def test_eta_expand_constructor(): mod = tvm.parser.fromtext(r""" #[version = "0.0.5"] type List[A] { