diff --git a/.gitignore b/.gitignore
index 0411de3941d790fd1668fe2328b248cd3c09be08..f838c1695130d232ac6a2b888aed0cea31aafaa7 100644
--- a/.gitignore
+++ b/.gitignore
@@ -76,13 +76,5 @@ MANIFEST
# Per-project virtualenvs
.venv*/
-# Cloned dependencies for Docker
-/brevitas/
-/brevitas_cnv_lfc/
-/cnpy/
-/finn-hlslib/
-/pyverilator/
-/PYNQ-HelloWorld/
-
# Jenkins cfg dir
/docker/jenkins_home
diff --git a/README.md b/README.md
index b408b1a69d6833382763795f35002e2b3322f09d..8e122fdad693d90cbabb738cb6da81d31a82dfd3 100644
--- a/README.md
+++ b/README.md
@@ -23,6 +23,8 @@ Please see the [Getting Started](https://finn.readthedocs.io/en/latest/getting_s
## What's New in FINN?
+* **2020-05-08:** v0.3b (beta) is released, with initial support for convolutions, parallel transformations, more flexible memory allocation for MVAUs, throughput testing and many other smaller improvements and bugfixes. Read more on the <a href="https://xilinx.github.io/finn/2020/05/08/finn-v03b-beta-is-released.html">release blog post</a>.
+* **2020-04-15:** FINN v0.2.1b (beta): use fixed commit versions for dependency repos, otherwise identical to 0.2b
* **2020-02-28:** FINN v0.2b (beta) is released, which is a clean-slate reimplementation of the framework. Currently only fully-connected networks are supported for the end-to-end flow. Please see the release blog post for a summary of the key features.
## Documentation
@@ -31,7 +33,9 @@ You can view the documentation on [readthedocs](https://finn.readthedocs.io) or
## Community
-We have a [gitter channel](https://gitter.im/xilinx-finn/community) where you can ask questions. You can use the GitHub issue tracker to report bugs, but please don't file issues to ask questions as this is better handled in the gitter channel. We also heartily welcome contributors to the project but do not yet have guidelines in place for this, so if you are interested just get in touch over gitter.
+We have a [gitter channel](https://gitter.im/xilinx-finn/community) where you can ask questions. You can use the GitHub issue tracker to report bugs, but please don't file issues to ask questions as this is better handled in the gitter channel.
+
+We also heartily welcome contributions to the project, please check out the [contribution guidelines](CONTRIBUTING.md) and the [list of open issues](https://github.com/Xilinx/finn/issues). Don't hesitate to get in touch over [Gitter](https://gitter.im/xilinx-finn/community) to discuss your ideas.
## Citation
diff --git a/docker/Dockerfile.finn_ci b/docker/Dockerfile.finn_ci
index fb257b05c7c5e63922fe9c51241c18ab671ec0ba..41f6a88f5dd4c9b0822a74cf4a0e7b4663dce910 100644
--- a/docker/Dockerfile.finn_ci
+++ b/docker/Dockerfile.finn_ci
@@ -44,8 +44,6 @@ RUN echo "StrictHostKeyChecking no" >> /etc/ssh/ssh_config
# cloning dependency repos
# Brevitas
RUN git clone https://github.com/Xilinx/brevitas.git /workspace/brevitas
-# Brevitas examples
-RUN git clone https://github.com/maltanar/brevitas_cnv_lfc.git /workspace/brevitas_cnv_lfc
# CNPY
RUN git clone https://github.com/rogersce/cnpy.git /workspace/cnpy
# FINN hlslib
@@ -63,8 +61,6 @@ RUN apt update; apt install nano
RUN pip install pytest-dependency
ENV PYTHONPATH "${PYTHONPATH}:/workspace/finn/src"
-ENV PYTHONPATH "${PYTHONPATH}:/workspace/brevitas_cnv_lfc/training_scripts"
-ENV PYTHONPATH "${PYTHONPATH}:/workspace/brevitas"
ENV PYTHONPATH "${PYTHONPATH}:/workspace/pyverilator"
ENV PYNQSHELL_PATH "/workspace/PYNQ-HelloWorld/boards"
ENV VIVADO_IP_CACHE "$BUILD_PATH/vivado_ip_cache"
diff --git a/docker/Dockerfile.finn_dev b/docker/Dockerfile.finn_dev
index 48ad00a555aebf678f68036d1f5ec576f4c70c3f..b7cfc299a2999662672225aa5f8912653d189559 100644
--- a/docker/Dockerfile.finn_dev
+++ b/docker/Dockerfile.finn_dev
@@ -73,8 +73,6 @@ USER $UNAME
# cloning dependency repos (as user)
# Brevitas
RUN git clone https://github.com/Xilinx/brevitas.git /workspace/brevitas
-# Brevitas examples
-RUN git clone https://github.com/maltanar/brevitas_cnv_lfc.git /workspace/brevitas_cnv_lfc
# CNPY
RUN git clone https://github.com/rogersce/cnpy.git /workspace/cnpy
# FINN hlslib
@@ -87,8 +85,6 @@ RUN git clone https://github.com/maltanar/PYNQ-HelloWorld.git /workspace/PYNQ-He
# for this developer-oriented Docker container we assume the FINN repo is cloned and mounted from the host
# at /workspace/finn -- see run-docker.sh for an example of how to do this.
ENV PYTHONPATH "${PYTHONPATH}:/workspace/finn/src"
-ENV PYTHONPATH "${PYTHONPATH}:/workspace/brevitas_cnv_lfc/training_scripts"
-ENV PYTHONPATH "${PYTHONPATH}:/workspace/brevitas"
ENV PYTHONPATH "${PYTHONPATH}:/workspace/pyverilator"
ENV PYNQSHELL_PATH "/workspace/PYNQ-HelloWorld/boards"
diff --git a/docker/Jenkinsfile b/docker/Jenkinsfile
index e64280222a6d2e558f00d20a25a4a79d55526a97..80be261fb3da057186259598f84d915176577a5d 100644
--- a/docker/Jenkinsfile
+++ b/docker/Jenkinsfile
@@ -35,7 +35,7 @@ pipeline {
stage('Test') {
steps {
sh """
- docker run --name $DOCKER_INST_NAME \
+ docker run --name $DOCKER_INST_NAME --init \
--hostname $DOCKER_INST_NAME \
-v ${params.VIVADO_PATH}:${params.VIVADO_PATH}:ro \
-e NUM_DEFAULT_WORKERS=${params.NUM_DEFAULT_WORKERS} \
diff --git a/docker/finn_entrypoint.sh b/docker/finn_entrypoint.sh
index 035bba3b53d85a8457eff1e7c1a23e0efff60caa..9cc239319fe94f482a4a6564399943b4dfe6ff53 100644
--- a/docker/finn_entrypoint.sh
+++ b/docker/finn_entrypoint.sh
@@ -13,11 +13,10 @@ gecho () {
# checkout the correct dependency repo commits
# the repos themselves are cloned in the Dockerfile
-BREVITAS_COMMIT=215cf44c76d562339fca368c8c3afee3110033e8
-BREVITAS_EXAMPLES_COMMIT=2059f96bd576bf71f32c757e7f92617a70190c90
+BREVITAS_COMMIT=989cdfdba4700fdd900ba0b25a820591d561c21a
CNPY_COMMIT=4e8810b1a8637695171ed346ce68f6984e585ef4
HLSLIB_COMMIT=6b88db826bb023937506913a23d964775a7606af
-PYVERILATOR_COMMIT=fb1afefa5b207acf6fec28f8abb72a862f2ca1d2
+PYVERILATOR_COMMIT=1d89cb0d4e0c97469cc6352c611f876ec13edfa6
PYNQSHELL_COMMIT=0c82a61b0ec1a07fa275a14146233824ded7a13d
@@ -26,10 +25,7 @@ gecho "Setting up known-good commit versions for FINN dependencies"
gecho "brevitas @ $BREVITAS_COMMIT"
git -C /workspace/brevitas pull --quiet
git -C /workspace/brevitas checkout $BREVITAS_COMMIT --quiet
-# Brevitas examples
-gecho "brevitas_cnv_lfc @ $BREVITAS_EXAMPLES_COMMIT"
-git -C /workspace/brevitas_cnv_lfc pull --quiet
-git -C /workspace/brevitas_cnv_lfc checkout $BREVITAS_EXAMPLES_COMMIT --quiet
+pip install --user -e /workspace/brevitas
# CNPY
gecho "cnpy @ $CNPY_COMMIT"
git -C /workspace/cnpy pull --quiet
diff --git a/docs/_posts/2020-05-08-finn-v03b-beta-is-released.md b/docs/_posts/2020-05-08-finn-v03b-beta-is-released.md
new file mode 100644
index 0000000000000000000000000000000000000000..8119b7338a7e14e5b9093a4a177f43e68267a911
--- /dev/null
+++ b/docs/_posts/2020-05-08-finn-v03b-beta-is-released.md
@@ -0,0 +1,46 @@
+---
+layout: post
+title: "FINN v0.3b (beta) is released"
+author: "Yaman Umuroglu"
+---
+
+We're happy to announce the v0.3b (beta) release of the FINN compiler.
+The full changelog is quite large as we've been working on a lot of exciting
+new features, but here is a summary:
+
+<img src="https://xilinx.github.io/finn/img/cnv-mp-fc.png" width="800" align="center"/>
+
+
+**Initial support for ConvNets and end-to-end notebook example.** The
+preliminary support for convolutions is now in place. Head over to the new
+<a href="https://github.com/Xilinx/finn/blob/staging/v0.3b/notebooks/end2end_example/cnv_end2end_example.ipynb">
+end-to-end notebook</a> to try out the end-to-end flow for convolutions
+and build the demonstrator for a simple binarized CNN on CIFAR-10.
+
+<img src="https://xilinx.github.io/finn/img/parallel-speedup.png" width="500" align="center"/>
+
+**Parallel transformations.** When working with larger designs, HLS synthesis
+and simulation compile times can be quite long. Thanks to a contribution by
+@HenniOVP we now support multi-process parallelization several FINN transformations.
+You can read more about those <a href="https://github.com/Xilinx/finn/blob/staging/v0.3b/notebooks/advanced/1_custom_transformation_pass.ipynb">here</a>.
+
+<img src="https://xilinx.github.io/finn/finn/img/mem_mode.png" width="600" align="center"/>
+
+**Decoupled memory mode for MVAUs.** To have more control over how the weight
+memories are implemented, you can now specify the `mem_mode` and `ram_style`
+attributes when instantiating compute engines. Read more <a href="https://finn.readthedocs.io/en/latest/internals.html#streamingfclayer-mem-mode">here.</a>
+
+**Throughput testing and optimizations.** To do a quick assessment of the
+customized accelerators you build, we now support a throughput test mode that
+lets you benchmark the accelerator with a configurable number of samples.
+To get better utilization from the heterogeneous streaming architectures FINN
+builds, we have also introduced a FIFO insertion transformation.
+You can see these in action in the updated <a href="https://github.com/Xilinx/finn/blob/staging/v0.3b/notebooks/end2end_example/tfc_end2end_example.ipynb">
+TFC-w1a1 end2end notebook.</a>
+
+We have a slew of other smaller features, bugfixes and various other improvements.
+The release (tagged 0.3b) is now available on GitHub.
+We're continuously working to improve FINN in terms of layer, network and
+infrastructure.
+If you'd like to help out, please check out the <a href="https://github.com/Xilinx/finn/blob/staging/v0.3b/CONTRIBUTING.md">contribution guidelines</a> and
+share your ideas on the <a href="https://gitter.im/xilinx-finn/community">FINN Gitter channel</a>!
diff --git a/docs/finn/brevitas_export.rst b/docs/finn/brevitas_export.rst
index 443b692a2d05b48b2e395373411c3d5382825c6c..83684ae092609ef0f83a5525508febf4676b2d7a 100644
--- a/docs/finn/brevitas_export.rst
+++ b/docs/finn/brevitas_export.rst
@@ -10,7 +10,7 @@ Brevitas Export
:scale: 70%
:align: center
-FINN expects an ONNX model as input. This can be a model trained with `Brevitas <https://github.com/Xilinx/brevitas>`_. Brevitas is a PyTorch library for quantization-aware training and the FINN Docker image comes with several `example Brevitas networks <https://github.com/maltanar/brevitas_cnv_lfc>`_. Brevitas provides an export of a quantized network in ONNX representation. The resulting model consists only of `ONNX standard nodes <https://github.com/onnx/onnx/blob/master/docs/Operators.md>`_, but also contains additional attributes for the ONNX nodes to represent low precision datatypes. To work with the model it is wrapped into :ref:`modelwrapper` provided by FINN.
+FINN expects an ONNX model as input. This can be a model trained with `Brevitas <https://github.com/Xilinx/brevitas>`_. Brevitas is a PyTorch library for quantization-aware training and the FINN Docker image comes with several `example Brevitas networks <https://github.com/Xilinx/brevitas/tree/master/brevitas_examples/bnn_pynq>`_. Brevitas provides an export of a quantized network in ONNX representation. The resulting model consists only of `ONNX standard nodes <https://github.com/onnx/onnx/blob/master/docs/Operators.md>`_, but also contains additional attributes for the ONNX nodes to represent low precision datatypes. To work with the model it is wrapped into :ref:`modelwrapper` provided by FINN.
At this stage we can already use the functional verification flow to simulate the model using Python, this is marked in the graphic with the dotted arrow. For more details please have look at :ref:`verification`.
diff --git a/docs/finn/example_networks.rst b/docs/finn/example_networks.rst
index e4193668b4c77136c3acc70275260bc4e99de4e8..9f221871f09bf655db9d81988d6fa83e53473634 100644
--- a/docs/finn/example_networks.rst
+++ b/docs/finn/example_networks.rst
@@ -4,7 +4,7 @@
Example Networks
****************
-FINN uses `several pre-trained QNNs <https://github.com/maltanar/brevitas_cnv_lfc>`_ that serve as examples and testcases.
+FINN uses `several pre-trained QNNs <https://github.com/Xilinx/brevitas/tree/master/brevitas_examples/bnn_pynq>`_ that serve as examples and testcases.
You can find a status summary below for each network.
* TFC, SFC, LFC... are fully-connected networks trained on the MNIST dataset
@@ -18,19 +18,19 @@ version, this is indicated by an x mark in the table.
+-----------------------+------------+----------+----------+----------+----------+----------+----------+
| FINN step | Basic test | TFC-w1a1 | TFC-w1a2 | TFC-w2a2 | CNV-w1a1 | CNV-w1a2 | CNV-w2a2 |
+-----------------------+------------+----------+----------+----------+----------+----------+----------+
-| Export/Import | x | x | x | x | x | | |
+| Export/Import | x | x | x | x | x | x | x |
+-----------------------+------------+----------+----------+----------+----------+----------+----------+
| Streamlining | x | x | x | x | x | | |
+-----------------------+------------+----------+----------+----------+----------+----------+----------+
| Convert to HLS layers | x | x | x | x | x | | |
+-----------------------+------------+----------+----------+----------+----------+----------+----------+
-| Stitched IP | x | x | x | x | | | |
+| Stitched IP | x | x | x | x | x | | |
+-----------------------+------------+----------+----------+----------+----------+----------+----------+
-| Hardware test | x | x | x | | | | |
+| Hardware test | x | x | x | | x | | |
+-----------------------+------------+----------+----------+----------+----------+----------+----------+
-| npysim | x | x | x | x | x | | |
+| cppsim | x | x | x | x | x | | |
+-----------------------+------------+----------+----------+----------+----------+----------+----------+
-| rtlsim node-by-node | x | x | x | x | | | |
+| rtlsim node-by-node | x | x | x | x | x | | |
+-----------------------+------------+----------+----------+----------+----------+----------+----------+
-| rtlsim stitched IP | x | x | x | x | | | |
+| rtlsim stitched IP | x | x | x | x | x | | |
+-----------------------+------------+----------+----------+----------+----------+----------+----------+
diff --git a/docs/finn/getting_started.rst b/docs/finn/getting_started.rst
index 72ec0c32f4f797476097e61920c72077f6801ddd..e2b56b7f75a489f4e4f08bccdc3b889822f81838 100644
--- a/docs/finn/getting_started.rst
+++ b/docs/finn/getting_started.rst
@@ -55,14 +55,6 @@ Running the test suite directly
FINN comes with a set of tests which can be launched using the command above. Note that some of the tests involve extra compilation and the entire test suite may take some time to complete.
-Running the test suite using Jenkins
-************************************
-::
-
- sh run-docker.sh jenkins
-
-This will launch `Jenkins <https://jenkins.io/>`_ inside a Docker container and print an initial password for the user to use together with the username "admin" to open Jenkins in the webbrowser. The script forwards port 8080 for Jenkins and also configures a smee client to access port 8080. `Smee <https://smee.io/>`_ is a webhook payload delivery service and the FINN GitHub repository has a webhook set up to trigger the smee client (that is set in the run_docker script) when a push event is happening. Through Jenkins the user can set up a test for FINN, which is started at every push event.
-
Environment variables
**********************
@@ -72,6 +64,8 @@ These are summarized below:
* `VIVADO_PATH` points to your Vivado installation on the host
* `JUPYTER_PORT` (default 8888) changes the port for Jupyter inside Docker
* `NETRON_PORT` (default 8081) changes the port for Netron inside Docker
+* `NUM_DEFAULT_WORKERS` (default 1) specifies the degree of parallelization for the transformations that can be run in parallel
* `PYNQ_BOARD` specifies the type of PYNQ board used (Pynq-Z1, Pynq-Z2, Ultra96, ZCU104) for the test suite
+* `PYNQ_IP` and `PYNQ_PORT` specify ip address and port number to access the PYNQ board
* `PYNQ_USERNAME` and `PYNQ_PASSWORD` specify the PYNQ board access credentials for the test suite
* `PYNQ_TARGET_DIR` specifies the target dir on the PYNQ board for the test suite
diff --git a/docs/finn/img/mem_mode.png b/docs/finn/img/mem_mode.png
new file mode 100755
index 0000000000000000000000000000000000000000..27783c5f3233cdf1d646e330d85ecdad39b8b922
Binary files /dev/null and b/docs/finn/img/mem_mode.png differ
diff --git a/docs/finn/img/verification.png b/docs/finn/img/verification.png
deleted file mode 100755
index e95a4d98e3b6c5a46ad6d4c2749679831b740044..0000000000000000000000000000000000000000
Binary files a/docs/finn/img/verification.png and /dev/null differ
diff --git a/docs/finn/internals.rst b/docs/finn/internals.rst
index 1e4ea189db614e2ec9d0713ed3d63696b705762d..7a4bc687eeb827320991f7d3f1ef8cc35e97f3da 100644
--- a/docs/finn/internals.rst
+++ b/docs/finn/internals.rst
@@ -24,7 +24,7 @@ FINN uses many custom operations (op_type in ONNX NodeProto) that are not define
Custom ONNX Execution Flow
==========================
-To verify correct operation of FINN-ONNX graphs, FINN provides its own ONNX execution flow (:py:mod:`finn.core.onnx_exec`). This flow supports the standard set of ONNX operations as well as the custom FINN operations.
+To verify correct operation of FINN-ONNX graphs, FINN provides its own ONNX execution flow (:py:mod:`finn.core.onnx_exec`). This flow supports the standard set of ONNX operations as well as the custom FINN operations.
.. warning:: This execution flow is only meant for checking the correctness of models after applying transformations, and not for high performance inference.
@@ -39,7 +39,7 @@ Some of the helper functions are described in more detail below.
Create a ModelWrapper instance
------------------------------
-The ModelWrapper instance can be created using a model in .onnx format or by directly passing a ModelProto instance to the wrapper. The code block below gives an example of how to use the wrapper on a model in .onnx format.
+The ModelWrapper instance can be created using a model in .onnx format or by directly passing a ModelProto instance to the wrapper. The code block below gives an example of how to use the wrapper on a model in .onnx format.
::
from finn.core.modelwrapper import ModelWrapper
@@ -49,7 +49,7 @@ Access the ONNX GraphProto through ModelWrapper
-----------------------------------------------
The ONNX ModelProto can be accessed with following command:
::
-
+
modelproto = model.model
The graph can be accessed using:
@@ -62,7 +62,7 @@ The node list is accessed by:
nodes = model.graph.node
-The individual nodes can be selected via their indices.
+The individual nodes can be selected via their indices.
::
# first node
@@ -79,7 +79,7 @@ Helper functions for tensors
A list of all tensors (names) can easily be accessed using:
::
-
+
tensor_list = model.get_all_tensor_names()
If we take a single tensor from that list (by index), we can determine their producer or consumer node by using one of the following functions. Note that it may be that a tensor does not have a producer or consumer node, for example if the tensor represents a constant that is already set. In that case `None` will be returned.
@@ -105,13 +105,13 @@ Optionally, the dtype (container datatype) of the tensor can also be specified a
As mentioned above there are FINN DataTypes additional to the container datatype, these can be accessed and set for a tensor with the following functions:
::
-
+
# get tensor dataype of third tensor in model tensor list
model.get_tensor_datatype(tensor_list[2])
# set tensor datatype of third tensor in model tensor list
from finn.core.datatype import DataType
-
+
finn_dtype = DataType.BIPOLAR
model.set_tensor_datatype(tensor_list[2], finn_dtype)
@@ -138,3 +138,60 @@ Transformation Pass
A transformation passes changes (transforms) the given model, it gets the model in the ModelWrapper as input and returns the changed model (ModelWrapper) to the FINN flow. Additional the flag *model_was_changed* which indicates if a transformation has to be performed more than once, is returned. If you are interested in how to write a transformation pass for FINN, please take a look at the Jupyter notebook about how to write a transformation pass, see chapter :ref:`tutorials` for details. For more information about existing transformation passes in FINN, see module :py:mod:`finn.transformation`.
+.. _mem_mode:
+
+StreamingFCLayer *mem_mode*
+===========================
+
+FINN supports two types of the so-called *mem_mode* attrıbute for the node StreamingFCLayer. This mode controls how the weight values are accessed during the execution. That means the mode setting has direct influence on the resulting circuit. Currently two settings for the *mem_mode* are supported in FINN:
+
+* "const"
+
+* "decoupled"
+
+The following picture shows the idea behind the two modes.
+
+.. image:: img/mem_mode.png
+ :scale: 55%
+ :align: center
+
+Const mode
+----------
+In *const* mode the weights are "baked in" into the Matrix-Vector-Activate-Unit (MVAU), which means they are part of the HLS code. During the IP block generation the weight values are integrated as *params.h* file in the HLS code and synthesized together with it. For the *const* mode IP block generation the `StreamingFCLayer_Batch function <https://github.com/Xilinx/finn-hlslib/blob/07a8353f6cdfd8bcdd81e309a5581044c2a93d3b/fclayer.h#L94>`_ from the finn-hls library is used, which implements a standard MVAU. The resulting IP block has an input and an output stream, as shown in the above picture on the left. FIFOs in the form of verilog components are connected to these.
+
+Advantages:
+
+* smaller resource footprint
+
+* easier to debug layer in cppsim since no additional components
+
+* well-tested and mature components
+
+Disadvantages:
+
+* can lead to very long HLS synthesis times for certain weight array shapes
+
+* less control over the weight memory FPGA primitives, Vivado HLS doesn't always make the best resource allocation decisions
+
+Decoupled mode
+--------------
+In *decoupled* mode a different variant of the MVAU with three ports is used. Besides the input and output streams, which are fed into the circuit via Verilog FIFOs, there is another input, which is used to stream the weights. For this the `streaming MVAU <https://github.com/Xilinx/finn-hlslib/blob/07a8353f6cdfd8bcdd81e309a5581044c2a93d3b/mvau.hpp#L213>`_ from the finn-hls library is used. To make the streaming possible a Verilog weight streamer component accesses the weight memory and sends the values via another FIFO to the MVAU. This component can be found in the `finn-rtllib <https://github.com/Xilinx/finn/tree/dev/finn-rtllib>`_ under the name *memstream.v*. For the IP block generation this component, the IP block resulting from the synthesis of the HLS code of the streaming MVAU and a FIFO for the weight stream are combined in a verilog wrapper. The weight values are saved in .dat files and stored in the weight memory from which the weight streamer reads. The resulting verilog component, which is named after the name of the node and has the suffix "_memstream.v", exposes only two ports to the outside, the data input and output. It therefore behaves externally in the same way as the MVAU in *const* mode.
+
+Advantages:
+
+* better control over the used memory primivites used (see the ram_style attribute in StreamingFCLayer)
+
+* potentially faster HLS synthesis time since weight array shape is no longer part of HLS synthesis
+
+* (future work) will enable placing memory and compute into different clock domains, combining different layers into same weight memory for higher packing efficiency, sourcing the weight stream from other sources such as DRAM
+
+Disadvantages:
+
+* somewhat less well-tested compared to the const mode
+
+* higher resource footprint due to additional weight streamer and weight FIFO
+
+
+How to set *mem_mode*
+---------------------
+When the nodes in the network are converted to HLS layers, the *mem_mode* can be passed. More detailed information about the transformations that prepare the network and the transformation that performs the conversion to HLS layers can be found in chapter :ref:`nw_prep`. The *mem_mode* is passed as argument. Note that if no argument is passed, the default is *const*.
diff --git a/docs/finn/nw_prep.rst b/docs/finn/nw_prep.rst
index 96dfcc184261b5e24a4e7c51eef74fff55d0d841..f9909d2befdff14b546c850b3cf56820785b2ffc 100644
--- a/docs/finn/nw_prep.rst
+++ b/docs/finn/nw_prep.rst
@@ -12,7 +12,7 @@ Network Preparation
The main principle of FINN are analysis and transformation passes. If you like to have more information about these please have a look at section :ref:`analysis_pass` and :ref:`transformation_pass` or at chapter :ref:`tutorials` about the provided Jupyter notebooks.
-This page is about the network preparation, the flow step that comes after the :ref:`brevitas_export`. Its main idea is to optimize the network and convert the nodes to custom nodes that correspond to `finn-hlslib <https://github.com/Xilinx/finn-hlslib>`_ functions. In this way we get a network that we can bring to hardware with the help of Vivado. For that we have to apply several transformations on the ONNX model, which this flow step receives wrapped in the :ref:`modelwrapper`.
+This page is about the network preparation, the flow step that comes after the :ref:`brevitas_export`. Its main idea is to optimize the network and convert the nodes to custom nodes that correspond to `finn-hlslib <https://github.com/Xilinx/finn-hlslib>`_ functions. In this way we get a network that we can bring to hardware with the help of Vivado. For that we have to apply several transformations on the ONNX model, which this flow step receives wrapped in the :ref:`modelwrapper`.
Various transformations are involved in the network preparation. The following is a short overview of these.
@@ -37,16 +37,16 @@ After this transformation the ONNX model is streamlined and contains now custom
Convert to HLS Layers
=====================
-Pairs of binary XNORPopcountMatMul layers are converted to StreamingFCLayers and following Multithreshold layers are absorbed into the MVTU. The result is a model consisting of a mixture of HLS and non-HLS layers. For more details, see :py:mod:`finn.transformation.fpgadataflow.convert_to_hls_layers`.
+Pairs of binary XNORPopcountMatMul layers are converted to StreamingFCLayers and following Multithreshold layers are absorbed into the Matrix-Vector-Activate-Unit (MVAU). The result is a model consisting of a mixture of HLS and non-HLS layers. For more details, see :py:mod:`finn.transformation.fpgadataflow.convert_to_hls_layers`. The MVAU can be implemented in two different modes, *const* and *decoupled*, see chapter :ref:`mem_mode`.
Dataflow Partitioning
=====================
-In the next step the graph is split and the part consisting of HLS layers is further processed in the FINN flow. The parent graph containing the non-HLS layers remains. The PE and SIMD are set to 1 by default, so the result is a network of only HLS layers with maximum folding. The model can be verified using the *npysim* simulation. It is a simulation using C++ and is described in more detail in chapter :ref:`verification`.
+In the next step the graph is split and the part consisting of HLS layers is further processed in the FINN flow. The parent graph containing the non-HLS layers remains. The PE and SIMD are set to 1 by default, so the result is a network of only HLS layers with maximum folding. The model can be verified using the *cppsim* simulation. It is a simulation using C++ and is described in more detail in chapter :ref:`verification`.
Folding
=======
-To adjust the folding, the values for PE and SIMD can be increased to achieve also an increase in the performance. The result can be verified using the same simulation flow as for the network with maximum folding (*npysim* using C++), for details please have a look at chapter :ref:`verification`.
+To adjust the folding, the values for PE and SIMD can be increased to achieve also an increase in the performance. The result can be verified using the same simulation flow as for the network with maximum folding (*cppsim* using C++), for details please have a look at chapter :ref:`verification`.
The result is a network of HLS layers with desired folding and it can be passed to :ref:`vivado_synth`.
diff --git a/docs/finn/pynq_deploy.rst b/docs/finn/pynq_deploy.rst
index 70bd6d72194a255323c4ce13337789c37da5482a..ef36da9e7f8ceee19abfd061fde10bf06d3afb62 100644
--- a/docs/finn/pynq_deploy.rst
+++ b/docs/finn/pynq_deploy.rst
@@ -15,7 +15,7 @@ This chapter is about the hardware generation and deployment on PYNQ. If you nee
Create PYNQ Shell Project
=========================
-To deploy the network on A PYNQ platform, it needs to be put inside an appropriate *shell*. This *shell* bridges the network with the interfaces the underlying system exposes. This can be done using the transformation MakePYNQProject, see :py:mod:`finn.transformation.fpgadataflow.make_pynq_proj.MakePYNQProject`.
+To deploy the network on A PYNQ platform, it needs to be put inside an appropriate *shell*. This *shell* bridges the network with the interfaces the underlying system exposes. This can be done using the transformation MakePYNQProject, see :py:mod:`finn.transformation.fpgadataflow.make_pynq_proj.MakePYNQProject`.
Test on Hardware
================
@@ -28,9 +28,14 @@ After integrating the model into the PYNQ shell, Vivado *Synthesis, Place and Ro
Generate PYNQ runtime code
--------------------------
-Additionally, a Python code is necessary to execute the model on the board. This is done by transformation pass :py:mod:`finn.transformation.fpgadataflow.make_pynq_driver.MakePYNQDriver`.
+Additionally, a Python code is necessary to execute the model on the board. This is done by transformation pass :py:mod:`finn.transformation.fpgadataflow.make_pynq_driver.MakePYNQDriver`.
Deployment and Remote Execution
-------------------------------
The bitfile and the driver file(s) are copied to the PYNQ board and can be executed there using the *onnx_exec* function with the right *exec_mode* settings. For details please have a look at transformation :py:mod:`finn.transformation.fpgadataflow.make_deployment.DeployToPYNQ` and the execution function :py:mod:`finn.core.onnx_exec`.
+
+Throughput Test
+---------------
+
+Finn also offers the possibility to measure the network performance directly on the PYNQ board. This can be done by using :py:mod:`finn.core.throughput_test`. When running this function the metrics of the network are returned as dictionary.
diff --git a/docs/finn/source_code/finn.analysis.fpgadataflow.rst b/docs/finn/source_code/finn.analysis.fpgadataflow.rst
index 9c1b0dd99a324929f5c8dd182f7208c27baa5ba8..72f8fb811b97b34759d346289a0421f5af7f9a1c 100644
--- a/docs/finn/source_code/finn.analysis.fpgadataflow.rst
+++ b/docs/finn/source_code/finn.analysis.fpgadataflow.rst
@@ -1,22 +1,29 @@
-Analysis - fpgadataflow
+Analysis - fpgadataflow
***********************
Analysis Passes (fpgadataflow)
==============================
finn.analysis.fpgadataflow.hls\_synth\_res\_estimation
-------------------------------------------------------
+-------------------------------------------------------------
.. automodule:: finn.analysis.fpgadataflow.hls_synth_res_estimation
:members:
:undoc-members:
:show-inheritance:
+finn.analysis.fpgadataflow.post\_synth\_res
+--------------------------------------------------
+
+.. automodule:: finn.analysis.fpgadataflow.post_synth_res
+ :members:
+ :undoc-members:
+ :show-inheritance:
+
finn.analysis.fpgadataflow.res\_estimation
-------------------------------------------
+-------------------------------------------------
.. automodule:: finn.analysis.fpgadataflow.res_estimation
:members:
:undoc-members:
:show-inheritance:
-
diff --git a/docs/finn/source_code/finn.analysis.rst b/docs/finn/source_code/finn.analysis.rst
index 9ce1b99990ed2facc430eaf318bb0480f7fba5e0..61946f8fe6d2be894dd9c3334a09c485e1ee1673 100644
--- a/docs/finn/source_code/finn.analysis.rst
+++ b/docs/finn/source_code/finn.analysis.rst
@@ -1,5 +1,5 @@
********
-Analysis
+Analysis
********
If you want to know more details about analysis passes, please take a look at section ":ref:`analysis_pass`" in chapter *Internals*.
@@ -8,7 +8,7 @@ Submodules
==========
.. toctree::
- :maxdepth: 2
+ :maxdepth: 2
finn.analysis.fpgadataflow
@@ -16,7 +16,7 @@ Analysis Passes
===============
finn.analysis.topology
-----------------------
+-----------------------------
.. automodule:: finn.analysis.topology
:members:
@@ -24,10 +24,9 @@ finn.analysis.topology
:show-inheritance:
finn.analysis.verify\_custom\_nodes
------------------------------------
+------------------------------------------
.. automodule:: finn.analysis.verify_custom_nodes
:members:
:undoc-members:
:show-inheritance:
-
diff --git a/docs/finn/source_code/finn.core.rst b/docs/finn/source_code/finn.core.rst
index f9d59fc0f68a37761f4c7fa8b25f5ee016092a0a..3ac077c694aafe938d36162dae86e1aafd6913dd 100644
--- a/docs/finn/source_code/finn.core.rst
+++ b/docs/finn/source_code/finn.core.rst
@@ -6,7 +6,7 @@ Modules
=======
finn.core.datatype
-------------------
+-------------------------
.. automodule:: finn.core.datatype
:members:
@@ -14,7 +14,7 @@ finn.core.datatype
:show-inheritance:
finn.core.execute\_custom\_node
--------------------------------
+--------------------------------------
.. automodule:: finn.core.execute_custom_node
:members:
@@ -22,9 +22,7 @@ finn.core.execute\_custom\_node
:show-inheritance:
finn.core.modelwrapper
-----------------------
-
-If you want to have more details about the module ModelWrapper, please have a look at section ":ref:`modelwrapper`" in chapter *Internals*.
+-----------------------------
.. automodule:: finn.core.modelwrapper
:members:
@@ -32,7 +30,7 @@ If you want to have more details about the module ModelWrapper, please have a lo
:show-inheritance:
finn.core.onnx\_exec
---------------------
+---------------------------
.. automodule:: finn.core.onnx_exec
:members:
@@ -40,7 +38,7 @@ finn.core.onnx\_exec
:show-inheritance:
finn.core.remote\_exec
-----------------------
+-----------------------------
.. automodule:: finn.core.remote_exec
:members:
@@ -48,9 +46,17 @@ finn.core.remote\_exec
:show-inheritance:
finn.core.rtlsim\_exec
-----------------------
+-----------------------------
.. automodule:: finn.core.rtlsim_exec
:members:
:undoc-members:
:show-inheritance:
+
+finn.core.throughput\_test
+---------------------------------
+
+.. automodule:: finn.core.throughput_test
+ :members:
+ :undoc-members:
+ :show-inheritance:
diff --git a/docs/finn/source_code/finn.custom_op.fpgadataflow.rst b/docs/finn/source_code/finn.custom_op.fpgadataflow.rst
index 7e9ebcbec1ebb958faa5b2cf1cdb87c02b17011b..5345fed596484c95c7204c16a4c5f57aa3101a81 100644
--- a/docs/finn/source_code/finn.custom_op.fpgadataflow.rst
+++ b/docs/finn/source_code/finn.custom_op.fpgadataflow.rst
@@ -14,7 +14,7 @@ Base Class
:show-inheritance:
finn.custom\_op.fpgadataflow.convolutioninputgenerator
-------------------------------------------------------
+-------------------------------------------------------------
.. automodule:: finn.custom_op.fpgadataflow.convolutioninputgenerator
:members:
@@ -22,7 +22,7 @@ finn.custom\_op.fpgadataflow.convolutioninputgenerator
:show-inheritance:
finn.custom\_op.fpgadataflow.streamingdatawidthconverter\_batch
----------------------------------------------------------------
+----------------------------------------------------------------------
.. automodule:: finn.custom_op.fpgadataflow.streamingdatawidthconverter_batch
:members:
@@ -30,15 +30,23 @@ finn.custom\_op.fpgadataflow.streamingdatawidthconverter\_batch
:show-inheritance:
finn.custom\_op.fpgadataflow.streamingfclayer\_batch
-----------------------------------------------------
+-----------------------------------------------------------
.. automodule:: finn.custom_op.fpgadataflow.streamingfclayer_batch
:members:
:undoc-members:
:show-inheritance:
+finn.custom\_op.fpgadataflow.streamingfifo
+-------------------------------------------------
+
+.. automodule:: finn.custom_op.fpgadataflow.streamingfifo
+ :members:
+ :undoc-members:
+ :show-inheritance:
+
finn.custom\_op.fpgadataflow.streamingmaxpool\_batch
-----------------------------------------------------
+-----------------------------------------------------------
.. automodule:: finn.custom_op.fpgadataflow.streamingmaxpool_batch
:members:
@@ -46,7 +54,7 @@ finn.custom\_op.fpgadataflow.streamingmaxpool\_batch
:show-inheritance:
finn.custom\_op.fpgadataflow.templates
---------------------------------------
+---------------------------------------------
.. automodule:: finn.custom_op.fpgadataflow.templates
:members:
@@ -54,10 +62,9 @@ finn.custom\_op.fpgadataflow.templates
:show-inheritance:
finn.custom\_op.fpgadataflow.tlastmarker
-----------------------------------------
+-----------------------------------------------
.. automodule:: finn.custom_op.fpgadataflow.tlastmarker
:members:
:undoc-members:
:show-inheritance:
-
diff --git a/docs/finn/source_code/finn.custom_op.rst b/docs/finn/source_code/finn.custom_op.rst
index cb436b1c1303020deeed1ca2f6dc2eb2f8678287..72dd4beb90e87d527543ab11ac1ce1d6ac0604b3 100644
--- a/docs/finn/source_code/finn.custom_op.rst
+++ b/docs/finn/source_code/finn.custom_op.rst
@@ -21,9 +21,8 @@ Base Class
:undoc-members:
:show-inheritance:
-
finn.custom\_op.im2col
-----------------------
+-----------------------------
.. automodule:: finn.custom_op.im2col
:members:
@@ -31,7 +30,7 @@ finn.custom\_op.im2col
:show-inheritance:
finn.custom\_op.maxpoolnhwc
----------------------------
+----------------------------------
.. automodule:: finn.custom_op.maxpoolnhwc
:members:
@@ -39,7 +38,7 @@ finn.custom\_op.maxpoolnhwc
:show-inheritance:
finn.custom\_op.multithreshold
-------------------------------
+-------------------------------------
.. automodule:: finn.custom_op.multithreshold
:members:
@@ -47,7 +46,7 @@ finn.custom\_op.multithreshold
:show-inheritance:
finn.custom\_op.registry
-------------------------
+-------------------------------
.. automodule:: finn.custom_op.registry
:members:
@@ -55,7 +54,7 @@ finn.custom\_op.registry
:show-inheritance:
finn.custom\_op.streamingdataflowpartition
-------------------------------------------
+-------------------------------------------------
.. automodule:: finn.custom_op.streamingdataflowpartition
:members:
@@ -63,11 +62,9 @@ finn.custom\_op.streamingdataflowpartition
:show-inheritance:
finn.custom\_op.xnorpopcount
-----------------------------
+-----------------------------------
.. automodule:: finn.custom_op.xnorpopcount
:members:
:undoc-members:
:show-inheritance:
-
-
diff --git a/docs/finn/source_code/finn.rst b/docs/finn/source_code/finn.rst
index 6f6b591e1e6a4b7b3900b834c8f5ff2765644f52..1197c50a035b77ec24f51f9e95f6208db162db8e 100644
--- a/docs/finn/source_code/finn.rst
+++ b/docs/finn/source_code/finn.rst
@@ -14,4 +14,3 @@ Modules
finn.custom_op
finn.transformation
finn.util
-
diff --git a/docs/finn/source_code/finn.transformation.fpgadataflow.rst b/docs/finn/source_code/finn.transformation.fpgadataflow.rst
index e80ddbdd05595ab3ca1e6a81da95f96e92f5452a..cd6e443675974b8b59e92ec2142cf848e62ba3c6 100644
--- a/docs/finn/source_code/finn.transformation.fpgadataflow.rst
+++ b/docs/finn/source_code/finn.transformation.fpgadataflow.rst
@@ -1,84 +1,84 @@
*****************************
-Transformation - fpgadataflow
+Transformation - fpgadataflow
*****************************
Transformations (fpgadataflow)
==============================
-finn.transformation.fpgadataflow.cleanup
-----------------------------------------
+finn.transformation.fpgadataflow.annotate\_resources
+-----------------------------------------------------------
-.. automodule:: finn.transformation.fpgadataflow.cleanup
+.. automodule:: finn.transformation.fpgadataflow.annotate_resources
:members:
:undoc-members:
:show-inheritance:
-finn.transformation.fpgadataflow.codegen\_ipgen
+finn.transformation.fpgadataflow.cleanup
-----------------------------------------------
-.. automodule:: finn.transformation.fpgadataflow.codegen_ipgen
+.. automodule:: finn.transformation.fpgadataflow.cleanup
:members:
:undoc-members:
:show-inheritance:
-finn.transformation.fpgadataflow.codegen\_ipstitch
---------------------------------------------------
+finn.transformation.fpgadataflow.compile\_cppsim
+-------------------------------------------------------
-.. automodule:: finn.transformation.fpgadataflow.codegen_ipstitch
+.. automodule:: finn.transformation.fpgadataflow.compile_cppsim
:members:
:undoc-members:
:show-inheritance:
-finn.transformation.fpgadataflow.codegen\_npysim
-------------------------------------------------
+finn.transformation.fpgadataflow.convert\_to\_hls\_layers
+----------------------------------------------------------------
-.. automodule:: finn.transformation.fpgadataflow.codegen_npysim
+.. automodule:: finn.transformation.fpgadataflow.convert_to_hls_layers
:members:
:undoc-members:
:show-inheritance:
-finn.transformation.fpgadataflow.compile
-----------------------------------------
+finn.transformation.fpgadataflow.create\_dataflow\_partition
+-------------------------------------------------------------------
-.. automodule:: finn.transformation.fpgadataflow.compile
+.. automodule:: finn.transformation.fpgadataflow.create_dataflow_partition
:members:
:undoc-members:
:show-inheritance:
-finn.transformation.fpgadataflow.convert\_to\_hls\_layers
----------------------------------------------------------
+finn.transformation.fpgadataflow.create\_stitched\_ip
+------------------------------------------------------------
-.. automodule:: finn.transformation.fpgadataflow.convert_to_hls_layers
+.. automodule:: finn.transformation.fpgadataflow.create_stitched_ip
:members:
:undoc-members:
:show-inheritance:
-finn.transformation.fpgadataflow.create\_dataflow\_partition
-------------------------------------------------------------
+finn.transformation.fpgadataflow.hlssynth\_ip
+----------------------------------------------------
-.. automodule:: finn.transformation.fpgadataflow.create_dataflow_partition
+.. automodule:: finn.transformation.fpgadataflow.hlssynth_ip
:members:
:undoc-members:
:show-inheritance:
-finn.transformation.fpgadataflow.hlssynth\_ipgen
-------------------------------------------------
+finn.transformation.fpgadataflow.insert\_dwc
+---------------------------------------------------
-.. automodule:: finn.transformation.fpgadataflow.hlssynth_ipgen
+.. automodule:: finn.transformation.fpgadataflow.insert_dwc
:members:
:undoc-members:
:show-inheritance:
-finn.transformation.fpgadataflow.insert\_dwc
---------------------------------------------
+finn.transformation.fpgadataflow.insert\_fifo
+----------------------------------------------------
-.. automodule:: finn.transformation.fpgadataflow.insert_dwc
+.. automodule:: finn.transformation.fpgadataflow.insert_fifo
:members:
:undoc-members:
:show-inheritance:
finn.transformation.fpgadataflow.insert\_tlastmarker
-----------------------------------------------------
+-----------------------------------------------------------
.. automodule:: finn.transformation.fpgadataflow.insert_tlastmarker
:members:
@@ -86,7 +86,7 @@ finn.transformation.fpgadataflow.insert\_tlastmarker
:show-inheritance:
finn.transformation.fpgadataflow.make\_deployment
--------------------------------------------------
+--------------------------------------------------------
.. automodule:: finn.transformation.fpgadataflow.make_deployment
:members:
@@ -94,7 +94,7 @@ finn.transformation.fpgadataflow.make\_deployment
:show-inheritance:
finn.transformation.fpgadataflow.make\_pynq\_driver
----------------------------------------------------
+----------------------------------------------------------
.. automodule:: finn.transformation.fpgadataflow.make_pynq_driver
:members:
@@ -102,15 +102,39 @@ finn.transformation.fpgadataflow.make\_pynq\_driver
:show-inheritance:
finn.transformation.fpgadataflow.make\_pynq\_proj
--------------------------------------------------
+--------------------------------------------------------
.. automodule:: finn.transformation.fpgadataflow.make_pynq_proj
:members:
:undoc-members:
:show-inheritance:
+finn.transformation.fpgadataflow.prepare\_cppsim
+-------------------------------------------------------
+
+.. automodule:: finn.transformation.fpgadataflow.prepare_cppsim
+ :members:
+ :undoc-members:
+ :show-inheritance:
+
+finn.transformation.fpgadataflow.prepare\_ip
+---------------------------------------------------
+
+.. automodule:: finn.transformation.fpgadataflow.prepare_ip
+ :members:
+ :undoc-members:
+ :show-inheritance:
+
+finn.transformation.fpgadataflow.prepare\_rtlsim
+-------------------------------------------------------
+
+.. automodule:: finn.transformation.fpgadataflow.prepare_rtlsim
+ :members:
+ :undoc-members:
+ :show-inheritance:
+
finn.transformation.fpgadataflow.replace\_verilog\_relpaths
------------------------------------------------------------
+------------------------------------------------------------------
.. automodule:: finn.transformation.fpgadataflow.replace_verilog_relpaths
:members:
@@ -118,7 +142,7 @@ finn.transformation.fpgadataflow.replace\_verilog\_relpaths
:show-inheritance:
finn.transformation.fpgadataflow.set\_exec\_mode
-------------------------------------------------
+-------------------------------------------------------
.. automodule:: finn.transformation.fpgadataflow.set_exec_mode
:members:
@@ -126,7 +150,7 @@ finn.transformation.fpgadataflow.set\_exec\_mode
:show-inheritance:
finn.transformation.fpgadataflow.synth\_pynq\_proj
---------------------------------------------------
+---------------------------------------------------------
.. automodule:: finn.transformation.fpgadataflow.synth_pynq_proj
:members:
@@ -134,10 +158,9 @@ finn.transformation.fpgadataflow.synth\_pynq\_proj
:show-inheritance:
finn.transformation.fpgadataflow.templates
-------------------------------------------
+-------------------------------------------------
.. automodule:: finn.transformation.fpgadataflow.templates
:members:
:undoc-members:
:show-inheritance:
-
diff --git a/docs/finn/source_code/finn.transformation.rst b/docs/finn/source_code/finn.transformation.rst
index 480c8030245ae5bc0add11ff629095b2d46ff225..4378cbf8e3bae09d49f20c4fe460e822a2a03993 100644
--- a/docs/finn/source_code/finn.transformation.rst
+++ b/docs/finn/source_code/finn.transformation.rst
@@ -16,8 +16,16 @@ Submodules
Transformation Passes
=====================
+Base Class
+----------
+
+.. automodule:: finn.transformation
+ :members:
+ :undoc-members:
+ :show-inheritance:
+
finn.transformation.batchnorm\_to\_affine
------------------------------------------
+------------------------------------------------
.. automodule:: finn.transformation.batchnorm_to_affine
:members:
@@ -25,7 +33,7 @@ finn.transformation.batchnorm\_to\_affine
:show-inheritance:
finn.transformation.bipolar\_to\_xnor
--------------------------------------
+--------------------------------------------
.. automodule:: finn.transformation.bipolar_to_xnor
:members:
@@ -33,7 +41,7 @@ finn.transformation.bipolar\_to\_xnor
:show-inheritance:
finn.transformation.double\_to\_single\_float
----------------------------------------------
+----------------------------------------------------
.. automodule:: finn.transformation.double_to_single_float
:members:
@@ -41,7 +49,7 @@ finn.transformation.double\_to\_single\_float
:show-inheritance:
finn.transformation.fold\_constants
------------------------------------
+------------------------------------------
.. automodule:: finn.transformation.fold_constants
:members:
@@ -49,7 +57,7 @@ finn.transformation.fold\_constants
:show-inheritance:
finn.transformation.general
----------------------------
+----------------------------------
.. automodule:: finn.transformation.general
:members:
@@ -57,7 +65,7 @@ finn.transformation.general
:show-inheritance:
finn.transformation.infer\_datatypes
-------------------------------------
+-------------------------------------------
.. automodule:: finn.transformation.infer_datatypes
:members:
@@ -65,18 +73,33 @@ finn.transformation.infer\_datatypes
:show-inheritance:
finn.transformation.infer\_shapes
----------------------------------
+----------------------------------------
.. automodule:: finn.transformation.infer_shapes
:members:
:undoc-members:
:show-inheritance:
+finn.transformation.insert\_topk
+---------------------------------------
+
+.. automodule:: finn.transformation.insert_topk
+ :members:
+ :undoc-members:
+ :show-inheritance:
+
finn.transformation.lower\_convs\_to\_matmul
---------------------------------------------
+---------------------------------------------------
.. automodule:: finn.transformation.lower_convs_to_matmul
:members:
:undoc-members:
:show-inheritance:
+finn.transformation.move\_reshape
+----------------------------------------
+
+.. automodule:: finn.transformation.move_reshape
+ :members:
+ :undoc-members:
+ :show-inheritance:
diff --git a/docs/finn/source_code/finn.transformation.streamline.rst b/docs/finn/source_code/finn.transformation.streamline.rst
index 56d7f827213381edd260ff43eb36e32394c6d03d..017622f04a313afa51fa5a20ac8a10317e9658bb 100644
--- a/docs/finn/source_code/finn.transformation.streamline.rst
+++ b/docs/finn/source_code/finn.transformation.streamline.rst
@@ -10,9 +10,8 @@ Transformation (Streamline)
:undoc-members:
:show-inheritance:
-
finn.transformation.streamline.absorb
--------------------------------------
+--------------------------------------------
.. automodule:: finn.transformation.streamline.absorb
:members:
@@ -20,7 +19,7 @@ finn.transformation.streamline.absorb
:show-inheritance:
finn.transformation.streamline.collapse\_repeated
--------------------------------------------------
+--------------------------------------------------------
.. automodule:: finn.transformation.streamline.collapse_repeated
:members:
@@ -28,7 +27,7 @@ finn.transformation.streamline.collapse\_repeated
:show-inheritance:
finn.transformation.streamline.reorder
---------------------------------------
+---------------------------------------------
.. automodule:: finn.transformation.streamline.reorder
:members:
@@ -36,7 +35,7 @@ finn.transformation.streamline.reorder
:show-inheritance:
finn.transformation.streamline.round\_thresholds
-------------------------------------------------
+-------------------------------------------------------
.. automodule:: finn.transformation.streamline.round_thresholds
:members:
@@ -44,7 +43,7 @@ finn.transformation.streamline.round\_thresholds
:show-inheritance:
finn.transformation.streamline.sign\_to\_thres
-----------------------------------------------
+-----------------------------------------------------
.. automodule:: finn.transformation.streamline.sign_to_thres
:members:
diff --git a/docs/finn/source_code/finn.util.rst b/docs/finn/source_code/finn.util.rst
index 7bcfde705f35a0deb9bb82d2d24831f718802070..d5bbd62357a8d807375d44e91e1cf95ca642ab9d 100644
--- a/docs/finn/source_code/finn.util.rst
+++ b/docs/finn/source_code/finn.util.rst
@@ -6,7 +6,7 @@ Utility Modules
===============
finn.util.basic
----------------
+----------------------
.. automodule:: finn.util.basic
:members:
@@ -14,7 +14,7 @@ finn.util.basic
:show-inheritance:
finn.util.data\_packing
------------------------
+------------------------------
.. automodule:: finn.util.data_packing
:members:
@@ -22,7 +22,7 @@ finn.util.data\_packing
:show-inheritance:
finn.util.fpgadataflow
-----------------------
+-----------------------------
.. automodule:: finn.util.fpgadataflow
:members:
@@ -30,7 +30,7 @@ finn.util.fpgadataflow
:show-inheritance:
finn.util.onnx
---------------
+---------------------
.. automodule:: finn.util.onnx
:members:
@@ -38,10 +38,17 @@ finn.util.onnx
:show-inheritance:
finn.util.test
---------------
+---------------------
.. automodule:: finn.util.test
:members:
:undoc-members:
:show-inheritance:
+finn.util.visualization
+------------------------------
+
+.. automodule:: finn.util.visualization
+ :members:
+ :undoc-members:
+ :show-inheritance:
diff --git a/docs/finn/source_code/modules.rst b/docs/finn/source_code/modules.rst
index dada6936c0d4e144acb991f32731b68e6995c23c..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 100644
--- a/docs/finn/source_code/modules.rst
+++ b/docs/finn/source_code/modules.rst
@@ -1,7 +0,0 @@
-finn
-====
-
-.. toctree::
- :maxdepth: 4
-
- finn
diff --git a/docs/finn/tutorials.rst b/docs/finn/tutorials.rst
index 2a234b15e61031ca994770c3590c013c4aaf5237..cda9d1b7cfccde9b77c09b6ac2776dfaaaa9daff 100644
--- a/docs/finn/tutorials.rst
+++ b/docs/finn/tutorials.rst
@@ -6,56 +6,47 @@ Tutorials
.. note:: **This website is currently under construction.**
-FINN provides several Jupyter notebooks that can help to get familiar with the basics, the internals and the end-to-end flow in FINN. All Jupyter notebooks can be found in the repo in the `notebook folder <https://github.com/Xilinx/finn/tree/dev/notebooks>`_.
+FINN provides several Jupyter notebooks that can help to get familiar with the basics, the internals and the end-to-end flow in FINN. All Jupyter notebooks can be found in the repo in the `notebook folder <https://github.com/Xilinx/finn/tree/master/notebooks>`_.
Basics
======
The notebooks in this folder should give a basic insight into FINN, how to get started and the basic concepts.
-* `0_getting_started <https://github.com/Xilinx/finn/blob/master/notebooks/basics/0_getting_started.ipynb>`_
-
- * This notebook corresponds to the chapter :ref:`getting_started` and gives an overview how to start working with FINN.
-
-* `1_how_to_work_with_onnx <https://github.com/Xilinx/finn/blob/master/notebooks/basics/1_how_to_work_with_onnx.ipynb>`_
+* 0_how_to_work_with_onnx
* This notebook can help you to learn how to create and manipulate a simple ONNX model, also by using FINN
-* `2_modelwrapper <https://github.com/Xilinx/finn/blob/master/notebooks/basics/2_modelwrapper.ipynb>`_
-
- * This notebook corresponds to the section :ref:`modelwrapper` in the chapter about internals.
-
-* `3_brevitas_network_import <https://github.com/Xilinx/finn/blob/master/notebooks/basics/3_brevitas_network_import.ipynb>`_
+* 1_brevitas_network_import
* This notebook shows how to import a brevitas network and prepare it for the FINN flow.
