Feature/validation steps

AUTHORS.rst

@@ -2,7 +2,7 @@
 Contributors
 ============
 
-* Mirza Mrahorovic (@mmrahorovic)
+* Mirza Mrahorovic (@mmrahorovic) (maintainer)
 * Jakoba Petri-Koenig (@auphelia)
 * Yaman Umuroglu (@maltanar)
 * Lucian Petrica (@quetric)
@@ -16,4 +16,3 @@ Contributors
 * Matthias Gehre (@mgehre-amd)
 * NaelF (@NaelF)
 * Tim Paine (@timkpaine)
-* Hannah Yan (@hannahxy13)

README.md

@@ -4,14 +4,14 @@
     <a>
         <img src="https://img.shields.io/github/v/release/Xilinx/finn-examples?color=%09%23228B22&display_name=tag&label=Release" />
     </a>
-    <a href="https://github.com/Xilinx/finn/tree/v0.10.1">
-        <img src="https://img.shields.io/badge/FINN-v0.10.1-blue" />
+    <a href="https://github.com/Xilinx/finn/tree/v0.9">
+        <img src="https://img.shields.io/badge/FINN-v0.9.0-blue" />
     </a>
     <a href="https://github.com/Xilinx/PYNQ/tree/v3.0.1">
         <img src="https://img.shields.io/badge/PYNQ-v3.0.1-blue" />
     </a>
     <a href="https://www.xilinx.com/support/download/index.html/content/xilinx/en/downloadNav/vivado-design-tools/2022-1.html">
-        <img src="https://img.shields.io/badge/Vivado%2FVitis-v2022.2-blue" />
+        <img src="https://img.shields.io/badge/Vivado%2FVitis-v2022.1-blue" />
     </a>
 </p>
 
@@ -79,7 +79,7 @@ jupyter-notebook --no-browser --allow-root --port=8888
 
 First, create & source a virtual environment:
 ```shell
-conda create -n <virtual-env> python=3.8
+conda create -n <virtual-env> python=3.10
 conda activate <virtual-env>
 ```
 
@@ -128,14 +128,14 @@ dummy_out = accel.execute(dummy_in)
 | CIFAR-10     | CNV (VGG-11-like)       | several variants:<br>1/2-bit weights/activations           | Pynq-Z1<br>ZCU104<br>Ultra96<br>U250              | Pynq-Z1<br>ZCU104<br>Ultra96<br>U250 |
 | MNIST       | 3-layer fully-connected | several variants:<br>1/2-bit weights/activations           | Pynq-Z1<br>ZCU104<br>Ultra96<br>U250              | Pynq-Z1<br>ZCU104<br>Ultra96<br>U250 |
 | ImageNet | MobileNet-v1            | 4-bit weights & activations<br>8-bit first layer weights | Alveo U250       | Alveo U250 |
-| ImageNet | ResNet-50            | 1-bit weights 2-bit activations<br>4-bit residuals<br>8-bit first/last layer weights | Alveo U250       | Alveo U250 |
+| ImageNet | ResNet-50            | 1-bit weights 2-bit activations<br>4-bit residuals<br>8-bit first/last layer weights | Alveo U250       | - |
 | RadioML 2018 | 1D CNN (VGG10)     |  4-bit weights & activations | ZCU104  | ZCU104 |
 | MaskedFace-Net | [BinaryCoP](https://arxiv.org/pdf/2102.03456)<br/>*Contributed by TU Munich+BMW*  | 1-bit weights & activations | Pynq-Z1       | Pynq-Z1 |
 | Google Speech Commands v2 | 3-layer fully-connected  | 3-bit weights & activations | Pynq-Z1       | Pynq-Z1 |
 | UNSW-NB15 | 4-layer fully-connected  | 2-bit weights & activations | Pynq-Z1 <br> ZCU104 <br> Ultra96       | Pynq-Z1 <br> ZCU104 <br> Ultra96 |
-| GTSRB | CNV (VGG-11-like) | 1-bit weights & activations | Pynq-Z1 | Pynq-Z1 |
 
