[Transform] introduce ExternalizeParams

src/finn/transformation/fpgadataflow/externalize_params.py

+# Copyright (c) 2021, 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.transformation.base import Transformation
+from finn.util.basic import get_by_name
+
+
+class ExternalizeParams(Transformation):
+    """Create top-level graph inputs for IODMAs serving layers where weights are
+    marked as external using mem_mode="external"."""
+
+    def __init__(self):
+        super().__init__()
+
+    def apply(self, model):
+        graph_modified = False
+
+        def filter_fc_extw(x):
+            if x.op_type == "IODMA":
+                burst_mode = get_by_name(x.attribute, "burstMode")
+                if burst_mode is not None:
+                    burst_mode = burst_mode.s.decode("UTF-8")
+                    return burst_mode == "wrap"
+
+        dma_extw_nodes = list(filter(filter_fc_extw, model.graph.node))
+
+        for dma_extw in dma_extw_nodes:
+            extw_tensor_name = dma_extw.input[0]
+            extw_tensor_name_out = dma_extw.output[0]
+            if extw_tensor_name in [x.name for x in model.graph.input]:
+                continue
+            else:
+                extw_vi = model.get_tensor_valueinfo(extw_tensor_name)
+                assert extw_vi is not None
+                model.graph.value_info.remove(extw_vi)
+                model.graph.input.append(extw_vi)
+                iodma_init = model.get_initializer(extw_vi.name)
+                assert iodma_init is not None
+                # remove output-side initializer to get correct dataflow partitioning
+                model.graph.initializer.remove(
+                    [
+                        x
+                        for x in model.graph.initializer
+                        if x.name == extw_tensor_name_out
+                    ][0]
+                )
+                graph_modified = True
+
+        return (model, graph_modified)