-Internals
-=========
+Advanced
+========
The notebooks in this folder are more developer oriented. They should help you to get familiar with the principles in FINN and how to add new content regarding these concepts.
-* `0_custom_analysis_pass <https://github.com/Xilinx/finn/blob/master/notebooks/internals/0_custom_analysis_pass.ipynb>`_
+* 0_custom_analysis_pass
* This notebook explains what an analysis pass is and how to write one for FINN.
-* `1_custom_transformation_pass <https://github.com/Xilinx/finn/blob/master/notebooks/internals/1_custom_transformation_pass.ipynb>`_
+* 1_custom_transformation_pass
* This notebook explains what a transformation pass is and how to write one for FINN.
-* `2_custom_op <https://github.com/Xilinx/finn/blob/master/notebooks/internals/2_custom_op.ipynb>`_
+End-to-End Flow
+===============
- * This notebooks explains what a custom operation/node is and how to create one for FINN.
+This notebook shows the FINN end-to-end flow step by step using an example of a simple, binarized, fully-connected network trained on the MNIST data set. Starting with the brevitas export and taking this particular network all the way down to hardware by using a specific sequence of transformations.
-* `3_verify_hls_custom_op <https://github.com/Xilinx/finn/blob/master/notebooks/internals/3_verify_hls_custom_op.ipynb>`_
+* cnv_end2end_example
- * This notebook shows the functional verification flow for hls custom operations/nodes.
+ * This notebook takes a simple convolutional model step-by-step from a trained Brevitas net to a running FPGA bitfile.
-End-to-End Flow
-===============
+* tfc_end2end_example
-This notebook shows the FINN end-to-end flow step by step using an example of a simple, binarized, fully-connected network trained on the MNIST data set. Starting with the brevitas export and taking this particular network all the way down to hardware by using a specific sequence of transformations.
+ * This notebook takes a simple fully-connected model step-by-step from a trained Brevitas net to a running FPGA bitfile.
+
+* tfc_end2end_verification
-* `tfc_end2end_example <https://github.com/Xilinx/finn/blob/master/notebooks/end2end_example/tfc_end2end_example.ipynb>`_
- * This notebook takes the model step-by-step from a trained Brevitas net to a running FPGA bitfile.
-* `tfc_end2end_verification <https://github.com/Xilinx/finn/blob/master/notebooks/end2end_example/tfc_end2end_verification.ipynb>`_
* This notebook runs parellel to the tfc_end2end_example notebook above, and shows how the output of each step can be verified.
diff --git a/docs/finn/verification.rst b/docs/finn/verification.rst
index 13e6fae7a22caf8fbab3d2569434e3cdc30d90e0..391c6f999312839daca0d4161336c7c0ae822f89 100644
--- a/docs/finn/verification.rst
+++ b/docs/finn/verification.rst
@@ -6,11 +6,11 @@ Functional Verification
.. note:: **This website is currently under construction.**
-.. image:: /img/verification.png
+.. image:: ../../notebooks/end2end_example/verification.png
:scale: 70%
:align: center
-"* This part of the flow is covered by the `this notebook <https://github.com/Xilinx/finn/tree/master/notebooks/end2end_example/tfc_end2end_verification.ipynb>`_. *"
+This part of the flow is covered by the Jupyter notebook about the verification of a simple fully-connected network, which you can find in the `end2end notebook folder <https://github.com/Xilinx/finn/tree/master/notebooks/end2end_example/tfc_end2end_verification.ipynb>`_.
When the network is transformed it is important to verify the functionality to make sure the transformation did not change the behaviour of the model. There are multiple ways of verification that can be applied in different stages of the network inside FINN. All can be accessed using the execution function in module :py:mod:`finn.core.onnx_exec`. The execution happens in most cases node by node, which supports networks that have a mixture of standard ONNX nodes, custom nodes and HLS custom nodes. A single node can be executed using one or more of the following methods:
diff --git a/docs/img/cnv-mp-fc.png b/docs/img/cnv-mp-fc.png
new file mode 100644
index 0000000000000000000000000000000000000000..1d1fb0045e764be2ecf6773b3701553a928fbd23
Binary files /dev/null and b/docs/img/cnv-mp-fc.png differ
diff --git a/docs/img/parallel-speedup.png b/docs/img/parallel-speedup.png
new file mode 100644
index 0000000000000000000000000000000000000000..abea8f9bd5251d51732c9281e9b0705adeeabe64
Binary files /dev/null and b/docs/img/parallel-speedup.png differ
diff --git a/notebooks/internals/0_custom_analysis_pass.ipynb b/notebooks/advanced/0_custom_analysis_pass.ipynb
similarity index 52%
rename from notebooks/internals/0_custom_analysis_pass.ipynb
rename to notebooks/advanced/0_custom_analysis_pass.ipynb
index 3db1d1c47acef301f7b89a05980aa68477ba567f..617bfa0897f64f13a0cf34be469ff8d0bfe2ee40 100644
--- a/notebooks/internals/0_custom_analysis_pass.ipynb
+++ b/notebooks/advanced/0_custom_analysis_pass.ipynb
@@ -6,9 +6,9 @@
"source": [
"# FINN - Analysis Passes\n",
"--------------------------------------\n",
- "<font size=\"3\"> This notebook is about analysis passes in FINN. The procedure for creating an analysis pass is shown using an example.\n",
+ "This notebook is about analysis passes in FINN. The procedure for creating an analysis pass is shown using an example.\n",
"\n",
- "Following showSrc function is used to print the source code of function calls in the Jupyter notebook:</font>"
+ "We'll use the following utility functions to print the source code for function calls (`showSrc()`) and to visualize a network using netron (`showInNetron()`) in the Jupyter notebook:"
]
},
{
@@ -17,10 +17,7 @@
"metadata": {},
"outputs": [],
"source": [
- "import inspect\n",
- "\n",
- "def showSrc(what):\n",
- " print(\"\".join(inspect.getsourcelines(what)[0]))"
+ "from finn.util.visualization import showSrc, showInNetron"
]
},
{
@@ -29,9 +26,9 @@
"source": [
"## General Information\n",
"------------------------------\n",
- "* <font size=\"3\">traverses the graph structure and produces information about certain properties</font>\n",
- "* <font size=\"3\">input: ModelWrapper</font>\n",
- "* <font size=\"3\">returns dictionary of named properties that the analysis extracts</font>"
+ "* traverses the graph structure and produces information about certain properties\n",
+ "* input: ModelWrapper\n",
+ "* returns dictionary of named properties that the analysis extracts"
]
},
{
@@ -39,19 +36,19 @@
"metadata": {},
"source": [
"### Example - Quantity analysis of operation types\n",
- "<font size=\"3\">As an example, an analysis is designed that returns the number of nodes of the same operation types. </font>"
+ "As an example, an analysis is designed that returns the number of nodes of the same operation types."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "<font size=\"3\">First the model is shown to illustrate the analysis. For this netron is used. Netron is a visualizer for neural network, deep learning and machine learning models. </font>"
+ "First the model is shown to illustrate the analysis. For this netron is used. Netron is a visualizer for neural network, deep learning and machine learning models."
]
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 2,
"metadata": {},
"outputs": [
{
@@ -60,46 +57,43 @@
"text": [
"Serving '../LFCW1A1.onnx' at http://0.0.0.0:8081\n"
]
- }
- ],
- "source": [
- "import netron\n",
- "netron.start('../LFCW1A1.onnx', port=8081, host=\"0.0.0.0\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
+ },
{
"data": {
"text/html": [
- "<iframe src=\"http://0.0.0.0:8081/\" style=\"position: relative; width: 100%;\" height=\"400\"></iframe>\n"
+ "\n",
+ " <iframe\n",
+ " width=\"100%\"\n",
+ " height=\"400\"\n",
+ " src=\"http://0.0.0.0:8081/\"\n",
+ " frameborder=\"0\"\n",
+ " allowfullscreen\n",
+ " ></iframe>\n",
+ " "
],
"text/plain": [
- "<IPython.core.display.HTML object>"
+ "<IPython.lib.display.IFrame at 0x7f14142de3c8>"
]
},
+ "execution_count": 2,
"metadata": {},
- "output_type": "display_data"
+ "output_type": "execute_result"
}
],
"source": [
- "%%html\n",
- "<iframe src=\"http://0.0.0.0:8081/\" style=\"position: relative; width: 100%;\" height=\"400\"></iframe>"
+ "showInNetron(\"../LFCW1A1.onnx\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "<font size=\"3\">The onnx model has to be converted to a format that can be processed by FINN. This is done with ModelWrapper. As described in the short introduction, this is the format an analysis pass takes as input.</font>"
+ "The onnx model has to be converted to a format that can be processed by FINN. This is done with ModelWrapper. As described in the short introduction, this is the format an analysis pass takes as input."
]
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
@@ -111,12 +105,12 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "<font size=\"3\">The idea is to count all nodes that have the same operation type. The result should contain the operation types and the corresponding number of nodes that occur in the model. In the beginning an empty dictionary is created which is filled by the function and returned as result to the user at the end of the analysis.</font>"
+ "The idea is to count all nodes that have the same operation type. The result should contain the operation types and the corresponding number of nodes that occur in the model. In the beginning an empty dictionary is created which is filled by the function and returned as result to the user at the end of the analysis."
]
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
@@ -134,19 +128,19 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "<font size=\"3\">The function takes the model as input and iterates over the nodes. Then it is checked whether there is already an entry for the operation type in the dictionary. If this is not the case, an entry is created and set to `1`. If there is already an entry, it is incremented. If all nodes in the model have been iterated, the filled dictionary is returned.</font>"
+ "The function takes the model as input and iterates over the nodes. Then it is checked whether there is already an entry for the operation type in the dictionary. If this is not the case, an entry is created and set to `1`. If there is already an entry, it is incremented. If all nodes in the model have been iterated, the filled dictionary is returned."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "<font size=\"3\">The analysis function of ModelWrapper is used to perform the analysis just designed. It is shown below and takes the function as input and performs it by passing the model to the function </font>"
+ "The analysis function of ModelWrapper is used to perform the analysis just designed. It is shown below and takes the function as input and performs it by passing the model to the function."
]
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
@@ -168,12 +162,12 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "<font size=\"3\">The result can now simply be determined by calling the `.analysis` function.</font>"
+ "The result can now simply be determined by calling the `.analysis` function."
]
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
@@ -187,13 +181,6 @@
"source": [
"print(model.analysis(count_equal_nodes))"
]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
}
],
"metadata": {
diff --git a/notebooks/advanced/1_custom_transformation_pass.ipynb b/notebooks/advanced/1_custom_transformation_pass.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..a9345401e28b89ba3a206b81e7c3b022bedae023
--- /dev/null
+++ b/notebooks/advanced/1_custom_transformation_pass.ipynb
@@ -0,0 +1,461 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# FINN - Transformation passes\n",
+ "--------------------------------------\n",
+ "In this notebook the idea behind transformation passes in FINN will be explained and with the help of an example the procedure of a transformation will be shown.\n",
+ "\n",
+ "We'll use the following utility functions to print the source code for function calls (`showSrc()`) and to visualize a network using netron (`showInNetron()`) in the Jupyter notebook:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from finn.util.visualization import showSrc, showInNetron"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## General Information\n",
+ "-----------------------------\n",
+ "* changes (transforms) the given graph\n",
+ "* input: ModelWrapper\n",
+ "* returns the changed model (ModelWrapper) and flag `model_was_changed`\n",
+ "\n",
+ "Transformation passes have a base class and must inherit from that. Transformations are meant to be applied using .transform function from the ModelWrapper. This function makes a deep copy of the input model by default. The next cell shows .transform of ModelWrapper.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### .transform() from ModelWrapper"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " def transform(self, transformation, make_deepcopy=True):\n",
+ " \"\"\"Applies given Transformation repeatedly until no more changes can be made\n",
+ " and returns a transformed ModelWrapper instance.\n",
+ "\n",
+ " If make_deepcopy is specified, operates on a new (deep)copy of model.\n",
+ " \"\"\"\n",
+ " transformed_model = self\n",
+ " if make_deepcopy:\n",
+ " transformed_model = copy.deepcopy(self)\n",
+ " model_was_changed = True\n",
+ " while model_was_changed:\n",
+ " (transformed_model, model_was_changed) = transformation.apply(\n",
+ " transformed_model\n",
+ " )\n",
+ " return transformed_model\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "from finn.core.modelwrapper import ModelWrapper\n",
+ "showSrc(ModelWrapper.transform)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "When the function is called, the model, the name of the transformation and, if required, the flag make_deepcopy are passed. It is also possible not to make a copy of the model. In this case `make_deepcopy` must be set to False. Then the branch `if make_deepcopy:` would not be taken and no copy of the model would be made. \n",
+ "\n",
+ "The unchanged model is first passed to the variable `transformed_model` to pass this variable on to the transformation later. \n",
+ "\n",
+ "`model_was_changed` indicates whether the transformation needs to be applied more then once. Because it needs to be applied at least one time `model_was_changed` is first set to True and then depending on the return values of the transformation function the transformation can be applied more then once. \n",
+ "\n",
+ "**Important**: Due to the structure of this function, `model_was_changed` must be set to False at some point. Otherwise the loop is infinite.\n",
+ " \n",
+ "\n",
+ "Each new transformation must correspond to the scheme of the base class and contain at least the function `apply(model)`, which returns the changed model and a bool value for `model_was_changed`."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Transformation Base Class "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "class Transformation(ABC):\n",
+ " \"\"\"Transformation class all transformations are based on. Contains only\n",
+ " abstract method apply() every transformation has to fill.\"\"\"\n",
+ "\n",
+ " def __init__(self):\n",
+ " super().__init__()\n",
+ "\n",
+ " @abstractmethod\n",
+ " def apply(self, model):\n",
+ " pass\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "from finn.transformation import Transformation\n",
+ "\n",
+ "showSrc(Transformation)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Base class is abstract class (`import ABC`) with only one abstract method (`apply()`) which gets the model as input. To show what a transformation should look like, the following example is taken from FINN."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Example - ConvertSubToAdd\n",
+ "-----------------------------\n",
+ "The transformation replaces all subtraction nodes in a model with addition nodes with appropriate sign. For that an onnx model is loaded which contains one subtraction node. \n",
+ " \n",
+ "Netron is used to visualize the result before and after."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import onnx\n",
+ "onnx_model = onnx.load('../LFCW1A1.onnx')\n",
+ "from finn.core.modelwrapper import ModelWrapper\n",
+ "onnx_model = ModelWrapper(onnx_model)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Serving '../LFCW1A1.onnx' at http://0.0.0.0:8081\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " <iframe\n",
+ " width=\"100%\"\n",
+ " height=\"400\"\n",
+ " src=\"http://0.0.0.0:8081/\"\n",
+ " frameborder=\"0\"\n",
+ " allowfullscreen\n",
+ " ></iframe>\n",
+ " "
+ ],
+ "text/plain": [
+ "<IPython.lib.display.IFrame at 0x7fc625ac0a20>"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "showInNetron('../LFCW1A1.onnx')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from finn.transformation import Transformation\n",
+ "\n",
+ "class ConvertSubToAdd(Transformation):\n",
+ " def apply(self, model):\n",
+ " graph = model.graph\n",
+ " for n in graph.node:\n",
+ " if n.op_type == \"Sub\":\n",
+ " A = model.get_initializer(n.input[1])\n",
+ " if A is not None:\n",
+ " n.op_type = \"Add\"\n",
+ " model.set_initializer(n.input[1], -A)\n",
+ " return (model, False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "First the transformation class must be imported. Then a class can be created for the new transformation, which is derived from the base class. In this case the transformation has only the `apply()` function. \n",
+ "\n",
+ "All nodes are checked by first extracting the graph from the model and then iterating over the node list. With the help of .op_type the operation type of the node can be checked, if the node is a subtraction node the condition `if n.op_type == \"Sub\"` is true. It may be that the subtraction input of the node has no value, this is checked with `model.get_initializer(n.input[1])`. \n",
+ " \n",
+ " \n",
+ "**Important:** FINN always assumes a certain order of inputs, this is especially important if you want to create additional custom operation nodes.\n",
+ "\n",
+ "When the input is initialized, the operation type of the node is converted to `\"Add\"`, this can simply be done by using the equal sign. Then the sign of the initial value must be changed. For this the ModelWrapper function `.set_initializer` can be used.\n",
+ "\n",
+ "At the end the changed model is returned and `model_was_changed` is set to False, because the transformation has to be executed only once.\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "onnx_model_transformed = onnx_model.transform(ConvertSubToAdd())\n",
+ "onnx_model_transformed.save('/tmp/LFCW1A1_changed.onnx')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Stopping http://0.0.0.0:8081\n",
+ "Serving '/tmp/LFCW1A1_changed.onnx' at http://0.0.0.0:8081\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " <iframe\n",
+ " width=\"100%\"\n",
+ " height=\"400\"\n",
+ " src=\"http://0.0.0.0:8081/\"\n",
+ " frameborder=\"0\"\n",
+ " allowfullscreen\n",
+ " ></iframe>\n",
+ " "
+ ],
+ "text/plain": [
+ "<IPython.lib.display.IFrame at 0x7fc625ac09b0>"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "showInNetron('/tmp/LFCW1A1_changed.onnx')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Parallel Transformation\n",
+ "---------------------------------\n",
+ "Some of the transformations in FINN can be performed in parallel on individual nodes. The following `NodeLocalTransformation` is required for this:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "class NodeLocalTransformation(Transformation):\n",
+ " \"\"\"\n",
+ " Parent class for transformations, which can be executed locally to one node\n",
+ " by accessing and modifying the attributes of only that node.\n",
+ " This class can then automatically parallelize the transformation.\n",
+ " Transformations sublcassing NodeLocalTransformation must implement the\n",
+ " abstract method applyNodeLocal().\n",
+ "\n",
+ " To control the degree of parallelization, specify the num_workers argument\n",
+ " in the constructor, using one of the following values:\n",
+ " * None: use NUM_DEFAULT_WORKERS environment variable\n",
+ " * 0: use all available CPU cores\n",
+ " * (any other int>0): set number of parallel workers\n",
+ " \"\"\"\n",
+ "\n",
+ " def __init__(self, num_workers=None):\n",
+ " super().__init__()\n",
+ " if num_workers is None:\n",
+ " self._num_workers = get_num_default_workers()\n",
+ " else:\n",
+ " self._num_workers = num_workers\n",
+ " assert self._num_workers >= 0, \"Number of workers must be nonnegative.\"\n",
+ " if self._num_workers == 0:\n",
+ " self._num_workers = mp.cpu_count()\n",
+ "\n",
+ " @abstractmethod\n",
+ " def applyNodeLocal(self, node):\n",
+ " pass\n",
+ "\n",
+ " def apply(self, model):\n",
+ " # Remove old nodes from the current model\n",
+ " old_nodes = []\n",
+ " for i in range(len(model.graph.node)):\n",
+ " old_nodes.append(model.graph.node.pop())\n",
+ "\n",
+ " # Execute transformation in parallel\n",
+ " with mp.Pool(self._num_workers) as p:\n",
+ " new_nodes_and_bool = p.map(self.applyNodeLocal, old_nodes, chunksize=1)\n",
+ "\n",
+ " # extract nodes and check if the transformation needs to run again\n",
+ " # Note: .pop() had initially reversed the node order\n",
+ " run_again = False\n",
+ " for node, run in reversed(new_nodes_and_bool):\n",
+ " # Reattach new nodes to old model\n",
+ " model.graph.node.append(node)\n",
+ " if run is True:\n",
+ " run_again = True\n",
+ "\n",
+ " return (model, run_again)\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "from finn.transformation import NodeLocalTransformation\n",
+ "\n",
+ "showSrc(NodeLocalTransformation)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Transformations that are to be executed in parallel must have the method `applyNodeLocal()` implemented. Please note that the transformation is only executed on a single node, the parallel transformations do not have access to the entire model or tensors. Parallelization has the advantage that especially time-consuming transformations such as compilation can be executed more effectively. \n",
+ "\n",
+ "To control the degree of parallelization the argument `num_workers` can be specified. When the Docker container is started, the env variable `NUM_DEFAULT_WORKERS` is set to 1 by default, this can be increased depending on the system. You can also set the number of workers manually to a specific value when calling a transformation that allows parallelization. If the value is set to 0, all available CPU cores are used.\n",
+ "\n",
+ "In the following we want to take a closer look at the implementation using the compile transformation as example."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "class CompileCppSim(NodeLocalTransformation):\n",
+ " \"\"\"For every node: compile C++ code in node attribute \"code_gen_dir_cppsim\"\n",
+ " and save path to executables in node attribute \"executable_path\".\n",
+ " All nodes in the graph must have the fpgadataflow backend attribute.\n",
+ "\n",
+ " To use these executables, exec_mode must be set to \"cppsim\" (using transformation\n",
+ " SetExecMode) and the model has to be executed using execute_onnx() from\n",
+ " finn.core.onnx_exec\n",
+ "\n",
+ " * num_workers (int or None) number of parallel workers, see documentation in\n",
+ " NodeLocalTransformation for more details.\n",
+ " \"\"\"\n",
+ "\n",
+ " def __init__(self, num_workers=None):\n",
+ " super().__init__(num_workers=num_workers)\n",
+ "\n",
+ " def applyNodeLocal(self, node):\n",
+ " op_type = node.op_type\n",
+ " if is_fpgadataflow_node(node) is True:\n",
+ " try:\n",
+ " # lookup op_type in registry of CustomOps\n",
+ " inst = registry.custom_op[op_type](node)\n",
+ " # ensure that code is generated\n",
+ " assert (\n",
+ " inst.get_nodeattr(\"code_gen_dir_cppsim\") != \"\"\n",
+ " ), \"\"\"Node\n",
+ " attribute \"code_gen_dir_cppsim\" is not set. Please run\n",
+ " Transformation PrepareCppSim first.\"\"\"\n",
+ " # call the compilation function for this node\n",
+ " inst.compile_singlenode_code()\n",
+ " # ensure that executable path is now set\n",
+ " assert (\n",
+ " inst.get_nodeattr(\"executable_path\") != \"\"\n",
+ " ), \"\"\"Transformation\n",
+ " compile was not successful, there is no path to executables set\n",
+ " in node attribute \"executable_path\".\"\"\"\n",
+ " except KeyError:\n",
+ " # exception if op_type is not supported\n",
+ " raise Exception(\n",
+ " \"Custom op_type %s is currently not supported.\" % op_type\n",
+ " )\n",
+ " return (node, False)\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "from finn.transformation.fpgadataflow.compile_cppsim import CompileCppSim\n",
+ "\n",
+ "showSrc(CompileCppSim)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The class is derived from the NodeLocalTransformation class and performs the compilation at every node that is fpgadataflow node."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/basics/0_getting_started.ipynb b/notebooks/basics/0_getting_started.ipynb
deleted file mode 100644
index 07b2a2ba6d5a21be15de5c4061500d83b2aefdf3..0000000000000000000000000000000000000000
--- a/notebooks/basics/0_getting_started.ipynb
+++ /dev/null
@@ -1,129 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# FINN Basics\n",
- "\n",
- "\n",
- "## What is FINN?\n",
- "\n",
- "'FINN' is colloquially used to refer to two separate but highly related things:\n",
- "\n",
- "* The [FINN project](https://xilinx.github.io/finn/), which includes tools for training quantized neural networks such as [Brevitas](github.com/Xilinx/brevitas), the FINN compiler, and the [finn-hlslib](github.com/Xilinx/finn-hlslib) Vivado HLS library of FPGA components for QNNs.\n",
- "* This repository, referred to as the *FINN compiler*, which is the centerpiece of the FINN project.\n",
- "\n",
- "## How to use the FINN compiler?\n",
- "\n",
- "The FINN compiler should not be thought of a single pushbutton tool that does everything for you, but rather as a collection of scripts/tools that will help you convert a QNN into a custom FPGA accelerator that performs high-performance inference. We do provide several examples of taking trained networks all the way down to FPGA bitfiles, but if you are trying to do this for custom networks you will have to write your own Python scripts that call the appropriate FINN Compiler functions that process your design correctly, or adding new functions as required."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Requirements\n",
- "\n",
- "* Ubuntu 18.04\n",
- "* Docker\n",
- "* A working Vivado installation\n",
- "* A `VIVADO_PATH` environment variable pointing to the Vivado installation directory (e.g. the directory where settings64.sh is located)\n",
- "\n",
- "\n",
- "## Running FINN with Docker\n",
- "\n",
- "We use Docker extensively for developing and deploying FINN. If you are not familiar with Docker, there are many excellent [online resources]( https://docker-curriculum.com/) to get started. There is a Dockerfile in the root of the repository, as well as a `run-docker.sh` script that can be launched in the following modes:\n",
- "\n",
- "### Getting an interactive shell for development or experimentation\n",
- "\n",
- "Simply running `sh run-docker.sh` without any additional arguments will clone the dependency repos, create a Docker container and give you a terminal with you can use for development for experimentation. \n",
- "\n",
- "*Important:* the Docker container is spawned with the `--rm` option, so make sure that any important files you created inside the container are either in the /workspace/finn folder (which is mounted from the host cinoyter) or otherwise backed up.\n",
- "\n",
- "*Develop from host, run inside container:* The FINN repository directory will be mounted from the host, so that you can use a text editor on your host computer to develop and the changes will be reflected directly inside the container.\n",
- "\n",
- "### Running the Jupyter notebooks\n",
- "\n",
- "```sh run-docker.sh notebook```\n",
- "\n",
- "This will launch the Jupyter notebook server inside a Docker container, and print a link on the terminal that you can open in your browser to run the FINN notebooks or create new ones. The link will look something like this (the token you get will be different):\n",
- "\n",
- "`http://127.0.0.1:8888/?token=f5c6bd32ae93ec103a88152214baedff4ce1850d81065bfc`\n",
- "\n",
- "The `run-docker.sh` script forwards ports 8888 for Jupyter and 8081 for Netron, and launches the notebook server with appropriate arguments.\n",
- "\n",
- "### Running the test suite\n",
- "\n",
- "FINN comes with a set of tests which you can easily launch in Docker as follows:\n",
- "\n",
- "```sh run-docker.sh test```\n",
- "\n",
- "Note that some of the tests involve extra compilation and the entire test suite may take some time to complete. "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Intermediate Representation: FINN-ONNX\n",
- "\n",
- "FINN uses [ONNX](onnx.ai) as an intermediate representation (IR) for neural networks. As such, almost every component inside FINN uses ONNX and its [Python API](https://github.com/onnx/onnx/blob/master/docs/PythonAPIOverview.md), so you may want to familiarize yourself with how ONNX represents DNNs. Specifically, the [ONNX protobuf description](https://github.com/onnx/onnx/blob/master/onnx/onnx.proto) (or its [human-readable documentation](https://github.com/onnx/onnx/blob/master/docs/IR.md) and the [operator schemas](https://github.com/onnx/onnx/blob/master/docs/Operators.md) are useful as reference documents.\n",
- "\n",
- "FINN uses ONNX is a specific way that we refer to as FINN-ONNX, and not all ONNX graphs are supported by FINN (and vice versa). Here is a list of key points to keep in mind:\n",
- "\n",
- "* *Custom quantization annotations but data stored as float.* ONNX does not support datatypes smaller than 8-bit integers, whereas in FINN we are interested in smaller integers down to ternary and bipolar. To make this work, FINN uses the `quantization_annotation` field in ONNX to annotate tensors with their [FINN DataType](https://github.com/Xilinx/finn/blob/master/src/finn/core/datatype.py) information. However, all tensors are expected to use single-precision floating point (float32) storage in FINN. This means we store even a 1-bit value as floating point for the purposes of representation. The FINN compiler flow is responsible for eventually producing a packed representation for the target hardware, where the 1-bit is actually stored as 1-bit.\n",
- "\n",
- "* *Custom operations/nodes.* FINN uses many custom operations (`op_type` in ONNX NodeProto) that are not defined in the ONNX operator schema. These custom nodes are marked with `domain=\"finn\"` in the protobuf to identify them as such. These nodes can represent specific operations that we need for low-bit networks, or operations that are specific to a particular hardware backend.\n",
- "\n",
- "* *Custom ONNX execution flow* To verify correct operation of FINN-ONNX graphs, FINN provides its own [ONNX execution flow](https://github.com/Xilinx/finn/blob/master/src/finn/core/onnx_exec.py). This flow supports the standard set of ONNX operations as well as the custom FINN operations. *Important:* this execution flow is *only* meant for checking the correctness of models after applying transformations, and *not* for high performance inference. \n",
- "\n",
- "* *ModelWrapper* FINN provides a [`ModelWrapper`](https://github.com/Xilinx/finn/blob/master/src/finn/core/modelwrapper.py) class as a thin wrapper around ONNX to make it easier to analyze and manipulate ONNX graphs. This wrapper provides many helper functions, while still giving full access to the ONNX protobuf representation. \n",
- "\n",
- "[Netron](https://lutzroeder.github.io/netron/) is very useful for visualizing ONNX models, including FINN-ONNX models."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## More FINN Resources\n",
- "\n",
- "* **[List of publications](https://github.com/Xilinx/finn/blob/master/docs/publications.md)**\n",
- "* **[Roadmap](https://github.com/Xilinx/finn/projects/1)**\n",
- "* **[Status of example networks](https://github.com/Xilinx/finn/blob/master/docs/example-networks.md)**\n",
- "\n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/basics/1_how_to_work_with_onnx.ipynb b/notebooks/basics/0_how_to_work_with_onnx.ipynb
similarity index 88%
rename from notebooks/basics/1_how_to_work_with_onnx.ipynb
rename to notebooks/basics/0_how_to_work_with_onnx.ipynb
index 29b2751aff73706d5590c6641b86104368816922..58f53c32981edcba669f15a6c52499a8f89938d6 100644
--- a/notebooks/basics/1_how_to_work_with_onnx.ipynb
+++ b/notebooks/basics/0_how_to_work_with_onnx.ipynb
@@ -6,7 +6,7 @@
"source": [
"# FINN - How to work with ONNX\n",
"\n",
- "This notebook should give an overview of ONNX ProtoBuf, help to create and manipulate an ONNX model and use FINN functions to work with it. There may be overlaps to other notebooks, like [ModelWrapper](2_modelwrapper.ipynb) and [CustomOps](../internals/2_custom_op.ipynb), but this notebook will give an overview about the handling of ONNX models in FINN."
+ "This notebook should give an overview of ONNX ProtoBuf, help to create and manipulate an ONNX model and use FINN functions to work with it."
]
},
{
@@ -14,7 +14,7 @@
"metadata": {},
"source": [
"## Outline\n",
- "* #### How to create a simple model\n",
+ "* #### How to create a simple ONNX model\n",
"* #### How to manipulate an ONNX model"
]
},
@@ -22,7 +22,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### How to create a simple model\n",
+ "### How to create a simple ONNX model\n",
"\n",
"To explain how to create an ONNX model a simple example with mathematical operations is used. All nodes are from the [standard operations library of ONNX](https://github.com/onnx/onnx/blob/master/docs/Operators.md).\n",
"\n",
@@ -93,7 +93,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "The names of the inputs and outputs of the nodes give already an idea of the structure of the resulting graph. In order to integrate the nodes into a graph environment, the inputs and outputs of the graph have to be specified first. In ONNX all data edges are processed as tensors. So with the helper function tensor value infos are created for the input and output tensors of the graph. Float from ONNX is used as data type. "
+ "The names of the inputs and outputs of the nodes give already an idea of the structure of the resulting graph. In order to integrate the nodes into a graph environment, the inputs and outputs of the graph have to be specified first. In ONNX all data edges are processed as tensors. So with onnx helper function tensors value infos are created for the input and output tensors of the graph. Float from ONNX is used as data type. "
]
},
{
@@ -159,32 +159,23 @@
"outputs": [],
"source": [
"onnx_model = onnx.helper.make_model(graph, producer_name=\"simple-model\")\n",
- "onnx.save(onnx_model, 'simple_model.onnx')"
+ "onnx.save(onnx_model, '/tmp/simple_model.onnx')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "To visualize the created model, [netron](https://github.com/lutzroeder/netron) can be used. Netron is a visualizer for neural network, deep learning and machine learning models."
+ "To visualize the created model, [netron](https://github.com/lutzroeder/netron) can be used. Netron is a visualizer for neural network, deep learning and machine learning models. FINN provides a utility function for visualization with netron, which we import and use in the following."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Serving 'simple_model.onnx' at http://0.0.0.0:8081\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
- "import netron\n",
- "netron.start('simple_model.onnx', port=8081, host=\"0.0.0.0\")"
+ "from finn.util.visualization import showInNetron"
]
},
{
@@ -194,22 +185,37 @@
"scrolled": true
},
"outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Serving '/tmp/simple_model.onnx' at http://0.0.0.0:8081\n"
+ ]
+ },
{
"data": {
"text/html": [
- "<iframe src=\"http://0.0.0.0:8081/\" style=\"position: relative; width: 100%;\" height=\"400\"></iframe>\n"
+ "\n",
+ " <iframe\n",
+ " width=\"100%\"\n",
+ " height=\"400\"\n",
+ " src=\"http://0.0.0.0:8081/\"\n",
+ " frameborder=\"0\"\n",
+ " allowfullscreen\n",
+ " ></iframe>\n",
+ " "
],
"text/plain": [
- "<IPython.core.display.HTML object>"
+ "<IPython.lib.display.IFrame at 0x7fcdfc956b70>"
]
},
+ "execution_count": 7,
"metadata": {},
- "output_type": "display_data"
+ "output_type": "execute_result"
}
],
"source": [
- "%%html\n",
- "<iframe src=\"http://0.0.0.0:8081/\" style=\"position: relative; width: 100%;\" height=\"400\"></iframe>"
+ "showInNetron('/tmp/simple_model.onnx')"
]
},
{
@@ -278,7 +284,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "To run the model and calculate the output, [onnxruntime](https://github.com/microsoft/onnxruntime) can be used. ONNX Runtime is a performance-focused complete scoring engine for Open Neural Network Exchange (ONNX) models from Microsoft. The `.InferenceSession` function is used to create a session of the model and `.run` is used to execute the model."
+ "To run the model and calculate the output, [onnxruntime](https://github.com/microsoft/onnxruntime) can be used. ONNX Runtime is a performance-focused complete scoring engine for ONNX models from Microsoft. The `.InferenceSession` function is used to create a session of the model and `.run` is used to execute the model."
]
},
{
@@ -310,16 +316,16 @@
"output_type": "stream",
"text": [
"The output of the ONNX model is: \n",
- "[[12. 9. 14. 8.]\n",
- " [ 9. 9. 4. 6.]\n",
- " [ 3. 19. 9. 5.]\n",
- " [ 8. 22. 7. 2.]]\n",
+ "[[22. 13. 21. 8.]\n",
+ " [ 0. 8. 11. 1.]\n",
+ " [ 3. 12. 8. 2.]\n",
+ " [ 0. 6. 1. 4.]]\n",
"\n",
"The output of the reference function is: \n",
- "[[12. 9. 14. 8.]\n",
- " [ 9. 9. 4. 6.]\n",
- " [ 3. 19. 9. 5.]\n",
- " [ 8. 22. 7. 2.]]\n",
+ "[[22. 13. 21. 8.]\n",
+ " [ 0. 8. 11. 1.]\n",
+ " [ 3. 12. 8. 2.]\n",
+ " [ 0. 6. 1. 4.]]\n",
"\n",
"The results are the same!\n"
]
@@ -349,7 +355,7 @@
"source": [
"### How to manipulate an ONNX model\n",
"\n",
- "In the model there are two successive adder nodes. An adder node in ONNX can only add two inputs, but there is also the [**sum**](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Sum) node, which can process more than one input. So it would be a reasonable change of the graph to combine the two successive adder nodes to one sum node."
+ "In the model there are two successive adder nodes. An adder node in ONNX can only add two inputs, but there is also the [**sum**](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Sum) node, which can process more than two inputs. So it would be a reasonable change of the graph to combine the two successive adder nodes to one sum node."
]
},
{
@@ -358,12 +364,12 @@
"source": [
"In the following we assume that we do not know the appearance of the model, so we first try to identify whether there are two consecutive adders in the graph and then convert them into a sum node. \n",
"\n",
- "Here we make use of FINN. FINN provides a thin wrapper around the model which provides several additional helper functions to manipulate the graph. The code can be found [here](https://github.com/Xilinx/finn/blob/master/src/finn/core/modelwrapper.py) and you can find a more detailed description in the notebook [ModelWrapper](2_modelwrapper.ipynb)."
+ "Here we make use of FINN. FINN provides a thin wrapper around the model which provides several additional helper functions to manipulate the graph. The code can be found [here](https://github.com/Xilinx/finn/blob/master/src/finn/core/modelwrapper.py)."
]
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
@@ -380,7 +386,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
@@ -404,7 +410,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
@@ -427,7 +433,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 16,
"metadata": {},
"outputs": [
{
@@ -455,7 +461,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
@@ -484,7 +490,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
@@ -514,7 +520,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 19,
"metadata": {},
"outputs": [
{
@@ -550,7 +556,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 20,
"metadata": {},
"outputs": [
{
@@ -585,7 +591,7 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
@@ -601,7 +607,7 @@
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
@@ -622,7 +628,7 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
@@ -655,7 +661,7 @@
"outputs": [],
"source": [
"onnx_model1 = onnx.helper.make_model(graph, producer_name=\"simple-model1\")\n",
- "onnx.save(onnx_model1, 'simple_model1.onnx')"
+ "onnx.save(onnx_model1, '/tmp/simple_model1.onnx')"
]
},
{
@@ -669,36 +675,33 @@
"text": [
"\n",
"Stopping http://0.0.0.0:8081\n",
- "Serving 'simple_model1.onnx' at http://0.0.0.0:8081\n"
+ "Serving '/tmp/simple_model1.onnx' at http://0.0.0.0:8081\n"
]
- }
- ],
- "source": [
- "import netron\n",
- "netron.start('simple_model1.onnx', port=8081, host=\"0.0.0.0\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "metadata": {},
- "outputs": [
+ },
{
"data": {
"text/html": [
- "<iframe src=\"http://0.0.0.0:8081/\" style=\"position: relative; width: 100%;\" height=\"400\"></iframe>\n"
+ "\n",
+ " <iframe\n",
+ " width=\"100%\"\n",
+ " height=\"400\"\n",
+ " src=\"http://0.0.0.0:8081/\"\n",
+ " frameborder=\"0\"\n",
+ " allowfullscreen\n",
+ " ></iframe>\n",
+ " "
],
"text/plain": [
- "<IPython.core.display.HTML object>"
+ "<IPython.lib.display.IFrame at 0x7fcdfc130cc0>"
]
},
+ "execution_count": 26,
"metadata": {},
- "output_type": "display_data"
+ "output_type": "execute_result"
}
],
"source": [
- "%%html\n",
- "<iframe src=\"http://0.0.0.0:8081/\" style=\"position: relative; width: 100%;\" height=\"400\"></iframe>"
+ "showInNetron('/tmp/simple_model1.onnx')"
]
},
{
@@ -710,7 +713,7 @@
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
@@ -720,7 +723,7 @@
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 28,
"metadata": {},
"outputs": [
{
@@ -728,16 +731,16 @@
"output_type": "stream",
"text": [
"The output of the manipulated ONNX model is: \n",
- "[[12. 9. 14. 8.]\n",
- " [ 9. 9. 4. 6.]\n",
- " [ 3. 19. 9. 5.]\n",
- " [ 8. 22. 7. 2.]]\n",
+ "[[22. 13. 21. 8.]\n",
+ " [ 0. 8. 11. 1.]\n",
+ " [ 3. 12. 8. 2.]\n",
+ " [ 0. 6. 1. 4.]]\n",
"\n",
"The output of the reference function is: \n",
- "[[12. 9. 14. 8.]\n",
- " [ 9. 9. 4. 6.]\n",
- " [ 3. 19. 9. 5.]\n",
- " [ 8. 22. 7. 2.]]\n",
+ "[[22. 13. 21. 8.]\n",
+ " [ 0. 8. 11. 1.]\n",
+ " [ 3. 12. 8. 2.]\n",
+ " [ 0. 6. 1. 4.]]\n",
"\n",
"The results are the same!\n"
]
@@ -752,13 +755,6 @@
"else:\n",
" raise Exception(\"Something went wrong, the output of the model doesn't match the expected output!\")"
]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
}
],
"metadata": {
diff --git a/notebooks/basics/3_brevitas_network_import.ipynb b/notebooks/basics/1_brevitas_network_import.ipynb
similarity index 58%
rename from notebooks/basics/3_brevitas_network_import.ipynb
rename to notebooks/basics/1_brevitas_network_import.ipynb
index 30026e7aaa541641d4068ca0a7a0a3cf7c14088f..3c9cad615e168e19c7f5dfef45e7c7c60965d1e3 100644
--- a/notebooks/basics/3_brevitas_network_import.ipynb
+++ b/notebooks/basics/1_brevitas_network_import.ipynb
@@ -12,7 +12,7 @@
"2. Call Brevitas FINN-ONNX export and visualize with Netron\n",
"3. Import into FINN and call cleanup transformations\n",
"\n",
- "We'll use the following showSrc function to print the source code for function calls in the Jupyter notebook:"
+ "We'll use the following utility functions to print the source code for function calls (`showSrc()`) and to visualize a network using netron (`showInNetron()`) in the Jupyter notebook:"
]
},
{
@@ -22,10 +22,7 @@
"outputs": [],
"source": [
"import onnx\n",
- "import inspect\n",
- "\n",
- "def showSrc(what):\n",
- " print(\"\".join(inspect.getsourcelines(what)[0]))"
+ "from finn.util.visualization import showSrc, showInNetron"
]
},
{
@@ -34,18 +31,55 @@
"source": [
"## 1. Load up the trained PyTorch model\n",
"\n",
- "The FINN Docker image comes with several [example Brevitas networks](https://github.com/maltanar/brevitas_cnv_lfc), and we'll use the LFC-w1a1 model as the example network here. This is a binarized fully connected network trained on the MNIST dataset. Let's start by looking at what the PyTorch network definition looks like:"
+ "The FINN Docker image comes with several [example Brevitas networks](https://github.com/Xilinx/brevitas/tree/master/brevitas_examples/bnn_pynq), and we'll use the LFC-w1a1 model as the example network here. This is a binarized fully connected network trained on the MNIST dataset. Let's start by looking at what the PyTorch network definition looks like:"
]
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
+ "# MIT License\n",
+ "#\n",
+ "# Copyright (c) 2019 Xilinx\n",
+ "#\n",
+ "# Permission is hereby granted, free of charge, to any person obtaining a copy\n",
+ "# of this software and associated documentation files (the \"Software\"), to deal\n",
+ "# in the Software without restriction, including without limitation the rights\n",
+ "# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n",
+ "# copies of the Software, and to permit persons to whom the Software is\n",
+ "# furnished to do so, subject to the following conditions:\n",
+ "#\n",
+ "# The above copyright notice and this permission notice shall be included in all\n",
+ "# copies or substantial portions of the Software.\n",
+ "#\n",
+ "# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n",
+ "# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n",
+ "# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n",
+ "# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n",
+ "# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n",
+ "# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n",
+ "# SOFTWARE.\n",
+ "\n",
+ "from functools import reduce\n",
+ "from operator import mul\n",
+ "\n",
+ "from torch.nn import Module, ModuleList, BatchNorm1d, Dropout\n",
+ "import torch\n",
+ "\n",
+ "from .common import get_quant_linear, get_act_quant, get_quant_type, QuantLinear\n",
+ "\n",
+ "FC_OUT_FEATURES = [1024, 1024, 1024]\n",
+ "INTERMEDIATE_FC_PER_OUT_CH_SCALING = True\n",
+ "LAST_FC_PER_OUT_CH_SCALING = False\n",
+ "IN_DROPOUT = 0.2\n",
+ "HIDDEN_DROPOUT = 0.2\n",
+ "\n",
+ "\n",
"class LFC(Module):\n",
"\n",
" def __init__(self, num_classes=10, weight_bit_width=None, act_bit_width=None,\n",
@@ -55,7 +89,6 @@
" weight_quant_type = get_quant_type(weight_bit_width)\n",
" act_quant_type = get_quant_type(act_bit_width)\n",
" in_quant_type = get_quant_type(in_bit_width)\n",
- " stats_op = get_stats_op(weight_quant_type)\n",
"\n",
" self.features = ModuleList()\n",
" self.features.append(get_act_quant(in_bit_width, in_quant_type))\n",
@@ -66,33 +99,54 @@
" out_features=out_features,\n",
" per_out_ch_scaling=INTERMEDIATE_FC_PER_OUT_CH_SCALING,\n",
" bit_width=weight_bit_width,\n",
- " quant_type=weight_quant_type,\n",
- " stats_op=stats_op))\n",
+ " quant_type=weight_quant_type))\n",
" in_features = out_features\n",
" self.features.append(BatchNorm1d(num_features=in_features))\n",
" self.features.append(get_act_quant(act_bit_width, act_quant_type))\n",
" self.features.append(Dropout(p=HIDDEN_DROPOUT))\n",
- " self.fc = get_quant_linear(in_features=in_features,\n",
+ " self.features.append(get_quant_linear(in_features=in_features,\n",
" out_features=num_classes,\n",
" per_out_ch_scaling=LAST_FC_PER_OUT_CH_SCALING,\n",
" bit_width=weight_bit_width,\n",
- " quant_type=weight_quant_type,\n",
- " stats_op=stats_op)\n",
+ " quant_type=weight_quant_type))\n",
+ " self.features.append(BatchNorm1d(num_features=num_classes))\n",
"\n",
+ " for m in self.modules():\n",
+ " if isinstance(m, QuantLinear):\n",
+ " torch.nn.init.uniform_(m.weight.data, -1, 1)\n",
+ "\n",
+ " def clip_weights(self, min_val, max_val):\n",
+ " for mod in self.features:\n",
+ " if isinstance(mod, QuantLinear):\n",
+ " mod.weight.data.clamp_(min_val, max_val)\n",
+ " \n",
" def forward(self, x):\n",
" x = x.view(x.shape[0], -1)\n",
- " x = 2.0 * x - torch.tensor([1.0])\n",
+ " x = 2.0 * x - torch.tensor([1.0], device=x.device)\n",
" for mod in self.features:\n",
" x = mod(x)\n",
- " out = self.fc(x)\n",
- " return out\n",
+ " return x\n",
+ "\n",
+ "\n",
+ "def lfc(cfg):\n",
+ " weight_bit_width = cfg.getint('QUANT', 'WEIGHT_BIT_WIDTH')\n",
+ " act_bit_width = cfg.getint('QUANT', 'ACT_BIT_WIDTH')\n",
+ " in_bit_width = cfg.getint('QUANT', 'IN_BIT_WIDTH')\n",
+ " num_classes = cfg.getint('MODEL', 'NUM_CLASSES')\n",
+ " in_channels = cfg.getint('MODEL', 'IN_CHANNELS')\n",
+ " net = LFC(weight_bit_width=weight_bit_width,\n",
+ " act_bit_width=act_bit_width,\n",
+ " in_bit_width=in_bit_width,\n",
+ " num_classes=num_classes,\n",
+ " in_ch=in_channels)\n",
+ " return net\n",
"\n"
]
}
],
"source": [
- "from models.LFC import LFC\n",
- "showSrc(LFC)"
+ "from brevitas_examples import bnn_pynq\n",
+ "showSrc(bnn_pynq.models.LFC)"
]
},
{
@@ -104,7 +158,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 8,
"metadata": {},
"outputs": [
{
@@ -135,22 +189,12 @@
" (weight_reg): WeightReg()\n",
" (weight_quant): WeightQuantProxy(\n",
" (tensor_quant): BinaryQuant(\n",
- " (scaling_impl): ParameterStatsScaling(\n",
- " (parameter_list_stats): ParameterListStats(\n",
- " (first_tracked_param): _ViewParameterWrapper()\n",
- " (stats): Stats(\n",
- " (stats_impl): AbsAve()\n",
- " )\n",
- " )\n",
- " (stats_scaling_impl): StatsScaling(\n",
- " (affine_rescaling): Identity()\n",
- " (restrict_scaling): RestrictValue(\n",
- " (forward_impl): Sequential(\n",
- " (0): PowerOfTwo()\n",
- " (1): ClampMin()\n",
- " )\n",
+ " (scaling_impl): StandaloneScaling(\n",
+ " (restrict_value): RestrictValue(\n",
+ " (forward_impl): Sequential(\n",
+ " (0): PowerOfTwo()\n",
+ " (1): Identity()\n",
" )\n",
- " (restrict_scaling_preprocess): LogTwo()\n",
" )\n",
" )\n",
" )\n",
@@ -181,22 +225,12 @@
" (weight_reg): WeightReg()\n",
" (weight_quant): WeightQuantProxy(\n",
" (tensor_quant): BinaryQuant(\n",
- " (scaling_impl): ParameterStatsScaling(\n",
- " (parameter_list_stats): ParameterListStats(\n",
- " (first_tracked_param): _ViewParameterWrapper()\n",
- " (stats): Stats(\n",
- " (stats_impl): AbsAve()\n",
- " )\n",
- " )\n",
- " (stats_scaling_impl): StatsScaling(\n",
- " (affine_rescaling): Identity()\n",
- " (restrict_scaling): RestrictValue(\n",
- " (forward_impl): Sequential(\n",
- " (0): PowerOfTwo()\n",
- " (1): ClampMin()\n",
- " )\n",
+ " (scaling_impl): StandaloneScaling(\n",
+ " (restrict_value): RestrictValue(\n",
+ " (forward_impl): Sequential(\n",
+ " (0): PowerOfTwo()\n",
+ " (1): Identity()\n",
" )\n",
- " (restrict_scaling_preprocess): LogTwo()\n",
" )\n",
" )\n",
" )\n",
@@ -227,22 +261,12 @@
" (weight_reg): WeightReg()\n",
" (weight_quant): WeightQuantProxy(\n",
" (tensor_quant): BinaryQuant(\n",
- " (scaling_impl): ParameterStatsScaling(\n",
- " (parameter_list_stats): ParameterListStats(\n",
- " (first_tracked_param): _ViewParameterWrapper()\n",
- " (stats): Stats(\n",
- " (stats_impl): AbsAve()\n",
- " )\n",
- " )\n",
- " (stats_scaling_impl): StatsScaling(\n",
- " (affine_rescaling): Identity()\n",
- " (restrict_scaling): RestrictValue(\n",
- " (forward_impl): Sequential(\n",
- " (0): PowerOfTwo()\n",
- " (1): ClampMin()\n",
- " )\n",
+ " (scaling_impl): StandaloneScaling(\n",
+ " (restrict_value): RestrictValue(\n",
+ " (forward_impl): Sequential(\n",
+ " (0): PowerOfTwo()\n",
+ " (1): Identity()\n",
" )\n",
- " (restrict_scaling_preprocess): LogTwo()\n",
" )\n",
" )\n",
" )\n",
@@ -268,49 +292,36 @@
" )\n",
" )\n",
" (13): Dropout(p=0.2)\n",
- " )\n",
- " (fc): QuantLinear(\n",
- " in_features=1024, out_features=10, bias=False\n",
- " (weight_reg): WeightReg()\n",
- " (weight_quant): WeightQuantProxy(\n",
- " (tensor_quant): BinaryQuant(\n",
- " (scaling_impl): ParameterStatsScaling(\n",
- " (parameter_list_stats): ParameterListStats(\n",
- " (first_tracked_param): _ViewParameterWrapper()\n",
- " (stats): Stats(\n",
- " (stats_impl): AbsAve()\n",
- " )\n",
- " )\n",
- " (stats_scaling_impl): StatsScaling(\n",
- " (affine_rescaling): Identity()\n",
- " (restrict_scaling): RestrictValue(\n",
+ " (14): QuantLinear(\n",
+ " in_features=1024, out_features=10, bias=False\n",
+ " (weight_reg): WeightReg()\n",
+ " (weight_quant): WeightQuantProxy(\n",
+ " (tensor_quant): BinaryQuant(\n",
+ " (scaling_impl): StandaloneScaling(\n",
+ " (restrict_value): RestrictValue(\n",
" (forward_impl): Sequential(\n",
" (0): PowerOfTwo()\n",
- " (1): ClampMin()\n",
+ " (1): Identity()\n",
" )\n",
" )\n",
- " (restrict_scaling_preprocess): LogTwo()\n",
" )\n",
" )\n",
" )\n",
+ " (bias_quant): BiasQuantProxy()\n",
" )\n",
- " (bias_quant): BiasQuantProxy()\n",
+ " (15): BatchNorm1d(10, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
")"
]
},
- "execution_count": 3,
+ "execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "import torch\n",
- "\n",
- "trained_lfc_w1a1_checkpoint = \"/workspace/brevitas_cnv_lfc/pretrained_models/LFC_1W1A/checkpoints/best.tar\"\n",
- "lfc = LFC(weight_bit_width=1, act_bit_width=1, in_bit_width=1).eval()\n",
- "checkpoint = torch.load(trained_lfc_w1a1_checkpoint, map_location=\"cpu\")\n",
- "lfc.load_state_dict(checkpoint[\"state_dict\"])\n",
+ "from finn.util.test import get_test_model\n",
+ "lfc = get_test_model(netname = \"LFC\", wbits = 1, abits = 1, pretrained = True)\n",
"lfc"
]
},
@@ -323,12 +334,12 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -340,6 +351,7 @@
}
],
"source": [
+ "import torch\n",
"import matplotlib.pyplot as plt\n",
"from pkgutil import get_data\n",
"import onnx\n",
@@ -353,17 +365,17 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "tensor([2.4663e-03, 6.8211e-06, 8.9177e-01, 2.1330e-05, 3.6883e-04, 3.0418e-06,\n",
- " 1.1795e-04, 5.0158e-05, 1.0517e-01, 2.4597e-05])"
+ "tensor([0.0602, 0.0147, 0.5844, 0.0445, 0.0270, 0.0185, 0.0595, 0.0082, 0.1689,\n",
+ " 0.0141])"
]
},
- "execution_count": 11,
+ "execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
@@ -378,12 +390,12 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -422,9 +434,18 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 15,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/workspace/brevitas/brevitas_examples/bnn_pynq/models/LFC.py:80: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.\n",
+ " x = 2.0 * x - torch.tensor([1.0], device=x.device)\n"
+ ]
+ }
+ ],
"source": [
"import brevitas.onnx as bo\n",
"export_onnx_path = \"/tmp/LFCW1A1.onnx\"\n",
@@ -441,45 +462,40 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "\n",
- "Stopping http://0.0.0.0:8081\n",
"Serving '/tmp/LFCW1A1.onnx' at http://0.0.0.0:8081\n"
]
- }
- ],
- "source": [
- "import netron\n",
- "netron.start(export_onnx_path, port=8081, host=\"0.0.0.0\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [
+ },
{
"data": {
"text/html": [
- "<iframe src=\"http://0.0.0.0:8081/\" style=\"position: relative; width: 100%;\" height=\"400\"></iframe>\n"
+ "\n",
+ " <iframe\n",
+ " width=\"100%\"\n",
+ " height=\"400\"\n",
+ " src=\"http://0.0.0.0:8081/\"\n",
+ " frameborder=\"0\"\n",
+ " allowfullscreen\n",
+ " ></iframe>\n",
+ " "
],
"text/plain": [
- "<IPython.core.display.HTML object>"
+ "<IPython.lib.display.IFrame at 0x7f3a27be9ac8>"
]
},
+ "execution_count": 16,
"metadata": {},
- "output_type": "display_data"
+ "output_type": "execute_result"
}
],
"source": [
- "%%html\n",
- "<iframe src=\"http://0.0.0.0:8081/\" style=\"position: relative; width: 100%;\" height=\"400\"></iframe>"
+ "showInNetron('/tmp/LFCW1A1.onnx')"
]
},
{
@@ -500,19 +516,19 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "input: \"32\"\n",
- "input: \"33\"\n",
- "output: \"35\"\n",
- "op_type: \"MatMul\""
+ "input: \"40\"\n",
+ "input: \"38\"\n",
+ "output: \"41\"\n",
+ "op_type: \"Add\""
]
},
- "execution_count": 16,
+ "execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
@@ -532,22 +548,16 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "array([[ 1., 1., 1., ..., 1., 1., -1.],\n",
- " [ 1., 1., -1., ..., 1., 1., -1.],\n",
- " [-1., 1., -1., ..., -1., 1., -1.],\n",
- " ...,\n",
- " [-1., 1., -1., ..., -1., -1., 1.],\n",
- " [ 1., 1., -1., ..., 1., 1., -1.],\n",
- " [-1., 1., 1., ..., -1., -1., 1.]], dtype=float32)"
+ "array(-0.5, dtype=float32)"
]
},
- "execution_count": 17,
+ "execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
@@ -565,16 +575,16 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "<DataType.BIPOLAR: 8>"
+ "<DataType.FLOAT32: 16>"
]
},
- "execution_count": 18,
+ "execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
@@ -585,16 +595,16 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "[784, 1024]"
+ "[]"
]
},
- "execution_count": 19,
+ "execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
@@ -612,50 +622,56 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 21,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "Stopping http://0.0.0.0:8081\n",
- "Serving '/tmp/LFCW1A1-clean.onnx' at http://0.0.0.0:8081\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from finn.transformation.fold_constants import FoldConstants\n",
"from finn.transformation.infer_shapes import InferShapes\n",
"model = model.transform(InferShapes())\n",
"model = model.transform(FoldConstants())\n",
"export_onnx_path_transformed = \"/tmp/LFCW1A1-clean.onnx\"\n",
- "model.save(export_onnx_path_transformed)\n",
- "netron.start(export_onnx_path_transformed, port=8081, host=\"0.0.0.0\")"
+ "model.save(export_onnx_path_transformed)"
]
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 22,
"metadata": {},
"outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Stopping http://0.0.0.0:8081\n",
+ "Serving '/tmp/LFCW1A1-clean.onnx' at http://0.0.0.0:8081\n"
+ ]
+ },
{
"data": {
"text/html": [
- "<iframe src=\"http://0.0.0.0:8081/\" style=\"position: relative; width: 100%;\" height=\"400\"></iframe>\n"
+ "\n",
+ " <iframe\n",
+ " width=\"100%\"\n",
+ " height=\"400\"\n",
+ " src=\"http://0.0.0.0:8081/\"\n",
+ " frameborder=\"0\"\n",
+ " allowfullscreen\n",
+ " ></iframe>\n",
+ " "
],
"text/plain": [
- "<IPython.core.display.HTML object>"
+ "<IPython.lib.display.IFrame at 0x7f3a27b49e10>"
]
},
+ "execution_count": 22,
"metadata": {},
- "output_type": "display_data"
+ "output_type": "execute_result"
}
],
"source": [
- "%%html\n",
- "<iframe src=\"http://0.0.0.0:8081/\" style=\"position: relative; width: 100%;\" height=\"400\"></iframe>"
+ "showInNetron('/tmp/LFCW1A1-clean.onnx')"
]
},
{
@@ -667,18 +683,18 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "array([[ 3.3252678 , -2.5652065 , 9.215742 , -1.4251148 , 1.4251148 ,\n",
- " -3.3727715 , 0.28502294, -0.5700459 , 7.07807 , -1.2826033 ]],\n",
+ "array([[-1.5095654 , -2.915617 , 0.764004 , -1.8118242 , -2.308991 ,\n",
+ " -2.6900144 , -1.520713 , -3.4965858 , -0.47711682, -2.9628415 ]],\n",
" dtype=float32)"
]
},
- "execution_count": 22,
+ "execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
@@ -694,7 +710,7 @@
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 17,
"metadata": {},
"outputs": [
{
@@ -703,7 +719,7 @@
"True"
]
},
- "execution_count": 23,
+ "execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
@@ -718,13 +734,6 @@
"source": [
"We have succesfully verified that the transformed and cleaned-up FINN graph still produces the same output, and can now use this model for further processing in FINN."
