Merge pull request fastmachinelearning#87 from iksnagreb/fix/remove_i…

…dentity_ops

Fix RemoveIdentityOps not correctly handling ops following fork-nodes

src/qonnx/core/modelwrapper.py

@@ -429,14 +429,24 @@ def is_fork_node(self, node):
         """Checks if the given node is a fork, that is, the node has multiple
         direct successors"""
         direct_successors = self.find_direct_successors(node)
-        is_fork = False if direct_successors is None else (len(direct_successors) > 1)
+        # if the node output is also wired to a top-level output, it is still
+        # a fork with only 1 direct successor
+        if node.output[0] in [x.name for x in self.graph.output]:
+            is_fork = False if direct_successors is None else (len(direct_successors) > 0)
+        else:
+            is_fork = False if direct_successors is None else (len(direct_successors) > 1)
         return is_fork
 
     def is_join_node(self, node):
         """Checks if the given node is a join, that is, the node has multiple
         direct predecessors"""
         direct_predecessors = self.find_direct_predecessors(node)
-        is_join = False if direct_predecessors is None else (len(direct_predecessors) > 1)
+        # if the node input is also wired to a top-level input, it is still
+        # a fork with only 1 direct predecessor
+        if node.input[0] in [x.name for x in self.graph.input]:
+            is_join = False if direct_predecessors is None else (len(direct_predecessors) > 0)
+        else:
+            is_join = False if direct_predecessors is None else (len(direct_predecessors) > 1)
         return is_join
 
     def get_all_tensor_names(self):

src/qonnx/transformation/remove.py

@@ -25,9 +25,8 @@
 # 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
+import warnings
 
 from qonnx.core.modelwrapper import ModelWrapper
 from qonnx.transformation.base import Transformation
@@ -58,21 +57,43 @@ def apply(self, model: ModelWrapper):
 
 
 def remove_node_and_rewire(model, node):
+    # Currently cannot remove and rewire join-nodes, probably not necessary to
+    # support this
+    if model.is_join_node(node):
+        # Log this as a warning, so the user is aware of this, there might be
+        # somthing wrong or some checks missing at the caller site
+        warnings.warn("Removing join-node operation is currently not supported")
+        # Exit the function here without doing anything
+        return
+    # We already know that node is not a join-node, thus to rewire, we only need
+    # to check the single producer
     producer = model.find_producer(node.input[0])
-    if producer is not None:
-        # wire output tensor to
-        # output of producer node
+    # If there is a producer which is not a fork-node, rewiring is simple
+    if producer is not None and not model.is_fork_node(producer):
+        # Rewire by skipping the node, letting the producer directly feed the
+        # nodes output.
+        #   TODO: Check whether this already covers fork-node identities?
         producer.output[0] = node.output[0]
+    # If there is no producer or the producer forks, rewiring is a bit more
+    # complicated
     else:
-        # node is first in graph
+        # Now it depends on the successor nodes to rewire their inputs
         successors = model.find_direct_successors(node)
+        # Singular node detached from the rest of the graph?
         assert successors is not None, "Whole graph is one node."
-        for succ in successors:
-            for i, s_inp in enumerate(succ.input):
+        # We need to rewire the input of each successor to not detach parts of
+        # the graph
+        for successor in successors:
+            # Find the inputs of the successor which are produced by the node to
+            # be removed
+            for i, s_inp in enumerate(successor.input):
+                # Note: This might happen multiple times?
                 if s_inp == node.output[0]:
-                    # rewire successor's input directly to graph input
-                    succ.input[i] = node.input[0]
-    # remove node
+                    # Rewire successor's input directly to nodes input
+                    # Note: Node may not be a join-node, but there is probably
+                    # no such thing as join-node identity anyway
+                    successor.input[i] = node.input[0]
+    # Remove node
     model.graph.node.remove(node)
 
 
@@ -117,5 +138,9 @@ def apply(self, model):
                     remove_node_and_rewire(model, n)
                     graph_modified = True
                     break
+            elif n.op_type == "Identity":
+                remove_node_and_rewire(model, n)
+                graph_modified = True
+                break
         model = model.transform(InferShapes())
         return (model, graph_modified)

tests/transformation/test_remove_identity_ops.py

@@ -51,28 +51,34 @@ def insert_identity_op(model, op, as_first_node, approx):
         val = np.asarray([zero_val], dtype=np.float32)
     elif op in ["Mul", "Div"]:
         val = np.asarray([one_val], dtype=np.float32)
+    elif op in ["Identity"]:
+        val = None
     else:
         return
 
     graph = model.graph
+    if val is None:
+        inplist = ["inp" if as_first_node else "div_out"]
+    else:
+        model.set_initializer("value", val)
+        inplist = ["inp" if as_first_node else "div_out", "value"]
+    identity_node = helper.make_node(op, inplist, ["ident_out"])
     if as_first_node:
-        identity_node = helper.make_node(op, ["inp", "value"], ["ident_out"])
         graph.node.insert(0, identity_node)
         graph.node[1].input[0] = "ident_out"
     else:
-        identity_node = helper.make_node(op, ["div_out", "value"], ["ident_out"])
         graph.node.insert(3, identity_node)
         graph.node[-1].input[0] = "ident_out"
-    model.set_initializer("value", val)
 
     return model
 
 
 # identity operations to be inserted
-@pytest.mark.parametrize("op", ["Add", "Sub", "Mul", "Div"])
+@pytest.mark.parametrize("op", ["Add", "Sub", "Mul", "Div", "Identity"])
 @pytest.mark.parametrize("approx", [False, True])
 @pytest.mark.parametrize("as_first_node", [False, True])
-def test_remove_identity_ops(op, as_first_node, approx):
+@pytest.mark.parametrize("fork_before_id", [False, True])
+def test_remove_identity_ops(op, as_first_node, approx, fork_before_id):
     # set up onnx model
     inp = helper.make_tensor_value_info("inp", TensorProto.FLOAT, [1, 4, 1, 1])
     mul = helper.make_tensor_value_info("mul", TensorProto.FLOAT, [])
@@ -109,14 +115,16 @@ def test_remove_identity_ops(op, as_first_node, approx):
     model = model.transform(InferShapes())
     model = model.transform(InferDataTypes())
     idict = {"inp": inp_values}
-    odict = oxe.execute_onnx(model, idict)
-    out_before = odict["outp"]
+    odict_before = oxe.execute_onnx(model, idict)
     num_of_nodes_before = len(model.graph.node)
-
+    if fork_before_id and not as_first_node:
+        divout_vi = model.get_tensor_valueinfo("div_out")
+        model.graph.output.append(divout_vi)
+        model.graph.value_info.remove(divout_vi)
     model = model.transform(RemoveIdentityOps())
     num_of_nodes_after = len(model.graph.node)
     assert num_of_nodes_before - 1 == num_of_nodes_after
 
-    odict = oxe.execute_onnx(model, idict)
-    out_after = odict["outp"]
-    assert np.isclose(out_before, out_after, atol=1e-3).all()
+    odict_after = oxe.execute_onnx(model, idict)
+    outputs_same = [np.isclose(odict_before[tname], odict_after[tname], atol=1e-3).all() for tname in odict_before.keys()]
+    assert all(outputs_same)