[RELAY] reorg testcase, make checked_type property, fix constructor error handling (#1850)

python/tvm/_ffi/_ctypes/node.py

@@ -76,6 +76,8 @@ class NodeBase(object):
         So the return handle is directly set into the Node object
         instead of creating a new Node.
         """
+        # assign handle first to avoid error raising
+        self.handle = None
         handle = __init_by_constructor__(fconstructor, args)
         if not isinstance(handle, NodeHandle):
             handle = NodeHandle(handle)

python/tvm/_ffi/_cython/node.pxi

@@ -82,6 +82,8 @@ cdef class NodeBase:
         So the return handle is directly set into the Node object
         instead of creating a new Node.
         """
+        # avoid error raised during construction.
+        self.chandle = NULL
         cdef void* chandle
         ConstructorCall(
             (<FunctionBase>fconstructor).chandle,

python/tvm/relay/expr.py

@@ -9,7 +9,15 @@ from .. import convert
 
 class Expr(NodeBase):
     """The base type for all Relay expressions."""
+    @property
     def checked_type(self):
+        """Get the checked type of relay.
+
+        Returns
+        -------
+        checked_type : relay.Type
+            The checked type.
+        """
         ret = self._checked_type_
         if ret is None:
             raise ValueError("The type checker has not populated"

tests/python/relay/test_ir_nodes.py

@@ -3,6 +3,13 @@ import tvm
 from tvm import relay
 from tvm.expr import *
 
+def test_bad_constructor():
+    try:
+        x = relay.ty.TensorType("xx", "xx")
+    except tvm.TVMError:
+        pass
+
+
 # Span
 def test_span():
     span = relay.Span(None, 1, 1)
@@ -169,6 +176,7 @@ def test_if():
 
 
 if __name__ == "__main__":
+    test_bad_constructor()
     test_span()
     test_tensor_type()
     test_type_param()

tests/python/relay/test_op_level1.py

@@ -11,7 +11,7 @@ def test_expand_dims_infer_type():
         ib.ret(relay.expand_dims(x, axis=2))
     ib.ret(func)
     func = relay.ir_pass.infer_type(ib.env, func.to_func())
-    ftype = func.checked_type()
+    ftype = func.checked_type
     assert ftype.ret_type == relay.ty.TensorType(
         (n, t, 1, 100), "float32")
 
@@ -27,7 +27,7 @@ def test_unary_op():
             ib.ret(op(x.var))
         ib.ret(func)
         func = relay.ir_pass.infer_type(ib.env, func.to_func())
-        ftype = func.checked_type()
+        ftype = func.checked_type
         assert ftype.ret_type == relay.TensorType((10, 4), "int32")
 
 

tests/python/relay/test_op_level2.py

@@ -16,7 +16,7 @@ def test_conv2d_infer_type():
                                channels=2))
     ib.ret(func)
     func = relay.ir_pass.infer_type(ib.env, func.to_func())
-    ftype = func.checked_type()
+    ftype = func.checked_type
     assert ftype.ret_type == relay.ty.TensorType(
         (n, 2, 224, 224), "float32")
     assert ftype.arg_types[1] == relay.ty.TensorType(
@@ -31,7 +31,7 @@ def test_conv2d_infer_type():
         ib.ret(relay.nn.conv2d(x.var, w.var, out_dtype="int32"))
     ib.ret(func)
     func = relay.ir_pass.infer_type(ib.env, func.to_func())
-    ftype = func.checked_type()
+    ftype = func.checked_type
     assert ftype.ret_type == relay.ty.TensorType(
         (n, 2, 222, 222), "int32")
 
@@ -50,7 +50,7 @@ def test_conv2d_infer_type():
                                out_dtype="int32"))
     ib.ret(func)
     func = relay.ir_pass.infer_type(ib.env, func.to_func())
-    ftype = func.checked_type()
+    ftype = func.checked_type
     assert ftype.ret_type == relay.ty.TensorType(
         (1, 4, 224, 224, 4, 4), "int32")
     assert ftype.arg_types[1] == relay.ty.TensorType(

tests/python/relay/test_op_level3.py

@@ -9,5 +9,5 @@ def test_unary_identity():
             ib.ret(op(x.var))
         ib.ret(func)
         func = relay.ir_pass.infer_type(ib.env, func.to_func())
-        ftype = func.checked_type()
+        ftype = func.checked_type
         assert ftype.ret_type == relay.TensorType((8, 9, 4), "int32")

tests/python/relay/test_op_level4.py

@@ -16,7 +16,7 @@ def test_cmp_type():
             ib.ret(op(x.var, y.var))
         ib.ret(func)
         func = relay.ir_pass.infer_type(ib.env, func.to_func())
-        ftype = func.checked_type()
+        ftype = func.checked_type
         assert ftype.ret_type == relay.TensorType((5, 10, 4), "uint1")
 
 
@@ -32,7 +32,7 @@ def test_binary_broadcast():
             ib.ret(op(x.var, y.var))
         ib.ret(func)
         func = relay.ir_pass.infer_type(ib.env, func.to_func())
-        ftype = func.checked_type()
+        ftype = func.checked_type
         assert ftype.ret_type == relay.TensorType((5, 10, 4), "int32")
 
 

tests/python/relay/test_type_infer.py

@@ -12,7 +12,7 @@ from tvm.relay.expr import Function
 
 def assert_has_type(expr, typ, env=Environment({})):
     checked_expr = infer_type(env, expr)
-    checked_type = checked_expr.checked_type()
+    checked_type = checked_expr.checked_type
     if checked_type != typ:
         raise RuntimeError("Type mismatch %s vs %s" % (
             checked_type, typ))
@@ -20,7 +20,7 @@ def assert_has_type(expr, typ, env=Environment({})):
 
 def assert_decl_has_type(env, name, typ):
     func = env[name]
-    assert func.checked_type() == typ
+    assert func.checked_type == typ
 
 
 def test_monomorphic_let():