]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
}
],
"metadata": {
diff --git a/notebooks/basics/2_modelwrapper.ipynb b/notebooks/basics/2_modelwrapper.ipynb
deleted file mode 100644
index 6b3cd0337d938c100e0f71e61f8505a5b7377505..0000000000000000000000000000000000000000
--- a/notebooks/basics/2_modelwrapper.ipynb
+++ /dev/null
@@ -1,365 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# FINN - ModelWrapper\n",
- "--------------------------------------\n",
- "<font size=\"3\"> This notebook is about the ModelWrapper class within FINN. \n",
- "\n",
- "Following showSrc function is used to print the source code of function calls in the Jupyter notebook:</font>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "import inspect\n",
- "\n",
- "def showSrc(what):\n",
- " print(\"\".join(inspect.getsourcelines(what)[0]))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## General Information\n",
- "------------------------------\n",
- "* <font size=\"3\"> wrapper around ONNX ModelProto that exposes several utility\n",
- " functions for graph manipulation and exploration </font>\n",
- "* <font size=\"3\"> ModelWrapper instance takes ONNX ModelProto and `make_deepcopy` flag as input </font>\n",
- "* <font size=\"3\"> ONNX ModelProto can either be a string with the path to a stored .onnx file on disk, or serialized bytes </font>\n",
- "* <font size=\"3\"> `make_deepcopy` is by default False but can be set to True if a (deep) copy should be created </font>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Create a ModelWrapper instance\n",
- "\n",
- "<font size=\"3\">Here we use a premade ONNX file on disk to load up the ModelWrapper, but this could have been produced from e.g. a trained Brevitas PyTorch model. See [this notebook](3_brevitas_network_import.ipynb) for more details.</font>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "from finn.core.modelwrapper import ModelWrapper\n",
- "model = ModelWrapper(\"../LFCW1A1.onnx\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Access the ONNX GraphProto through ModelWrapper\n",
- "\n",
- "<font size=\"3\">ModelWrapper is a thin wrapper around the ONNX protobuf, and it offers a range of helper functions as well as direct access to the underlying protobuf. The `.model` member gives access to the full ONNX ModelProto, whereas `.graph` gives access to the GraphProto, as follows:</font>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "ModelProto IR version is 4\n",
- "GraphProto top-level outputs are [name: \"60\"\n",
- "type {\n",
- " tensor_type {\n",
- " elem_type: 1\n",
- " shape {\n",
- " dim {\n",
- " dim_value: 1\n",
- " }\n",
- " dim {\n",
- " dim_value: 10\n",
- " }\n",
- " }\n",
- " }\n",
- "}\n",
- "]\n",
- "There are 29 nodes in the graph\n",
- "The first node is \n",
- "input: \"0\"\n",
- "output: \"21\"\n",
- "op_type: \"Shape\"\n",
- "\n"
- ]
- }
- ],
- "source": [
- "# access the ONNX ModelProto\n",
- "modelproto = model.model\n",
- "print(\"ModelProto IR version is %d\" % modelproto.ir_version)\n",
- "\n",
- "# the graph\n",
- "graphproto = model.graph\n",
- "print(\"GraphProto top-level outputs are %s\" % str(graphproto.output))\n",
- "\n",
- "# the node list\n",
- "nodes = model.graph.node\n",
- "print(\"There are %d nodes in the graph\" % len(nodes))\n",
- "print(\"The first node is \\n%s\" % str(nodes[0]))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Helper functions for tensors\n",
- "<font size=\"3\"> Every input and output of every node in the onnx model is represented as tensor with several properties (i.e. name, shape, data type). ModelWrapper provides some utility functions to work with the tensors. </font>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "##### Get all tensor names\n",
- "\n",
- "<font size=\"3\">Produces a list of all tensor names (inputs, activations, weights, outputs...) in the graph.</font>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "['0', 'features.3.weight', 'features.3.bias', 'features.3.running_mean', 'features.3.running_var', 'features.7.weight', 'features.7.bias', 'features.7.running_mean', 'features.7.running_var', 'features.11.weight', 'features.11.bias', 'features.11.running_mean', 'features.11.running_var', '20', '23', '28', '30', '33', '34', '41', '42', '49', '50', '57', '58', '60']\n"
- ]
- }
- ],
- "source": [
- "# get all tensor names\n",
- "tensor_list = model.get_all_tensor_names()\n",
- "print(tensor_list)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "##### Producer and consumer of a tensor\n",
- "\n",
- "<font size=\"3\">A tensor can have a producer node and/or a consumer node in the onnx model. ModelWrapper provides two helper functions to access these nodes, they are shown in the following.\n",
- "\n",
- "It may be that a tensor does not have a producer or consumer node, for example if the tensor represents a constant that is already set. In that case `None` will be returned.</font>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Producer node of tensor 60:\n",
- "input: \"59\"\n",
- "input: \"58\"\n",
- "output: \"60\"\n",
- "op_type: \"Mul\"\n",
- "\n",
- "Consumer node of tensor 0:\n",
- "input: \"0\"\n",
- "output: \"21\"\n",
- "op_type: \"Shape\"\n",
- "\n",
- "Producer of tensor 0: None\n"
- ]
- }
- ],
- "source": [
- "# get random tensor and find producer and consumer (returns node)\n",
- "\n",
- "tensor_name = tensor_list[25]\n",
- "print(\"Producer node of tensor {}:\".format(tensor_name))\n",
- "print(model.find_producer(tensor_name))\n",
- "\n",
- "tensor_name = tensor_list[0]\n",
- "print(\"Consumer node of tensor {}:\".format(tensor_name))\n",
- "print(model.find_consumer(tensor_name))\n",
- "\n",
- "print(\"Producer of tensor 0: %s\" % str(model.find_producer(\"0\")))\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "##### Tensor shape\n",
- "<font size=\"3\">Each tensor has a specific shape which can be accessed with the following ModelWrapper helper functions.</font>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Shape of tensor 0 is [1, 1, 28, 28]\n"
- ]
- }
- ],
- "source": [
- "# get tensor_shape\n",
- "\n",
- "print(\"Shape of tensor 0 is %s\" % str(model.get_tensor_shape(\"0\")))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<font size=\"3\">It is also possible to set the tensor shape with a helper function. The syntax would be the following:\n",
- " \n",
- "`onnx_model.set_tensor_shape(tensor_name, tensor_shape)`\n",
- "\n",
- "Optionally, the dtype (container datatype) of the tensor can also be specified as third argument. By default it is set to TensorProto.FLOAT. \n",
- " \n",
- "**Important:** dtype should not be confused with FINN data type, which specifies the quantization annotation. See the remarks about FINN-ONNX in [this notebook](0_getting_started.ipynb). It is safest to use floating point tensors as the container data type for best compatibility inside FINN.</font>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "##### Tensor FINN DataType"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<font size=\"3\">FINN introduces its [own data types](https://github.com/Xilinx/finn/blob/master/src/finn/core/datatype.py) because ONNX does not natively support precisions less than 8 bits. FINN is about quantized neural networks, so precision of i.e. 4 bits, 3 bits, 2 bits or 1 bit are of interest. To represent the data within FINN, float tensors are used with additional annotation to specify the quantized data type of a tensor. The following helper functions are about this quantization annotation.</font>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "The FINN DataType of tensor 0 is DataType.FLOAT32\n",
- "The FINN DataType of tensor 32 is DataType.BIPOLAR\n"
- ]
- }
- ],
- "source": [
- "# get tensor data type (FINN data type)\n",
- "print(\"The FINN DataType of tensor 0 is \" + str(model.get_tensor_datatype(\"0\")))\n",
- "print(\"The FINN DataType of tensor 32 is \" + str(model.get_tensor_datatype(\"32\")))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<font size=\"3\">In addition to the get_tensor_datatatype() function, the (FINN) datatype of a tensor can be set using the `set_tensor_datatype(tensor_name, datatype)` function.</font>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "##### Tensor initializers\n",
- "<font size=\"3\">Some tensors have *initializers*, like tensors that represent constants or i.e. the trained weight values. \n",
- "\n",
- "ModelWrapper contains two helper functions for this case, one to determine the current initializer and one to set the initializer of a tensor. If there is no initializer, `None` is returned.</font>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Initializer for tensor 33:\n",
- "[[ 1. 1. 1. ... 1. 1. -1.]\n",
- " [ 1. 1. -1. ... 1. 1. -1.]\n",
- " [-1. 1. -1. ... -1. 1. -1.]\n",
- " ...\n",
- " [-1. 1. -1. ... -1. -1. 1.]\n",
- " [ 1. 1. -1. ... 1. 1. -1.]\n",
- " [-1. 1. 1. ... -1. -1. 1.]]\n",
- "Initializer for tensor 0:\n",
- "None\n"
- ]
- }
- ],
- "source": [
- "# get tensor initializer\n",
- "tensor_name = tensor_list[1]\n",
- "print(\"Initializer for tensor 33:\\n\" + str(model.get_initializer(\"33\")))\n",
- "print(\"Initializer for tensor 0:\\n\" + str(model.get_initializer(\"0\")))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<font size=\"3\">Like for the other tensor helper functions there is a corresponding set function (`set_initializer(tensor_name, tensor_value)`).</font>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### More helper functions\n",
- "<font size=\"3\">ModelWrapper contains more useful functions, if you are interested please have a look at the [Python code](https://github.com/Xilinx/finn/blob/master/src/finn/core/modelwrapper.py) directly. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/end2end_example/cnv-mp-fc.png b/notebooks/end2end_example/cnv-mp-fc.png
new file mode 100644
index 0000000000000000000000000000000000000000..1d1fb0045e764be2ecf6773b3701553a928fbd23
Binary files /dev/null and b/notebooks/end2end_example/cnv-mp-fc.png differ
diff --git a/notebooks/end2end_example/cnv_end2end_example.ipynb b/notebooks/end2end_example/cnv_end2end_example.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..ce8c9decf4aaa6b7be2e556b6053abf380d0d373
--- /dev/null
+++ b/notebooks/end2end_example/cnv_end2end_example.ipynb
@@ -0,0 +1,778 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# End-to-End FINN Flow for a Simple Convolutional Net\n",
+ "-----------------------------------------------------------------\n",
+ "\n",
+ "In this notebook, we will go through the FINN steps needed to take a binarized convolutional network all the way down to a heterogeneous streaming dataflow accelerator running on the FPGA. \n",
+ "\n",
+ "It's recommended to go through the simpler [end-to-end notebook for a fully connected network](tfc_end2end_example.ipynb) first, since many steps here are very similar and we will focus on what is done differently for convolutions.\n",
+ "\n",
+ "This notebook is quite lengthy, and some of the cells (involving Vivado synthesis) may take up to an hour to finish running. To let you save and resume your progress, we will save the intermediate ONNX models that are generated in the various steps to disk, so that you can jump back directly to where you left off."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Quick Introduction to the CNV-w1a1 Network\n",
+ "\n",
+ "The particular quantized neural network (QNN) we will be targeting in this notebook is referred to as CNV-w1a1 and it classifies 32x32 RGB images into one of ten CIFAR-10 classes. All weights and activations in this network are quantized to bipolar values (either -1 or +1), with the exception of the input (which is RGB with 8 bits per channel) and the final output (which is 32-bit numbers). It first appeared in the original [FINN paper](https://arxiv.org/abs/1612.07119) from ISFPGA'17 with the name CNV, as a variant of the binarized convolutional network from the [BinaryNet paper](https://arxiv.org/abs/1602.02830), in turn inspired by the VGG-11 topology which was the runner-up for the 2014 [ImageNet Large Scale Visual Recognition Challenge](http://www.image-net.org/challenges/LSVRC/).\n",
+ "\n",
+ "\n",
+ "You'll have a chance to interactively examine the layers that make up the network in Netron in a moment, so that's enough about the network for now. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Quick Recap of the End-to-End Flow\n",
+ "\n",
+ "The FINN compiler comes with many *transformations* that modify the ONNX representation of the network according to certain patterns. This notebook will demonstrate a *possible* sequence of such transformations to take a particular trained network all the way down to hardware, as shown in the figure below."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The white fields show the state of the network representation in the respective step. The colored fields represent the transformations that are applied to the network to achieve a certain result. The diagram is divided into 5 sections represented by a different color, each of it includes several flow steps. The flow starts in top left corner with Brevitas export (green section), followed by the preparation of the network (blue section) for the Vivado HLS synthesis and Vivado IPI stitching (orange section), and finally building a PYNQ overlay bitfile and testing it on a PYNQ board (yellow section).\n",
+ "There is an additional section for functional verification (red section) on the left side of the diagram, which we will not cover in this notebook. For details please take a look in the verification notebook which you can find [here](tfc_end2end_verification.ipynb)\n",
+ "\n",
+ "\n",
+ "We will use the helper function `showInNetron` to show the ONNX model at the current transformation step. The Netron displays are interactive, but they only work when running the notebook actively and not on GitHub (i.e. if you are viewing this on GitHub you'll only see blank squares)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from finn.util.basic import make_build_dir\n",
+ "from finn.util.visualization import showInNetron\n",
+ " \n",
+ "build_dir = \"/workspace/finn\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 1. Brevitas Export, FINN Import and Tidy-Up\n",
+ "\n",
+ "Similar to what we did in the TFC-w1a1 end-to-end notebook, we will start by exporting the [pretrained CNV-w1a1 network](https://github.com/Xilinx/brevitas/tree/master/brevitas_examples/bnn_pynq) to ONNX, importing that into FINN and running the \"tidy-up\" transformations to have a first look at the topology."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Downloading: \"https://github.com/Xilinx/brevitas/releases/download/bnn_pynq-r0/cnv_1w1a-758c8fef.pth\" to /home/maltanar/.cache/torch/checkpoints/cnv_1w1a-758c8fef.pth\n",
+ "100%|██████████| 6227617/6227617 [00:01<00:00, 4429264.32it/s]\n",
+ "/workspace/brevitas/brevitas_examples/bnn_pynq/models/CNV.py:107: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.\n",
+ " x = 2.0 * x - torch.tensor([1.0], device=x.device)\n"
+ ]
+ }
+ ],
+ "source": [
+ "import onnx\n",
+ "from finn.util.test import get_test_model_trained\n",
+ "import brevitas.onnx as bo\n",
+ "from finn.core.modelwrapper import ModelWrapper\n",
+ "from finn.transformation.double_to_single_float import DoubleToSingleFloat\n",
+ "from finn.transformation.infer_shapes import InferShapes\n",
+ "from finn.transformation.fold_constants import FoldConstants\n",
+ "from finn.transformation.general import GiveReadableTensorNames, GiveUniqueNodeNames\n",
+ "\n",
+ "cnv = get_test_model_trained(\"CNV\", 1, 1)\n",
+ "bo.export_finn_onnx(cnv, (1, 3, 32, 32), build_dir + \"/end2end_cnv_w1a1_export.onnx\")\n",
+ "model = ModelWrapper(build_dir + \"/end2end_cnv_w1a1_export.onnx\")\n",
+ "model = model.transform(DoubleToSingleFloat())\n",
+ "model = model.transform(InferShapes())\n",
+ "model = model.transform(FoldConstants())\n",
+ "model = model.transform(GiveUniqueNodeNames())\n",
+ "model = model.transform(GiveReadableTensorNames())\n",
+ "model.save(build_dir + \"/end2end_cnv_w1a1_tidy.onnx\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that the model is exported, let's have a look at its layer structure with Netron. Remember that the visualization below is interactive, you can click on the individual nodes and view the layer attributes, trained weights and so on."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Serving '/workspace/finn/end2end_cnv_w1a1_tidy.onnx' at http://0.0.0.0:8081\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " <iframe\n",
+ " width=\"100%\"\n",
+ " height=\"400\"\n",
+ " src=\"http://0.0.0.0:8081/\"\n",
+ " frameborder=\"0\"\n",
+ " allowfullscreen\n",
+ " ></iframe>\n",
+ " "
+ ],
+ "text/plain": [
+ "<IPython.lib.display.IFrame at 0x7f7b24ef8b00>"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "showInNetron(build_dir+\"/end2end_cnv_w1a1_tidy.onnx\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can see that the network is composed of a repeating convolution-convolution-maxpool layer pattern to extract features using 3x3 convolution kernels (with weights binarized) and `Sign` activations, followed by fully connected layers acting as the classifier. Also notice the initial `MultiThreshold` layer at the beginning of the network, which is quantizing float inputs to 8-bit ones."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 2. How FINN Implements Convolutions: Lowering and Streamlining\n",
+ "\n",
+ "In FINN, we implement convolutions with the *lowering* approach: we convert them to matrix-matrix multiply operations, where one of the matrices is generated by sliding a window over the input image. You can read more about the sliding window operator and how convolution lowering works [in this notebook](https://github.com/maltanar/qnn-inference-examples/blob/master/3-convolutional-binarized-gtsrb.ipynb). The streaming dataflow architecture we will end up with is going to look something like this figure from the [FINN-R paper](https://arxiv.org/abs/1809.04570):\n",
+ "\n",
+ "\n",
+ "\n",
+ "Note how the convolution layer looks very similar to the fully connected one in terms of the matrix-vector-threshold unit (MVTU), but now the MVTU is preceded by a sliding window unit that produces the matrix from the input image. All of these building blocks, including the `MaxPool` layer you see in this figure, exist as templated Vivado HLS C++ functions in [finn-hlslib](https://github.com/Xilinx/finn-hlslib).\n",
+ "\n",
+ "\n",
+ "To target this kind of hardware architecture with our network we'll apply a convolution lowering transformation, in addition to streamlining. You may recall the *streamlining transformation* that we applied to the TFC-w1a1 network, which is a series of mathematical simplifications that allow us to get rid of floating point scaling operations by implementing few-bit activations as thresholding operations. **The current implementation of streamlining is highly network-specific and may not work for your network if its topology is very different than the example network here. We hope to rectify this in future releases.**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from finn.transformation.streamline import Streamline\n",
+ "from finn.transformation.lower_convs_to_matmul import LowerConvsToMatMul\n",
+ "from finn.transformation.bipolar_to_xnor import ConvertBipolarMatMulToXnorPopcount\n",
+ "import finn.transformation.streamline.absorb as absorb\n",
+ "from finn.transformation.streamline.reorder import MakeMaxPoolNHWC\n",
+ "\n",
+ "model = ModelWrapper(build_dir + \"/end2end_cnv_w1a1_tidy.onnx\")\n",
+ "model = model.transform(Streamline())\n",
+ "model = model.transform(LowerConvsToMatMul())\n",
+ "model = model.transform(MakeMaxPoolNHWC())\n",
+ "model = model.transform(absorb.AbsorbTransposeIntoMultiThreshold())\n",
+ "model = model.transform(ConvertBipolarMatMulToXnorPopcount())\n",
+ "model = model.transform(Streamline())\n",
+ "model.save(build_dir + \"/end2end_cnv_w1a1_streamlined.onnx\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We won't go into too much detail about what happens in each transformation and why they are called in the particular order they are (feel free to visualize the intermediate steps using Netron yourself if you are curious) but here is a brief summmmary:\n",
+ "\n",
+ "* `Streamline` moves floating point scaling and addition operations closer to the input of the nearest thresholding activation and absorbs them into thresholds\n",
+ "* `LowerConvsToMatMul` converts ONNX `Conv` nodes into sequences of `Im2Col, MatMul` nodes as discussed above. `Im2Col` is a custom FINN ONNX high-level node type that implements the sliding window operator.\n",
+ "* `MakeMaxPoolNHWC` and `AbsorbTransposeIntoMultiThreshold` convert the *data layout* of the network into the NHWC data layout that finn-hlslib primitives use. NCHW means the tensor dimensions are ordered as `(N : batch, H : height, W : width, C : channels)` (assuming 2D images). The ONNX standard ops normally use the NCHW layout, but the ONNX intermediate representation itself does not dictate any data layout.\n",
+ "* You may recall `ConvertBipolarMatMulToXnorPopcount` from the TFC-w1a1 example, which is needed to implement bipolar-by-bipolar (w1a1) networks correctly using finn-hlslib.\n",
+ "\n",
+ "Let's visualize the streamlined and lowered network with Netron. Observe how all the `Conv` nodes have turned into pairs of `Im2Col, MatMul` nodes, and many nodes including `BatchNorm, Mul, Add` nodes have disappeared and replaced with `MultiThreshold` nodes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Stopping http://0.0.0.0:8081\n",
+ "Serving '/workspace/finn/end2end_cnv_w1a1_streamlined.onnx' at http://0.0.0.0:8081\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " <iframe\n",
+ " width=\"100%\"\n",
+ " height=\"400\"\n",
+ " src=\"http://0.0.0.0:8081/\"\n",
+ " frameborder=\"0\"\n",
+ " allowfullscreen\n",
+ " ></iframe>\n",
+ " "
+ ],
+ "text/plain": [
+ "<IPython.lib.display.IFrame at 0x7f0cb1098f28>"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "showInNetron(build_dir+\"/end2end_cnv_w1a1_streamlined.onnx\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 3. Partitioning, Conversion to HLS Layers and Folding\n",
+ "\n",
+ "The next steps will be (again) very similar to what we did for the TFC-w1a1 network. We'll first convert the layers that we can put into the FPGA into their HLS equivalents and separate them out into a *dataflow partition*:\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import finn.transformation.fpgadataflow.convert_to_hls_layers as to_hls\n",
+ "from finn.transformation.fpgadataflow.create_dataflow_partition import (\n",
+ " CreateDataflowPartition,\n",
+ ")\n",
+ "from finn.transformation.move_reshape import RemoveCNVtoFCFlatten\n",
+ "from finn.custom_op.registry import getCustomOp\n",
+ "\n",
+ "# choose the memory mode for the MVTU units, decoupled or const\n",
+ "mem_mode = \"decoupled\"\n",
+ "\n",
+ "model = ModelWrapper(build_dir + \"/end2end_cnv_w1a1_streamlined.onnx\")\n",
+ "model = model.transform(to_hls.InferBinaryStreamingFCLayer(mem_mode))\n",
+ "model = model.transform(to_hls.InferQuantizedStreamingFCLayer(mem_mode))\n",
+ "model = model.transform(to_hls.InferConvInpGen())\n",
+ "model = model.transform(to_hls.InferStreamingMaxPool())\n",
+ "# get rid of Reshape(-1, 1) operation between hlslib nodes\n",
+ "model = model.transform(RemoveCNVtoFCFlatten())\n",
+ "parent_model = model.transform(CreateDataflowPartition())\n",
+ "parent_model.save(build_dir + \"/end2end_cnv_w1a1_dataflow_parent.onnx\")\n",
+ "sdp_node = parent_model.get_nodes_by_op_type(\"StreamingDataflowPartition\")[0]\n",
+ "sdp_node = getCustomOp(sdp_node)\n",
+ "dataflow_model_filename = sdp_node.get_nodeattr(\"model\")\n",
+ "# save the dataflow partition with a different name for easier access\n",
+ "dataflow_model = ModelWrapper(dataflow_model_filename)\n",
+ "dataflow_model.save(build_dir + \"/end2end_cnv_w1a1_dataflow_model.onnx\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Notice the additional `RemoveCNVtoFCFlatten` transformation that was not used for TFC-w1a1. In the last Netron visualization you may have noticed a `Reshape` operation towards the end of the network where the convolutional part of the network ends and the fully-connected layers started. That `Reshape` is essentialy a tensor flattening operation, which we can remove for the purposes of hardware implementation. We can examine the contents of the dataflow partition with Netron, and observe the `ConvolutionInputGenerator`, `StreamingFCLayer_Batch` and `StreamingMaxPool_Batch` nodes that implement the sliding window, matrix multiply and maxpool operations in hlslib. *Note that the StreamingFCLayer instances following the ConvolutionInputGenerator nodes are really implementing the convolutions, despite the name. The final three StreamingFCLayer instances implement actual FC layers.*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Stopping http://0.0.0.0:8081\n",
+ "Serving '/workspace/finn/end2end_cnv_w1a1_dataflow_model.onnx' at http://0.0.0.0:8081\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " <iframe\n",
+ " width=\"100%\"\n",
+ " height=\"400\"\n",
+ " src=\"http://0.0.0.0:8081/\"\n",
+ " frameborder=\"0\"\n",
+ " allowfullscreen\n",
+ " ></iframe>\n",
+ " "
+ ],
+ "text/plain": [
+ "<IPython.lib.display.IFrame at 0x7f0cb063e208>"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "showInNetron(build_dir + \"/end2end_cnv_w1a1_dataflow_model.onnx\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we have to set the *folding factors* for certain layers to adjust the performance of our accelerator, similar to the TFC-w1a1 example. We'll also set the desired FIFO depths around those layers, which are important to achieve full throughput in the accelerator."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from finn.transformation.fpgadataflow.insert_dwc import InsertDWC\n",
+ "from finn.transformation.fpgadataflow.insert_tlastmarker import InsertTLastMarker\n",
+ "from finn.transformation.fpgadataflow.insert_fifo import InsertFIFO\n",
+ "\n",
+ "model = ModelWrapper(build_dir + \"/end2end_cnv_w1a1_dataflow_model.onnx\")\n",
+ "fc_layers = model.get_nodes_by_op_type(\"StreamingFCLayer_Batch\")\n",
+ "# each tuple is (PE, SIMD, in_fifo_depth) for a layer\n",
+ "folding = [\n",
+ " (16, 3, 128),\n",
+ " (32, 32, 128),\n",
+ " (16, 32, 128),\n",
+ " (16, 32, 128),\n",
+ " (4, 32, 81),\n",
+ " (1, 32, 2),\n",
+ " (1, 4, 2),\n",
+ " (1, 8, 128),\n",
+ " (5, 1, 3),\n",
+ "]\n",
+ "for fcl, (pe, simd, ififodepth) in zip(fc_layers, folding):\n",
+ " fcl_inst = getCustomOp(fcl)\n",
+ " fcl_inst.set_nodeattr(\"PE\", pe)\n",
+ " fcl_inst.set_nodeattr(\"SIMD\", simd)\n",
+ " fcl_inst.set_nodeattr(\"inFIFODepth\", ififodepth)\n",
+ "\n",
+ "# use same SIMD values for the sliding window operators\n",
+ "swg_layers = model.get_nodes_by_op_type(\"ConvolutionInputGenerator\")\n",
+ "for i in range(len(swg_layers)):\n",
+ " swg_inst = getCustomOp(swg_layers[i])\n",
+ " simd = folding[i][1]\n",
+ " swg_inst.set_nodeattr(\"SIMD\", simd)\n",
+ "\n",
+ "model = model.transform(InsertDWC())\n",
+ "model = model.transform(InsertFIFO())\n",
+ "model = model.transform(InsertTLastMarker())\n",
+ "model = model.transform(GiveUniqueNodeNames())\n",
+ "model.save(build_dir + \"/end2end_cnv_w1a1_folded.onnx\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Below we visualize in Netron to observe the `StreamingDataWidthConverter` and `StreamingFIFO` nodes that have been inserted into graph, as well as the folding factors in the `PE` and `SIMD` attributes of each `StreamingFCLayer_Batch`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Stopping http://0.0.0.0:8081\n",
+ "Serving '/workspace/finn/end2end_cnv_w1a1_folded.onnx' at http://0.0.0.0:8081\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " <iframe\n",
+ " width=\"100%\"\n",
+ " height=\"400\"\n",
+ " src=\"http://0.0.0.0:8081/\"\n",
+ " frameborder=\"0\"\n",
+ " allowfullscreen\n",
+ " ></iframe>\n",
+ " "
+ ],
+ "text/plain": [
+ "<IPython.lib.display.IFrame at 0x7f0cb1098748>"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "showInNetron(build_dir + \"/end2end_cnv_w1a1_folded.onnx\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Our network is now ready and we can start with the hardware generation."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 4. Hardware Generation\n",
+ "\n",
+ "From this point onward, the steps we have to follow do not depend on the particular network and will be exactly the same as the TFC-w1a1 example. We first proceed with HLS synthesis, **which may take 10-20 minutes depending on your host computer**."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from finn.transformation.fpgadataflow.prepare_ip import PrepareIP\n",
+ "from finn.transformation.fpgadataflow.hlssynth_ip import HLSSynthIP\n",
+ "from finn.util.basic import pynq_part_map\n",
+ "\n",
+ "test_pynq_board = \"Pynq-Z1\"\n",
+ "test_fpga_part = pynq_part_map[test_pynq_board]\n",
+ "target_clk_ns = 5\n",
+ "\n",
+ "model = ModelWrapper(build_dir + \"/end2end_cnv_w1a1_folded.onnx\")\n",
+ "model = model.transform(PrepareIP(test_fpga_part, target_clk_ns))\n",
+ "model = model.transform(HLSSynthIP())\n",
+ "model.save(build_dir + \"/end2end_cnv_w1a1_ipgen.onnx\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Once the HLS synthesis is complete, we can stitch together the generated IP blocks into a larger IP that is the implementation of our network:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from finn.transformation.fpgadataflow.replace_verilog_relpaths import (\n",
+ " ReplaceVerilogRelPaths,\n",
+ ")\n",
+ "from finn.transformation.fpgadataflow.create_stitched_ip import CreateStitchedIP\n",
+ "\n",
+ "model = ModelWrapper(build_dir + \"/end2end_cnv_w1a1_ipgen.onnx\")\n",
+ "model = model.transform(ReplaceVerilogRelPaths())\n",
+ "model = model.transform(CreateStitchedIP(test_fpga_part))\n",
+ "model.save(build_dir + \"/end2end_cnv_w1a1_ipstitch.onnx\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Finally, we create a PYNQ project that includes the hardware \"shell\" that will support our accelerator, including the data movers, and run Vivado synthesis, **which may take around 30 minutes depending on your host computer.**\n",
+ "\n",
+ "*If you'd like to watch the progress, you can open the generated project file (printed below) with the Vivado GUI.*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Vivado synthesis project is at /tmp/finn_dev_maltanar/vivado_pynq_proj_96qtjweo/resizer.xpr\n"
+ ]
+ }
+ ],
+ "source": [
+ "from finn.transformation.fpgadataflow.make_pynq_proj import MakePYNQProject\n",
+ "from finn.transformation.fpgadataflow.synth_pynq_proj import SynthPYNQProject\n",
+ "\n",
+ "model = ModelWrapper(build_dir + \"/end2end_cnv_w1a1_ipstitch.onnx\")\n",
+ "model = model.transform(MakePYNQProject(test_pynq_board))\n",
+ "vivado_proj = model.get_metadata_prop(\"vivado_pynq_proj\")\n",
+ "print(\"Vivado synthesis project is at %s/resizer.xpr\" % vivado_proj)\n",
+ "model.save(build_dir + \"/end2end_cnv_w1a1_pynqproj.onnx\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model = ModelWrapper(build_dir + \"/end2end_cnv_w1a1_pynqproj.onnx\")\n",
+ "model = model.transform(SynthPYNQProject())\n",
+ "model.save(build_dir + \"/end2end_cnv_w1a1_synth.onnx\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 5. Deployment and Remote Execution\n",
+ "\n",
+ "Now that we're done with the hardware generation, we can generate a Python driver for accelerator and copy the necessary files onto our PYNQ board."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "from finn.transformation.fpgadataflow.make_pynq_driver import MakePYNQDriver\n",
+ "from finn.transformation.fpgadataflow.make_deployment import DeployToPYNQ\n",
+ "\n",
+ "# set up the following values according to your own environment\n",
+ "# FINN will use ssh to deploy and run the generated accelerator\n",
+ "ip = os.getenv(\"PYNQ_IP\", \"192.168.1.99\")\n",
+ "username = os.getenv(\"PYNQ_USERNAME\", \"xilinx\")\n",
+ "password = os.getenv(\"PYNQ_PASSWORD\", \"xilinx\")\n",
+ "port = os.getenv(\"PYNQ_PORT\", 22)\n",
+ "target_dir = os.getenv(\"PYNQ_TARGET_DIR\", \"/home/xilinx/finn\")\n",
+ "\n",
+ "model = ModelWrapper(build_dir + \"/end2end_cnv_w1a1_synth.onnx\")\n",
+ "model = model.transform(MakePYNQDriver())\n",
+ "model = model.transform(DeployToPYNQ(ip, port, username, password, target_dir))\n",
+ "model.save(build_dir + \"/end2end_cnv_w1a1_pynq_deploy.onnx\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "total 4260\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 6380 May 7 15:14 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 May 7 15:14 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045675 May 7 15:14 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 302015 May 7 15:14 resizer.hwh\r\n"
+ ]
+ }
+ ],
+ "source": [
+ "! sshpass -p {password} ssh {username}@{ip} -p {port} 'ls -l {target_dir}/*'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We only have two more steps to be able to remotely execute the deployed bitfile with some test data from the CIFAR-10 dataset. Let's load up some test data that comes bundled with FINN -- and before you ask, that's supposed to be a cat (CIFAR-10 class number 3)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.image.AxesImage at 0x7f0c2b2c6908>"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import pkg_resources as pk\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "\n",
+ "fn = pk.resource_filename(\"finn\", \"data/cifar10/cifar10-test-data-class3.npz\")\n",
+ "x = np.load(fn)[\"arr_0\"].astype(np.float32)\n",
+ "x = x / 255\n",
+ "plt.imshow(x.reshape(3, 32,32).transpose(1, 2, 0))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Recall that we partitioned our original network into a parent graph that contained the non-synthesizable nodes and a child graph that contained the bulk of the network, which we turned into a bitfile. We'll load up the parent graph, modify the `StreamingDataflowPartition` node so that it points to the deployed ONNX graph."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# point to the PYNQ-deployed model as the StreamingDataflowPartition in the parent\n",
+ "parent_model = ModelWrapper(build_dir+\"/end2end_cnv_w1a1_dataflow_parent.onnx\")\n",
+ "sdp_node = parent_model.get_nodes_by_op_type(\"StreamingDataflowPartition\")[0]\n",
+ "sdp_node = getCustomOp(sdp_node)\n",
+ "sdp_node.set_nodeattr(\"model\", build_dir + \"/end2end_cnv_w1a1_pynq_deploy.onnx\")\n",
+ "parent_model.save(build_dir+\"/end2end_cnv_w1a1_dataflow_parent_with_remote_bitfile_exec.onnx\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Finally, we can call `execute_onnx` on the parent graph, which will internally call remote execution with the bitfile once the `StreamingDataflowPartition` node is reached, grab the results, then continue executing the last portion of the network. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "from finn.core.onnx_exec import execute_onnx\n",
+ "iname = parent_model.graph.input[0].name\n",
+ "oname = parent_model.graph.output[0].name\n",
+ "ishape = parent_model.get_tensor_shape(iname)\n",
+ "input_dict = {iname: x.reshape(ishape)}\n",
+ "ret = execute_onnx(parent_model, input_dict, True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We'll pass the output of the network through a softmax function to interpret it as probabilities, and plot the per-class probabilities as a bar chart."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<BarContainer object of 10 artists>"
+ ]
+ },
+ "execution_count": 28,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1440x216 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "def softmax(x):\n",
+ " \"\"\"Compute softmax values for each sets of scores in x.\"\"\"\n",
+ " e_x = np.exp(x - np.max(x))\n",
+ " return e_x / e_x.sum()\n",
+ "\n",
+ "logits = ret[oname].flatten()\n",
+ "prob = softmax(logits)\n",
+ "\n",
+ "classes = [\"airplane\", \"automobile\", \"bird\", \"cat\", \"deer\", \"dog\", \"frog\", \"horse\", \"ship\", \"truck\"]\n",
+ "\n",
+ "plt.figure(figsize=(20, 3)) \n",
+ "plt.bar(classes, prob)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We see that the network correctly predicts this as a class 3 (\"cat\") with high probability. This concludes our tutorial on how to take a convolutional BNN all the way down to hardware with FINN, and execute it remotely on a PYNQ board."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/end2end_example/finn-design-flow-example.svg b/notebooks/end2end_example/finn-design-flow-example.svg
index ca2b82bedb84f4cb9a36ccfef2ad9848b5d5b131..04f568c1f77cdbdc0dfbc8cd50b76f4c5d45d954 100755
--- a/notebooks/end2end_example/finn-design-flow-example.svg
+++ b/notebooks/end2end_example/finn-design-flow-example.svg
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diff --git a/notebooks/end2end_example/tfc_end2end_example.ipynb b/notebooks/end2end_example/tfc_end2end_example.ipynb
index a0e905c83eab7a52f70bfb45923b9b59d1c8cea6..d573061487de204084e0d3242da8ad1b791f44d8 100644
--- a/notebooks/end2end_example/tfc_end2end_example.ipynb
+++ b/notebooks/end2end_example/tfc_end2end_example.ipynb
@@ -34,7 +34,7 @@
"metadata": {},
"source": [
"The white fields show the state of the network representation in the respective step. The colored fields represent the transformations that are applied to the network to achieve a certain result. The diagram is divided into 5 sections represented by a different color, each of it includes several flow steps. The flow starts in top left corner with Brevitas export (green section), followed by the preparation of the network (blue section) for the Vivado HLS synthesis and Vivado IPI stitching (orange section), and finally building a PYNQ overlay bitfile and testing it on a PYNQ board (yellow section).\n",
- "There is an additional section for functional verification (red section) on the left side of the diagram, which we will not cover in this notebook. For details please take a look in the verification notebook which you can find [here](tfc_end2end_verification.ipynb)\n",
+ "There is an additional section for functional verification (red section) on the right side of the diagram, which we will not cover in this notebook. For details please take a look in the verification notebook which you can find [here](tfc_end2end_verification.ipynb)\n",
"\n",
"\n",
"This Jupyter notebook is organized based on the sections described above. We will use the following helper functions, `showSrc` to show source code of FINN library calls and `showInNetron` to show the ONNX model at the current transformation step. The Netron displays are interactive, but they only work when running the notebook actively and not on GitHub (i.e. if you are viewing this on GitHub you'll only see blank squares)."
@@ -46,17 +46,9 @@
"metadata": {},
"outputs": [],
"source": [
- "import inspect\n",
- "import netron\n",
+ "from finn.util.visualization import showSrc, showInNetron\n",
"from finn.util.basic import make_build_dir\n",
- "from IPython.display import IFrame\n",
"\n",
- "def showSrc(what):\n",
- " print(\"\".join(inspect.getsourcelines(what)[0]))\n",
- " \n",
- "def showInNetron(model_filename):\n",
- " netron.start(model_filename, port=8081, host=\"0.0.0.0\")\n",
- " return IFrame(src=\"http://0.0.0.0:8081/\", width=\"100%\", height=400)\n",
" \n",
"build_dir = \"/workspace/finn\""
]
@@ -78,7 +70,7 @@
"metadata": {},
"source": [
"## 1. Brevitas export <a id='brev_exp'></a>\n",
- "FINN expects an ONNX model as input. This can be a model trained with [Brevitas](https://github.com/Xilinx/brevitas). Brevitas is a PyTorch library for quantization-aware training and the FINN Docker image comes with several [example Brevitas networks](https://github.com/maltanar/brevitas_cnv_lfc). To show the FINN end-to-end flow, we'll use the TFC-w1a1 model as example network.\n",
+ "FINN expects an ONNX model as input. This can be a model trained with [Brevitas](https://github.com/Xilinx/brevitas). Brevitas is a PyTorch library for quantization-aware training and the FINN Docker image comes with several [example Brevitas networks](https://github.com/Xilinx/brevitas/tree/master/brevitas_examples/bnn_pynq). To show the FINN end-to-end flow, we'll use the TFC-w1a1 model as example network.\n",
"\n",
"First a few things have to be imported. Then the model can be loaded with the pretrained weights."
]
@@ -92,8 +84,8 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "/workspace/brevitas_cnv_lfc/training_scripts/models/TFC.py:85: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.\n",
- " x = 2.0 * x - torch.tensor([1.0]).to(self.device)\n"
+ "/workspace/brevitas/brevitas_examples/bnn_pynq/models/TFC.py:80: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.\n",
+ " x = 2.0 * x - torch.tensor([1.0], device=x.device)\n"
]
}
],
@@ -140,7 +132,7 @@
" "
],
"text/plain": [
- "<IPython.lib.display.IFrame at 0x7f4310b476a0>"
+ "<IPython.lib.display.IFrame at 0x7f8890385828>"
]
},
"execution_count": 3,
@@ -173,7 +165,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Now the model is prepared and could be simulated using Python. How this works is described in subsection [Simulation using Python](#simpy) in the section about *Simulation & Emulation Flows*.\n",
+ "Now the model is prepared and could be simulated using Python. How this works is described in the Jupyter notebook about verification and can be found [here](tfc_end2end_verification.ipynb#simpy).\n",
"\n",
"The model can now also be processed in different ways. The principle of FINN are analysis and transformation passes, which can be applied to the model. An analysis pass extracts specific information about the model and returns it to the user in the form of a dictionary. A transformation pass changes the model and returns the changed model back to the FINN flow.\n",
"\n",
@@ -186,10 +178,12 @@
"source": [
"## 2. Network preparation <a id='nw_prep'></a>\n",
"\n",
+ "* [FINN-style Dataflow Architectures](#dataflow_arch)\n",
"* [Tidy-up transformations](#basic_trafo)\n",
"* [Streamlining](#streamline)\n",
"* [Conversion to HLS layers](#hls_layers)\n",
- "* [Folding](#folding)\n",
+ "* [Creating a Dataflow Partition](#dataflow_partition)\n",
+ "* [Folding and Datawidth Converter, FIFO and TLastMarker Insertion](#folding)\n",
"\n",
"\n",
"In this section, we will put the network through a series of transformations that puts it in a form that can be stitched together to form a FINN-style dataflow architecture, yielding a high-performance, high-efficiency FPGA accelerator."
@@ -199,7 +193,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### FINN-style Dataflow Architectures\n",
+ "### FINN-style Dataflow Architectures <a id='dataflow_arch'></a>\n",
"\n",
"We start with a quick recap of FINN-style dataflow architectures. The key idea in such architectures is to parallelize across layers as well as within layers by dedicating a proportionate amount of compute resources to each layer, as illustrated in the figure below taken from the [FINN-R paper](https://arxiv.org/pdf/1809.04570.pdf):\n",
"\n",
@@ -299,7 +293,7 @@
" "
],
"text/plain": [
- "<IPython.lib.display.IFrame at 0x7f43177c2a20>"
+ "<IPython.lib.display.IFrame at 0x7fe1ad0639e8>"
]
},
"execution_count": 6,
@@ -373,7 +367,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "As can be seen, several transformations are involved in the streamlining transformation. There are move and collapse transformations. In the last step the operations are transformed into multithresholds. The involved transformations can be viewed in detail [here](https://github.com/Xilinx/finn/tree/dev/src/finn/transformation/streamline). After each transformation, three of the tidy-up transformations (`GiveUniqueNodeNames`, `GiveReadableTensorNames` and `InferDataTypes`) are applied to the model.\n",
+ "As can be seen, several transformations are involved in the streamlining transformation. There are move and collapse transformations. In the last step the operations are transformed into multithresholds. The involved transformations can be viewed in detail [here](https://github.com/Xilinx/finn/tree/master/src/finn/transformation/streamline). After each transformation, three of the tidy-up transformations (`GiveUniqueNodeNames`, `GiveReadableTensorNames` and `InferDataTypes`) are applied to the model.\n",
"\n",
"After streamlining the network looks as follows:"
]
@@ -406,7 +400,7 @@
" "
],
"text/plain": [
- "<IPython.lib.display.IFrame at 0x7f431826d860>"
+ "<IPython.lib.display.IFrame at 0x7fe1346e4ef0>"
]
},
"execution_count": 8,
@@ -460,7 +454,7 @@
" "
],
"text/plain": [
- "<IPython.lib.display.IFrame at 0x7f42977e39b0>"
+ "<IPython.lib.display.IFrame at 0x7fe1346f7780>"
]
},
"execution_count": 9,
@@ -494,11 +488,18 @@
"metadata": {},
"source": [
"### Conversion to HLS layers <a id='hls_layers'></a>\n",
- "Converts the nodes to HLS layers that correspond to the functions in [finn-hls library](https://finn-hlslib.readthedocs.io/en/latest/). In our case this transformation onverts pairs of binary XnorPopcountMatMul layers to StreamingFCLayer_Batch layers. Any immediately following MultiThreshold layers will also be absorbed into the MVTU.\n",
+ "Converts the nodes to HLS layers that correspond to the functions in [finn-hls library](https://finn-hlslib.readthedocs.io/en/latest/). In our case this transformation converts pairs of binary XnorPopcountMatMul layers to StreamingFCLayer_Batch layers. Any immediately following MultiThreshold layers will also be absorbed into the MVTU.\n",
"\n",
"Below is the code for the transformation and the network is visualized using netron to create the new structure with `StreamingFCLayer_Batch` nodes, which will correspond to a function call from the [finn-hlslib](https://finn-hlslib.readthedocs.io/en/latest/library/fclayer.html#_CPPv4I_j_j_j_j000_i_i000E22StreamingFCLayer_BatchvRN3hls6streamI7ap_uintI9InStreamWEEERN3hls6streamI7ap_uintI10OutStreamWEEERK2TWRK2TAKjRK1R) library."
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Note:** The transformation `to_hls.InferBinaryStreamingFCLayer` gets the string \"decoupled\" as argument, this indicates the `mem_mode` for the weights. In FINN there are different options to set the way the weights are stored and accessed. For details please have a look on the [FINN readthedocs website](https://finn.readthedocs.io/) under Internals."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 10,
@@ -529,7 +530,7 @@
" "
],
"text/plain": [
- "<IPython.lib.display.IFrame at 0x7f43177c73c8>"
+ "<IPython.lib.display.IFrame at 0x7fe1346f1080>"
]
},
"execution_count": 10,
@@ -540,7 +541,7 @@
"source": [
"import finn.transformation.fpgadataflow.convert_to_hls_layers as to_hls\n",
"model = ModelWrapper(build_dir+\"/tfc_w1a1_ready_for_hls_conversion.onnx\")\n",
- "model = model.transform(to_hls.InferBinaryStreamingFCLayer())\n",
+ "model = model.transform(to_hls.InferBinaryStreamingFCLayer(\"decoupled\"))\n",
"model.save(build_dir+\"/tfc_w1_a1_hls_layers.onnx\")\n",
"showInNetron(build_dir+\"/tfc_w1_a1_hls_layers.onnx\")"
]
@@ -589,7 +590,7 @@
" "
],
"text/plain": [
- "<IPython.lib.display.IFrame at 0x7f43177c2f60>"
+ "<IPython.lib.display.IFrame at 0x7fe1ad0b6e48>"
]
},
"execution_count": 11,
@@ -624,7 +625,7 @@
"text": [
"\n",
"Stopping http://0.0.0.0:8081\n",
- "Serving '/tmp/finn_jakobap/dataflow_partition_sqcfkplo/df_model.onnx' at http://0.0.0.0:8081\n"
+ "Serving '/tmp/finn_dev_jakobap/dataflow_partition_pbrjefjg/df_model.onnx' at http://0.0.0.0:8081\n"
]
},
{
@@ -641,7 +642,7 @@
" "
],
"text/plain": [
- "<IPython.lib.display.IFrame at 0x7f42977d4978>"
+ "<IPython.lib.display.IFrame at 0x7fe1346f3550>"
]
},
"execution_count": 12,
@@ -676,50 +677,23 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Folding and TLastMarker Insertion <a id='folding'></a>\n",
+ "### Folding and Datawidth Converter, FIFO and TLastMarker Insertion <a id='folding'></a>\n",
"\n",
"*Folding* in FINN describes how much a layer is time-multiplexed in terms of execution resources. There are several *folding factors* for each layer, controlled by the PE (parallelization over outputs) and SIMD (parallelization over inputs) parameters as described by the original [FINN paper](https://arxiv.org/pdf/1612.07119). The higher the PE and SIMD values are set, the faster the generated accelerator will run, and the more FPGA resources it will consume. \n",
"\n",
- "Since the folding parameters are node attributes, they can be easily accessed and changed using a helper function of the `ModelWrapper`. But first we have to extract the nodes which are StreamingFCLayer_Batch operations. This is where the Netron visualization helps us, in the above diagram we can see that the first four nodes are StreamingFCLayer_Batch. Through the `print`s we can check if the extracted nodes all have the op_type \"StreamingFCLayer_Batch\"."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "fc0 has the op_type: StreamingFCLayer_Batch\n",
- "fc1 has the op_type: StreamingFCLayer_Batch\n",
- "fc2 has the op_type: StreamingFCLayer_Batch\n",
- "fc3 has the op_type: StreamingFCLayer_Batch\n"
- ]
- }
- ],
- "source": [
- "fc0 = model.graph.node[0]\n",
- "fc1 = model.graph.node[1]\n",
- "fc2 = model.graph.node[2]\n",
- "fc3 = model.graph.node[3]\n",
- "print(\"fc0 has the op_type: \" + str(fc0.op_type))\n",
- "print(\"fc1 has the op_type: \" + str(fc1.op_type))\n",
- "print(\"fc2 has the op_type: \" + str(fc2.op_type))\n",
- "print(\"fc3 has the op_type: \" + str(fc3.op_type))"
+ "Since the folding parameters are node attributes, they can be easily accessed and changed using a helper function of the `ModelWrapper`. But first we take a closer look at one of the nodes that implement a StreamingFCLayer_Batch operation. This is where the Netron visualization helps us, in the above diagram we can see that the first four nodes are StreamingFCLayer_Batch. So as an example we extract the first node."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "We can use the higher-level [HLSCustomOp](https://github.com/Xilinx/finn/blob/master/src/finn/custom_op/fpgadataflow/__init__.py) wrappers for these nodes. These wrappers provide easy access to specific properties of these nodes, such as the folding factors (PE and SIMD). Let's have a look at which node attributes are defined by the CustomOp wrapper, and adjust the SIMD and PE attributes."
+ "We can use the higher-level [HLSCustomOp](https://github.com/Xilinx/finn/blob/master/src/finn/custom_op/fpgadataflow/__init__.py) wrappers for this node. These wrappers provide easy access to specific properties of these nodes, such as the folding factors (PE and SIMD). Let's have a look at which node attributes are defined by the CustomOp wrapper, and adjust the SIMD and PE attributes."
