Merge branch 'dev' into feature/util_fct_is_fpgadataflow_node

src/finn/core/modelwrapper.py

@@ -253,14 +253,12 @@ class ModelWrapper:
             return None
 
     def find_producer(self, tensor_name):
-        """Finds and returns the node that produces the tensor with given name.
-        Currently only works for linear graphs."""
-        all_outputs = [x.output[0] for x in self._model_proto.graph.node]
-        try:
-            producer_ind = all_outputs.index(tensor_name)
-            return self._model_proto.graph.node[producer_ind]
-        except ValueError:
-            return None
+        """Finds and returns the node that produces the tensor with given name."""
+        ret = None
+        for x in self._model_proto.graph.node:
+            if tensor_name in x.output:
+                ret = x
+        return ret
 
     def find_upstream(self, tensor_name, finder_fxn):
         """Follow the producer chain upstream, calling finder_fxn on each upstream

src/finn/core/onnx_exec.py

@@ -61,6 +61,10 @@ def execute_node(node, context, graph):
             # onnxruntime unfortunately does not implement run_node as defined by ONNX,
             # it can only execute entire models -- so we create a model which solely
             # consists of our current node.
+            # note: ensure that the same ValueInfo does not appear both in
+            # graph.value_info as well as graph.output or graph.input
+            # nodes with multiple outputs that are a mix of value_info and
+            # input/outputs may get them reordered below
             node_inputs = list(filter(lambda x: x.name in node.input, graph.input))
             node_inputs += list(
                 filter(lambda x: x.name in node.input, graph.value_info)
@@ -84,17 +88,25 @@ def execute_node(node, context, graph):
             output_list = sess.run(None, input_dict)
 
             for output_ind in range(len(node.output)):
+                # get the name of the target buffer from node.output
                 outp = node.output[output_ind]
-                if output_list[output_ind].shape != context[outp].shape:
+
+                # retrieve the index of that name in node_outputs
+                for i in range(len(node_outputs)):
+                    if outp == node_outputs[i].name:
+                        list_ind = i
+
+                # use that index to index output_list
+                if output_list[list_ind].shape != context[outp].shape:
                     raise Exception(
                         """Output shapes disagree after node execution:
                         found %s vs expected %s"""
                         % (
-                            str(output_list[output_ind].shape.shape),
+                            str(output_list[list_ind].shape.shape),
                             str(context[outp].shape),
                         )
                     )
-                context[outp] = output_list[output_ind]
+                context[outp] = output_list[list_ind]
 
 
 def execute_onnx(model, input_dict, return_full_exec_context=False):

src/finn/transformation/insert_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 numpy as np
+
+from onnx import TensorProto
+from onnx import helper as oh
+
+from finn.transformation import Transformation
+from finn.core.datatype import DataType
+
+
+class InsertTopK(Transformation):
+    """Add TopK node at the network output and replace the graph output with
+    the TopK indices."""
+
+    def __init__(self, k=5, axis=-1, largest=1, sorted=1):
+        super().__init__()
+        self.k = k
+        self.axis = axis
+        self.largest = largest
+        self.sorted = sorted
+
+    def apply(self, model):
+        # get name of output tensor
+        graph_out_name = model.graph.output[0].name
+        # find final node
+        final_node = model.find_producer(graph_out_name)
+        # if a top-select op is already present, do nothing
+        if final_node.op_type == "TopK":
+            return (model, False)
+        else:
+            out_shape = model.get_tensor_shape(graph_out_name)
+            out_dtype = model.get_tensor_datatype(graph_out_name)
+            # adjust shape
+            out_shape[self.axis] = self.k
+            # make new buffer
+            k_tensor = np.array([self.k]).astype(np.int64)
+            k_value = oh.make_tensor_value_info(
+                model.make_new_valueinfo_name(), TensorProto.INT64, [1]
+            )
+            topk_values = oh.make_tensor_value_info(
+                model.make_new_valueinfo_name(), TensorProto.FLOAT, out_shape
+            )
+            topk_indices = oh.make_tensor_value_info(
+                model.make_new_valueinfo_name(), TensorProto.INT64, out_shape
+            )
+            model.graph.value_info.append(k_value)
+            model.set_tensor_datatype(k_value.name, out_dtype)  # TODO set to int64
+            model.graph.value_info.append(topk_values)
+            model.set_tensor_datatype(topk_values.name, out_dtype)
+            # create and append topk node
+            model.set_initializer(k_value.name, k_tensor)
+            topk_node = oh.make_node(
+                "TopK",
+                inputs=[graph_out_name, k_value.name],
+                outputs=[topk_values.name, topk_indices.name],
+                axis=self.axis,
+                largest=self.largest,
+                sorted=self.sorted,
+            )
+            model.graph.node.append(topk_node)
+            # replace the existing output definition with topk indices
+            model.graph.output.insert(0, topk_indices)
+            model.graph.output.pop(1)
+            # set quantization annotation for indices
+            # minimal output dtype for TopK indices dependens on num. classes
+            # assuming UINT32 is large enough for now (FINN has currently no
+            # DataType.INT64)
+            model.set_tensor_datatype(topk_indices.name, DataType.UINT32)
+            return (model, True)

