add test case

tests/python/relay/test_ir_parser.py

@@ -910,6 +910,45 @@ def test_load_prelude():
     tvm.parser.parse(mod.astext())
 
 
+def test_call_attrs():
+    def get_func(shape, dtype):
+        x0 = relay.var("data", shape=shape, dtype=dtype)
+        w0 = relay.var("weight", shape=shape, dtype=dtype)
+        a = relay.nn.dense(x0, w0)
+        b = relay.nn.relu(a)
+        d = relay.add(b, relay.const(1.0, dtype=dtype))
+        return relay.Function([x0, w0], d)
+
+    # build relay graph
+    shape = (2, 4)
+    dtype = "float32"
+    sub_func = get_func(shape, dtype)
+    p0 = relay.var("p0", shape=shape, dtype=dtype)
+    p1 = relay.var("p1", shape=shape, dtype=dtype)
+    attr = tvm.ir.make_node("attrs.TestAttrs", name="func_call_attrs")
+    call = relay.Call(sub_func, [p0, p1], attrs=attr)
+    func = relay.Function([p0, p1], call)
+
+    # build relay module
+    mod = tvm.IRModule()
+    mod["main"] = func
+    mod = tvm.relay.transform.InferType()(mod)
+
+    # assert equal
+    program = """
+    def @main(%p0: Tensor[(2, 4), float32], %p1: Tensor[(2, 4), float32]) {
+    %2 = fn (%data: Tensor[(2, 4), float32], %weight: Tensor[(2, 4), float32]) {
+        %0 = nn.dense(%data, %weight, units=None);
+        %1 = nn.relu(%0);
+        add(%1, 1f)
+    };
+    %2(%p0, %p1, name="func_call_attrs", attrs_type_key="attrs.TestAttrs")
+    }
+    """
+    parsed = parse_module(program)
+    assert_graph_equal(parsed, mod)
+
+
 if __name__ == "__main__":
     import sys