Improve dtype detection in loop to fix onnx tests. (apache#7934)

python/tvm/relay/frontend/onnx.py

@@ -43,7 +43,7 @@
 
 
 class onnx_input:
-    """ Dual purpose list or dictionary access object."""
+    """Dual purpose list or dictionary access object."""
 
     def __init__(self):
         self.input_keys = []
@@ -126,7 +126,10 @@ def get_info(info_proto):
         shape.append(value)
 
     name = info_proto.name
-    dtype = get_type(info_proto.type.tensor_type.elem_type)
+    if info_proto.type.tensor_type.elem_type:
+        dtype = get_type(info_proto.type.tensor_type.elem_type)
+    else:
+        dtype = None
     return name, shape, dtype, shape_name
 
 
@@ -2495,6 +2498,8 @@ def get_var(name, val, scan=False):
         scan_output_init = []
         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"
             scan_output_vars.append(

tests/python/frontend/onnx/test_forward.py

@@ -45,7 +45,7 @@ def get_input_data_shape_dict(graph_def, input_data):
 def get_tvm_output_with_vm(
     graph_def, input_data, target, device, opset=None, freeze_params=False, convert_to_static=False
 ):
-    """ Generic function to execute and get tvm output with vm executor"""
+    """Generic function to execute and get tvm output with vm executor"""
     if not isinstance(input_data, list):
         input_data = [input_data]
     _, shape_dict = get_input_data_shape_dict(graph_def, input_data)
@@ -67,7 +67,7 @@ def get_tvm_output_with_vm(
 def get_tvm_output(
     graph_def, input_data, target, device, output_shape=None, output_dtype="float32", opset=None
 ):
-    """ Generic function to execute and get tvm output"""
+    """Generic function to execute and get tvm output"""
     # TODO: Resolve the issues and remove the following lines
     target = "llvm"
     device = tvm.cpu(0)
@@ -4218,8 +4218,6 @@ def verify_cumsum(indata, axis, exclusive=0, reverse=0, type="float32"):
     "test_qlinearconv/",
     "test_qlinearmatmul_2D/",
     "test_qlinearmatmul_3D/",
-    "test_range_float_type_positive_delta_expanded/",
-    "test_range_int32_type_negative_delta_expanded/",
     "test_resize_tf_crop_and_resize/",
     ## For these three tests, ONNX 1.6.0 has incorrect graphs, they pass with ONNX 1.7.0
     "test_resize_upsample_sizes_nearest_ceil_half_pixel/",