a configuration file. + +**Arguments** + + +* ``-h, --help``\ : show help message and exit +* ``-m MODEL``\ , or ``--model MODEL``\ : model file to convert (we currently support Keras's ``.h5`` or ``.json`` file, ONNX's ``.onnx``\ , Tensorflow's ``pb``\ , Pytorch's ``pt``\ ) +* ``-w WEIGHT``\ , or ``--weights WEIGHTS``\ : optional weights file (if Keras's ``.json`` file is provided)) +* ``-o OUTPUT``\ , or ``--output OUTPUT``\ : output file name + +---- + +hls4ml convert +================ + +.. code-block:: + + hls4ml convert [-h] [-c CONFIG] + +Suppose you have a configuration file called ``keras-config.yml``. You can use this command with the configuration file like the following: + +.. code-block:: + + hls4ml convert -c keras-config.yml + +**Arguments** + + +* ``-h, --help``\ : show help message and exit +* ``-c CONFIG``\ , or ``--config CONFIG``\ : configuration file + +---- + +hls4ml build +============== + +.. code-block:: + + hls4ml build [-h] [-p PROJECT] [-c] [-s] [-r] [-v] [-e] [-l] [-a] [--reset] + +Build your HLS project. Suppose that you have a project directory called ``my-hls-test``\ , you can often do the following to build the whole project with all the steps described in the arguments section: + +.. code-block:: + + hls4ml build -p my-hls-test -a + +**Arguments** + + +* ``-h, --help``\ : show help message and exit. +* ``-p PROJECT``\ , or ``--project PROJECT``\ : project directory. +* ``-c, --csimulation``\ : run C simulation. +* ``-s, --synthesis``\ : run C/RTL synthesis +* ``-r, --co-simulation``\ : run C/RTL co-simulation. +* ``-v, --validation``\ : run C/RTL validation. +* ``-e, --export``\ : export IP (implies -s) +* ``-l, --vivado_synthesis``\ : run Vivado synthesis (implies -s). +* ``-a, --all``\ : run C simulation, C/RTL synthesis, C/RTL co-simulation and Vivado synthesis. +* ``--reset``\ : remove any previous builds + +---- + +hls4ml report +=============== + +.. code-block:: + + hls4ml report [-h] [-p PROJECT] [-f] + +Suppose that you have a project directory called ``my-hls-test``\ , you can get the full report about the project by doing the following: + +.. code-block:: + + hls4ml report my-hls-test -f + +**Arguments** + + +* ``-h, --help``\ : show help message and exit. +* ``-p PROJECT``\ , or ``--project PROJECT``\ : project directory. +* ``-f, --full``\ : show full report diff --git a/sphinx-docs/concepts.rst b/sphinx-docs/concepts.rst new file mode 100644 index 000000000..4f4ba7596 --- /dev/null +++ b/sphinx-docs/concepts.rst @@ -0,0 +1,52 @@ +======== +Concepts +======== + +The goal of hls4ml is to provide an efficient and fast translation of machine learning models from open-source packages (like Keras and PyTorch) for training machine learning algorithms to high level synthesis (HLS) code that can then be transpiled to run on an FPGA. The resulting HLS project can be then used to produce an IP which can be plugged into more complex designs or be used to create a kernel for CPU co-processing. The user has freedom to define many of the parameters of their algorithm to best suit their needs. + +The hls4ml package enables fast prototyping of a machine learning algorithm implementation in FPGAs, +greatly reducing the time to results and giving the user intuition for how to best design a machine learning algorithm for their application while balancing performance, resource utilization and latency requirements. + +The Inspiration +=============== + +The inspiration for the creation of the hls4ml package stems from the high energy physics community at the Large Hadron Collider (LHC). While machine learning has already been proven to be extremely useful in analysis of data from detectors at the LHC, it is typically performed in an "offline" environment after the data is taken and agglomerated. However, one of the largest problems at detectors on the LHC is that collisions, or "events", generate too much data for everything to be saved. As such, filters called "triggers" are used to determine whether a given event should be kept. Using FPGAs allows for significantly lower latency so machine learning algorithms can essentially be run "live" at the detector level for event selection. As a result, more events with potential signs of new physics can be preserved for analysis. + +The Solution: hls4ml +==================== + +.. image:: img/overview.jpg + + +With this in mind, let's take a look at how hls4ml helps to achieve such a goal. First, it's important to realize the architecture differences between an FPGA and a CPU or GPU. An FPGA can be specifically programmed to do a certain task, in this case evaluate neural networks given a set of inputs, and as such can be highly optimized for the task, with tricks like pipelining and parallel evaluation. However, this means dynamic remapping while running isn't really a possibility. FPGAs also often come at a comparatively low power cost with respect to CPUs and GPUs. This allows hls4ml to build HLS code from compressed neural networks that results in predictions on the microsecond scale for latency. The hls4ml tool saves the time investment needed to convert a neural network to a hardware design language or even HLS code, thus allowing for rapid prototyping. + +How it Works +============= + +.. image:: img/nn_map_paper_fig_2.png + + +Consider a multi-layered neural network. At each neuron in a layer :math:`m` (containing :math:`N_m` neurons), we calculate an output value (part of the output vector :math:`\mathbf{x}_m` of said layer) using the sum of output values of the previous layer multiplied by independent weights for each of these values and a bias value. An activation function is performed on the result to get the final output value for the neuron. Representing the weights as a :math:`N_m` by :math:`N_{m-1}` matrix :math:`W_{m,m-1}`, the bias values as :math:`\mathbf{b}_m`, and the activation function as :math:`g_m`, we can express this compactly as: + + +.. math:: + + \mathbf{x}_m = g_m (W_{m,m-1} \mathbf{x}_{m-1} +\mathbf{b}_m) + +With hls4ml, each layer of output values is calculated independently in sequence, using pipelining to speed up the process by accepting new inputs after an initiation interval. The activations, if nontrivial, are precomputed. + +To ensure optimal performance, the user can control aspects of their model, principally: + + +* **Size/Compression** - Though not explicitly part of the hls4ml package, this is an important optimization to efficiently use the FPGA resources +* **Precision** - Define the :doc:`precision <api/profiling>` of the calculations in your model +* **Dataflow/Resource Reuse** - Control parallel or serial model implementations with varying levels of pipelining +* **Quantization Aware Training** - Achieve best performance at low precision with tools like QKeras, and benefit automatically during inference with hls4ml parsing of QKeras models + + +.. image:: img/reuse_factor_paper_fig_8.png + + +Often, these decisions will be hardware dependent to maximize performance. Of note is that simplifying the input network must be done before using hls4ml to generate HLS code, for optimal compression to provide a sizable speedup. Also important to note is the use of fixed point arithmetic in hls4ml. This improves processing speed relative to floating point implementations. The hls4ml package also offers the functionality of configuring binning and output bit width of the precomputed activation functions as necessary. With respect to parallelization and resource reuse, hls4ml offers a "reuse factor" parameter that determines the number of times each multiplier is used in order to compute a layer of neuron's values. Therefore, a reuse factor of one would split the computation so each multiplier had to only perform one multiplication in the computation of the output values of a layer, as shown above. Conversely, a reuse factor of four, in this case, uses a single multiplier four times sequentially. Low reuse factor achieves the lowest latency and highest throughput but uses the most resources, while high reuse factor save resources at the expense of longer latency and lower throughput. The reuse factor can be set using the configuration options defined on the :doc:`Setup <setup>` page. + +Thereby, the hls4ml package builds efficient HLS code to implement neural networks on FPGAs for microsecond-scale latency on predictions. For more detailed information, take a look at our :doc:`References <reference>` page. All figures on this page are taken from the following paper: `JINST 13 P07027 (2018) <https://dx.doi.org/10.1088/1748-0221/13/07/P07027>`_. diff --git a/sphinx-docs/conf.py b/sphinx-docs/conf.py new file mode 100644 index 000000000..853b93a31 --- /dev/null +++ b/sphinx-docs/conf.py @@ -0,0 +1,89 @@ +# Configuration file for the Sphinx documentation builder. +# +# This file only contains a selection of the most common options. For a full +# list see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html + +# -- Path setup -------------------------------------------------------------- + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. +# +import os +import sys +sys.path.insert(0, os.path.abspath('../hls4ml')) + +import datetime +def get_version(rel_path): + for line in open(rel_path): + if line.startswith('__version__'): + delim = '"' if '"' in line else "'" + return line.split(delim)[1] + else: + raise RuntimeError("Unable to find version string.") + +# -- Project information ----------------------------------------------------- + +project = 'hls4ml' +copyright = str(datetime.datetime.now().year)+', Fast Machine Learning Lab' +author = 'Fast Machine Learning Lab' + +# The full version, including alpha/beta/rc tags +release = get_version("../hls4ml/__init__.py") + +# -- General configuration --------------------------------------------------- + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = [ + 'sphinx.ext.mathjax', + 'sphinx.ext.autodoc', + 'sphinx.ext.githubpages', + 'sphinx_rtd_theme', + 'sphinx.ext.napoleon' +] + +# Add any paths that contain templates here, relative to this directory. +templates_path = ['_templates'] + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This pattern also affects html_static_path and html_extra_path. +exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] + + +# -- Options for HTML output ------------------------------------------------- + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. +# +html_theme = 'sphinx_rtd_theme' + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. They are copied after the builtin static files, +# so a file named "default.css" will overwrite the builtin "default.css". +html_static_path = ['_static'] + + +# -- Extension configuration ------------------------------------------------- +html_show_sourcelink = False +html_logo = "img/hls4ml_logo_navbar.png" + +html_theme_options = { + 'canonical_url': '', + 'analytics_id': '', # Provided by Google in your dashboard + 'logo_only': True, + 'display_version': True, + 'prev_next_buttons_location': 'bottom', + 'style_external_links': False, + + 'style_nav_header_background': '#2980B9', + # Toc options + 'collapse_navigation': True, + 'sticky_navigation': True, + 'navigation_depth': 2, + 'includehidden': True, + 'titles_only': False +} diff --git a/sphinx-docs/img/activations.png b/sphinx-docs/img/activations.png new file mode 100644 index 0000000000000000000000000000000000000000..36fb8adc8f96da82b7982bbcde54d8f0360fd1ef GIT binary patch literal 47691 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learning inference in FPGAs. We create firmware implementations of machine learning algorithms using high level synthesis language (HLS). We translate traditional open-source machine learning package models into HLS that can be configured for your use-case! + +The project is currently in development, so please let us know if you are interested, your experiences with the package, and if you would like new features to be added. + +Contact: hls4ml.help@gmail.com + + +Project Status +================================= +For the latest status including current and planned features, see the :doc:`Status and Features <status>` page. + +Tutorials +================================= +Detailed tutorials on how to use ``hls4ml``'s various functionalities can be found at: + +https://github.com/hls-fpga-machine-learning/hls4ml-tutorial diff --git a/sphinx-docs/reference.rst b/sphinx-docs/reference.rst new file mode 100644 index 000000000..1f785b77f --- /dev/null +++ b/sphinx-docs/reference.rst @@ -0,0 +1,53 @@ +============================ +Reference and Contributors +============================ + + +Citation +======== + +If you are using the package please cite: + + +* + .. image:: https://zenodo.org/badge/108329371.svg + :target: https://zenodo.org/badge/latestdoi/108329371 + :alt: DOI + +* J. Duarte *et al.*\ , "Fast inference of deep neural networks in FPGAs for particle physics", `JINST 13 P07027 (2018) <https://dx.doi.org/10.1088/1748-0221/13/07/P07027>`_\ , `arXiv:1804.06913 <https://arxiv.org/abs/1804.06913>`_. + +If you are using the boosted decision tree implementation, please cite also: + + +* S. Summers *et al.*\ , "Fast inference of boosted decision trees in FPGAs for particle physics", `arXiv:2002.02534 <https://arxiv.org/abs/2002.02534>`_. + If you are using the binary or ternary neural network implementation, please also cite: +* G. Di Guglielmo *et al.*\ , "Compressing deep neural networks on FPGAs to binary and ternary precision with hls4ml", `arXiv:2003.06308 <https://arxiv.org/abs/2003.06308>`_ + +Additional Talks and Presentations +================================== + + +* eScience 2019: `talk <https://escience2019.sched.com/event/Uuiy/machine-learning-on-fpgas-for-low-latency-and-high-throughput-inference?iframe=yes&w=100%&sidebar=yes&bg=no#>`_ +* ACAT 2019: `talk <https://indico.cern.ch/event/708041/contributions/3269690/>`_ +* Zurich Hands-on Course: `course <https://indico.cern.ch/event/769727/>`_ +* TWEPP 2018: `talk <https://indico.cern.ch/event/697988/contributions/3055990/>`_ +* CHEP 2018: `talk <https://indico.cern.ch/event/587955/contributions/2937529/>`_ +* Connecting the Dots 2018: `talk <https://indico.cern.ch/event/658267/contributions/2813688/>`_ +* Fermilab Research Techniques Seminar: `talk <https://indico.fnal.gov/event/16908/>`_ +* CERN EP/IT Data Science Seminar: `talk <https://indico.cern.ch/event/721567/>`_ + +Contributors +============ + + +* Vladimir Loncar, Jennifer Ngadiuba, Maurizio Pierini, Sioni Summers [CERN] +* Javier Duarte [University of California San Diego] +* Sergo Jindariani, Benjamin Kreis, Ryan Rivera, Nhan Tran [Fermilab] +* Edward Kreinar [Hawkeye360] +* Song Han, Philip Harris, Dylan Rankin [MIT] +* Zhenbin Wu [University of Illinois at Chicago] +* Mark Neubauer [University of Illinois Urbana-Champaign] +* Shih-Chieh Hsu [University of Washington] +* Giuseppe Di Guglielmo [Columbia University] +* Duc Hoang [Rhodes College] + diff --git a/sphinx-docs/release_notes.rst b/sphinx-docs/release_notes.rst new file mode 100644 index 000000000..d132f94b2 --- /dev/null +++ b/sphinx-docs/release_notes.rst @@ -0,0 +1,69 @@ +======================== +Realease Notes +======================== + + +Go to `here <https://github.com/hls-fpga-machine-learning/hls4ml/releases>`_ for official releases on Github. + +**v0.3.0** + + +* Installing from ``PyPI`` +* Create configuration dictionary from model object +* Run 'C Simulation' from Python with ``hls_model.predict(X)`` +* Trace model layer output with ``hls_model.trace(X)`` +* Write HLS project, run synthesis flow from Python +* QKeras support: convert models trained using layers and quantizers from QKeras +* Example models moved to separate repo, added API to retrieve them +* New Softmax implementations +* Minor fixes: weights exported at higher precision, concatenate layer shape corrected + +---- + +**v0.2.0:** + + +* ``tf_to_hls`` tool for converting tensorflow models (protobufs ``.pb``\ ) +* Support for larger ``Conv1D/2D`` layers +* Support for binary and ternary layers from `QKeras <https://github.com/google/qkeras>`_. +* API enhancements (custom layers, multiple backends) +* :doc:`Profiling <api/profiling>` support +* ``hls4ml report``\ command to gather HLS build reports, ``hls4ml build -l`` for Logic Synthesis +* Support for all-in-one Keras's ``.h5`` files (obtained with Keras's ``save()`` function, without the need for separate ``.json`` and ``.h5`` weight file). +* Fused Batch Normalisation into Dense layer optimsation. + +---- + +**v0.1.6:** + + +* Support for larger Dense layers (enabled with Strategy: Resource in the configuration file) +* Binary/Ternary NN refinements +* Built-in optimization framework +* Optional C/RTL validation + +---- + +**v0.1.5**\ : Per-layer precision and reuse factor + +---- + +**v0.1.3**\ : Adding PyTorch support + +---- + +**v0.1.2**\ : First beta release + + +* some bug fixes for pipelining and support for layer types + +---- + +**v0.0.2**\ : first alpha release + + +* full translation of DNNs from Keras +* an example Conv1D exists +* parallel mode is supported (serial mode, not yet) + + diff --git a/sphinx-docs/setup.rst b/sphinx-docs/setup.rst new file mode 100644 index 000000000..a76b895b0 --- /dev/null +++ b/sphinx-docs/setup.rst @@ -0,0 +1,177 @@ +===== +Setup +===== + +This chapter is dedicated to setting up the tool. We discuss software dependencies of hls4ml. There is a quick start guide for beginners to get familiar quickly. Then we discuss in more detail the features of the tool and user configuration. + +Dependencies +============ + +**numpy, h5py**\ : required for the translation of keras model files + +http://www.numpy.org + +http://www.h5py.org + + +**pyyaml**\ : for configuration file parsing + +https://pypi.python.org/pypi/PyYAML + + +**PyTorch**\ : for reading in Torch models + +https://pytorch.org/ + + +**scikit-learn**\ : for BDT architectures, includes dependencies on numpy, etc. + +https://scikit-learn.org + + +**onnx**\ : note that you need an install of protobuf and numpy to build onnx. Detailed instructions are included in the link. + +https://github.com/onnx/onnx + + +**Xilinx Vivado license**\ : the package currently is only for Xilinx devices, a license is required for the simulation and synthesis of HLS code + + +Quick Start +============= + +For basic concepts to understand the tool, please visit the :doc:`Concepts <concepts>` chapter. Here we give line-by-line instructions for simply running the tool out-of-the-box and getting a feel for the workflow. + +Installation +------------ + +.. code-block:: + + pip install hls4ml + +If you want to use our :doc:`profiling <api/profiling>` toolbox, you might need to install extra dependencies: + +.. code-block:: + + pip install hls4ml[profiling] + +Getting started +--------------- + +To get started with ``hls4ml``, we provide some default example models for conversion: + +.. code-block:: python + + import hls4ml + + #Fetch a keras model from our example repository + #This will download our example model to your working directory and return an example configuration file + config = hls4ml.utils.fetch_example_model('KERAS_3layer.json') + + print(config) #You can print it to see some default parameters + + #Convert it to a hls project + hls_model = hls4ml.converters.keras_to_hls(config) + + # Print full list of example model if you want to explore more + hls4ml.utils.fetch_example_list() + +After that, you can use :code:`Vivado HLS` to synthesize the model: + +.. code-block:: python + + #Use Vivado HLS to synthesize the model + #This might take several minutes + hls_model.build() + + #Print out the report if you want + hls4ml.report.read_vivado_report('my-hls-test') + +Done! you've built your first project using ``hls4ml`` ! To learn more about our various API functionalities, check out our tutorials `here <https://github.com/hls-fpga-machine-learning/hls4ml-tutorial>`_. + +If you want to configure your model further, check out our :doc:`Configuration <api/configuration>` page. + +Apart from our main API, we also support model conversion using a command line interface, check out our next section to find out more: + +Getting started with hls4ml commands (optional) +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +To follow this tutorial, you must first download our ``example-models`` repository: + +.. code-block:: bash + + git clone https://github.com/hls-fpga-machine-learning/example-models.git + +The model files, along with other configuration parameters, are defined in the ``.yml`` files. +Further information about ``.yml`` files can be found in :doc:`Configuration <api/configuration>` page. + +In