diff --git a/tests/end2end/test_end2end.py b/tests/end2end/test_end2end.py
new file mode 100644
index 0000000000000000000000000000000000000000..cf9501ff56c36dce5ee963aa2b6e5df651db6b06
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+++ b/tests/end2end/test_end2end.py
@@ -0,0 +1,348 @@
+# Copyright (c) 2020, Xilinx
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# * Redistributions of source code must retain the above copyright notice, this
+# list of conditions and the following disclaimer.
+#
+# * Redistributions in binary form must reproduce the above copyright notice,
+# this list of conditions and the following disclaimer in the documentation
+# and/or other materials provided with the distribution.
+#
+# * Neither the name of FINN nor the names of its
+# contributors may be used to endorse or promote products derived from
+# this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+import os
+from pkgutil import get_data
+
+import pytest
+
+import numpy as np
+
+# as of Feb'20 there is a bug that segfaults ONNX shape inference if we
+# import pytorch before onnx, so we make sure to import onnx first
+import onnx # NOQA
+import onnx.numpy_helper as nph
+import torch
+import brevitas.onnx as bo
+
+import finn.transformation.fpgadataflow.convert_to_hls_layers as to_hls
+import finn.transformation.streamline.absorb as absorb
+from finn.core.onnx_exec import execute_onnx
+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.create_dataflow_partition import (
+ CreateDataflowPartition,
+)
+from finn.transformation.fpgadataflow.make_deployment import DeployToPYNQ
+from finn.transformation.general import (
+ RemoveUnusedTensors,
+ RemoveStaticGraphInputs,
+ GiveReadableTensorNames,
+ GiveUniqueNodeNames,
+)
+from finn.transformation.infer_datatypes import InferDataTypes
+from finn.transformation.infer_shapes import InferShapes
+from finn.transformation.streamline import Streamline
+from finn.util.basic import pynq_part_map
+from finn.util.test import get_test_model_trained, load_test_checkpoint_or_skip
+from finn.transformation.fpgadataflow.annotate_resources import AnnotateResources
+from finn.transformation.infer_data_layouts import InferDataLayouts
+from finn.transformation.fpgadataflow.make_zynq_proj import ZynqBuild
+from finn.transformation.double_to_single_float import DoubleToSingleFloat
+from finn.transformation.move_reshape import RemoveCNVtoFCFlatten
+from finn.transformation.lower_convs_to_matmul import LowerConvsToMatMul
+from finn.transformation.streamline.reorder import MakeMaxPoolNHWC
+import warnings
+import pkg_resources as pk
+
+build_dir = "/tmp/" + os.environ["FINN_INST_NAME"]
+test_pynq_board = os.getenv("PYNQ_BOARD", default="Pynq-Z1")
+test_fpga_part = pynq_part_map[test_pynq_board]
+target_clk_ns = 10
+mem_mode = "decoupled"
+
+
+def get_trained_network_and_ishape(topology, wbits, abits):
+ topology_to_ishape = {
+ "tfc": (1, 1, 28, 28),
+ "cnv": (1, 3, 32, 32),
+ }
+ ishape = topology_to_ishape[topology]
+ model = get_test_model_trained(topology.upper(), wbits, abits)
+ return (model, ishape)
+
+
+def get_checkpoint_name(topology, wbits, abits, step):
+ return build_dir + "/end2end_%s_w%da%d_%s.onnx" % (topology, wbits, abits, step)
+
+
+def fold_tfc(model):
