Merge pull request #175 from Xilinx/feature/move_flatten_past_topk

Feature/move flatten past topk

src/finn/transformation/streamline/reorder.py

@@ -29,6 +29,7 @@
 import numpy as np
 import warnings
 from onnx import helper as oh
+from onnx import TensorProto
 
 from finn.transformation import Transformation
 import finn.core.data_layout as DataLayout
@@ -676,6 +677,66 @@ class MoveMaxPoolPastMultiThreshold(Transformation):
         model = model.transform(InferShapes())
         return (model, graph_modified)
 
+class MoveFlattenPastTopK(Transformation):
+    """Move flatten node past a succeeding topk node, if the "axis" attribute in topk
+    is set to -1 and the data layout before the flatten is NHWC with H=W=1"""
+
+    def apply(self, model):
+        graph = model.graph
+        node_ind = 0
+        graph_modified = False
+        for n in graph.node:
+            node_ind += 1
+            if n.op_type == "Flatten":
+                consumer = model.find_consumer(n.output[0])
+                if consumer is not None and consumer.op_type == "TopK":
+                    axis = get_by_name(consumer.attribute, "axis")
+                    if axis is None or axis.i != -1:
+                        continue
+                    start_name = n.input[0]
+                    data_layout = model.get_tensor_layout(start_name)
+                    if data_layout != DataLayout.NHWC:
+                        warnings.warn(
+                            """Transformation can't be applied. The input
+                            to flatten has to have DataLayout.NHWC"""
+                        )
+                        continue
+                    (b, h, w, c) = model.get_tensor_shape(start_name)
+                    if h != 1 or w != 1:
+                        continue
+                    # get parameter k from topk
+                    k = model.get_tensor_shape(consumer.output[1])[-1]
+
+                    # swap conections
+                    # new tensor because dims change
+                    middle_name = model.make_new_valueinfo_name()
+                    topk_indices = oh.make_tensor_value_info(
+                        middle_name, TensorProto.INT64, [b, h, w, k]
+                    )
+                    end_name = consumer.output[1]
+                    graph.value_info.append(topk_indices)
+
+                    # remove old nodes
+                    graph.node.remove(n)
+                    graph.node.remove(consumer)
+
+                    # set inputs and outputs correctly
+                    consumer.input[0] = start_name
+                    consumer.output[1] = middle_name
+                    model.set_tensor_shape(consumer.output[0], (b, h, w, k))
+
+                    n.input[0] = middle_name
+                    n.output[0] = end_name
+
+                    # insert them back in
+                    graph.node.insert(node_ind - 1, consumer)
+                    graph.node.insert(node_ind, n)
+
+                    graph_modified = True
+
+        model = model.transform(InferShapes())
+        return (model, graph_modified)
+
 class MoveFlattenPastAffine(Transformation):
     """Moves a node that implements a (1, -1) reshape past a MatMul, Mul or Add node."""
 

tests/transformation/test_move_flatten_past_topk.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.
+import pytest
+
+from onnx import TensorProto, helper
+
+from finn.core.modelwrapper import ModelWrapper
+from finn.core.datatype import DataType
+import finn.core.data_layout as DataLayout
+from finn.util.basic import gen_finn_dt_tensor
+from finn.transformation.insert_topk import InsertTopK
+from finn.transformation.infer_shapes import InferShapes
+from finn.transformation.infer_datatypes import InferDataTypes
+from finn.transformation.infer_data_layouts import InferDataLayouts
+from finn.transformation.general import GiveUniqueNodeNames, GiveReadableTensorNames
+from finn.transformation.streamline.reorder import MoveFlattenPastTopK
+import finn.core.onnx_exec as oxe
+
+# data layout
+@pytest.mark.parametrize("data_layout", [DataLayout.NHWC, DataLayout.NCHW])
+# batch size
+@pytest.mark.parametrize("batch_size", [1, 2])
+def test_move_flatten_past_affine(data_layout, batch_size):
+    if data_layout == DataLayout.NHWC:
+        ishape = [batch_size, 1, 1, 1024]
+        oshape = [batch_size, 1024]
+    else:
+        ishape = [batch_size, 1024, 1, 1]
+        oshape = [batch_size, 1024]
+
+    inp = helper.make_tensor_value_info("inp", TensorProto.FLOAT, ishape)
+    outp = helper.make_tensor_value_info("outp", TensorProto.FLOAT, oshape)
+
+    flatten_node = helper.make_node("Flatten", ["inp"], ["outp"])
+
+    graph = helper.make_graph(
+        nodes=[flatten_node], name="move-flatten-graph", inputs=[inp], outputs=[outp],
+    )
+
+    model = helper.make_model(graph, producer_name="move_flatten_model")
+    model = ModelWrapper(model)
+
+    model.set_tensor_datatype("inp", DataType.INT2)
+    model.set_tensor_layout("inp", data_layout)
+    model = model.transform(InsertTopK())
+    model = model.transform(InferShapes())
+    model = model.transform(InferDataTypes())
+    model = model.transform(InferDataLayouts())
+    model = model.transform(GiveUniqueNodeNames())
+    model = model.transform(GiveReadableTensorNames())
+
+    # compare execution before and after transformation
+    inp_values = gen_finn_dt_tensor(DataType.INT2, ishape)
+    idict = {model.graph.input[0].name: inp_values}
+    model_transformed = model.transform(MoveFlattenPastTopK())
+    assert oxe.compare_execution(model, model_transformed, idict)
+
+    # depending on data layout check if graph is transformed or not
+    if data_layout == DataLayout.NHWC:
+        # check if nodes have new order in transformed graph
+        assert model.graph != model_transformed.graph
+        assert model_transformed.graph.node[-1].op_type == "Flatten"
+    else:
+        assert model.graph == model_transformed.graph