Merge pull request #149 from quetric/feature_floorplan

Added floorplanning transform

src/finn/custom_op/fpgadataflow/__init__.py

@@ -88,6 +88,8 @@ class HLSCustomOp(CustomOp):
             "res_hls": ("s", False, ""),
             "res_synth": ("s", False, ""),
             "rtlsim_so": ("s", False, ""),
+            # partitioning info
+            "partition_id": ("i", False, 0),
             # input and output FIFO depths
             "inFIFODepth": ("i", False, 2),
             "outFIFODepth": ("i", False, 2),

src/finn/custom_op/registry.py

@@ -52,6 +52,7 @@ from finn.custom_op.fpgadataflow.labelselect_batch import LabelSelect_Batch
 from finn.custom_op.quantavgpool2d import QuantAvgPool2d
 from finn.custom_op.fpgadataflow.duplicatestreams_batch import DuplicateStreams_Batch
 from finn.custom_op.fpgadataflow.channelwise_op_batch import ChannelwiseOp_Batch
+from finn.custom_op.fpgadataflow.iodma import IODMA
 
 # create a mapping of all known CustomOp names and classes
 custom_op = {}
@@ -76,6 +77,7 @@ custom_op["LabelSelect_Batch"] = LabelSelect_Batch
 custom_op["QuantAvgPool2d"] = QuantAvgPool2d
 custom_op["DuplicateStreams_Batch"] = DuplicateStreams_Batch
 custom_op["ChannelwiseOp_Batch"] = ChannelwiseOp_Batch
+custom_op["IODMA"] = IODMA
 
 
 def getCustomOp(node):

src/finn/transformation/fpgadataflow/floorplan.py

+# 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.
+
+from finn.custom_op.registry import getCustomOp
+from finn.transformation import Transformation
+from finn.util.basic import get_by_name
+
+
+class Floorplan(Transformation):
+    """Perform Floorplanning of the dataflow design. Separate DMAs into their own
+    partitions IDs, and TODO: split the design into sections of defined size"""
+
+    def __init__(self, limits=None):
+        super().__init__()
+        self.resource_limits = limits
+
+    def apply(self, model):
+        target_partition_id = 0
+        # we currently assume that all dataflow nodes belonging to the same partition
+        # are connected to each other and there is a single input/output to/from each.
+        all_nodes = list(model.graph.node)
+        df_nodes = list(
+            filter(lambda x: get_by_name(x.attribute, "backend") is not None, all_nodes)
+        )
+        dma_nodes = list(filter(lambda x: x.op_type == "IODMA", df_nodes))
+
+        non_dma_nodes = list(filter(lambda x: x not in dma_nodes, df_nodes))
+        dyn_tlastmarker_nodes = list(
+            filter(
+                lambda x: x.op_type == "TLastMarker"
+                and getCustomOp(x).get_nodeattr("DynIters") == "true",
+                non_dma_nodes,
+            )
+        )
+
+        non_dma_nodes = list(
+            filter(lambda x: x not in dyn_tlastmarker_nodes, non_dma_nodes)
+        )
+
+        for node in dma_nodes:
+            node_inst = getCustomOp(node)
+            node_inst.set_nodeattr("partition_id", target_partition_id)
+            target_partition_id += 1
+
+        for node in dyn_tlastmarker_nodes:
+            node_inst = getCustomOp(node)
+            node_inst.set_nodeattr("partition_id", target_partition_id)
+            target_partition_id += 1
+
+        for node in non_dma_nodes:
+            # TODO: implement proper floorplanning; for now just a single partition
+            node_inst = getCustomOp(node)
+            node_inst.set_nodeattr("partition_id", target_partition_id)
+
+        return (model, False)

tests/fpgadataflow/test_fpgadataflow_ipstitch.py

@@ -54,6 +54,10 @@ from finn.util.basic import gen_finn_dt_tensor, pynq_part_map
 from finn.util.fpgadataflow import pyverilate_stitched_ip
 from finn.util.test import load_test_checkpoint_or_skip
 from finn.transformation.fpgadataflow.synth_ooc import SynthOutOfContext
+from finn.transformation.infer_data_layouts import InferDataLayouts
+from finn.transformation.fpgadataflow.insert_iodma import InsertIODMA
+from finn.transformation.fpgadataflow.floorplan import Floorplan
+
 
 test_pynq_board = os.getenv("PYNQ_BOARD", default="Pynq-Z1")
 test_fpga_part = pynq_part_map[test_pynq_board]
@@ -390,3 +394,19 @@ def test_fpgadataflow_ipstitch_remote_execution():
         assert np.isclose(outp["outp"], x).all()
     except KeyError:
         pytest.skip("PYNQ board IP address not specified")
+
+
+def test_fpgadataflow_ipstitch_iodma_floorplan():
+    model = create_one_fc_model()
+    if model.graph.node[0].op_type == "StreamingDataflowPartition":
+        sdp_node = getCustomOp(model.graph.node[0])
+        assert sdp_node.__class__.__name__ == "StreamingDataflowPartition"
+        assert os.path.isfile(sdp_node.get_nodeattr("model"))
+        model = load_test_checkpoint_or_skip(sdp_node.get_nodeattr("model"))
+    model = model.transform(InferDataLayouts())
+    model = model.transform(InsertIODMA())
+    model = model.transform(Floorplan())
+    assert getCustomOp(model.graph.node[0]).get_nodeattr("partition_id") == 0
+    assert getCustomOp(model.graph.node[1]).get_nodeattr("partition_id") == 2
+    assert getCustomOp(model.graph.node[2]).get_nodeattr("partition_id") == 1
+    model.save(ip_stitch_model_dir + "/test_fpgadataflow_ipstitch_iodma_floorplan.onnx")