diff --git a/.gitignore b/.gitignore
index 796e3e78d2ff89e091be9ff2a072b214cc5ed20a..56a3d233020db94e69c8072b23aa5a66f75ff449 100644
--- a/.gitignore
+++ b/.gitignore
@@ -49,9 +49,9 @@ MANIFEST
.venv*/
# Cloned dependencies for Docker
-brevitas/
-brevitas_cnv_lfc/
-cnpy/
-finn-hlslib/
-pyverilator/
-PYNQ-HelloWorld/
+/brevitas/
+/brevitas_cnv_lfc/
+/cnpy/
+/finn-hlslib/
+/pyverilator/
+/PYNQ-HelloWorld/
diff --git a/notebooks/9-FINN-EndToEndFlow.ipynb b/notebooks/9-FINN-EndToEndFlow.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..37cbd2256ed1d19fc5e29859f64735e44035b49a
--- /dev/null
+++ b/notebooks/9-FINN-EndToEndFlow.ipynb
@@ -0,0 +1,1474 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# FINN - End-to-End Flow\n",
+ "-----------------------------------------------------------------\n",
+ "This notebook gives an overview about the end to end flow of FINN. From loading an ONNX model from Brevitas, followed by the numerous transformations in FINN and up to the generation of a bitstream that can be used to load an FPGA. \n",
+ "\n",
+ "We'll use the following showSrc function to print the source code for function calls in the Jupyter notebook."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import inspect\n",
+ "import netron\n",
+ "from finn.util.basic import make_build_dir\n",
+ "\n",
+ "def showSrc(what):\n",
+ " print(\"\".join(inspect.getsourcelines(what)[0]))\n",
+ " \n",
+ "# create a host-Docker shared folder for the build\n",
+ "build_dir = make_build_dir(\"end2end_\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Overview\n",
+ "The notebook is based on the following diagram. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The diagram visualizes the end-to-end flow of FINN. The cylinder-like fields show the state of the network representation in the respective step. The rectangular fields represent the transformations that are applied to the network to achieve a certain result. The diagram is divided into 5 blocks, each of it includes several flow steps. The flow starts in top left corner with Brevitas export (pink block), followed by the preparation of the network (grey block) for the Vivado HLS and Vivado synthesis (yellow block). There is also a section for testing and verification in software (green block) and the hardware test on the PYNQ board (red block).\n",
+ "The diagram leads to the following outline for this Jupyter notebook."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Outline\n",
+ "-------------\n",
+ "1. [Brevitas export](#brev_exp)\n",
+ "2. [Network preparation](#nw_prep)\n",
+ " * Basic transformations\n",
+ " * Streamlining\n",
+ " * Conversion to HLS layers\n",
+ " * Folding\n",
+ "3. [Vivado HLS and Vivado synthesis](#vivado)\n",
+ " * HLS IP per layer\n",
+ " * Creation of stitched design\n",
+ " * PYNQ shell project\n",
+ " * Synthesis, place and route\n",
+ "4. [Hardware Test](#hw_test)\n",
+ "5. [Simulation & Emulation flows for functional verification](#sim)\n",
+ " * Simulation using Python\n",
+ " * Simulation (npysim) using C++\n",
+ " * Emulation (rtlsim) using PyVerilator"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "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. The Brevitas export is only briefly described here, for details see Jupyter notebook [3-FINN-Brevitas-network-import](3-FINN-Brevitas-network-import.ipynb).\n",
+ "\n",
+ "First a few things have to be imported. Then the model can be loaded with the pretrained weights."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/workspace/brevitas_cnv_lfc/training_scripts/models/TFC.py:73: 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])\n"
+ ]
+ }
+ ],
+ "source": [
+ "from finn.util.test import get_fc_model_trained\n",
+ "import brevitas.onnx as bo\n",
+ "\n",
+ "tfc = get_fc_model_trained(\"TFC\", 1, 1)\n",
+ "bo.export_finn_onnx(tfc, (1, 1, 28, 28), build_dir+\"/tfc_w1_a1.onnx\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The model was now exported, loaded with the pretrained weights and saved under the name \"lfc_w1_a1.onnx\".\n",
+ "To visualize the exported model, Netron can be used. Netron is a visualizer for neural networks and allows interactive investigation of network properties. For example, you can click on the individual nodes and view the properties."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Serving '/tmp/finn_maltanar_9257/end2end_f4ebazeo/tfc_w1_a1.onnx' at http://0.0.0.0:8081\n"
+ ]
+ }
+ ],
+ "source": [
+ "netron.start(build_dir+\"/tfc_w1_a1.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": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that we have the model in .onnx format, we can work with it using FINN. For that FINN `ModelWrapper` is used. It is a wrapper around the ONNX model which provides several helper functions to make it easier to work with the model. For details see Jupyter notebook [2-FINN-ModelWrapper](2-FINN-ModelWrapper.ipynb)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from finn.core.modelwrapper import ModelWrapper\n",
+ "model = ModelWrapper(build_dir+\"/tfc_w1_a1.onnx\")"
+ ]
+ },
+ {
+ "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 for functional verification*.\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. For more details see [4-FINN-HowToAnalysisPass](4-FINN-HowToAnalysisPass.ipynb). A transformation pass changes the model and returns the changed model back to the FINN flow, for more information about transformation passes see notebook [5-FINN-HowToTransformationPass](5-FINN-HowToTransformationPass.ipynb).\n",
+ "\n",
+ "Since the goal in this notebook is to process the model to such an extent that a bitstream can be generated from it, the focus is on the transformations that are necessary for this. In the next section these are discussed in more detail."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 2. Network preparation <a id='nw_prep'></a>\n",
+ "\n",
+ "* [Basic transformations](#basic_trafo)\n",
+ "* [Streamlining](#streamline)\n",
+ "* [Conversion to HLS layers](#hls_layers)\n",
+ "* [Folding](#folding)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Basic transformations <a id='basic_trafo'></a>\n",
+ "This section deals with the basic transformations, which are applied to the model like a kind of clean up. They do not appear in the diagram above, but they are applied in many steps in the FINN flow to postprocess the model after a transformation and/or to prepare it for the next transformation."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The basis transformations are:\n",
+ "* GiveUniqueNodeNames\n",
+ "* GiveReadableTensorNames\n",
+ "* InferShapes\n",
+ "* InferDataTypes\n",
+ "* FoldConstants"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "These transformations work like a clean up. In the first two transformations (`GiveUniqueNodeNames`, `GiveReadableTensorNames`) the nodes and tensors are given unique and readable names. The following two transformations (`InferShapes`, `InferDataTypes`) derive the shapes and data types of the tensors from the model properties and set them in the `ValueInfo` of the model. Normally these transformations can always be applied and do not affect the structure of the graph, but ensure that all the information needed is available. \n",
+ "\n",
+ "The last listed transformation is `FoldConstants`. It identifies a node with constant inputs and determines its output. The result is then set as const-only inputs for the following node and the old node is removed. Although this transformation changes the structure of the model, it is a transformation that is usually always desired and can be applied to any model."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The transformations can be imported and applied as follows."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from finn.transformation.general import GiveReadableTensorNames, GiveUniqueNodeNames\n",
+ "from finn.transformation.infer_shapes import InferShapes\n",
+ "from finn.transformation.infer_datatypes import InferDataTypes\n",
+ "from finn.transformation.fold_constants import FoldConstants\n",
+ "\n",
+ "model = model.transform(InferShapes())\n",
+ "model = model.transform(FoldConstants())\n",
+ "model = model.transform(GiveUniqueNodeNames())\n",
+ "model = model.transform(GiveReadableTensorNames())\n",
+ "model = model.transform(InferDataTypes())\n",
+ "\n",
+ "\n",
+ "# save model with other name for section \"Simulation using Python\"\n",
+ "model.save(build_dir+\"/tfc_w1_a1_after_brevitas_export.onnx\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The result of these transformations can be viewed with netron after the model has been saved again. By clicking on the individual nodes, it can now be seen, for example, that each node has been given a name. Also the whole upper area could be folded, so that now the first node is \"Reshape\"."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Stopping http://0.0.0.0:8081\n",
+ "Serving '/tmp/finn_maltanar_9257/end2end_f4ebazeo/tfc_w1_a1.onnx' at http://0.0.0.0:8081\n"
+ ]
+ }
+ ],
+ "source": [
+ "model.save(build_dir+\"/tfc_w1_a1.onnx\")\n",
+ "netron.start(build_dir+\"/tfc_w1_a1.onnx\", port=8081, host=\"0.0.0.0\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "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": "markdown",
+ "metadata": {},
+ "source": [
+ "### Streamlining <a id='streamline'></a>\n",
+ "Streamlining is a transformation containing several sub-transformations. The goal of streamlining is to eliminate floating point operations by moving them around, then collapsing them into one operation and in the last step transform them into multithresholding nodes. For the theoretical background see arXiv:1709.04060.\n",
+ "\n",
+ "In the following the streamlining transformation is shown."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "class Streamline(Transformation):\n",