-*Please note that you can target other boards (such as the Pynq-Z2 or ZCU102) by changing the build script manually, but these accelerators have not been tested.*
+*Please note that the build flow for ResNet-50 for the Alveo U250 has known issues and we're currently working on resolving them. However, you can still execute the associated notebook, as we provide a pre-built FPGA bitfile generated with an older Vivado (/FINN) version targeting the [xilinx_u250_xdma_201830_2](https://www.xilinx.com/products/boards-and-kits/alveo/package-files-archive/u250-2018-3-1.html) platform.* <br>
+*Furthermore, please note that you can target other boards (such as the Pynq-Z2 or ZCU102) by changing the build script manually, but these accelerators have not been tested.*
 
 We welcome community contributions to add more examples to this repo!
 

build/bnn-pynq/build.py

@@ -44,6 +44,8 @@
     "cnv-w2a2",
 ]
 
+verif_en = os.getenv("VERIFICATION_EN", "0")
+
 # which platforms to build the networks for
 zynq_platforms = ["Pynq-Z1", "Ultra96", "ZCU104"]
 alveo_platforms = ["U250"]
@@ -86,15 +88,33 @@ def platform_to_shell(platform):
             board=platform_name,
             shell_flow_type=shell_flow_type,
             vitis_platform=vitis_platform,
-            generate_outputs=[build_cfg.DataflowOutputType.BITFILE],
+            generate_outputs=[
+                build_cfg.DataflowOutputType.BITFILE,
+                build_cfg.DataflowOutputType.STITCHED_IP,
+            ],
             save_intermediate_models=True,
             default_swg_exception=True,
             specialize_layers_config_file="specialize_layers_config/%s_specialize_layers.json"
             % model_name,
         )
         model_file = "models/%s.onnx" % model_name
-        # launch FINN compiler to build
-        build.build_dataflow_cfg(model_file, cfg)
+
+        if verif_en == "1":
+            # Build the model with verification
+            import sys
+
+            sys.path.append(os.path.abspath(os.getenv("FINN_EXAMPLES_ROOT") + "/ci/"))
+            from verification_funcs import init_verif, verify_build_output
+
+            cfg.verify_steps, cfg.verify_input_npy, cfg.verify_expected_output_npy = init_verif(
+                model_name
+            )
+            build.build_dataflow_cfg(model_file, cfg)
+            verify_build_output(cfg, model_name)
+        else:
+            # Build the model without verification
+            build.build_dataflow_cfg(model_file, cfg)
+
         # copy bitfiles into release dir if found
         bitfile_gen_dir = cfg.output_dir + "/bitfile"
         files_to_check_and_copy = [

build/cybersecurity-mlp/build.py

@@ -31,6 +31,12 @@
 from finn.util.basic import alveo_default_platform
 import os
 import shutil
+from custom_steps import custom_step_mlp_export
+
+# Define model name
+model_name = "unsw_nb15-mlp-w2a2"
+
+verif_en = os.getenv("VERIFICATION_EN", "0")
 
 # Which platforms to build the networks for
 zynq_platforms = ["Pynq-Z1", "Ultra96", "ZCU104"]
@@ -50,9 +56,6 @@ def platform_to_shell(platform):
         raise Exception("Unknown platform, can't determine ShellFlowType")
 
 
-# Define model name
-model_name = "unsw_nb15-mlp-w2a2"
-
 # Create a release dir, used for finn-examples release packaging
 os.makedirs("release", exist_ok=True)
 
@@ -85,13 +88,29 @@ def platform_to_shell(platform):
             build_cfg.DataflowOutputType.ESTIMATE_REPORTS,
             build_cfg.DataflowOutputType.BITFILE,
             build_cfg.DataflowOutputType.DEPLOYMENT_PACKAGE,
+            build_cfg.DataflowOutputType.STITCHED_IP,
         ],
-        save_intermediate_models=True,
     )
 
-    model = "models/%s.onnx" % model_name
+    # Export MLP model to FINN-ONNX
+    model = custom_step_mlp_export(model_name)
     # Launch FINN compiler to generate bitfile
-    build.build_dataflow_cfg(model, cfg)
+    if verif_en == "1":
+        # Build the model with verification
+        import sys
+
+        sys.path.append(os.path.abspath(os.getenv("FINN_EXAMPLES_ROOT") + "/ci/"))
+        from verification_funcs import init_verif, verify_build_output
+
+        cfg.verify_steps, cfg.verify_input_npy, cfg.verify_expected_output_npy = init_verif(
+            model_name
+        )
+        build.build_dataflow_cfg(model, cfg)
+        verify_build_output(cfg, model_name)
+    else:
+        # Build the model without verification
+        build.build_dataflow_cfg(model, cfg)
+
     # Copy bitfiles into release dir if found
     bitfile_gen_dir = cfg.output_dir + "/bitfile"
     filtes_to_check_and_copy = ["finn-accel.bit", "finn-accel.hwh", "finn-accel.xclbin"]