]
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
@@ -747,7 +721,7 @@
" 'mem_mode': ('s', False, 'const'),\n",
" 'ram_style': ('s', False, 'auto'),\n",
" 'backend': ('s', True, 'fpgadataflow'),\n",
- " 'code_gen_dir_npysim': ('s', False, ''),\n",
+ " 'code_gen_dir_cppsim': ('s', False, ''),\n",
" 'code_gen_dir_ipgen': ('s', False, ''),\n",
" 'executable_path': ('s', False, ''),\n",
" 'ipgen_path': ('s', False, ''),\n",
@@ -764,16 +738,14 @@
" 'outFIFODepth': ('i', False, 2)}"
]
},
- "execution_count": 15,
+ "execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
+ "fc0 = model.graph.node[0]\n",
"fc0w = getCustomOp(fc0)\n",
- "fc1w = getCustomOp(fc1)\n",
- "fc2w = getCustomOp(fc2)\n",
- "fc3w = getCustomOp(fc3)\n",
"\n",
"print(\"CustomOp wrapper is of class \" + fc0w.__class__.__name__)\n",
"\n",
@@ -790,48 +762,56 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
- "# SIMD controls the folding over the input vector\n",
- "# PE controls the folding over the output vector\n",
- "\n",
- "fc0w.set_nodeattr(\"inFIFODepth\", 50)\n",
- "fc0w.set_nodeattr(\"SIMD\", 16)\n",
- "fc0w.set_nodeattr(\"PE\", 16)\n",
- "fc0w.set_nodeattr(\"outFIFODepth\", 4)\n",
- "\n",
- "fc1w.set_nodeattr(\"inFIFODepth\", 4)\n",
- "fc1w.set_nodeattr(\"SIMD\", 16)\n",
- "fc1w.set_nodeattr(\"PE\", 16)\n",
- "fc1w.set_nodeattr(\"outFIFODepth\", 4)\n",
- "\n",
- "fc2w.set_nodeattr(\"inFIFODepth\", 4)\n",
- "fc2w.set_nodeattr(\"SIMD\", 16)\n",
- "fc2w.set_nodeattr(\"PE\", 16)\n",
- "fc2w.set_nodeattr(\"outFIFODepth\", 4)\n",
- "\n",
- "fc3w.set_nodeattr(\"inFIFODepth\", 4)\n",
- "fc3w.set_nodeattr(\"SIMD\", 16)\n",
- "fc3w.set_nodeattr(\"PE\", 10)\n",
- "fc3w.set_nodeattr(\"outFIFODepth\", 50)\n"
+ "fc_layers = model.get_nodes_by_op_type(\"StreamingFCLayer_Batch\")\n",
+ "# (PE, SIMD, in_fifo_depth, out_fifo_depth, ramstyle) for each layer\n",
+ "config = [\n",
+ " (16, 49, 16, 64, \"block\"),\n",
+ " (8, 8, 64, 64, \"auto\"),\n",
+ " (8, 8, 64, 64, \"auto\"),\n",
+ " (10, 8, 64, 10, \"distributed\"),\n",
+ "]\n",
+ "for fcl, (pe, simd, ififo, ofifo, ramstyle) in zip(fc_layers, config):\n",
+ " fcl_inst = getCustomOp(fcl)\n",
+ " fcl_inst.set_nodeattr(\"PE\", pe)\n",
+ " fcl_inst.set_nodeattr(\"SIMD\", simd)\n",
+ " fcl_inst.set_nodeattr(\"inFIFODepth\", ififo)\n",
+ " fcl_inst.set_nodeattr(\"outFIFODepth\", ofifo)\n",
+ " fcl_inst.set_nodeattr(\"ram_style\", ramstyle)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We are setting PE and SIMD so that each layer has a total folding of 16."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "After setting the FIFO node attributes, we can insert FIFO nodes inbetween the fpgadataflow nodes and in the beginning and end of the graph. This can be done using the transformation `InsertFIFO`."
+ "Besides PE and SIMD three other node attributes are set. `ram_style` specifies how the weights are to be stored (BRAM, LUTRAM, and so on). It can be selected explicitly or with the option `auto` you can let Vivado decide.\n",
+ "`inFIFODepth` and `outFIFODepth` specifies the FIFO depths that is needed by the node from the surrounding FIFOs. These attributes are used in the transformation 'InsertFIFO' to insert the appropriate FIFOs between the nodes.\n",
+ "\n",
+ "But before FIFOs can be added, it must be determined whether datawidth converters (DWC) are required and they must be inserted correctly. Because by setting the folding, the folded output shape of one node may not match the folded input shape of the next node. \n",
+ "\n",
+ "In the following, first DWCs and then FIFOs are inserted using the corresponding transformations in FINN."
]
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
+ "from finn.transformation.fpgadataflow.insert_dwc import InsertDWC\n",
"from finn.transformation.fpgadataflow.insert_fifo import InsertFIFO\n",
+ "\n",
+ "model = model.transform(InsertDWC())\n",
"model = model.transform(InsertFIFO())"
]
},
@@ -839,13 +819,15 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Finally, we will run the `InsertTLastMarker` transformation to get a `TLastMarker` node at the output of this graph, which is necessary to run the DMA engines correctly. Using netron we can observe that now the nodes contain the set folding, inbetween the nodes are FIFOs inserted and the last node is the `TLastMarker` node we insert in the following."
+ "Finally, we will run the `InsertTLastMarker` transformation to get a `TLastMarker` node at the output of this graph, which is necessary to run the DMA engines correctly. Using netron we can observe that now the nodes contain the set folding, if necessary a DWC is inserted, inbetween the nodes are FIFOs inserted and the last node is the `TLastMarker` node we insert in the following."
]
},
{
"cell_type": "code",
- "execution_count": 18,
- "metadata": {},
+ "execution_count": 17,
+ "metadata": {
+ "scrolled": true
+ },
"outputs": [
{
"name": "stdout",
@@ -870,7 +852,7 @@
" "
],
"text/plain": [
- "<IPython.lib.display.IFrame at 0x7f43177c7518>"
+ "<IPython.lib.display.IFrame at 0x7fe135b84780>"
]
},
"execution_count": 17,
@@ -889,7 +871,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "This completes the network preparation and the network can be passed on to the next block *Vivado HLS and Vivado synthesis*, which is described below."
+ "This completes the network preparation and the network can be passed on to the next block *Vivado HLS and IPI*, which is described below."
]
},
{
@@ -906,7 +888,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 18,
"metadata": {},
"outputs": [
{
@@ -925,7 +907,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
@@ -948,23 +930,23 @@
"metadata": {},
"source": [
"Two transformations are required to generate HLS IP blocks for each layer: \n",
- "* `CodeGen_ipgen` which generates the HLS C++ code for the node and a tcl-script which starts the HLS synthesis and exports the design as IP. \n",
- "* `HLSSynth_IPGen` which passes the tcl-script to Vivado HLS and thus performs the actual IP generation. \n",
+ "* `PrepareIP` which generates the HLS C++ code for the node and a tcl-script which starts the HLS synthesis and exports the design as IP. \n",
+ "* `HLSSynthIP` which passes the tcl-script to Vivado HLS and thus performs the actual IP generation. \n",
"\n",
- "We start off by giving unique node names using the basic transformation `GiveUniqueNodeNames`, and then proceed with the HLS C++ code generation with `CodeGen_ipgen`."
+ "We start off by giving unique node names using the basic transformation `GiveUniqueNodeNames`, and then proceed with the HLS C++ code generation with `PrepareIP`."
]
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"model = ModelWrapper(build_dir+\"/tfc_w1_a1_set_folding_factors.onnx\")\n",
"model = model.transform(GiveUniqueNodeNames())\n",
"\n",
- "from finn.transformation.fpgadataflow.codegen_ipgen import CodeGen_ipgen\n",
- "model = model.transform(CodeGen_ipgen(fpga_part, target_clk_ns))"
+ "from finn.transformation.fpgadataflow.prepare_ip import PrepareIP\n",
+ "model = model.transform(PrepareIP(fpga_part, target_clk_ns))"
]
},
{
@@ -973,18 +955,18 @@
"source": [
"### Synthesizing HLS to IP Blocks <a id='hls_synth'></a>\n",
"\n",
- "Now that we have generated the HLS code for each layer, we can call the `HLSSynth_IPGen` transformation to convert the generated HLS into Vivado IP blocks. **As this involves calling HLS synthesis, this transformation will run for some time (several minutes).**"
+ "Now that we have generated the HLS code for each layer, we can call the `HLSSynthIP` transformation to convert the generated HLS into Vivado IP blocks. **As this involves calling HLS synthesis, this transformation will run for some time (several minutes).**"
]
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
- "from finn.transformation.fpgadataflow.hlssynth_ipgen import HLSSynth_IPGen\n",
+ "from finn.transformation.fpgadataflow.hlssynth_ip import HLSSynthIP\n",
"\n",
- "model = model.transform(HLSSynth_IPGen())\n",
+ "model = model.transform(HLSSynthIP())\n",
"model.save(build_dir+\"/tfc_w1_a1_ipgen.onnx\")"
]
},
@@ -997,7 +979,7 @@
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 22,
"metadata": {},
"outputs": [
{
@@ -1023,10 +1005,10 @@
" "
],
"text/plain": [
- "<IPython.lib.display.IFrame at 0x7f42977edf60>"
+ "<IPython.lib.display.IFrame at 0x7fe1346f7588>"
]
},
- "execution_count": 23,
+ "execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
@@ -1048,19 +1030,23 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "project_StreamingFIFO_0\r\n"
+ "StreamingFCLayer_Batch_0_memstream.v thresh.h\r\n",
+ "hls_syn_StreamingFCLayer_Batch_0.tcl top_StreamingFCLayer_Batch_0.cpp\r\n",
+ "ipgen.sh\t\t\t vivado_hls.log\r\n",
+ "memblock_0.dat\t\t\t weights.npy\r\n",
+ "project_StreamingFCLayer_Batch_0\r\n"
]
}
],
"source": [
- "fc0w = getCustomOp(model.graph.node[0])\n",
+ "fc0w = getCustomOp(model.graph.node[1])\n",
"code_gen_dir = fc0w.get_nodeattr(\"code_gen_dir_ipgen\")\n",
"!ls {code_gen_dir}"
]
@@ -1069,12 +1055,14 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Directory *project_StreamingFCLayer_Batch_0* contains the project created by Vivado HLS into which the IP Block is exported, along with other files generated by Vivado HLS. If we compare it to the above visualization of the network with netron, this is exactly the name of the folder stored in the node attribute `ipgen_path`. The .cpp code that is passed to Vivado HLS can be found in the file *top_StreamingFCLayer_Batch_0.cpp*. The files *params.h* and *thresh.h* belong to that as well, they contain the values for the weights and thresholds. *vivado_hls.log* is the log file from Vivado HLS. Besides these files, the folder contains *ipgen.sh* and *hls_syn_StreamingFCLayer_Batch_0.tcl*. First we take a look at *ipgen.sh*."
+ "Directory *project_StreamingFCLayer_Batch_0* contains the project created by Vivado HLS into which the IP Block is exported, along with other files generated by Vivado HLS. If we compare it to the above visualization of the network with netron, this is exactly the name of the folder stored in the node attribute `ipgen_path`. The .cpp code that is passed to Vivado HLS can be found in the file *top_StreamingFCLayer_Batch_0.cpp*. The file *thresh.h* belongs to that as well, it contains the value for the thresholds. The weights are stored as .npy file and as .dat file (*memblock_0.dat*). *vivado_hls.log* is the log file from Vivado HLS. Besides these files, the folder contains *ipgen.sh* and *hls_syn_StreamingFCLayer_Batch_0.tcl* and because we use the StreamingFCLayer in \"decoupled\" mode a verilog wrapper (*StreamingFCLayer_Batch_0_memstream.v*) is produced, for more details on \"decoupled\" and \"const\" mode please see on the [FINN readthedocs website](https://finn.readthedocs.io/) under Internals. \n",
+ "\n",
+ "In the following we take a closer look at the two generated scripts. We start with *ipgen.sh*."
]
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 24,
"metadata": {},
"outputs": [
{
@@ -1082,8 +1070,8 @@
"output_type": "stream",
"text": [
"#!/bin/bash \r\n",
- "cd /tmp/finn_jakobap/code_gen_ipgen_StreamingFCLayer_Batch_0_pfp8r_i6\r\n",
- "vivado_hls /tmp/finn_jakobap/code_gen_ipgen_StreamingFCLayer_Batch_0_pfp8r_i6/hls_syn_StreamingFCLayer_Batch_0.tcl\r\n",
+ "cd /tmp/finn_dev_jakobap/code_gen_ipgen_StreamingFCLayer_Batch_0_edb__5oc\r\n",
+ "vivado_hls /tmp/finn_dev_jakobap/code_gen_ipgen_StreamingFCLayer_Batch_0_edb__5oc/hls_syn_StreamingFCLayer_Batch_0.tcl\r\n",
"cd /workspace/finn\r\n"
]
}
@@ -1104,7 +1092,7 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": 25,
"metadata": {},
"outputs": [
{
@@ -1114,14 +1102,14 @@
"\r\n",
"set config_proj_name project_StreamingFCLayer_Batch_0\r\n",
"puts \"HLS project: $config_proj_name\"\r\n",
- "set config_hwsrcdir \"/tmp/finn_jakobap/code_gen_ipgen_StreamingFCLayer_Batch_0_pfp8r_i6\"\r\n",
+ "set config_hwsrcdir \"/tmp/finn_dev_jakobap/code_gen_ipgen_StreamingFCLayer_Batch_0_edb__5oc\"\r\n",
"puts \"HW source dir: $config_hwsrcdir\"\r\n",
"set config_proj_part \"xc7z020clg400-1\"\r\n",
"\r\n",
"set config_bnnlibdir \"/workspace/finn-hlslib\"\r\n",
"\r\n",
"set config_toplevelfxn \"StreamingFCLayer_Batch_0\"\r\n",
- "set config_clkperiod 5\r\n",
+ "set config_clkperiod 10\r\n",
"\r\n",
"open_project $config_proj_name\r\n",
"add_files $config_hwsrcdir/top_StreamingFCLayer_Batch_0.cpp -cflags \"-std=c++0x -I$config_bnnlibdir\"\r\n",
@@ -1133,6 +1121,7 @@
"config_interface -m_axi_addr64\r\n",
"config_rtl -auto_prefix\r\n",
"\r\n",
+ "\r\n",
"create_clock -period $config_clkperiod -name default\r\n",
"csynth_design\r\n",
"export_design -format ip_catalog\r\n",
@@ -1160,20 +1149,20 @@
"source": [
"### IP Stitching <a id='ip_stitching'></a>\n",
"\n",
- "We now have IP blocks for each of our layers, and will stitch them together into a larger IP that implements the whole network using the `CodeGen_ipstitch` transformation. Bear in mind that this transformation can only be applied on a graph that only contains HLS nodes that already have been through the `HLSSynth_IPGen` transformation, which is the last step we performed. Prior to calling IP stitching, we'll also use the `ReplaceVerilogRelPaths` transformation to convert any relative `$readmemh` paths in the generated IP blocks to absolute ones, which prevents errors later on. **This step invokes Vivado and may take a few minutes to run.**"
+ "We now have IP blocks for each of our layers, and will stitch them together into a larger IP that implements the whole network using the `CreateStitchedIP` transformation. Bear in mind that this transformation can only be applied on a graph that only contains HLS nodes that already have been through the `HLSSynthIP` transformation, which is the last step we performed. Prior to calling IP stitching, we'll also use the `ReplaceVerilogRelPaths` transformation to convert any relative `$readmemh` paths in the generated IP blocks to absolute ones, which prevents errors later on. **This step invokes Vivado and may take a few minutes to run.**"
]
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
- "from finn.transformation.fpgadataflow.codegen_ipstitch import CodeGen_ipstitch\n",
+ "from finn.transformation.fpgadataflow.create_stitched_ip import CreateStitchedIP\n",
"from finn.transformation.fpgadataflow.replace_verilog_relpaths import ReplaceVerilogRelPaths\n",
"model = ModelWrapper(build_dir+\"/tfc_w1_a1_ipgen.onnx\")\n",
"model = model.transform(ReplaceVerilogRelPaths())\n",
- "model = model.transform(CodeGen_ipstitch(fpga_part))"
+ "model = model.transform(CreateStitchedIP(fpga_part))"
]
},
{
@@ -1185,22 +1174,22 @@
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[key: \"vivado_stitch_proj\"\n",
- "value: \"/tmp/finn_jakobap/vivado_stitch_proj_tqp4ib4j\"\n",
+ "value: \"/tmp/finn_dev_jakobap/vivado_stitch_proj_oa43bqzl\"\n",
", key: \"vivado_stitch_vlnv\"\n",
"value: \"xilinx_finn:finn:finn_design:1.0\"\n",
", key: \"wrapper_filename\"\n",
- "value: \"/tmp/finn_jakobap/vivado_stitch_proj_tqp4ib4j/finn_vivado_stitch_proj.srcs/sources_1/bd/finn_design/hdl/finn_design_wrapper.v\"\n",
+ "value: \"/tmp/finn_dev_jakobap/vivado_stitch_proj_oa43bqzl/finn_vivado_stitch_proj.srcs/sources_1/bd/finn_design/hdl/finn_design_wrapper.v\"\n",
"]"
]
},
- "execution_count": 28,
+ "execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
@@ -1211,16 +1200,16 @@
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "'/tmp/finn_jakobap/vivado_stitch_proj_tqp4ib4j'"
+ "'/tmp/finn_dev_jakobap/vivado_stitch_proj_oa43bqzl'"
]
},
- "execution_count": 29,
+ "execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
@@ -1245,7 +1234,7 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
@@ -1266,9 +1255,10 @@
"## 4. PYNQ hardware generation and deployment <a id='hw_test'></a>\n",
"\n",
"* [Inserting the IP into a PYNQ Overlay Shell](#pynq_shell)\n",
- "* [Synthesis, place and route](#synth_pl_ro)\n",
+ "* [Synthesis, Place and Route](#synth_pl_ro)\n",
"* [Driver Generation](#driver_gen)\n",
"* [Deployment and Remote Execution](#deploy)\n",
+ "* [Throughput Test on PYNQ Board](#throughput)\n",
"\n",
"\n",
"We are almost done preparing our hardware design. We'll now put it in a form suitable for use as a PYNQ overlay, synthesize and deploy it."
@@ -1280,12 +1270,12 @@
"source": [
"### Inserting the IP into a PYNQ Overlay Shell <a id='pynq_shell'></a>\n",
"\n",
- "We are almost done preparing our hardware design. To deploy our accelerator on a PYNQ platform, it needs to be put inside an appropriate *shell* that bridges it with the interfaces that the underlying system exposes. FINN makes it easy to create a PYNQ-compatible overlay by inserting the stitched IP into an appropriate PYNQ shell with the `MakePYNQProject` transformation, and view the created PYNQ shell project directory using the `metadata_props`. **This invokes Vivado and may take a few minutes to run.**"
+ "To deploy our accelerator on a PYNQ platform, it needs to be put inside an appropriate *shell* that bridges it with the interfaces that the underlying system exposes. FINN makes it easy to create a PYNQ-compatible overlay by inserting the stitched IP into an appropriate PYNQ shell with the `MakePYNQProject` transformation, and view the created PYNQ shell project directory using the `metadata_props`. **This invokes Vivado and may take a few minutes to run.**"
]
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": 30,
"metadata": {
"scrolled": true
},
@@ -1294,19 +1284,19 @@
"data": {
"text/plain": [
"[key: \"vivado_stitch_proj\"\n",
- "value: \"/tmp/finn_jakobap/vivado_stitch_proj_tqp4ib4j\"\n",
+ "value: \"/tmp/finn_dev_jakobap/vivado_stitch_proj_oa43bqzl\"\n",
", key: \"vivado_stitch_vlnv\"\n",
"value: \"xilinx_finn:finn:finn_design:1.0\"\n",
", key: \"wrapper_filename\"\n",
- "value: \"/tmp/finn_jakobap/vivado_stitch_proj_tqp4ib4j/finn_vivado_stitch_proj.srcs/sources_1/bd/finn_design/hdl/finn_design_wrapper.v\"\n",
+ "value: \"/tmp/finn_dev_jakobap/vivado_stitch_proj_oa43bqzl/finn_vivado_stitch_proj.srcs/sources_1/bd/finn_design/hdl/finn_design_wrapper.v\"\n",
", key: \"vivado_pynq_proj\"\n",
- "value: \"/tmp/finn_jakobap/vivado_pynq_proj_gkwfg31j\"\n",
+ "value: \"/tmp/finn_dev_jakobap/vivado_pynq_proj_ljn53hfs\"\n",
", key: \"vivado_synth_rpt\"\n",
- "value: \"/tmp/finn_jakobap/vivado_pynq_proj_gkwfg31j/synth_report.xml\"\n",
+ "value: \"/tmp/finn_dev_jakobap/vivado_pynq_proj_ljn53hfs/synth_report.xml\"\n",
"]"
]
},
- "execution_count": 31,
+ "execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
@@ -1320,7 +1310,7 @@
},
{
"cell_type": "code",
- "execution_count": 32,
+ "execution_count": 31,
"metadata": {},
"outputs": [
{
@@ -1346,7 +1336,7 @@
},
{
"cell_type": "code",
- "execution_count": 33,
+ "execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
@@ -1357,7 +1347,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Synthesis, place and route <a id='synth_pl_ro'></a>"
+ "### Synthesis, Place and Route <a id='synth_pl_ro'></a>"
]
},
{
@@ -1369,7 +1359,7 @@
},
{
"cell_type": "code",
- "execution_count": 34,
+ "execution_count": 33,
"metadata": {
"scrolled": true
},
@@ -1378,21 +1368,21 @@
"data": {
"text/plain": [
"[key: \"vivado_stitch_proj\"\n",
- "value: \"/tmp/finn_jakobap/vivado_stitch_proj_tqp4ib4j\"\n",
+ "value: \"/tmp/finn_dev_jakobap/vivado_stitch_proj_oa43bqzl\"\n",
", key: \"vivado_stitch_vlnv\"\n",
"value: \"xilinx_finn:finn:finn_design:1.0\"\n",
", key: \"wrapper_filename\"\n",
- "value: \"/tmp/finn_jakobap/vivado_stitch_proj_tqp4ib4j/finn_vivado_stitch_proj.srcs/sources_1/bd/finn_design/hdl/finn_design_wrapper.v\"\n",
+ "value: \"/tmp/finn_dev_jakobap/vivado_stitch_proj_oa43bqzl/finn_vivado_stitch_proj.srcs/sources_1/bd/finn_design/hdl/finn_design_wrapper.v\"\n",
", key: \"vivado_pynq_proj\"\n",
- "value: \"/tmp/finn_jakobap/vivado_pynq_proj_gkwfg31j\"\n",
+ "value: \"/tmp/finn_dev_jakobap/vivado_pynq_proj_ljn53hfs\"\n",
", key: \"vivado_synth_rpt\"\n",
- "value: \"/tmp/finn_jakobap/vivado_pynq_proj_gkwfg31j/synth_report.xml\"\n",
+ "value: \"/tmp/finn_dev_jakobap/vivado_pynq_proj_ljn53hfs/synth_report.xml\"\n",
", key: \"vivado_pynq_bitfile\"\n",
- "value: \"/tmp/finn_jakobap/vivado_pynq_proj_gkwfg31j/resizer.bit\"\n",
+ "value: \"/tmp/finn_dev_jakobap/vivado_pynq_proj_ljn53hfs/resizer.bit\"\n",
"]"
]
},
- "execution_count": 34,
+ "execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
@@ -1406,7 +1396,7 @@
},
{
"cell_type": "code",
- "execution_count": 35,
+ "execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
@@ -1417,14 +1407,14 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Driver generation <a id='driver_gen'></a>\n",
+ "### Driver Generation <a id='driver_gen'></a>\n",
"\n",
"Now that we have synthesized a bitfile for our network, we will generate some Python code for PYNQ that will act as the driver for this bitfile, package everything into a deployment folder and copy that to our PYNQ board."
]
},
{
"cell_type": "code",
- "execution_count": 36,
+ "execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
@@ -1442,7 +1432,7 @@
},
{
"cell_type": "code",
- "execution_count": 44,
+ "execution_count": 36,
"metadata": {},
"outputs": [
{
@@ -1462,110 +1452,142 @@
")\r\n",
"from finn.core.datatype import DataType\r\n",
"\r\n",
- "def load_input(N):\r\n",
- " ishape_normal = (N, 784)\r\n",
- " # load desired input .npy file\r\n",
- " ibuf_normal = np.load(\"input.npy\")\r\n",
- " # ensure that shape is as expected\r\n",
- " assert ibuf_normal.shape == ishape_normal\r\n",
- " return ibuf_normal\r\n",
+ "class FINNAccelDriver():\r\n",
+ " def __init__(self, N, bitfile):\r\n",
+ " \"\"\"Instantiate the FINN accelerator driver.\r\n",
+ " Gets batchsize (N) as integer and path to bitfile as string.\"\"\"\r\n",
+ " self.N = N\r\n",
+ " # input FINN DataType\r\n",
+ " self.idt = DataType.BINARY\r\n",
+ " # output FINN DataType\r\n",
+ " self.odt = DataType.UINT32\r\n",
+ " # input and output shapes\r\n",
+ " self.ishape_normal = (N, 784)\r\n",
+ " self.oshape_normal = (N, 10)\r\n",
+ " self.ishape_folded = (N, 16, 49)\r\n",
+ " self.oshape_folded = (N, 1, 10)\r\n",
+ " self.ishape_packed = (N, 16, 7) # datatype np.uint8\r\n",
+ " self.oshape_packed = (N, 1, 40) # datatype np.uint8\r\n",
+ " # load bitfile and set up accelerator\r\n",
+ " self.ol = Overlay(bitfile)\r\n",
+ " self.dma = self.ol.axi_dma_0\r\n",
+ " self.ctrl_regs = self.ol.resize_accel_0\r\n",
+ " # neuron folding factor of output = iterations per sample\r\n",
+ " self.itersPerSample = self.oshape_packed[-2]\r\n",
+ " # AXI lite register offset for number of iterations\r\n",
+ " # used by TLastMarker to signal end of transmission for AXI CDMA\r\n",
+ " self.REG_OFFSET_NUM_ITERS = 0x10\r\n",
+ " # set up TLastMarker with correct num. samples\r\n",
+ " self.ctrl_regs.write(self.REG_OFFSET_NUM_ITERS, self.N*self.itersPerSample)\r\n",
"\r\n",
- "def pack_input(ibuf_normal, N):\r\n",
- " # input FINN DataType\r\n",
- " idt = DataType.BINARY\r\n",
- " ishape_folded = (N, 49, 16)\r\n",
- " # convert to folded form\r\n",
- " ibuf_folded = ibuf_normal.reshape(ishape_folded)\r\n",
- " # pack the input buffer, reversing both SIMD dim and endianness\r\n",
- " ibuf_packed = finnpy_to_packed_bytearray(\r\n",
- " ibuf_folded, idt, reverse_endian=True, reverse_inner=True\r\n",
- " )\r\n",
- " return ibuf_packed\r\n",
+ " # allocate a PYNQ buffer for the packed input and buffer\r\n",
+ " self.ibuf_packed_device = allocate(shape=self.ishape_packed, dtype=np.uint8)\r\n",
+ " self.obuf_packed_device = allocate(shape=self.oshape_packed, dtype=np.uint8)\r\n",
"\r\n",
- "def unpack_output(obuf_packed, N):\r\n",
- " # output FINN DataType\r\n",
- " odt = DataType.UINT32\r\n",
- " oshape_folded = (N, 1, 10)\r\n",
- " # unpack the packed output buffer from accelerator\r\n",
- " obuf_folded = packed_bytearray_to_finnpy(\r\n",
- " obuf_packed, odt, oshape_folded, reverse_endian=True, reverse_inner=True\r\n",
- " )\r\n",
- " return obuf_folded\r\n",
+ " def fold_input(self, ibuf_normal):\r\n",
+ " \"\"\"Reshapes input in desired shape.\r\n",
+ " Gets input data (ibuf_normal), checks if data is in expected normal shape.\r\n",
+ " Returns folded input.\"\"\"\r\n",
+ " # ensure that shape is as expected\r\n",
+ " assert ibuf_normal.shape == self.ishape_normal\r\n",
+ " # convert to folded form\r\n",
+ " ibuf_folded = ibuf_normal.reshape(self.ishape_folded)\r\n",
+ " return ibuf_folded\r\n",
"\r\n",
- "def save_output(obuf_folded, N):\r\n",
- " # convert to normal reshape and save\r\n",
- " oshape_normal = (N, 10)\r\n",
- " obuf_normal = obuf_folded.reshape(oshape_normal)\r\n",
- " np.save(\"output.npy\", obuf_normal)\r\n",
+ " def pack_input(self, ibuf_folded):\r\n",
+ " \"\"\"Packs folded input and reverses both SIMD dim and endianness.\r\n",
+ " Gets input data in folded shape and returns packed input data.\"\"\"\r\n",
+ " ibuf_packed = finnpy_to_packed_bytearray(\r\n",
+ " ibuf_folded, self.idt, reverse_endian=True, reverse_inner=True\r\n",
+ " )\r\n",
+ " return ibuf_packed\r\n",
"\r\n",
- "if __name__ == \"__main__\":\r\n",
- " parser = argparse.ArgumentParser(description='Please select functional verification (\"remote_pynq\") or throughput test (\"throughput_test\")')\r\n",
- " parser.add_argument('exec_mode', help='metadata prop exec_mode as string')\r\n",
- " args = parser.parse_args()\r\n",
- " exec_mode = args.exec_mode\r\n",
+ " def unpack_output(self, obuf_packed):\r\n",
+ " \"\"\"Unpacks the packed output buffer from accelerator.\r\n",
+ " Gets packed output and returns output data in folded shape.\"\"\"\r\n",
+ " obuf_folded = packed_bytearray_to_finnpy(\r\n",
+ " obuf_packed, self.odt, self.oshape_folded, reverse_endian=True, reverse_inner=True\r\n",
+ " )\r\n",
+ " return obuf_folded\r\n",
"\r\n",
- " bitfile_path = \"resizer.bit\"\r\n",
- " ol = Overlay(bitfile_path)\r\n",
- " dma=ol.axi_dma_0\r\n",
- " ctrl_regs=ol.resize_accel_0\r\n",
- " # AXI lite register offset for number of iterations\r\n",
- " # used by TLastMarker to signal end of transmission for AXI CDMA\r\n",
- " REG_OFFSET_NUM_ITERS = 0x10\r\n",
+ " def unfold_output(self, obuf_folded):\r\n",
+ " \"\"\"Unfolds output data to normal shape.\r\n",
+ " Gets folded output data and returns output data in normal shape.\"\"\"\r\n",
+ " obuf_normal = obuf_folded.reshape(self.oshape_normal)\r\n",
+ " return obuf_normal\r\n",
"\r\n",
- " # number of samples for inference\r\n",
- " if exec_mode == \"remote_pynq\":\r\n",
- " N = 1\r\n",
- " elif exec_mode == \"throughput_test\":\r\n",
- " res={}\r\n",
- " N = 1000\r\n",
- " else:\r\n",
- " raise Exception(\"Exec mode has to be set to remote_pynq or throughput_test\")\r\n",
+ " def copy_input_data_to_device(self, data):\r\n",
+ " \"\"\"Copies given input data to PYNQ buffer.\"\"\"\r\n",
+ " np.copyto(self.ibuf_packed_device, data)\r\n",
"\r\n",
- " # declare input/output types and shapes for the accelerator\r\n",
- " ishape_packed = (N, 49, 2)\r\n",
- " oshape_packed = (N, 1, 40)\r\n",
- " \r\n",
- " if exec_mode == \"remote_pynq\":\r\n",
- " ibuf_normal = load_input(N)\r\n",
- " ibuf_packed = pack_input(ibuf_normal, N)\r\n",
- " elif exec_mode == \"throughput_test\":\r\n",
- " ibuf_packed = np.asarray(np.random.uniform(low=0, high=1, size=tuple(ishape_packed)), dtype=np.uint8)\r\n",
+ " def execute(self):\r\n",
+ " \"\"\"Executes accelerator by setting up the DMA and\r\n",
+ " waiting until all transfers complete. Uses only member variables and\r\n",
+ " returns nothing.\"\"\"\r\n",
+ " dma = self.dma\r\n",
+ " dma.sendchannel.transfer(self.ibuf_packed_device)\r\n",
+ " dma.recvchannel.transfer(self.obuf_packed_device)\r\n",
+ " dma.sendchannel.wait()\r\n",
+ " dma.recvchannel.wait()\r\n",
"\r\n",
- " # set up TLastMarker with correct num. samples\r\n",
- " ctrl_regs.write(REG_OFFSET_NUM_ITERS, N)\r\n",
"\r\n",
- " # allocate a PYNQ buffer for the packed input buffer\r\n",
- " ibuf_packed_device = allocate(shape=ishape_packed, dtype=np.uint8)\r\n",
- " # copy the packed data into the PYNQ buffer\r\n",
- " # TODO optimization: pack directly into the PYNQ buffer?\r\n",
- " np.copyto(ibuf_packed_device, ibuf_packed)\r\n",
+ "if __name__ == \"__main__\":\r\n",
+ " parser = argparse.ArgumentParser(description='Set exec mode, batchsize N, bitfile name, inputfile name and outputfile name')\r\n",
+ " parser.add_argument('--exec_mode', help='Please select functional verification (\"execute\") or throughput test (\"throughput_test\")', default=\"execute\")\r\n",
+ " parser.add_argument('--batchsize', help='number of samples for inference', type=int, default=1)\r\n",
+ " parser.add_argument('--bitfile', help='name of bitfile (i.e. \"resizer.bit\")', default=\"resizer.bit\")\r\n",
+ " parser.add_argument('--inputfile', help='name of input npy file (i.e. \"input.npy\")', default=\"input.npy\")\r\n",
+ " parser.add_argument('--outputfile', help='name of output npy file (i.e. \"output.npy\")', default=\"output.npy\")\r\n",
+ " # parse arguments\r\n",
+ " args = parser.parse_args()\r\n",
+ " exec_mode = args.exec_mode\r\n",
+ " N = args.batchsize\r\n",
+ " bitfile = args.bitfile\r\n",
+ " inputfile = args.inputfile\r\n",
+ " outputfile = args.outputfile\r\n",
"\r\n",
- " # allocate a PYNQ buffer for the returned packed output buffer\r\n",
- " obuf_packed = allocate(shape=oshape_packed, dtype=np.uint8)\r\n",
+ " # instantiate FINN accelerator driver and pass batchsize and bitfile\r\n",
+ " finnDriver = FINNAccelDriver(N, bitfile)\r\n",
+ "\r\n",
+ " # for the remote execution the data from the input npy file has to be loaded,\r\n",
+ " # packed and copied to the PYNQ buffer\r\n",
+ " if exec_mode == \"execute\":\r\n",
+ " # load desired input .npy file\r\n",
+ " ibuf_normal = np.load(inputfile)\r\n",
+ " ibuf_folded = finnDriver.fold_input(ibuf_normal)\r\n",
+ " ibuf_packed = finnDriver.pack_input(ibuf_folded)\r\n",
+ " finnDriver.copy_input_data_to_device(ibuf_packed)\r\n",
+ " elif exec_mode != \"throughput_test\":\r\n",
+ " raise Exception(\"Exec mode has to be set to remote_pynq or throughput_test\")\r\n",
"\r\n",
+ " # for the throughput test the runtime of the network has to be measured\r\n",
" if exec_mode == \"throughput_test\":\r\n",
" # measure runtime of network\r\n",
" start = time.time()\r\n",
+ " # dictionary for results of throughput test\r\n",
+ " res={}\r\n",
"\r\n",
- " # set up the DMA and wait until all transfers complete\r\n",
- " dma.sendchannel.transfer(ibuf_packed_device)\r\n",
- " dma.recvchannel.transfer(obuf_packed)\r\n",
- " dma.sendchannel.wait()\r\n",
- " dma.recvchannel.wait()\r\n",
- "\r\n",
+ " # execute accelerator\r\n",
+ " finnDriver.execute()\r\n",
"\r\n",
+ " # measure run time and fill dictionary with results of the throughput test\r\n",
" if exec_mode == \"throughput_test\":\r\n",
" end = time.time()\r\n",
" runtime = end - start\r\n",
" res[\"runtime[ms]\"] = runtime*1000\r\n",
" res[\"throughput[images/s]\"] = N / runtime\r\n",
+ " res[\"DRAM_in_bandwidth[Mb/s]\"] = np.prod(finnDriver.ishape_packed)*0.000001 / runtime\r\n",
+ " res[\"DRAM_out_bandwidth[Mb/s]\"] = np.prod(finnDriver.oshape_packed)*0.000001 / runtime\r\n",
" file = open(\"nw_metrics.txt\", \"w\")\r\n",
" file.write(str(res))\r\n",
" file.close()\r\n",
"\r\n",
+ " # if execution is selected unpack, unfold and save output to output npy file\r\n",
" else:\r\n",
- " obuf_folded = unpack_output(obuf_packed, N)\r\n",
- " save_output(obuf_folded, N)\r\n",
+ " obuf_folded = finnDriver.unpack_output(finnDriver.obuf_packed_device)\r\n",
+ " obuf_normal = finnDriver.unfold_output(obuf_folded)\r\n",
+ " np.save(outputfile, obuf_normal)\r\n",
+ "\r\n",
"\r\n"
]
}
@@ -1579,7 +1601,21 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "We can see that the generated driver contains the expected input/output shapes, expecting a file called `input.npy` to be provided prior to execution, which will be read in, packed into the format that the accelerator expects, running it and generating an `output.npy` file with the results. You can build your own applications around the accelerator by modifying the driver, or use the remote execution capabilities that FINN provides just to check if it is working, which will be our next step."
+ "We can see that in the generated driver a class is implemented which implements the FINN accelerator. The constructor gets the batchsize (N) as integer and the bitfile as string. It also contains the expected input/output shapes, and takes care of the instantiation of the accelerator by loading the bitfile and setting up dma and buffer. Several member functions take care of the data folding and packing. The function `copy_input_data_to_device` copies the input data into the PYNQ buffer and `execute` sets up the dma channels and waits until the transfer is completed. This class is used in the main function. But first the arguments are parsed, which are passed to the script. The driver can be used in two modes: \"execute\" and \"throughput_test\". By default all arguments are set to \"execute\" mode. In this mode the batch size is 1, and the passed files are set to the names used by the FINN transformations.\n",
+ "\n",
+ "In the \"execute\" mode works as follows:\n",
+ "1. the data is loaded from the \"inputfile\"\n",
+ "2. the data is folded using `fold_input`\n",
+ "3. the data is packed using `pack_input`\n",
+ "4. the data is copied to the device using `copy_input_data_to_device`\n",
+ "5. FINNAccelDriver is executed using `execute`\n",
+ "6. the data is unpacked using `unpack_output`\n",
+ "7. the data is unfolded using `unfold_output`\n",
+ "8. the data is stored in the \"outputfile\"\n",
+ "\n",
+ "If \"throughput_test\" is selected as `exec_mode`, no actual data needs to be loaded. The batchsize N should be set to a high value (i.e. 1000) and a time measurement is implemented in python. An empty dictionary (`res`) is created and after running the accelerator with the measured runtime it is filled with the metrics and saved in a .txt file.\n",
+ "\n",
+ "You can build your own applications around the accelerator by modifying the driver, or use the remote execution capabilities that FINN provides just to check if it is working, which will be our next step."
]
},
{
@@ -1593,15 +1629,15 @@
},
{
"cell_type": "code",
- "execution_count": 57,
+ "execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"from finn.transformation.fpgadataflow.make_deployment import DeployToPYNQ\n",
- "ip = \"51.37.26.64\"\n",
- "port = \"23\"\n",
+ "ip = \"192.168.3.1\"\n",
+ "port = \"22\"\n",
"username = \"xilinx\"\n",
- "password = \"x1l1nx_f1nn\"\n",
+ "password = \"xilinx\"\n",
"target_dir = \"/home/xilinx/finn_tfc_end2end_example\"\n",
"model = model.transform(DeployToPYNQ(ip, port, username, password, target_dir))\n",
"model.save(build_dir + \"/tfc_w1_a1_pynq_deploy.onnx\")"
@@ -1616,46 +1652,46 @@
},
{
"cell_type": "code",
- "execution_count": 58,
+ "execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[key: \"vivado_stitch_proj\"\n",
- "value: \"/tmp/finn_jakobap/vivado_stitch_proj_tqp4ib4j\"\n",
+ "value: \"/tmp/finn_dev_jakobap/vivado_stitch_proj_oa43bqzl\"\n",
", key: \"vivado_stitch_vlnv\"\n",
"value: \"xilinx_finn:finn:finn_design:1.0\"\n",
", key: \"wrapper_filename\"\n",
- "value: \"/tmp/finn_jakobap/vivado_stitch_proj_tqp4ib4j/finn_vivado_stitch_proj.srcs/sources_1/bd/finn_design/hdl/finn_design_wrapper.v\"\n",
+ "value: \"/tmp/finn_dev_jakobap/vivado_stitch_proj_oa43bqzl/finn_vivado_stitch_proj.srcs/sources_1/bd/finn_design/hdl/finn_design_wrapper.v\"\n",
", key: \"vivado_pynq_proj\"\n",
- "value: \"/tmp/finn_jakobap/vivado_pynq_proj_gkwfg31j\"\n",
+ "value: \"/tmp/finn_dev_jakobap/vivado_pynq_proj_ljn53hfs\"\n",
", key: \"vivado_synth_rpt\"\n",
- "value: \"/tmp/finn_jakobap/vivado_pynq_proj_gkwfg31j/synth_report.xml\"\n",
+ "value: \"/tmp/finn_dev_jakobap/vivado_pynq_proj_ljn53hfs/synth_report.xml\"\n",
", key: \"vivado_pynq_bitfile\"\n",
- "value: \"/tmp/finn_jakobap/vivado_pynq_proj_gkwfg31j/resizer.bit\"\n",
+ "value: \"/tmp/finn_dev_jakobap/vivado_pynq_proj_ljn53hfs/resizer.bit\"\n",
", key: \"pynq_driver_dir\"\n",
- "value: \"/tmp/finn_jakobap/pynq_driver_1r1_0kz6\"\n",
+ "value: \"/tmp/finn_dev_jakobap/pynq_driver_j_9suyqm\"\n",
", key: \"pynq_ip\"\n",
- "value: \"51.37.26.64\"\n",
+ "value: \"51.37.47.42\"\n",
", key: \"pynq_port\"\n",
"value: \"23\"\n",
", key: \"pynq_username\"\n",
"value: \"xilinx\"\n",
", key: \"pynq_password\"\n",
- "value: \"x1l1nx_f1nn\"\n",
+ "value: \"x1l1nx_f!nn\"\n",
", key: \"pynq_target_dir\"\n",
"value: \"/home/xilinx/finn_tfc_end2end_example\"\n",
", key: \"pynq_deployment_dir\"\n",
- "value: \"/tmp/finn_jakobap/pynq_deployment_kvurnk0c\"\n",
+ "value: \"/tmp/finn_dev_jakobap/pynq_deployment_962qxwkv\"\n",
", key: \"pynq_deploy_dir\"\n",
- "value: \"/tmp/finn_jakobap/pynq_deployment_kvurnk0c\"\n",
+ "value: \"/tmp/finn_dev_jakobap/pynq_deployment_962qxwkv\"\n",
", key: \"exec_mode\"\n",
"value: \"remote_pynq\"\n",
"]"
]
},
- "execution_count": 58,
+ "execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
@@ -1666,18 +1702,63 @@
},
{
"cell_type": "code",
- "execution_count": 59,
+ "execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "total 4284\r\n",
+ "/home/xilinx/finn_tfc_end2end_example/pynq_deployment_26e8h5jo:\r\n",
+ "total 4276\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 6363 May 7 10:35 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 May 7 10:35 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 3264 May 7 10:55 input.npy\r\n",
+ "-rw-r--r-- 1 root root 172 May 7 10:37 nw_metrics.txt\r\n",
+ "-rw-r--r-- 1 root root 120 May 7 10:55 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045675 May 7 10:35 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 302015 May 7 10:35 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 May 7 10:55 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_tfc_end2end_example/pynq_deployment_962qxwkv:\r\n",
+ "total 4260\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 6363 May 7 17:44 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 May 7 17:44 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045675 May 7 17:44 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 302015 May 7 17:44 resizer.hwh\r\n",
+ "\r\n",
+ "/home/xilinx/finn_tfc_end2end_example/pynq_deployment_kvurnk0c:\r\n",
+ "total 4300\r\n",
"-rw-r--r-- 1 xilinx xilinx 3861 Apr 27 12:36 driver.py\r\n",
"drwxr-xr-x 4 xilinx xilinx 4096 Apr 27 12:37 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 3264 Apr 27 12:37 input.npy\r\n",
+ "-rw-r--r-- 1 root root 78 Apr 27 12:38 nw_metrics.txt\r\n",
+ "-rw-r--r-- 1 root root 120 Apr 27 12:37 output.npy\r\n",
"-rw-r--r-- 1 xilinx xilinx 4045675 Apr 27 12:36 resizer.bit\r\n",
- "-rw-r--r-- 1 xilinx xilinx 329531 Apr 27 12:36 resizer.hwh\r\n"
+ "-rw-r--r-- 1 xilinx xilinx 329531 Apr 27 12:36 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Apr 27 12:38 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_tfc_end2end_example/pynq_deployment__tnbutz_:\r\n",
+ "total 4276\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 6363 May 6 17:34 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 May 6 17:34 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 3264 May 6 17:34 input.npy\r\n",
+ "-rw-r--r-- 1 root root 173 May 6 17:35 nw_metrics.txt\r\n",
+ "-rw-r--r-- 1 root root 120 May 6 17:34 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045675 May 6 17:34 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 302015 May 6 17:34 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 May 6 17:35 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_tfc_end2end_example/pynq_deployment_w4aa1r9k:\r\n",
+ "total 4276\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 6363 May 7 15:05 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 May 7 15:05 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 3264 May 7 15:06 input.npy\r\n",
+ "-rw-r--r-- 1 root root 172 May 7 15:11 nw_metrics.txt\r\n",
+ "-rw-r--r-- 1 root root 120 May 7 15:06 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045675 May 7 15:05 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 302015 May 7 15:05 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 May 7 15:11 sds_trace_data.dat\r\n"
]
}
],
@@ -1694,18 +1775,30 @@
},
{
"cell_type": "code",
- "execution_count": 60,
+ "execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "<matplotlib.image.AxesImage at 0x7f4277550ef0>"
+ "<matplotlib.image.AxesImage at 0x7fe11dda48d0>"
]
},
- "execution_count": 60,
+ "execution_count": 41,
"metadata": {},
"output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
}
],
"source": [
@@ -1727,7 +1820,7 @@
},
{
"cell_type": "code",
- "execution_count": 61,
+ "execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
@@ -1747,7 +1840,7 @@
},
{
"cell_type": "code",
- "execution_count": 62,
+ "execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
@@ -1769,7 +1862,7 @@
},
{
"cell_type": "code",
- "execution_count": 63,
+ "execution_count": 44,
"metadata": {},
"outputs": [
{
@@ -1778,13 +1871,13 @@
"<BarContainer object of 10 artists>"
]
},
- "execution_count": 63,
+ "execution_count": 44,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": "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\n",
+ "image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -1820,20 +1913,23 @@
"source": [
"### Throughput Test on PYNQ Board <a id='throughput'></a>\n",
"In addition to the functional verification, FINN also offers the possibility to measure the network performance directly on the PYNQ board. This can be done using the core function `throughput_test`. In the next section we import the function and execute it.\n",
- "First we extract the `remote_exec_model` again and pass it to the function. The function returns the metrics of the network as dictionary."
+ "First we extract the `remote_exec_model` again and pass it to the function. The function returns the metrics of the network as dictionary. "
]
},
{
"cell_type": "code",
- "execution_count": 66,
+ "execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Network metrics: \n",
- "{'runtime[ms]': 3.5953521728515625, 'throughput[images/s]': 278136.8700265252}\n"
+ "Network metrics:\n",
+ "runtime[ms]: 1.4772415161132812\n",
+ "throughput[images/s]: 676937.378954164\n",
+ "DRAM_in_bandwidth[Mb/s]: 75.81698644286635\n",
+ "DRAM_out_bandwidth[Mb/s]: 27.07749515816656\n"
]
}
],
@@ -1842,15 +1938,49 @@
"\n",
"child_model = ModelWrapper(getCustomOp(sdp_node).get_nodeattr(\"model\"))\n",
"res = throughput_test(child_model)\n",
- "print(\"Network metrics: \\n\" + str(res))"
+ "print(\"Network metrics:\")\n",
+ "for key in res:\n",
+ " print(str(key) + \": \" + str(res[key]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Together with the values for folding we can evaluate the performance of our accelerator. Each layer has a total folding factor of 64 and because the network is fully pipelined, it follows: `II = 64`. II is the initiation interval and indicates how many cycles are needed for one input to be processed. "
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 46,
"metadata": {},
- "outputs": [],
- "source": []
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "We reach approximately 43% of the ideal performance.\n"
+ ]
+ }
+ ],
+ "source": [
+ "II = 64\n",
+ "# frequency in MHz\n",
+ "f_MHz = 100\n",
+ "# expected throughput in MFPS\n",
+ "expected_throughput = f_MHz / II\n",
+ "# measured throughput (FPS) from throughput test, converted to MFPS\n",
+ "measured_throughput = res[\"throughput[images/s]\"] * 0.000001\n",
+ "# peformance\n",
+ "print(\"We reach approximately \" + str(round((measured_throughput / expected_throughput)*100)) + \"% of the ideal performance.\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The measured values were recorded with a batch size of 1000 and at a frequency of 100 MHz. We will be improving the efficiency of the generated accelerator examples in the coming FINN releases."
+ ]
}
],
"metadata": {
diff --git a/notebooks/end2end_example/tfc_end2end_verification.ipynb b/notebooks/end2end_example/tfc_end2end_verification.ipynb
index f03add2da37f1c7d0b33824fb0d1b9d19ba8f7fa..1ce17d3da3e4313f2b89eb6a790efcee704f70e9 100644
--- a/notebooks/end2end_example/tfc_end2end_verification.ipynb
+++ b/notebooks/end2end_example/tfc_end2end_verification.ipynb
@@ -9,7 +9,7 @@
"\n",
"**Important: This notebook depends on the tfc_end2end_example notebook, because we are using models that are available at intermediate steps in the end-to-end flow. So please make sure the needed .onnx files are generated to run this notebook.**\n",
"\n",
- "In this notebook, we will show how to take the intermediate results of the end-to-end tfc example and verify their functionality with different methods. In the following picture you can see the block in the end-to-end flow about the *Simulation & Emulation Flows*. Besides the methods in this notebook, there is another one that is covered in the Jupyter notebook [tfc_end2end_example](tfc_end2end_example.ipynb): remote execution. The remote execution allows functional verification directly on the PYNQ board, for details please have a look at the mentioned Jupyter notebook."
+ "In this notebook, we will show how to take the intermediate results of the end-to-end tfc example and verify their functionality with different methods. In the following picture you can see the section in the end-to-end flow about the *Simulation & Emulation Flows*. Besides the methods in this notebook, there is another one that is covered in the Jupyter notebook [tfc_end2end_example](tfc_end2end_example.ipynb): remote execution. The remote execution allows functional verification directly on the PYNQ board, for details please have a look at the mentioned Jupyter notebook."
]
},
{
@@ -32,18 +32,9 @@
"metadata": {},
"outputs": [],
"source": [
- "import inspect\n",
- "import netron\n",
"from finn.util.basic import make_build_dir\n",
- "from IPython.display import IFrame\n",
- "\n",
- "def showSrc(what):\n",
- " print(\"\".join(inspect.getsourcelines(what)[0]))\n",
- " \n",
- "def showInNetron(model_filename):\n",
- " netron.start(model_filename, port=8081, host=\"0.0.0.0\")\n",
- " return IFrame(src=\"http://0.0.0.0:8081/\", width=\"100%\", height=400)\n",
- " \n",
+ "from finn.util.visualization import showSrc, showInNetron\n",
+ " \n",
"build_dir = \"/workspace/finn\""
]
},
@@ -51,7 +42,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "To verify the simulations a \"golden\" output is calculated as a reference. This is calculated directly from the Brevitas model using PyTorch, by running some example data from the MNIST dataset through the trained model."
+ "To verify the simulations, a \"golden\" output is calculated as a reference. This is calculated directly from the Brevitas model using PyTorch, by running some example data from the MNIST dataset through the trained model."
]
},
{
@@ -62,8 +53,8 @@
{
"data": {
"text/plain": [
- "array([[-0.4992097 , -0.24960485, 6.489726 , 0.99841946, -0.24960482,\n",
- " -2.2464437 , 0.7488146 , -1.4976292 , -0.49920973, -2.7456534 ]],\n",
+ "array([[-1.119972 , -1.7596636, 0.8423852, -1.0705007, -1.3218282,\n",
+ " -1.5030646, -1.4598225, -1.2803943, -1.0334575, -1.7878995]],\n",
" dtype=float32)"
]
},
@@ -91,9 +82,9 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Simulation using Python \n",
+ "## Simulation using Python <a id='simpy'></a>\n",
"\n",
- "If an ONNX model consists of [standard ONNX](https://github.com/onnx/onnx/blob/master/docs/Operators.md) nodes and/or FINN custom operations that do not belong to the fpgadataflow (backend $\\neq$ \"fpgadataflow\") this model can be checked for functionality using Python. General information about FINN custom op nodes can be found in Jupyter notebook [2_custom_op.ipynb](../internals/2_custom_op.ipynb).\n",
+ "If an ONNX model consists of [standard ONNX](https://github.com/onnx/onnx/blob/master/docs/Operators.md) nodes and/or FINN custom operations that do not belong to the fpgadataflow (backend $\\neq$ \"fpgadataflow\") this model can be checked for functionality using Python.\n",
"\n",
"To simulate a standard ONNX node [onnxruntime](https://github.com/microsoft/onnxruntime) is used. onnxruntime is an open source tool developed by Microsoft to run standard ONNX nodes. For the FINN custom op nodes execution functions are defined. The following is an example of the execution function of a XNOR popcount node.\n"
]
@@ -111,8 +102,10 @@
" \"\"\"Simulates XNOR-popcount matrix multiplication as a regular bipolar\n",
" matrix multiplication followed by some post processing.\"\"\"\n",
" # extract the operand shapes\n",
- " (M, K0) = inp0.shape\n",
- " (K1, N) = inp1.shape\n",
+ " # (M, K0) = inp0.shape\n",
+ " # (K1, N) = inp1.shape\n",
+ " K0 = inp0.shape[-1]\n",
+ " K1 = inp1.shape[0]\n",
" # make sure shapes are compatible with matmul\n",
" assert K0 == K1, \"Matrix shapes are not compatible with matmul.\"\n",
" K = K0\n",
@@ -200,9 +193,9 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Simulation (npysim) using C++\n",
+ "## Simulation (cppsim) using C++\n",
"\n",
- "When dealing with HLS custom op nodes in FINN the simulation using Python is no longer sufficient. After the nodes have been converted to HLS layers, the simulation using C++ can be used. To do this, the input tensor is stored in an .npy file and C++ code is generated that reads the values from the .npy array, streams them to the corresponding finn-hlslib function and writes the result to a new .npy file. This in turn can be read in Python and processed in the FINN flow. For this example the model after the conversion to HLS layers is used."
+ "When dealing with HLS custom op nodes in FINN the simulation using Python is no longer sufficient. After the nodes have been converted to HLS layers, the simulation using C++ can be used. To do this, the input tensor is stored in an .npy file and C++ code is generated that reads the values from the .npy array, streams them to the corresponding finn-hlslib function and writes the result to a new .npy file. This in turn can be read in Python and processed in the FINN flow. For this example the model after setting the folding factors in the HLS layers is used, please be aware that this is not the full model, but the dataflow partition, so before executing at the end of this section we have to integrate the model back into the parent model."