+ " \"\"\"Apply the streamlining transform, see arXiv:1709.04060.\"\"\"\n",
+ "\n",
+ " def apply(self, model):\n",
+ " streamline_transformations = [\n",
+ " ConvertSubToAdd(),\n",
+ " BatchNormToAffine(),\n",
+ " ConvertSignToThres(),\n",
+ " MoveScalarAddPastMatMul(),\n",
+ " MoveScalarMulPastMatMul(),\n",
+ " MoveAddPastMul(),\n",
+ " CollapseRepeatedAdd(),\n",
+ " CollapseRepeatedMul(),\n",
+ " AbsorbAddIntoMultiThreshold(),\n",
+ " FactorOutMulSignMagnitude(),\n",
+ " AbsorbMulIntoMultiThreshold(),\n",
+ " Absorb1BitMulIntoMatMul(),\n",
+ " RoundAndClipThresholds(),\n",
+ " ]\n",
+ " for trn in streamline_transformations:\n",
+ " model = model.transform(trn)\n",
+ " model = model.transform(GiveUniqueNodeNames())\n",
+ " model = model.transform(GiveReadableTensorNames())\n",
+ " model = model.transform(InferDataTypes())\n",
+ " return (model, False)\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "from finn.transformation.streamline import Streamline\n",
+ "showSrc(Streamline)"
+ ]
+ },
+ {
+ "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 basic transformations (`GiveUniqueNodeNames`, `GiveReadableTensorNames` and `InferDataTypes`) are applied to the model as clean up.\n",
+ "\n",
+ "After streamlining the network looks as follows."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Stopping http://0.0.0.0:8081\n",
+ "Serving '/tmp/finn_maltanar_9257/end2end_f4ebazeo/tfc_w1_a1.onnx' at http://0.0.0.0:8081\n"
+ ]
+ }
+ ],
+ "source": [
+ "model = model.transform(Streamline())\n",
+ "model.save(build_dir+\"/tfc_w1_a1.onnx\")\n",
+ "netron.start(build_dir+\"/tfc_w1_a1.onnx\", port=8081, host=\"0.0.0.0\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "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": "markdown",
+ "metadata": {},
+ "source": [
+ "Our example network is a quantized network with 1 bit precision. For this reason, after streamlining, the resulting bipolar matrix multiplications are converted into xnorpopcount operations. This transformation produces operations that are again collapsed and converted into thresholds. This procedure is shown below. \n",
+ "In this state the model can still be simulated with Python, even if it no longer contains only standard ONNX nodes. For details, see section [Simulation using Python](#simpy).\n",
+ "\n",
+ "After these finishing transformations, the nodes can be converted to HLS layers for further processing."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from finn.transformation.bipolar_to_xnor import ConvertBipolarMatMulToXnorPopcount\n",
+ "import finn.transformation.streamline.absorb as absorb\n",
+ "from finn.transformation.streamline.round_thresholds import RoundAndClipThresholds\n",
+ "\n",
+ "model = model.transform(ConvertBipolarMatMulToXnorPopcount())\n",
+ "model = model.transform(absorb.AbsorbAddIntoMultiThreshold())\n",
+ "model = model.transform(absorb.AbsorbMulIntoMultiThreshold())\n",
+ "model = model.transform(RoundAndClipThresholds())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "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",
+ "\n",
+ "Below is the code for the transformation and the network is visualized using netron to create the new structure which now corresponds to the finn-hls library."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Stopping http://0.0.0.0:8081\n",
+ "Serving '/tmp/finn_maltanar_9257/end2end_f4ebazeo/tfc_w1_a1.onnx' at http://0.0.0.0:8081\n"
+ ]
+ }
+ ],
+ "source": [
+ "import finn.transformation.fpgadataflow.convert_to_hls_layers as to_hls\n",
+ "model = model.transform(to_hls.InferBinaryStreamingFCLayer())\n",
+ "model.save(build_dir+\"/tfc_w1_a1.onnx\")\n",
+ "netron.start(build_dir+\"/tfc_w1_a1.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": [
+ "Each StreamingFCLayer_Batch node has two attributes that specify the degree of folding, PE and SIMD. In all nodes the values for these attributes are set as default to 1, which would correspond to a maximum folding. The user can now adjust the folding as desired. This is described in the next section.\n",
+ "\n",
+ "At this point the model can also be simulated using C++. The exact procedure is described in section [Simulation using C++](#npysim)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Folding <a id='folding'></a>\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 netron helps us, in the above diagram we can see that the third to sixth nodes are StreamingFCLayer_Batch. Through the `print`s we can check if the extracted nodes all have the op_type \"StreamingFCLayer_Batch\". For more details on how to use ONNX model, see Jupyter notebook [1-FINN-HowToWorkWithONNX](1-FINN-HowToWorkWithONNX.ipynb)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "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[2]\n",
+ "fc1 = model.graph.node[3]\n",
+ "fc2 = model.graph.node[4]\n",
+ "fc3 = model.graph.node[5]\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))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can use the higher-level [HLSCustomOp](https://github.com/Xilinx/finn/blob/dev/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). For more details about custom ops, see Jupyter notebook [7-FINN-CustomOps](7-FINN-CustomOps.ipynb). 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": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CustomOp wrapper is of class StreamingFCLayer_Batch\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "{'PE': ('i', True, 0),\n",
+ " 'SIMD': ('i', True, 0),\n",
+ " 'MW': ('i', True, 0),\n",
+ " 'MH': ('i', True, 0),\n",
+ " 'resType': ('s', True, ''),\n",
+ " 'ActVal': ('i', False, 0),\n",
+ " 'inputDataType': ('s', True, ''),\n",
+ " 'weightDataType': ('s', True, ''),\n",
+ " 'outputDataType': ('s', True, ''),\n",
+ " 'binaryXnorMode': ('i', False, 0),\n",
+ " 'noActivation': ('i', False, 0),\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",
+ " 'sim_mode': ('s', False, ''),\n",
+ " 'sim_cycles': ('i', False, 0)}"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from finn.custom_op.registry import getCustomOp\n",
+ "\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",
+ "fc0w.get_nodeattr_types()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "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(\"SIMD\", 16)\n",
+ "fc0w.set_nodeattr(\"PE\", 16)\n",
+ "\n",
+ "fc1w.set_nodeattr(\"SIMD\", 16)\n",
+ "fc1w.set_nodeattr(\"PE\", 16)\n",
+ "\n",
+ "fc2w.set_nodeattr(\"SIMD\", 16)\n",
+ "fc2w.set_nodeattr(\"PE\", 16)\n",
+ "\n",
+ "fc3w.set_nodeattr(\"SIMD\", 16)\n",
+ "fc3w.set_nodeattr(\"PE\", 10)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# save model with other name for section \"Simulation using C++\"\"\n",
+ "model.save(build_dir+\"/tfc_w1_a1_after_conv_to_hls.onnx\")"
+ ]
+ },
+ {
+ "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."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 3. Vivado HLS and Vivado synthesis <a id='vivado'></a>\n",
+ "* [Generating HLS Code](#hls_per_layer)\n",
+ "* [Synthesizing HLS to IP Blocks](#hls_synth)\n",
+ "* [Creation of stitched design](#stitched_design)\n",
+ "* [PYNQ shell project](#pynq_shell)\n",
+ "* [Synthesis, place and route](#synth_pl_ro)\n",
+ "\n",
+ "As we will be performing FPGA synthesis in these tasks, we'll define two helper variables that describe the Xilinx FPGA part name and the PYNQ board name that we are targeting."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fpga_part = \"xczu3eg-sbva484-1-e\"\n",
+ "pynq_board = \"Ultra96\"\n",
+ "target_clk_ns = 5"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Generating HLS Code <a id='hls_per_layer'></a>\n",
+ "This section deals with the generation of an IP block from the different layers. These can then be stitched to a block design that corresponds to the complete model. The single conversion into IP blocks allows a good transparency and we can check the functionality of each IP block and compare it with the behaviour of the corresponding ONNX node. The emulation of such an IP block is performed with PyVerilator and is described in detail in section [Emulation (rtlsim) using Pyverilator](#rtlsim)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "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",
+ "\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`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Each `fpgadataflow` node will have its own code generation directory, which we can examine as follows:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "hls_syn_StreamingFCLayer_Batch_0.tcl thresh.h\r\n",
+ "params.h\t\t\t top_StreamingFCLayer_Batch_0.cpp\r\n"
+ ]
+ }
+ ],
+ "source": [
+ "fc0w = getCustomOp(model.graph.node[2])\n",
+ "codegen_dir = fc0w.get_nodeattr(\"code_gen_dir_ipgen\")\n",
+ "! ls {codegen_dir}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can see the various generated files. In particular, the `top*.cpp` will contain the Vivado HLS function call that instantiates the correct `finn-hlslib` library component for this node:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\r\n",
+ "#include \"bnn-library.h\"\r\n",
+ "// includes for network parameters\r\n",
+ "#include \"weights.hpp\"\r\n",
+ "#include \"activations.hpp\"\r\n",
+ "#include \"params.h\"\r\n",
+ "#include \"thresh.h\"\r\n",
+ "\r\n",
+ "// defines for network parameters\r\n",
+ "#define MW1 784\r\n",
+ " #define MH1 64\r\n",
+ " #define SIMD1 16\r\n",
+ "\r\n",
+ " #define PE1 16\r\n",
+ " #define WMEM1 196\r\n",
+ " #define TMEM1 4\r\n",
+ "\r\n",
+ " #define numReps 1\r\n",
+ "#define PRAGMA_SUB(x) _Pragma (#x)\r\n",
+ "#define DO_PRAGMA(x) PRAGMA_SUB(x)\r\n",
+ "\r\n",
+ "void StreamingFCLayer_Batch_0(hls::stream<ap_uint<16>> &in0,\r\n",
+ " hls::stream<ap_uint<16>> &out\r\n",
+ " )\r\n",
+ "{\r\n",
+ "#pragma HLS INTERFACE axis port=in0\r\n",
