From 55a8828ff89ff167dd9397c8e02aa7a63efac49c Mon Sep 17 00:00:00 2001
From: Tobi-Alonso <tobi.alonso@gmail.com>
Date: Tue, 7 Jul 2020 22:38:59 +0100
Subject: [PATCH] [FPGADataflow] Add InferDuplicateStreamsLayer
---
.../fpgadataflow/convert_to_hls_layers.py | 74 +++++++++++++++++++
1 file changed, 74 insertions(+)
diff --git a/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py b/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py
index 844c21726..6fe6e97df 100644
--- a/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py
+++ b/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py
@@ -35,6 +35,7 @@ from finn.transformation import Transformation
from finn.custom_op.registry import getCustomOp
from finn.transformation.infer_shapes import InferShapes
from finn.transformation.infer_datatypes import InferDataTypes
+from finn.transformation.general import SortGraph
import finn.core.data_layout as DataLayout
from finn.util.onnx import nchw_to_nhwc
import warnings
@@ -780,6 +781,79 @@ class InferAddStreamsLayer(Transformation):
return (model, graph_modified)
+class InferDuplicateStreamsLayer(Transformation):
+ """Insert a DuplicateStreams HLS layer for any tensor with fanout == 2 """
+
+ def apply(self, model):
+ graph = model.graph
+ node_ind = 0
+ graph_modified = False
+ for node in graph.node:
+ node_ind += 1
+ successors = model.find_consumers(node.output[0])
+ if successors is not None and len(successors) == 2:
+ output_tensor = node.output[0]
+
+ dt = model.get_tensor_datatype(output_tensor)
+
+ # skip conversion for layers with float input
+ if not dt.is_integer():
+ continue
+
+ # create clone tensors
+ out_shape = model.get_tensor_shape(output_tensor)
+ out_tensor_clones = []
+ for i in range(2):
+ clone = helper.make_tensor_value_info(
+ model.make_new_valueinfo_name(), TensorProto.FLOAT, out_shape
+ )
+ model.graph.value_info.append(clone)
+ out_tensor_clones += [clone.name]
+
+ num_ch = int(out_shape[-1])
+ vecs = out_shape[:-1]
+
+ # create node with no parallelization first
+ pe = 1
+ assert (
+ num_ch % pe == 0
+ ), "Requirement channels divisable by PE is violated."
+
+ dup_node = helper.make_node(
+ "DuplicateStreams_Batch",
+ [output_tensor],
+ out_tensor_clones,
+ domain="finn",
+ backend="fpgadataflow",
+ NumChannels=num_ch,
+ PE=pe,
+ inputDataType=dt.name,
+ numInputVectors=vecs,
+ )
+
+ graph.node.insert(node_ind, dup_node)
+
+ # connect successors to out tensor clone
+ clone_idx = 0
+ for successor in successors:
+ for i, succ_input in enumerate(successor.input):
+ if succ_input == output_tensor:
+ successor.input[i] = out_tensor_clones[clone_idx]
+ clone_idx += 1
+ # if one node has multiple connections to the same output
+ # find_direct_successors will return one node per input
+ # so break the inner loop will result in correct behaviour
+ break
+
+ graph_modified = True
+
+ if graph_modified:
+ model = model.transform(SortGraph())
+ model = model.transform(InferShapes())
+ model = model.transform(InferDataTypes())
+ return (model, graph_modified)
+
+
class InferChannelwiseLinearLayer(Transformation):
"""Convert any channel-wise Add/Mul into a HLS layer."""
--
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