build/cybersecurity-mlp/custom_steps.py

@@ -0,0 +1,118 @@
+# Copyright (C) 2023, Advanced Micro Devices, Inc.
+# 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 numpy as np
+import pkg_resources as pk
+import os
+
+from brevitas.nn import QuantLinear, QuantReLU, QuantIdentity
+import torch
+import torch.nn as nn
+import brevitas.onnx as bo
+from qonnx.core.modelwrapper import ModelWrapper
+from qonnx.core.datatype import DataType
+
+
+# Define export wrapper
+class CybSecMLPForExport(nn.Module):
+    def __init__(self, my_pretrained_model):
+        super(CybSecMLPForExport, self).__init__()
+        self.pretrained = my_pretrained_model
+        self.qnt_output = QuantIdentity(
+            quant_type="binary", scaling_impl_type="const", bit_width=1, min_val=-1.0, max_val=1.0
+        )
+
+    def forward(self, x):
+        # assume x contains bipolar {-1,1} elems
+        # shift from {-1,1} -> {0,1} since that is the
+        # input range for the trained network
+        x = (x + torch.tensor([1.0]).to("cpu")) / 2.0
+        out_original = self.pretrained(x)
+        out_final = self.qnt_output(out_original)  # output as {-1, 1}
+        return out_final
+
+
+def custom_step_mlp_export(model_name):
+    # Define model parameters
+    input_size = 593
+    hidden1 = 64
+    hidden2 = 64
+    hidden3 = 64
+    weight_bit_width = 2
+    act_bit_width = 2
+    num_classes = 1
+
+    # Create model definition from Brevitas library
+    model = nn.Sequential(
+        QuantLinear(input_size, hidden1, bias=True, weight_bit_width=weight_bit_width),
+        nn.BatchNorm1d(hidden1),
+        nn.Dropout(0.5),
+        QuantReLU(act_bit_width=act_bit_width),
+        QuantLinear(hidden1, hidden2, bias=True, weight_bit_width=weight_bit_width),
+        nn.BatchNorm1d(hidden2),
+        nn.Dropout(0.5),
+        QuantReLU(bit_width=act_bit_width),
+        QuantLinear(hidden2, hidden3, bias=True, weight_bit_width=weight_bit_width),
+        nn.BatchNorm1d(hidden3),
+        nn.Dropout(0.5),
+        QuantReLU(bit_width=act_bit_width),
+        QuantLinear(hidden3, num_classes, bias=True, weight_bit_width=weight_bit_width),
+    )
+
+    # Load pre-trained weights
+    assets_dir = pk.resource_filename("finn.qnn-data", "cybsec-mlp/")
+    trained_state_dict = torch.load(assets_dir + "/state_dict.pth")["models_state_dict"][0]
+    model.load_state_dict(trained_state_dict, strict=False)
+
+    # Network surgery: pad input size from 593 to 600 and convert bipolar to binary
+    W_orig = model[0].weight.data.detach().numpy()
+    W_new = np.pad(W_orig, [(0, 0), (0, 7)])
+    model[0].weight.data = torch.from_numpy(W_new)
+
+    model_for_export = CybSecMLPForExport(model)
+
+    # Create directory to save model
+    os.makedirs("models", exist_ok=True)
+    ready_model_filename = "models/%s.onnx" % model_name
+    input_shape = (1, 600)
+
+    # create a QuantTensor instance to mark input as bipolar during export
+    input_a = np.random.randint(0, 1, size=input_shape).astype(np.float32)
+    input_a = 2 * input_a - 1
+    scale = 1.0
+    input_t = torch.from_numpy(input_a * scale)
+
+    # Export to ONNX
+    bo.export_qonnx(model_for_export, export_path=ready_model_filename, input_t=input_t)
+
+    # Set input datatype for FINN's InferDataType transformation to infer the right datatypes
+    model = ModelWrapper(ready_model_filename)
+    model.set_tensor_datatype(model.graph.input[0].name, DataType["BIPOLAR"])
+    model.save(ready_model_filename)
+
+    return ready_model_filename