[Test] start adding end2end for tfc as test

tests/end2end/test_end2end_tfc.py

+import os
+
+# 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 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.registry import getCustomOp
+from finn.transformation.bipolar_to_xnor import ConvertBipolarMatMulToXnorPopcount
+from finn.transformation.fold_constants import FoldConstants
+from finn.transformation.fpgadataflow.codegen_ipgen import CodeGen_ipgen
+from finn.transformation.fpgadataflow.codegen_ipstitch import CodeGen_ipstitch
+from finn.transformation.fpgadataflow.create_dataflow_partition import (
+    CreateDataflowPartition,
+)
+from finn.transformation.fpgadataflow.hlssynth_ipgen import HLSSynth_IPGen
+from finn.transformation.fpgadataflow.insert_tlastmarker import InsertTLastMarker
+from finn.transformation.fpgadataflow.make_pynq_driver import MakePYNQDriver
+from finn.transformation.fpgadataflow.make_pynq_proj import MakePYNQProject
+from finn.transformation.fpgadataflow.replace_verilog_relpaths import (
+    ReplaceVerilogRelPaths,
+)
+from finn.transformation.fpgadataflow.synth_pynq_proj import SynthPYNQProject
+from finn.transformation.general import GiveReadableTensorNames, GiveUniqueNodeNames
+from finn.transformation.infer_datatypes import InferDataTypes
+from finn.transformation.infer_shapes import InferShapes
+from finn.transformation.streamline import Streamline
+from finn.transformation.streamline.round_thresholds import RoundAndClipThresholds
+from finn.util.basic import pynq_part_map
+from finn.util.test import get_test_model_trained
+
+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 = 5
+
+
+def test_end2end_tfc_export():
+    import brevitas.onnx as bo
+
+    tfc = get_test_model_trained("TFC", 1, 1)
+    bo.export_finn_onnx(
+        tfc, (1, 1, 28, 28), build_dir + "/end2end_tfc_w1_a1_export.onnx"
+    )
+
+
+def test_end2end_tfc_import_and_tidy():
+    model = ModelWrapper(build_dir + "/end2end_tfc_w1_a1_export.onnx")
+    model = model.transform(InferShapes())
+    model = model.transform(FoldConstants())
+    model = model.transform(GiveUniqueNodeNames())
+    model = model.transform(GiveReadableTensorNames())
+    model = model.transform(InferDataTypes())
+    model.save(build_dir + "/end2end_tfc_w1_a1_tidy.onnx")
+
+
+def test_end2end_tfc_streamline():
+    model = ModelWrapper(build_dir + "/end2end_tfc_w1_a1_tidy.onnx")
+    model = model.transform(Streamline())
+    model.save(build_dir + "/end2end_tfc_w1_a1_streamlined.onnx")
+
+
+def test_end2end_tfc_convert_to_hls_layers():
+    model = ModelWrapper(build_dir + "/end2end_tfc_w1_a1_streamlined.onnx")
+    model = model.transform(ConvertBipolarMatMulToXnorPopcount())
+    model = model.transform(absorb.AbsorbAddIntoMultiThreshold())
+    model = model.transform(absorb.AbsorbMulIntoMultiThreshold())
+    model = model.transform(RoundAndClipThresholds())
+    model = model.transform(to_hls.InferBinaryStreamingFCLayer())
+    model.save(build_dir + "/end2end_tfc_w1_a1_hls_layers.onnx")
+
+
+def test_end2end_tfc_create_dataflow_partition():
+    model = ModelWrapper(build_dir + "/end2end_tfc_w1_a1_hls_layers.onnx")
+    parent_model = model.transform(CreateDataflowPartition())
+    parent_model.save(build_dir + "/end2end_tfc_w1_a1_dataflow_parent.onnx")
+    sdp_node = getCustomOp(parent_model.graph.node[2])
+    dataflow_model_filename = sdp_node.get_nodeattr("model")
+    dataflow_model = ModelWrapper(dataflow_model_filename)
+    dataflow_model.save(build_dir + "/end2end_tfc_w1_a1_dataflow_model.onnx")
+
+
+def test_end2end_tfc_fold_and_tlastmarker():
+    model = ModelWrapper(build_dir + "/end2end_tfc_w1_a1_dataflow_model.onnx")
+    fc0 = model.graph.node[0]
+    fc1 = model.graph.node[1]
+    fc2 = model.graph.node[2]
+    fc3 = model.graph.node[3]
+    fc0w = getCustomOp(fc0)
+    fc1w = getCustomOp(fc1)
+    fc2w = getCustomOp(fc2)
+    fc3w = getCustomOp(fc3)
+    fc0w.set_nodeattr("inFIFODepth", 50)
+    fc0w.set_nodeattr("SIMD", 16)
+    fc0w.set_nodeattr("PE", 16)
+    fc0w.set_nodeattr("outFIFODepth", 4)
+    fc1w.set_nodeattr("SIMD", 16)
+    fc1w.set_nodeattr("PE", 16)
+    fc1w.set_nodeattr("outFIFODepth", 4)
+    fc2w.set_nodeattr("SIMD", 16)
+    fc2w.set_nodeattr("PE", 16)
+    fc2w.set_nodeattr("outFIFODepth", 4)
+    fc3w.set_nodeattr("SIMD", 16)
+    fc3w.set_nodeattr("PE", 10)
+    fc3w.set_nodeattr("outFIFODepth", 50)
+    model = model.transform(InsertTLastMarker())
+    model.save(build_dir + "/end2end_tfc_w1_a1_folded.onnx")
+
+
+def test_end2end_tfc_gen_hls_ip():
+    model = ModelWrapper(build_dir + "/end2end_tfc_w1_a1_folded.onnx")
+    model = model.transform(GiveUniqueNodeNames())
+    model = model.transform(CodeGen_ipgen(test_fpga_part, target_clk_ns))
+    model = model.transform(HLSSynth_IPGen())
+    model.save(build_dir + "/end2end_tfc_w1_a1_ipgen.onnx")
+
+
+def test_end2end_tfc_ip_stitch():
+    model = ModelWrapper(build_dir + "/end2end_tfc_w1_a1_ipgen.onnx")
+    model = model.transform(ReplaceVerilogRelPaths())
+    model = model.transform(CodeGen_ipstitch(test_fpga_part))
+    model.save(build_dir + "/end2end_tfc_w1_a1_ipstitch.onnx")
+
+
+def test_end2end_tfc_make_pynq_proj():
+    model = ModelWrapper(build_dir + "/end2end_tfc_w1_a1_ipstitch.onnx")
+    model = model.transform(MakePYNQProject(test_pynq_board))
+    model.save(build_dir + "/end2end_tfc_w1_a1_pynq_project.onnx")
+
+
+def test_end2end_synth_pynq_project():
+    model = ModelWrapper(build_dir + "/end2end_tfc_w1_a1_pynq_project.onnx")
+    model = model.transform(SynthPYNQProject())
+    model.save(build_dir + "/end2end_tfc_w1_a1_synth.onnx")
+
+
+def test_end2end_tfc_make_driver():
+    model = ModelWrapper(build_dir + "/end2end_tfc_w1_a1_synth.onnx")
+    model = model.transform(MakePYNQDriver())
+    model.save(build_dir + "/end2end_tfc_w1_a1_pynq_driver.onnx")