[Relay][Frontend][Onnx] SequenceAt and SplitToSequence Operators (apa…

…che#13602)

* Add support for SequenceAt and SplitToSequence to onnx importer

* Formatting

* Change keepdims comparison

* Only unify non-tuples in If

python/tvm/relay/frontend/onnx.py

@@ -4008,6 +4008,23 @@ def _impl_v1(cls, inputs, attr, params):
         for var in else_free_vars:
             graph_scope._nodes.update({var.name_hint: var})
 
+        # Sometimes pytorch to onnx will insert silly if statements that produce dynamic ranks.
+        # Often these dont contribute anything. If we see a dynamic rank output, try to unify
+        # them so we can continue without breaking.
+        if not isinstance(then_expr, _expr.Tuple) and not isinstance(else_expr, _expr.Tuple):
+            then_shape = infer_shape(then_expr)
+            else_shape = infer_shape(else_expr)
+            if len(then_shape) != len(else_shape):
+                warning_msg = (
+                    "If statement produced outputs with different rank. "
+                    "Attempting to unify ranks but this may produce incorrect results."
+                )
+                warnings.warn(warning_msg)
+                if len(then_shape) < len(else_shape):
+                    then_expr = _op.broadcast_to_like(then_expr, else_expr)
+                else:
+                    else_expr = _op.broadcast_to_like(else_expr, then_expr)
+
         # Now we can construct the relay if statement and return.
         ret = _expr.If(cond, then_expr, else_expr)
         if len(then_branch.output) > 1:
@@ -5565,6 +5582,66 @@ def _impl_v11(cls, inputs, attr, params):
         return _op.concatenate(inputs[0], axis=axis)
 
 
+class SplitToSequence(OnnxOpConverter):
+    """Operator converter for split to sequence op."""
+
+    @classmethod
+    def _impl_v11(cls, inputs, attr, params):
+        axis = attr.get("axis", 0)
+        keepdims = attr.get("keepdims", 1)
+
+        input_tensor = inputs[0]
+        input_shape = infer_shape(input_tensor)
+        split = inputs[1]
+
+        # If split is not provided, we split all values along axis.
+        if split is None:
+            output = _op.split(input_tensor, input_shape[axis], axis=axis)
+            # If keepdims is 0, then we need to squeeze off the axis.
+            if not keepdims:
+                output = [_op.squeeze(tensor_slice, axis=[axis]) for tensor_slice in output]
+            return _expr.Tuple(list(output))
+
+        # Otherwise, split based on provided split value.
+        else:
+            # For now we only support constant valued split.
+            assert isinstance(
+                split, _expr.Constant
+            ), "Only constant split supported for SplitToSequence"
+            split = split.data.numpy()
+            if len(split.shape) == 1 and split.shape[0] > 1:
+                # If split is a 1D tensor, it must be converted to indices for relay compatibility.
+                split = np.cumsum(split)
+                # Remove final invalid index.
+                split = split[:-1]
+            else:
+                # Otherwise get split as an integer.
+                split = int(split)
+
+            output = _op.split(input_tensor, split, axis=axis)
+
+            # If keepdims is set to 0 remove split axis. Note that this is
+            # an inconsistency with the onnx spec but is needed for pytorch compatibility.
+            if not keepdims:
+                output = [_op.squeeze(tensor_slice, axis=[axis]) for tensor_slice in output]
+            return _expr.Tuple(list(output))
+
+
+class SequenceAt(OnnxOpConverter):
+    """Operator converter for sequence at op."""
+
+    @classmethod
+    def _impl_v11(cls, inputs, attr, params):
+        input_sequence = inputs[0]
+        position = inputs[1]
+        assert isinstance(
+            position, _expr.Constant
+        ), "Only constant position supported for SequenceAt"
+        # Convert position to integer.
+        position = int(position.data.numpy())
+        return input_sequence[position]
+
+
 # compatible operators that do NOT require any conversion.
 _identity_list = []
 
@@ -5793,6 +5870,8 @@ def _get_convert_map(opset):
         "SequenceConstruct": SequenceConstruct.get_converter(opset),
         "SequenceInsert": SequenceInsert.get_converter(opset),
         "ConcatFromSequence": ConcatFromSequence.get_converter(opset),
+        "SplitToSequence": SplitToSequence.get_converter(opset),
+        "SequenceAt": SequenceAt.get_converter(opset),
     }
 
 

python/tvm/relay/op/_transform.py

@@ -432,8 +432,6 @@ def _concatenate_shape_func(inputs, axis):
     for i in const_range(ndim):
         if i != axis:
             out[i] = inputs[0][i]
-            for j in const_range(1, len(inputs)):
-                assert out[i] == inputs[j][i], "Dims mismatch in the inputs of concatenate."
         else:
             out[i] = int64(0)
             for j in const_range(len(inputs)):

tests/python/frontend/onnx/test_forward.py

@@ -7043,7 +7043,7 @@ def verify_linear_regressor(a_shape, c_shape, i_shape, targets=1, batch=1):
 def test_sequence(target, dev):
     """test_sequence"""
 
-    def verify_sequence_ops(tensor_shape, num_tensors, axis=0, position=None, new_axis=None):
+    def verify_sequence_ops(tensor_shape, num_tensors, axis=0, position=0, new_axis=None):
         tensor_shape = list(tensor_shape)
         tensor_values = []
         for i in range(num_tensors):
@@ -7062,20 +7062,30 @@ def verify_sequence_ops(tensor_shape, num_tensors, axis=0, position=None, new_ax
             outputs=["sequence"],
         )
 
-        insert_inputs = ["sequence", input_tensor_names[0]]
-        position_node = None
-        if position is not None:
-            insert_inputs.append("position")
-            position_node = make_constant_node("position", TensorProto.INT32, (), [position])
+        position_node = make_constant_node("position", TensorProto.INT32, (), [position])
 
         # Test sequence insertion.
         insert_node = helper.make_node(
-            "SequenceInsert", inputs=insert_inputs, outputs=["inserted_sequence"]
+            "SequenceInsert",
+            inputs=["sequence", input_tensor_names[0], "position"],
+            outputs=["inserted_sequence"],
         )
 
         # Test sequence concatenation.
         concat_node = helper.make_node(
-            "ConcatFromSequence", inputs=["inserted_sequence"], outputs=["output"], axis=axis
+            "ConcatFromSequence",
+            inputs=["inserted_sequence"],
+            outputs=["concat_sequence"],
+            axis=axis,
+        )
+
+        # Test splitting a tensor into a sequence.
+        split_node = helper.make_node(
+            "SplitToSequence", inputs=["concat_sequence"], outputs=["split_sequence"], axis=axis
+        )
+
+        at_node = helper.make_node(
+            "SequenceAt", inputs=["split_sequence", "position"], outputs=["output"]
         )
 
         if new_axis is not None:
@@ -7097,10 +7107,7 @@ def verify_sequence_ops(tensor_shape, num_tensors, axis=0, position=None, new_ax
             output_shape[axis] = (num_tensors + 1) * output_shape[axis]
         graph_outputs = [helper.make_tensor_value_info("output", TensorProto.FLOAT, output_shape)]
 
-        graph_nodes = []
-        if position_node is not None:
-            graph_nodes.append(position_node)
-        graph_nodes += [construct_node, insert_node, concat_node]
+        graph_nodes = [position_node, construct_node, insert_node, concat_node, split_node, at_node]
 
         graph = helper.make_graph(
             graph_nodes,