+ "#pragma HLS INTERFACE axis port=out\r\n",
+ "#pragma HLS INTERFACE ap_ctrl_none port=return\r\n",
+ "DO_PRAGMA(HLS ARRAY_PARTITION variable=weights.m_weights complete dim=1)\r\n",
+ "DO_PRAGMA(HLS ARRAY_PARTITION variable=threshs.m_thresholds complete dim=1)\r\n",
+ "DO_PRAGMA(HLS ARRAY_PARTITION variable=threshs.m_thresholds complete dim=3)\r\n",
+ "StreamingFCLayer_Batch<MW1, MH1, SIMD1, PE1, Recast<XnorMul>, Identity, Identity>\r\n",
+ " (in0, out, weights, threshs, numReps, ap_resource_lut());\r\n",
+ "}\r\n"
+ ]
+ }
+ ],
+ "source": [
+ "! cat {codegen_dir}/top_StreamingFCLayer_Batch_0.cpp"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "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."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from finn.transformation.fpgadataflow.hlssynth_ipgen import HLSSynth_IPGen\n",
+ "\n",
+ "model = model.transform(HLSSynth_IPGen())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Each StreamingFCLayer_Batch node now has new attributes which can be examined more closely with netron."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Serving 'lfc_w1_a1.onnx' at http://0.0.0.0:8081\n"
+ ]
+ }
+ ],
+ "source": [
+ "model.save(build_dir+\"/tfc_w1_a1.onnx\")\n",
+ "netron.start(build_dir+\"/tfc_w1_a1.onnx\", port=8081, host=\"0.0.0.0\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "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": [
+ "There are two additional attributes: \n",
+ "* `code_gen_dir_ipgen` which contains the directory path where all the files generated by the ipgen transformations are stored\n",
+ "* `ipgen_path` which contains the path to the project directory in which the generated IP block is stored\n",
+ "\n",
+ "We can further investigate which files are produced by taking a look in this directory. For example for the first StreamingFCLayer_Batch node."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "hls_syn_StreamingFCLayer_Batch_0.tcl thresh.h\r\n",
+ "ipgen.sh\t\t\t top_StreamingFCLayer_Batch_0.cpp\r\n",
+ "params.h\t\t\t vivado_hls.log\r\n",
+ "project_StreamingFCLayer_Batch_0\r\n"
+ ]
+ }
+ ],
+ "source": [
+ "fc0w = getCustomOp(model.graph.node[2])\n",
+ "code_gen_dir = fc0w.get_nodeattr(\"code_gen_dir_ipgen\")\n",
+ "!ls {code_gen_dir}"
+ ]
+ },
+ {
+ "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*."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "#!/bin/bash \r\n",
+ "cd /tmp/code_gen_ipgen_StreamingFCLayer_Batch_a00d34e6\r\n",
+ "vivado_hls /tmp/code_gen_ipgen_StreamingFCLayer_Batch_a00d34e6/hls_syn_StreamingFCLayer_Batch_0.tcl\r\n",
+ "cd /workspace/finn\r\n"
+ ]
+ }
+ ],
+ "source": [
+ "shell_script = code_gen_dir + \"/ipgen.sh\"\n",
+ "!cat {shell_script}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The script consists only of two framing `cd` commands and a command to pass the tcl script to *vivado_hls*. The directory has to be changed to create the files in the correct folder and will then be changed back to the original directory. \n",
+ "\n",
+ "Below is the tcl script which is passed to *vivado_hls*."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\r\n",
+ "set config_proj_name project_StreamingFCLayer_Batch_0\r\n",
+ "puts \"HLS project: $config_proj_name\"\r\n",
+ "set config_hwsrcdir \"/tmp/code_gen_ipgen_StreamingFCLayer_Batch_a00d34e6\"\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",
+ "\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",
+ "\r\n",
+ "set_top $config_toplevelfxn\r\n",
+ "open_solution sol1\r\n",
+ "set_part $config_proj_part\r\n",
+ "\r\n",
+ "config_interface -m_axi_addr64\r\n",
+ "\r\n",
+ "create_clock -period $config_clkperiod -name default\r\n",
+ "csynth_design\r\n",
+ "export_design -format ip_catalog\r\n",
+ "exit 0\r\n"
+ ]
+ }
+ ],
+ "source": [
+ "tcl_script = code_gen_dir + \"/hls_syn_StreamingFCLayer_Batch_0.tcl\"\n",
+ "!cat {tcl_script}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In the first part of the script the project is configured. For example the FPGA part and the clock are set. Then the project is opened and the files are added. The toplevel function is set and after creating a clock, the design is first synthesized with `csynth` and then exported as an IP block.\n",
+ "\n",
+ "Now that all IP blocks are in place, they can be stitched together to create an IP design that matches the ONNX model. This is covered in the next section."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# save model with other name for section \"Emulation using PyVerilator\"\"\n",
+ "model.save(build_dir+\"/tfc_w1_a1_after_hls_ip_per_layer.onnx\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Creation of stitched design <a id='stitched_design'></a>\n",
+ "The goal of this transformation is the creation of a Vivado IP Block Design project from all the generated IPs of a model. All nodes in the model must have the fpgadataflow backend attribute, and the CodeGen_ipgen transformation must have been previously run on the model. The resulting block design is also packaged as IP."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### PYNQ shell project <a id='pynq_shell'></a>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Synthesis, place and route <a id='synth_pl_ro'></a>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 4. Hardware test <a id='hw_test'></a>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 5. Simulation & Emulation flows for functional verification <a id='sim'></a>\n",
+ "* [Simulation using Python](#simpy)\n",
+ "* [Simulation (npysim) using C++](#npysim)\n",
+ "* [Emulation (rtlsim) using PyVerilator](#rtlsim)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Simulation using Python <a id='simpy'></a>\n",
+ "If an ONNX model consists of [standard ONNX nodes](https://github.com/onnx/onnx/blob/master/docs/Operators.md) 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 [7-FINN-CustomOps](7-FINN-CustomOps.ipynb)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To simulate an ONNX node [ONNX Runtime](https://github.com/microsoft/onnxruntime) is used. ONNX Runtime 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."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "def xnorpopcountmatmul(inp0, inp1):\n",
+ " # extract the operand shapes\n",
+ " (M, K0) = inp0.shape\n",
+ " (K1, N) = inp1.shape\n",
+ " # make sure shapes are compatible with matmul\n",
+ " assert K0 == K1\n",
+ " K = K0\n",
+ " # we simulate XNOR-popcount matrix multiplication as a regular bipolar\n",
+ " # matrix multiplication followed by some post processing\n",
+ " # first, convert binary inputs to bipolar\n",
+ " inp0_bipolar = 2.0 * inp0 - 1.0\n",
+ " inp1_bipolar = 2.0 * inp1 - 1.0\n",
+ " # call regular numpy matrix multiplication\n",
+ " out = np.matmul(inp0_bipolar, inp1_bipolar)\n",
+ " # XNOR-popcount does not produce the regular dot product result --\n",
+ " # it returns the number of +1s after XNOR. let P be the number of +1s\n",
+ " # and N be the number of -1s. XNOR-popcount returns P, whereas the\n",
+ " # regular dot product result from numpy is P-N, so we need to apply\n",
+ " # some correction.\n",
+ " # out = P-N\n",
+ " # K = P+N\n",
+ " # out + K = 2P, so P = (out + K)/2\n",
+ " return (out + K) * 0.5\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "from finn.custom_op.xnorpopcount import xnorpopcountmatmul\n",
+ "showSrc(xnorpopcountmatmul)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The function contains a description of the behaviour in Python and can thus calculate the result of the node."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This execution function and onnxruntime is used when `execute_onnx` from [onnx_exec](https://github.com/Xilinx/finn/blob/dev/src/finn/core/onnx_exec.py) is applied to the model. The model is then simulated node by node and the result is stored in a context dictionary, which contains the values of each tensor at the end of the execution. To get the result, only the output tensor has to be extracted.\n",
+ "\n",
+ "The procedure is shown below. We take the model after the Brevitas export and the basic netwok transformations and generate an input tensor to pass to the execution function. The input tensor is generated from the Brevitas example inputs."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from pkgutil import get_data\n",
+ "import onnx\n",
+ "import onnx.numpy_helper as np_helper\n",
+ "\n",
+ "raw_i = get_data(\"finn\", \"data/onnx/mnist-conv/test_data_set_0/input_0.pb\")\n",
+ "input_tensor = onnx.load_tensor_from_string(raw_i)\n",
+ "input_dict = {\"global_in\": np_helper.to_array(input_tensor)}\n",
+ "\n",
+ "model_for_sim = ModelWrapper(build_dir+\"/tfc_w1_a1_after_brevitas_export.onnx\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "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",
+ " dtype=float32)"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import finn.core.onnx_exec as oxe\n",
+ "output_dict = oxe.execute_onnx(model_for_sim, input_dict)\n",
+ "pysim_output = output_dict[list(output_dict.keys())[0]]\n",
+ "pysim_output"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This result can now be compared with a theoretical calculated value for verification. This is not done in this notebook, but there are some tests in the FINN repository that demonstrate such a procedure. They can be found [here](https://github.com/Xilinx/finn/tree/dev/tests)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Simulation (npysim) using C++ <a id='npysim'></a>\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."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model_for_npysim = ModelWrapper(build_dir+\"/tfc_w1_a1_after_conv_to_hls.onnx\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "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 compiles the C++ code and stores the path to the executable"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from finn.transformation.fpgadataflow.codegen_npysim import CodeGen_npysim\n",
