[Relay][Frontend][ONNX] Allow importing models with malformed Loop no…

…des. (#8475)

* Snapshot

* Undo comments.

* Add testing for malformed loop nodes.

* Format oops.

python/tvm/relay/frontend/onnx.py

@@ -2742,24 +2742,24 @@ def get_var(name, val, scan=False):
         loop_var_names = [v.name_hint for v in loop_vars]
 
         num_scan_outputs = len(body.output) - (1 + num_deps)
-        # TODO (jwfromm) Test with strided slice once type unifier for this case is fixed.
-        if num_scan_outputs != 0 and "Slice" in [n.op_type for n in body.node]:
-            warnings.warn(
-                """
-                Using scan outputs in a loop with strided slice
-                currently may cause errors during compilation.
-                """
-            )
 
         # Construct variables and intial empty tensors for any scan outputs.
+        # To do this, we'll figure out the output shapes of the body subgraph by importing
+        # it and doing type inference.
         scan_output_vars = []
         scan_output_init = []
+        if num_scan_outputs > 0:
+            with subgraph_scope:
+                loop_outputs = subgraph_scope.from_onnx(
+                    body, graph_scope.opset, get_output_expr=True
+                )
+            loop_outputs = _expr.TupleWrapper(loop_outputs, len(body.output))
+
         for i in range(num_scan_outputs):
-            name, shape, dtype, _ = get_info(body.output[i + 1 + num_deps])
-            if dtype is None:
-                dtype = infer_type(loop_deps[i]).checked_type.dtype
-            if dtype == "float":
-                dtype = "float32"
+            name, _, _, _ = get_info(body.output[i + 1 + num_deps])
+            output_node = infer_type(loop_outputs[i + 1 + num_deps])
+            shape = get_const_tuple(output_node.checked_type.shape)
+            dtype = output_node.checked_type.dtype
             scan_output_vars.append(
                 _expr.var(name, shape=([_ty.Any()] * (len(shape) + 1)), dtype=dtype)
             )

tests/python/frontend/onnx/test_forward.py

@@ -4043,7 +4043,7 @@ def verify_count_loop():
     verify_with_ort_with_inputs(loop_model, input_vals, use_vm=True, freeze_params=True)
 
 
-def verify_tensor_loop():
+def verify_tensor_loop(shapeless_output=False):
     y_in = helper.make_tensor_value_info("y_in", TensorProto.FLOAT, [3, 3, 3, 3])
     y_out = helper.make_tensor_value_info("y_out", TensorProto.FLOAT, [3, 3, 3, 3])
     scan_out = helper.make_tensor_value_info("scan_out", TensorProto.FLOAT, [3, 3, 3, 3])
@@ -4076,6 +4076,13 @@ def verify_tensor_loop():
 
     trip_count = np.array(5).astype(np.int64)
     cond = np.array(1).astype(bool)
+
+    # Allow testing of malformed nodes since pytorch likes to create these.
+    if shapeless_output:
+        scan_shape = None
+    else:
+        scan_shape = [5, 3, 3, 3, 3]
+
     loop_graph = onnx.helper.make_graph(
         [loop_node],
         "loop_outer",
@@ -4086,7 +4093,7 @@ def verify_tensor_loop():
         ],
         outputs=[
             onnx.helper.make_tensor_value_info("res_y", onnx.TensorProto.FLOAT, [3, 3, 3, 3]),
-            onnx.helper.make_tensor_value_info("res_scan", onnx.TensorProto.FLOAT, [5, 3, 3, 3, 3]),
+            onnx.helper.make_tensor_value_info("res_scan", onnx.TensorProto.FLOAT, scan_shape),
         ],
     )
     loop_model = onnx.helper.make_model(loop_graph)
@@ -4106,6 +4113,8 @@ def test_loop():
     verify_count_loop()
     # Test a loop that uses an array output.
     verify_tensor_loop()
+    # Test a loop that is malformed and has no output shape defined.
+    verify_tensor_loop(shapeless_output=True)
 
 
 def verify_if(cond_array, num_outputs):