+ fc_layers = model.get_nodes_by_op_type("StreamingFCLayer_Batch")
+ # (PE, SIMD, in_fifo_depth, out_fifo_depth, ramstyle) for each layer
+ config = [
+ (16, 49, 16, 64, "block"),
+ (8, 8, 64, 64, "auto"),
+ (8, 8, 64, 64, "auto"),
+ (10, 8, 64, 10, "distributed"),
+ ]
+ for fcl, (pe, simd, ififo, ofifo, ramstyle) in zip(fc_layers, config):
+ fcl_inst = getCustomOp(fcl)
+ fcl_inst.set_nodeattr("PE", pe)
+ fcl_inst.set_nodeattr("SIMD", simd)
+ fcl_inst.set_nodeattr("inFIFODepth", ififo)
+ fcl_inst.set_nodeattr("outFIFODepth", ofifo)
+ fcl_inst.set_nodeattr("ram_style", ramstyle)
+ return model
+
+
+def fold_cnv_large(model):
+ fc_layers = model.get_nodes_by_op_type("StreamingFCLayer_Batch")
+ # each tuple is (PE, SIMD, in_fifo_depth) for a layer
+ folding = [
+ (16, 3, 256),
+ (32, 32, 256),
+ (16, 32, 256),
+ (16, 32, 256),
+ (4, 32, 214),
+ (1, 32, 2),
+ (1, 4, 126),
+ (1, 8, 62),
+ (5, 1, 6),
+ ]
+ for fcl, (pe, simd, ififodepth) in zip(fc_layers, folding):
+ fcl_inst = getCustomOp(fcl)
+ fcl_inst.set_nodeattr("PE", pe)
+ fcl_inst.set_nodeattr("SIMD", simd)
+ fcl_inst.set_nodeattr("inFIFODepth", ififodepth)
+
+ swg_layers = model.get_nodes_by_op_type("ConvolutionInputGenerator")
+ swg_idepth = [2, 51, 9, 106, 2, 2]
+ for i in range(len(swg_layers)):
+ swg_inst = getCustomOp(swg_layers[i])
+ simd = folding[i][1]
+ swg_inst.set_nodeattr("SIMD", simd)
+ swg_inst.set_nodeattr("inFIFODepth", swg_idepth[i])
+ return model
+
+
+def fold_cnv_small(model):
+ fc_layers = model.get_nodes_by_op_type("StreamingFCLayer_Batch")
+ # each tuple is (PE, SIMD, in_fifo_depth) for a layer
+ folding = [
+ (8, 3, 256, "auto"),
+ (16, 16, 256, "auto"),
+ (8, 16, 256, "auto"),
+ (8, 16, 256, "block"),
+ (4, 8, 214, "auto"),
+ (1, 8, 2, "auto"),
+ (1, 2, 126, "distributed"),
+ (2, 2, 62, "block"),
+ (5, 1, 6, "distributed"),
+ ]
+ for fcl, (pe, simd, ififodepth, ramstyle) in zip(fc_layers, folding):
+ fcl_inst = getCustomOp(fcl)
+ fcl_inst.set_nodeattr("PE", pe)
+ fcl_inst.set_nodeattr("SIMD", simd)
+ fcl_inst.set_nodeattr("inFIFODepth", ififodepth)
+ fcl_inst.set_nodeattr("ram_style", ramstyle)
+
+ swg_layers = model.get_nodes_by_op_type("ConvolutionInputGenerator")
+ swg_idepth = [2, 51, 9, 106, 2, 2]
+ for i in range(len(swg_layers)):
+ swg_inst = getCustomOp(swg_layers[i])
+ simd = folding[i][1]
+ swg_inst.set_nodeattr("SIMD", simd)
+ swg_inst.set_nodeattr("inFIFODepth", swg_idepth[i])
+ return model
+
+
+def get_folding_function(topology, wbits, abits):
+ if "tfc" in topology:
+ return fold_tfc
+ elif "cnv" in topology:
+ if wbits == 1 and abits == 1:
+ return fold_cnv_large
+ else:
+ return fold_cnv_small
+ else:
+ raise Exception("Unknown topology/quantization combo for predefined folding")
+
+
+def get_example_input(topology):
+ if "fc" in topology:
+ raw_i = get_data("finn", "data/onnx/mnist-conv/test_data_set_0/input_0.pb")
+ onnx_tensor = onnx.load_tensor_from_string(raw_i)
+ return nph.to_array(onnx_tensor)
+ elif topology == "cnv":
+ fn = pk.resource_filename("finn", "data/cifar10/cifar10-test-data-class3.npz")
+ input_tensor = np.load(fn)["arr_0"].astype(np.float32)
+ input_tensor = input_tensor / 255
+ return input_tensor
+ else:
+ raise Exception("Unknown topology, can't return example input")
+
+