+ "from finn.transformation.fpgadataflow.compile import Compile\n",
+ "\n",
+ "model_for_npysim = model_for_npysim.transform(CodeGen_npysim())\n",
+ "model_for_npysim = model_for_npysim.transform(Compile())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "When we take a look at the model using netron, we can see that the transformations introduced new attributes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Stopping http://0.0.0.0:8081\n",
+ "Serving 'lfc_w1_a1_after_conv_to_hls.onnx' at http://0.0.0.0:8081\n"
+ ]
+ }
+ ],
+ "source": [
+ "model_for_npysim.save(build_dir+\"/tfc_w1_a1_after_conv_to_hls.onnx\")\n",
+ "netron.start(build_dir+\"/tfc_w1_a1_after_conv_to_hls.onnx\", port=8081, host=\"0.0.0.0\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "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 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",
+ "* `executable_path` specifies the path to the executable\n",
+ "\n",
+ "We take now a closer look into the files that were generated:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "compile.sh execute_StreamingFCLayer_Batch.cpp\tnode_model params.h thresh.h\r\n"
+ ]
+ }
+ ],
+ "source": [
+ "fc0 = model_for_npysim.graph.node[2]\n",
+ "fc0w = StreamingFCLayer_Batch(fc0)\n",
+ "code_gen_dir = fc0w.get_nodeattr(\"code_gen_dir_npysim\")\n",
+ "!ls {code_gen_dir}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Besides the .cpp file, the folder contains .h files with the weights and thresholds. The shell script contains the compile command and *node_model* is the executable generated by compilation. Comparing this with the `executable_path` node attribute, it can be seen that it specifies exactly the path to *node_model*.\n",
+ "\n",
+ "To simulate the model the *simulation mode*(`sim_mode`) must be set to \"npysim\". This is done using the transformation `SetSimMode`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from finn.transformation.fpgadataflow.set_sim_mode import SetSimMode\n",
+ "\n",
+ "model_for_npysim = model_for_npysim.transform(SetSimMode(\"npysim\"))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now the model can be executed using `execute_onnx`. The function reads the `sim_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/dev/src/finn/data/cpp/npy2apintstream.hpp)) in FINN, which uses [cnpy](https://github.com/rogersce/cnpy) to read .npy files and convert them into streams, or to read a stream and write it into an .npy. cnpy is a helper to read and write .npy and .npz formates in C++. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "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",
+ " dtype=float32)"
+ ]
+ },
+ "execution_count": 44,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "output_dict = oxe.execute_onnx(model_for_npysim, input_dict)\n",
+ "npysim_output = output_dict[list(output_dict.keys())[0]]\n",
+ "npysim_output"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This result can now be compared with the previous simulation using Python."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Results are the same\n"
+ ]
+ }
+ ],
+ "source": [
+ "if (pysim_output == npysim_output).all():\n",
+ " print(\"Results are the same\")\n",
+ "else:\n",
+ " raise Exception"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Emulation (rtlsim) using Pyverilator <a id='rtlsim'></a>\n",
+ "The emulation using [PyVerilator](https://github.com/maltanar/pyverilator) can be done after IP blocks are generated from the corresponding HLS layers. Pyverilator is a tool which makes it possible to simulate verilog files using verilator via a python interface.\n",
+ "\n",
+ "In the first step we load the model after IP blocks have been created from the layers and set `sim_mode` to \"rtlsim\"."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model_for_rtlsim = ModelWrapper(build_dir+\"/tfc_w1_a1_after_hls_ip_per_layer.onnx\")\n",
+ "\n",
+ "model_for_rtlsim = model_for_rtlsim.transform(SetSimMode(\"rtlsim\"))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Because the necessary files for the emulation are already generated in section [](), in the next step the execution of the model can be done directly."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Stopping http://0.0.0.0:8081\n",
+ "Serving 'lfc_w1_a1_after_hls_ip_per_layer.onnx' at http://0.0.0.0:8081\n"
+ ]
+ }
+ ],
+ "source": [
+ "model_for_rtlsim.save(build_dir+\"/tfc_w1_a1_after_hls_ip_per_layer.onnx\")\n",
+ "netron.start(build_dir+\"/tfc_w1_a1_after_hls_ip_per_layer.onnx\", port=8081, host=\"0.0.0.0\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "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": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "MultiThreshold\n",
+ "StreamingFCLayer_Batch\n"
+ ]
+ },
+ {
+ "ename": "Exception",
+ "evalue": "Found no verilog files for this node,\n did you run the codegen_ipgen transformation?",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mException\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-12-96db733648ff>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0moutput_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moxe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute_onnx\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_for_rtlsim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mrtlsim_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moutput_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput_dict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mrtlsim_output\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/workspace/finn/src/finn/core/onnx_exec.py\u001b[0m in \u001b[0;36mexecute_onnx\u001b[0;34m(model, input_dict, return_full_exec_context)\u001b[0m\n\u001b[1;32m 118\u001b[0m \u001b[0;31m# topologically sorted\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 119\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mnode\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 120\u001b[0;31m \u001b[0mexecute_node\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexecution_context\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgraph\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 121\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mreturn_full_exec_context\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 122\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mexecution_context\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/workspace/finn/src/finn/core/onnx_exec.py\u001b[0m in \u001b[0;36mexecute_node\u001b[0;34m(node, context, graph)\u001b[0m\n\u001b[1;32m 41\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdomain\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"finn\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 43\u001b[0;31m \u001b[0mex_cu_node\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute_custom_node\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgraph\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 44\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 45\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/workspace/finn/src/finn/core/execute_custom_node.py\u001b[0m in \u001b[0;36mexecute_custom_node\u001b[0;34m(node, context, graph)\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0minst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mregistry\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcustom_op\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mop_type\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0minst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute_node\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcontext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgraph\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 13\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;31m# exception if op_type is not supported\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/workspace/finn/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py\u001b[0m in \u001b[0;36mexecute_node\u001b[0;34m(self, context, graph)\u001b[0m\n\u001b[1;32m 496\u001b[0m raise Exception(\n\u001b[1;32m 497\u001b[0m \"\"\"Found no verilog files for this node,\n\u001b[0;32m--> 498\u001b[0;31m did you run the codegen_ipgen transformation?\"\"\"\n\u001b[0m\u001b[1;32m 499\u001b[0m )\n\u001b[1;32m 500\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mException\u001b[0m: Found no verilog files for this node,\n did you run the codegen_ipgen transformation?"
+ ]
+ }
+ ],
+ "source": [
+ "output_dict = oxe.execute_onnx(model_for_rtlsim, input_dict)\n",
+ "rtlsim_output = output_dict[list(output_dict.keys())[0]]\n",
+ "rtlsim_output"
+ ]
+ },
+ {
+ "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
+}
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diff --git a/src/finn/core/modelwrapper.py b/src/finn/core/modelwrapper.py
index 1d5d3a978948998dce95d74ee780dfb998376465..473a33c47885a9e21e0c331233b85fb265933456 100644
--- a/src/finn/core/modelwrapper.py
+++ b/src/finn/core/modelwrapper.py
@@ -115,6 +115,19 @@ class ModelWrapper:
qa.quant_parameter_tensor_names.append(dt)
qnt_annotations.append(qa)
+ def get_tensor_valueinfo(self, tensor_name):
+ """Returns ValueInfoProto of tensor with given name, if it has one."""
+ graph = self._model_proto.graph
+ vi_names = [(x.name, x) for x in graph.input]
+ vi_names += [(x.name, x) for x in graph.output]
+ vi_names += [(x.name, x) for x in graph.value_info]
+ try:
+ vi_ind = [x[0] for x in vi_names].index(tensor_name)
+ vi = vi_names[vi_ind][1]
+ return vi
+ except ValueError:
+ return None
+
def get_tensor_shape(self, tensor_name):
"""Returns the shape of tensor with given name, if it has ValueInfoProto."""