order to create an example HLS project: + + +* Go to ``example-models/`` from the main directory: + +.. code-block:: bash + + cd example-models/ + + +* And use this command to translate a Keras model: + +.. code-block:: bash + + hls4ml convert -c keras-config.yml + +This will create a new HLS project directory with an implementation of a model from the ``example-models/keras/`` directory. +To build the HLS project, do: + +.. code-block:: bash + + hls4ml build -p my-hls-test -a + +This will create a Vivado HLS project with your model implmentation! + +**NOTE:** For the last step, you can alternatively do the following to build the HLS project: + +.. code-block:: Bash + + cd my-hls-test + vivado_hls -f build_prj.tcl + +``vivado_hls`` can be controlled with: + +.. code-block:: bash + + vivado_hls -f build_prj.tcl "csim=1 synth=1 cosim=1 export=1" + +Setting the additional parameters to ``1`` to ``0`` disables that step, but disabling ``synth`` also disables ``cosim`` and ``export``. + +**3) Further help:** + + +* + For further information about how to use ``hls4ml``\ , do: ``hls4ml --help`` or ``hls4ml -h`` + +* + If you need help for a particular ``command``\ , ``hls4ml command -h`` will show help for the requested ``command`` + +* + We provide a detailed documentation for each of the command in the :doc:`Command Help <../command>` section + +**To uninstall ``hls4ml``\ :** + +.. code-block:: bash + + pip uninstall hls4ml + +Existing examples +----------------- + + +* + Examples of model files and weights can be found in `example_models <https://github.com/hls-fpga-machine-learning/example-models>`_ directory. + +* + Training codes and examples of resources needed to train the models can be found `here <https://github.com/hls-fpga-machine-learning/keras-training>`_. + +* + Other examples of various HLS projects with examples of different machine learning algorithm implementations is in the directory `example-prjs <https://github.com/hls-fpga-machine-learning/hls4ml/tree/master/example-prjs>`_. \ No newline at end of file diff --git a/sphinx-docs/status.rst b/sphinx-docs/status.rst new file mode 100644 index 000000000..8a77b9dd6 --- /dev/null +++ b/sphinx-docs/status.rst @@ -0,0 +1,65 @@ +=================== +Status and Features +=================== + +Status +======== + +The latest stable release is :doc:`v0.2.0 <release_notes>`, including a validated boosted decision tree implementation (`arXiv:2002.02534 <https://arxiv.org/abs/2002.02534>`_) and binary/ternary neural networks (\ `arXiv:2003.06308 <https://arxiv.org/abs/2003.06308>`_). + + +Features +======== + +A list of suppported ML codes and architectures, including a summary table is below. Dependences are given in the :doc:`Setup <setup>` page. + +ML code support: + + +* Keras/Tensorflow, PyTorch, scikit-learn +* Planned: xgboost + +Neural network architectures: + + +* Fully Connected NNs (multi-layer perceptron) +* Boosted Decision Trees +* Convolutional NNs (1D/2D), in beta testing +* Recurrent NN/LSTM, in prototyping + +A summary of the on-going status of the ``hls4ml`` tool is in the table below. + +.. list-table:: + :header-rows: 1 + + * - Architectures/Toolkits + - Keras/TensorFlow + - PyTorch + - scikit-learn + * - MLP + - ``supported`` + - ``supported`` + - - + * - Conv1D/Conv2D + - ``supported`` + - ``in development`` + - - + * - BDT + - - + - - + - ``supported`` + * - RNN/LSTM + - ``in development`` + - - + - - + + +Other random feature notes: + + +* There is a known Vivado HLS issue where the large loop unrolls create memory issues during synthesis. We are working to solve this issue but you may see errors related to this depending on the memory of your machine. Please feel free to email the ``hls4ml`` team if you have any further questions. + +Example Models +============== + +We also provide and documented several example models that have been implemented in ``hls4ml`` in `this Github repository <https://github.com/hls-fpga-machine-learning/models>`_. -- GitLab From 39d92b9c6b9a65ecfafdf7ecd677545050685342 Mon Sep 17 00:00:00 2001 From: ngpaladi <ngpaladi@users.noreply.github.com> Date: Tue, 22 Dec 2020 18:27:14 -0500 Subject: [PATCH 2/7] Update to forked branch --- .github/workflows/build-sphinx.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/build-sphinx.yml b/.github/workflows/build-sphinx.yml index e46d0a863..e99781bfb 100644 --- a/.github/workflows/build-sphinx.yml +++ b/.github/workflows/build-sphinx.yml @@ -17,7 +17,7 @@ jobs: pre-build-command: "pip install sphinx-rtd-theme" - name: Commit Documentation Changes run: | - git clone https://github.com/fastmachinelearning/hls4ml.git --branch gh-pages --single-branch gh-pages + git clone https://github.com/ngpaladi/hls4ml.git --branch gh-pages --single-branch gh-pages cp -r sphinx-docs/_build/html/* gh-pages/ cd gh-pages touch .nojekyll -- GitLab From 4d76a8632714e0c93ff34414d4131e93e2d575f4 Mon Sep 17 00:00:00 2001 From: ngpaladi <ngpaladi@users.noreply.github.com> Date: Wed, 23 Dec 2020 02:05:05 -0500 Subject: [PATCH 3/7] Add Initial Autodoc --- sphinx-docs/api/configuration.rst | 4 +- sphinx-docs/api/profiling.rst | 2 +- .../autodoc/hls4ml.converters.keras.rst | 85 +++ sphinx-docs/autodoc/hls4ml.converters.rst | 53 ++ .../autodoc/hls4ml.model.optimizer.passes.rst | 85 +++ .../autodoc/hls4ml.model.optimizer.rst | 29 + sphinx-docs/autodoc/hls4ml.model.rst | 45 ++ sphinx-docs/autodoc/hls4ml.report.rst | 21 + sphinx-docs/autodoc/hls4ml.rst | 23 + sphinx-docs/autodoc/hls4ml.templates.rst | 29 + sphinx-docs/autodoc/hls4ml.utils.rst | 37 ++ sphinx-docs/autodoc/hls4ml.writer.rst | 29 + sphinx-docs/concepts.rst | 20 +- sphinx-docs/conf.py | 2 +- sphinx-docs/img/hls4ml_logo.png | Bin 26295 -> 26453 bytes sphinx-docs/img/hls4ml_logo.svg | 514 ++++++++++++++++ sphinx-docs/img/hls4ml_logo_lightgrey.png | Bin 0 -> 25377 bytes sphinx-docs/img/hls4ml_logo_lightgrey.svg | 562 ++++++++++++++++++ sphinx-docs/img/hls4ml_logo_navbar.png | Bin 10910 -> 7506 bytes sphinx-docs/index.rst | 10 + sphinx-docs/setup.rst | 14 +- 21 files changed, 1547 insertions(+), 17 deletions(-) create mode 100644 sphinx-docs/autodoc/hls4ml.converters.keras.rst create mode 100644 sphinx-docs/autodoc/hls4ml.converters.rst create mode 100644 sphinx-docs/autodoc/hls4ml.model.optimizer.passes.rst create mode 100644 sphinx-docs/autodoc/hls4ml.model.optimizer.rst create mode 100644 sphinx-docs/autodoc/hls4ml.model.rst create mode 100644 sphinx-docs/autodoc/hls4ml.report.rst create mode 100644 sphinx-docs/autodoc/hls4ml.rst create mode 100644 sphinx-docs/autodoc/hls4ml.templates.rst create mode 100644 sphinx-docs/autodoc/hls4ml.utils.rst create mode 100644 sphinx-docs/autodoc/hls4ml.writer.rst create mode 100644 sphinx-docs/img/hls4ml_logo.svg create mode 100644 sphinx-docs/img/hls4ml_logo_lightgrey.png create mode 100644 sphinx-docs/img/hls4ml_logo_lightgrey.svg diff --git a/sphinx-docs/api/configuration.rst b/sphinx-docs/api/configuration.rst index 300a3570e..5a5d3f308 100644 --- a/sphinx-docs/api/configuration.rst +++ b/sphinx-docs/api/configuration.rst @@ -14,7 +14,7 @@ We currently support two ways of setting hls4ml's model configuration. This page * - One important part of hls4ml to remember is that the user is responsible for the format of the inputs. There is no automatic formatting or normalization so this must be done in the training. + One important part of ``hls4ml`` to remember is that the user is responsible for the format of the inputs. There is no automatic formatting or normalization so this must be done in the training. * For developers, you might also want to checkout this section: `Detailed configuration in converted hls codes <#detailed-configuration-in-converted-hls-codes>`_. @@ -100,7 +100,7 @@ There are a number of configuration options that you have. Let's go through the 2.2 Per-Layer Configuration --------------------------- -In the hls4ml configuration file, it is possible to specify the model *Precision* and *ReuseFactor* with finer granularity. +In the ``hls4ml`` configuration file, it is possible to specify the model *Precision* and *ReuseFactor* with finer granularity. Under the ``HLSConfig`` heading, these can be set for the ``Model``\ , per ``LayerType``\ , per ``LayerName``\ , and for named variables within the layer (for precision only). The most basic configuration may look like this: diff --git a/sphinx-docs/api/profiling.rst b/sphinx-docs/api/profiling.rst index 5b70962c5..1d71d67f7 100644 --- a/sphinx-docs/api/profiling.rst +++ b/sphinx-docs/api/profiling.rst @@ -53,7 +53,7 @@ When different combinations of the input objects are given, different plots will 4) Keras model, HLSModel, and data: both weights and activation profiles are produced, with grey boxes indicating the data types from the HLSModel. -Each box shows the median and quartiles of the distribution. The grey shaded boxes show the range which can be represented with the hls4ml config file used. +Each box shows the median and quartiles of the distribution. The grey shaded boxes show the range which can be represented with the ``hls4ml`` config file used. As a starting point, a good configuration would at least cover the box and whisker for each variable with the grey box. Make sure the box and whisker is contained to the right by using sufficient integer bits to avoid overflow. It might be that more precision is needed (grey boxes extend further to the left) to achieve satisfactory performance. In some cases, it is safe to barely cover the values and still achieve good accuracy. diff --git a/sphinx-docs/autodoc/hls4ml.converters.keras.rst b/sphinx-docs/autodoc/hls4ml.converters.keras.rst new file mode 100644 index 000000000..51c8953ae --- /dev/null +++ b/sphinx-docs/autodoc/hls4ml.converters.keras.rst @@ -0,0 +1,85 @@ +hls4ml.converters.keras +=============================== + +Submodules +---------- + +hls4ml.converters.keras.convolution module +------------------------------------------ + +.. automodule:: hls4ml.converters.keras.convolution + :members: + :undoc-members: + :show-inheritance: + +hls4ml.converters.keras.core module +----------------------------------- + +.. automodule:: hls4ml.converters.keras.core + :members: + :undoc-members: + :show-inheritance: + +hls4ml.converters.keras.graph module +------------------------------------ + +.. automodule:: hls4ml.converters.keras.graph + :members: + :undoc-members: + :show-inheritance: + +hls4ml.converters.keras.merge module +------------------------------------ + +.. automodule:: hls4ml.converters.keras.merge + :members: + :undoc-members: + :show-inheritance: + +hls4ml.converters.keras.pooling module +-------------------------------------- + +.. automodule:: hls4ml.converters.keras.pooling + :members: + :undoc-members: + :show-inheritance: + +hls4ml.converters.keras.qkeras module +------------------------------------- + +.. automodule:: hls4ml.converters.keras.qkeras + :members: + :undoc-members: + :show-inheritance: + +hls4ml.converters.keras.qkeras\_layers module +--------------------------------------------- + +.. automodule:: hls4ml.converters.keras.qkeras_layers + :members: + :undoc-members: + :show-inheritance: + +hls4ml.converters.keras.reshape module +-------------------------------------- + +.. automodule:: hls4ml.converters.keras.reshape + :members: + :undoc-members: + :show-inheritance: + +hls4ml.converters.keras.reshaping module +---------------------------------------- + +.. automodule:: hls4ml.converters.keras.reshaping + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: hls4ml.converters.keras + :members: + :undoc-members: + :show-inheritance: diff --git a/sphinx-docs/autodoc/hls4ml.converters.rst b/sphinx-docs/autodoc/hls4ml.converters.rst new file mode 100644 index 000000000..aa58f0b70 --- /dev/null +++ b/sphinx-docs/autodoc/hls4ml.converters.rst @@ -0,0 +1,53 @@ +hls4ml.converters +========================= + +Subpackages +----------- + +.. toctree:: + :maxdepth: 4 + + hls4ml.converters.keras + +Submodules +---------- + +hls4ml.converters.keras\_to\_hls module +--------------------------------------- + +.. automodule:: hls4ml.converters.keras_to_hls + :members: + :undoc-members: + :show-inheritance: + +hls4ml.converters.onnx\_to\_hls module +-------------------------------------- + +.. automodule:: hls4ml.converters.onnx_to_hls + :members: + :undoc-members: + :show-inheritance: + +hls4ml.converters.pytorch\_to\_hls module +----------------------------------------- + +.. automodule:: hls4ml.converters.pytorch_to_hls + :members: + :undoc-members: + :show-inheritance: + +hls4ml.converters.tf\_to\_hls module +------------------------------------ + +.. automodule:: hls4ml.converters.tf_to_hls + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: hls4ml.converters + :members: + :undoc-members: + :show-inheritance: diff --git a/sphinx-docs/autodoc/hls4ml.model.optimizer.passes.rst b/sphinx-docs/autodoc/hls4ml.model.optimizer.passes.rst new file mode 100644 index 000000000..48087f991 --- /dev/null +++ b/sphinx-docs/autodoc/hls4ml.model.optimizer.passes.rst @@ -0,0 +1,85 @@ +hls4ml.model.optimizer.passes +===================================== + +Submodules +---------- + +hls4ml.model.optimizer.passes.bn\_fuse module +--------------------------------------------- + +.. automodule:: hls4ml.model.optimizer.passes.bn_fuse + :members: + :undoc-members: + :show-inheritance: + +hls4ml.model.optimizer.passes.bn\_quant module +---------------------------------------------- + +.. automodule:: hls4ml.model.optimizer.passes.bn_quant + :members: + :undoc-members: + :show-inheritance: + +hls4ml.model.optimizer.passes.clone module +------------------------------------------ + +.. automodule:: hls4ml.model.optimizer.passes.clone + :members: + :undoc-members: + :show-inheritance: + +hls4ml.model.optimizer.passes.conv\_same\_pad module +---------------------------------------------------- + +.. automodule:: hls4ml.model.optimizer.passes.conv_same_pad + :members: + :undoc-members: + :show-inheritance: + +hls4ml.model.optimizer.passes.fuse\_biasadd module +-------------------------------------------------- + +.. automodule:: hls4ml.model.optimizer.passes.fuse_biasadd + :members: + :undoc-members: + :show-inheritance: + +hls4ml.model.optimizer.passes.nop module +---------------------------------------- + +.. automodule:: hls4ml.model.optimizer.passes.nop + :members: + :undoc-members: + :show-inheritance: + +hls4ml.model.optimizer.passes.pointwise module +---------------------------------------------- + +.. automodule:: hls4ml.model.optimizer.passes.pointwise + :members: + :undoc-members: + :show-inheritance: + +hls4ml.model.optimizer.passes.qkeras module +------------------------------------------- + +.. automodule:: hls4ml.model.optimizer.passes.qkeras + :members: + :undoc-members: + :show-inheritance: + +hls4ml.model.optimizer.passes.repack\_stream module +--------------------------------------------------- + +.. automodule:: hls4ml.model.optimizer.passes.repack_stream + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: hls4ml.model.optimizer.passes + :members: + :undoc-members: + :show-inheritance: diff --git a/sphinx-docs/autodoc/hls4ml.model.optimizer.rst b/sphinx-docs/autodoc/hls4ml.model.optimizer.rst new file mode 100644 index 000000000..838b5ace4 --- /dev/null +++ b/sphinx-docs/autodoc/hls4ml.model.optimizer.rst @@ -0,0 +1,29 @@ +hls4ml.model.optimizer +============================== + +Subpackages +----------- + +.. toctree:: + :maxdepth: 4 + + hls4ml.model.optimizer.passes + +Submodules +---------- + +hls4ml.model.optimizer.optimizer module +--------------------------------------- + +.. automodule:: hls4ml.model.optimizer.optimizer + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: hls4ml.model.optimizer + :members: + :undoc-members: + :show-inheritance: diff --git a/sphinx-docs/autodoc/hls4ml.model.rst b/sphinx-docs/autodoc/hls4ml.model.rst new file mode 100644 index 000000000..17f4b98de --- /dev/null +++ b/sphinx-docs/autodoc/hls4ml.model.rst @@ -0,0 +1,45 @@ +hls4ml.model +==================== + +Subpackages +----------- + +.. toctree:: + :maxdepth: 4 + + hls4ml.model.optimizer + +Submodules +---------- + +hls4ml.model.hls\_layers module +------------------------------- + +.. automodule:: hls4ml.model.hls_layers + :members: + :undoc-members: + :show-inheritance: + +hls4ml.model.hls\_model module +------------------------------ + +.. automodule:: hls4ml.model.hls_model + :members: + :undoc-members: + :show-inheritance: + +hls4ml.model.profiling module +----------------------------- + +.. automodule:: hls4ml.model.profiling + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: hls4ml.model + :members: + :undoc-members: + :show-inheritance: diff --git a/sphinx-docs/autodoc/hls4ml.report.rst b/sphinx-docs/autodoc/hls4ml.report.rst new file mode 100644 index 000000000..90cc408fc --- /dev/null +++ b/sphinx-docs/autodoc/hls4ml.report.rst @@ -0,0 +1,21 @@ +hls4ml.report +===================== + +Submodules +---------- + +hls4ml.report.vivado\_report module +----------------------------------- + +.. automodule:: hls4ml.report.vivado_report + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: hls4ml.report + :members: + :undoc-members: + :show-inheritance: diff --git a/sphinx-docs/autodoc/hls4ml.rst b/sphinx-docs/autodoc/hls4ml.rst new file mode 100644 index 000000000..dcb63c2a6 --- /dev/null +++ b/sphinx-docs/autodoc/hls4ml.rst @@ -0,0 +1,23 @@ +hls4ml +============== + +Subpackages +----------- + +.. toctree:: + :maxdepth: 4 + + hls4ml.converters + hls4ml.model + hls4ml.report + hls4ml.templates + hls4ml.utils + hls4ml.writer + +Module contents +--------------- + +.. automodule:: hls4ml + :members: + :undoc-members: + :show-inheritance: diff --git a/sphinx-docs/autodoc/hls4ml.templates.rst b/sphinx-docs/autodoc/hls4ml.templates.rst new file mode 100644 index 000000000..a3c679526 --- /dev/null +++ b/sphinx-docs/autodoc/hls4ml.templates.rst @@ -0,0 +1,29 @@ +hls4ml.templates +======================== + +Submodules +---------- + +hls4ml.templates.templates module +--------------------------------- + +.. automodule:: hls4ml.templates.templates + :members: + :undoc-members: + :show-inheritance: + +hls4ml.templates.vivado\_template module +---------------------------------------- + +.. automodule:: hls4ml.templates.vivado_template + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: hls4ml.templates + :members: + :undoc-members: + :show-inheritance: diff --git a/sphinx-docs/autodoc/hls4ml.utils.rst b/sphinx-docs/autodoc/hls4ml.utils.rst new file mode 100644 index 000000000..e29dd96c9 --- /dev/null +++ b/sphinx-docs/autodoc/hls4ml.utils.rst @@ -0,0 +1,37 @@ +hls4ml.utils +==================== + +Submodules +---------- + +hls4ml.utils.config module +-------------------------- + +.. automodule:: hls4ml.utils.config + :members: + :undoc-members: + :show-inheritance: + +hls4ml.utils.example\_models module +----------------------------------- + +.. automodule:: hls4ml.utils.example_models + :members: + :undoc-members: + :show-inheritance: + +hls4ml.utils.plot module +------------------------ + +.. automodule:: hls4ml.utils.plot + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: hls4ml.utils + :members: + :undoc-members: + :show-inheritance: diff --git a/sphinx-docs/autodoc/hls4ml.writer.rst b/sphinx-docs/autodoc/hls4ml.writer.rst new file mode 100644 index 000000000..af4ba6bd2 --- /dev/null +++ b/sphinx-docs/autodoc/hls4ml.writer.rst @@ -0,0 +1,29 @@ +hls4ml.writer +===================== + +Submodules +---------- + +hls4ml.writer.vivado\_writer module +----------------------------------- + +.. automodule:: hls4ml.writer.vivado_writer + :members: + :undoc-members: + :show-inheritance: + +hls4ml.writer.writers module +---------------------------- + +.. automodule:: hls4ml.writer.writers + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: hls4ml.writer + :members: + :undoc-members: + :show-inheritance: diff --git a/sphinx-docs/concepts.rst b/sphinx-docs/concepts.rst index 4f4ba7596..8b56182ce 100644 --- a/sphinx-docs/concepts.rst +++ b/sphinx-docs/concepts.rst @@ -2,15 +2,15 @@ Concepts ======== -The goal of hls4ml is to provide an efficient and fast translation of machine learning models from open-source packages (like Keras and PyTorch) for training machine learning algorithms to high level synthesis (HLS) code that can then be transpiled to run on an FPGA. The resulting HLS project can be then used to produce an IP which can be plugged into more complex designs or be used to create a kernel for CPU co-processing. The user has freedom to define many of the parameters of their algorithm to best suit their needs. +The goal of ``hls4ml`` is to provide an efficient and fast translation of machine learning models from open-source packages (like Keras and PyTorch) for training machine learning algorithms to high level synthesis (HLS) code that can then be transpiled to run on an FPGA. The resulting HLS project can be then used to produce an IP which can be plugged into more complex designs or be used to create a kernel for CPU co-processing. The user has freedom to define many of the parameters of their algorithm to best suit their needs. -The hls4ml package enables fast prototyping of a machine learning algorithm implementation in FPGAs, +The ``hls4ml`` package enables fast prototyping of a machine learning algorithm implementation in FPGAs, greatly reducing the time to results and giving the user intuition for how to best design a machine learning algorithm for their application while balancing performance, resource utilization and latency requirements. The Inspiration =============== -The inspiration for the creation of the hls4ml package stems from the high energy physics community at the Large Hadron Collider (LHC). While machine learning has already been proven to be extremely useful in analysis of data from detectors at the LHC, it is typically performed in an "offline" environment after the data is taken and agglomerated. However, one of the largest problems at detectors on the LHC is that collisions, or "events", generate too much data for everything to be saved. As such, filters called "triggers" are used to determine whether a given event should be kept. Using FPGAs allows for significantly lower latency so machine learning algorithms can essentially be run "live" at the detector level for event selection. As a result, more events with potential signs of new physics can be preserved for analysis. +The inspiration for the creation of the ``hls4ml`` package stems from the high energy physics community at the Large Hadron Collider (LHC). While machine learning has already been proven to be extremely useful in analysis of data from detectors at the LHC, it is typically performed in an "offline" environment after the data is taken and agglomerated. However, one of the largest problems at detectors on the LHC is that collisions, or "events", generate too much data for everything to be saved. As such, filters called "triggers" are used to determine whether a given event should be kept. Using FPGAs allows for significantly lower latency so machine learning algorithms can essentially be run "live" at the detector level for event selection. As a result, more events with potential signs of new physics can be preserved for analysis. The Solution: hls4ml ==================== @@ -18,12 +18,14 @@ The Solution: hls4ml .. image:: img/overview.jpg -With this in mind, let's take a look at how hls4ml helps to achieve such a goal. First, it's important to realize the architecture differences between an FPGA and a CPU or GPU. An FPGA can be specifically programmed to do a certain task, in this case evaluate neural networks given a set of inputs, and as such can be highly optimized for the task, with tricks like pipelining and parallel evaluation. However, this means dynamic remapping while running isn't really a possibility. FPGAs also often come at a comparatively low power cost with respect to CPUs and GPUs. This allows hls4ml to build HLS code from compressed neural networks that results in predictions on the microsecond scale for latency. The hls4ml tool saves the time investment needed to convert a neural network to a hardware design language or even HLS code, thus allowing for rapid prototyping. +With this in mind, let's take a look at how ``hls4ml`` helps to achieve such a goal. First, it's important to realize the architecture differences between an FPGA and a CPU or GPU. An FPGA can be specifically programmed to do a certain task, in this case evaluate neural networks given a set of inputs, and as such can be highly optimized for the task, with tricks like pipelining and parallel evaluation. However, this means dynamic remapping while running isn't really a possibility. FPGAs also often come at a comparatively low power cost with respect to CPUs and GPUs. This allows ``hls4ml`` to build HLS code from compressed neural networks that results in predictions on the microsecond scale for latency. The ``hls4ml`` tool saves the time investment needed to convert a neural network to a hardware design language or even HLS code, thus allowing for rapid prototyping. How it Works ============= .. image:: img/nn_map_paper_fig_2.png + :width: 70% + :align: center Consider a multi-layered neural network. At each neuron in a layer :math:`m` (containing :math:`N_m` neurons), we calculate an output value (part of the output vector :math:`\mathbf{x}_m` of said layer) using the sum of output values of the previous layer multiplied by independent weights for each of these values and a bias value. An activation function is performed on the result to get the final output value for the neuron. Representing the weights as a :math:`N_m` by :math:`N_{m-1}` matrix :math:`W_{m,m-1}`, the bias values as :math:`\mathbf{b}_m`, and the activation function as :math:`g_m`, we can express this compactly as: @@ -38,15 +40,17 @@ With hls4ml, each layer of output values is calculated independently in sequence To ensure optimal performance, the user can control aspects of their model, principally: -* **Size/Compression** - Though not explicitly part of the hls4ml package, this is an important optimization to efficiently use the FPGA resources +* **Size/Compression** - Though not explicitly part of the ``hls4ml`` package, this is an important optimization to efficiently use the FPGA resources * **Precision** - Define the :doc:`precision <api/profiling>` of the calculations in your model * **Dataflow/Resource Reuse** - Control parallel or serial model implementations with varying levels of pipelining -* **Quantization Aware Training** - Achieve best performance at low precision with tools like QKeras, and benefit automatically during inference with hls4ml parsing of QKeras models +* **Quantization Aware Training** - Achieve best performance at low precision with tools like QKeras, and benefit automatically during inference with ``hls4ml`` parsing of QKeras models .. image:: img/reuse_factor_paper_fig_8.png + :width: 70% + :align: center -Often, these decisions will be hardware dependent to maximize performance. Of note is that simplifying the input network must be done before using hls4ml to generate HLS code, for optimal compression to provide a sizable speedup. Also important to note is the use of fixed point arithmetic in hls4ml. This improves processing speed relative to floating point implementations. The hls4ml package also offers the functionality of configuring binning and output bit width of the precomputed activation functions as necessary. With respect to parallelization and resource reuse, hls4ml offers a "reuse factor" parameter that determines the number of times each multiplier is used in order to compute a layer of neuron's values. Therefore, a reuse factor of one would split the computation so each multiplier had to only perform one multiplication in the computation of the output values of a layer, as shown above. Conversely, a reuse factor of four, in this case, uses a single multiplier four times sequentially. Low reuse factor achieves the lowest latency and highest throughput but uses the most resources, while high reuse factor save resources at the expense of longer latency and lower throughput. The reuse factor can be set using the configuration options defined on the :doc:`Setup <setup>` page. +Often, these decisions will be hardware dependent to maximize performance. Of note is that simplifying the input network must be done before using ``hls4ml`` to generate HLS code, for optimal compression to provide a sizable speedup. Also important to note is the use of fixed point arithmetic in ``hls4ml``. This improves processing speed relative to floating point implementations. The ``hls4ml`` package also offers the functionality of configuring binning and output bit width of the precomputed activation functions as necessary. With respect to parallelization and resource reuse, ``hls4ml`` offers a "reuse factor" parameter that determines the number of times each multiplier is used in order to compute a layer of neuron's values. Therefore, a reuse factor of one would split the computation so each multiplier had to only perform one multiplication in the computation of the output values of a layer, as shown above. Conversely, a reuse factor of four, in this case, uses a single multiplier four times sequentially. Low reuse factor achieves the lowest latency and highest throughput but uses the most resources, while high reuse factor save resources at the expense of longer latency and lower throughput. The reuse factor can be set using the configuration options defined on the :doc:`Setup <setup>` page. -Thereby, the hls4ml package builds efficient HLS code to implement neural networks on FPGAs for microsecond-scale latency on predictions. For more detailed information, take a look at our :doc:`References <reference>` page. All figures on this page are taken from the following paper: `JINST 13 P07027 (2018) <https://dx.doi.org/10.1088/1748-0221/13/07/P07027>`_. +Thereby, the ``hls4ml`` package builds efficient HLS code to implement neural networks on FPGAs for microsecond-scale latency on predictions. For more detailed information, take a look at our :doc:`References <reference>` page. All figures on this page are taken from the following paper: `JINST 13 P07027 (2018) <https://dx.doi.org/10.1088/1748-0221/13/07/P07027>`_. diff --git a/sphinx-docs/conf.py b/sphinx-docs/conf.py index 853b93a31..5a04a95e7 100644 --- a/sphinx-docs/conf.py +++ b/sphinx-docs/conf.py @@ -12,7 +12,7 @@ # import os import sys -sys.path.insert(0, os.path.abspath('../hls4ml')) +sys.path.insert(0, os.path.abspath('../')) import datetime def get_version(rel_path): diff --git a/sphinx-docs/img/hls4ml_logo.png b/sphinx-docs/img/hls4ml_logo.png index 4aaed9367ebc1ca613fe0a12e7b59f1359f82d9d..5d1d4ab6a10f0a0cd55db0e782c7419ad51b4a02 100644 GIT binary patch literal 26453 zcmeFZS5#Bo7YBF&kxvk?qjVJz73oDfhzOyBA|g$s8LG5|4uXJ%X6U_z-lY=(1qFf7 zLhl`<Bp|&7=EVQZnt7gwd77-nB3|w}cb{E;yIwzPX(-WBGg3nkL<>`XtOG%3z*lnU zzq8;Mb0=#p_;tql5$xH2;N$h5`3Latb8nUPogs)NnDj#yzVO!qT)g3;_`>C>gQW}7 z)X4%uB9XjsdmCpn)3+A94o+5y1Sv)cVuxUl<)67FtxdRl8M!8({@q&|=drtMW1!L( zV+rR}|DG`&Mm<L;<yO6V^|4h&jHsz&RPR)-W0IqS+*d!