+def get_golden_io_pair(topology, wbits, abits):
+ (model, ishape) = get_trained_network_and_ishape(topology, wbits, abits)
+ input_tensor_npy = get_example_input(topology)
+ input_tensor_torch = torch.from_numpy(input_tensor_npy).float()
+ output_tensor_npy = model.forward(input_tensor_torch).detach().numpy()
+ return (input_tensor_npy, output_tensor_npy)
+
+
+@pytest.mark.parametrize("wbits", [1, 2])
+@pytest.mark.parametrize("abits", [1, 2])
+@pytest.mark.parametrize("topology", ["tfc", "cnv"])
+class TestEnd2End:
+ def test_export(self, topology, wbits, abits):
+ (model, ishape) = get_trained_network_and_ishape(topology, wbits, abits)
+ chkpt_name = get_checkpoint_name(topology, wbits, abits, "export")
+ bo.export_finn_onnx(model, ishape, chkpt_name)
+ assert os.path.isfile(chkpt_name)
+
+ def test_import_and_tidy(self, topology, wbits, abits):
+ prev_chkpt_name = get_checkpoint_name(topology, wbits, abits, "export")
+ model = load_test_checkpoint_or_skip(prev_chkpt_name)
+ model = model.transform(DoubleToSingleFloat())
+ model = model.transform(InferShapes())
+ model = model.transform(FoldConstants())
+ model = model.transform(GiveUniqueNodeNames())
+ model = model.transform(GiveReadableTensorNames())
+ model = model.transform(InferDataTypes())
+ model = model.transform(RemoveStaticGraphInputs())
+ model.save(get_checkpoint_name(topology, wbits, abits, "import_and_tidy"))
+
+ def test_streamline(self, topology, wbits, abits):
+ prev_chkpt_name = get_checkpoint_name(topology, wbits, abits, "import_and_tidy")
+ model = load_test_checkpoint_or_skip(prev_chkpt_name)
+ model = model.transform(Streamline())
+ if "fc" not in topology:
+ model = model.transform(LowerConvsToMatMul())
+ model = model.transform(MakeMaxPoolNHWC())
+ model = model.transform(absorb.AbsorbTransposeIntoMultiThreshold())
+ model = model.transform(ConvertBipolarMatMulToXnorPopcount())
+ model = model.transform(Streamline())
+ model = model.transform(RemoveUnusedTensors())
+ model.save(get_checkpoint_name(topology, wbits, abits, "streamline"))
+
+ def test_convert_to_hls_layers(self, topology, wbits, abits):
+ prev_chkpt_name = get_checkpoint_name(topology, wbits, abits, "streamline")
+ model = load_test_checkpoint_or_skip(prev_chkpt_name)
+ # needed for bipolar MatMul layers
+ model = model.transform(to_hls.InferBinaryStreamingFCLayer(mem_mode))
+ # needed for non-bipolar MatMul layers
+ model = model.transform(to_hls.InferQuantizedStreamingFCLayer(mem_mode))
+ # needed for convolutions
+ if "fc" not in topology:
+ model = model.transform(to_hls.InferConvInpGen())
+ model = model.transform(to_hls.InferStreamingMaxPool())
+ model = model.transform(RemoveCNVtoFCFlatten())
+ model = model.transform(GiveUniqueNodeNames())
+ model = model.transform(InferDataLayouts())
+ model.save(get_checkpoint_name(topology, wbits, abits, "convert_to_hls_layers"))
+
+ def test_create_dataflow_partition(self, topology, wbits, abits):
+ prev_chkpt_name = get_checkpoint_name(
+ topology, wbits, abits, "convert_to_hls_layers"
+ )
+ model = load_test_checkpoint_or_skip(prev_chkpt_name)
+ parent_model = model.transform(CreateDataflowPartition())
+ parent_model_chkpt = get_checkpoint_name(
+ topology, wbits, abits, "dataflow_parent"
+ )
+ parent_model.save(parent_model_chkpt)