]
},
{
@@ -211,7 +204,7 @@
"metadata": {},
"outputs": [],
"source": [
- "model_for_npysim = ModelWrapper(build_dir+\"/tfc_w1_a1_hls_layers.onnx\")"
+ "model_for_cppsim = ModelWrapper(build_dir+\"/tfc_w1_a1_set_folding_factors.onnx\")"
]
},
{
@@ -219,8 +212,8 @@
"metadata": {},
"source": [
"To generate the code for this simulation and to generate the executable two transformations are used:\n",
- "* `CodeGen_npysim` which generates the C++ code for the corresponding hls layer\n",
- "* `Compile` which compules the C++ code and stores the path to the executable"
+ "* `PrepareCppSim` which generates the C++ code for the corresponding hls layer\n",
+ "* `CompileCppSim` which compules the C++ code and stores the path to the executable"
]
},
{
@@ -229,11 +222,13 @@
"metadata": {},
"outputs": [],
"source": [
- "from finn.transformation.fpgadataflow.codegen_npysim import CodeGen_npysim\n",
- "from finn.transformation.fpgadataflow.compile import Compile\n",
+ "from finn.transformation.fpgadataflow.prepare_cppsim import PrepareCppSim\n",
+ "from finn.transformation.fpgadataflow.compile_cppsim import CompileCppSim\n",
+ "from finn.transformation.general import GiveUniqueNodeNames\n",
"\n",
- "model_for_npysim = model_for_npysim.transform(CodeGen_npysim())\n",
- "model_for_npysim = model_for_npysim.transform(Compile())"
+ "model_for_cppsim = model_for_cppsim.transform(GiveUniqueNodeNames())\n",
+ "model_for_cppsim = model_for_cppsim.transform(PrepareCppSim())\n",
+ "model_for_cppsim = model_for_cppsim.transform(CompileCppSim())"
]
},
{
@@ -252,7 +247,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Serving '/workspace/finn/tfc_w1_a1_for_npysim.onnx' at http://0.0.0.0:8081\n"
+ "Serving '/workspace/finn/tfc_w1_a1_for_cppsim.onnx' at http://0.0.0.0:8081\n"
]
},
{
@@ -269,7 +264,7 @@
" "
],
"text/plain": [
- "<IPython.lib.display.IFrame at 0x7fb461dd6710>"
+ "<IPython.lib.display.IFrame at 0x7f776245ac18>"
]
},
"execution_count": 8,
@@ -278,8 +273,8 @@
}
],
"source": [
- "model_for_npysim.save(build_dir+\"/tfc_w1_a1_for_npysim.onnx\")\n",
- "showInNetron(build_dir+\"/tfc_w1_a1_for_npysim.onnx\")"
+ "model_for_cppsim.save(build_dir+\"/tfc_w1_a1_for_cppsim.onnx\")\n",
+ "showInNetron(build_dir+\"/tfc_w1_a1_for_cppsim.onnx\")"
]
},
{
@@ -287,7 +282,7 @@
"metadata": {},
"source": [
"The following node attributes have been added:\n",
- "* `code_gen_dir_npysim` indicates the directory where the files for the simulation using C++ are stored\n",
+ "* `code_gen_dir_cppsim` indicates the directory where the files for the simulation using C++ are stored\n",
"* `executable_path` specifies the path to the executable\n",
"\n",
"We take now a closer look into the files that were generated:"
@@ -302,16 +297,17 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "compile.sh execute_StreamingFCLayer_Batch.cpp\tnode_model params.h thresh.h\r\n"
+ "compile.sh\t\t\t memblock_0.dat thresh.h\r\n",
+ "execute_StreamingFCLayer_Batch.cpp node_model\t weights.npy\r\n"
]
}
],
"source": [
"from finn.custom_op.registry import getCustomOp\n",
"\n",
- "fc0 = model_for_npysim.graph.node[2]\n",
+ "fc0 = model_for_cppsim.graph.node[1]\n",
"fc0w = getCustomOp(fc0)\n",
- "code_gen_dir = fc0w.get_nodeattr(\"code_gen_dir_npysim\")\n",
+ "code_gen_dir = fc0w.get_nodeattr(\"code_gen_dir_cppsim\")\n",
"!ls {code_gen_dir}"
]
},
@@ -326,7 +322,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "To simulate the model the execution mode(exec_mode) must be set to \"npysim\". This is done using the transformation SetExecMode."
+ "To simulate the model the execution mode(exec_mode) must be set to \"cppsim\". This is done using the transformation SetExecMode."
]
},
{
@@ -337,14 +333,15 @@
"source": [
"from finn.transformation.fpgadataflow.set_exec_mode import SetExecMode\n",
"\n",
- "model_for_npysim = model_for_npysim.transform(SetExecMode(\"npysim\"))"
+ "model_for_cppsim = model_for_cppsim.transform(SetExecMode(\"cppsim\"))\n",
+ "model_for_cppsim.save(build_dir+\"/tfc_w1_a1_for_cppsim.onnx\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "Now the model can be executed using `execute_onnx`. The function reads the `exec_mode` and writes the input into the correct directory in a .npy file. To be able to read this in C++, there is an additional .hpp file ([npy2apintstream.hpp](https://github.com/Xilinx/finn/blob/master/src/finn/data/cpp/npy2apintstream.hpp)) in FINN, which uses cnpy to read .npy files and convert them into streams, or to read a stream and write it into an .npy. [cnpy](https://github.com/rogersce/cnpy) is a helper to read and write .npy and .npz formates in C++.\n",
+ "Before the model can be executed using `execute_onnx`, we integrate the child model in the parent model. The function reads then the `exec_mode` and writes the input into the correct directory in a .npy file. To be able to read this in C++, there is an additional .hpp file ([npy2apintstream.hpp](https://github.com/Xilinx/finn/blob/master/src/finn/data/cpp/npy2apintstream.hpp)) in FINN, which uses cnpy to read .npy files and convert them into streams, or to read a stream and write it into an .npy. [cnpy](https://github.com/rogersce/cnpy) is a helper to read and write .npy and .npz formates in C++.\n",
"\n",
"The result is again compared to the \"golden\" output."
]
@@ -363,10 +360,14 @@
}
],
"source": [
- "output_dict = oxe.execute_onnx(model_for_npysim, input_dict)\n",
- "output_npysim = output_dict[list(output_dict.keys())[0]]\n",
+ "parent_model = ModelWrapper(build_dir+\"/tfc_w1_a1_dataflow_parent.onnx\")\n",
+ "sdp_node = parent_model.graph.node[2]\n",
+ "child_model = build_dir + \"/tfc_w1_a1_for_cppsim.onnx\"\n",
+ "getCustomOp(sdp_node).set_nodeattr(\"model\", child_model)\n",
+ "output_dict = oxe.execute_onnx(parent_model, input_dict)\n",
+ "output_cppsim = output_dict[list(output_dict.keys())[0]]\n",
"\n",
- "if np.isclose(output_npysim, output_golden, atol=1e-3).all():\n",
+ "if np.isclose(output_cppsim, output_golden, atol=1e-3).all():\n",
" print(\"Results are the same!\")\n",
"else:\n",
" print(\"The results are not the same!\")"
@@ -398,7 +399,7 @@
"source": [
"### Emulation of model node-by-node\n",
"\n",
- "The child model is loaded and the `exec_mode` for each node is set. Then it is saved in a new .onnx file so that the changed model can be referenced in the parent model."
+ "The child model is loaded and the `exec_mode` for each node is set. To prepare the node-by-node emulation the transformation `PrepareRTLSim` is applied to the child model. With this transformation the emulation files are created for each node and can be used directly when calling `execute_onnx()`. Each node has a new node attribute \"rtlsim_so\" after transformation, which contains the path to the corresponding emulation files. Then it is saved in a new .onnx file so that the changed model can be referenced in the parent model."
]
},
{
@@ -407,8 +408,10 @@
"metadata": {},
"outputs": [],
"source": [
+ "from finn.transformation.fpgadataflow.prepare_rtlsim import PrepareRTLSim\n",
"child_model = ModelWrapper(build_dir + \"/tfc_w1_a1_ipgen.onnx\")\n",
"child_model = child_model.transform(SetExecMode(\"rtlsim\"))\n",
+ "child_model = child_model.transform(PrepareRTLSim())\n",
"child_model.save(build_dir + \"/tfc_w1_a1_dataflow_child.onnx\")"
]
},
@@ -519,13 +522,6 @@
"else:\n",
" print(\"The results are not the same!\")"
]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
}
],
"metadata": {
diff --git a/notebooks/end2end_example/verification.png b/notebooks/end2end_example/verification.png
index e95a4d98e3b6c5a46ad6d4c2749679831b740044..cb50ba1b67508b45322f6b86bfcbcfb02d3cc9d5 100755
Binary files a/notebooks/end2end_example/verification.png and b/notebooks/end2end_example/verification.png differ
diff --git a/notebooks/internals/1_custom_transformation_pass.ipynb b/notebooks/internals/1_custom_transformation_pass.ipynb
deleted file mode 100644
index f0405c0db3b02cf19476ed6cc8d293a93df00b30..0000000000000000000000000000000000000000
--- a/notebooks/internals/1_custom_transformation_pass.ipynb
+++ /dev/null
@@ -1,320 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# FINN - Transformation passes\n",
- "--------------------------------------\n",
- "<font size=\"3\">In this notebook the idea behind transformation passes in FINN will be explained and with the help of an example the procedure of a transformation will be shown.\n",
- "\n",
- "Following showSrc function is used to print the source code of function calls in the Jupyter notebook:</font>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import inspect\n",
- "\n",
- "def showSrc(what):\n",
- " print(\"\".join(inspect.getsourcelines(what)[0]))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## General Information\n",
- "-----------------------------\n",
- "* <font size=\"3\">changes (transforms) the given graph</font>\n",
- "* <font size=\"3\">input: ModelWrapper</font>\n",
- "* <font size=\"3\">returns the changed model (ModelWrapper) and flag `model_was_changed`</font>\n",
- "\n",
- "<font size=\"3\">Transformation passes have a base class and must inherit from that. Transformations are meant to be applied using .transform function from the ModelWrapper. This function makes a deep copy of the input model by default. The next cell shows .transform of ModelWrapper. </font>\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### .transform() from ModelWrapper"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " def transform(self, transformation, make_deepcopy=True):\n",
- " \"\"\"Applies given Transformation repeatedly until no more changes can be made\n",
- " and returns a transformed ModelWrapper instance.\n",
- " \n",
- " If make_deepcopy is specified, operates on a new (deep)copy of model.\n",
- " \"\"\"\n",
- " transformed_model = self\n",
- " if make_deepcopy:\n",
- " transformed_model = copy.deepcopy(self)\n",
- " model_was_changed = True\n",
- " while model_was_changed:\n",
- " (transformed_model, model_was_changed) = transformation.apply(\n",
- " transformed_model\n",
- " )\n",
- " return transformed_model\n",
- "\n"
- ]
- }
- ],
- "source": [
- "from finn.core.modelwrapper import ModelWrapper\n",
- "showSrc(ModelWrapper.transform)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<font size=\"3\">When the function is called, the model, the name of the transformation and, if required, the flag make_deepcopy are passed. It is also possible not to make a copy of the model. In this case `make_deepcopy` must be set to False. Then the branch `if make_deepcopy:` would not be taken and no copy of the model would be made. \n",
- "\n",
- "The unchanged model is first passed to the variable `transformed_model` to pass this variable on to the transformation later. \n",
- "\n",
- "`model_was_changed` indicates whether the transformation needs to be applied more then once. Because it needs to be applied at least one time `model_was_changed` is first set to True and then depending on the return values of the transformation function the transformation can be applied more then once. \n",
- "\n",
- "**Important**: Due to the structure of this function, `model_was_changed` must be set to False at some point. Otherwise the loop is infinite.\n",
- " \n",
- "\n",
- "Each new transformation must correspond to the scheme of the base class and contain at least the function `apply(model)`, which returns the changed model and a bool value for `model_was_changed`.\n",
- "</font>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### transformation base class "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "class Transformation(ABC):\n",
- " \"\"\"Transformation class all transformations are based on. Contains only \n",
- " abstract method apply() every transformation has to fill.\"\"\"\n",
- " def __init__(self):\n",
- " super().__init__()\n",
- "\n",
- " @abstractmethod\n",
- " def apply(self, model):\n",
- " pass\n",
- "\n"
- ]
- }
- ],
- "source": [
- "from finn.transformation import Transformation\n",
- "\n",
- "showSrc(Transformation)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<font size=\"3\"> Base class is abstract class (`import ABC`) with only one abstract method (`apply()`) which gets the model as input. To show what a transformation should look like, the following example is taken from FINN. </font>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Example - ConvertSubToAdd\n",
- "-----------------------------\n",
- "<font size=\"3\">The transformation replaces all subtraction nodes in a model with addition nodes with appropriate sign. For that an onnx model is loaded which contains one subtraction node. \n",
- " \n",
- "Netron is used to visualize the result before and after. </font>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "import onnx\n",
- "onnx_model = onnx.load('../LFCW1A1.onnx')\n",
- "from finn.core.modelwrapper import ModelWrapper\n",
- "onnx_model = ModelWrapper(onnx_model)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Serving '../LFCW1A1.onnx' at http://0.0.0.0:8081\n"
- ]
- }
- ],
- "source": [
- "import netron\n",
- "netron.start('../LFCW1A1.onnx', port=8081, host=\"0.0.0.0\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<iframe src=\"http://0.0.0.0:8081/\" style=\"position: relative; width: 100%;\" height=\"400\"></iframe>\n"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "%%html\n",
- "<iframe src=\"http://0.0.0.0:8081/\" style=\"position: relative; width: 100%;\" height=\"400\"></iframe>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [],
- "source": [
- "from finn.transformation import Transformation\n",
- "\n",
- "class ConvertSubToAdd(Transformation):\n",
- " def apply(self, model):\n",
- " graph = model.graph\n",
- " for n in graph.node:\n",
- " if n.op_type == \"Sub\":\n",
- " A = model.get_initializer(n.input[1])\n",
- " if A is not None:\n",
- " n.op_type = \"Add\"\n",
- " model.set_initializer(n.input[1], -A)\n",
- " return (model, False)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<font size=\"3\">First the transformation class must be imported. Then a class can be created for the new transformation, which is derived from the base class. In this case the transformation has only the `apply()` function. \n",
- "\n",
- "All nodes are checked by first extracting the graph from the model and then iterating over the node list. With the help of .op_type the operation type of the node can be checked, if the node is a subtraction node the condition `if n.op_type == \"Sub\"` is true. It may be that the subtraction input of the node has no value, this is checked with `model.get_initializer(n.input[1])`. \n",
- " \n",
- " \n",
- "**Important:** FINN always assumes a certain order of inputs, this is especially important if you want to create additional custom operation nodes.\n",
- "\n",
- "When the input is initialized, the operation type of the node is converted to `\"Add\"`, this can simply be done by using the equal sign. Then the sign of the initial value must be changed. For this the ModelWrapper function `.set_initializer` can be used.\n",
- "\n",
- "At the end the changed model is returned and `model_was_changed` is set to False, because the transformation has to be executed only once.</font>\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [],
- "source": [
- "onnx_model_transformed = onnx_model.transform(ConvertSubToAdd())\n",
- "onnx_model_transformed.save('LFCW1A1_changed.onnx')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "Stopping http://0.0.0.0:8081\n",
- "Serving 'LFCW1A1_changed.onnx' at http://0.0.0.0:8081\n"
- ]
- }
- ],
- "source": [
- "netron.start('LFCW1A1_changed.onnx', port=8081, host=\"0.0.0.0\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<iframe src=\"http://0.0.0.0:8081/\" style=\"position: relative; width: 100%;\" height=\"400\"></iframe>\n"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "%%html\n",
- "<iframe src=\"http://0.0.0.0:8081/\" style=\"position: relative; width: 100%;\" height=\"400\"></iframe>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/internals/2_custom_op.ipynb b/notebooks/internals/2_custom_op.ipynb
deleted file mode 100644
index 9aaef9d42ccde42a8f3a0213f1c287a8d72c164a..0000000000000000000000000000000000000000
--- a/notebooks/internals/2_custom_op.ipynb
+++ /dev/null
@@ -1,982 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# FINN - CustomOps\n",
- "-----------------------------------------------------------------\n",
- "<font size=\"3\">This notebook should give a more detailed insight into FINN custom operation nodes. </font>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<font size=\"3\">Following showSrc function is used to print the source code of function calls in the Jupyter notebook: </font>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import inspect\n",
- "\n",
- "def showSrc(what):\n",
- " print(\"\".join(inspect.getsourcelines(what)[0]))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<font size=\"3\">FINN uses many custom operations (`op_type` in ONNX NodeProto) that are not defined in the ONNX operator schema. These custom nodes are marked with `domain=\"finn\"` in the protobuf to identify them as such. These nodes can represent specific operations that we need for low-bit networks, or operations that are specific to a particular hardware backend.\n",
- "\n",
- "A very abstract version of a custom op node representing a streaming fc layer is shown below. </font>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Outline\n",
- "---------------------------\n",
- "* <font size=\"3\">Basic FINN-ONNX node</font>\n",
- "* <font size=\"3\">CustomOp class</font>\n",
- "* <font size=\"3\">HLS FINN-ONNX node</font>\n",
- "* <font size=\"3\">HLSCustomOp class</font>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Basic FINN-ONNX node\n",
- "\n",
- "<font size=\"3\">To create a FINN-ONNX node you can use the helper function of ONNX. Because it is an ONNX NodeProtobuf, but with several additional attributes. The procedure is shown with an example for a multithreshold node. </font>\n",
- "\n",
- "`multithreshold_node = helper.make_node(\n",
- " \"MultiThreshold\",\n",
- " [\"v\", \"thresholds\"],\n",
- " [\"out\"],\n",
- " domain=\"finn\",\n",
- " out_scale=2.0,\n",
- " out_bias=-1.0,\n",
- " out_dtype=\"\",\n",
- ")`\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<font size=\"3\">The `helper.make_node` function gets the op_type as first argument. In this case it is *MultiThreshold*. Then the inputs and outputs are passed. Beside the data input the multithreshold node has an additional input to pass the threshold values. \n",
- "\n",
- "The next attribute (`domain`) is to specify that it is a FINN-ONNX node. It must be set to `\"finn\"`, so that the functions that work with FINN-ONNX nodes can directly recognize that it is a CustomOp. The attributes `out_scale` and `out_bias` are special multithreshold attributes to manipulate the output value. `out_dtype` contains the output data type.\n",
- " \n",
- "**Note**: each FINN-ONNX node has its own special attributes, which must be set correctly to ensure proper processing.</font>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## CustomOp class\n",
- "\n",
- "<font size=\"3\">Custom Ops are represented in FINN as ONNX nodes on the one hand and by a CustomOp class on the other hand. This allows easier access to different attributes and introduces special custom op functions. See below for the standard CustomOp class.</font>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "class CustomOp(ABC):\n",
- " \"\"\"CustomOp class all custom op nodes are based on. Contains different functions \n",
- " every custom node should have. Some as abstract methods, these have to be filled when\n",
- " writing a new custom op node.\"\"\"\n",
- " def __init__(self, onnx_node):\n",
- " super().__init__()\n",
- " self.onnx_node = onnx_node\n",
- "\n",
- " def get_nodeattr(self, name):\n",
- " \"\"\"Get a node attribute by name. Data is stored inside the ONNX node's\n",
- " AttributeProto container. Attribute must be part of get_nodeattr_types.\n",
- " Default value is returned if attribute is not set.\"\"\"\n",
- " try:\n",
- " (dtype, req, def_val) = self.get_nodeattr_types()[name]\n",
- " attr = get_by_name(self.onnx_node.attribute, name)\n",
- " if attr is not None:\n",
- " # dtype indicates which ONNX Attribute member to use\n",
- " # (such as i, f, s...)\n",
- " ret = attr.__getattribute__(dtype)\n",
- " if dtype == \"s\":\n",
- " # decode string attributes\n",
- " ret = ret.decode(\"utf-8\")\n",
- " return ret\n",
- " else:\n",
- " # not set, return default value\n",
- " return def_val\n",
- " except KeyError:\n",
- " raise AttributeError(\"Op has no such attribute: \" + name)\n",
- "\n",
- " def set_nodeattr(self, name, value):\n",
- " \"\"\"Set a node attribute by name. Data is stored inside the ONNX node's\n",
- " AttributeProto container. Attribute must be part of get_nodeattr_types.\"\"\"\n",
- " try:\n",
- " (dtype, req, def_val) = self.get_nodeattr_types()[name]\n",
- " attr = get_by_name(self.onnx_node.attribute, name)\n",
- " if attr is not None:\n",
- " # dtype indicates which ONNX Attribute member to use\n",
- " # (such as i, f, s...)\n",
- " if dtype == \"s\":\n",
- " # encode string attributes\n",
- " value = value.encode(\"utf-8\")\n",
- " attr.__setattr__(dtype, value)\n",
- " else:\n",
- " # not set, create and insert AttributeProto\n",
- " attr_proto = helper.make_attribute(name, value)\n",
- " self.onnx_node.attribute.append(attr_proto)\n",
- " except KeyError:\n",
- " raise AttributeError(\"Op has no such attribute: \" + name)\n",
- "\n",
- " @abstractmethod\n",
- " def get_nodeattr_types(self):\n",
- " \"\"\"Returns a dict of permitted attributes for node, where:\n",
- " returned_dict[attribute_name] = (dtype, require, default_value)\n",
- " - dtype indicates which member of the ONNX AttributeProto\n",
- " will be utilized\n",
- " - require indicates whether this attribute is required\n",
- " - default_val indicates the default value that will be used if the\n",
- " attribute is not set\n",
- " \"\"\"\n",
- " pass\n",
- "\n",
- " @abstractmethod\n",
- " def make_shape_compatible_op(self):\n",
- " \"\"\"Returns a standard ONNX op which is compatible with this CustomOp\n",
- " for performing shape inference.\"\"\"\n",
- " pass\n",
- "\n",
- " @abstractmethod\n",
- " def infer_node_datatype(self, model):\n",
- " \"\"\"Set the DataType annotations corresponding to the outputs of this\n",
- " node.\"\"\"\n",
- " pass\n",
- "\n",
- " @abstractmethod\n",
- " def execute_node(self, context, graph):\n",
- " \"\"\"Execute this CustomOp instance, given the execution context and\n",
- " ONNX graph.\"\"\"\n",
- " pass\n",
- "\n",
- " @abstractmethod\n",
- " def verify_node(self):\n",
- " \"\"\"Verifies that all attributes the node needs are there and\n",
- " that particular attributes are set correctly. Also checks if\n",
- " the number of inputs is equal to the expected number.\"\"\"\n",
- " pass\n",
- "\n"
- ]
- }
- ],
- "source": [
- "from finn.custom_op import CustomOp\n",
- "showSrc(CustomOp)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<font size=\"3\">When instantiating the class, the ONNX node is passed to access all attributes of the node within the class. This is accompanied by the functions `get_nodeattr()`and `set_nodeattr()`, which each instance of this class has. Furthermore 4 abstract methods are implemented, which are described in more detail in the commands of the code and will be exemplarily explained for the multithreshold node in the following. </font>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "class MultiThreshold(CustomOp):\n",
- " \"\"\"Class that corresponds to a multithresholding node.\"\"\"\n",
- " def get_nodeattr_types(self):\n",
- " return {\n",
- " \"out_dtype\": (\"s\", True, \"\"),\n",
- " \"out_scale\": (\"f\", False, 1.0),\n",
- " \"out_bias\": (\"f\", False, 0.0),\n",
- " }\n",
- "\n",
- " def make_shape_compatible_op(self):\n",
- " node = self.onnx_node\n",
- " return helper.make_node(\"Relu\", [node.input[0]], [node.output[0]])\n",
- "\n",
- " def infer_node_datatype(self, model):\n",
- " node = self.onnx_node\n",
- " odt = self.get_nodeattr(\"out_dtype\")\n",
- " model.set_tensor_datatype(node.output[0], DataType[odt])\n",
- "\n",
- " def execute_node(self, context, graph):\n",
- " node = self.onnx_node\n",
- " # save inputs\n",
- " v = context[node.input[0]]\n",
- " thresholds = context[node.input[1]]\n",
- " # retrieve attributes if output scaling is used\n",
- " out_scale = self.get_nodeattr(\"out_scale\")\n",
- " out_bias = self.get_nodeattr(\"out_bias\")\n",
- " # calculate output\n",
- " output = multithreshold(v, thresholds, out_scale, out_bias)\n",
- " # setting context according to output\n",
- " context[node.output[0]] = output\n",
- "\n",
- " def verify_node(self):\n",
- " info_messages = []\n",
- "\n",
- " # verify number of attributes\n",
- " num_of_attr = 3\n",
- " if len(self.onnx_node.attribute) == num_of_attr:\n",
- " info_messages.append(\"The number of attributes is correct\")\n",
- " else:\n",
- " info_messages.append(\n",
- " \"\"\"The number of attributes is incorrect,\n",
- " {} should have {} attributes\"\"\".format(\n",
- " self.onnx_node.op_type, num_of_attr\n",
- " )\n",
- " )\n",
- "\n",
- " # verify that \"domain\" is set to \"finn\"\n",
- " domain_value = self.onnx_node.domain\n",
- " if domain_value == \"finn\":\n",
- " info_messages.append(\"Attribute domain is set correctly\")\n",
- " else:\n",
- " info_messages.append('Attribute domain should be set to \"finn\"')\n",
- "\n",
- " # verify that all necessary attributes exist\n",
- " try:\n",
- " self.get_nodeattr(\"out_scale\")\n",
- " self.get_nodeattr(\"out_bias\")\n",
- " self.get_nodeattr(\"out_dtype\")\n",
- " info_messages.append(\"All necessary attributes exist\")\n",
- " except Exception:\n",
- " info_messages.append(\n",
- " \"\"\"The necessary attributes do not exist.\n",
- " MultiThreshold needs the following attributes:\n",
- " out_scale, out_bias, out_dtype\"\"\"\n",
- " )\n",
- "\n",
- " # verify the number of inputs\n",
- " if len(self.onnx_node.input) == 2:\n",
- " info_messages.append(\"The number of inputs is correct\")\n",
- " else:\n",
- " info_messages.append(\n",
- " \"\"\"MultiThreshold needs 2 inputs\n",
- " (data input and threshold values)\"\"\"\n",
- " )\n",
- "\n",
- " return info_messages\n",
- "\n"
- ]
- }
- ],
- "source": [
- "from finn.custom_op.multithreshold import MultiThreshold\n",
- "showSrc(MultiThreshold)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<font size=\"3\"> `get_nodeattr_types`: returns a dict for the permitted attributes for node. It returns a triple with following values for each of the special multithreshold attributes. </font>\n",
- "* <font size=\"3\">`dtype`: indicates which member of the ONNX AttributeProto will be utilized </font>\n",
- "* <font size=\"3\">`require`: indicates whether this attribute is required </font>\n",
- "* <font size=\"3\">`default_value`: indicates the default value that will be used if the attribute is not set </font>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<font size=\"3\">`make_shape_compatible_op`: To use the flow of FINN, the transformation pass [infer_shapes](https://github.com/Xilinx/finn/blob/master/src/finn/transformation/infer_shapes.py) is applied to the graphs in various places. In order for this transformation to be applied to CustomOps, they must first be converted to standard ONNX nodes with the same shape behavior. This means, nodes where the relationship between input and output shape is the same. \n",
- "\n",
- "This is done at this point. Since the output shape of a multithreshold node is the same as the input shape, it can be replaced by a `\"Relu\"` node from the standard node library of onnx.</font>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<font size=\"3\">`infer_node_datatype`: sets the output tensor data type accordingly to the attribute `out_dtype` </font>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<font size=\"3\">`execute_node`: This function allows the execution of the node, depending on the CustomOp a different functionality has to be implemented. In the case of the multithreshold node the input values and the thresholds are first extracted and after the attributes for the output scaling have been retrieved, the output is calculated with the help of a separate function. For more details regarding this function please take a look in the code [here](https://github.com/Xilinx/finn/blob/master/src/finn/custom_op/multithreshold.py). </font>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<font size=\"3\">FINN has a subset of CustomOps that correspond to the [finn-hls](https://finn-hlslib.readthedocs.io/en/latest/) library. In the next part of the Jupyter notebook these are described in more detail. </font>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## HLS FINN-ONNX node\n",
- "\n",
- "<font size=\"3\">The creation of an HLS FINN-ONNX node looks very similar to the creation of a basic FINN-ONNX node. But three new attributes are introduced that are necessary to enable the processing of HLS FINN-ONNX nodes in FINN.</font>\n",
- "\n",
- "`FCLayer_node = helper.make_node(\n",
- " \"StreamingFCLayer_Batch\",\n",
- " node_inp_list,\n",
- " node_outp_list,\n",
- " domain=\"finn\",\n",
- " backend=\"fpgadataflow\",\n",
- " code_gen_dir=\"\",\n",
- " executable_path=\"\",\n",
- " resType=\"ap_resource_lut()\",\n",
- " MW=mw,\n",
- " MH=mh,\n",
- " SIMD=simd,\n",
- " PE=pe,\n",
- " inputDataType=<FINN DataType>,\n",
- " weightDataType=<FINN DataType>,\n",
- " outputDataType=<FINN DataType>,\n",
- " ActVal=actval,\n",
- " binaryXnorMode=<0/1>,\n",
- " noActivation=<0/1>\n",
- ")`"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<font size=\"3\">`\"StreamingFCLayer_Batch\"` describes the op_type, then the inputs and outputs are declared. This is still like building a default onnx node without additional attributes. But since this is a custom op node of FINN, the attribute `domain=\"finn\"` must be set. The streaming fc layer is a custom op from the [finn-hls](https://finn-hlslib.readthedocs.io/en/latest/) library, this information is set in the node using the `backend` attribute. To execute a custom op from the [finn-hls](https://finn-hlslib.readthedocs.io/en/latest/) library, the corresponding c++ code must be created and an executable must be produced. Where the generated code is stored is specified in the `code_gen_dir` attribute and `executable_path` specifies the path to the produced executable. In addition to the data types of the input and output tensors, the node also contains various other attributes resulting from the parameters of the corresponding [finn-hls](https://finn-hlslib.readthedocs.io/en/latest/) library function. More detailed information can be found in the documentation of [finn-hlslib](https://finn-hlslib.readthedocs.io/en/latest/).</font>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## HLSCustomOp class\n",
- "\n",
- "<font size=\"3\">If it is a node from the [finn-hls](https://finn-hlslib.readthedocs.io/en/latest/) library another class is used which is derived from the CustomOp class:</font>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "class HLSCustomOp(CustomOp):\n",
- " \"\"\"HLSCustomOp class all custom ops that correspond to a finn-hlslib \n",
- " function are based on. Contains different functions every fpgadataflow \n",
- " custom node should have. Some as abstract methods, these have to be filled\n",
- " when writing a new fpgadataflow custom op node.\"\"\"\n",
- " def __init__(self, onnx_node):\n",
- " super().__init__(onnx_node)\n",
- "\n",
- " self.code_gen_dict = {}\n",
- "\n",
- " # getting templates from templates.py\n",
- "\n",
- " # template for single node execution\n",
- " self.docompute_template = templates.docompute_template\n",
- "\n",
- " # templates for single node ip generation\n",
- " # cpp file\n",
- " self.ipgen_template = templates.ipgen_template\n",
- " # tcl script\n",
- " self.ipgentcl_template = templates.ipgentcl_template\n",
- "\n",
- " def get_nodeattr_types(self):\n",
- " return {\n",
- " \"backend\": (\"s\", True, \"fpgadataflow\"),\n",
- " \"code_gen_dir_npysim\": (\"s\", False, \"\"),\n",
- " \"code_gen_dir_ipgen\": (\"s\", False, \"\"),\n",
- " \"executable_path\": (\"s\", False, \"\"),\n",
- " \"ipgen_path\": (\"s\", False, \"\"),\n",
- " \"exec_mode\": (\"s\", False, \"\"),\n",
- " \"sim_cycles\": (\"i\", False, 0),\n",
- " \"rtlsim_trace\": (\"s\", False, \"\"),\n",
- " }\n",
- "\n",
- " def node_res_estimation(self):\n",
- " \"\"\"Returns summarized resource estimation of BRAMs and LUTs \n",
- " of the node.\"\"\"\n",
- " resources = []\n",
- " resources.append(\"BRAMs: \" + str(self.bram_estimation()))\n",
- " resources.append(\"LUTs: \" + str(self.lut_estimation()))\n",
- " return resources\n",
- "\n",
- " def bram_estimation(self):\n",
- " \"\"\"Function for BRAM resource estimation, is member function of \n",
- " HLSCustomOp class but has to be filled by every node\"\"\"\n",
- " return 0\n",
- "\n",
- " def lut_estimation(self):\n",
- " \"\"\"Function for LUT resource estimation, is member function of\n",
- " HLSCustomOp class but has to be filled by every node\"\"\"\n",
- " return 0\n",
- "\n",
- " def code_generation_ipgen(self, model, fpgapart, clk):\n",
- " \"\"\"Generates c++ code and tcl script for ip generation.\"\"\"\n",
- " node = self.onnx_node\n",
- "\n",
- " # generate top cpp file for ip generation\n",
- " path = self.get_nodeattr(\"code_gen_dir_ipgen\")\n",
- " self.generate_params(model, path)\n",
- " self.global_includes()\n",
- " self.defines(\"ipgen\")\n",
- " self.blackboxfunction()\n",
- " self.pragmas()\n",
- " self.docompute()\n",
- "\n",
- " template = self.ipgen_template\n",
- "\n",
- " for key in self.code_gen_dict:\n",
- " # transform list into long string separated by '\\n'\n",
- " code_gen_line = \"\\n\".join(self.code_gen_dict[key])\n",
- " template = template.replace(key, code_gen_line)\n",
- " code_gen_dir = self.get_nodeattr(\"code_gen_dir_ipgen\")\n",
- " f = open(os.path.join(code_gen_dir, \"top_{}.cpp\".format(node.name)), \"w\")\n",
- " f.write(template)\n",
- " f.close()\n",
- " self.code_gen_dict.clear()\n",
- "\n",
- " # generate tcl script for ip generation\n",
- " self.code_gen_dict[\"$PROJECTNAME$\"] = [\"project_{}\".format(node.name)]\n",
- " self.code_gen_dict[\"$HWSRCDIR$\"] = [code_gen_dir]\n",
- " self.code_gen_dict[\"$FPGAPART$\"] = [fpgapart]\n",
- " self.code_gen_dict[\"$FINNHLSLIBDIR$\"] = [\"/workspace/finn-hlslib\"]\n",
- " self.code_gen_dict[\"$TOPFXN$\"] = [node.name]\n",
- " self.code_gen_dict[\"$CLKPERIOD$\"] = [str(clk)]\n",
- "\n",
- " template = self.ipgentcl_template\n",
- "\n",
- " for key in self.code_gen_dict:\n",
- " # transform list into long string separated by '\\n'\n",
- " code_gen_line = \"\\n\".join(self.code_gen_dict[key])\n",
- " template = template.replace(key, code_gen_line)\n",
- " code_gen_dir = self.get_nodeattr(\"code_gen_dir_ipgen\")\n",
- " f = open(os.path.join(code_gen_dir, \"hls_syn_{}.tcl\".format(node.name)), \"w\")\n",
- " f.write(template)\n",
- " f.close()\n",
- " self.code_gen_dict.clear()\n",
- "\n",
- " def ipgen_singlenode_code(self):\n",
- " \"\"\"Builds the bash script for ip generation using the IPGenBuilder from \n",
- " finn.util.fpgadataflow.\"\"\"\n",
- " node = self.onnx_node\n",
- " code_gen_dir = self.get_nodeattr(\"code_gen_dir_ipgen\")\n",
- " builder = IPGenBuilder()\n",
- " builder.append_tcl(code_gen_dir + \"/hls_syn_{}.tcl\".format(node.name))\n",
- " builder.set_ipgen_path(code_gen_dir + \"/project_{}\".format(node.name))\n",
- " builder.build(code_gen_dir)\n",
- " self.set_nodeattr(\"ipgen_path\", builder.ipgen_path)\n",
- "\n",
- " def code_generation_npysim(self, model):\n",
- " \"\"\"Generates c++ code for simulation (npysim).\"\"\"\n",
- " node = self.onnx_node\n",
- " path = self.get_nodeattr(\"code_gen_dir_npysim\")\n",
- " self.generate_params(model, path)\n",
- " self.global_includes()\n",
- " self.defines(\"npysim\")\n",
- " self.read_npy_data()\n",
- " self.strm_decl()\n",
- " self.docompute()\n",
- " self.dataoutstrm()\n",
- " self.save_as_npy()\n",
- "\n",
- " template = self.docompute_template\n",
- "\n",
- " for key in self.code_gen_dict:\n",
- " # transform list into long string separated by '\\n'\n",
- " code_gen_line = \"\\n\".join(self.code_gen_dict[key])\n",
- " template = template.replace(key, code_gen_line)\n",
- " code_gen_dir = self.get_nodeattr(\"code_gen_dir_npysim\")\n",
- " f = open(os.path.join(code_gen_dir, \"execute_{}.cpp\".format(node.op_type)), \"w\")\n",
- " f.write(template)\n",
- " f.close()\n",
- " self.code_gen_dict.clear()\n",
- "\n",
- " def compile_singlenode_code(self):\n",
- " \"\"\"Builds the bash script for compilation using the CppBuilder from\n",
- " finn.util.basic and executes the script to produce the executable.\"\"\"\n",
- " code_gen_dir = self.get_nodeattr(\"code_gen_dir_npysim\")\n",
- " builder = CppBuilder()\n",
- " # to enable additional debug features please uncommand the next line\n",
- " # builder.append_includes(\"-DDEBUG\")\n",
- " builder.append_includes(\"-I/workspace/finn/src/finn/data/cpp\")\n",
- " builder.append_includes(\"-I/workspace/cnpy/\")\n",
- " builder.append_includes(\"-I/workspace/finn-hlslib\")\n",
- " builder.append_includes(\"-I{}/include\".format(os.environ[\"VIVADO_PATH\"]))\n",
- " builder.append_includes(\"--std=c++11\")\n",
- " builder.append_sources(code_gen_dir + \"/*.cpp\")\n",
- " builder.append_sources(\"/workspace/cnpy/cnpy.cpp\")\n",
- " builder.append_includes(\"-lz\")\n",
- " builder.set_executable_path(code_gen_dir + \"/node_model\")\n",
- " builder.build(code_gen_dir)\n",
- " self.set_nodeattr(\"executable_path\", builder.executable_path)\n",
- "\n",
- " def dynamic_input_to_npy(self, context, count):\n",
- " \"\"\"Saves input (given context) into .npy files. \n",
- " \n",
- " Count indicates the number of inputs that have to be saved.\"\"\"\n",
- " node = self.onnx_node\n",
- " code_gen_dir = self.get_nodeattr(\"code_gen_dir_npysim\")\n",
- " if code_gen_dir == \"\":\n",
- " raise Exception(\n",
- " \"\"\"\n",
- "Found no codegen dir for this node, did you run the codegen_npysim transformation?\n",
- " \"\"\"\n",
- " )\n",
- " # create a npy file for each input of the node (in_ind is input index)\n",
- " # assuming dynamic inputs start from 0\n",
- " for in_ind in range(count):\n",
- " current_input_name = node.input[in_ind]\n",
- " np.save(\n",
- " os.path.join(code_gen_dir, \"input_{}.npy\".format(in_ind)),\n",
- " context[current_input_name],\n",
- " )\n",
- "\n",
- " def npy_to_dynamic_output(self, context):\n",
- " \"\"\"Reads the output from a .npy file and saves it at the right place in \n",
- " the context dictionary.\"\"\"\n",
- " # TODO support multi-output nodes as needed\n",
- " node = self.onnx_node\n",
- " code_gen_dir = self.get_nodeattr(\"code_gen_dir_npysim\")\n",
- " output = np.load(\"{}/output.npy\".format(code_gen_dir))\n",
- " context[node.output[0]] = output\n",
- "\n",
- " def exec_precompiled_singlenode_model(self):\n",
- " \"\"\"Executes precompiled executable.\"\"\"\n",
- " executable_path = self.get_nodeattr(\"executable_path\")\n",
- " if executable_path == \"\":\n",
- " raise Exception(\n",
- " \"\"\"\n",
- "Found no executable for this node, did you run the codegen and\n",
- "compilation transformations?\n",
- " \"\"\"\n",
- " )\n",
- " process_execute = subprocess.Popen(executable_path, stdout=subprocess.PIPE)\n",
- " process_execute.communicate()\n",
- "\n",
- " def reset_rtlsim(self, sim):\n",
- " \"\"\"Sets reset input in pyverilator to zero, toggles the clock and set it\n",
- " back to one\"\"\"\n",
- " sim.io.ap_rst_n = 0\n",
- " sim.io.ap_clk = 1\n",
- " sim.io.ap_clk = 0\n",
- " sim.io.ap_rst_n = 1\n",
- "\n",
- " def toggle_clk(self, sim):\n",
- " \"\"\"Toggles the clock input in pyverilator once.\"\"\"\n",
- " sim.io.ap_clk = 1\n",
- " sim.io.ap_clk = 0\n",
- "\n",
- " def rtlsim(self, sim, inp):\n",
- " \"\"\"Runs the pyverilator simulation by passing the input values to the simulation,\n",
- " toggle the clock and observing the execution time. Function contains also an \n",
- " observation loop that can abort the simulation if no output value is produced \n",
- " after 100 cycles.\"\"\"\n",
- " \n",
- " trace_file = self.get_nodeattr(\"rtlsim_trace\")\n",
- " if trace_file != \"\":\n",
- " if trace_file == \"default\":\n",
- " trace_file = self.onnx_node.name + \".vcd\"\n",
- " sim.start_vcd_trace(trace_file)\n",
- " inputs = inp\n",
- " outputs = []\n",
- " sim.io.out_V_V_TREADY = 1\n",
- "\n",
- " # observe if output is completely calculated\n",
- " # observation_count will contain the number of cycles the calculation ran\n",
- " num_out_values = self.get_number_output_values()\n",
- " output_observed = False\n",
- " observation_count = 0\n",
- "\n",
- " # avoid infinite looping of simulation by aborting when there is no change in\n",
- " # output values after 100 cycles\n",
- " no_change_count = 0\n",
- " old_outputs = outputs\n",
- " liveness_threshold = pyverilate_get_liveness_threshold_cycles()\n",
- "\n",
- " while not (output_observed):\n",
- " sim.io.in0_V_V_TVALID = 1 if len(inputs) > 0 else 0\n",
- " sim.io.in0_V_V_TDATA = inputs[0] if len(inputs) > 0 else 0\n",
- " if sim.io.in0_V_V_TREADY == 1 and sim.io.in0_V_V_TVALID == 1:\n",
- " inputs = inputs[1:]\n",
- " if sim.io.out_V_V_TVALID == 1 and sim.io.out_V_V_TREADY == 1:\n",
- " outputs = outputs + [sim.io.out_V_V_TDATA]\n",
- " sim.io.ap_clk = 1\n",
- " sim.io.ap_clk = 0\n",
- "\n",
- " observation_count = observation_count + 1\n",
- " no_change_count = no_change_count + 1\n",
- "\n",
- " if len(outputs) == num_out_values:\n",
- " self.set_nodeattr(\"sim_cycles\", observation_count)\n",
- " output_observed = True\n",
- "\n",
- " if no_change_count == liveness_threshold:\n",
- " if old_outputs == outputs:\n",
- " if trace_file != \"\":\n",
- " sim.flush_vcd_trace()\n",
- " sim.stop_vcd_trace()\n",
- " raise Exception(\n",
- " \"Error in simulation! Takes too long to produce output. \"\n",
- " \"Consider setting the LIVENESS_THRESHOLD env.var. to a \"\n",
- " \"larger value.\"\n",
- " )\n",
- " else:\n",
- " no_change_count = 0\n",
- " old_outputs = outputs\n",
- " if trace_file != \"\":\n",
- " sim.flush_vcd_trace()\n",
- " sim.stop_vcd_trace()\n",
- " return outputs\n",
- "\n",
- " def execute_node(self, context, graph):\n",
- " \"\"\"Executes single node using npysim or rtlsim.\"\"\"\n",
- " mode = self.get_nodeattr(\"exec_mode\")\n",
- " if mode == \"npysim\":\n",
- " # save input(s)\n",
- " self.dynamic_input_to_npy(context, 1)\n",
- " # execute the precompiled model\n",
- " self.exec_precompiled_singlenode_model()\n",
- " # load output npy file\n",
- " self.npy_to_dynamic_output(context)\n",
- " elif mode == \"rtlsim\":\n",
- " pass\n",
- "\n",
- " else:\n",
- " raise Exception(\n",
- " \"\"\"Invalid value for attribute exec_mode! Is currently set to: {}\n",
- " has to be set to one of the following value (\"npysim\", \"rtlsim\")\"\"\".format(\n",
- " mode\n",
- " )\n",
- " )\n",
- "\n",
- " def generate_params(self, model, path):\n",
- " \"\"\"Function to generate parameters (i.e. weights and thresholds), \n",
- " is member function of HLSCustomOp class but has to be filled \n",
- " by every node.\"\"\"\n",
- " pass\n",
- "\n",
- " @abstractmethod\n",
- " def get_number_output_values(self):\n",
- " \"\"\"Function to get the number of expected output values, \n",
- " is member function of HLSCustomOp class but has to be filled \n",
- " by every node.\"\"\"\n",
- " pass\n",
- "\n",
- " @abstractmethod\n",
- " def global_includes(self):\n",
- " \"\"\"Function to set the global includes for c++ code that has to be generated\n",
- " for npysim or rtlsim, is member function of HLSCustomOp class but has to \n",
- " be filled by every node.\"\"\"\n",
- " pass\n",
- "\n",
- " @abstractmethod\n",
- " def defines(self, var):\n",
- " \"\"\"Function to set the define commands for c++ code that has to be generated\n",
- " for npysim or rtlsim, is member function of HLSCustomOp class but has to \n",
- " be filled by every node.\n",
- " \n",
- " var: makes it possible to reuse the function for different c++ code generation.\n",
- " I.e. if set to \"ipgen\" in StreamingFCLayer_Batch additional PRAGMA defines are\n",
- " added.\"\"\"\n",
- " pass\n",
- "\n",
- " @abstractmethod\n",
- " def read_npy_data(self):\n",
- " \"\"\"Function to generate the commands for reading data from .npy file in c++, \n",
- " is member function of HLSCustomOp class but has to be filled by every node.\"\"\"\n",
- " pass\n",
- "\n",
- " @abstractmethod\n",
- " def strm_decl(self):\n",
- " \"\"\"Function to generate the commands for the stream declaration in c++,\n",
- " is member function of HLSCustomOp class but has to be filled\n",
- " by every node.\"\"\"\n",
- " pass\n",
- "\n",
- " @abstractmethod\n",
- " def docompute(self):\n",
- " \"\"\"Function to generate the commands for the computational part of the \n",
- " c++ code, is member function of HLSCustomOp class but has to be filled\n",
- " by every node.\"\"\"\n",
- " pass\n",
- "\n",
- " @abstractmethod\n",
- " def dataoutstrm(self):\n",
- " \"\"\"Function to generate the commands for reading out data from c++ and convert \n",
- " into npy format, is member function of HLSCustomOp class but has to be filled \n",
- " by every node.\"\"\"\n",
- " pass\n",
- "\n",
- " @abstractmethod\n",
- " def save_as_npy(self):\n",
- " \"\"\"Function to generate the commands for saving data in .npy file in c++,\n",
- " is member function of HLSCustomOp class but has to be filled by every node.\"\"\"\n",
- " pass\n",
- "\n",
- " @abstractmethod\n",
- " def blackboxfunction(self):\n",
- " \"\"\"Function to generate a blackbock function in c++ from which an IP block \n",
- " will be generated, is member function of HLSCustomOp class but has to be filled \n",
- " by every node.\"\"\"\n",
- " pass\n",
- "\n",
- " @abstractmethod\n",
- " def pragmas(self):\n",
- " \"\"\"Function to generate the pragma commands in c++, is member function of \n",
- " HLSCustomOp class but has to be filled by every node.\"\"\"\n",
- " pass\n",
- "\n",
- " def get_folded_input_shape(self):\n",
- " \"\"\"Returns folded input shape (according to synapse folding), if implemented.\"\"\"\n",
- " raise Exception(\"get_folded_input_shape not implemented for this op\")\n",
- "\n",
- " def get_folded_output_shape(self):\n",
- " \"\"\"Returns folded output shape (according to neuron folding), if implemented.\"\"\"\n",
- " raise Exception(\"get_folded_output_shape not implemented for this op\")\n",
- "\n",
- " def get_instream_width(self):\n",
- " \"\"\"Returns input stream width, if implemented.\"\"\"\n",
- " raise Exception(\"get_instream_width not implemented for this op\")\n",
- "\n",
- " def get_outstream_width(self):\n",
- " \"\"\"Returns output stream width, if implemented.\"\"\"\n",
- " raise Exception(\"get_outstream_width not implemented for this op\")\n",
- "\n"
- ]
- }
- ],
- "source": [
- "from finn.custom_op.fpgadataflow import HLSCustomOp\n",
- "showSrc(HLSCustomOp)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<font size=\"3\">When creating an instance of this class, a template is introduced, which forms the layout for the c++ code to execute the node. It has some general constructs, like the inclusion of bnn-library.h, which contains the references to the finn-hls library, and of cnpy.h and npy2apintstream.hpp, which support the transfer of python numpy arrays in c++. The idea of this template is to replace the variables marked with `$ $` with c++ calls during code generation. Then the template can be written into a .cpp file and be compiled.\n",
- "\n",
- "**`get_nodeattr_types()`**: each instance of the HLSCustomOp class must have the attributes `code_gen_dir` and `executable_path`, since to execute these nodes c++ code must be generated and correspondingly the executables.\n",
- "\n",
- "</font>\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<font size=\"3\">**`code_generation(model)`**: all functions required for code generation are called and the `$ $` variables in the template are replaced accordingly and written into a .cpp file. Almost all of these subfunctions are implemented as abstract methods in the class, so they are completely customized for each custom op node. A special function is `generate_params()`. This is not implemented as an abstract method, but as a normal function, but contains by default only `pass`. This is because some custom op nodes do not have parameters that need to be generated and in this way the function is skipped. For example for a streaming fc layer node a parameter generation is necessary. How such a parameter generation can look like is described in more detail in the course of this notebook.\n",
- "</font>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<font size=\"3\">**`compile_singlenode_code()`**: To compile the generated code, the compile command must be built. This is done in this function. It creates an instance of the `CppBuilder()` class and assembles the various components for the function. The `.build` function creates the executable and then sets the corresponding attribute. The class `CppBuilder` is a transformation and a more detailed description can be found in Jupyter notebook [FINN-CodeGenerationAndCompilation](FINN-CodeGenerationAndCompilation.ipynb).\n",
- "</font>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<font size=\"3\">**`dynamic_input_to_npy(context, count)`**: creates a .npy file for all inputs of the node. These files will be stored in the directory specified by code_gen_dir. The argument `count` must be used to specify the number of inputs. `context` contains the values for the inputs.</font>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<font size=\"3\">**`npy_to_dynamic_output(context)`**: reads the output values and sets `context` dictionary accordingly. When executing the c++ executable of the node, the output values are written to a .npy file. </font>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<font size=\"3\">**`exec_precompiled_singlenode_model()`**: executes precompiled executable which is specified in `executable_path`</font>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<font size=\"3\">**`execute_node(context,graph)`**: calls first `dynamic_input_to_npy()`, then executes the executable using `exec_precompiled_singlenode_model()` and at the end reads the output .npy file with `npy_to_dynamic_output`</font>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Generate Parameter\n",
- "<font size=\"3\">Parameters have to be generated for specific types of HLSCustomOps. For example if the node is a streaming fc layer, there are weights and activation values, which are written to separate .h files and added to the template using `#include`. For streaming fc layer the parameter generation looks like this:\n",
- "</font>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " def generate_params(self, model, path):\n",
- " \"\"\"Saves weights into params.h and if existing thresholds into thresh.h.\"\"\"\n",
- " code_gen_dir = path\n",
- " # weights\n",
- " weights = model.get_initializer(self.onnx_node.input[1])\n",
- " # convert weights into hlslib-compatible format\n",
- " weight_tensor = self.get_hls_compatible_weight_tensor(weights)\n",
- " export_wdt = self.get_weight_datatype()\n",
- " # we have converted bipolar weights to binary for export,\n",
- " # so use it as such for weight generation\n",
- " if self.get_weight_datatype() == DataType.BIPOLAR:\n",
- " export_wdt = DataType.BINARY\n",
- " weight_hls_code = numpy_to_hls_code(\n",
- " weight_tensor, export_wdt, \"weights\", True, True\n",
- " )\n",
- " # write weights into params.h\n",
- " # code_gen_dir = self.get_nodeattr(\"code_gen_dir_npysim\")\n",
- " f_weights = open(\"{}/params.h\".format(code_gen_dir), \"w\")\n",
- "\n",
- " if export_wdt.bitwidth() != 1:\n",
- " f_weights.write(\n",
- " \"static FixedPointWeights<{},{},{},{}> weights = \".format(\n",
- " self.get_nodeattr(\"SIMD\"),\n",
- " export_wdt.get_hls_datatype_str(),\n",
- " self.get_nodeattr(\"PE\"),\n",
- " self.calc_wmem(),\n",
- " )\n",
- " )\n",
- " else:\n",
- " f_weights.write(\n",
- " \"static BinaryWeights<{},{},{}> weights = \".format(\n",
- " self.get_nodeattr(\"SIMD\"), self.get_nodeattr(\"PE\"), self.calc_wmem()\n",
- " )\n",
- " )\n",
- " f_weights.write(weight_hls_code)\n",
- " f_weights.close()\n",
- "\n",
- " # thresholds\n",
- " if len(self.onnx_node.input) > 2:\n",
- " thresholds = model.get_initializer(self.onnx_node.input[2])\n",
- " if thresholds is not None:\n",
- " threshold_tensor = self.get_hls_compatible_threshold_tensor(thresholds)\n",
- " tdt = DataType.INT32\n",
- " # use UINT32 threshold export for bipolar times bipolar\n",
- " inp_is_bipolar = self.get_input_datatype() == DataType.BIPOLAR\n",
- " wt_is_bipolar = self.get_weight_datatype() == DataType.BIPOLAR\n",
- " # reinterpret inp/wt as bipolar if bin_xnor_mode is iset\n",
- " inp_is_binary = self.get_input_datatype() == DataType.BINARY\n",
- " wt_is_binary = self.get_weight_datatype() == DataType.BINARY\n",
- " bin_xnor_mode = self.get_nodeattr(\"binaryXnorMode\") == 1\n",
- " inp_is_bipolar = inp_is_bipolar or (inp_is_binary and bin_xnor_mode)\n",
- " wt_is_bipolar = wt_is_bipolar or (wt_is_binary and bin_xnor_mode)\n",
- " if inp_is_bipolar and wt_is_bipolar:\n",
- " tdt = DataType.UINT32\n",
- " thresholds_hls_code = numpy_to_hls_code(\n",
- " threshold_tensor, tdt, \"thresholds\", False, True\n",
- " )\n",
- " # write thresholds into thresh.h\n",
- " # code_gen_dir = self.get_nodeattr(\"code_gen_dir_npysim\")\n",
- " f_thresh = open(\"{}/thresh.h\".format(code_gen_dir), \"w\")\n",
- " tdt_hls = tdt.get_hls_datatype_str()\n",
- " # use binary to export bipolar activations\n",
- " export_odt = self.get_output_datatype()\n",
- " if self.get_output_datatype() == DataType.BIPOLAR:\n",
- " export_odt = DataType.BINARY\n",
- " odt_hls = export_odt.get_hls_datatype_str()\n",
- " f_thresh.write(\n",
- " \"static ThresholdsActivation<{},{},{},{},{},{},{}> threshs \\\n",
- " = \".format(\n",
- " self.calc_tmem(),\n",
- " self.get_nodeattr(\"PE\"),\n",
- " threshold_tensor.shape[-1],\n",
- " tdt_hls,\n",
- " odt_hls,\n",
- " self.get_nodeattr(\"ActVal\"),\n",
- " \"std::less_equal<%s>\" % tdt_hls,\n",
- " )\n",
- " )\n",
- " f_thresh.write(thresholds_hls_code)\n",
- " f_thresh.close()\n",
- "\n"
- ]
- }
- ],
- "source": [
- "from finn.custom_op.fpgadataflow.streamingfclayer_batch import StreamingFCLayer_Batch\n",
- "showSrc(StreamingFCLayer_Batch.generate_params)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<font size=\"3\">First, the values for the weights are extracted with `get_initializer()` using the ModelWrapper. At this point it is assumed that the second input of the streamingfclayer specifies the weights. After a few manipulations the weights are written in `params.h`. If there are threshold values, they will be prepared and written to `thresh.h`. </font>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/internals/3_verify_hls_custom_op.ipynb b/notebooks/internals/3_verify_hls_custom_op.ipynb
deleted file mode 100644
index 5c3b43cd03d45be03a6c853a19169fbcc5c5acbf..0000000000000000000000000000000000000000
--- a/notebooks/internals/3_verify_hls_custom_op.ipynb
+++ /dev/null
@@ -1,569 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# FINN - Verification of an HLSCustomOp node\n",
- "-----------------------------------------------------------------\n",
- "This notebook is about the verification flow and options for FINN custom operation nodes. "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Outline\n",
- "-------------\n",
- "* Example model (sliding window function)\n",
- "* c++ high level simulation\n",
- "* Vivado IP synthesis and pyverilator execution flow"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Example model\n",
- "To show the possibilities of how to verify a FINN HLSCustomOp node, an example model with the [sliding window function](https://finn-hlslib.readthedocs.io/en/latest/library/swg.html) of the finn-hlslib is used. For that a corresponding ONNX node is created. The ONNX node contains all the template parameters of the corresponding finn-hlslib function as attributes. The function is shown below."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In the next step the individual parameters are defined. At first the class 'DataType' is imported from FINN to be able to use data types like bipolar. With the member function `bitwidth()` the parameter `Input_precision` can be derived directly from this data type. The other parameters are set to reasonable values. The output dimension can be calculated using the input dimension, the kernel size and the value for stride."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "from finn.core.datatype import DataType\n",
- "idt = DataType.BIPOLAR # input data type\n",
- "ip = idt.bitwidth() # input precision\n",
- "k = 2 # kernel size\n",
- "ifm_dim = 4 # input dimension\n",
- "ifm_ch = 1 # input channels\n",
- "stride = 2 # stride\n",
- "simd = ifm_ch # simd\n",
- "\n",
- "# output dimension\n",
- "ofm_dim = int(((ifm_dim - k) / stride) + 1)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "An additional variable is defined to be able to infer the shape of the output tensor. Furthermore the output data type is set to the same value as the input data type."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "out_pix = ofm_dim * ofm_dim\n",
- "odt = idt"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "To create an ONNX node, first TensorProto and helper are imported from ONNX. These can be used to create tensors, nodes, graphs and models in ONNX. After importing, the input and output tensors can be created."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "from onnx import TensorProto, helper\n",
- "\n",
- "inp = helper.make_tensor_value_info(\n",
- " \"inp\", TensorProto.FLOAT, [1, ifm_ch, ifm_dim, ifm_dim]\n",
- ")\n",
- "outp = helper.make_tensor_value_info(\n",
- " \"outp\", TensorProto.FLOAT, [1, out_pix, k * k * ifm_ch]\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now the node can be built. This node is directly integrated into a graph environment and from this the ONNX model is created. For more information about the creation and manipulation of an ONNX model, please refer to jupyter notebook [FINN-HowToWorkWithONNX](FINN-HowToWorkWithONNX.ipynb)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "SlidingWindow_node = helper.make_node(\n",
- " \"ConvolutionInputGenerator\",\n",
- " [\"inp\"],\n",
- " [\"outp\"],\n",
- " domain=\"finn\",\n",
- " backend=\"fpgadataflow\",\n",
- " ConvKernelDim=k,\n",
- " IFMChannels=ifm_ch,\n",
- " Input_precision=ip,\n",
- " IFMDim=ifm_dim,\n",
- " OFMDim=ofm_dim,\n",
- " SIMD=simd,\n",
- " Stride=stride,\n",
- " inputDataType=idt.name,\n",
- " outputDataType=odt.name,\n",
- " )\n",
- "graph = helper.make_graph(\n",
- " nodes=[SlidingWindow_node],\n",
- " name=\"slidingwindow_graph\",\n",
- " inputs=[inp],\n",
- " outputs=[outp],\n",
- " )\n",
- "\n",
- "model = helper.make_model(graph, producer_name=\"slidingwindow-model\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "FINN provides a thin wrapper around the ONNX model with a lot of helper functions that can be used by importing the class `ModelWrapper`. More information about `ModelWrapper` can be found in Jupyter notebook [FINN-ModelWrapper](FINN-ModelWrapper.ipynb). Here it is used to assign FINN data types to the tensors."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [],
- "source": [
- "from finn.core.modelwrapper import ModelWrapper\n",
- "\n",
- "model = ModelWrapper(model)\n",
- "\n",
- "model.set_tensor_datatype(\"inp\", idt)\n",
- "model.set_tensor_datatype(\"outp\", odt)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "What the model looks like can be visualized with netron. Netron is a visualizer for neural network, deep learning and machine learning models. For this the model is first saved."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [],
- "source": [
- "model.save(\"original_model.onnx\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Serving 'original_model.onnx' at http://0.0.0.0:8081\n"
- ]
- }
- ],
- "source": [
- "import netron\n",
- "netron.start('original_model.onnx', port=8081, host=\"0.0.0.0\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<iframe src=\"http://0.0.0.0:8081/\" style=\"position: relative; width: 100%;\" height=\"400\"></iframe>\n"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "%%html\n",
- "<iframe src=\"http://0.0.0.0:8081/\" style=\"position: relative; width: 100%;\" height=\"400\"></iframe>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now that we have the model, we can use various features of FINN to manipulate it. The basic principle of FINN is that there are transformation and analysis passes that can be applied to a model. A transformation pass changes a given model and returns the changed model. An analysis pass traverses the graph structure and produces information about certain properties. It returns a dictionary of named properties.\n",
- "\n",
- "The following section describes the transformation passes that can be used to verify an HLSCustomOp node. Firstly the verification with a c++ high level simulation is shown and afterwards with a Vivado IP synthesis and pyverilator execution flow."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### c++ high level simulation\n",
- "\n",
- "First, an additional attribute must be set to specify which of the two verification types should be used when executing the node. This is done with the transformation pass `SetExecMode`, to which the desired mode is passed. After that the transformation pass `CodeGen_npysim` can be applied. With this transformation c++ code is generated and stored in a temporary directory. In addition, a further attribute is set, which contains the path to this directory."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [],
- "source": [
- "from finn.transformation.fpgadataflow.set_exec_mode import SetExecMode\n",
- "from finn.transformation.fpgadataflow.codegen_npysim import CodeGen_npysim\n",
- "\n",
- "model = model.transform(SetExecMode(\"npysim\"))\n",
- "model = model.transform(CodeGen_npysim())"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "If you now save the model again and display it, these changes can be seen."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "Stopping http://0.0.0.0:8081\n",
- "Serving 'modified_model.onnx' at http://0.0.0.0:8081\n"
- ]
- }
- ],
- "source": [
- "model.save(\"modified_model.onnx\")\n",
- "netron.start('modified_model.onnx', port=8081, host=\"0.0.0.0\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<iframe src=\"http://0.0.0.0:8081/\" style=\"position: relative; width: 100%;\" height=\"400\"></iframe>\n"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "%%html\n",
- "<iframe src=\"http://0.0.0.0:8081/\" style=\"position: relative; width: 100%;\" height=\"400\"></iframe>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The next step is to create the executable from the .cpp file using the `Compile` transformation. The path to the executable is also stored in a new attribute."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [],
- "source": [
- "from finn.transformation.fpgadataflow.compile import Compile\n",
- "model = model.transform(Compile())"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "All required files are now available and we can execute the node. This is done with the `execute_onnx` function, which gets the model and an input dictionary. That means we have to create an input tensor first. For this we use a numpy array."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[[[[-1. -1. 1. 1.]\n",
- " [-1. -1. -1. -1.]\n",
- " [ 1. -1. 1. -1.]\n",
- " [ 1. 1. 1. -1.]]]]\n"
- ]
- }
- ],
- "source": [
- "import numpy as np\n",
- "x = np.asarray([-1, -1, 1, 1, -1, -1, -1, -1, 1, -1, 1, -1, 1, 1, 1, -1], dtype=np.float32).reshape(1, ifm_ch, ifm_dim, ifm_dim)\n",
- "print(x)\n",
- "input_dict = {\"inp\": (x + 1) /2}"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "To be able to use `execute_onnx()` `onnx_exec` must be imported. Inside `execute_onnx()` the attribute `exec_mode` is read and if \"npysim\" is selected, the input array is saved in a .npy file and the previously created executable is executed. The output is saved in another .npy file and is read by `execute_onnx()` and saved as output."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[[[-1. -1. -1. -1.]\n",
- " [ 1. 1. -1. -1.]\n",
- " [ 1. -1. 1. 1.]\n",
- " [ 1. -1. 1. -1.]]]\n"
- ]
- }
- ],
- "source": [
- "import finn.core.onnx_exec as oxe\n",
- "y_npysim = oxe.execute_onnx(model, input_dict)[\"outp\"]\n",
- "print(y_npysim)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "A different transformation flow can be used for verification. This will be discussed in the next section."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Vivado IP synthesis and pyverilator execution flow\n",
- "\n",
- "In this verification a .cpp code is generated from the node, which is synthesized to an IP block using Vivado. Afterwards the functionality can be simulated with [pyverilator](https://github.com/maltanar/pyverilator). Pyverilator is a tool which makes it possible to simulate verilog files using verilator via a python interface."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In the first step `exec_mode` must be set to \"rtlsim\" in order to select the corresponding functionality when executing the node. In addition, the nodes in the model are assigned unique names using the `GiveUniqueNodeNames()` transformation. Then the transformation `CodeGen_ipgen()` can be executed. Two arguments are passed to this transformation, one is an fpga part and the other is a value for the clock."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [],
- "source": [
- "from finn.transformation.general import GiveUniqueNodeNames\n",
- "from finn.transformation.fpgadataflow.codegen_ipgen import CodeGen_ipgen\n",
- "model = model.transform(SetExecMode(\"rtlsim\"))\n",
- "model = model.transform(GiveUniqueNodeNames())\n",
- "model = model.transform(CodeGen_ipgen(\"xc7z020clg400-1\", 5))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "During the transformation a new attribute with the temporary directory is set, in which the .cpp and a .tcl script are stored, with which the synthesis can be started. This can be seen in the following using netron."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "Stopping http://0.0.0.0:8081\n",
- "Serving 'modified_model.onnx' at http://0.0.0.0:8081\n"
- ]
- }
- ],
- "source": [
- "model.save(\"modified_model.onnx\")\n",
- "netron.start('modified_model.onnx', port=8081, host=\"0.0.0.0\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<iframe src=\"http://0.0.0.0:8081/\" style=\"position: relative; width: 100%;\" height=\"400\"></iframe>\n"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "%%html\n",
- "<iframe src=\"http://0.0.0.0:8081/\" style=\"position: relative; width: 100%;\" height=\"400\"></iframe>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The next step is to perform the synthesis using the `HLSSynth_IPGen()` transformation and set another attribute with the project directory, which contains the IP block. \n",
- "\n",
- "So that the execution can run without errors, two env variables must be set inside the jupyter notebook."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "env: PWD=/workspace/finn/notebooks\n"
- ]
- }
- ],
- "source": [
- "# env variable has to be set because it is used inside the trafo\n",
- "%env PWD=/workspace/finn/notebooks\n",
- "\n",
- "from finn.transformation.fpgadataflow.hlssynth_ipgen import HLSSynth_IPGen\n",
- "model = model.transform(HLSSynth_IPGen())"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now the execution can run again and pyverilator is used in the background to simulate the generated verilog files."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[[-1., -1., -1., -1.],\n",
- " [ 1., 1., -1., -1.],\n",
- " [ 1., -1., 1., 1.],\n",
- " [ 1., -1., 1., -1.]]], dtype=float32)"
- ]
- },
- "execution_count": 19,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "y_rtlsim = oxe.execute_onnx(model, input_dict)[\"outp\"]\n",
- "y_rtlsim"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In the last step it can be checked whether the two results from the simulations match."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [],
- "source": [
- "assert (y_npysim == y_rtlsim).all()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/internals/im2col_finnhlslib.PNG b/notebooks/internals/im2col_finnhlslib.PNG
deleted file mode 100755
index 4df7c3041426576fe5b422aba345a52b3d3ab51c..0000000000000000000000000000000000000000
Binary files a/notebooks/internals/im2col_finnhlslib.PNG and /dev/null differ
diff --git a/requirements.txt b/requirements.txt
index a19b03d2179bfcdb73d04f9b71b0317de29575ee..4eac22f03165ba934cf9a9517a14ed4cd8a4c7c6 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,11 +1,11 @@
bitstring
docrep
future
-numpy
-onnx
-onnxruntime
+numpy==1.18.0
+onnx==1.5.0
+onnxruntime==1.2.0
pre-commit
+pyverilator
scipy
sphinx
wget
-pyverilator
diff --git a/run-docker.sh b/run-docker.sh
index 018bd9aa8c39666a1b9c0ef7f426587f265769f7..b7f844d314c5fb67e11e0933f42b3edfa4d96036 100755
--- a/run-docker.sh
+++ b/run-docker.sh
@@ -104,7 +104,9 @@ docker build -f docker/Dockerfile.finn_dev --tag=$DOCKER_TAG \
--build-arg NETRON_PORT=$NETRON_PORT \
.