tests/transformation/test_topk_insert.py

+import onnx
+from finn.util.test import get_test_model_trained
+import brevitas.onnx as bo
+import numpy as np
+import onnx.numpy_helper as nph
+import torch
+
+from finn.core.modelwrapper import ModelWrapper
+from finn.transformation.general import GiveReadableTensorNames, GiveUniqueNodeNames
+from finn.transformation.infer_shapes import InferShapes
+from finn.transformation.infer_datatypes import InferDataTypes
+from finn.transformation.fold_constants import FoldConstants
+from finn.transformation.insert_topk import InsertTopK
+
+import finn.core.onnx_exec as oxe
+from pkgutil import get_data
+
+import pytest
+
+export_onnx_path = "test_output_lfc.onnx"
+
+
+@pytest.mark.parametrize("k", [1, 5, 10])
+def test_topk_insert(k):
+    tfc = get_test_model_trained("TFC", 1, 1)
+    bo.export_finn_onnx(tfc, (1, 1, 28, 28), export_onnx_path)
+    model = ModelWrapper(export_onnx_path)
+
+    # do transformations (no topk)
+    model = model.transform(InferShapes())
+    model = model.transform(FoldConstants())
+    model = model.transform(GiveUniqueNodeNames())
+    model = model.transform(GiveReadableTensorNames())
+    model = model.transform(InferDataTypes())
+
+    # verification: generate random input, run through net, streamline,
+    # run again, check that output is top-k
+    raw_i = get_data("finn", "data/onnx/mnist-conv/test_data_set_0/input_0.pb")
+    input_tensor = onnx.load_tensor_from_string(raw_i)
+    input_brevitas = torch.from_numpy(nph.to_array(input_tensor)).float()
+    output_golden = tfc.forward(input_brevitas).detach().numpy()
+    output_golden_topk = np.flip(output_golden.flatten().argsort())[:k]
+    output_golden_topk = output_golden_topk.flatten()
+
+    input_dict = {"global_in": nph.to_array(input_tensor)}
+
+    # insert top-k
+    model = model.transform(InsertTopK(k))
+    model = model.transform(GiveUniqueNodeNames())
+    model = model.transform(GiveReadableTensorNames())
+    model = model.transform(InferShapes())
+
+    # verify output of top-k
+    output_dict_topk = oxe.execute_onnx(model, input_dict)
+    output_pysim_topk = output_dict_topk[list(output_dict_topk.keys())[0]]
+    output_pysim_topk = output_pysim_topk.astype(np.int).flatten()
+
+    assert np.array_equal(output_golden_topk, output_pysim_topk)