graph = self._model_proto.graph
diff --git a/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py b/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
index a423974149054cdf11b5a01af7b6ed201188bb3e..ce21ad38c842bf967e96c06ad39525d4b7690297 100644
--- a/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
+++ b/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
@@ -13,6 +13,12 @@ from finn.util.data_packing import (
rtlsim_output_to_npy,
)
+# ONNX i/o tensor shape assumptions for StreamingFCLayer:
+# input 0 is the input vector, shape (1, i_size) = (1, MW)
+# input 1 is the weight vector, shape (i_size, o_size) = (MW, MH)
+# (optional) input 2 is the threshold vector, shape (o_size, n_thres)
+# output 0 is the output vector, shape (1, o_size) = (1, MH)
+
class StreamingFCLayer_Batch(HLSCustomOp):
def __init__(self, onnx_node):
@@ -628,18 +634,27 @@ class StreamingFCLayer_Batch(HLSCustomOp):
self.code_gen_dict["$PRAGMAS$"].append(
"#pragma HLS INTERFACE ap_ctrl_none port=return"
)
+ # the weight tensor is ap_uint<simd*prec> [PE][WMEM]
+ # partition for parallel access along the PE dimension (dim 1)
self.code_gen_dict["$PRAGMAS$"].append(
- "DO_PRAGMA(HLS ARRAY_PARTITION variable=weights complete dim=1)"
- )
-
- self.code_gen_dict["$PRAGMAS$"].append(
- "DO_PRAGMA(HLS ARRAY_PARTITION variable=weights complete dim=2)"
+ (
+ "DO_PRAGMA(HLS ARRAY_PARTITION "
+ "variable=weights.m_weights complete dim=1)"
+ )
)
+ # the threshold tensor is acc_type [PE][TMEM][N_THRES]
+ # partition for parallel access along PE and N_THRES dimensions (dims 1 and 3)
if self.calc_tmem() != 0:
# TODO find a better way of checking for no pregenerated thresholds
self.code_gen_dict["$PRAGMAS$"].append(
- "DO_PRAGMA(HLS ARRAY_PARTITION variable=threshs complete dim=1)"
+ (
+ "DO_PRAGMA(HLS ARRAY_PARTITION variable=threshs.m_thresholds "
+ "complete dim=1)"
+ )
)
self.code_gen_dict["$PRAGMAS$"].append(
- "DO_PRAGMA(HLS ARRAY_PARTITION variable=threshs complete dim=3)"
+ (
+ "DO_PRAGMA(HLS ARRAY_PARTITION variable=threshs.m_thresholds "
+ "complete dim=3)"
+ )
)
diff --git a/src/finn/custom_op/registry.py b/src/finn/custom_op/registry.py
index dc1a05a018a2767767cf2f2b811d763539df0841..c38a28dae46f6e39ed45e58647385b79fe271bcc 100644
--- a/src/finn/custom_op/registry.py
+++ b/src/finn/custom_op/registry.py
@@ -1,12 +1,13 @@
# make sure new CustomOp subclasses are imported here so that they get
# registered and plug in correctly into the infrastructure
from finn.custom_op.fpgadataflow.convolutioninputgenerator import (
- ConvolutionInputGenerator
+ ConvolutionInputGenerator,
)
from finn.custom_op.fpgadataflow.streamingfclayer_batch import StreamingFCLayer_Batch
from finn.custom_op.fpgadataflow.streamingmaxpool_batch import StreamingMaxPool_Batch
from finn.custom_op.fpgadataflow.tlastmarker import TLastMarker
from finn.custom_op.multithreshold import MultiThreshold
+from finn.custom_op.streamingdataflowpartition import StreamingDataflowPartition
from finn.custom_op.xnorpopcount import XnorPopcountMatMul
# create a mapping of all known CustomOp names and classes
@@ -18,3 +19,16 @@ custom_op["StreamingMaxPool_Batch"] = StreamingMaxPool_Batch
custom_op["StreamingFCLayer_Batch"] = StreamingFCLayer_Batch
custom_op["ConvolutionInputGenerator"] = ConvolutionInputGenerator
custom_op["TLastMarker"] = TLastMarker
+custom_op["StreamingDataflowPartition"] = StreamingDataflowPartition
+
+
+def getCustomOp(node):
+ "Return a FINN CustomOp instance for the given ONNX node, if it exists."
+ op_type = node.op_type
+ try:
+ # lookup op_type in registry of CustomOps
+ inst = custom_op[op_type](node)
+ return inst
+ except KeyError:
+ # exception if op_type is not supported
+ raise Exception("Custom op_type %s is currently not supported." % op_type)
diff --git a/src/finn/custom_op/streamingdataflowpartition.py b/src/finn/custom_op/streamingdataflowpartition.py
new file mode 100644
index 0000000000000000000000000000000000000000..856ba6d5604c37440985648bb91b18fffeaf79fd
--- /dev/null
+++ b/src/finn/custom_op/streamingdataflowpartition.py
@@ -0,0 +1,65 @@
+from finn.custom_op import CustomOp
+
+# note that the StreamingDataflowPartition node is only a meta/container node,
+# it does not produce any HLS or bitfile by itself. it's a placeholder for
+# a group of fpgadataflow nodes that have been separated out into a FINN-ONNX
+# model of its own.
+
+
+class StreamingDataflowPartition(CustomOp):
+ def get_nodeattr_types(self):
+ return {
+ "model": ("s", True, ""),
+ }
+
+ def make_shape_compatible_op(self):
+ pass
+
+ def infer_node_datatype(self, model):
+ pass
+
+ def execute_node(self, context, graph):
+ # TODO add RPC execution with synthesized bitfile?
+ # whole-design rtlsim with PyVerilator may also be an alternative
+ pass
+
+ def verify_node(self):
+ info_messages = []
+
+ # verify number of attributes
+ num_of_attr = 1
+ if len(self.onnx_node.attribute) == num_of_attr:
+ info_messages.append("The number of attributes is correct")
+ else:
+ info_messages.append(
+ """The number of attributes is incorrect,
+ {} should have {} attributes""".format(
+ self.onnx_node.op_type, num_of_attr
+ )
+ )
+
+ # verify that "domain" is set to "finn"
+ domain_value = self.onnx_node.domain
+ if domain_value == "finn":
+ info_messages.append("Attribute domain is set correctly")
+ else:
+ info_messages.append('Attribute domain should be set to "finn"')
+
+ # verify that all necessary attributes exist
+ try:
+ self.get_nodeattr("model")
+ info_messages.append("All necessary attributes exist")
+ except Exception:
+ info_messages.append(
+ """The necessary attributes do not exist.
+ StreamingDataflowPartition needs the following attribute(s):
+ model"""
+ )
+
+ # verify the number of inputs
+ if len(self.onnx_node.input) == 1:
+ info_messages.append("The number of inputs is correct")
+ else:
+ info_messages.append("StreamingDataflowPartition needs 1 data input")
+
+ return info_messages
diff --git a/src/finn/data/onnx/finn-hls-model/tfc_w1_a1_after_conv_to_hls.onnx b/src/finn/data/onnx/finn-hls-model/tfc_w1_a1_after_conv_to_hls.onnx
new file mode 100644
index 0000000000000000000000000000000000000000..aada6f07e9d3910122d2eb357d8a8c1224e9fbab
Binary files /dev/null and b/src/finn/data/onnx/finn-hls-model/tfc_w1_a1_after_conv_to_hls.onnx differ
diff --git a/src/finn/transformation/fpgadataflow/create_dataflow_partition.py b/src/finn/transformation/fpgadataflow/create_dataflow_partition.py
new file mode 100644
index 0000000000000000000000000000000000000000..af43b7ea55c30155ff0ff326d2a26942ab69bad2
--- /dev/null
+++ b/src/finn/transformation/fpgadataflow/create_dataflow_partition.py
@@ -0,0 +1,74 @@
+import copy
+
+from onnx import helper
+
+from finn.transformation import Transformation
+from finn.util.basic import get_by_name, make_build_dir
+
+
+class CreateDataflowPartition(Transformation):
+ """Split a graph into two graphs; one which contains non-FINN-dataflow nodes
+ and a StreamingDataflowPartition node, and another which only contains
+ FINN dataflow nodes. The StreamingDataflowPartition has a model attribute
+ that indicates the filename for the second graph that only contains
+ dataflow nodes. No action is taken if there are no dataflow nodes."""
+
+ def __init__(self):
+ super().__init__()
+
+ def apply(self, model):
+ # TODO we currently assume that all dataflow nodes are connected to
+ # each other, forming a single partition. check the assumption and/or
+ # improve this.