# Launch container with current directory mounted
-docker run -t --rm --name $DOCKER_INST_NAME -it \
+# important to pass the --init flag here for correct Vivado operation, see:
+# https://stackoverflow.com/questions/55733058/vivado-synthesis-hangs-in-docker-container-spawned-by-jenkins
+docker run -t --rm --name $DOCKER_INST_NAME -it --init \
--hostname $DOCKER_INST_NAME \
-e "XILINX_VIVADO=$VIVADO_PATH" \
-e "SHELL=/bin/bash" \
diff --git a/setup.cfg b/setup.cfg
index 0a54c3b3682c8512e456623954ef2e9fc7813128..5974cda20e37449a879f7528516895fb7cea4264 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -100,7 +100,6 @@ extras = True
# e.g. --cov-report html (or xml) for html/xml output or --junitxml junit.xml
# in order to write a coverage file that can be read by Jenkins.
addopts =
- --cov finn --cov-report term-missing
--verbose
norecursedirs =
dist
diff --git a/src/finn/analysis/fpgadataflow/hls_synth_res_estimation.py b/src/finn/analysis/fpgadataflow/hls_synth_res_estimation.py
index 78fc2ccfc92f9b7ca3ae6beafe7d24bdbfada2bc..ad30282d93034f8d043a05a2172790349c31ec83 100644
--- a/src/finn/analysis/fpgadataflow/hls_synth_res_estimation.py
+++ b/src/finn/analysis/fpgadataflow/hls_synth_res_estimation.py
@@ -54,8 +54,8 @@ def hls_synth_res_estimation(model):
if code_gen_dir == "":
warnings.warn(
"""Could not find report files, values will be set to zero
- for this node. Please run "CodeGen_ipgen" transformation and
- "HLSSynth_IPGen" first to generate the report files"""
+ for this node. Please run "PrepareIP" transformation and
+ "HLSSynthIP" first to generate the report files"""
)
else:
xmlfile = "{}/project_{}/sol1/syn/report/{}_csynth.xml".format(
@@ -71,7 +71,7 @@ def hls_synth_res_estimation(model):
else:
warnings.warn(
"""Could not find report files, values will be set to zero
- for this node. Please run "CodeGen_ipgen" transformation and
- "HLSSynth_IPGen" first to generate the report files"""
+ for this node. Please run "PrepareIP" transformation and
+ "HLSSynthIP" first to generate the report files"""
)
return res_dict
diff --git a/src/finn/analysis/topology.py b/src/finn/analysis/topology.py
index c825a221ec178ee89b4e3747c982e59a3005cadd..acdb8ed7fcf41fd041c3601b2ee4fe67b6dc5f19 100644
--- a/src/finn/analysis/topology.py
+++ b/src/finn/analysis/topology.py
@@ -79,3 +79,26 @@ def node_inputs_in_expected_order(model):
if n.op_type != "Add":
all_OK = all_OK and (model.get_initializer(n.input[1]) is not None)
return {"node_inputs_in_expected_order": all_OK}
+
+
+def nodes_topologically_sorted(model):
+ """Verifies that graph.node is topologically sorted. This is required by the
+ ONNX specification.
+
+ Returns {"nodes_topologically_sorted": Bool}."""
+
+ # get successors of every node and check that
+ # successor index > current node index
+
+ all_OK = True
+ for n in model.graph.node:
+ successors = model.find_direct_successors(n)
+ if successors is not None:
+ for successor in successors:
+ # check the condition by checking the antithesis
+ index_n = model.get_node_index(n)
+ index_suc = model.get_node_index(successor)
+ if index_n > index_suc:
+ all_OK = False
+
+ return {"nodes_topologically_sorted": all_OK}
diff --git a/src/finn/core/modelwrapper.py b/src/finn/core/modelwrapper.py
index e99a6ef4cd40d6323d77354d3c9b4be341d7649c..dc5b36920a5639933463d682dc66fb8bc15b35f2 100644
--- a/src/finn/core/modelwrapper.py
+++ b/src/finn/core/modelwrapper.py
@@ -288,6 +288,46 @@ class ModelWrapper:
except ValueError:
return None
+ def find_consumers(self, tensor_name):
+ """Finds and returns a list of the nodes that consume tensor with
+ given name."""
+ consumers = []
+ for n in self._model_proto.graph.node:
+ for inp_tensor in n.input:
+ if inp_tensor == tensor_name:
+ consumers.append(n)
+ if consumers != []:
+ return consumers
+ else:
+ return None
+
+ def find_direct_successors(self, node):
+ """Finds and returns a list of the nodes that are successors of
+ given node."""
+ successors = []
+ for outp_tensor in node.output:
+ tensor_consumer_list = self.find_consumers(outp_tensor)
+ if tensor_consumer_list is not None:
+ for consumer in tensor_consumer_list:
+ successors.append(consumer)
+ if successors != []:
+ return successors
+ else:
+ return None
+
+ def find_direct_predecessors(self, node):
+ """Finds and returns a list of the nodes that are predecessors of
+ given node."""
+ predecessors = []
+ for inp_tensor in node.input:
+ producer = self.find_producer(inp_tensor)
+ if producer is not None:
+ predecessors.append(producer)
+ if predecessors != []:
+ return predecessors
+ else:
+ return None
+
def get_all_tensor_names(self):
"""Returns a list of all (input, output and value_info) tensor names
in the graph."""
@@ -383,3 +423,14 @@ class ModelWrapper:
def get_non_finn_nodes(self):
"""Returns a list of nodes where domain != 'finn'."""
return list(filter(lambda x: x.domain != "finn", self.graph.node))
+
+ def get_node_index(self, node):
+ """Returns current index of given node."""
+ n_ind = 0
+ try:
+ for n in self.graph.node:
+ if n == node:
+ return n_ind
+ n_ind += 1
+ except ValueError:
+ return None
diff --git a/src/finn/core/onnx_exec.py b/src/finn/core/onnx_exec.py
index 172ba25b223fd087df134add460a42d0a9935e0e..44787e1d26049e6075e2222316b45ab3898acbc7 100644
--- a/src/finn/core/onnx_exec.py
+++ b/src/finn/core/onnx_exec.py
@@ -38,6 +38,7 @@ from finn.core.modelwrapper import ModelWrapper
from finn.core.remote_exec import remote_exec
from finn.core.rtlsim_exec import rtlsim_exec
from finn.custom_op.registry import getCustomOp
+import finn.analysis.topology as ta
def execute_node(node, context, graph):
@@ -121,6 +122,11 @@ def execute_onnx(model, input_dict, return_full_exec_context=False):
if not model.check_all_tensor_shapes_specified():
raise Exception("Found unspecified tensor shapes, try infer_shapes")
+ ret = model.analysis(ta.nodes_topologically_sorted)
+ assert (
+ ret["nodes_topologically_sorted"] is True
+ ), """Nodes must be
+ topologically sorted."""
graph = model.graph
# first, we need to make sure that every variable required by the graph has
diff --git a/src/finn/core/remote_exec.py b/src/finn/core/remote_exec.py
index e97eb19a101e83f9d9603637e131b2ec9b7d16a4..335dfec04e4abee41f914c5d912ce291a0d31a91 100644
--- a/src/finn/core/remote_exec.py
+++ b/src/finn/core/remote_exec.py
@@ -65,7 +65,8 @@ def remote_exec(model, execution_context):
cmd = (
"sshpass -p {} ssh {}@{} -p {} "
'"cd {}/{}; echo "{}" | '
- 'sudo -S python3.6 driver.py remote_pynq 1 resizer.bit input.npy output.npy"'
+ 'sudo -S python3.6 driver.py --exec_mode="execute" --batchsize=1" '
+ '--bitfile="resizer.bit" --inputfile="input.npy" --outputfile="output.npy"'
).format(
pynq_password,
pynq_username,
diff --git a/src/finn/core/rtlsim_exec.py b/src/finn/core/rtlsim_exec.py
index af84a75e299d666c059df54211be42b691f5ccf2..e5e6d29bd8d8ed23f6a4958856ed1ddea3617175 100644
--- a/src/finn/core/rtlsim_exec.py
+++ b/src/finn/core/rtlsim_exec.py
@@ -94,7 +94,7 @@ def rtlsim_exec(model, execution_context):
model.set_metadata_prop("sim_cycles", str(ret[1]))
# unpack output and put into context
o_folded_tensor = rtlsim_output_to_npy(
- packed_output, "out.npy", o_dt, o_folded_shape, packedBits, targetBits
+ packed_output, None, o_dt, o_folded_shape, packedBits, targetBits
)
execution_context[o_name] = o_folded_tensor.reshape(o_shape)
diff --git a/src/finn/core/throughput_test.py b/src/finn/core/throughput_test.py
index fc929237bf6c985997e49cc3f74c7d492d79839a..c82d540e29fc59b92a22bf011e823a9f8c076843 100644
--- a/src/finn/core/throughput_test.py
+++ b/src/finn/core/throughput_test.py
@@ -47,8 +47,7 @@ def throughput_test(model):
cmd = (
"sshpass -p {} ssh {}@{} -p {} "
'"cd {}/{}; echo "{}" | '
- "sudo -S python3.6 driver.py throughput_test 1000 "
- 'resizer.bit input.npy output.npy"'
+ 'sudo -S python3.6 driver.py --exec_mode="throughput_test" --batchsize=1000"'
).format(
pynq_password,
pynq_username,
diff --git a/src/finn/custom_op/fpgadataflow/__init__.py b/src/finn/custom_op/fpgadataflow/__init__.py
index 8430a56bc2688627f82da6ae92140f5cff82cb60..d47b687b65d93ec45d936afd91c08c117cf8dbc8 100644
--- a/src/finn/custom_op/fpgadataflow/__init__.py
+++ b/src/finn/custom_op/fpgadataflow/__init__.py
@@ -74,7 +74,7 @@ class HLSCustomOp(CustomOp):
def get_nodeattr_types(self):
return {
"backend": ("s", True, "fpgadataflow"),
- "code_gen_dir_npysim": ("s", False, ""),
+ "code_gen_dir_cppsim": ("s", False, ""),
"code_gen_dir_ipgen": ("s", False, ""),
"executable_path": ("s", False, ""),
"ipgen_path": ("s", False, ""),
@@ -121,7 +121,7 @@ class HLSCustomOp(CustomOp):
assert (
code_gen_dir != ""
), """Node attribute "code_gen_dir_ipgen" is
- not set. Please run HLSSynth_IPGen first."""
+ not set. Please run HLSSynthIP first."""
verilog_file = self.get_verilog_top_filename()
assert os.path.isfile(verilog_file), "Cannot find top-level Verilog file."
# build the Verilator emu library
@@ -232,14 +232,14 @@ class HLSCustomOp(CustomOp):
vlnv = "xilinx.com:hls:%s:1.0" % node.name
self.set_nodeattr("ip_vlnv", vlnv)
- def code_generation_npysim(self, model):
- """Generates c++ code for simulation (npysim)."""
+ def code_generation_cppsim(self, model):
+ """Generates c++ code for simulation (cppsim)."""
node = self.onnx_node
- path = self.get_nodeattr("code_gen_dir_npysim")
+ path = self.get_nodeattr("code_gen_dir_cppsim")
self.code_gen_dict["$AP_INT_MAX_W$"] = [str(self.get_ap_int_max_w())]
self.generate_params(model, path)
self.global_includes()
- self.defines("npysim")
+ self.defines("cppsim")
self.read_npy_data()
self.strm_decl()
self.pragmas()
@@ -253,7 +253,7 @@ class HLSCustomOp(CustomOp):
# transform list into long string separated by '\n'
code_gen_line = "\n".join(self.code_gen_dict[key])
template = template.replace(key, code_gen_line)
- code_gen_dir = self.get_nodeattr("code_gen_dir_npysim")
+ code_gen_dir = self.get_nodeattr("code_gen_dir_cppsim")
f = open(os.path.join(code_gen_dir, "execute_{}.cpp".format(node.op_type)), "w")
f.write(template)
f.close()
@@ -262,7 +262,7 @@ class HLSCustomOp(CustomOp):
def compile_singlenode_code(self):
"""Builds the bash script for compilation using the CppBuilder from
finn.util.basic and executes the script to produce the executable."""
- code_gen_dir = self.get_nodeattr("code_gen_dir_npysim")
+ code_gen_dir = self.get_nodeattr("code_gen_dir_cppsim")
builder = CppBuilder()
# to enable additional debug features please uncommand the next line
# builder.append_includes("-DDEBUG")
@@ -284,11 +284,11 @@ class HLSCustomOp(CustomOp):
Count indicates the number of inputs that have to be saved."""
node = self.onnx_node
- code_gen_dir = self.get_nodeattr("code_gen_dir_npysim")
+ code_gen_dir = self.get_nodeattr("code_gen_dir_cppsim")
if code_gen_dir == "":
raise Exception(
"""
-Found no codegen dir for this node, did you run the codegen_npysim transformation?
+Found no codegen dir for this node, did you run the prepare_cppsim transformation?
"""
)
# create a npy file for each input of the node (in_ind is input index)
@@ -306,7 +306,7 @@ Found no codegen dir for this node, did you run the codegen_npysim transformatio
the context dictionary."""
# TODO support multi-output nodes as needed
node = self.onnx_node
- code_gen_dir = self.get_nodeattr("code_gen_dir_npysim")
+ code_gen_dir = self.get_nodeattr("code_gen_dir_cppsim")
output = np.load("{}/output.npy".format(code_gen_dir))
context[node.output[0]] = output
@@ -399,9 +399,9 @@ compilation transformations?
return outputs
def execute_node(self, context, graph):
- """Executes single node using npysim or rtlsim."""
+ """Executes single node using cppsim or rtlsim."""
mode = self.get_nodeattr("exec_mode")
- if mode == "npysim":
+ if mode == "cppsim":
# save input(s)
self.dynamic_input_to_npy(context, 1)
# execute the precompiled model
@@ -414,7 +414,7 @@ compilation transformations?
else:
raise Exception(
"""Invalid value for attribute exec_mode! Is currently set to: {}
- has to be set to one of the following value ("npysim", "rtlsim")""".format(
+ has to be set to one of the following value ("cppsim", "rtlsim")""".format(
mode
)
)
@@ -435,14 +435,14 @@ compilation transformations?
@abstractmethod
def global_includes(self):
"""Function to set the global includes for c++ code that has to be generated
- for npysim or rtlsim, is member function of HLSCustomOp class but has to
+ for cppsim or rtlsim, is member function of HLSCustomOp class but has to
be filled by every node."""
pass
@abstractmethod
def defines(self, var):
"""Function to set the define commands for c++ code that has to be generated
- for npysim or rtlsim, is member function of HLSCustomOp class but has to
+ for cppsim or rtlsim, is member function of HLSCustomOp class but has to
be filled by every node.
var: makes it possible to reuse the function for different c++ code generation.
@@ -496,6 +496,14 @@ compilation transformations?
HLSCustomOp class but has to be filled by every node."""
pass
+ def get_normal_input_shape(self):
+ """Returns normal input shape if implemented."""
+ raise Exception("get_normal_input_shape not implemented for this op")
+
+ def get_normal_output_shape(self):
+ """Returns folded output shape if implemented."""
+ raise Exception("get_normal_output_shape not implemented for this op")
+
def get_folded_input_shape(self):
"""Returns folded input shape (according to synapse folding), if implemented."""
raise Exception("get_folded_input_shape not implemented for this op")
diff --git a/src/finn/custom_op/fpgadataflow/convolutioninputgenerator.py b/src/finn/custom_op/fpgadataflow/convolutioninputgenerator.py
index 2b469f7b0d6e5ddc3068fa3fd2d6cb487a560d92..e4d106068d4d128c66b2ce5f3d6c925dfe414b90 100644
--- a/src/finn/custom_op/fpgadataflow/convolutioninputgenerator.py
+++ b/src/finn/custom_op/fpgadataflow/convolutioninputgenerator.py
@@ -177,14 +177,14 @@ class ConvolutionInputGenerator(HLSCustomOp):
folded_oshape = self.get_folded_output_shape()
# TODO ensure codegen dir exists
- if mode == "npysim":
- code_gen_dir = self.get_nodeattr("code_gen_dir_npysim")
+ if mode == "cppsim":
+ code_gen_dir = self.get_nodeattr("code_gen_dir_cppsim")
elif mode == "rtlsim":
code_gen_dir = self.get_nodeattr("code_gen_dir_ipgen")
else:
raise Exception(
"""Invalid value for attribute exec_mode! Is currently set to: {}
- has to be set to one of the following value ("npysim", "rtlsim")""".format(
+ has to be set to one of the following value ("cppsim", "rtlsim")""".format(
mode
)
)
@@ -207,14 +207,14 @@ class ConvolutionInputGenerator(HLSCustomOp):
reshaped_input = inp.copy()
np.save(os.path.join(code_gen_dir, "input_0.npy"), reshaped_input)
- if mode == "npysim":
+ if mode == "cppsim":
# execute the precompiled model
super().exec_precompiled_singlenode_model()
# load output npy file
super().npy_to_dynamic_output(context)
assert (
context[node.output[0]].shape == folded_oshape
- ), "npysim \
+ ), "cppsim \
did not produce expected ofolded utput shape"
context[node.output[0]] = context[node.output[0]].reshape(*exp_oshape)
elif mode == "rtlsim":
@@ -241,7 +241,7 @@ class ConvolutionInputGenerator(HLSCustomOp):
else:
raise Exception(
"""Invalid value for attribute exec_mode! Is currently set to: {}
- has to be set to one of the following value ("npysim", "rtlsim")""".format(
+ has to be set to one of the following value ("cppsim", "rtlsim")""".format(
mode
)
)
@@ -277,7 +277,7 @@ class ConvolutionInputGenerator(HLSCustomOp):
]
def read_npy_data(self):
- code_gen_dir = self.get_nodeattr("code_gen_dir_npysim")
+ code_gen_dir = self.get_nodeattr("code_gen_dir_cppsim")
dtype = self.get_input_datatype()
if dtype == DataType.BIPOLAR:
# use binary for bipolar storage
@@ -313,7 +313,7 @@ class ConvolutionInputGenerator(HLSCustomOp):
]
def dataoutstrm(self):
- code_gen_dir = self.get_nodeattr("code_gen_dir_npysim")
+ code_gen_dir = self.get_nodeattr("code_gen_dir_cppsim")
dtype = self.get_output_datatype()
if dtype == DataType.BIPOLAR:
# use binary for bipolar storage
diff --git a/src/finn/custom_op/fpgadataflow/streamingdatawidthconverter_batch.py b/src/finn/custom_op/fpgadataflow/streamingdatawidthconverter_batch.py
index f30871909b1c70f3b5df148f1b6eae22fdbadc25..f666becdbcceca6ca202907610595f8c0069c5a0 100644
--- a/src/finn/custom_op/fpgadataflow/streamingdatawidthconverter_batch.py
+++ b/src/finn/custom_op/fpgadataflow/streamingdatawidthconverter_batch.py
@@ -146,10 +146,6 @@ class StreamingDataWidthConverter_Batch(HLSCustomOp):
folded_oshape = self.get_folded_output_shape()
return np.prod(folded_oshape[:-1])
- def get_number_input_values(self):
- folded_ishape = self.get_folded_input_shape()
- return np.prod(folded_ishape[:-1])
-
def get_instream_width(self):
in_width = self.get_nodeattr("inWidth")
return in_width
@@ -226,7 +222,7 @@ class StreamingDataWidthConverter_Batch(HLSCustomOp):
]
def read_npy_data(self):
- code_gen_dir = self.get_nodeattr("code_gen_dir_npysim")
+ code_gen_dir = self.get_nodeattr("code_gen_dir_cppsim")
dtype = self.get_input_datatype()
if dtype == DataType.BIPOLAR:
# use binary for bipolar storage
@@ -260,7 +256,7 @@ class StreamingDataWidthConverter_Batch(HLSCustomOp):
]
def dataoutstrm(self):
- code_gen_dir = self.get_nodeattr("code_gen_dir_npysim")
+ code_gen_dir = self.get_nodeattr("code_gen_dir_cppsim")
dtype = self.get_output_datatype()
if dtype == DataType.BIPOLAR:
# use binary for bipolar storage
@@ -313,14 +309,14 @@ class StreamingDataWidthConverter_Batch(HLSCustomOp):
folded_ishape = self.get_folded_input_shape()
# TODO ensure codegen dir exists
- if mode == "npysim":
- code_gen_dir = self.get_nodeattr("code_gen_dir_npysim")
+ if mode == "cppsim":
+ code_gen_dir = self.get_nodeattr("code_gen_dir_cppsim")
elif mode == "rtlsim":
code_gen_dir = self.get_nodeattr("code_gen_dir_ipgen")
else:
raise Exception(
"""Invalid value for attribute exec_mode! Is currently set to: {}
- has to be set to one of the following value ("npysim", "rtlsim")""".format(
+ has to be set to one of the following value ("cppsim", "rtlsim")""".format(
mode
)
)
@@ -343,7 +339,7 @@ class StreamingDataWidthConverter_Batch(HLSCustomOp):
reshaped_input = reshaped_input.copy()
np.save(os.path.join(code_gen_dir, "input_0.npy"), reshaped_input)
- if mode == "npysim":
+ if mode == "cppsim":
output = inp
output = np.asarray([output], dtype=np.float32).reshape(*exp_shape)
context[node.output[0]] = output
diff --git a/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py b/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
index 46920711e13057178be9fca5fe3a18ce3e14feda..f650442401b49f1ad0a602b6b2ad3e50fbb5e5c2 100644
--- a/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
+++ b/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
@@ -181,7 +181,7 @@ class StreamingFCLayer_Batch(HLSCustomOp):
# verify that all necessary attributes exist
# TODO collect automatically from get_nodeattr_types
try:
- self.get_nodeattr("code_gen_dir_npysim")
+ self.get_nodeattr("code_gen_dir_cppsim")
self.get_nodeattr("executable_path")
self.get_nodeattr("resType")
self.get_nodeattr("MW")
@@ -290,6 +290,7 @@ class StreamingFCLayer_Batch(HLSCustomOp):
return out_width
def get_weightstream_width(self):
+ """Returns weight stream width. Used in decoupled mode."""
pe = self.get_nodeattr("PE")
simd = self.get_nodeattr("SIMD")
wp = self.get_weight_datatype().bitwidth()
@@ -297,6 +298,8 @@ class StreamingFCLayer_Batch(HLSCustomOp):
return w_width
def get_weightstream_width_padded(self):
+ """Returns weight stream width padded to a multiple of 8. This is required
+ by the AXI Stream spec. Used in decoupled mode."""
weight_width = self.get_weightstream_width()
return roundup_to_integer_multiple(weight_width, 8)
@@ -508,10 +511,10 @@ class StreamingFCLayer_Batch(HLSCustomOp):
f_weights.close()
elif mem_mode == "decoupled":
- """Saves weights in corresponding file format for npysim or rtlsim"""
+ """Saves weights in corresponding file format for cppsim or rtlsim"""
# transpose weight tensor from (1, PE, WMEM, SIMD) to (1, WMEM, PE, SIMD)
# and save as unflipped weight tensor to be able to differentiate between
- # flipped an unflipped weight tensor (has to be flipped for npysim)
+ # flipped an unflipped weight tensor (has to be flipped for cppsim)
weight_tensor_unflipped = np.transpose(weight_tensor, (0, 2, 1, 3))
@@ -613,14 +616,14 @@ class StreamingFCLayer_Batch(HLSCustomOp):
node = self.onnx_node
# TODO ensure codegen dir exists
- if mode == "npysim":
- code_gen_dir = self.get_nodeattr("code_gen_dir_npysim")
+ if mode == "cppsim":
+ code_gen_dir = self.get_nodeattr("code_gen_dir_cppsim")
elif mode == "rtlsim":
code_gen_dir = self.get_nodeattr("code_gen_dir_ipgen")
else:
raise Exception(
"""Invalid value for attribute exec_mode! Is currently set to: {}
- has to be set to one of the following value ("npysim", "rtlsim")""".format(
+ has to be set to one of the following value ("cppsim", "rtlsim")""".format(
mode
)
)
@@ -654,7 +657,7 @@ class StreamingFCLayer_Batch(HLSCustomOp):
raise Exception("Unexpected input found for StreamingFCLayer")
in_ind += 1
- if mode == "npysim":
+ if mode == "cppsim":
# execute the precompiled model
super().exec_precompiled_singlenode_model()
# load output npy file
@@ -696,7 +699,7 @@ class StreamingFCLayer_Batch(HLSCustomOp):
else:
raise Exception(
"""Invalid value for attribute exec_mode! Is currently set to: {}
- has to be set to one of the following value ("npysim", "rtlsim")""".format(
+ has to be set to one of the following value ("cppsim", "rtlsim")""".format(
mode
)
)
@@ -744,7 +747,7 @@ class StreamingFCLayer_Batch(HLSCustomOp):
)
def read_npy_data(self):
- code_gen_dir = self.get_nodeattr("code_gen_dir_npysim")
+ code_gen_dir = self.get_nodeattr("code_gen_dir_cppsim")
dtype = self.get_input_datatype()
if dtype == DataType.BIPOLAR:
# use binary for bipolar storage
@@ -841,7 +844,7 @@ class StreamingFCLayer_Batch(HLSCustomOp):
)
def dataoutstrm(self):
- code_gen_dir = self.get_nodeattr("code_gen_dir_npysim")
+ code_gen_dir = self.get_nodeattr("code_gen_dir_cppsim")
dtype = self.get_output_datatype()
if dtype == DataType.BIPOLAR:
# use binary for bipolar storage
diff --git a/src/finn/custom_op/fpgadataflow/streamingfifo.py b/src/finn/custom_op/fpgadataflow/streamingfifo.py
index eb96c6c04eb0b7b83c3f925e10f86b17ec399e42..66190333ce8d71dafba99aaeae4fb2c973d67410 100644
--- a/src/finn/custom_op/fpgadataflow/streamingfifo.py
+++ b/src/finn/custom_op/fpgadataflow/streamingfifo.py
@@ -121,7 +121,7 @@ class StreamingFIFO(HLSCustomOp):
# transform list into long string separated by '\n'
code_gen_line = "\n".join(self.code_gen_dict[key])
template = template.replace(key, code_gen_line)
- f = open(os.path.join(verilog_dir, "{}.v".format(self.onnx_node.name,)), "w",)
+ f = open(os.path.join(verilog_dir, "{}.v".format(self.onnx_node.name)), "w")
f.write(template)
f.close()
self.code_gen_dict.clear()
@@ -222,7 +222,7 @@ class StreamingFIFO(HLSCustomOp):
inp = context[node.input[0]]
exp_shape = self.get_normal_input_shape()
- if mode == "npysim":
+ if mode == "cppsim":
output = inp
output = np.asarray([output], dtype=np.float32).reshape(*exp_shape)
context[node.output[0]] = output
@@ -243,9 +243,7 @@ class StreamingFIFO(HLSCustomOp):
export_idt = DataType[self.get_nodeattr("dataType")]
# make copy before saving the array
reshaped_input = reshaped_input.copy()
- np.save(
- os.path.join(code_gen_dir, "input_0.npy"), reshaped_input,
- )
+ np.save(os.path.join(code_gen_dir, "input_0.npy"), reshaped_input)
sim = self.get_rtlsim()
nbits = self.get_instream_width()
inp = npy_to_rtlsim_input(
@@ -269,16 +267,17 @@ class StreamingFIFO(HLSCustomOp):
context[node.output[0]] = output
else:
- raise Exception("Test")
+ raise Exception(
+ """Invalid value for attribute exec_mode! Is currently set to: {}
+ has to be set to one of the following value ("cppsim", "rtlsim")""".format(
+ mode
+ )
+ )
def get_number_output_values(self):
folded_oshape = self.get_folded_output_shape()
return np.prod(folded_oshape[:-1])
- def get_number_input_values(self):
- folded_ishape = self.get_folded_input_shape()
- return np.prod(folded_ishape[:-1])
-
def global_includes(self):
pass
diff --git a/src/finn/custom_op/fpgadataflow/streamingmaxpool_batch.py b/src/finn/custom_op/fpgadataflow/streamingmaxpool_batch.py
index 7334c913b6f85cad4835b6e65eb14c488432af6b..2344e12f7e87634c189563f9cde7b1c861a3606e 100644
--- a/src/finn/custom_op/fpgadataflow/streamingmaxpool_batch.py
+++ b/src/finn/custom_op/fpgadataflow/streamingmaxpool_batch.py
@@ -171,7 +171,7 @@ class StreamingMaxPool_Batch(HLSCustomOp):
]
def read_npy_data(self):
- code_gen_dir = self.get_nodeattr("code_gen_dir_npysim")
+ code_gen_dir = self.get_nodeattr("code_gen_dir_cppsim")
dtype = self.get_input_datatype()
if dtype == DataType.BIPOLAR:
# use binary for bipolar storage
@@ -215,7 +215,7 @@ class StreamingMaxPool_Batch(HLSCustomOp):
]
def dataoutstrm(self):
- code_gen_dir = self.get_nodeattr("code_gen_dir_npysim")
+ code_gen_dir = self.get_nodeattr("code_gen_dir_cppsim")
dtype = self.get_output_datatype()
if dtype == DataType.BIPOLAR:
# use binary for bipolar storage
@@ -267,14 +267,14 @@ class StreamingMaxPool_Batch(HLSCustomOp):
folded_oshape = self.get_folded_output_shape()
# TODO ensure codegen dir exists
- if mode == "npysim":
- code_gen_dir = self.get_nodeattr("code_gen_dir_npysim")
+ if mode == "cppsim":
+ code_gen_dir = self.get_nodeattr("code_gen_dir_cppsim")
elif mode == "rtlsim":
code_gen_dir = self.get_nodeattr("code_gen_dir_ipgen")
else:
raise Exception(
"""Invalid value for attribute exec_mode! Is currently set to: {}
- has to be set to one of the following value ("npysim", "rtlsim")""".format(
+ has to be set to one of the following value ("cppsim", "rtlsim")""".format(
mode
)
)
@@ -296,14 +296,14 @@ class StreamingMaxPool_Batch(HLSCustomOp):
reshaped_input = inp.copy()
np.save(os.path.join(code_gen_dir, "input_0.npy"), reshaped_input)
- if mode == "npysim":
+ if mode == "cppsim":
# execute the precompiled model
super().exec_precompiled_singlenode_model()
# load output npy file
super().npy_to_dynamic_output(context)
assert (
context[node.output[0]].shape == folded_oshape
- ), "npysim \
+ ), "cppsim \
did not produce expected ofolded utput shape"
context[node.output[0]] = context[node.output[0]].reshape(*exp_oshape)
elif mode == "rtlsim":
@@ -330,7 +330,7 @@ class StreamingMaxPool_Batch(HLSCustomOp):
else:
raise Exception(
"""Invalid value for attribute exec_mode! Is currently set to: {}
- has to be set to one of the following value ("npysim", "rtlsim")""".format(
+ has to be set to one of the following value ("cppsim", "rtlsim")""".format(
mode
)
)
diff --git a/src/finn/custom_op/im2col.py b/src/finn/custom_op/im2col.py
index 2bd039c3acbcb3dfa99b478b012a4e89d5399f45..16446c15d46ee7996162f864708f7fde6cfedaf3 100644
--- a/src/finn/custom_op/im2col.py
+++ b/src/finn/custom_op/im2col.py
@@ -11,13 +11,14 @@ from finn.core.datatype import DataType
def compute_conv_output_dim(ifm_dim, k, stride, pad=0):
- "Return spatial output dimension size for convolution with given params."
+ """Returns spatial output dimension size for convolution with given params."""
return int(((ifm_dim + 2 * pad - k) / stride) + 1)
def get_im2col_indices_nchw(
x_shape, field_height, field_width, padding=0, stride_y=1, stride_x=1
):
+ """Returns im2col indices."""
# First figure out what the size of the output should be
N, C, H, W = x_shape
assert (H + 2 * padding - field_height) % stride_y == 0
@@ -41,7 +42,9 @@ def get_im2col_indices_nchw(
def im2col_indices_nchw(
x, field_height, field_width, padding=0, stride_y=1, stride_x=1, pad_val=0
):
- """ An implementation of im2col based on some fancy indexing """
+ """Performs im2col on x with given field height and width, as well as values
+ for padding and stride size.
+ Returns result of im2col."""
# Zero-pad the input
p = padding
x_padded = np.pad(
diff --git a/src/finn/transformation/bipolar_to_xnor.py b/src/finn/transformation/bipolar_to_xnor.py
index 4c7ebaf04e35f94e84e52e0b4520ee2369502120..8b65cfee17edd5d89fcca0bd86da12415d38fe78 100644
--- a/src/finn/transformation/bipolar_to_xnor.py
+++ b/src/finn/transformation/bipolar_to_xnor.py
@@ -35,6 +35,7 @@ from finn.transformation import Transformation
from finn.transformation.infer_shapes import InferShapes
from finn.transformation.infer_datatypes import InferDataTypes
from finn.util.basic import get_by_name
+from finn.custom_op.registry import getCustomOp
class ConvertBipolarMatMulToXnorPopcount(Transformation):
@@ -71,10 +72,19 @@ class ConvertBipolarMatMulToXnorPopcount(Transformation):
)
graph_modified = True
mt = mt_chain[-1]
- bin_dt_attr = "BINARY".encode("utf-8")
- get_by_name(mt.attribute, "out_dtype").s = bin_dt_attr
- get_by_name(mt.attribute, "out_scale").f = 1.0
- get_by_name(mt.attribute, "out_bias").f = 0
+ mt_inst = getCustomOp(mt)
+ # ensure old scale/bias were correct for BIPOLAR
+ scale_ok = mt_inst.get_nodeattr("out_scale") == 2.0
+ bias_ok = mt_inst.get_nodeattr("out_bias") == -1.0
+ assert (
+ scale_ok and bias_ok
+ ), """Unexpected scale/bias
+ attributes for BIPOLAR MultiThreshold node."""
+ # start conversion, set MT output to binary
+ # (this is what XnorPopcountMatMul expects)
+ mt_inst.set_nodeattr("out_dtype", "BINARY")
+ mt_inst.set_nodeattr("out_scale", 1.0)
+ mt_inst.set_nodeattr("out_bias", 0.0)
model.set_tensor_datatype(mm_input, DataType.BINARY)
# change node type and domain
n.op_type = "XnorPopcountMatMul"
diff --git a/src/finn/transformation/fpgadataflow/annotate_resources.py b/src/finn/transformation/fpgadataflow/annotate_resources.py
index d192372a7d9c1f6ee2f088c6a058b994d21f6c99..207075b00de1871da19ea78472125d435449ed6e 100644
--- a/src/finn/transformation/fpgadataflow/annotate_resources.py
+++ b/src/finn/transformation/fpgadataflow/annotate_resources.py
@@ -41,7 +41,7 @@ class AnnotateResources(Transformation):
* 'hls' -- use results from the HLS synthesis report
No annotations can be provided unless the relevant transformation for the
- chosen mode (e.g. HLSSynth_IPGen for hls) was previously run.
+ chosen mode (e.g. HLSSynthIP for hls) was previously run.
"""
def __init__(self, mode):
diff --git a/src/finn/transformation/fpgadataflow/cleanup.py b/src/finn/transformation/fpgadataflow/cleanup.py
index a31cbfa7dd30eff37ceb2d7bf3c162093a5a3a1c..248a99b57aed7f38f63cc25ad7ecf93bd1930e63 100644
--- a/src/finn/transformation/fpgadataflow/cleanup.py
+++ b/src/finn/transformation/fpgadataflow/cleanup.py
@@ -57,11 +57,11 @@ class CleanUp(Transformation):
try:
# lookup op_type in registry of CustomOps
inst = registry.custom_op[op_type](node)
- # delete code_gen_dir from npysim
- code_gen_dir = inst.get_nodeattr("code_gen_dir_npysim")
+ # delete code_gen_dir from cppsim
+ code_gen_dir = inst.get_nodeattr("code_gen_dir_cppsim")
if os.path.isdir(code_gen_dir):
shutil.rmtree(code_gen_dir)
- inst.set_nodeattr("code_gen_dir_npysim", "")
+ inst.set_nodeattr("code_gen_dir_cppsim", "")
inst.set_nodeattr("executable_path", "")
# delete code_gen_dir from ipgen and project folder
code_gen_dir = inst.get_nodeattr("code_gen_dir_ipgen")
diff --git a/src/finn/transformation/fpgadataflow/compile.py b/src/finn/transformation/fpgadataflow/compile_cppsim.py
similarity index 91%
rename from src/finn/transformation/fpgadataflow/compile.py
rename to src/finn/transformation/fpgadataflow/compile_cppsim.py
index 40c7da8f77efeaa655459402699a401b642b776c..ddf00c799b8a53c428d0854551d0078a6e264111 100644
--- a/src/finn/transformation/fpgadataflow/compile.py
+++ b/src/finn/transformation/fpgadataflow/compile_cppsim.py
@@ -31,12 +31,12 @@ from finn.util.fpgadataflow import is_fpgadataflow_node
from finn.transformation import NodeLocalTransformation
-class Compile(NodeLocalTransformation):
- """For every node: compile C++ code in node attribute "code_gen_dir_npysim"
+class CompileCppSim(NodeLocalTransformation):
+ """For every node: compile C++ code in node attribute "code_gen_dir_cppsim"
and save path to executables in node attribute "executable_path".
All nodes in the graph must have the fpgadataflow backend attribute.
- To use these executables, exec_mode must be set to "npysim" (using transformation
+ To use these executables, exec_mode must be set to "cppsim" (using transformation
SetExecMode) and the model has to be executed using execute_onnx() from
finn.core.onnx_exec
@@ -55,10 +55,10 @@ class Compile(NodeLocalTransformation):
inst = registry.custom_op[op_type](node)
# ensure that code is generated
assert (
- inst.get_nodeattr("code_gen_dir_npysim") != ""
+ inst.get_nodeattr("code_gen_dir_cppsim") != ""
), """Node
- attribute "code_gen_dir_npysim" is not set. Please run
- Transformation CodeGen_npysim first."""
+ attribute "code_gen_dir_cppsim" is not set. Please run
+ Transformation PrepareCppSim first."""
# call the compilation function for this node
inst.compile_singlenode_code()
# ensure that executable path is now set
diff --git a/src/finn/transformation/fpgadataflow/codegen_ipstitch.py b/src/finn/transformation/fpgadataflow/create_stitched_ip.py
similarity index 98%
rename from src/finn/transformation/fpgadataflow/codegen_ipstitch.py
rename to src/finn/transformation/fpgadataflow/create_stitched_ip.py
index bc1fce836a16f49e6549f6b24de2973b902bf066..c22a21ebdfd19178d3937de3a235dfadb7ee1d71 100644
--- a/src/finn/transformation/fpgadataflow/codegen_ipstitch.py
+++ b/src/finn/transformation/fpgadataflow/create_stitched_ip.py
@@ -34,10 +34,10 @@ from finn.util.basic import get_by_name, make_build_dir
from finn.custom_op.registry import getCustomOp
-class CodeGen_ipstitch(Transformation):
+class CreateStitchedIP(Transformation):
"""Create a Vivado IP Block Design project from all the generated IPs of a
graph. All nodes in the graph must have the fpgadataflow backend attribute,
- and the CodeGen_ipgen transformation must have been previously run on
+ and the PrepareIP transformation must have been previously run on
the graph. The resulting block design is also packaged as IP. The
transformation gets the fpgapart as a string.
diff --git a/src/finn/transformation/fpgadataflow/hlssynth_ipgen.py b/src/finn/transformation/fpgadataflow/hlssynth_ip.py
similarity index 94%
rename from src/finn/transformation/fpgadataflow/hlssynth_ipgen.py
rename to src/finn/transformation/fpgadataflow/hlssynth_ip.py
index 2af623818fe0e830883ef5065e5e7c9c7364ef1e..6d7c4025653948d6958672177ae5e36ab08bf279 100644
--- a/src/finn/transformation/fpgadataflow/hlssynth_ipgen.py
+++ b/src/finn/transformation/fpgadataflow/hlssynth_ip.py
@@ -31,7 +31,7 @@ from finn.util.fpgadataflow import is_fpgadataflow_node
from finn.transformation import NodeLocalTransformation
-class HLSSynth_IPGen(NodeLocalTransformation):
+class HLSSynthIP(NodeLocalTransformation):
"""For each node: generate IP block from code in folder
that is referenced in node attribute "code_gen_dir_ipgen"
and save path of generated project in node attribute "ipgen_path".
@@ -58,14 +58,14 @@ class HLSSynth_IPGen(NodeLocalTransformation):
inst.get_nodeattr("code_gen_dir_ipgen") != ""
), """Node
attribute "code_gen_dir_ipgen" is empty. Please run
- transformation CodeGen_ipgen first."""
+ transformation PrepareIP first."""
