def test_tuple():
ib = IRBuilder()
dup = ib.global_var('dup')
x = ib.param('x')
with ib.decl(dup, x):
ib.ret(relay.Tuple([x, x]))
# todo: why is this not generalized?
fn_ty = func_type([tensor_type()],
relay.TupleType([tensor_type(),
tensor_type()]))
assert_decl_has_type(ib.env, dup, fn_ty)
def test_concat():
"""
Program:
def try_concat2(x: Float(3, 2), y: Float(2, 2)) -> Float(5, 2) {
return concat(x, y);
}
"""
ib = IRBuilder()
try_concat2 = ib.global_var('try_concat2')
x = ib.param('x', ty=tensor_type(3, 2))
y = ib.param('y', ty=tensor_type(2, 2))
with ib.decl(try_concat2, x, y):
ib.ret(concat(x, y))
fn_ty = func_type([tensor_type(3, 2), tensor_type(2, 2)], tensor_type(5, 2))
assert_decl_has_type(ib.env, try_concat2, fn_ty)
def test_recursion():
"""
Program:
def f(n: i32, data: f32) -> f32 {
if (n == 0) {
return f(n - 1, log(data));
} else {
return data;
}
}
f(2, 10000);
"""
b = IRBuilder()
f = b.global_var('f')
n = b.param('n', ty='int32')
data = b.param('data', ty='float32')
with b.decl(f, n, data):
with b.if_scope(equal(n, convert(0))):
b.ret(f(subtract(n, convert(1)), log(data)))
with b.else_scope():
b.ret(data)
b.ret(f(convert(2.0), convert(10000.0)))
assert_decl_has_type(b.env, 'f', func_type(
['int32', 'float32'], 'float32'))