From 57f42b20cc93a92a01ba1b29ed36fcd1ab50f038 Mon Sep 17 00:00:00 2001
From: Tobi-Alonso <tobi.alonso@gmail.com>
Date: Mon, 15 Jun 2020 14:03:59 +0100
Subject: [PATCH] [FPGADataFlow] Create parallel version of PrepareCppSim
---
.../fpgadataflow/prepare_cppsim.py | 48 ++++++++++++++++---
1 file changed, 42 insertions(+), 6 deletions(-)
diff --git a/src/finn/transformation/fpgadataflow/prepare_cppsim.py b/src/finn/transformation/fpgadataflow/prepare_cppsim.py
index a1524322e..2ebd2df7c 100644
--- a/src/finn/transformation/fpgadataflow/prepare_cppsim.py
+++ b/src/finn/transformation/fpgadataflow/prepare_cppsim.py
@@ -32,14 +32,17 @@ import finn.custom_op.registry as registry
from finn.transformation import Transformation
from finn.util.basic import make_build_dir
from finn.util.fpgadataflow import is_fpgadataflow_node
-
+from finn.transformation import Transformation
+from finn.util.basic import get_num_default_workers
+import multiprocessing as mp
+import copy
def _codegen_single_node(node, model):
"""Calls C++ code generation for one node. Resulting code can be used
to simulate node using cppsim."""
op_type = node.op_type
- try:
+ try:
# lookup op_type in registry of CustomOps
inst = registry.custom_op[op_type](node)
# get the path of the code generation directory
@@ -66,8 +69,41 @@ class PrepareCppSim(Transformation):
that contains generated C++ code that can be used to simulate node using cppsim.
The subsequent transformation is CompileCppSim"""
+ def __init__(self, num_workers=None):
+ super().__init__()
+ if num_workers is None:
+ self._num_workers = get_num_default_workers()
+ else:
+ self._num_workers = num_workers
+ assert self._num_workers >= 0, "Number of workers must be nonnegative."
+ if self._num_workers == 0:
+ self._num_workers = mp.cpu_count()
+
+ def prepareCppSim_node(self, node):
+ print(node.name)
+ if is_fpgadataflow_node(node) is True:
+ _codegen_single_node(node, self.model)
+ return (node, False)
+
+
def apply(self, model):
- for node in model.graph.node:
- if is_fpgadataflow_node(node) is True:
- _codegen_single_node(node, model)
- return (model, False)
+ # Remove old nodes from the current model
+ self.model = copy.deepcopy(model)
+ old_nodes = []
+ for i in range(len(model.graph.node)):
+ old_nodes.append(model.graph.node.pop())
+
+ # Execute transformation in parallel
+ with mp.Pool(self._num_workers) as p:
+ new_nodes_and_bool = p.map(self.prepareCppSim_node, old_nodes, chunksize=1)
+
+ # extract nodes and check if the transformation needs to run again
+ # Note: .pop() had initially reversed the node order
+ run_again = False
+ for node, run in reversed(new_nodes_and_bool):
+ # Reattach new nodes to old model
+ model.graph.node.append(node)
+ if run is True:
+ run_again = True
+
+ return (model, run_again)
\ No newline at end of file
--
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