# call the compilation function for this node
inst.ipgen_singlenode_code()
# ensure that executable path is now set
assert (
inst.get_nodeattr("ipgen_path") != ""
), """Transformation
- HLSSynth_IPGen was not successful. Node attribute "ipgen_path"
+ HLSSynthIP was not successful. Node attribute "ipgen_path"
is empty."""
except KeyError:
# exception if op_type is not supported
diff --git a/src/finn/transformation/fpgadataflow/insert_fifo.py b/src/finn/transformation/fpgadataflow/insert_fifo.py
index 95a7a7154f10c15b38d94d4bef653649aa30a569..b01f8cbe5c48db6c5288b2db1a8b009ea09ce6c0 100644
--- a/src/finn/transformation/fpgadataflow/insert_fifo.py
+++ b/src/finn/transformation/fpgadataflow/insert_fifo.py
@@ -165,7 +165,7 @@ class InsertFIFO(Transformation):
# determine fifo node attributes
fld_shape = n0.get_folded_output_shape()
dtype = n0.get_output_datatype()
- fifo_depth = n0.get_nodeattr("inFIFODepth")
+ fifo_depth = n0.get_nodeattr("outFIFODepth")
# create fifo node
fifo_input_tensor = oh.make_tensor_value_info(
diff --git a/src/finn/transformation/fpgadataflow/make_pynq_driver.py b/src/finn/transformation/fpgadataflow/make_pynq_driver.py
index c5b8d35dba1069ac749e0a0d92060c8216ada507..049ede5064d252bd6391184c4227e5367a8c1e2b 100644
--- a/src/finn/transformation/fpgadataflow/make_pynq_driver.py
+++ b/src/finn/transformation/fpgadataflow/make_pynq_driver.py
@@ -70,8 +70,8 @@ class MakePYNQDriver(Transformation):
# extract HLSCustomOp instances to get folded i/o shapes
first_node = getCustomOp(model.find_consumer(i_tensor_name))
last_node = getCustomOp(model.find_producer(o_tensor_name))
- i_tensor_shape_folded = first_node.get_folded_input_shape()
- o_tensor_shape_folded = last_node.get_folded_output_shape()
+ i_tensor_shape_folded = tuple(first_node.get_folded_input_shape())
+ o_tensor_shape_folded = tuple(last_node.get_folded_output_shape())
# generate dummy folded i/o tensors and their packed versions
i_tensor_dummy_folded = gen_finn_dt_tensor(i_tensor_dt, i_tensor_shape_folded)
o_tensor_dummy_folded = gen_finn_dt_tensor(o_tensor_dt, o_tensor_shape_folded)
diff --git a/src/finn/transformation/fpgadataflow/make_pynq_proj.py b/src/finn/transformation/fpgadataflow/make_pynq_proj.py
index 9fe5781ecd3aa885281bde772571d307ad0669c8..429b74bb5ea7e359ea720a0a86706f2c653ee6ce 100644
--- a/src/finn/transformation/fpgadataflow/make_pynq_proj.py
+++ b/src/finn/transformation/fpgadataflow/make_pynq_proj.py
@@ -40,7 +40,7 @@ class MakePYNQProject(Transformation):
"""Create a Vivado PYNQ overlay project (including the shell infrastructure)
from the already-stitched IP block for this graph.
All nodes in the graph must have the fpgadataflow backend attribute,
- and the CodeGen_ipstitch transformation must have been previously run on
+ and the CreateStitchedIP transformation must have been previously run on
the graph.
Outcome if successful: sets the vivado_pynq_proj attribute in the ONNX
@@ -59,12 +59,12 @@ class MakePYNQProject(Transformation):
ipstitch_path = model.get_metadata_prop("vivado_stitch_proj")
if ipstitch_path is None or (not os.path.isdir(ipstitch_path)):
raise Exception(
- "No stitched IPI design found, apply CodeGen_ipstitch first."
+ "No stitched IPI design found, apply CreateStitchedIP first."
)
vivado_stitch_vlnv = model.get_metadata_prop("vivado_stitch_vlnv")
if vivado_stitch_vlnv is None:
raise Exception(
- "No vlnv for stitched IP found, apply CodeGen_ipstitch first."
+ "No vlnv for stitched IP found, apply CreateStitchedIP first."
)
# collect list of all IP dirs
diff --git a/src/finn/transformation/fpgadataflow/codegen_npysim.py b/src/finn/transformation/fpgadataflow/prepare_cppsim.py
similarity index 86%
rename from src/finn/transformation/fpgadataflow/codegen_npysim.py
rename to src/finn/transformation/fpgadataflow/prepare_cppsim.py
index 02200e76db3f9c8207605bb93c4b07f0ebc76cab..a1524322ec03a4e96ef41f999144e3eed349c5af 100644
--- a/src/finn/transformation/fpgadataflow/codegen_npysim.py
+++ b/src/finn/transformation/fpgadataflow/prepare_cppsim.py
@@ -36,35 +36,35 @@ from finn.util.fpgadataflow import is_fpgadataflow_node
def _codegen_single_node(node, model):
"""Calls C++ code generation for one node. Resulting code can be used
- to simulate node using npysim."""
+ to simulate node using cppsim."""
op_type = node.op_type
try:
# lookup op_type in registry of CustomOps
inst = registry.custom_op[op_type](node)
# get the path of the code generation directory
- code_gen_dir = inst.get_nodeattr("code_gen_dir_npysim")
+ code_gen_dir = inst.get_nodeattr("code_gen_dir_cppsim")
# ensure that there is a directory
if code_gen_dir == "" or not os.path.isdir(code_gen_dir):
code_gen_dir = make_build_dir(
- prefix="code_gen_npysim_" + str(node.name) + "_"
+ prefix="code_gen_cppsim_" + str(node.name) + "_"
)
- inst.set_nodeattr("code_gen_dir_npysim", code_gen_dir)
+ inst.set_nodeattr("code_gen_dir_cppsim", code_gen_dir)
# ensure that there is generated code inside the dir
- inst.code_generation_npysim(model)
+ inst.code_generation_cppsim(model)
except KeyError:
# exception if op_type is not supported
raise Exception("Custom op_type %s is currently not supported." % op_type)
-class CodeGen_npysim(Transformation):
+class PrepareCppSim(Transformation):
"""Call custom implementation to generate code for single custom node
and create folder that contains all the generated files.
All nodes in the graph must have the fpgadataflow backend attribute.
- Outcome if succesful: Node attribute "code_gen_dir_npysim" contains path to folder
- that contains generated C++ code that can be used to simulate node using npysim.
- The subsequent transformation is Compile"""
+ Outcome if succesful: Node attribute "code_gen_dir_cppsim" contains path to folder
+ that contains generated C++ code that can be used to simulate node using cppsim.
+ The subsequent transformation is CompileCppSim"""
def apply(self, model):
for node in model.graph.node:
diff --git a/src/finn/transformation/fpgadataflow/codegen_ipgen.py b/src/finn/transformation/fpgadataflow/prepare_ip.py
similarity index 97%
rename from src/finn/transformation/fpgadataflow/codegen_ipgen.py
rename to src/finn/transformation/fpgadataflow/prepare_ip.py
index fa7725ae1fa03cc204aa58969d6fbc6cf71e7d97..00182773558ec30ab0271de6599615233785bdd7 100644
--- a/src/finn/transformation/fpgadataflow/codegen_ipgen.py
+++ b/src/finn/transformation/fpgadataflow/prepare_ip.py
@@ -57,7 +57,7 @@ def _codegen_single_node(node, model, fpgapart, clk):
raise Exception("Custom op_type %s is currently not supported." % op_type)
-class CodeGen_ipgen(Transformation):
+class PrepareIP(Transformation):
"""Call custom implementation to generate code for single custom node
and create folder that contains all the generated files.
All nodes in the graph must have the fpgadataflow backend attribute and
@@ -69,7 +69,7 @@ class CodeGen_ipgen(Transformation):
Outcome if succesful: Node attribute "code_gen_dir_ipgen" contains path to folder
that contains generated C++ code that can be used to generate a Vivado IP block.
- The subsequent transformation is HLSSynth_IPGen"""
+ The subsequent transformation is HLSSynthIP"""
def __init__(self, fpgapart, clk):
super().__init__()
diff --git a/src/finn/transformation/fpgadataflow/prepare_rtlsim.py b/src/finn/transformation/fpgadataflow/prepare_rtlsim.py
index baac3e7bdf5ac936a963ac8346f01638657b042c..5f0b89e85dc5f33319f64ef885db20ed9c4046af 100644
--- a/src/finn/transformation/fpgadataflow/prepare_rtlsim.py
+++ b/src/finn/transformation/fpgadataflow/prepare_rtlsim.py
@@ -38,7 +38,7 @@ except ModuleNotFoundError:
class PrepareRTLSim(NodeLocalTransformation):
- """For a graph with generated RTL sources (after HLSSynth_IPGen), create a
+ """For a graph with generated RTL sources (after HLSSynthIP), create a
Verilator emulation library for each node to prepare for rtlsim
execution and set the rtlsim_so property to the path to the generated
emulation library.
diff --git a/src/finn/transformation/fpgadataflow/set_exec_mode.py b/src/finn/transformation/fpgadataflow/set_exec_mode.py
index 83dda7ceccfd26fa1c43ab517ade2e19ccae4a61..40996e5f64fb812ea3766b71a9a8275514dec4a0 100644
--- a/src/finn/transformation/fpgadataflow/set_exec_mode.py
+++ b/src/finn/transformation/fpgadataflow/set_exec_mode.py
@@ -33,7 +33,7 @@ from finn.transformation import Transformation
class SetExecMode(Transformation):
"""Set attribute exec_mode in all fpgadataflow nodes to specify which
- kind of execution should be used ("npysim" or "rtlsim")"""
+ kind of execution should be used ("cppsim" or "rtlsim")"""
def __init__(self, mode):
super().__init__()
diff --git a/src/finn/transformation/fpgadataflow/templates.py b/src/finn/transformation/fpgadataflow/templates.py
index 55a5af2ad887e4a8cfa5e3836bef00f2defe7284..55ecb57decd2ac4fa08331b5ebbcb7fd2f0cd5c6 100644
--- a/src/finn/transformation/fpgadataflow/templates.py
+++ b/src/finn/transformation/fpgadataflow/templates.py
@@ -102,144 +102,141 @@ from finn.util.data_packing import (
)
from finn.core.datatype import DataType
-class RemoteTest():
- def __init__(
- self,
- exec_mode,
- N,
- bitfile="resizer.bit",
- inputfile="input.npy",
- outputfile="output.npy"):
-
- self.exec_mode = exec_mode
+class FINNAccelDriver():
+ def __init__(self, N, bitfile):
+ \"\"\"Instantiate the FINN accelerator driver.
+ Gets batchsize (N) as integer and path to bitfile as string.\"\"\"
self.N = N
- self.inputfile = inputfile
- self.outputfile = outputfile
+ # input FINN DataType
+ self.idt = $INPUT_FINN_DATATYPE$
+ # output FINN DataType
+ self.odt = $OUTPUT_FINN_DATATYPE$
+ # input and output shapes
+ self.ishape_normal = $INPUT_SHAPE_NORMAL$
+ self.oshape_normal = $OUTPUT_SHAPE_NORMAL$
+ self.ishape_folded = $INPUT_SHAPE_FOLDED$
+ self.oshape_folded = $OUTPUT_SHAPE_FOLDED$
+ self.ishape_packed = $INPUT_SHAPE_PACKED$ # datatype np.uint8
+ self.oshape_packed = $OUTPUT_SHAPE_PACKED$ # datatype np.uint8
+ # load bitfile and set up accelerator
self.ol = Overlay(bitfile)
self.dma = self.ol.axi_dma_0
self.ctrl_regs = self.ol.resize_accel_0
- self.ishape_packed = $INPUT_SHAPE_PACKED$
- self.oshape_packed = $OUTPUT_SHAPE_PACKED$
# neuron folding factor of output = iterations per sample
self.itersPerSample = self.oshape_packed[-2]
# AXI lite register offset for number of iterations
# used by TLastMarker to signal end of transmission for AXI CDMA
self.REG_OFFSET_NUM_ITERS = 0x10
+ # set up TLastMarker with correct num. samples
+ self.ctrl_regs.write(self.REG_OFFSET_NUM_ITERS, self.N*self.itersPerSample)
- def load_input(self):
- N = self.N
- ishape_normal = $INPUT_SHAPE_NORMAL$
- # load desired input .npy file
- ibuf_normal = np.load(self.inputfile)
- # ensure that shape is as expected
- assert ibuf_normal.shape == ishape_normal
- return ibuf_normal
+ # allocate a PYNQ buffer for the packed input and buffer
+ self.ibuf_packed_device = allocate(shape=self.ishape_packed, dtype=np.uint8)
+ self.obuf_packed_device = allocate(shape=self.oshape_packed, dtype=np.uint8)
- def pack_input(self, ibuf_normal):
- N = self.N
- # input FINN DataType
- idt = $INPUT_FINN_DATATYPE$
- ishape_folded = $INPUT_SHAPE_FOLDED$
+ def fold_input(self, ibuf_normal):
+ \"\"\"Reshapes input in desired shape.
+ Gets input data (ibuf_normal), checks if data is in expected normal shape.
+ Returns folded input.\"\"\"
+ # ensure that shape is as expected
+ assert ibuf_normal.shape == self.ishape_normal
# convert to folded form
- ibuf_folded = ibuf_normal.reshape(ishape_folded)
- # pack the input buffer, reversing both SIMD dim and endianness
+ ibuf_folded = ibuf_normal.reshape(self.ishape_folded)
+ return ibuf_folded
+
+ def pack_input(self, ibuf_folded):
+ \"\"\"Packs folded input and reverses both SIMD dim and endianness.
+ Gets input data in folded shape and returns packed input data.\"\"\"
ibuf_packed = finnpy_to_packed_bytearray(
- ibuf_folded, idt, reverse_endian=True, reverse_inner=True
+ ibuf_folded, self.idt, reverse_endian=True, reverse_inner=True
)
return ibuf_packed
def unpack_output(self, obuf_packed):
- N = self.N
- # output FINN DataType
- odt = $OUTPUT_FINN_DATATYPE$
- oshape_folded = $OUTPUT_SHAPE_FOLDED$
- # unpack the packed output buffer from accelerator
+ \"\"\"Unpacks the packed output buffer from accelerator.
+ Gets packed output and returns output data in folded shape.\"\"\"
obuf_folded = packed_bytearray_to_finnpy(
- obuf_packed, odt, oshape_folded, reverse_endian=True, reverse_inner=True
+ obuf_packed, self.odt, self.oshape_folded, reverse_endian=True, reverse_inner=True
)
return obuf_folded
- def save_output(self, obuf_folded):
- N = self.N
- # convert to normal reshape and save
- oshape_normal = $OUTPUT_SHAPE_NORMAL$
- obuf_normal = obuf_folded.reshape(oshape_normal)
- np.save(self.outputfile, obuf_normal)
-
- def allocate_pynqbuffer(self, shape, data=None):
- buf_device = allocate(shape=shape, dtype=np.uint8)
-
- # if necessary copy the packed data into the PYNQ buffer
- # TODO optimization: pack directly into the PYNQ buffer?
- if data is not None:
- np.copyto(buf_device, data)
-
- return buf_device
+ def unfold_output(self, obuf_folded):
+ \"\"\"Unfolds output data to normal shape.
+ Gets folded output data and returns output data in normal shape.\"\"\"
+ obuf_normal = obuf_folded.reshape(self.oshape_normal)
+ return obuf_normal
+ def copy_input_data_to_device(self, data):
+ \"\"\"Copies given input data to PYNQ buffer.\"\"\"
+ np.copyto(self.ibuf_packed_device, data)
- def run_nw(self):
- exec_mode = self.exec_mode
- if exec_mode == "remote_pynq":
- ibuf_normal = self.load_input()
- ibuf_packed = self.pack_input(ibuf_normal)
- elif exec_mode != "throughput_test":
- raise Exception("Exec mode has to be set to remote_pynq or throughput_test")
-
- # set up TLastMarker with correct num. samples
- self.ctrl_regs.write(self.REG_OFFSET_NUM_ITERS, N*self.itersPerSample)
-
- # allocate a PYNQ buffer for the packed input buffer
- if exec_mode == "remote_pynq":
- ibuf_packed_device = self.allocate_pynqbuffer(self.ishape_packed, ibuf_packed)
- else:
- ibuf_packed_device = self.allocate_pynqbuffer(self.ishape_packed)
-
- # allocate a PYNQ buffer for the returned packed output buffer
- obuf_packed = self.allocate_pynqbuffer(self.oshape_packed)
-
- if exec_mode == "throughput_test":
- # measure runtime of network
- start = time.time()
- res={}
-
- # set up the DMA and wait until all transfers complete
+ def execute(self):
+ \"\"\"Executes accelerator by setting up the DMA and
+ waiting until all transfers complete. Uses only member variables and
+ returns nothing.\"\"\"
dma = self.dma
- dma.sendchannel.transfer(ibuf_packed_device)
- dma.recvchannel.transfer(obuf_packed)
+ dma.sendchannel.transfer(self.ibuf_packed_device)
+ dma.recvchannel.transfer(self.obuf_packed_device)
dma.sendchannel.wait()
dma.recvchannel.wait()
- if exec_mode == "throughput_test":
- end = time.time()
- runtime = end - start
- res["runtime[ms]"] = runtime*1000
- res["throughput[images/s]"] = N / runtime
- res["DRAM_in_bandwidth[Mb/s]"] = np.prod(self.ishape_packed)*0.000001 / runtime
- res["DRAM_out_bandwidth[Mb/s]"] = np.prod(self.oshape_packed)*0.000001 / runtime
- file = open("nw_metrics.txt", "w")
- file.write(str(res))
- file.close()
- else:
- obuf_folded = self.unpack_output(obuf_packed)
- self.save_output(obuf_folded)
-
-
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Set exec mode, batchsize N, bitfile name, inputfile name and outputfile name')
- parser.add_argument('exec_mode', help='Please select functional verification ("remote_pynq") or throughput test ("throughput_test")')
- parser.add_argument('N', help='number of samples for inference', type=int)
- parser.add_argument('bitfile', default="resizer.bit")
- parser.add_argument('inputfile', default="input.npy")
- parser.add_argument('outputfile', default="output.npy")
+ parser.add_argument('--exec_mode', help='Please select functional verification ("execute") or throughput test ("throughput_test")', default="execute")
+ parser.add_argument('--batchsize', help='number of samples for inference', type=int, default=1)
+ parser.add_argument('--bitfile', help='name of bitfile (i.e. "resizer.bit")', default="resizer.bit")
+ parser.add_argument('--inputfile', help='name of input npy file (i.e. "input.npy")', default="input.npy")
+ parser.add_argument('--outputfile', help='name of output npy file (i.e. "output.npy")', default="output.npy")
+ # parse arguments
args = parser.parse_args()
exec_mode = args.exec_mode
- N = args.N
+ N = args.batchsize
bitfile = args.bitfile
inputfile = args.inputfile
outputfile = args.outputfile
- Test = RemoteTest(exec_mode, N, bitfile, inputfile, outputfile)
- Test.run_nw()
+ # instantiate FINN accelerator driver and pass batchsize and bitfile
+ finnDriver = FINNAccelDriver(N, bitfile)
+
+ # for the remote execution the data from the input npy file has to be loaded,
+ # packed and copied to the PYNQ buffer
+ if exec_mode == "execute":
+ # load desired input .npy file
+ ibuf_normal = np.load(inputfile)
+ ibuf_folded = finnDriver.fold_input(ibuf_normal)
+ ibuf_packed = finnDriver.pack_input(ibuf_folded)
+ finnDriver.copy_input_data_to_device(ibuf_packed)
+ elif exec_mode != "throughput_test":
+ raise Exception("Exec mode has to be set to remote_pynq or throughput_test")
+
+ # for the throughput test the runtime of the network has to be measured
+ if exec_mode == "throughput_test":
+ # measure runtime of network
+ start = time.time()
+ # dictionary for results of throughput test
+ res={}
+
+ # execute accelerator
+ finnDriver.execute()
+
+ # measure run time and fill dictionary with results of the throughput test
+ if exec_mode == "throughput_test":
+ end = time.time()
+ runtime = end - start
+ res["runtime[ms]"] = runtime*1000
+ res["throughput[images/s]"] = N / runtime
+ res["DRAM_in_bandwidth[Mb/s]"] = np.prod(finnDriver.ishape_packed)*0.000001 / runtime
+ res["DRAM_out_bandwidth[Mb/s]"] = np.prod(finnDriver.oshape_packed)*0.000001 / runtime
+ file = open("nw_metrics.txt", "w")
+ file.write(str(res))
+ file.close()
+
+ # if execution is selected unpack, unfold and save output to output npy file
+ else:
+ obuf_folded = finnDriver.unpack_output(finnDriver.obuf_packed_device)
+ obuf_normal = finnDriver.unfold_output(obuf_folded)
+ np.save(outputfile, obuf_normal)
+
"""
diff --git a/src/finn/transformation/move_reshape.py b/src/finn/transformation/move_reshape.py
index 6a30fd93cc0bdc322b6ec7d892d42d3c3ca96fd6..2ddaf4f840f449d3f5ec5cb83eaf461d624eb7a2 100644
--- a/src/finn/transformation/move_reshape.py
+++ b/src/finn/transformation/move_reshape.py
@@ -17,7 +17,7 @@ def _is_fpgadataflow_node(node):
return False
-class MoveReshape(Transformation):
+class RemoveCNVtoFCFlatten(Transformation):
"""Removes a node that implements a (1, -1) reshape if it is
between two fpgadataflow nodes"""
@@ -27,13 +27,13 @@ class MoveReshape(Transformation):
graph_modified = False
for n in graph.node:
if n.op_type == "Reshape":
- graph_modified = True
shape = model.get_initializer(n.input[1])
if (shape == [1, -1]).all():
producer = model.find_producer(n.input[0])
if _is_fpgadataflow_node(producer) is True:
consumer = model.find_consumer(n.output[0])
if _is_fpgadataflow_node(consumer) is True:
+ graph_modified = True
consumer.input[0] = n.input[0]
graph.node.remove(n)
diff --git a/src/finn/util/data_packing.py b/src/finn/util/data_packing.py
index e67638bd1ca81d933fbfbffec9efcd7f84fa961e..a087fd2ff2f8a3db8a6caa8332810be9b6188367 100644
--- a/src/finn/util/data_packing.py
+++ b/src/finn/util/data_packing.py
@@ -296,7 +296,8 @@ def rtlsim_output_to_npy(
"""Convert a flattened sequence of Python arbitrary-precision integers
output into a NumPy array, saved as npy file at path. Each arbitrary-precision
integer is assumed to be a packed array of targetBits-bit elements, which
- will be unpacked as the innermost dimension of the NumPy array."""
+ will be unpacked as the innermost dimension of the NumPy array. If path is
+ not None it will also be saved as a npy file."""
# TODO should have its own testbench?
output = np.asarray([hex(int(x)) for x in output])
@@ -305,7 +306,8 @@ def rtlsim_output_to_npy(
)
# make copy before saving the array
out_array = out_array.copy()
- np.save(path, out_array)
+ if path is not None:
+ np.save(path, out_array)
return out_array
diff --git a/src/finn/util/fpgadataflow.py b/src/finn/util/fpgadataflow.py
index 7a404cd53e0fcbc758a960fa2d31792fb6263a18..9a2708439c0fed1e25c0d955af21cd2e9e705446 100644
--- a/src/finn/util/fpgadataflow.py
+++ b/src/finn/util/fpgadataflow.py
@@ -103,6 +103,7 @@ def pyverilate_get_liveness_threshold_cycles():
def is_fpgadataflow_node(node):
+ """Returns True if given node is fpgadataflow node. Otherwise False."""
is_node = False
if node is not None:
if node.domain == "finn":
diff --git a/src/finn/util/test.py b/src/finn/util/test.py
index 34edc3cacdecc461d1254c35c026c56ff8813549..4cad01b1f7ec58da7ba6d5460c072faa01202c55 100644
--- a/src/finn/util/test.py
+++ b/src/finn/util/test.py
@@ -26,49 +26,38 @@
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-import torch
-from models.CNV import CNV
-from models.LFC import LFC
-from models.SFC import SFC
-from models.TFC import TFC
-
-
-def get_trained_checkpoint(netname, wbits, abits):
- """Returns the weights and activations from the FINN Brevitas test networks
- for given netname and the number of bits for weights and activations"""
- # TODO get from config instead, hardcoded to Docker path for now
- nname = "%s_%dW%dA" % (netname, wbits, abits)
- root = "/workspace/brevitas_cnv_lfc/pretrained_models/%s/checkpoints/best.tar"
- return root % nname
-
-
-def get_test_model_def_fxn(netname):
- """Returns the PyTorch model instantation function related to netname."""
- model_def_map = {"LFC": LFC, "SFC": SFC, "TFC": TFC, "CNV": CNV}
- return model_def_map[netname]
+from brevitas_examples import bnn_pynq
+
+# map of (wbits,abits) -> model
+example_map = {
+ ("CNV", 1, 1): bnn_pynq.cnv_1w1a,
+ ("CNV", 1, 2): bnn_pynq.cnv_1w2a,
+ ("CNV", 2, 2): bnn_pynq.cnv_2w2a,
+ ("LFC", 1, 1): bnn_pynq.lfc_1w1a,
+ ("LFC", 1, 2): bnn_pynq.lfc_1w2a,
+ ("SFC", 1, 1): bnn_pynq.sfc_1w1a,
+ ("SFC", 1, 2): bnn_pynq.sfc_1w2a,
+ ("SFC", 2, 2): bnn_pynq.sfc_2w2a,
+ ("TFC", 1, 1): bnn_pynq.tfc_1w1a,
+ ("TFC", 1, 2): bnn_pynq.tfc_1w2a,
+ ("TFC", 2, 2): bnn_pynq.tfc_2w2a,
+}
+
+
+def get_test_model(netname, wbits, abits, pretrained):
+ """Returns the model specified by input arguments from the Brevitas BNN-PYNQ
+ test networks. Pretrained weights loaded if pretrained is True."""
+ model_cfg = (netname, wbits, abits)
+ model_def_fxn = example_map[model_cfg]
+ fc = model_def_fxn(pretrained)
+ return fc.eval()
def get_test_model_trained(netname, wbits, abits):
- """Returns the pretrained model specified by input arguments loaded with weights
- and activations from the FINN Brevitas test networks."""
- model_def_fxn = get_test_model_def_fxn(netname)
- checkpoint_loc = get_trained_checkpoint(netname, wbits, abits)
- if netname == "CNV":
- ibits = 8
- else:
- ibits = abits
- fc = model_def_fxn(weight_bit_width=wbits, act_bit_width=abits, in_bit_width=ibits)
- checkpoint = torch.load(checkpoint_loc, map_location="cpu")
- fc.load_state_dict(checkpoint["state_dict"])
- return fc.eval()
+ "get_test_model with pretrained=True"
+ return get_test_model(netname, wbits, abits, pretrained=True)
def get_test_model_untrained(netname, wbits, abits):
- """Returns untrained model specified by input arguments."""
- model_def_fxn = get_test_model_def_fxn(netname)
- if netname == "CNV":
- ibits = 8
- else:
- ibits = abits
- fc = model_def_fxn(weight_bit_width=wbits, act_bit_width=abits, in_bit_width=ibits)
- return fc.eval()
+ "get_test_model with pretrained=False"
+ return get_test_model(netname, wbits, abits, pretrained=False)
diff --git a/src/finn/util/visualization.py b/src/finn/util/visualization.py
new file mode 100644
index 0000000000000000000000000000000000000000..9f0e9e8d773dd3cb4521d44db486a3f916b86ff7
--- /dev/null
+++ b/src/finn/util/visualization.py
@@ -0,0 +1,40 @@
+# Copyright (c) 2020, Xilinx
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# * Redistributions of source code must retain the above copyright notice, this
+# list of conditions and the following disclaimer.
+#
+# * Redistributions in binary form must reproduce the above copyright notice,
+# this list of conditions and the following disclaimer in the documentation
+# and/or other materials provided with the distribution.
+#
+# * Neither the name of FINN nor the names of its
+# contributors may be used to endorse or promote products derived from
+# this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+import inspect
+import netron
+from IPython.display import IFrame
+
+
+def showSrc(what):
+ print("".join(inspect.getsourcelines(what)[0]))
+
+
+def showInNetron(model_filename):
+ netron.start(model_filename, port=8081, host="0.0.0.0")
+ return IFrame(src="http://0.0.0.0:8081/", width="100%", height=400)
diff --git a/tests/analysis/test_topology_checks.py b/tests/analysis/test_topology_checks.py
index 41fbdb6cac8e81d6b1e3eed54a71d0e1d43c3adc..7f7f800da05e38fefa9350928ab6ddc94acbe2b6 100644
--- a/tests/analysis/test_topology_checks.py
+++ b/tests/analysis/test_topology_checks.py
@@ -26,11 +26,13 @@
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+import os
from pkgutil import get_data
import onnx.helper as oh
from onnx import TensorProto
-
+import brevitas.onnx as bo
+from finn.util.test import get_test_model_trained
import finn.analysis.topology as ta
from finn.core.modelwrapper import ModelWrapper
from finn.transformation.infer_shapes import InferShapes
@@ -88,3 +90,116 @@ def test_node_inputs_in_expected_order():
# this model has an (unnecessary) dynamic reshape for its weight tensor
# and so it fails the check
assert ret["node_inputs_in_expected_order"] is False
+
+
+def test_nodes_topologically_sorted():
+ # test analysis pass (nodes_topologically_sorted) with different models
+
+ # test with data/onnx/finn-hls-model/tfc_w1_a1_after_conv_to_hls.onnx
+ raw_m = get_data(
+ "finn", "data/onnx/finn-hls-model/tfc_w1_a1_after_conv_to_hls.onnx"
+ )
+ model = ModelWrapper(raw_m)
+ ret = model.analysis(ta.nodes_topologically_sorted)
+ assert ret["nodes_topologically_sorted"] is True
+
+ # remove first node and add it at the end
+ graph = model.graph
+ first_node = graph.node[0]
+ graph.node.remove(first_node)
+ graph.node.append(first_node)
+ ret = model.analysis(ta.nodes_topologically_sorted)
+ assert ret["nodes_topologically_sorted"] is False
+
+ # test with data/onnx/mnist-conv/model.onnx
+ raw_m = get_data("finn", "data/onnx/mnist-conv/model.onnx")
+ model = ModelWrapper(raw_m)
+ ret = model.analysis(ta.nodes_topologically_sorted)
+ assert ret["nodes_topologically_sorted"] is True
+
+ # remove first node and add it at the end
+ graph = model.graph
+ first_node = graph.node[0]
+ graph.node.remove(first_node)
+ graph.node.append(first_node)
+ ret = model.analysis(ta.nodes_topologically_sorted)
+ assert ret["nodes_topologically_sorted"] is False
+
+ # test with manually created small network
+ Neg_node = oh.make_node("Neg", inputs=["in1"], outputs=["neg1"])
+ Round_node = oh.make_node("Round", inputs=["neg1"], outputs=["round1"])
+
+ Ceil_node = oh.make_node("Ceil", inputs=["neg1"], outputs=["ceil1"])
+ Add_node = oh.make_node("Add", inputs=["round1", "ceil1"], outputs=["out1"])
+
+ in1 = oh.make_tensor_value_info("in1", TensorProto.FLOAT, [4, 4])
+ out1 = oh.make_tensor_value_info("out1", TensorProto.FLOAT, [4, 4])
+
+ graph = oh.make_graph(
+ nodes=[Neg_node, Round_node, Ceil_node, Add_node],
+ name="simple_graph",
+ inputs=[in1],
+ outputs=[out1],
+ value_info=[
+ oh.make_tensor_value_info("neg1", TensorProto.FLOAT, [4, 4]),
+ oh.make_tensor_value_info("round1", TensorProto.FLOAT, [4, 4]),
+ oh.make_tensor_value_info("ceil1", TensorProto.FLOAT, [4, 4]),
+ ],
+ )
+
+ onnx_model = oh.make_model(graph, producer_name="simple-model")
+ model = ModelWrapper(onnx_model)
+
+ ret = model.analysis(ta.nodes_topologically_sorted)
+ assert ret["nodes_topologically_sorted"] is True
+
+ # create same graph but with "wrong" node order
+ graph = oh.make_graph(
+ nodes=[Round_node, Ceil_node, Neg_node, Add_node],
+ name="simple_graph",
+ inputs=[in1],
+ outputs=[out1],
+ value_info=[
+ oh.make_tensor_value_info("neg1", TensorProto.FLOAT, [4, 4]),
+ oh.make_tensor_value_info("round1", TensorProto.FLOAT, [4, 4]),
+ oh.make_tensor_value_info("ceil1", TensorProto.FLOAT, [4, 4]),
+ ],
+ )
+
+ onnx_model = oh.make_model(graph, producer_name="simple-model")
+ model = ModelWrapper(onnx_model)
+
+ ret = model.analysis(ta.nodes_topologically_sorted)
+ assert ret["nodes_topologically_sorted"] is False
+
+ # test with data/onnx/finn-hls-model/finn-hls-onnx-model.onnx
+ raw_m = get_data("finn", "data/onnx/finn-hls-model/finn-hls-onnx-model.onnx")
+ model = ModelWrapper(raw_m)
+ ret = model.analysis(ta.nodes_topologically_sorted)
+ assert ret["nodes_topologically_sorted"] is True
+
+ # remove first node and add it at the end
+ graph = model.graph
+ first_node = graph.node[0]
+ graph.node.remove(first_node)
+ graph.node.append(first_node)
+ ret = model.analysis(ta.nodes_topologically_sorted)
+ assert ret["nodes_topologically_sorted"] is False
+
+ # test with cnv_w1a1
+ build_dir = "/tmp/" + os.environ["FINN_INST_NAME"]
+ cnv = get_test_model_trained("CNV", 1, 1)
+ bo.export_finn_onnx(
+ cnv, (1, 3, 32, 32), build_dir + "/end2end_cnv_w1a1_export.onnx"
+ )
+ model = ModelWrapper(build_dir + "/end2end_cnv_w1a1_export.onnx")
+ ret = model.analysis(ta.nodes_topologically_sorted)
+ assert ret["nodes_topologically_sorted"] is True
+
+ # remove first node and add it at the end
+ graph = model.graph
+ first_node = graph.node[0]
+ graph.node.remove(first_node)
+ graph.node.append(first_node)
+ ret = model.analysis(ta.nodes_topologically_sorted)
+ assert ret["nodes_topologically_sorted"] is False
diff --git a/tests/brevitas/test_brevitas_act_export.py b/tests/brevitas/test_brevitas_act_export.py
index 77ad1fb1cb2f9cb116ad2f8961b0878d95d73d34..2090d15bbfadf97566e2ff4c6fb73cae06ad7960 100644
--- a/tests/brevitas/test_brevitas_act_export.py
+++ b/tests/brevitas/test_brevitas_act_export.py
@@ -1,11 +1,12 @@
import onnx # noqa
+import os
import numpy as np
import torch
import brevitas.onnx as bo
from brevitas.nn import QuantHardTanh
from brevitas.core.restrict_val import RestrictValueType
+from brevitas.core.quant import QuantType
from brevitas.core.scaling import ScalingImplType
-from models.common import get_quant_type
import pytest
from finn.core.modelwrapper import ModelWrapper
import finn.core.onnx_exec as oxe
@@ -22,6 +23,14 @@ export_onnx_path = "test_act.onnx"
"scaling_impl_type", [ScalingImplType.CONST, ScalingImplType.PARAMETER]
)
def test_brevitas_act_export(abits, narrow_range, min_val, max_val, scaling_impl_type):
+ def get_quant_type(bit_width):
+ if bit_width is None:
+ return QuantType.FP
+ elif bit_width == 1:
+ return QuantType.BINARY
+ else:
+ return QuantType.INT
+
act_quant_type = get_quant_type(abits)
ishape = (1, 15)
b_act = QuantHardTanh(
@@ -79,3 +88,4 @@ tensor_quant.scaling_impl.learned_value": torch.tensor(
print("expec:", ", ".join(["{:8.4f}".format(x) for x in expected[0]]))
assert np.isclose(produced, expected, atol=1e-3).all()
+ os.remove(export_onnx_path)
diff --git a/tests/brevitas/test_brevitas_cnv.py b/tests/brevitas/test_brevitas_cnv.py
index 8d21a33f78ca6f1229bdc11e753fc17cdf170242..c04e16ad1923609c81240235057cc7a190c90ffb 100644
--- a/tests/brevitas/test_brevitas_cnv.py
+++ b/tests/brevitas/test_brevitas_cnv.py
@@ -59,6 +59,7 @@ def test_brevitas_cnv_export_exec(wbits, abits):
model = model.transform(FoldConstants())
fn = pk.resource_filename("finn", "data/cifar10/cifar10-test-data-class3.npz")
input_tensor = np.load(fn)["arr_0"].astype(np.float32)
+ input_tensor = input_tensor / 255
assert input_tensor.shape == (1, 3, 32, 32)
# run using FINN-based execution
input_dict = {model.graph.input[0].name: input_tensor}
@@ -68,4 +69,5 @@ def test_brevitas_cnv_export_exec(wbits, abits):
input_tensor = torch.from_numpy(input_tensor).float()
expected = cnv.forward(input_tensor).detach().numpy()
assert np.isclose(produced, expected, atol=1e-3).all()
+ assert np.argmax(produced) == 3
os.remove(export_onnx_path)
diff --git a/tests/core/test_modelwrapper.py b/tests/core/test_modelwrapper.py
index 942eda19ca4c2cdbded9f906a5e7772f50acbd6e..4d2029093f09a705eb562a7f706f21b64172e435 100644
--- a/tests/core/test_modelwrapper.py
+++ b/tests/core/test_modelwrapper.py
@@ -27,8 +27,8 @@
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import os
+import onnx
from collections import Counter
-
import brevitas.onnx as bo
import numpy as np
@@ -68,3 +68,56 @@ def test_modelwrapper():
out_prod = model.find_producer(l0_inp_tensor_name)
assert out_prod.op_type == "Sign"
os.remove(export_onnx_path)
+
+
+def test_modelwrapper_graph_order():
+ # create small network with properties to be tested
+ Neg_node = onnx.helper.make_node("Neg", inputs=["in1"], outputs=["neg1"],)
+ Round_node = onnx.helper.make_node("Round", inputs=["neg1"], outputs=["round1"],)
+
+ Ceil_node = onnx.helper.make_node("Ceil", inputs=["neg1"], outputs=["ceil1"],)
+ Add_node = onnx.helper.make_node(
+ "Add", inputs=["round1", "ceil1"], outputs=["out1"],
+ )
+
+ in1 = onnx.helper.make_tensor_value_info("in1", onnx.TensorProto.FLOAT, [4, 4])
+ out1 = onnx.helper.make_tensor_value_info("out1", onnx.TensorProto.FLOAT, [4, 4])
+
+ graph = onnx.helper.make_graph(
+ nodes=[Neg_node, Round_node, Ceil_node, Add_node],
+ name="simple_graph",
+ inputs=[in1],
+ outputs=[out1],
+ value_info=[
+ onnx.helper.make_tensor_value_info("neg1", onnx.TensorProto.FLOAT, [4, 4]),
+ onnx.helper.make_tensor_value_info(
+ "round1", onnx.TensorProto.FLOAT, [4, 4]
+ ),
+ onnx.helper.make_tensor_value_info("ceil1", onnx.TensorProto.FLOAT, [4, 4]),
+ ],
+ )
+
+ onnx_model = onnx.helper.make_model(graph, producer_name="simple-model")
+ model = ModelWrapper(onnx_model)
+
+ # test graph order functions
+ assert model.find_consumers("in1") == [Neg_node]
+ assert model.find_consumers("neg1") == [Round_node, Ceil_node]
+ assert model.find_consumers("round1") == [Add_node]
+ assert model.find_consumers("ceil1") == [Add_node]
+ assert model.find_consumers("out1") is None
+
+ assert model.find_direct_successors(Neg_node) == [Round_node, Ceil_node]
+ assert model.find_direct_successors(Round_node) == [Add_node]
+ assert model.find_direct_successors(Ceil_node) == [Add_node]
+ assert model.find_direct_successors(Add_node) is None
+
+ assert model.find_direct_predecessors(Neg_node) is None
+ assert model.find_direct_predecessors(Round_node) == [Neg_node]
+ assert model.find_direct_predecessors(Ceil_node) == [Neg_node]
+ assert model.find_direct_predecessors(Add_node) == [Round_node, Ceil_node]
+
+ assert model.get_node_index(Neg_node) == 0
+ assert model.get_node_index(Round_node) == 1
+ assert model.get_node_index(Ceil_node) == 2
+ assert model.get_node_index(Add_node) == 3
diff --git a/tests/custom_op/test_xnorpopcountmatmul.py b/tests/custom_op/test_xnorpopcountmatmul.py
index 6b59283667ac05f569e5c3d80dbfc1530616d045..37d9b7e5968bdb70023be9b70515410e941f51ce 100644
--- a/tests/custom_op/test_xnorpopcountmatmul.py
+++ b/tests/custom_op/test_xnorpopcountmatmul.py
@@ -48,10 +48,6 @@ from finn.transformation.streamline.sign_to_thres import ConvertSignToThres
from finn.util.test import get_test_model_trained
export_onnx_path = "test_output_lfc.onnx"
-# TODO get from config instead, hardcoded to Docker path for now
-trained_lfc_checkpoint = (
- "/workspace/brevitas_cnv_lfc/pretrained_models/LFC_1W1A/checkpoints/best.tar"
-)
def test_xnorpopcountmatmul():
diff --git a/tests/end2end/test_end2end_cnv_w1a1.py b/tests/end2end/test_end2end_cnv_w1a1.py
index 34e0df8402ea0d1b880781185cd17e3ccb1a0ae0..703cb7ad92d6f4ec6dd67a05345f323a73ee178d 100644
--- a/tests/end2end/test_end2end_cnv_w1a1.py
+++ b/tests/end2end/test_end2end_cnv_w1a1.py
@@ -41,7 +41,7 @@ from finn.custom_op.registry import getCustomOp
from finn.core.onnx_exec import execute_onnx
from finn.transformation.double_to_single_float import DoubleToSingleFloat
from finn.transformation.infer_shapes import InferShapes
-from finn.transformation.move_reshape import MoveReshape
+from finn.transformation.move_reshape import RemoveCNVtoFCFlatten
from finn.transformation.fold_constants import FoldConstants
from finn.transformation.general import GiveReadableTensorNames, GiveUniqueNodeNames
from finn.transformation.streamline import Streamline
@@ -55,15 +55,15 @@ from finn.transformation.fpgadataflow.create_dataflow_partition import (
)
from finn.transformation.fpgadataflow.insert_dwc import InsertDWC
from finn.transformation.fpgadataflow.insert_tlastmarker import InsertTLastMarker
-from finn.transformation.fpgadataflow.codegen_ipgen import CodeGen_ipgen
-from finn.transformation.fpgadataflow.hlssynth_ipgen import HLSSynth_IPGen
+from finn.transformation.fpgadataflow.prepare_ip import PrepareIP
+from finn.transformation.fpgadataflow.hlssynth_ip import HLSSynthIP
from finn.transformation.fpgadataflow.replace_verilog_relpaths import (
ReplaceVerilogRelPaths,
)
-from finn.transformation.fpgadataflow.codegen_ipstitch import CodeGen_ipstitch
+from finn.transformation.fpgadataflow.create_stitched_ip import CreateStitchedIP
from finn.transformation.fpgadataflow.set_exec_mode import SetExecMode
-from finn.transformation.fpgadataflow.codegen_npysim import CodeGen_npysim
-from finn.transformation.fpgadataflow.compile import Compile
+from finn.transformation.fpgadataflow.prepare_cppsim import PrepareCppSim
+from finn.transformation.fpgadataflow.compile_cppsim import CompileCppSim
from finn.transformation.fpgadataflow.make_pynq_driver import MakePYNQDriver
from finn.transformation.fpgadataflow.make_pynq_proj import MakePYNQProject
from finn.transformation.fpgadataflow.synth_pynq_proj import SynthPYNQProject
@@ -117,7 +117,7 @@ def test_end2end_cnv_w1a1_convert_to_hls_layers():
model = model.transform(to_hls.InferQuantizedStreamingFCLayer(mem_mode))
model = model.transform(to_hls.InferConvInpGen())
model = model.transform(to_hls.InferStreamingMaxPool())
- model = model.transform(MoveReshape())
+ model = model.transform(RemoveCNVtoFCFlatten())
model.save(build_dir + "/end2end_cnv_w1a1_hls_layers.onnx")
@@ -169,8 +169,8 @@ def test_end2end_cnv_w1a1_fold_and_tlastmarker():
def test_end2end_cnv_w1a1_gen_hls_ip():
model = ModelWrapper(build_dir + "/end2end_cnv_w1a1_folded.onnx")
- model = model.transform(CodeGen_ipgen(test_fpga_part, target_clk_ns))
- model = model.transform(HLSSynth_IPGen())
+ model = model.transform(PrepareIP(test_fpga_part, target_clk_ns))
+ model = model.transform(HLSSynthIP())
model = model.transform(AnnotateResources("hls"))
model.save(build_dir + "/end2end_cnv_w1a1_ipgen.onnx")
@@ -178,7 +178,7 @@ def test_end2end_cnv_w1a1_gen_hls_ip():
def test_end2end_cnv_w1a1_ip_stitch():
model = ModelWrapper(build_dir + "/end2end_cnv_w1a1_ipgen.onnx")
model = model.transform(ReplaceVerilogRelPaths())
- model = model.transform(CodeGen_ipstitch(test_fpga_part))
+ model = model.transform(CreateStitchedIP(test_fpga_part))
model.save(build_dir + "/end2end_cnv_w1a1_ipstitch.onnx")
@@ -188,13 +188,13 @@ def test_end2end_cnv_w1a1_verify_dataflow_part():
inp_name = model.graph.input[0].name
out_name = model.graph.output[0].name
inp_dict = {inp_name: x}
- # npysim
- model = model.transform(CodeGen_npysim())
- model = model.transform(Compile())
- model = model.transform(SetExecMode("npysim"))
- model.save(build_dir + "/end2end_cnv_w1a1_ipgen_npysim.onnx")
- ret_npysim = execute_onnx(model, inp_dict, True)
- res_npysim = ret_npysim[out_name]
+ # cppsim
+ model = model.transform(PrepareCppSim())
+ model = model.transform(CompileCppSim())
+ model = model.transform(SetExecMode("cppsim"))
+ model.save(build_dir + "/end2end_cnv_w1a1_ipgen_cppsim.onnx")
+ ret_cppsim = execute_onnx(model, inp_dict, True)
+ res_cppsim = ret_cppsim[out_name]
# node-by-node rtlsim
model = model.transform(SetExecMode("rtlsim"))
model = model.transform(PrepareRTLSim())
@@ -208,8 +208,8 @@ def test_end2end_cnv_w1a1_verify_dataflow_part():
os.environ["LIVENESS_THRESHOLD"] = "-1"
ret_rtlsim_whole = execute_onnx(model, inp_dict, True)
res_rtlsim_whole = ret_rtlsim_whole[out_name]
- assert np.isclose(res_npysim, res_rtlsim_nodebynode).all()
- assert np.isclose(res_npysim, res_rtlsim_whole).all()
+ assert np.isclose(res_cppsim, res_rtlsim_nodebynode).all()
+ assert np.isclose(res_cppsim, res_rtlsim_whole).all()
def test_end2end_cnv_w1a1_verify_all():
@@ -220,6 +220,7 @@ def test_end2end_cnv_w1a1_verify_all():
# load one of the test vectors
fn = pk.resource_filename("finn", "data/cifar10/cifar10-test-data-class3.npz")
input_tensor = np.load(fn)["arr_0"].astype(np.float32)
+ input_tensor = input_tensor / 255
assert input_tensor.shape == (1, 3, 32, 32)
x = input_tensor
# x = np.zeros(ishape, dtype=np.float32)
@@ -230,12 +231,12 @@ def test_end2end_cnv_w1a1_verify_all():
parent_model = ModelWrapper(build_dir + "/end2end_cnv_w1a1_dataflow_parent.onnx")
iname = parent_model.graph.input[0].name
oname = parent_model.graph.output[0].name
- # produce results with npysim
+ # produce results with cppsim
sdp_node = parent_model.get_nodes_by_op_type("StreamingDataflowPartition")[0]
sdp_node = getCustomOp(sdp_node)
- sdp_node.set_nodeattr("model", build_dir + "/end2end_cnv_w1a1_ipgen_npysim.onnx")
- ret_npysim = execute_onnx(parent_model, {iname: x}, True)
- y_npysim = ret_npysim[oname]
+ sdp_node.set_nodeattr("model", build_dir + "/end2end_cnv_w1a1_ipgen_cppsim.onnx")
+ ret_cppsim = execute_onnx(parent_model, {iname: x}, True)
+ y_cppsim = ret_cppsim[oname]
# produce results with node-by-node rtlsim
sdp_node.set_nodeattr(
"model", build_dir + "/end2end_cnv_w1a1_ipgen_nodebynode_rtlsim.onnx"
@@ -250,9 +251,10 @@ def test_end2end_cnv_w1a1_verify_all():
os.environ["LIVENESS_THRESHOLD"] = "-1"
ret_whole_rtlsim = execute_onnx(parent_model, {iname: x}, True)
y_whole_rtlsim = ret_whole_rtlsim[oname]
- assert np.isclose(y_golden, y_npysim).all()
+ assert np.isclose(y_golden, y_cppsim).all()
assert np.isclose(y_golden, y_nodebynode_rtlsim).all()
assert np.isclose(y_golden, y_whole_rtlsim).all()
+ assert np.argmax(y_golden) == 3
def test_end2end_cnv_w1a1_make_pynq_proj():
@@ -299,6 +301,7 @@ def test_end2end_cnv_w1a1_run_on_pynq():
# load one of the test vectors
fn = pk.resource_filename("finn", "data/cifar10/cifar10-test-data-class3.npz")
input_tensor = np.load(fn)["arr_0"].astype(np.float32)
+ input_tensor = input_tensor / 255
assert input_tensor.shape == (1, 3, 32, 32)
x = input_tensor
# run using FINN-based execution
@@ -313,13 +316,14 @@ def test_end2end_cnv_w1a1_run_on_pynq():
ip = os.environ["PYNQ_IP"] # NOQA
if ip == "":
pytest.skip("PYNQ board IP address not specified")
- # produce results with npysim
+ # produce results with cppsim
sdp_node = parent_model.get_nodes_by_op_type("StreamingDataflowPartition")[0]
sdp_node = getCustomOp(sdp_node)
sdp_node.set_nodeattr("model", build_dir + "/end2end_cnv_w1a1_pynq_deploy.onnx")
ret = execute_onnx(parent_model, {iname: x}, True)
y = ret[oname]
assert np.isclose(y, y_golden).all()
+ assert np.argmax(y) == 3
except KeyError:
pytest.skip("PYNQ board IP address not specified")
diff --git a/tests/end2end/test_end2end_tfc_w1a1_throughput_test.py b/tests/end2end/test_end2end_tfc_w1a1_throughput_test.py
index ded0bd107ab9f15a72018137c79eac640e09d3a2..b5f3f4e27ff24723db69f887cb7f1cce9c4df617 100644
--- a/tests/end2end/test_end2end_tfc_w1a1_throughput_test.py
+++ b/tests/end2end/test_end2end_tfc_w1a1_throughput_test.py
@@ -46,14 +46,14 @@ from finn.core.throughput_test import throughput_test
from finn.custom_op.registry import getCustomOp
from finn.transformation.bipolar_to_xnor import ConvertBipolarMatMulToXnorPopcount
from finn.transformation.fold_constants import FoldConstants
-from finn.transformation.fpgadataflow.codegen_ipgen import CodeGen_ipgen
-from finn.transformation.fpgadataflow.codegen_ipstitch import CodeGen_ipstitch
-from finn.transformation.fpgadataflow.codegen_npysim import CodeGen_npysim
-from finn.transformation.fpgadataflow.compile import Compile
+from finn.transformation.fpgadataflow.prepare_ip import PrepareIP
+from finn.transformation.fpgadataflow.create_stitched_ip import CreateStitchedIP
+from finn.transformation.fpgadataflow.prepare_cppsim import PrepareCppSim
+from finn.transformation.fpgadataflow.compile_cppsim import CompileCppSim
from finn.transformation.fpgadataflow.create_dataflow_partition import (
CreateDataflowPartition,
)
-from finn.transformation.fpgadataflow.hlssynth_ipgen import HLSSynth_IPGen
+from finn.transformation.fpgadataflow.hlssynth_ip import HLSSynthIP
from finn.transformation.fpgadataflow.insert_dwc import InsertDWC
from finn.transformation.fpgadataflow.insert_tlastmarker import InsertTLastMarker
from finn.transformation.fpgadataflow.insert_fifo import InsertFIFO
@@ -131,54 +131,32 @@ def test_end2end_tfc_w1a1_create_dataflow_partition():
def test_end2end_tfc_w1a1_fold_and_tlastmarker():
model = ModelWrapper(build_dir + "/end2end_tfc_w1a1_dataflow_model.onnx")
fc_layers = model.get_nodes_by_op_type("StreamingFCLayer_Batch")
- fc0w = getCustomOp(fc_layers[0])
- fc1w = getCustomOp(fc_layers[1])
- fc2w = getCustomOp(fc_layers[2])
- fc3w = getCustomOp(fc_layers[3])
- fc0w.set_nodeattr("inFIFODepth", 256)
- fc0w.set_nodeattr("SIMD", 196)
- fc0w.set_nodeattr("PE", 16)
- fc0w.set_nodeattr("outFIFODepth", 64)
- fc1w.set_nodeattr("inFIFODepth", 64)
- fc0w.set_nodeattr("ram_style", "block")
- fc1w.set_nodeattr("SIMD", 16)
- fc1w.set_nodeattr("PE", 16)
- fc1w.set_nodeattr("outFIFODepth", 64)
- fc2w.set_nodeattr("inFIFODepth", 64)
- fc2w.set_nodeattr("SIMD", 16)
- fc2w.set_nodeattr("PE", 16)
- fc2w.set_nodeattr("outFIFODepth", 64)
- fc3w.set_nodeattr("inFIFODepth", 64)
- fc3w.set_nodeattr("SIMD", 16)
- fc3w.set_nodeattr("PE", 10)
- fc3w.set_nodeattr("outFIFODepth", 10)
- fc3w.set_nodeattr("ram_style", "distributed")
+ # (PE, SIMD, in_fifo_depth, out_fifo_depth, ramstyle) for each layer
+ config = [
+ (16, 49, 16, 64, "block"),
+ (8, 8, 64, 64, "auto"),
+ (8, 8, 64, 64, "auto"),
+ (10, 8, 64, 10, "distributed"),
+ ]
+ for fcl, (pe, simd, ififo, ofifo, ramstyle) in zip(fc_layers, config):