+ all_nodes = list(model.graph.node)
+ df_nodes = filter(
+ lambda x: get_by_name(x.attribute, "backend") is not None, all_nodes
+ )
+ df_nodes = filter(
+ lambda x: get_by_name(x.attribute, "backend").s.decode("UTF-8")
+ == "fpgadataflow",
+ df_nodes,
+ )
+ df_nodes = list(df_nodes)
+ non_df_nodes = filter(lambda x: x not in df_nodes, all_nodes)
+ non_df_nodes = list(non_df_nodes)
+
+ if len(df_nodes) == 0:
+ # no changes if no dataflow nodes are present
+ return (model, False)
+ else:
+ # partition the model into two models
+ df_model = copy.deepcopy(model)
+ non_df_model = model
+ # remove all non-dataflow nodes from the dataflow model
+ for node_to_remove in non_df_nodes:
+ df_model.graph.node.remove(node_to_remove)
+ # identify the entry and exit points for the dataflow part
+ df_in = df_model.graph.node[0].input[0]
+ df_out = df_model.graph.node[-1].output[0]
+ df_in_vi = df_model.get_tensor_valueinfo(df_in)
+ df_out_vi = df_model.get_tensor_valueinfo(df_out)
+ # set df graph in/out to be df_in/df_out
+ df_model.graph.input.remove(df_model.graph.input[0])
+ df_model.graph.input.insert(0, df_in_vi)
+ df_model.graph.output.remove(df_model.graph.output[0])
+ df_model.graph.output.insert(0, df_out_vi)
+ df_model_dir = make_build_dir("dataflow_partition_")
+ df_model_filename = df_model_dir + "/df_model.onnx"
+ df_model.save(df_model_filename)
+ # remove all dataflow nodes from the non-dataflow model
+ # keep track of where the dataflow part starts
+ df_start_ind = all_nodes.index(df_nodes[0])
+ for node_to_remove in df_nodes:
+ non_df_model.graph.node.remove(node_to_remove)
+ # create StreamingDataflow node with df_in/df_out io
+ df_node = helper.make_node(
+ "StreamingDataflowPartition",
+ [df_in],
+ [df_out],
+ # use the model attribute to mark the df model
+ model=df_model_filename,
+ )
+ non_df_model.graph.node.insert(df_start_ind, df_node)
+
+ return (non_df_model, False)
diff --git a/src/finn/util/test.py b/src/finn/util/test.py
new file mode 100644
index 0000000000000000000000000000000000000000..a1bd96b2b5f3c75c072333d9efdec4bb00bef7b5
--- /dev/null
+++ b/src/finn/util/test.py
@@ -0,0 +1,31 @@
+import torch
+from models.LFC import LFC
+from models.SFC import SFC
+from models.TFC import TFC
+
+
+def get_trained_fc_checkpoint(size, wbits, abits):
+ # TODO get from config instead, hardcoded to Docker path for now
+ nname = "%s_%dW%dA" % (size, wbits, abits)
+ root = "/workspace/brevitas_cnv_lfc/pretrained_models/%s/checkpoints/best.tar"
+ return root % nname
+
+
+def get_fc_model_def_fxn(size):
+ model_def_map = {"LFC": LFC, "SFC": SFC, "TFC": TFC}
+ return model_def_map[size]
+
+
+def get_fc_model_trained(size, wbits, abits):
+ model_def_fxn = get_fc_model_def_fxn(size)
+ checkpoint_loc = get_trained_fc_checkpoint(size, wbits, abits)
+ fc = model_def_fxn(weight_bit_width=wbits, act_bit_width=abits, in_bit_width=abits)
+ checkpoint = torch.load(checkpoint_loc, map_location="cpu")
+ fc.load_state_dict(checkpoint["state_dict"])
+ return fc
+
+
+def get_fc_model_untrained(size, wbits, abits):
+ model_def_fxn = get_fc_model_def_fxn(size)
+ fc = model_def_fxn(weight_bit_width=wbits, act_bit_width=abits, in_bit_width=abits)
+ return fc
diff --git a/tests/test_brevitas_cnv.py b/tests/brevitas/test_brevitas_cnv.py
similarity index 100%
rename from tests/test_brevitas_cnv.py
rename to tests/brevitas/test_brevitas_cnv.py
diff --git a/tests/brevitas/test_brevitas_fc.py b/tests/brevitas/test_brevitas_fc.py
new file mode 100644
index 0000000000000000000000000000000000000000..a3b651e37aff5690f841cb848c7035f8a30a1ac5
--- /dev/null
+++ b/tests/brevitas/test_brevitas_fc.py
@@ -0,0 +1,46 @@
+from pkgutil import get_data
+
+import pytest
+
+import brevitas.onnx as bo
+import numpy as np
+import onnx
+import onnx.numpy_helper as nph
+import torch
+
+import finn.core.onnx_exec as oxe
+from finn.core.modelwrapper import ModelWrapper
+from finn.transformation.fold_constants import FoldConstants
+from finn.transformation.infer_shapes import InferShapes
+from finn.util.basic import make_build_dir
+from finn.util.test import get_fc_model_trained
+
+export_onnx_path = make_build_dir("test_brevitas_fc_")
+
+# activation: None or DataType
+@pytest.mark.parametrize("size", ["TFC", "SFC", "LFC"])
+# weight bits
+@pytest.mark.parametrize("wbits", [1])
+# act bits
+@pytest.mark.parametrize("abits", [1, 2])
+def test_brevitas_fc_onnx_export_and_exec(size, wbits, abits):
+ nname = "%s_%dW%dA" % (size, wbits, abits)
+ finn_onnx = export_onnx_path + "/%s.onnx" % nname
+ fc = get_fc_model_trained(size, wbits, abits)
+ bo.export_finn_onnx(fc, (1, 1, 28, 28), finn_onnx)
+ model = ModelWrapper(finn_onnx)
+ model = model.transform(InferShapes())
+ model = model.transform(FoldConstants())
+ # load one of the test vectors
+ 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
+ input_dict = {"0": nph.to_array(input_tensor)}
+ output_dict = oxe.execute_onnx(model, input_dict)
+ produced = output_dict[list(output_dict.keys())[0]]
+ # run using PyTorch/Brevitas
+ input_tensor = torch.from_numpy(nph.to_array(input_tensor)).float()
+ assert input_tensor.shape == (1, 1, 28, 28)
+ # do forward pass in PyTorch/Brevitas
+ expected = fc.forward(input_tensor).detach().numpy()
+ assert np.isclose(produced, expected, atol=1e-3).all()
diff --git a/tests/core/test_modelwrapper.py b/tests/core/test_modelwrapper.py
index 162278da13af9766b1ebbe081616c1865bec03e2..c0864d81276363e0a446c448e212ab98fed3017d 100644
--- a/tests/core/test_modelwrapper.py
+++ b/tests/core/test_modelwrapper.py
@@ -3,22 +3,15 @@ from collections import Counter
import brevitas.onnx as bo
import numpy as np
-import torch
-from models.LFC import LFC
from finn.core.modelwrapper import ModelWrapper
+from finn.util.test import get_fc_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_modelwrapper():
- lfc = LFC(weight_bit_width=1, act_bit_width=1, in_bit_width=1)
- checkpoint = torch.load(trained_lfc_checkpoint, map_location="cpu")
- lfc.load_state_dict(checkpoint["state_dict"])
+ lfc = get_fc_model_trained("LFC", 1, 1)
bo.export_finn_onnx(lfc, (1, 1, 28, 28), export_onnx_path)
model = ModelWrapper(export_onnx_path)
assert model.check_all_tensor_shapes_specified() is False
diff --git a/tests/fpgadataflow/test_convert_to_hls_layers.py b/tests/fpgadataflow/test_convert_to_hls_layers.py
index 14246fea56ffb2d595dd755fd11e0cb2c056f651..d319c0bf070fe436d8b5835eb54998828332e7df 100644
--- a/tests/fpgadataflow/test_convert_to_hls_layers.py
+++ b/tests/fpgadataflow/test_convert_to_hls_layers.py
@@ -6,13 +6,12 @@ import numpy as np
import onnx
import onnx.numpy_helper as nph
import torch
-from models.LFC import LFC
import finn.core.onnx_exec as oxe
import finn.transformation.fpgadataflow.convert_to_hls_layers as to_hls
import finn.transformation.streamline.absorb as absorb
from finn.core.modelwrapper import ModelWrapper
-from finn.custom_op.fpgadataflow.streamingfclayer_batch import StreamingFCLayer_Batch
+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
@@ -22,18 +21,13 @@ from finn.transformation.general import GiveReadableTensorNames, GiveUniqueNodeN
from finn.transformation.infer_shapes import InferShapes
from finn.transformation.streamline import Streamline
from finn.transformation.streamline.round_thresholds import RoundAndClipThresholds
+from finn.util.test import get_fc_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"
-)
+export_onnx_path = "test_output_tfc.onnx"
-def test_convert_to_hls_layers_lfc_w1a1():
- lfc = LFC(weight_bit_width=1, act_bit_width=1, in_bit_width=1)
- checkpoint = torch.load(trained_lfc_checkpoint, map_location="cpu")
- lfc.load_state_dict(checkpoint["state_dict"])
+def test_convert_to_hls_layers_tfc_w1a1():
+ lfc = get_fc_model_trained("TFC", 1, 1)
bo.export_finn_onnx(lfc, (1, 1, 28, 28), export_onnx_path)
model = ModelWrapper(export_onnx_path)
model = model.transform(InferShapes())
@@ -49,38 +43,38 @@ def test_convert_to_hls_layers_lfc_w1a1():
fc0 = model.graph.node[2]
assert fc0.op_type == "StreamingFCLayer_Batch"
assert model.get_tensor_shape(fc0.input[0]) == [1, 784]
- assert model.get_tensor_shape(fc0.input[1]) == [784, 1024]
- assert model.get_tensor_shape(fc0.input[2]) == [1024, 1]
+ assert model.get_tensor_shape(fc0.input[1]) == [784, 64]
+ assert model.get_tensor_shape(fc0.input[2]) == [64, 1]
fc1 = model.graph.node[3]
assert fc1.op_type == "StreamingFCLayer_Batch"
- assert model.get_tensor_shape(fc1.input[0]) == [1, 1024]
- assert model.get_tensor_shape(fc1.input[1]) == [1024, 1024]
- assert model.get_tensor_shape(fc1.input[2]) == [1024, 1]
