From 341a8a3ef25383396edba6d28aaa7ee3e0c51761 Mon Sep 17 00:00:00 2001
From: Yaman Umuroglu <yamanu@amd.com>
Date: Thu, 21 Jul 2022 20:11:30 +0200
Subject: [PATCH] [ToHLS] draft a InferStreamingEltwiseAbsDiff conversion
---
.../fpgadataflow/convert_to_hls_layers.py | 92 +++++++++++++++++++
1 file changed, 92 insertions(+)
diff --git a/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py b/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py
index 429bc34ff..e8f6372ab 100644
--- a/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py
+++ b/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py
@@ -1671,3 +1671,95 @@ class InferConcatLayer(Transformation):
model = model.transform(InferShapes())
model = model.transform(InferDataTypes())
return (model, graph_modified)
+
+
+class InferStreamingEltwiseAbsDiff(Transformation):
+ """Convert eltwise Sub -> Abs to StreamingEltwise layer
+ with AbsDiffEltwise op."""
+
+ def apply(self, model):
+ graph = model.graph
+ node_ind = 0
+ graph_modified = False
+ for node in graph.node:
+ node_ind += 1
+ if node.op_type == "Sub":
+ in0 = node.input[0]
+ in1 = node.input[1]
+ result = node.output[0]
+ in0_shape = model.get_tensor_shape(in0)
+ in1_shape = model.get_tensor_shape(in1)
+
+ # skip if different shapes on inputs
+ if in0_shape != in1_shape:
+ continue
+
+ idt0 = model.get_tensor_datatype(in0)
+ idt1 = model.get_tensor_datatype(in1)
+
+ # skip conversion for layers with float input
+ if not (idt0.is_integer() and idt1.is_integer()):
+ continue
+
+ # look for a downstream Abs node
+ res_consumer = model.find_consumer(result)
+ if res_consumer is None:
+ continue
+ if res_consumer.op_type != "Abs":
+ continue
+
+ result = res_consumer.output[0]
+
+ # check layout and convert if necessary
+ in0_layout = model.get_tensor_layout(in0)
+ in1_layout = model.get_tensor_layout(in1)
+ result_layout = model.get_tensor_layout(result)
+
+ if in0_layout == DataLayout.NCHW:
+ in0 = nchw_to_nhwc(in0, model, node_ind)
+ node_ind += 1
+ in0_shape = model.get_tensor_shape(in0)
+
+ if in1_layout == DataLayout.NCHW:
+ in1 = nchw_to_nhwc(in1, model, node_ind)
+ node_ind += 1
+ in1_shape = model.get_tensor_shape(in1)
+
+ # keep track of where we need to insert the HLS Op
+ # it has to be ahead of the output transform
+ insert_point = node_ind
+
+ if result_layout == DataLayout.NCHW:
+ result = nchw_to_nhwc(result, model, node_ind, reverse=True)
+ node_ind += 1
+
+ # now safe to assume num_channels is size of last dimension
+ num_channels = int(in0_shape[-1])
+ # create node with no parallelization first
+ pe = 1
+
+ # create and insert new Eltwise node
+ new_node = helper.make_node(
+ "StreamingEltwise",
+ [in0, in1],
+ [result],
+ domain="finn.custom_op.fpgadataflow",
+ backend="fpgadataflow",
+ NumChannels=num_channels,
+ PE=pe,
+ inputDataType0=idt0.name,
+ inputDataType1=idt1.name,
+ eltwiseOp="AbsDiff",
+ numInputVectors=in0_shape[:-1],
+ name="StreamingEltwise_" + node.name,
+ )
+ graph.node.insert(insert_point, new_node)
+ # remove old nodes
+ graph.node.remove(node)
+ graph.node.remove(res_consumer)
+ graph_modified = True
+
+ # if graph_modified:
+ # model = model.transform(InferShapes())
+ # model = model.transform(InferDataTypes())
+ return (model, graph_modified)
--
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