+ fcl_inst = getCustomOp(fcl)
+ fcl_inst.set_nodeattr("PE", pe)
+ fcl_inst.set_nodeattr("SIMD", simd)
+ fcl_inst.set_nodeattr("inFIFODepth", ififo)
+ fcl_inst.set_nodeattr("outFIFODepth", ofifo)
+ fcl_inst.set_nodeattr("ram_style", ramstyle)
model = model.transform(InsertDWC())
model = model.transform(InsertFIFO())
model = model.transform(InsertTLastMarker())
model = model.transform(GiveUniqueNodeNames())
- fifos = []
- for n in model.graph.node:
- if n.op_type == "StreamingFIFO":
- fifos.append(n)
- fifo0 = getCustomOp(fifos[0])
- fifo1 = getCustomOp(fifos[1])
- fifo2 = getCustomOp(fifos[2])
- fifo3 = getCustomOp(fifos[3])
- fifo4 = getCustomOp(fifos[4])
- fifo0.set_nodeattr("depth", 256)
- fifo1.set_nodeattr("depth", 64)
- fifo2.set_nodeattr("depth", 64)
- fifo3.set_nodeattr("depth", 64)
- fifo4.set_nodeattr("depth", 10)
model = model.transform(AnnotateResources("estimate"))
model.save(build_dir + "/end2end_tfc_w1a1_folded.onnx")
def test_end2end_tfc_w1a1_gen_hls_ip():
model = ModelWrapper(build_dir + "/end2end_tfc_w1a1_folded.onnx")
- model = model.transform(CodeGen_ipgen(test_fpga_part, target_clk_ns))
- model = model.transform(HLSSynth_IPGen())
+ model = model.transform(PrepareIP(test_fpga_part, target_clk_ns))
+ model = model.transform(HLSSynthIP())
model = model.transform(AnnotateResources("hls"))
model.save(build_dir + "/end2end_tfc_w1a1_ipgen.onnx")
@@ -186,7 +164,7 @@ def test_end2end_tfc_w1a1_gen_hls_ip():
def test_end2end_tfc_w1a1_ip_stitch():
model = ModelWrapper(build_dir + "/end2end_tfc_w1a1_ipgen.onnx")
model = model.transform(ReplaceVerilogRelPaths())
- model = model.transform(CodeGen_ipstitch(test_fpga_part))
+ model = model.transform(CreateStitchedIP(test_fpga_part))
model.save(build_dir + "/end2end_tfc_w1a1_ipstitch.onnx")
@@ -196,13 +174,13 @@ def test_end2end_tfc_w1a1_verify_dataflow_part():
inp_name = model.graph.input[0].name
out_name = model.graph.output[0].name
inp_dict = {inp_name: x}
- # npysim
- model = model.transform(CodeGen_npysim())
- model = model.transform(Compile())
- model = model.transform(SetExecMode("npysim"))
- model.save(build_dir + "/end2end_tfc_w1a1_ipstitch_npysim.onnx")
- ret_npysim = execute_onnx(model, inp_dict, True)
- res_npysim = ret_npysim[out_name]
+ # cppsim
+ model = model.transform(PrepareCppSim())
+ model = model.transform(CompileCppSim())
+ model = model.transform(SetExecMode("cppsim"))
+ model.save(build_dir + "/end2end_tfc_w1a1_ipstitch_cppsim.onnx")
+ ret_cppsim = execute_onnx(model, inp_dict, True)
+ res_cppsim = ret_cppsim[out_name]
# node-by-node rtlsim
model = model.transform(SetExecMode("rtlsim"))
model = model.transform(PrepareRTLSim())
@@ -214,8 +192,8 @@ def test_end2end_tfc_w1a1_verify_dataflow_part():
model.save(build_dir + "/end2end_tfc_w1a1_ipstitch_whole_rtlsim.onnx")
ret_rtlsim_whole = execute_onnx(model, inp_dict, True)
res_rtlsim_whole = ret_rtlsim_whole[out_name]
- assert np.isclose(res_npysim, res_rtlsim_nodebynode).all()
- assert np.isclose(res_npysim, res_rtlsim_whole).all()
+ assert np.isclose(res_cppsim, res_rtlsim_nodebynode).all()
+ assert np.isclose(res_cppsim, res_rtlsim_whole).all()
def test_end2end_tfc_w1a1_verify_all():
@@ -234,12 +212,12 @@ def test_end2end_tfc_w1a1_verify_all():
parent_model = ModelWrapper(build_dir + "/end2end_tfc_w1a1_dataflow_parent.onnx")
iname = parent_model.graph.input[0].name
oname = parent_model.graph.output[0].name
- # produce results with npysim
+ # produce results with cppsim
sdp_node = parent_model.get_nodes_by_op_type("StreamingDataflowPartition")[0]
sdp_node = getCustomOp(sdp_node)
- sdp_node.set_nodeattr("model", build_dir + "/end2end_tfc_w1a1_ipstitch_npysim.onnx")
- ret_npysim = execute_onnx(parent_model, {iname: x}, True)
- y_npysim = ret_npysim[oname]
+ sdp_node.set_nodeattr("model", build_dir + "/end2end_tfc_w1a1_ipstitch_cppsim.onnx")
+ ret_cppsim = execute_onnx(parent_model, {iname: x}, True)
+ y_cppsim = ret_cppsim[oname]
# produce results with node-by-node rtlsim
sdp_node.set_nodeattr(
"model", build_dir + "/end2end_tfc_w1a1_ipstitch_nodebynode_rtlsim.onnx"
@@ -252,7 +230,7 @@ def test_end2end_tfc_w1a1_verify_all():
)
ret_whole_rtlsim = execute_onnx(parent_model, {iname: x}, True)
y_whole_rtlsim = ret_whole_rtlsim[oname]
- assert np.isclose(y_golden, y_npysim).all()
+ assert np.isclose(y_golden, y_cppsim).all()
assert np.isclose(y_golden, y_nodebynode_rtlsim).all()
assert np.isclose(y_golden, y_whole_rtlsim).all()
@@ -314,7 +292,7 @@ def test_end2end_tfc_w1a1_run_on_pynq():
ip = os.environ["PYNQ_IP"] # NOQA
if ip == "":
pytest.skip("PYNQ board IP address not specified")
- # produce results with npysim
+ # produce results with cppsim
sdp_node = parent_model.get_nodes_by_op_type("StreamingDataflowPartition")[0]
sdp_node = getCustomOp(sdp_node)
sdp_node.set_nodeattr("model", build_dir + "/end2end_tfc_w1a1_pynq_deploy.onnx")
diff --git a/tests/end2end/test_end2end_tfc_w1a2.py b/tests/end2end/test_end2end_tfc_w1a2.py
index 52771e6d149810d70f908ac2af07e1d81f8f46ec..ecc0d48a6af37bc2bdd48f9306976aa8582ca1b0 100644
--- a/tests/end2end/test_end2end_tfc_w1a2.py
+++ b/tests/end2end/test_end2end_tfc_w1a2.py
@@ -43,14 +43,16 @@ from finn.core.modelwrapper import ModelWrapper
from finn.core.onnx_exec import execute_onnx
from finn.custom_op.registry import getCustomOp
from finn.transformation.fold_constants import FoldConstants
-from finn.transformation.fpgadataflow.codegen_ipgen import CodeGen_ipgen
-from finn.transformation.fpgadataflow.codegen_ipstitch import CodeGen_ipstitch
-from finn.transformation.fpgadataflow.codegen_npysim import CodeGen_npysim
-from finn.transformation.fpgadataflow.compile import Compile
+from finn.transformation.fpgadataflow.prepare_ip import PrepareIP
+from finn.transformation.fpgadataflow.create_stitched_ip import CreateStitchedIP
+from finn.transformation.fpgadataflow.prepare_cppsim import PrepareCppSim
+from finn.transformation.fpgadataflow.compile_cppsim import CompileCppSim
from finn.transformation.fpgadataflow.create_dataflow_partition import (
CreateDataflowPartition,
)
-from finn.transformation.fpgadataflow.hlssynth_ipgen import HLSSynth_IPGen
+from finn.transformation.fpgadataflow.hlssynth_ip import HLSSynthIP
+from finn.transformation.fpgadataflow.insert_dwc import InsertDWC
+from finn.transformation.fpgadataflow.insert_fifo import InsertFIFO
from finn.transformation.fpgadataflow.insert_tlastmarker import InsertTLastMarker
from finn.transformation.fpgadataflow.make_deployment import DeployToPYNQ
from finn.transformation.fpgadataflow.make_pynq_driver import MakePYNQDriver
@@ -121,23 +123,22 @@ def test_end2end_tfc_w1a2_create_dataflow_partition():
def test_end2end_tfc_w1a2_fold_and_tlastmarker():
model = ModelWrapper(build_dir + "/end2end_tfc_w1a2_dataflow_model.onnx")
fc_layers = model.get_nodes_by_op_type("StreamingFCLayer_Batch")
- fc0w = getCustomOp(fc_layers[0])
- fc1w = getCustomOp(fc_layers[1])
- fc2w = getCustomOp(fc_layers[2])
- fc3w = getCustomOp(fc_layers[3])
- fc0w.set_nodeattr("inFIFODepth", 50)
- fc0w.set_nodeattr("SIMD", 8)
- fc0w.set_nodeattr("PE", 16)
- fc0w.set_nodeattr("outFIFODepth", 4)
- fc1w.set_nodeattr("SIMD", 16)
- fc1w.set_nodeattr("PE", 16)
- fc1w.set_nodeattr("outFIFODepth", 4)
- fc2w.set_nodeattr("SIMD", 16)
- fc2w.set_nodeattr("PE", 16)
- fc2w.set_nodeattr("outFIFODepth", 4)
- fc3w.set_nodeattr("SIMD", 16)
- fc3w.set_nodeattr("PE", 10)
- fc3w.set_nodeattr("outFIFODepth", 50)
+ # (PE, SIMD, in_fifo_depth, out_fifo_depth, ramstyle) for each layer
+ config = [
+ (16, 49, 16, 64, "block"),
+ (8, 8, 64, 64, "auto"),
+ (8, 8, 64, 64, "auto"),
+ (10, 8, 64, 10, "distributed"),
+ ]
+ for fcl, (pe, simd, ififo, ofifo, ramstyle) in zip(fc_layers, config):
+ fcl_inst = getCustomOp(fcl)
+ fcl_inst.set_nodeattr("PE", pe)
+ fcl_inst.set_nodeattr("SIMD", simd)
+ fcl_inst.set_nodeattr("inFIFODepth", ififo)
+ fcl_inst.set_nodeattr("outFIFODepth", ofifo)
+ fcl_inst.set_nodeattr("ram_style", ramstyle)
+ model = model.transform(InsertDWC())
+ model = model.transform(InsertFIFO())
model = model.transform(InsertTLastMarker())
model = model.transform(GiveUniqueNodeNames())
model = model.transform(AnnotateResources("estimate"))
@@ -146,8 +147,8 @@ def test_end2end_tfc_w1a2_fold_and_tlastmarker():
def test_end2end_tfc_w1a2_gen_hls_ip():
model = ModelWrapper(build_dir + "/end2end_tfc_w1a2_folded.onnx")
- model = model.transform(CodeGen_ipgen(test_fpga_part, target_clk_ns))
- model = model.transform(HLSSynth_IPGen())
+ model = model.transform(PrepareIP(test_fpga_part, target_clk_ns))
+ model = model.transform(HLSSynthIP())
model = model.transform(AnnotateResources("hls"))
model.save(build_dir + "/end2end_tfc_w1a2_ipgen.onnx")
@@ -155,7 +156,7 @@ def test_end2end_tfc_w1a2_gen_hls_ip():
def test_end2end_tfc_w1a2_ip_stitch():
model = ModelWrapper(build_dir + "/end2end_tfc_w1a2_ipgen.onnx")
model = model.transform(ReplaceVerilogRelPaths())
- model = model.transform(CodeGen_ipstitch(test_fpga_part))
+ model = model.transform(CreateStitchedIP(test_fpga_part))
model.save(build_dir + "/end2end_tfc_w1a2_ipstitch.onnx")
@@ -165,13 +166,13 @@ def test_end2end_tfc_w1a2_verify_dataflow_part():
inp_name = model.graph.input[0].name
out_name = model.graph.output[0].name
inp_dict = {inp_name: x}
- # npysim
- model = model.transform(CodeGen_npysim())
- model = model.transform(Compile())
- model = model.transform(SetExecMode("npysim"))
- model.save(build_dir + "/end2end_tfc_w1a2_ipstitch_npysim.onnx")
- ret_npysim = execute_onnx(model, inp_dict, True)
- res_npysim = ret_npysim[out_name]
+ # cppsim
+ model = model.transform(PrepareCppSim())
+ model = model.transform(CompileCppSim())
+ model = model.transform(SetExecMode("cppsim"))
+ model.save(build_dir + "/end2end_tfc_w1a2_ipstitch_cppsim.onnx")
+ ret_cppsim = execute_onnx(model, inp_dict, True)
+ res_cppsim = ret_cppsim[out_name]
# node-by-node rtlsim
model = model.transform(SetExecMode("rtlsim"))
model = model.transform(PrepareRTLSim())
@@ -183,8 +184,8 @@ def test_end2end_tfc_w1a2_verify_dataflow_part():
model.save(build_dir + "/end2end_tfc_w1a2_ipstitch_whole_rtlsim.onnx")
ret_rtlsim_whole = execute_onnx(model, inp_dict, True)
res_rtlsim_whole = ret_rtlsim_whole[out_name]
- assert np.isclose(res_npysim, res_rtlsim_nodebynode).all()
- assert np.isclose(res_npysim, res_rtlsim_whole).all()
+ assert np.isclose(res_cppsim, res_rtlsim_nodebynode).all()
+ assert np.isclose(res_cppsim, res_rtlsim_whole).all()
def test_end2end_tfc_w1a2_verify_all():
@@ -203,12 +204,12 @@ def test_end2end_tfc_w1a2_verify_all():
parent_model = ModelWrapper(build_dir + "/end2end_tfc_w1a2_dataflow_parent.onnx")
iname = parent_model.graph.input[0].name
oname = parent_model.graph.output[0].name
- # produce results with npysim
+ # produce results with cppsim
sdp_node = parent_model.get_nodes_by_op_type("StreamingDataflowPartition")[0]
sdp_node = getCustomOp(sdp_node)
- sdp_node.set_nodeattr("model", build_dir + "/end2end_tfc_w1a2_ipstitch_npysim.onnx")
- ret_npysim = execute_onnx(parent_model, {iname: x}, True)
- y_npysim = ret_npysim[oname]
+ sdp_node.set_nodeattr("model", build_dir + "/end2end_tfc_w1a2_ipstitch_cppsim.onnx")
+ ret_cppsim = execute_onnx(parent_model, {iname: x}, True)
+ y_cppsim = ret_cppsim[oname]
# produce results with node-by-node rtlsim
sdp_node.set_nodeattr(
"model", build_dir + "/end2end_tfc_w1a2_ipstitch_nodebynode_rtlsim.onnx"
@@ -221,7 +222,7 @@ def test_end2end_tfc_w1a2_verify_all():
)
ret_whole_rtlsim = execute_onnx(parent_model, {iname: x}, True)
y_whole_rtlsim = ret_whole_rtlsim[oname]
- assert np.isclose(y_golden, y_npysim).all()
+ assert np.isclose(y_golden, y_cppsim).all()
assert np.isclose(y_golden, y_nodebynode_rtlsim).all()
assert np.isclose(y_golden, y_whole_rtlsim).all()
@@ -283,7 +284,7 @@ def test_end2end_tfc_w1a2_run_on_pynq():
ip = os.environ["PYNQ_IP"] # NOQA
if ip == "":
pytest.skip("PYNQ board IP address not specified")
- # produce results with npysim
+ # produce results with cppsim
sdp_node = parent_model.get_nodes_by_op_type("StreamingDataflowPartition")[0]
sdp_node = getCustomOp(sdp_node)
sdp_node.set_nodeattr("model", build_dir + "/end2end_tfc_w1a2_pynq_deploy.onnx")
diff --git a/tests/end2end/test_end2end_tfc_w2a2.py b/tests/end2end/test_end2end_tfc_w2a2.py
index 67111da400d475311cc29b45bb24573128981958..8c13352d9e9d146d58d76b1cf1e17878f27513f5 100644
--- a/tests/end2end/test_end2end_tfc_w2a2.py
+++ b/tests/end2end/test_end2end_tfc_w2a2.py
@@ -43,15 +43,17 @@ from finn.core.modelwrapper import ModelWrapper
from finn.core.onnx_exec import execute_onnx
from finn.custom_op.registry import getCustomOp
from finn.transformation.fold_constants import FoldConstants
-from finn.transformation.fpgadataflow.codegen_ipgen import CodeGen_ipgen
-from finn.transformation.fpgadataflow.codegen_ipstitch import CodeGen_ipstitch
-from finn.transformation.fpgadataflow.codegen_npysim import CodeGen_npysim
-from finn.transformation.fpgadataflow.compile import Compile
+from finn.transformation.fpgadataflow.prepare_ip import PrepareIP
+from finn.transformation.fpgadataflow.create_stitched_ip import CreateStitchedIP
+from finn.transformation.fpgadataflow.prepare_cppsim import PrepareCppSim
+from finn.transformation.fpgadataflow.compile_cppsim import CompileCppSim
from finn.transformation.fpgadataflow.create_dataflow_partition import (
CreateDataflowPartition,
)
-from finn.transformation.fpgadataflow.hlssynth_ipgen import HLSSynth_IPGen
+from finn.transformation.fpgadataflow.hlssynth_ip import HLSSynthIP
from finn.transformation.fpgadataflow.insert_tlastmarker import InsertTLastMarker
+from finn.transformation.fpgadataflow.insert_dwc import InsertDWC
+from finn.transformation.fpgadataflow.insert_fifo import InsertFIFO
from finn.transformation.fpgadataflow.make_deployment import DeployToPYNQ
from finn.transformation.fpgadataflow.make_pynq_driver import MakePYNQDriver
from finn.transformation.fpgadataflow.make_pynq_proj import MakePYNQProject
@@ -121,23 +123,22 @@ def test_end2end_tfc_w2a2_create_dataflow_partition():
def test_end2end_tfc_w2a2_fold_and_tlastmarker():
model = ModelWrapper(build_dir + "/end2end_tfc_w2a2_dataflow_model.onnx")
fc_layers = model.get_nodes_by_op_type("StreamingFCLayer_Batch")
- fc0w = getCustomOp(fc_layers[0])
- fc1w = getCustomOp(fc_layers[1])
- fc2w = getCustomOp(fc_layers[2])
- fc3w = getCustomOp(fc_layers[3])
- fc0w.set_nodeattr("inFIFODepth", 50)
- fc0w.set_nodeattr("SIMD", 8)
- fc0w.set_nodeattr("PE", 16)
- fc0w.set_nodeattr("outFIFODepth", 4)
- fc1w.set_nodeattr("SIMD", 16)
- fc1w.set_nodeattr("PE", 16)
- fc1w.set_nodeattr("outFIFODepth", 4)
- fc2w.set_nodeattr("SIMD", 16)
- fc2w.set_nodeattr("PE", 16)
- fc2w.set_nodeattr("outFIFODepth", 4)
- fc3w.set_nodeattr("SIMD", 16)
- fc3w.set_nodeattr("PE", 10)
- fc3w.set_nodeattr("outFIFODepth", 50)
+ # (PE, SIMD, in_fifo_depth, out_fifo_depth, ramstyle) for each layer
+ config = [
+ (16, 49, 16, 64, "block"),
+ (8, 8, 64, 64, "auto"),
+ (8, 8, 64, 64, "auto"),
+ (10, 8, 64, 10, "distributed"),
+ ]
+ for fcl, (pe, simd, ififo, ofifo, ramstyle) in zip(fc_layers, config):
+ fcl_inst = getCustomOp(fcl)
+ fcl_inst.set_nodeattr("PE", pe)
+ fcl_inst.set_nodeattr("SIMD", simd)
+ fcl_inst.set_nodeattr("inFIFODepth", ififo)
+ fcl_inst.set_nodeattr("outFIFODepth", ofifo)
+ fcl_inst.set_nodeattr("ram_style", ramstyle)
+ model = model.transform(InsertDWC())
+ model = model.transform(InsertFIFO())
model = model.transform(InsertTLastMarker())
model = model.transform(GiveUniqueNodeNames())
model = model.transform(AnnotateResources("estimate"))
@@ -146,8 +147,8 @@ def test_end2end_tfc_w2a2_fold_and_tlastmarker():
def test_end2end_tfc_w2a2_gen_hls_ip():
model = ModelWrapper(build_dir + "/end2end_tfc_w2a2_folded.onnx")
- model = model.transform(CodeGen_ipgen(test_fpga_part, target_clk_ns))
- model = model.transform(HLSSynth_IPGen())
+ model = model.transform(PrepareIP(test_fpga_part, target_clk_ns))
+ model = model.transform(HLSSynthIP())
model = model.transform(AnnotateResources("hls"))
model.save(build_dir + "/end2end_tfc_w2a2_ipgen.onnx")
@@ -155,7 +156,7 @@ def test_end2end_tfc_w2a2_gen_hls_ip():
def test_end2end_tfc_w2a2_ip_stitch():
model = ModelWrapper(build_dir + "/end2end_tfc_w2a2_ipgen.onnx")
model = model.transform(ReplaceVerilogRelPaths())
- model = model.transform(CodeGen_ipstitch(test_fpga_part))
+ model = model.transform(CreateStitchedIP(test_fpga_part))
model.save(build_dir + "/end2end_tfc_w2a2_ipstitch.onnx")
@@ -165,13 +166,13 @@ def test_end2end_tfc_w2a2_verify_dataflow_part():
inp_name = model.graph.input[0].name
out_name = model.graph.output[0].name
inp_dict = {inp_name: x}
- # npysim
- model = model.transform(CodeGen_npysim())
- model = model.transform(Compile())
- model = model.transform(SetExecMode("npysim"))
- model.save(build_dir + "/end2end_tfc_w2a2_ipstitch_npysim.onnx")
- ret_npysim = execute_onnx(model, inp_dict, True)
- res_npysim = ret_npysim[out_name]
+ # cppsim
+ model = model.transform(PrepareCppSim())
+ model = model.transform(CompileCppSim())
+ model = model.transform(SetExecMode("cppsim"))
+ model.save(build_dir + "/end2end_tfc_w2a2_ipstitch_cppsim.onnx")
+ ret_cppsim = execute_onnx(model, inp_dict, True)
+ res_cppsim = ret_cppsim[out_name]
# node-by-node rtlsim
model = model.transform(SetExecMode("rtlsim"))
model = model.transform(PrepareRTLSim())
@@ -183,8 +184,8 @@ def test_end2end_tfc_w2a2_verify_dataflow_part():
model.save(build_dir + "/end2end_tfc_w2a2_ipstitch_whole_rtlsim.onnx")
ret_rtlsim_whole = execute_onnx(model, inp_dict, True)
res_rtlsim_whole = ret_rtlsim_whole[out_name]
- assert np.isclose(res_npysim, res_rtlsim_nodebynode).all()
- assert np.isclose(res_npysim, res_rtlsim_whole).all()
+ assert np.isclose(res_cppsim, res_rtlsim_nodebynode).all()
+ assert np.isclose(res_cppsim, res_rtlsim_whole).all()
def test_end2end_tfc_w2a2_verify_all():
@@ -203,12 +204,12 @@ def test_end2end_tfc_w2a2_verify_all():
parent_model = ModelWrapper(build_dir + "/end2end_tfc_w2a2_dataflow_parent.onnx")
iname = parent_model.graph.input[0].name
oname = parent_model.graph.output[0].name
- # produce results with npysim
+ # produce results with cppsim
sdp_node = parent_model.get_nodes_by_op_type("StreamingDataflowPartition")[0]
sdp_node = getCustomOp(sdp_node)
- sdp_node.set_nodeattr("model", build_dir + "/end2end_tfc_w2a2_ipstitch_npysim.onnx")
- ret_npysim = execute_onnx(parent_model, {iname: x}, True)
- y_npysim = ret_npysim[oname]
+ sdp_node.set_nodeattr("model", build_dir + "/end2end_tfc_w2a2_ipstitch_cppsim.onnx")
+ ret_cppsim = execute_onnx(parent_model, {iname: x}, True)
+ y_cppsim = ret_cppsim[oname]
# produce results with node-by-node rtlsim
sdp_node.set_nodeattr(
"model", build_dir + "/end2end_tfc_w2a2_ipstitch_nodebynode_rtlsim.onnx"
@@ -221,7 +222,7 @@ def test_end2end_tfc_w2a2_verify_all():
)
ret_whole_rtlsim = execute_onnx(parent_model, {iname: x}, True)
y_whole_rtlsim = ret_whole_rtlsim[oname]
- assert np.isclose(y_golden, y_npysim).all()
+ assert np.isclose(y_golden, y_cppsim).all()
assert np.isclose(y_golden, y_nodebynode_rtlsim).all()
assert np.isclose(y_golden, y_whole_rtlsim).all()
@@ -283,7 +284,7 @@ def test_end2end_tfc_w2a2_run_on_pynq():
ip = os.environ["PYNQ_IP"] # NOQA
if ip == "":
pytest.skip("PYNQ board IP address not specified")
- # produce results with npysim
+ # produce results with cppsim
sdp_node = parent_model.get_nodes_by_op_type("StreamingDataflowPartition")[0]
sdp_node = getCustomOp(sdp_node)
sdp_node.set_nodeattr("model", build_dir + "/end2end_tfc_w2a2_pynq_deploy.onnx")
diff --git a/tests/fpgadataflow/test_code_gen_trafo.py b/tests/fpgadataflow/test_code_gen_trafo.py
index a9b9cd84de77d3393ff76760528ea53780482bfb..1228a9c79608a1c7eb44900ddb7df54ed900a3c2 100644
--- a/tests/fpgadataflow/test_code_gen_trafo.py
+++ b/tests/fpgadataflow/test_code_gen_trafo.py
@@ -33,7 +33,7 @@ from onnx import TensorProto, helper
import finn.util.basic as util
from finn.core.datatype import DataType
from finn.core.modelwrapper import ModelWrapper
-from finn.transformation.fpgadataflow.codegen_npysim import CodeGen_npysim
+from finn.transformation.fpgadataflow.prepare_cppsim import PrepareCppSim
def test_code_gen_trafo():
@@ -77,9 +77,9 @@ def test_code_gen_trafo():
W = util.gen_finn_dt_tensor(wdt, (mw, mh))
model.set_initializer("weights", W)
- model = model.transform(CodeGen_npysim())
+ model = model.transform(PrepareCppSim())
for node in model.graph.node:
- code_gen_attribute = util.get_by_name(node.attribute, "code_gen_dir_npysim")
+ code_gen_attribute = util.get_by_name(node.attribute, "code_gen_dir_cppsim")
tmp_dir = code_gen_attribute.s.decode("UTF-8")
assert os.path.isdir(
tmp_dir
diff --git a/tests/fpgadataflow/test_compilation_trafo.py b/tests/fpgadataflow/test_compilation_trafo.py
index 7a50888545b7c464d187f77122f89b255e9063da..35eed02f4e71a96f9f4e8957c372f93e6cd7927c 100644
--- a/tests/fpgadataflow/test_compilation_trafo.py
+++ b/tests/fpgadataflow/test_compilation_trafo.py
@@ -33,8 +33,8 @@ from onnx import TensorProto, helper
import finn.util.basic as util
from finn.core.datatype import DataType
from finn.core.modelwrapper import ModelWrapper
-from finn.transformation.fpgadataflow.codegen_npysim import CodeGen_npysim
-from finn.transformation.fpgadataflow.compile import Compile
+from finn.transformation.fpgadataflow.prepare_cppsim import PrepareCppSim
+from finn.transformation.fpgadataflow.compile_cppsim import CompileCppSim
def test_compilation_trafo():
@@ -78,8 +78,8 @@ def test_compilation_trafo():
W = util.gen_finn_dt_tensor(wdt, (mw, mh))
model.set_initializer("weights", W)
- model = model.transform(CodeGen_npysim())
- model = model.transform(Compile())
+ model = model.transform(PrepareCppSim())
+ model = model.transform(CompileCppSim())
for node in model.graph.node:
compilation_attribute = util.get_by_name(node.attribute, "executable_path")
executable = compilation_attribute.s.decode("UTF-8")
diff --git a/tests/fpgadataflow/test_convert_to_hls_layers_cnv.py b/tests/fpgadataflow/test_convert_to_hls_layers_cnv.py
index 4effc0da9702850565d2651819d64f1ab3489877..220f8a7966a146f954a7fcb3f32058e231b83e23 100644
--- a/tests/fpgadataflow/test_convert_to_hls_layers_cnv.py
+++ b/tests/fpgadataflow/test_convert_to_hls_layers_cnv.py
@@ -45,8 +45,8 @@ from finn.transformation.double_to_single_float import DoubleToSingleFloat
from finn.transformation.lower_convs_to_matmul import LowerConvsToMatMul
from finn.transformation.bipolar_to_xnor import ConvertBipolarMatMulToXnorPopcount
import finn.transformation.fpgadataflow.convert_to_hls_layers as to_hls
-from finn.transformation.fpgadataflow.codegen_npysim import CodeGen_npysim
-from finn.transformation.fpgadataflow.compile import Compile
+from finn.transformation.fpgadataflow.prepare_cppsim import PrepareCppSim
+from finn.transformation.fpgadataflow.compile_cppsim import CompileCppSim
from finn.transformation.fpgadataflow.set_exec_mode import SetExecMode
from finn.custom_op.registry import getCustomOp
@@ -72,6 +72,7 @@ def test_convert_to_hls_layers_cnv_w1a1():
# load one of the test vectors
fn = pk.resource_filename("finn", "data/cifar10/cifar10-test-data-class3.npz")
input_tensor = np.load(fn)["arr_0"].astype(np.float32)
+ input_tensor = input_tensor / 255
assert input_tensor.shape == (1, 3, 32, 32)
# generate expected value from streamlined net
input_dict = {"global_in": input_tensor}
@@ -112,11 +113,12 @@ def test_convert_to_hls_layers_cnv_w1a1():
mp_nodes = model.get_nodes_by_op_type("StreamingMaxPool_Batch")
assert len(mp_nodes) == 2
# model.save("cnv-pre-compile.onnx")
- model = model.transform(CodeGen_npysim())
- model = model.transform(Compile())
- model = model.transform(SetExecMode("npysim"))
+ model = model.transform(PrepareCppSim())
+ model = model.transform(CompileCppSim())
+ model = model.transform(SetExecMode("cppsim"))
# model.save("cnv-post-compile.onnx")
produced_ctx = oxe.execute_onnx(model, input_dict, True)
produced = produced_ctx[model.graph.output[0].name]
assert np.isclose(expected, produced, atol=1e-3).all()
+ assert np.argmax(produced) == 3
os.remove(export_onnx_path_cnv)
diff --git a/tests/fpgadataflow/test_convert_to_hls_layers_fc.py b/tests/fpgadataflow/test_convert_to_hls_layers_fc.py
index 1a2d65de001a484fc41dbe63a38194a254ac8490..b7dea03797bc5de5e7517d0d8b816c438027008b 100644
--- a/tests/fpgadataflow/test_convert_to_hls_layers_fc.py
+++ b/tests/fpgadataflow/test_convert_to_hls_layers_fc.py
@@ -42,8 +42,8 @@ from finn.core.modelwrapper import ModelWrapper
from finn.custom_op.registry import getCustomOp
from finn.transformation.bipolar_to_xnor import ConvertBipolarMatMulToXnorPopcount
from finn.transformation.fold_constants import FoldConstants
-from finn.transformation.fpgadataflow.codegen_npysim import CodeGen_npysim
-from finn.transformation.fpgadataflow.compile import Compile
+from finn.transformation.fpgadataflow.prepare_cppsim import PrepareCppSim
+from finn.transformation.fpgadataflow.compile_cppsim import CompileCppSim
from finn.transformation.fpgadataflow.set_exec_mode import SetExecMode
from finn.transformation.general import GiveReadableTensorNames, GiveUniqueNodeNames
from finn.transformation.infer_shapes import InferShapes
@@ -107,9 +107,9 @@ def test_convert_to_hls_layers_tfc_w1a1():
fc3w.set_nodeattr("SIMD", 16)
fc3w.set_nodeattr("PE", 10)
- model = model.transform(CodeGen_npysim())
- model = model.transform(Compile())
- model = model.transform(SetExecMode("npysim"))
+ model = model.transform(PrepareCppSim())
+ model = model.transform(CompileCppSim())
+ model = model.transform(SetExecMode("cppsim"))
raw_i = get_data("finn", "data/onnx/mnist-conv/test_data_set_0/input_0.pb")
input_tensor = onnx.load_tensor_from_string(raw_i)
@@ -171,9 +171,9 @@ def test_convert_to_hls_layers_tfc_w1a2():
fc3w = getCustomOp(fc3)
fc3w.set_nodeattr("SIMD", 16)
fc3w.set_nodeattr("PE", 10)
- model = model.transform(CodeGen_npysim())
- model = model.transform(Compile())
- model = model.transform(SetExecMode("npysim"))
+ model = model.transform(PrepareCppSim())
+ model = model.transform(CompileCppSim())
+ model = model.transform(SetExecMode("cppsim"))
raw_i = get_data("finn", "data/onnx/mnist-conv/test_data_set_0/input_0.pb")
input_tensor = onnx.load_tensor_from_string(raw_i)
# run using FINN-based execution
diff --git a/tests/fpgadataflow/test_fpgadataflow_convinputgenerator.py b/tests/fpgadataflow/test_fpgadataflow_convinputgenerator.py
index 7c19ebbfaeed09cb1e367cf6567e5b149aa4236c..02a9acae5e0e90d2a8dfa7d4d4afb03aa11f4239 100644
--- a/tests/fpgadataflow/test_fpgadataflow_convinputgenerator.py
+++ b/tests/fpgadataflow/test_fpgadataflow_convinputgenerator.py
@@ -33,10 +33,10 @@ from onnx import TensorProto, helper
import finn.core.onnx_exec as oxe
from finn.core.datatype import DataType
from finn.core.modelwrapper import ModelWrapper
-from finn.transformation.fpgadataflow.codegen_ipgen import CodeGen_ipgen
-from finn.transformation.fpgadataflow.codegen_npysim import CodeGen_npysim
-from finn.transformation.fpgadataflow.compile import Compile
-from finn.transformation.fpgadataflow.hlssynth_ipgen import HLSSynth_IPGen
+from finn.transformation.fpgadataflow.prepare_ip import PrepareIP
+from finn.transformation.fpgadataflow.prepare_cppsim import PrepareCppSim
+from finn.transformation.fpgadataflow.compile_cppsim import CompileCppSim
+from finn.transformation.fpgadataflow.hlssynth_ip import HLSSynthIP
from finn.transformation.fpgadataflow.set_exec_mode import SetExecMode
from finn.transformation.fpgadataflow.prepare_rtlsim import PrepareRTLSim
from finn.transformation.general import GiveUniqueNodeNames
@@ -134,7 +134,7 @@ def prepare_inputs(input_tensor):
# Stride
@pytest.mark.parametrize("stride", [1, 2])
# execution mode
-@pytest.mark.parametrize("exec_mode", ["npysim", "rtlsim"])
+@pytest.mark.parametrize("exec_mode", ["cppsim", "rtlsim"])
# input channel parallelism ("SIMD")
@pytest.mark.parametrize("simd", [1, 2])
def test_fpgadataflow_slidingwindow(idt, k, ifm_dim, ifm_ch, stride, exec_mode, simd):
@@ -145,15 +145,15 @@ def test_fpgadataflow_slidingwindow(idt, k, ifm_dim, ifm_ch, stride, exec_mode,
k, ifm_ch, ifm_dim, ofm_dim, simd, stride, idt
)
- if exec_mode == "npysim":
- model = model.transform(SetExecMode("npysim"))
- model = model.transform(CodeGen_npysim())
- model = model.transform(Compile())
+ if exec_mode == "cppsim":
+ model = model.transform(SetExecMode("cppsim"))
+ model = model.transform(PrepareCppSim())
+ model = model.transform(CompileCppSim())
elif exec_mode == "rtlsim":
model = model.transform(SetExecMode("rtlsim"))
model = model.transform(GiveUniqueNodeNames())
- model = model.transform(CodeGen_ipgen("xc7z020clg400-1", 5))
- model = model.transform(HLSSynth_IPGen())
+ model = model.transform(PrepareIP("xc7z020clg400-1", 5))
+ model = model.transform(HLSSynthIP())
model = model.transform(PrepareRTLSim())
else:
raise Exception("Unknown exec_mode in test_fpgadataflow_slidingwindow")
diff --git a/tests/fpgadataflow/test_fpgadataflow_dwc.py b/tests/fpgadataflow/test_fpgadataflow_dwc.py
index 1465881830b4fec61d1b1aa6e8465a41766fd9de..a230fb4201673e3bf0a31cf9ec82f21250fd9e40 100644
--- a/tests/fpgadataflow/test_fpgadataflow_dwc.py
+++ b/tests/fpgadataflow/test_fpgadataflow_dwc.py
@@ -4,8 +4,8 @@ from onnx import TensorProto, helper
from finn.core.datatype import DataType
from finn.core.modelwrapper import ModelWrapper
-from finn.transformation.fpgadataflow.codegen_ipgen import CodeGen_ipgen
-from finn.transformation.fpgadataflow.hlssynth_ipgen import HLSSynth_IPGen
+from finn.transformation.fpgadataflow.prepare_ip import PrepareIP
+from finn.transformation.fpgadataflow.hlssynth_ip import HLSSynthIP
from finn.transformation.fpgadataflow.set_exec_mode import SetExecMode
from finn.transformation.fpgadataflow.prepare_rtlsim import PrepareRTLSim
from finn.transformation.general import GiveUniqueNodeNames
@@ -65,8 +65,8 @@ def test_fpgadataflow_dwc_rtlsim(Shape, INWidth, OUTWidth, finn_dtype):
model = model.transform(SetExecMode("rtlsim"))
model = model.transform(GiveUniqueNodeNames())
- model = model.transform(CodeGen_ipgen("xc7z020clg400-1", 5))
- model = model.transform(HLSSynth_IPGen())
+ model = model.transform(PrepareIP("xc7z020clg400-1", 5))
+ model = model.transform(HLSSynthIP())
model = model.transform(PrepareRTLSim())
y = oxe.execute_onnx(model, input_dict)["outp"]
diff --git a/tests/fpgadataflow/test_fpgadataflow_fclayer.py b/tests/fpgadataflow/test_fpgadataflow_fclayer.py
index 7552fecd85ee0e36216f6c934d454f057a2a41ce..416d96d5dbfa1125d878eb8339ae38f5d572d1ce 100644
--- a/tests/fpgadataflow/test_fpgadataflow_fclayer.py
+++ b/tests/fpgadataflow/test_fpgadataflow_fclayer.py
@@ -38,10 +38,10 @@ from finn.analysis.fpgadataflow.hls_synth_res_estimation import hls_synth_res_es
from finn.core.datatype import DataType
from finn.core.modelwrapper import ModelWrapper
from finn.custom_op.multithreshold import multithreshold
-from finn.transformation.fpgadataflow.codegen_ipgen import CodeGen_ipgen
-from finn.transformation.fpgadataflow.codegen_npysim import CodeGen_npysim
-from finn.transformation.fpgadataflow.compile import Compile
-from finn.transformation.fpgadataflow.hlssynth_ipgen import HLSSynth_IPGen
+from finn.transformation.fpgadataflow.prepare_ip import PrepareIP
+from finn.transformation.fpgadataflow.prepare_cppsim import PrepareCppSim
+from finn.transformation.fpgadataflow.compile_cppsim import CompileCppSim
+from finn.transformation.fpgadataflow.hlssynth_ip import HLSSynthIP
from finn.transformation.fpgadataflow.set_exec_mode import SetExecMode
from finn.transformation.general import GiveUniqueNodeNames
from finn.transformation.fpgadataflow.prepare_rtlsim import PrepareRTLSim
@@ -149,7 +149,7 @@ def prepare_inputs(input_tensor, idt, wdt):
@pytest.mark.parametrize("mw", [16])
# HLS matrix height (output features)
@pytest.mark.parametrize("mh", [16])
-def test_fpgadataflow_fclayer_npysim(mem_mode, idt, wdt, act, nf, sf, mw, mh):
+def test_fpgadataflow_fclayer_cppsim(mem_mode, idt, wdt, act, nf, sf, mw, mh):
if nf == -1:
nf = mh
if sf == -1:
@@ -190,9 +190,9 @@ def test_fpgadataflow_fclayer_npysim(mem_mode, idt, wdt, act, nf, sf, mw, mh):
# lookup op_type in registry of CustomOps
inst = getCustomOp(node)
inst.set_nodeattr("mem_mode", mem_mode)
- model = model.transform(SetExecMode("npysim"))
- model = model.transform(CodeGen_npysim())
- model = model.transform(Compile())
+ model = model.transform(SetExecMode("cppsim"))
+ model = model.transform(PrepareCppSim())
+ model = model.transform(CompileCppSim())
# prepare input data
input_dict = prepare_inputs(x, idt, wdt)
if wdt == DataType.BIPOLAR and idt == DataType.BIPOLAR:
@@ -215,7 +215,7 @@ def test_fpgadataflow_fclayer_npysim(mem_mode, idt, wdt, act, nf, sf, mw, mh):
y_produced = y_produced.reshape(y_expected.shape)
- assert (y_produced == y_expected).all(), "npysim failed"
+ assert (y_produced == y_expected).all(), "cppsim failed"
# mem_mode: const or decoupled
@@ -297,8 +297,8 @@ def test_fpgadataflow_fclayer_rtlsim(mem_mode, idt, wdt, act, nf, sf, mw, mh):
# works for parametrized tests...
model = model.transform(SetExecMode("rtlsim"))
model = model.transform(GiveUniqueNodeNames())
- model = model.transform(CodeGen_ipgen("xc7z020clg400-1", 5))
- model = model.transform(HLSSynth_IPGen())
+ model = model.transform(PrepareIP("xc7z020clg400-1", 5))
+ model = model.transform(HLSSynthIP())
model = model.transform(ReplaceVerilogRelPaths())
model = model.transform(PrepareRTLSim())
y_produced = oxe.execute_onnx(model, input_dict)["outp"]
@@ -389,8 +389,8 @@ def test_fpgadataflow_fclayer_large_depth_decoupled_mode(
# works for parametrized tests...
model = model.transform(SetExecMode("rtlsim"))
model = model.transform(GiveUniqueNodeNames())
- model = model.transform(CodeGen_ipgen("xc7z020clg400-1", 5))
- model = model.transform(HLSSynth_IPGen())
+ model = model.transform(PrepareIP("xc7z020clg400-1", 5))
+ model = model.transform(HLSSynthIP())
model = model.transform(ReplaceVerilogRelPaths())
model = model.transform(PrepareRTLSim())
y_produced = oxe.execute_onnx(model, input_dict)["outp"]
diff --git a/tests/fpgadataflow/test_fpgadataflow_fifo.py b/tests/fpgadataflow/test_fpgadataflow_fifo.py
index 8ab4809928d91d8456b7720f897763b206c4e5f5..fe27d7d4273be2b938e5bf70338bb374ce16b6b2 100644
--- a/tests/fpgadataflow/test_fpgadataflow_fifo.py
+++ b/tests/fpgadataflow/test_fpgadataflow_fifo.py
@@ -5,10 +5,10 @@ from onnx import TensorProto, helper
from finn.core.datatype import DataType
from finn.core.modelwrapper import ModelWrapper
-from finn.transformation.fpgadataflow.codegen_ipgen import CodeGen_ipgen
-from finn.transformation.fpgadataflow.codegen_ipstitch import CodeGen_ipstitch
+from finn.transformation.fpgadataflow.prepare_ip import PrepareIP
+from finn.transformation.fpgadataflow.create_stitched_ip import CreateStitchedIP
-from finn.transformation.fpgadataflow.hlssynth_ipgen import HLSSynth_IPGen
+from finn.transformation.fpgadataflow.hlssynth_ip import HLSSynthIP
from finn.transformation.fpgadataflow.set_exec_mode import SetExecMode
from finn.transformation.general import GiveUniqueNodeNames
@@ -87,8 +87,8 @@ def test_fpgadataflow_fifo_rtlsim(Shape, folded_shape, depth, finn_dtype):
model = model.transform(SetExecMode("rtlsim"))
model = model.transform(InsertTLastMarker())
model = model.transform(GiveUniqueNodeNames())
- model = model.transform(CodeGen_ipgen(test_fpga_part, target_clk_ns))
- model = model.transform(HLSSynth_IPGen())
+ model = model.transform(PrepareIP(test_fpga_part, target_clk_ns))
+ model = model.transform(HLSSynthIP())
model = model.transform(PrepareRTLSim())
y = oxe.execute_onnx(model, input_dict)["outp"]
assert (
@@ -98,7 +98,7 @@ def test_fpgadataflow_fifo_rtlsim(Shape, folded_shape, depth, finn_dtype):
assert y.shape == tuple(Shape), """The output shape is incorrect."""
model = model.transform(ReplaceVerilogRelPaths())
- model = model.transform(CodeGen_ipstitch(test_fpga_part))
+ model = model.transform(CreateStitchedIP(test_fpga_part))
model = model.transform(MakePYNQProject(test_pynq_board))
model = model.transform(SynthPYNQProject())
model = model.transform(MakePYNQDriver())
diff --git a/tests/fpgadataflow/test_fpgadataflow_ip_stitch.py b/tests/fpgadataflow/test_fpgadataflow_ip_stitch.py
index af0c7b0755c7aad5dd145ea5ea8ace59941dd74a..f26ba428bf4cbe174c048dcd35a4d63dc58519ab 100644
--- a/tests/fpgadataflow/test_fpgadataflow_ip_stitch.py
+++ b/tests/fpgadataflow/test_fpgadataflow_ip_stitch.py
@@ -37,12 +37,12 @@ from finn.core.datatype import DataType
from finn.core.modelwrapper import ModelWrapper
from finn.core.onnx_exec import execute_onnx
from finn.custom_op.registry import getCustomOp
-from finn.transformation.fpgadataflow.codegen_ipgen import CodeGen_ipgen
-from finn.transformation.fpgadataflow.codegen_ipstitch import CodeGen_ipstitch
+from finn.transformation.fpgadataflow.prepare_ip import PrepareIP
+from finn.transformation.fpgadataflow.create_stitched_ip import CreateStitchedIP
from finn.transformation.fpgadataflow.create_dataflow_partition import (
CreateDataflowPartition,
)
-from finn.transformation.fpgadataflow.hlssynth_ipgen import HLSSynth_IPGen
+from finn.transformation.fpgadataflow.hlssynth_ip import HLSSynthIP
from finn.transformation.fpgadataflow.insert_tlastmarker import InsertTLastMarker
from finn.transformation.fpgadataflow.make_deployment import DeployToPYNQ
from finn.transformation.fpgadataflow.make_pynq_driver import MakePYNQDriver
@@ -208,8 +208,8 @@ def test_fpgadataflow_ipstitch_gen_model(): # exec_mode):
model.set_metadata_prop("exec_mode", "remote_pynq")
model = model.transform(InsertTLastMarker())
model = model.transform(GiveUniqueNodeNames())
- model = model.transform(CodeGen_ipgen(test_fpga_part, 5))
- model = model.transform(HLSSynth_IPGen())
+ model = model.transform(PrepareIP(test_fpga_part, 5))
+ model = model.transform(HLSSynthIP())
assert model.graph.node[0].op_type == "StreamingFCLayer_Batch"
assert model.graph.node[-1].op_type == "TLastMarker"
model.save(ip_stitch_model_dir + "/test_fpgadataflow_ipstitch_gen_model.onnx")
@@ -220,7 +220,7 @@ def test_fpgadataflow_ipstitch_do_stitch():
ip_stitch_model_dir + "/test_fpgadataflow_ipstitch_gen_model.onnx"
)
model = model.transform(rvp.ReplaceVerilogRelPaths())
- model = model.transform(CodeGen_ipstitch(test_fpga_part))
+ model = model.transform(CreateStitchedIP(test_fpga_part))
vivado_stitch_proj_dir = model.get_metadata_prop("vivado_stitch_proj")
assert vivado_stitch_proj_dir is not None
assert os.path.isdir(vivado_stitch_proj_dir)
diff --git a/tests/fpgadataflow/test_layer_streaming_maxpool_batch.py b/tests/fpgadataflow/test_layer_streaming_maxpool_batch.py
index a7a731aaa5593a9fd680061d2b8ad3fc47e9f490..ac4ab33469c7720c3d7b9f30f5d13be888e1439d 100644
--- a/tests/fpgadataflow/test_layer_streaming_maxpool_batch.py
+++ b/tests/fpgadataflow/test_layer_streaming_maxpool_batch.py
@@ -33,10 +33,10 @@ from onnx import TensorProto, helper
import finn.core.onnx_exec as oxe
from finn.core.datatype import DataType
from finn.core.modelwrapper import ModelWrapper
-from finn.transformation.fpgadataflow.codegen_ipgen import CodeGen_ipgen
-from finn.transformation.fpgadataflow.codegen_npysim import CodeGen_npysim
-from finn.transformation.fpgadataflow.compile import Compile
-from finn.transformation.fpgadataflow.hlssynth_ipgen import HLSSynth_IPGen
+from finn.transformation.fpgadataflow.prepare_ip import PrepareIP
+from finn.transformation.fpgadataflow.prepare_cppsim import PrepareCppSim
+from finn.transformation.fpgadataflow.compile_cppsim import CompileCppSim
+from finn.transformation.fpgadataflow.hlssynth_ip import HLSSynthIP
from finn.transformation.fpgadataflow.set_exec_mode import SetExecMode
from finn.transformation.fpgadataflow.prepare_rtlsim import PrepareRTLSim
from finn.transformation.general import GiveUniqueNodeNames
@@ -120,7 +120,7 @@ def prepare_inputs(input_tensor):
# input channels
@pytest.mark.parametrize("ifm_ch", [1, 2]) # , 2, 3, 4])
# execution mode
-@pytest.mark.parametrize("exec_mode", ["rtlsim", "npysim"])
+@pytest.mark.parametrize("exec_mode", ["rtlsim", "cppsim"])
def test_fpgadataflow_streamingmaxpool(idt, k, ifm_dim, ifm_ch, exec_mode):
stride = k
ofm_dim = int(((ifm_dim - k) / stride) + 1)
@@ -136,15 +136,15 @@ def test_fpgadataflow_streamingmaxpool(idt, k, ifm_dim, ifm_ch, exec_mode):
model = make_single_streamingmaxpool_modelwrapper(k, ifm_ch, ifm_dim, ofm_dim, idt)
- if exec_mode == "npysim":
- model = model.transform(SetExecMode("npysim"))
- model = model.transform(CodeGen_npysim())
- model = model.transform(Compile())
+ if exec_mode == "cppsim":
+ model = model.transform(SetExecMode("cppsim"))
+ model = model.transform(PrepareCppSim())
+ model = model.transform(CompileCppSim())
elif exec_mode == "rtlsim":
model = model.transform(SetExecMode("rtlsim"))
model = model.transform(GiveUniqueNodeNames())
- model = model.transform(CodeGen_ipgen("xc7z020clg400-1", 5))
- model = model.transform(HLSSynth_IPGen())
+ model = model.transform(PrepareIP("xc7z020clg400-1", 5))
+ model = model.transform(HLSSynthIP())
model = model.transform(PrepareRTLSim())
else:
raise Exception("Unknown exec_mode in test_fpgadataflow_slidingwindow")
diff --git a/tests/transformation/streamline/test_streamline_cnv.py b/tests/transformation/streamline/test_streamline_cnv.py
index ec5bf441b736b5faed0024749b5b77f213949029..56dcd26076ec0a5fba6e9be6acac7f5e13572c3d 100644
--- a/tests/transformation/streamline/test_streamline_cnv.py
+++ b/tests/transformation/streamline/test_streamline_cnv.py
@@ -65,15 +65,17 @@ def test_streamline_cnv(size, wbits, abits):
# load one of the test vectors
fn = pk.resource_filename("finn", "data/cifar10/cifar10-test-data-class3.npz")
input_tensor = np.load(fn)["arr_0"].astype(np.float32)
+ input_tensor = input_tensor / 255
assert input_tensor.shape == (1, 3, 32, 32)
# run using FINN-based execution
input_dict = {"global_in": input_tensor}
expected_ctx = oxe.execute_onnx(model, input_dict, True)
expected = expected_ctx[model.graph.output[0].name]
- model.save("orig_cnv.onnx")
+ # model.save("orig_cnv.onnx")
model = model.transform(Streamline())
- model.save("streamlined_cnv.onnx")
+ # model.save("streamlined_cnv.onnx")
produced_ctx = oxe.execute_onnx(model, input_dict, True)
produced = produced_ctx[model.graph.output[0].name]
assert np.isclose(expected, produced, atol=1e-3).all()
assert model.graph.node[0].op_type == "MultiThreshold"
+ assert np.argmax(produced) == 3
diff --git a/tests/transformation/test_batchnorm_to_affine.py b/tests/transformation/test_batchnorm_to_affine.py
index 997ca5ab110bf3612a4db1152ac844180daf7d43..43110c6bf9e5469b2ca21ac667d7f92808017fb8 100644
--- a/tests/transformation/test_batchnorm_to_affine.py
+++ b/tests/transformation/test_batchnorm_to_affine.py
@@ -55,6 +55,7 @@ def test_batchnorm_to_affine_cnv_w1a1():
model = model.transform(FoldConstants())
fn = pk.resource_filename("finn", "data/cifar10/cifar10-test-data-class3.npz")
input_tensor = np.load(fn)["arr_0"].astype(np.float32)
+ input_tensor = input_tensor / 255
assert input_tensor.shape == (1, 3, 32, 32)
input_dict = {"0": input_tensor}
output_dict = oxe.execute_onnx(model, input_dict)
@@ -66,6 +67,7 @@ def test_batchnorm_to_affine_cnv_w1a1():
output_dict_p = oxe.execute_onnx(new_model, input_dict)
produced = output_dict_p[list(output_dict_p.keys())[0]]
assert np.isclose(expected, produced).all()
+ assert np.argmax(produced) == 3
os.remove(export_onnx_path)
diff --git a/tests/transformation/test_conv_lowering.py b/tests/transformation/test_conv_lowering.py
index 647a2b454a1a609c41707707e7fc9cf90dadd59c..2cbc8e558940517168678b05c3bb46af8170abce 100644
--- a/tests/transformation/test_conv_lowering.py
+++ b/tests/transformation/test_conv_lowering.py
@@ -52,6 +52,7 @@ def test_conv_lowering_cnv_w1a1():
model = model.transform(FoldConstants())
fn = pk.resource_filename("finn", "data/cifar10/cifar10-test-data-class3.npz")
input_tensor = np.load(fn)["arr_0"].astype(np.float32)
+ input_tensor = input_tensor / 255
assert input_tensor.shape == (1, 3, 32, 32)
# execute imported model to get expected answer
input_dict = {"0": input_tensor}
@@ -62,4 +63,5 @@ def test_conv_lowering_cnv_w1a1():
output_dict_p = oxe.execute_onnx(model, input_dict)
produced = output_dict_p[list(output_dict_p.keys())[0]]
assert np.isclose(produced, expected).all()
+ assert np.argmax(produced) == 3
os.remove(export_onnx_path)
diff --git a/tests/transformation/test_infer_datatypes.py b/tests/transformation/test_infer_datatypes.py
index ae8a52882a9126470dad6ca15d8c35000a8edaff..77b6a94f8ed891a4fe761fe864a6e18d35e84382 100644
--- a/tests/transformation/test_infer_datatypes.py
+++ b/tests/transformation/test_infer_datatypes.py
@@ -39,10 +39,6 @@ from finn.transformation.infer_shapes import InferShapes
from finn.util.test import get_test_model_trained
export_onnx_path = "test_output_lfc.onnx"
-# TODO get from config instead, hardcoded to Docker path for now
-trained_lfc_checkpoint = (
- "/workspace/brevitas_cnv_lfc/pretrained_models/LFC_1W1A/checkpoints/best.tar"
-)
def test_infer_datatypes():
diff --git a/tests/transformation/test_sign_to_thres.py b/tests/transformation/test_sign_to_thres.py
index 1033a313560c714b02e256e5940694868fa41cbf..b10840df37a695986e54c0bdaa68baa0538f90f2 100644
--- a/tests/transformation/test_sign_to_thres.py
+++ b/tests/transformation/test_sign_to_thres.py
@@ -42,10 +42,6 @@ from finn.util.test import get_test_model_trained
export_onnx_path = "test_output_lfc.onnx"
transformed_onnx_path = "test_output_lfc_transformed.onnx"
-# TODO get from config instead, hardcoded to Docker path for now
-trained_lfc_checkpoint = (
- "/workspace/brevitas_cnv_lfc/pretrained_models/LFC_1W1A/checkpoints/best.tar"
-)
def test_sign_to_thres():
diff --git a/tests/transformation/test_topk_insert.py b/tests/transformation/test_topk_insert.py
index ac32c30edbbf466b2b441bcc92975a7d50f42bda..1af0f255d8fb1af8a6e571518f18d831aa71298b 100644
--- a/tests/transformation/test_topk_insert.py
+++ b/tests/transformation/test_topk_insert.py
@@ -1,3 +1,4 @@
+import os
import onnx
from finn.util.test import get_test_model_trained
import brevitas.onnx as bo
@@ -56,3 +57,4 @@ def test_topk_insert(k):
output_pysim_topk = output_pysim_topk.astype(np.int).flatten()
assert np.array_equal(output_golden_topk, output_pysim_topk)
+ os.remove(export_onnx_path)