+ assert model.get_tensor_shape(fc1.input[0]) == [1, 64]
+ assert model.get_tensor_shape(fc1.input[1]) == [64, 64]
+ assert model.get_tensor_shape(fc1.input[2]) == [64, 1]
fc2 = model.graph.node[4]
assert fc2.op_type == "StreamingFCLayer_Batch"
- assert model.get_tensor_shape(fc2.input[0]) == [1, 1024]
- assert model.get_tensor_shape(fc2.input[1]) == [1024, 1024]
- assert model.get_tensor_shape(fc2.input[2]) == [1024, 1]
+ assert model.get_tensor_shape(fc2.input[0]) == [1, 64]
+ assert model.get_tensor_shape(fc2.input[1]) == [64, 64]
+ assert model.get_tensor_shape(fc2.input[2]) == [64, 1]
fc3 = model.graph.node[5]
assert fc3.op_type == "StreamingFCLayer_Batch"
- assert model.get_tensor_shape(fc3.input[0]) == [1, 1024]
- assert model.get_tensor_shape(fc3.input[1]) == [1024, 10]
+ assert model.get_tensor_shape(fc3.input[0]) == [1, 64]
+ assert model.get_tensor_shape(fc3.input[1]) == [64, 10]
os.remove(export_onnx_path)
- fc0w = StreamingFCLayer_Batch(fc0)
+ fc0w = getCustomOp(fc0)
fc0w.set_nodeattr("SIMD", 784)
- fc0w.set_nodeattr("PE", 32)
+ fc0w.set_nodeattr("PE", 16)
- fc1w = StreamingFCLayer_Batch(fc1)
- fc1w.set_nodeattr("SIMD", 1024)
- fc1w.set_nodeattr("PE", 32)
+ fc1w = getCustomOp(fc1)
+ fc1w.set_nodeattr("SIMD", 16)
+ fc1w.set_nodeattr("PE", 16)
- fc2w = StreamingFCLayer_Batch(fc2)
- fc2w.set_nodeattr("SIMD", 1024)
- fc2w.set_nodeattr("PE", 32)
+ fc2w = getCustomOp(fc2)
+ fc2w.set_nodeattr("SIMD", 16)
+ fc2w.set_nodeattr("PE", 16)
- fc3w = StreamingFCLayer_Batch(fc3)
- fc3w.set_nodeattr("SIMD", 1024)
+ fc3w = getCustomOp(fc3)
+ fc3w.set_nodeattr("SIMD", 16)
fc3w.set_nodeattr("PE", 10)
model = model.transform(CodeGen_npysim())
diff --git a/tests/fpgadataflow/test_create_dataflow_partition.py b/tests/fpgadataflow/test_create_dataflow_partition.py
new file mode 100644
index 0000000000000000000000000000000000000000..e0b8b491b0ed926a56331a8e125f3de4ecd91615
--- /dev/null
+++ b/tests/fpgadataflow/test_create_dataflow_partition.py
@@ -0,0 +1,21 @@
+import os.path
+from pkgutil import get_data
+
+from finn.core.modelwrapper import ModelWrapper
+from finn.custom_op.registry import getCustomOp
+from finn.transformation.fpgadataflow.create_dataflow_partition import (
+ CreateDataflowPartition,
+)
+
+
+def test_create_dataflow_partition():
+ # load the onnx model
+ raw_m = get_data(
+ "finn", "data/onnx/finn-hls-model/tfc_w1_a1_after_conv_to_hls.onnx"
+ )
+ model = ModelWrapper(raw_m)
+ model = model.transform(CreateDataflowPartition())
+ assert model.graph.node[2].op_type == "StreamingDataflowPartition"
+ sdp_node = getCustomOp(model.graph.node[2])
+ assert sdp_node.__class__.__name__ == "StreamingDataflowPartition"
+ assert os.path.isfile(sdp_node.get_nodeattr("model"))
diff --git a/tests/test_brevitas_export.py b/tests/test_brevitas_export.py
deleted file mode 100644
index 641e5e3c49b917ad06d649d797e6f9bc9f30f170..0000000000000000000000000000000000000000
--- a/tests/test_brevitas_export.py
+++ /dev/null
@@ -1,132 +0,0 @@
-import os
-from pkgutil import get_data
-
-import brevitas.onnx as bo
-import numpy as np
-import onnx
-import onnx.numpy_helper as nph
-import torch
-from models.LFC import LFC
-
-import finn.core.onnx_exec as oxe
-from finn.core.modelwrapper import ModelWrapper
-from finn.transformation.fold_constants import FoldConstants
-from finn.transformation.infer_shapes import InferShapes
-
-export_onnx_path = "test_output_lfc.onnx"
-# TODO get from config instead, hardcoded to Docker path for now
-trained_lfc_w1a1_checkpoint = (
- "/workspace/brevitas_cnv_lfc/pretrained_models/LFC_1W1A/checkpoints/best.tar"
-)
-
-trained_lfc_w1a2_checkpoint = (
- "/workspace/brevitas_cnv_lfc/pretrained_models/LFC_1W2A/checkpoints/best.tar"
-)
-
-
-def test_brevitas_trained_lfc_w1a1_pytorch():
- # load pretrained weights into LFC-w1a1
- lfc = LFC(weight_bit_width=1, act_bit_width=1, in_bit_width=1).eval()
- checkpoint = torch.load(trained_lfc_w1a1_checkpoint, map_location="cpu")
- lfc.load_state_dict(checkpoint["state_dict"])
- # load one of the test vectors
- 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)
- input_tensor = torch.from_numpy(nph.to_array(input_tensor)).float()
- assert input_tensor.shape == (1, 1, 28, 28)
- # do forward pass in PyTorch/Brevitas
- produced = lfc.forward(input_tensor).detach().numpy()
- expected = [
- [
- 3.3253,
- -2.5652,
- 9.2157,
- -1.4251,
- 1.4251,
- -3.3728,
- 0.2850,
- -0.5700,
- 7.0781,
- -1.2826,
- ]
- ]
- assert np.isclose(produced, expected, atol=1e-4).all()
-
-
-def test_brevitas_trained_lfc_w1a2_pytorch():
- # load pretrained weights into LFC-w1a2
- lfc = LFC(weight_bit_width=1, act_bit_width=2, in_bit_width=2).eval()
- checkpoint = torch.load(trained_lfc_w1a2_checkpoint, map_location="cpu")
- lfc.load_state_dict(checkpoint["state_dict"])
- # load one of the test vectors
- 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)
- input_tensor = torch.from_numpy(nph.to_array(input_tensor)).float()
- assert input_tensor.shape == (1, 1, 28, 28)
- # do forward pass in PyTorch/Brevitas
- produced = lfc.forward(input_tensor).detach().numpy()
- expected = [
- [
- 4.598069,
- -6.3698025,
- 10.75695,
- 0.3796571,
- 1.4764442,
- -5.4417515,
- -1.8982856,
- -5.610488,
- 6.116698,
- 0.21092065,
- ]
- ]
- assert np.isclose(produced, expected, atol=1e-4).all()
-
-
-def test_brevitas_to_onnx_export_and_exec_lfc_w1a1():
- lfc = LFC(weight_bit_width=1, act_bit_width=1, in_bit_width=1)
- checkpoint = torch.load(trained_lfc_w1a1_checkpoint, map_location="cpu")
- lfc.load_state_dict(checkpoint["state_dict"])
- bo.export_finn_onnx(lfc, (1, 1, 28, 28), export_onnx_path)
- model = ModelWrapper(export_onnx_path)
- model = model.transform(InferShapes())
- model = model.transform(FoldConstants())
- # load one of the test vectors
- 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
- input_dict = {"0": nph.to_array(input_tensor)}
- output_dict = oxe.execute_onnx(model, input_dict)
- produced = output_dict[list(output_dict.keys())[0]]
- # run using PyTorch/Brevitas
- input_tensor = torch.from_numpy(nph.to_array(input_tensor)).float()
- assert input_tensor.shape == (1, 1, 28, 28)
- # do forward pass in PyTorch/Brevitas
- expected = lfc.forward(input_tensor).detach().numpy()
- assert np.isclose(produced, expected, atol=1e-3).all()
- # remove the downloaded model and extracted files
- os.remove(export_onnx_path)
-
-
-def test_brevitas_to_onnx_export_and_exec_lfc_w1a2():
- lfc = LFC(weight_bit_width=1, act_bit_width=2, in_bit_width=2)
- checkpoint = torch.load(trained_lfc_w1a2_checkpoint, map_location="cpu")
- lfc.load_state_dict(checkpoint["state_dict"])
- bo.export_finn_onnx(lfc, (1, 1, 28, 28), export_onnx_path)
- model = ModelWrapper(export_onnx_path)
- model = model.transform(InferShapes())
- model = model.transform(FoldConstants())
- # load one of the test vectors
- 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
- input_dict = {"0": nph.to_array(input_tensor)}
- output_dict = oxe.execute_onnx(model, input_dict)
- produced = output_dict[list(output_dict.keys())[0]]
- # run using PyTorch/Brevitas
- input_tensor = torch.from_numpy(nph.to_array(input_tensor)).float()
- assert input_tensor.shape == (1, 1, 28, 28)
- # do forward pass in PyTorch/Brevitas
- expected = lfc.forward(input_tensor).detach().numpy()
- assert np.isclose(produced, expected, atol=1e-3).all()
- # remove the downloaded model and extracted files
- os.remove(export_onnx_path)
diff --git a/tests/test_xnorpopcountmatmul.py b/tests/test_xnorpopcountmatmul.py
index fc174a098cb901869065f2ec92c8fee1e65c1968..a98e0044be7bf4bb5e888da72dbd391eacf2a355 100644
--- a/tests/test_xnorpopcountmatmul.py
+++ b/tests/test_xnorpopcountmatmul.py
@@ -6,8 +6,6 @@ import numpy as np
import onnx
import onnx.helper as helper
import onnx.numpy_helper as nph
-import torch
-from models.LFC import LFC
from onnx import TensorProto
import finn.core.onnx_exec as oxe
@@ -19,6 +17,7 @@ from finn.transformation.general import GiveReadableTensorNames, GiveUniqueNodeN
from finn.transformation.infer_datatypes import InferDataTypes
from finn.transformation.infer_shapes import InferShapes
from finn.transformation.streamline.sign_to_thres import ConvertSignToThres
+from finn.util.test import get_fc_model_trained
export_onnx_path = "test_output_lfc.onnx"
# TODO get from config instead, hardcoded to Docker path for now
@@ -63,9 +62,7 @@ def test_xnorpopcountmatmul():
def test_convert_bipolar_matmul_to_xnorpopcountmatmul():
- lfc = LFC(weight_bit_width=1, act_bit_width=1, in_bit_width=1)