+ sdp_node = parent_model.get_nodes_by_op_type("StreamingDataflowPartition")[0]
+ sdp_node = getCustomOp(sdp_node)
+ dataflow_model_filename = sdp_node.get_nodeattr("model")
+ dataflow_model = load_test_checkpoint_or_skip(dataflow_model_filename)
+ dataflow_model_chkpt = get_checkpoint_name(
+ topology, wbits, abits, "dataflow_model"
+ )
+ dataflow_model.save(dataflow_model_chkpt)
+
+ def test_fold(self, topology, wbits, abits):
+ prev_chkpt_name = get_checkpoint_name(topology, wbits, abits, "dataflow_model")
+ model = load_test_checkpoint_or_skip(prev_chkpt_name)
+ folding_fxn = get_folding_function(topology, wbits, abits)
+ model = folding_fxn(model)
+ model.save(get_checkpoint_name(topology, wbits, abits, "fold"))
+
+ def test_build(self, topology, wbits, abits):
+ prev_chkpt_name = get_checkpoint_name(topology, wbits, abits, "fold")
+ model = load_test_checkpoint_or_skip(prev_chkpt_name)
+ model = model.transform(ZynqBuild(test_pynq_board, target_clk_ns))
+ model = model.transform(AnnotateResources("synth"))
+ warnings.warn(
+ "Post-synthesis resources (excluding shell): "
+ + model.get_metadata_prop("res_total_synth")
+ )
+ warnings.warn(
+ "Post-synthesis resources (all inclusive): "
+ + model.get_metadata_prop("res_total_top_synth")
+ )
+ model.save(get_checkpoint_name(topology, wbits, abits, "build"))
+
+ def test_deploy(self, topology, wbits, abits):
+ prev_chkpt_name = get_checkpoint_name(topology, wbits, abits, "build")
+ model = load_test_checkpoint_or_skip(prev_chkpt_name)
+ try:
+ ip = os.environ["PYNQ_IP"] # no fault for this one; skip if not defined
+ if ip == "":
+ pytest.skip("PYNQ board IP address not specified")
+ username = os.getenv("PYNQ_USERNAME", "xilinx")
+ password = os.getenv("PYNQ_PASSWORD", "xilinx")
+ port = os.getenv("PYNQ_PORT", 22)
+ target_dir = os.getenv("PYNQ_TARGET_DIR", "/home/xilinx/finn")
+ model = model.transform(
+ DeployToPYNQ(ip, port, username, password, target_dir)
+ )
+ # save the model to be able to link it to the parent
+ model.save(get_checkpoint_name(topology, wbits, abits, "deploy"))
+ except KeyError:
+ pytest.skip("PYNQ board IP address not specified")
+
+ def test_run_on_pynq(self, topology, wbits, abits):
+ prev_chkpt_name = get_checkpoint_name(topology, wbits, abits, "deploy")
+ model = load_test_checkpoint_or_skip(prev_chkpt_name) # NOQA
+ try:
+ ip = os.environ["PYNQ_IP"] # no fault for this one; skip if not defined
+ if ip == "":
+ pytest.skip("PYNQ board IP address not specified")
+ (input_tensor_npy, output_tensor_npy) = get_golden_io_pair(
+ topology, wbits, abits
+ )
+ parent_model = load_test_checkpoint_or_skip(
+ get_checkpoint_name(topology, wbits, abits, "dataflow_parent")
+ )
+ iname = parent_model.graph.input[0].name
+ oname = parent_model.graph.output[0].name
+ sdp_node = parent_model.get_nodes_by_op_type("StreamingDataflowPartition")[
+ 0
+ ]
+ sdp_node = getCustomOp(sdp_node)
+ sdp_chkpt = get_checkpoint_name(topology, wbits, abits, "deploy")
+ load_test_checkpoint_or_skip(sdp_chkpt)
+ sdp_node.set_nodeattr("model", sdp_chkpt)
+ ret = execute_onnx(parent_model, {iname: input_tensor_npy}, True)
+ y = ret[oname]
+ assert np.isclose(y, output_tensor_npy).all()
+
+ except KeyError:
+ pytest.skip("PYNQ board IP address not specified")