[Analysis] introduce dataflow_performance to extract key perf info

src/finn/analysis/fpgadataflow/dataflow_performance.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.util.fpgadataflow import is_fpgadataflow_node
+
+
+def dataflow_performance(model):
+    """Extract key performance indicators from given model with dataflow nodes.
+
+    Preconditions:
+    - model consists of fpgadataflow nodes
+    - model has cycle estimates annotated (see AnnotateCycles transformation)
+    - nodes have unique names (see GiveUniqueNodeNames)
+
+    Returns:
+    - max_cycles : number of cycles for slowest node
+    - max_cycles_node_name : name of slowest node
+    - critical_path_cycles : total expected latency from input to output
+    """
+    latency_at_node_output = {}
+    max_cycles = 0
+    max_node_name = ""
+
+    for node in model.graph.node:
+        if is_fpgadataflow_node(node) is True:
+            inst = getCustomOp(node)
+            node_cycles = inst.get_nodeattr("cycles_estimate")
+            if node_cycles > max_cycles:
+                max_cycles = node_cycles
+                max_node_name = node.name
+            if node.name not in latency_at_node_output:
+                # calculate based on input(s)
+                predecessors = model.find_direct_predecessors(node)
+                if predecessors is None:
+                    # no predecessors, node is first node
+                    max_pred_latency = 0
+                else:
+                    # find max of any of predecessors
+                    pred_latencies = map(
+                        lambda x: latency_at_node_output[x.name], predecessors
+                    )
+                    max_pred_latency = max(pred_latencies)
+                latency_at_node_output[node.name] = node_cycles + max_pred_latency
+    critical_path_cycles = max(latency_at_node_output.values())
+    return {
+        "critical_path_cycles": critical_path_cycles,
+        "max_cycles": max_cycles,
+        "max_cycles_node_name": max_node_name,
+    }