{FN%%|5kF{=T34R5r@gYL 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zh%%-e_yQnaAOetcuWx;xV98HID)+HIH~`B>wS!u|x{ct$cp~m8!}o8q9uWq~bXaIm osI0Vam|B4D={biRcODUTNrOM<l?3P>|6iu2x}Mq-6}!;?16!zmy8r+H diff --git a/sphinx-docs/index.rst b/sphinx-docs/index.rst index ce25c120c..3dc476e9c 100644 --- a/sphinx-docs/index.rst +++ b/sphinx-docs/index.rst @@ -15,6 +15,14 @@ :caption: Quick API Reference api/* + +.. toctree:: + :hidden: + :glob: + :caption: Autogenerated API Reference + + autodoc/hls4ml + autodoc/hls4ml.* ================================== @@ -22,6 +30,8 @@ Welcome to hls4ml's documentation! ================================== .. image:: img/hls4ml_logo.png + :width: 60% + :align: center ``hls4ml`` is a Python package for machine learning inference in FPGAs. We create firmware implementations of machine learning algorithms using high level synthesis language (HLS). We translate traditional open-source machine learning package models into HLS that can be configured for your use-case! diff --git a/sphinx-docs/setup.rst b/sphinx-docs/setup.rst index a76b895b0..7a5516dea 100644 --- a/sphinx-docs/setup.rst +++ b/sphinx-docs/setup.rst @@ -2,7 +2,7 @@ Setup ===== -This chapter is dedicated to setting up the tool. We discuss software dependencies of hls4ml. There is a quick start guide for beginners to get familiar quickly. Then we discuss in more detail the features of the tool and user configuration. +This chapter is dedicated to setting up the tool. We discuss software dependencies of ``hls4ml``. There is a quick start guide for beginners to get familiar quickly. Then we discuss in more detail the features of the tool and user configuration. Dependencies ============ @@ -108,14 +108,14 @@ Further information about ``.yml`` files can be found in :doc:`Configuration <ap In order to create an example HLS project: -* Go to ``example-models/`` from the main directory: +Go to ``example-models/`` from the main directory: .. code-block:: bash cd example-models/ -* And use this command to translate a Keras model: +And use this command to translate a Keras model: .. code-block:: bash @@ -145,7 +145,8 @@ This will create a Vivado HLS project with your model implmentation! Setting the additional parameters to ``1`` to ``0`` disables that step, but disabling ``synth`` also disables ``cosim`` and ``export``. -**3) Further help:** +Further help +^^^^^^^^^^^^^^^^ * @@ -157,7 +158,10 @@ Setting the additional parameters to ``1`` to ``0`` disables that step, but disa * We provide a detailed documentation for each of the command in the :doc:`Command Help <../command>` section -**To uninstall ``hls4ml``\ :** +Uninstalling +^^^^^^^^^^^^^^ + +To uninstall ``hls4ml``: .. code-block:: bash -- GitLab From 7175eaa84dc3ab2b47aaefb91764bb682b91e5fd Mon Sep 17 00:00:00 2001 From: ngpaladi <ngpaladi@users.noreply.github.com> Date: Wed, 23 Dec 2020 13:29:21 -0500 Subject: [PATCH 4/7] Add regenerate autodoc to makefile --- .gitignore | 3 ++- sphinx-docs/Makefile | 1 + 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index 19ebfc5bf..ba949896e 100644 --- a/.gitignore +++ b/.gitignore @@ -8,4 +8,5 @@ vivado_prj .vscode my-hls-test *.tar.gz -sphinx-docs/_build \ No newline at end of file +sphinx-docs/_build +sphinx-docs/autodoc/* \ No newline at end of file diff --git a/sphinx-docs/Makefile b/sphinx-docs/Makefile index d4bb2cbb9..3f7357526 100644 --- a/sphinx-docs/Makefile +++ b/sphinx-docs/Makefile @@ -17,4 +17,5 @@ help: # Catch-all target: route all unknown targets to Sphinx using the new # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). %: Makefile + @sphinx-apidoc -f -T -o autodoc/ ../hls4ml @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) -- GitLab From a3af2e70a58666f0fa5b0e1453ab4b91dd42d016 Mon Sep 17 00:00:00 2001 From: ngpaladi <ngpaladi@users.noreply.github.com> Date: Wed, 23 Dec 2020 17:56:23 -0500 Subject: [PATCH 5/7] Prep for PR --- .github/workflows/build-sphinx.yml | 2 +- sphinx-docs/index.rst | 14 +++++++------- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/.github/workflows/build-sphinx.yml b/.github/workflows/build-sphinx.yml index e99781bfb..e46d0a863 100644 --- a/.github/workflows/build-sphinx.yml +++ b/.github/workflows/build-sphinx.yml @@ -17,7 +17,7 @@ jobs: pre-build-command: "pip install sphinx-rtd-theme" - name: Commit Documentation Changes run: | - git clone https://github.com/ngpaladi/hls4ml.git --branch gh-pages --single-branch gh-pages + git clone https://github.com/fastmachinelearning/hls4ml.git --branch gh-pages --single-branch gh-pages cp -r sphinx-docs/_build/html/* gh-pages/ cd gh-pages touch .nojekyll diff --git a/sphinx-docs/index.rst b/sphinx-docs/index.rst index 3dc476e9c..2830c41a4 100644 --- a/sphinx-docs/index.rst +++ b/sphinx-docs/index.rst @@ -16,13 +16,13 @@ api/* -.. toctree:: - :hidden: - :glob: - :caption: Autogenerated API Reference - - autodoc/hls4ml - autodoc/hls4ml.* +.. .. toctree:: +.. :hidden: +.. :glob: +.. :caption: Autogenerated API Reference +.. +.. autodoc/hls4ml +.. autodoc/hls4ml.* ================================== -- GitLab From c87740631c8e4cd4b67ea3d469ff2caea049d5b8 Mon Sep 17 00:00:00 2001 From: ngpaladi <ngpaladi@users.noreply.github.com> Date: Wed, 23 Dec 2020 18:51:48 -0500 Subject: [PATCH 6/7] Add Links to Autogenerated Docs and Update Build --- .github/workflows/build-sphinx.yml | 2 +- .../autodoc/hls4ml.converters.keras.rst | 85 ------------------- sphinx-docs/autodoc/hls4ml.converters.rst | 53 ------------ .../autodoc/hls4ml.model.optimizer.passes.rst | 85 ------------------- .../autodoc/hls4ml.model.optimizer.rst | 29 ------- sphinx-docs/autodoc/hls4ml.model.rst | 45 ---------- sphinx-docs/autodoc/hls4ml.report.rst | 21 ----- sphinx-docs/autodoc/hls4ml.rst | 23 ----- sphinx-docs/autodoc/hls4ml.templates.rst | 29 ------- sphinx-docs/autodoc/hls4ml.utils.rst | 37 -------- sphinx-docs/autodoc/hls4ml.writer.rst | 29 ------- sphinx-docs/index.rst | 14 +-- sphinx-docs/reference.rst | 1 + 13 files changed, 9 insertions(+), 444 deletions(-) delete mode 100644 sphinx-docs/autodoc/hls4ml.converters.keras.rst delete mode 100644 sphinx-docs/autodoc/hls4ml.converters.rst delete mode 100644 sphinx-docs/autodoc/hls4ml.model.optimizer.passes.rst delete mode 100644 sphinx-docs/autodoc/hls4ml.model.optimizer.rst delete mode 100644 sphinx-docs/autodoc/hls4ml.model.rst delete mode 100644 sphinx-docs/autodoc/hls4ml.report.rst delete mode 100644 sphinx-docs/autodoc/hls4ml.rst delete mode 100644 sphinx-docs/autodoc/hls4ml.templates.rst delete mode 100644 sphinx-docs/autodoc/hls4ml.utils.rst delete mode 100644 sphinx-docs/autodoc/hls4ml.writer.rst diff --git a/.github/workflows/build-sphinx.yml b/.github/workflows/build-sphinx.yml index e46d0a863..3d931653e 100644 --- a/.github/workflows/build-sphinx.yml +++ b/.github/workflows/build-sphinx.yml @@ -30,4 +30,4 @@ jobs: with: branch: gh-pages directory: gh-pages - github_token: ${{ secrets.GITHUB_TOKEN }} \ No newline at end of file + github_token: ${{ secrets.PERSONAL_TOKEN }} \ No newline at end of file diff --git a/sphinx-docs/autodoc/hls4ml.converters.keras.rst b/sphinx-docs/autodoc/hls4ml.converters.keras.rst deleted file mode 100644 index 51c8953ae..000000000 --- a/sphinx-docs/autodoc/hls4ml.converters.keras.rst +++ /dev/null @@ -1,85 +0,0 @@ -hls4ml.converters.keras -=============================== - -Submodules ----------- - -hls4ml.converters.keras.convolution module ------------------------------------------- - -.. automodule:: hls4ml.converters.keras.convolution - :members: - :undoc-members: - :show-inheritance: - -hls4ml.converters.keras.core module ------------------------------------ - -.. automodule:: hls4ml.converters.keras.core - :members: - :undoc-members: - :show-inheritance: - -hls4ml.converters.keras.graph module ------------------------------------- - -.. automodule:: hls4ml.converters.keras.graph - :members: - :undoc-members: - :show-inheritance: - -hls4ml.converters.keras.merge module ------------------------------------- - -.. automodule:: hls4ml.converters.keras.merge - :members: - :undoc-members: - :show-inheritance: - -hls4ml.converters.keras.pooling module --------------------------------------- - -.. automodule:: hls4ml.converters.keras.pooling - :members: - :undoc-members: - :show-inheritance: - -hls4ml.converters.keras.qkeras module -------------------------------------- - -.. automodule:: hls4ml.converters.keras.qkeras - :members: - :undoc-members: - :show-inheritance: - -hls4ml.converters.keras.qkeras\_layers module ---------------------------------------------- - -.. automodule:: hls4ml.converters.keras.qkeras_layers - :members: - :undoc-members: - :show-inheritance: - -hls4ml.converters.keras.reshape module --------------------------------------- - -.. automodule:: hls4ml.converters.keras.reshape - :members: - :undoc-members: - :show-inheritance: - -hls4ml.converters.keras.reshaping module ----------------------------------------- - -.. automodule:: hls4ml.converters.keras.reshaping - :members: - :undoc-members: - :show-inheritance: - -Module contents ---------------- - -.. automodule:: hls4ml.converters.keras - :members: - :undoc-members: - :show-inheritance: diff --git a/sphinx-docs/autodoc/hls4ml.converters.rst b/sphinx-docs/autodoc/hls4ml.converters.rst deleted file mode 100644 index aa58f0b70..000000000 --- a/sphinx-docs/autodoc/hls4ml.converters.rst +++ /dev/null @@ -1,53 +0,0 @@ -hls4ml.converters -========================= - -Subpackages ------------ - -.. toctree:: - :maxdepth: 4 - - hls4ml.converters.keras - -Submodules ----------- - -hls4ml.converters.keras\_to\_hls module ---------------------------------------- - -.. automodule:: hls4ml.converters.keras_to_hls - :members: - :undoc-members: - :show-inheritance: - -hls4ml.converters.onnx\_to\_hls module --------------------------------------- - -.. automodule:: hls4ml.converters.onnx_to_hls - :members: - :undoc-members: - :show-inheritance: - -hls4ml.converters.pytorch\_to\_hls module ------------------------------------------ - -.. automodule:: hls4ml.converters.pytorch_to_hls - :members: - :undoc-members: - :show-inheritance: - -hls4ml.converters.tf\_to\_hls module ------------------------------------- - -.. automodule:: hls4ml.converters.tf_to_hls - :members: - :undoc-members: - :show-inheritance: - -Module contents ---------------- - -.. automodule:: hls4ml.converters - :members: - :undoc-members: - :show-inheritance: diff --git a/sphinx-docs/autodoc/hls4ml.model.optimizer.passes.rst b/sphinx-docs/autodoc/hls4ml.model.optimizer.passes.rst deleted file mode 100644 index 48087f991..000000000 --- a/sphinx-docs/autodoc/hls4ml.model.optimizer.passes.rst +++ /dev/null @@ -1,85 +0,0 @@ -hls4ml.model.optimizer.passes -===================================== - -Submodules ----------- - -hls4ml.model.optimizer.passes.bn\_fuse module ---------------------------------------------- - -.. automodule:: hls4ml.model.optimizer.passes.bn_fuse - :members: - :undoc-members: - :show-inheritance: - -hls4ml.model.optimizer.passes.bn\_quant module ----------------------------------------------- - -.. automodule:: hls4ml.model.optimizer.passes.bn_quant - :members: - :undoc-members: - :show-inheritance: - -hls4ml.model.optimizer.passes.clone module ------------------------------------------- - -.. automodule:: hls4ml.model.optimizer.passes.clone - :members: - :undoc-members: - :show-inheritance: - -hls4ml.model.optimizer.passes.conv\_same\_pad module ----------------------------------------------------- - -.. automodule:: hls4ml.model.optimizer.passes.conv_same_pad - :members: - :undoc-members: - :show-inheritance: - -hls4ml.model.optimizer.passes.fuse\_biasadd module --------------------------------------------------- - -.. automodule:: hls4ml.model.optimizer.passes.fuse_biasadd - :members: - :undoc-members: - :show-inheritance: - -hls4ml.model.optimizer.passes.nop module ----------------------------------------- - -.. automodule:: hls4ml.model.optimizer.passes.nop - :members: - :undoc-members: - :show-inheritance: - -hls4ml.model.optimizer.passes.pointwise module ----------------------------------------------- - -.. automodule:: hls4ml.model.optimizer.passes.pointwise - :members: - :undoc-members: - :show-inheritance: - -hls4ml.model.optimizer.passes.qkeras module -------------------------------------------- - -.. automodule:: hls4ml.model.optimizer.passes.qkeras - :members: - :undoc-members: - :show-inheritance: - -hls4ml.model.optimizer.passes.repack\_stream module ---------------------------------------------------- - -.. automodule:: hls4ml.model.optimizer.passes.repack_stream - :members: - :undoc-members: - :show-inheritance: - -Module contents ---------------- - -.. automodule:: hls4ml.model.optimizer.passes - :members: - :undoc-members: - :show-inheritance: diff --git a/sphinx-docs/autodoc/hls4ml.model.optimizer.rst b/sphinx-docs/autodoc/hls4ml.model.optimizer.rst deleted file mode 100644 index 838b5ace4..000000000 --- a/sphinx-docs/autodoc/hls4ml.model.optimizer.rst +++ /dev/null @@ -1,29 +0,0 @@ -hls4ml.model.optimizer -============================== - -Subpackages ------------ - -.. toctree:: - :maxdepth: 4 - - hls4ml.model.optimizer.passes - -Submodules ----------- - -hls4ml.model.optimizer.optimizer module ---------------------------------------- - -.. automodule:: hls4ml.model.optimizer.optimizer - :members: - :undoc-members: - :show-inheritance: - -Module contents ---------------- - -.. automodule:: hls4ml.model.optimizer - :members: - :undoc-members: - :show-inheritance: diff --git a/sphinx-docs/autodoc/hls4ml.model.rst b/sphinx-docs/autodoc/hls4ml.model.rst deleted file mode 100644 index 17f4b98de..000000000 --- a/sphinx-docs/autodoc/hls4ml.model.rst +++ /dev/null @@ -1,45 +0,0 @@ -hls4ml.model -==================== - -Subpackages ------------ - -.. toctree:: - :maxdepth: 4 - - hls4ml.model.optimizer - -Submodules ----------- - -hls4ml.model.hls\_layers module -------------------------------- - -.. automodule:: hls4ml.model.hls_layers - :members: - :undoc-members: - :show-inheritance: - -hls4ml.model.hls\_model module ------------------------------- - -.. automodule:: hls4ml.model.hls_model - :members: - :undoc-members: - :show-inheritance: - -hls4ml.model.profiling module ------------------------------ - -.. automodule:: hls4ml.model.profiling - :members: - :undoc-members: - :show-inheritance: - -Module contents ---------------- - -.. automodule:: hls4ml.model - :members: - :undoc-members: - :show-inheritance: diff --git a/sphinx-docs/autodoc/hls4ml.report.rst b/sphinx-docs/autodoc/hls4ml.report.rst deleted file mode 100644 index 90cc408fc..000000000 --- a/sphinx-docs/autodoc/hls4ml.report.rst +++ /dev/null @@ -1,21 +0,0 @@ -hls4ml.report -===================== - -Submodules ----------- - -hls4ml.report.vivado\_report module ------------------------------------ - -.. automodule:: hls4ml.report.vivado_report - :members: - :undoc-members: - :show-inheritance: - -Module contents ---------------- - -.. automodule:: hls4ml.report - :members: - :undoc-members: - :show-inheritance: diff --git a/sphinx-docs/autodoc/hls4ml.rst b/sphinx-docs/autodoc/hls4ml.rst deleted file mode 100644 index dcb63c2a6..000000000 --- a/sphinx-docs/autodoc/hls4ml.rst +++ /dev/null @@ -1,23 +0,0 @@ -hls4ml -============== - -Subpackages ------------ - -.. toctree:: - :maxdepth: 4 - - hls4ml.converters - hls4ml.model - hls4ml.report - hls4ml.templates - hls4ml.utils - hls4ml.writer - -Module contents ---------------- - -.. automodule:: hls4ml - :members: - :undoc-members: - :show-inheritance: diff --git a/sphinx-docs/autodoc/hls4ml.templates.rst b/sphinx-docs/autodoc/hls4ml.templates.rst deleted file mode 100644 index a3c679526..000000000 --- a/sphinx-docs/autodoc/hls4ml.templates.rst +++ /dev/null @@ -1,29 +0,0 @@ -hls4ml.templates -======================== - -Submodules ----------- - -hls4ml.templates.templates module ---------------------------------- - -.. automodule:: hls4ml.templates.templates - :members: - :undoc-members: - :show-inheritance: - -hls4ml.templates.vivado\_template module ----------------------------------------- - -.. automodule:: hls4ml.templates.vivado_template - :members: - :undoc-members: - :show-inheritance: - -Module contents ---------------- - -.. automodule:: hls4ml.templates - :members: - :undoc-members: - :show-inheritance: diff --git a/sphinx-docs/autodoc/hls4ml.utils.rst b/sphinx-docs/autodoc/hls4ml.utils.rst deleted file mode 100644 index e29dd96c9..000000000 --- a/sphinx-docs/autodoc/hls4ml.utils.rst +++ /dev/null @@ -1,37 +0,0 @@ -hls4ml.utils -==================== - -Submodules ----------- - -hls4ml.utils.config module --------------------------- - -.. automodule:: hls4ml.utils.config - :members: - :undoc-members: - :show-inheritance: - -hls4ml.utils.example\_models module ------------------------------------ - -.. automodule:: hls4ml.utils.example_models - :members: - :undoc-members: - :show-inheritance: - -hls4ml.utils.plot module ------------------------- - -.. automodule:: hls4ml.utils.plot - :members: - :undoc-members: - :show-inheritance: - -Module contents ---------------- - -.. automodule:: hls4ml.utils - :members: - :undoc-members: - :show-inheritance: diff --git a/sphinx-docs/autodoc/hls4ml.writer.rst b/sphinx-docs/autodoc/hls4ml.writer.rst deleted file mode 100644 index af4ba6bd2..000000000 --- a/sphinx-docs/autodoc/hls4ml.writer.rst +++ /dev/null @@ -1,29 +0,0 @@ -hls4ml.writer -===================== - -Submodules ----------- - -hls4ml.writer.vivado\_writer module ------------------------------------ - -.. automodule:: hls4ml.writer.vivado_writer - :members: - :undoc-members: - :show-inheritance: - -hls4ml.writer.writers module ----------------------------- - -.. automodule:: hls4ml.writer.writers - :members: - :undoc-members: - :show-inheritance: - -Module contents ---------------- - -.. automodule:: hls4ml.writer - :members: - :undoc-members: - :show-inheritance: diff --git a/sphinx-docs/index.rst b/sphinx-docs/index.rst index 2830c41a4..3dc476e9c 100644 --- a/sphinx-docs/index.rst +++ b/sphinx-docs/index.rst @@ -16,13 +16,13 @@ api/* -.. .. toctree:: -.. :hidden: -.. :glob: -.. :caption: Autogenerated API Reference -.. -.. autodoc/hls4ml -.. autodoc/hls4ml.* +.. toctree:: + :hidden: + :glob: + :caption: Autogenerated API Reference + + autodoc/hls4ml + autodoc/hls4ml.* ================================== diff --git a/sphinx-docs/reference.rst b/sphinx-docs/reference.rst index 1f785b77f..fe650139f 100644 --- a/sphinx-docs/reference.rst +++ b/sphinx-docs/reference.rst @@ -50,4 +50,5 @@ Contributors * Shih-Chieh Hsu [University of Washington] * Giuseppe Di Guglielmo [Columbia University] * Duc Hoang [Rhodes College] +* Noah Paladino [Rutgers University] -- GitLab From 43d8d48ace026d691bce88bc12bd63e0045732f3 Mon Sep 17 00:00:00 2001 From: ngpaladi <ngpaladi@users.noreply.github.com> Date: Tue, 12 Jan 2021 11:51:05 -0500 Subject: [PATCH 7/7] Move contents of sphinx-docs to docs --- .github/workflows/build-sphinx.yml | 4 +-- .gitignore | 4 +-- {sphinx-docs => docs}/Makefile | 0 docs/README-KERAS.md | 29 ----------------- docs/README-ONNX.md | 29 ----------------- docs/README-PYTORCH.md | 30 ------------------ {sphinx-docs => docs}/api/configuration.rst | 0 {sphinx-docs => docs}/api/hls-model.rst | 0 {sphinx-docs => docs}/api/profiling.rst | 0 {sphinx-docs => docs}/command.rst | 0 {sphinx-docs => docs}/concepts.rst | 0 {sphinx-docs => docs}/conf.py | 0 {sphinx-docs => docs}/img/activations.png | Bin {sphinx-docs => docs}/img/hls4ml_logo.png | Bin {sphinx-docs => docs}/img/hls4ml_logo.svg | 0 .../img/hls4ml_logo_lightgrey.png | Bin .../img/hls4ml_logo_lightgrey.svg | 0 .../img/hls4ml_logo_navbar.png | Bin {sphinx-docs => docs}/img/logo.jpg | Bin {sphinx-docs => docs}/img/logo.png | Bin .../img/nn_map_paper_fig_2.png | Bin {sphinx-docs => docs}/img/overview.jpg | Bin {sphinx-docs => docs}/img/overview.pdf | Bin .../img/reuse_factor_paper_fig_8.png | Bin {sphinx-docs => docs}/img/weights.png | Bin {sphinx-docs => docs}/index.rst | 0 {sphinx-docs => docs}/reference.rst | 0 {sphinx-docs => docs}/release_notes.rst | 0 {sphinx-docs => docs}/setup.rst | 0 {sphinx-docs => docs}/status.rst | 0 30 files changed, 4 insertions(+), 92 deletions(-) rename {sphinx-docs => docs}/Makefile (100%) delete mode 100644 docs/README-KERAS.md delete mode 100644 docs/README-ONNX.md delete mode 100644 docs/README-PYTORCH.md rename {sphinx-docs => docs}/api/configuration.rst (100%) rename {sphinx-docs => docs}/api/hls-model.rst (100%) rename {sphinx-docs => docs}/api/profiling.rst (100%) rename {sphinx-docs => docs}/command.rst (100%) rename {sphinx-docs => docs}/concepts.rst (100%) rename {sphinx-docs => docs}/conf.py (100%) rename {sphinx-docs => docs}/img/activations.png (100%) rename {sphinx-docs => docs}/img/hls4ml_logo.png (100%) rename {sphinx-docs => docs}/img/hls4ml_logo.svg (100%) rename {sphinx-docs => docs}/img/hls4ml_logo_lightgrey.png (100%) rename {sphinx-docs => docs}/img/hls4ml_logo_lightgrey.svg (100%) rename {sphinx-docs => docs}/img/hls4ml_logo_navbar.png (100%) rename {sphinx-docs => docs}/img/logo.jpg (100%) rename {sphinx-docs => docs}/img/logo.png (100%) rename {sphinx-docs => docs}/img/nn_map_paper_fig_2.png (100%) rename {sphinx-docs => docs}/img/overview.jpg (100%) rename {sphinx-docs => docs}/img/overview.pdf (100%) rename {sphinx-docs => docs}/img/reuse_factor_paper_fig_8.png (100%) rename {sphinx-docs => docs}/img/weights.png (100%) rename {sphinx-docs => docs}/index.rst (100%) rename {sphinx-docs => docs}/reference.rst (100%) rename {sphinx-docs => docs}/release_notes.rst (100%) rename {sphinx-docs => docs}/setup.rst (100%) rename {sphinx-docs => docs}/status.rst (100%) diff --git a/.github/workflows/build-sphinx.yml b/.github/workflows/build-sphinx.yml index 3d931653e..755f7b433 100644 --- a/.github/workflows/build-sphinx.yml +++ b/.github/workflows/build-sphinx.yml @@ -13,12 +13,12 @@ jobs: - uses: actions/checkout@v1 - uses: ammaraskar/sphinx-action@master with: - docs-folder: "sphinx-docs/" + docs-folder: "docs/" pre-build-command: "pip install sphinx-rtd-theme" - name: Commit Documentation Changes run: | git clone https://github.com/fastmachinelearning/hls4ml.git --branch gh-pages --single-branch gh-pages - cp -r sphinx-docs/_build/html/* gh-pages/ + cp -r docs/_build/html/* gh-pages/ cd gh-pages touch .nojekyll git config --local user.email "action@github.com" diff --git a/.gitignore b/.gitignore index ba949896e..e25160c34 100644 --- a/.gitignore +++ b/.gitignore @@ -8,5 +8,5 @@ vivado_prj .vscode my-hls-test *.tar.gz -sphinx-docs/_build -sphinx-docs/autodoc/* \ No newline at end of file +docs/_build +docs/autodoc/* \ No newline at end of file diff --git a/sphinx-docs/Makefile b/docs/Makefile similarity index 100% rename from sphinx-docs/Makefile rename to docs/Makefile diff --git a/docs/README-KERAS.md b/docs/README-KERAS.md deleted file mode 100644 index 14b6afa85..000000000 --- a/docs/README-KERAS.md +++ /dev/null @@ -1,29 +0,0 @@ -# Keras to HLS - -Reads Keras model json for architecture and hdf5 for weights and biases. - -# Instructions to run - -```python keras-to-hls.py -c keras-config.yml``` - -# Configuration - -Configuration options for the HLS translation of Keras models. - -*KerasJson, KerasH5*: For Keras translation, you are required to provide `json` and `h5` model files. -Examples are in the directory: `example-keras-model-files` - -*OutputDir*: Directory where your HLS project will go - -*IOType*: We provide 2 options for the way inputs are input to the architecture, serially or in parallel. The keywords are `io_serial` or `io_parallel` - -*ReuseFactor*: For the running mode `io_parallel`, the calculations do not have to be fully parallelized but resources can be reused at the cost of higher latency. A `ReuseFactor: 1` means fully parallelized and no resources are reused - -*DefaultPrecision*: This is the default type of the weights, biases, accumulators, input and output vectors. This can then be further modified by the `firmware/parameters.h` file generated in your HLS project. - -# Running HLS - -``` -cd my-hls-test -vivado_hls -f build_prj.tcl -``` diff --git a/docs/README-ONNX.md b/docs/README-ONNX.md deleted file mode 100644 index 24852564a..000000000 --- a/docs/README-ONNX.md +++ /dev/null @@ -1,29 +0,0 @@ -# ONNX to HLS - -Reads ONNX model file which contains architecture as well as weights and biases. - -# Instructions to run - -```python onnx-to-hls.py -c onnx-config.yml``` - -# Configuration - -Configuration options for the HLS translation of Keras models. - -*OnnxModel*: For ONNX translation, you are required to provide `onnx` model file. -Examples are in the directory: `example-onnx-model-files` - -*OutputDir*: Directory where your HLS project will go. - -*IOType*: We provide 2 options for the way inputs are input to the architecture, serially or in parallel. The keywords are `io_serial` or `io_parallel`. - -*ReuseFactor*: For the running mode `io_parallel`, the calculations do not have to be fully parallelized but resources can be reused at the cost of higher latency. A `ReuseFactor: 1` means fully parallelized and no resources are reused. - -*DefaultPrecision*: This is the default type of the weights, biases, accumulators, input and output vectors. This can then be further modified by the `firmware/parameters.h` file generated in your HLS project. - -# Running HLS - -``` -cd my-hls-test -vivado_hls -f build_prj.tcl -``` diff --git a/docs/README-PYTORCH.md b/docs/README-PYTORCH.md deleted file mode 100644 index b094fff83..000000000 --- a/docs/README-PYTORCH.md +++ /dev/null @@ -1,30 +0,0 @@ -# Pytorch to HLS - -Reads Pytorch model pickle for architecture and state_dict pickle for weights and biases. - -# Instructions to run - -```python pytorch-to-hls.py -c pytorch-config.yml``` - -# Configuration - -Configuration options for the HLS translation of Keras models. - -*PytorchModel, PytorchDict*: For Pytorch translation, you are required to -store the model class into one pickle file, and the state_dict of the model -into another pickle file with PyTorch format. Examples are in the directory: `example-models` - -*OutputDir*: Directory where your HLS project will go - -*IOType*: We provide 2 options for the way inputs are input to the architecture, serially or in parallel. The keywords are `io_serial` or `io_parallel` - -*ReuseFactor*: For the running mode `io_parallel`, the calculations do not have to be fully parallelized but resources can be reused at the cost of higher latency. A `ReuseFactor: 1` means fully parallelized and no resources are reused - -*DefaultPrecision*: This is the default type of the weights, biases, accumulators, input and output vectors. This can then be further modified by the `firmware/parameters.h` file generated in your HLS project. - -# Running HLS - -``` -cd my-hls-dir-test -vivado_hls -f build_prj.tcl -``` diff --git a/sphinx-docs/api/configuration.rst b/docs/api/configuration.rst similarity index 100% rename from sphinx-docs/api/configuration.rst rename to docs/api/configuration.rst diff --git a/sphinx-docs/api/hls-model.rst b/docs/api/hls-model.rst similarity index 100% rename from sphinx-docs/api/hls-model.rst rename to docs/api/hls-model.rst diff --git a/sphinx-docs/api/profiling.rst b/docs/api/profiling.rst similarity index 100% rename from sphinx-docs/api/profiling.rst rename to docs/api/profiling.rst diff --git a/sphinx-docs/command.rst b/docs/command.rst similarity index 100% rename from sphinx-docs/command.rst rename to docs/command.rst diff --git a/sphinx-docs/concepts.rst b/docs/concepts.rst similarity index 100% rename from sphinx-docs/concepts.rst rename to docs/concepts.rst diff --git a/sphinx-docs/conf.py b/docs/conf.py similarity index 100% rename from sphinx-docs/conf.py rename to docs/conf.py diff --git a/sphinx-docs/img/activations.png b/docs/img/activations.png similarity index 100% rename from sphinx-docs/img/activations.png rename to docs/img/activations.png diff --git a/sphinx-docs/img/hls4ml_logo.png b/docs/img/hls4ml_logo.png similarity index 100% rename from sphinx-docs/img/hls4ml_logo.png rename to docs/img/hls4ml_logo.png diff --git a/sphinx-docs/img/hls4ml_logo.svg b/docs/img/hls4ml_logo.svg similarity index 100% rename from sphinx-docs/img/hls4ml_logo.svg rename to docs/img/hls4ml_logo.svg diff --git a/sphinx-docs/img/hls4ml_logo_lightgrey.png b/docs/img/hls4ml_logo_lightgrey.png similarity index 100% rename from sphinx-docs/img/hls4ml_logo_lightgrey.png rename to docs/img/hls4ml_logo_lightgrey.png diff --git a/sphinx-docs/img/hls4ml_logo_lightgrey.svg b/docs/img/hls4ml_logo_lightgrey.svg similarity index 100% rename from sphinx-docs/img/hls4ml_logo_lightgrey.svg rename to docs/img/hls4ml_logo_lightgrey.svg diff --git a/sphinx-docs/img/hls4ml_logo_navbar.png b/docs/img/hls4ml_logo_navbar.png similarity index 100% rename from sphinx-docs/img/hls4ml_logo_navbar.png rename to docs/img/hls4ml_logo_navbar.png diff --git a/sphinx-docs/img/logo.jpg b/docs/img/logo.jpg similarity index 100% rename from sphinx-docs/img/logo.jpg rename to docs/img/logo.jpg diff --git a/sphinx-docs/img/logo.png b/docs/img/logo.png similarity index 100% rename from sphinx-docs/img/logo.png rename to docs/img/logo.png diff --git a/sphinx-docs/img/nn_map_paper_fig_2.png b/docs/img/nn_map_paper_fig_2.png similarity index 100% rename from sphinx-docs/img/nn_map_paper_fig_2.png rename to docs/img/nn_map_paper_fig_2.png diff --git a/sphinx-docs/img/overview.jpg b/docs/img/overview.jpg similarity index 100% rename from sphinx-docs/img/overview.jpg rename to docs/img/overview.jpg diff --git a/sphinx-docs/img/overview.pdf b/docs/img/overview.pdf similarity index 100% rename from sphinx-docs/img/overview.pdf rename to docs/img/overview.pdf diff --git a/sphinx-docs/img/reuse_factor_paper_fig_8.png b/docs/img/reuse_factor_paper_fig_8.png similarity index 100% rename from sphinx-docs/img/reuse_factor_paper_fig_8.png rename to docs/img/reuse_factor_paper_fig_8.png diff --git a/sphinx-docs/img/weights.png b/docs/img/weights.png similarity index 100% rename from sphinx-docs/img/weights.png rename to docs/img/weights.png diff --git a/sphinx-docs/index.rst b/docs/index.rst similarity index 100% rename from sphinx-docs/index.rst rename to docs/index.rst diff --git a/sphinx-docs/reference.rst b/docs/reference.rst similarity index 100% rename from sphinx-docs/reference.rst rename to docs/reference.rst diff --git a/sphinx-docs/release_notes.rst b/docs/release_notes.rst similarity index 100% rename from sphinx-docs/release_notes.rst rename to docs/release_notes.rst diff --git a/sphinx-docs/setup.rst b/docs/setup.rst similarity index 100% rename from sphinx-docs/setup.rst rename to docs/setup.rst diff --git a/sphinx-docs/status.rst b/docs/status.rst similarity index 100% rename from sphinx-docs/status.rst rename to docs/status.rst -- GitLab