- checkpoint = torch.load(trained_lfc_checkpoint, map_location="cpu")
- lfc.load_state_dict(checkpoint["state_dict"])
+ lfc = get_fc_model_trained("LFC", 1, 1)
bo.export_finn_onnx(lfc, (1, 1, 28, 28), export_onnx_path)
model = ModelWrapper(export_onnx_path)
model = model.transform(InferShapes())
diff --git a/tests/transformation/streamline/test_streamline_lfc_w1a1.py b/tests/transformation/streamline/test_streamline_lfc_w1a1.py
index 06533a1d97b0734e7fd8a6aa0bf0a2a28548b9de..140bb3ad14552e7bbe6c543c6dcf5bd06989b622 100644
--- a/tests/transformation/streamline/test_streamline_lfc_w1a1.py
+++ b/tests/transformation/streamline/test_streamline_lfc_w1a1.py
@@ -5,8 +5,6 @@ import brevitas.onnx as bo
import numpy as np
import onnx
import onnx.numpy_helper as nph
-import torch
-from models.LFC import LFC
import finn.core.onnx_exec as oxe
from finn.core.modelwrapper import ModelWrapper
@@ -14,6 +12,7 @@ from finn.transformation.fold_constants import FoldConstants
from finn.transformation.general import GiveReadableTensorNames, GiveUniqueNodeNames
from finn.transformation.infer_shapes import InferShapes
from finn.transformation.streamline import Streamline
+from finn.util.test import get_fc_model_trained
export_onnx_path = "test_output_lfc.onnx"
# TODO get from config instead, hardcoded to Docker path for now
@@ -23,9 +22,7 @@ trained_lfc_w1a1_checkpoint = (
def test_streamline_lfc_w1a1():
- lfc = LFC(weight_bit_width=1, act_bit_width=1, in_bit_width=1)
- checkpoint = torch.load(trained_lfc_w1a1_checkpoint, map_location="cpu")
- lfc.load_state_dict(checkpoint["state_dict"])
+ lfc = get_fc_model_trained("LFC", 1, 1)
bo.export_finn_onnx(lfc, (1, 1, 28, 28), export_onnx_path)
model = ModelWrapper(export_onnx_path)
model = model.transform(InferShapes())
diff --git a/tests/transformation/streamline/test_streamline_lfc_w1a2.py b/tests/transformation/streamline/test_streamline_lfc_w1a2.py
index 9e9d347e85245e740605efea3a03e1792c3ea46b..8d10deac77a909d6b055f6dd4b9a6ecbf06c0db6 100644
--- a/tests/transformation/streamline/test_streamline_lfc_w1a2.py
+++ b/tests/transformation/streamline/test_streamline_lfc_w1a2.py
@@ -5,8 +5,6 @@ import brevitas.onnx as bo
import numpy as np
import onnx
import onnx.numpy_helper as nph
-import torch
-from models.LFC import LFC
import finn.core.onnx_exec as oxe
from finn.core.modelwrapper import ModelWrapper
@@ -14,18 +12,13 @@ from finn.transformation.fold_constants import FoldConstants
from finn.transformation.general import GiveReadableTensorNames, GiveUniqueNodeNames
from finn.transformation.infer_shapes import InferShapes
from finn.transformation.streamline import Streamline
+from finn.util.test import get_fc_model_trained
export_onnx_path = "test_output_lfc.onnx"
-# TODO get from config instead, hardcoded to Docker path for now
-trained_lfc_w1a2_checkpoint = (
- "/workspace/brevitas_cnv_lfc/pretrained_models/LFC_1W2A/checkpoints/best.tar"
-)
def test_streamline_lfc_w1a2():
- lfc = LFC(weight_bit_width=1, act_bit_width=2, in_bit_width=2).eval()
- checkpoint = torch.load(trained_lfc_w1a2_checkpoint, map_location="cpu")
- lfc.load_state_dict(checkpoint["state_dict"])
+ lfc = get_fc_model_trained("LFC", 1, 1)
bo.export_finn_onnx(lfc, (1, 1, 28, 28), export_onnx_path)
model = ModelWrapper(export_onnx_path)
model = model.transform(InferShapes())
@@ -43,5 +36,4 @@ def test_streamline_lfc_w1a2():
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()
- model.save("lfc-w1a2-streamlined.onnx")
os.remove(export_onnx_path)
diff --git a/tests/transformation/test_batchnorm_to_affine.py b/tests/transformation/test_batchnorm_to_affine.py
index dec01b53a9081c6c04143e4465d603d66fc771a6..2247672f7d20c1f7290eb2fd1c00d44a399bbeb6 100644
--- a/tests/transformation/test_batchnorm_to_affine.py
+++ b/tests/transformation/test_batchnorm_to_affine.py
@@ -4,14 +4,13 @@ from pkgutil import get_data
import brevitas.onnx as bo
import onnx
import onnx.numpy_helper as nph
-import torch
-from models.LFC import LFC
import finn.core.onnx_exec as oxe
from finn.core.modelwrapper import ModelWrapper
from finn.transformation.batchnorm_to_affine import BatchNormToAffine
from finn.transformation.fold_constants import FoldConstants
from finn.transformation.infer_shapes import InferShapes
+from finn.util.test import get_fc_model_trained
export_onnx_path = "test_output_lfc.onnx"
transformed_onnx_path = "test_output_lfc_transformed.onnx"
@@ -22,9 +21,7 @@ trained_lfc_checkpoint = (
def test_batchnorm_to_affine():
- lfc = LFC(weight_bit_width=1, act_bit_width=1, in_bit_width=1)
- checkpoint = torch.load(trained_lfc_checkpoint, map_location="cpu")
- lfc.load_state_dict(checkpoint["state_dict"])
+ lfc = get_fc_model_trained("LFC", 1, 1)
bo.export_finn_onnx(lfc, (1, 1, 28, 28), export_onnx_path)
model = ModelWrapper(export_onnx_path)
model = model.transform(InferShapes())
diff --git a/tests/transformation/test_fold_constants.py b/tests/transformation/test_fold_constants.py
index 09dbd95c27cec65183ec7dd6067ce187595fcf52..db9a23956504216a4d66da93121f063bd275d929 100644
--- a/tests/transformation/test_fold_constants.py
+++ b/tests/transformation/test_fold_constants.py
@@ -5,12 +5,12 @@ import brevitas.onnx as bo
import numpy as np
import onnx
import onnx.numpy_helper as np_helper
-from models.LFC import LFC
import finn.core.onnx_exec as oxe
from finn.core.modelwrapper import ModelWrapper
from finn.transformation.fold_constants import FoldConstants
from finn.transformation.infer_shapes import InferShapes
+from finn.util.test import get_fc_model_untrained
export_onnx_path = "test_output_lfc.onnx"
@@ -32,7 +32,7 @@ def test_const_folding():
def test_const_folding_shapes():
- lfc = LFC(weight_bit_width=1, act_bit_width=1, in_bit_width=1)
+ lfc = get_fc_model_untrained("LFC", 1, 1)
bo.export_finn_onnx(lfc, (1, 1, 28, 28), export_onnx_path)
model = ModelWrapper(export_onnx_path)
model = model.transform(InferShapes())
diff --git a/tests/transformation/test_infer_datatypes.py b/tests/transformation/test_infer_datatypes.py
index e4269499c55eb89d9a9c268f79456ca6ac588028..042866338af0841e643e1b9f8bec8cca212bd842 100644
--- a/tests/transformation/test_infer_datatypes.py
+++ b/tests/transformation/test_infer_datatypes.py
@@ -1,8 +1,6 @@
import os
import brevitas.onnx as bo
-import torch
-from models.LFC import LFC
from finn.core.datatype import DataType
from finn.core.modelwrapper import ModelWrapper
@@ -10,6 +8,7 @@ from finn.transformation.fold_constants import FoldConstants
from finn.transformation.general import GiveReadableTensorNames, GiveUniqueNodeNames
from finn.transformation.infer_datatypes import InferDataTypes
from finn.transformation.infer_shapes import InferShapes
+from finn.util.test import get_fc_model_trained
export_onnx_path = "test_output_lfc.onnx"
# TODO get from config instead, hardcoded to Docker path for now
@@ -19,9 +18,7 @@ trained_lfc_checkpoint = (
def test_infer_datatypes():
- lfc = LFC(weight_bit_width=1, act_bit_width=1, in_bit_width=1)
- checkpoint = torch.load(trained_lfc_checkpoint, map_location="cpu")
- lfc.load_state_dict(checkpoint["state_dict"])
+ lfc = get_fc_model_trained("LFC", 1, 1)
bo.export_finn_onnx(lfc, (1, 1, 28, 28), export_onnx_path)
model = ModelWrapper(export_onnx_path)
model = model.transform(InferShapes())
diff --git a/tests/transformation/test_sign_to_thres.py b/tests/transformation/test_sign_to_thres.py
index 75327df3e5194f48f71c914bc62fc5a08588faff..72df1f10037324c6d28da23d59ca0caa87091d57 100644
--- a/tests/transformation/test_sign_to_thres.py
+++ b/tests/transformation/test_sign_to_thres.py
@@ -4,14 +4,13 @@ from pkgutil import get_data
import brevitas.onnx as bo
import onnx
import onnx.numpy_helper as nph
-import torch
-from models.LFC import LFC
import finn.core.onnx_exec as oxe
from finn.core.modelwrapper import ModelWrapper
from finn.transformation.fold_constants import FoldConstants
from finn.transformation.infer_shapes import InferShapes
from finn.transformation.streamline import ConvertSignToThres
+from finn.util.test import get_fc_model_trained
export_onnx_path = "test_output_lfc.onnx"
transformed_onnx_path = "test_output_lfc_transformed.onnx"
@@ -22,9 +21,7 @@ trained_lfc_checkpoint = (
def test_sign_to_thres():
- lfc = LFC(weight_bit_width=1, act_bit_width=1, in_bit_width=1)
- checkpoint = torch.load(trained_lfc_checkpoint, map_location="cpu")
- lfc.load_state_dict(checkpoint["state_dict"])
+ lfc = get_fc_model_trained("LFC", 1, 1)
bo.export_finn_onnx(lfc, (1, 1, 28, 28), export_onnx_path)
model = ModelWrapper(export_onnx_path)
model = model.transform(InferShapes())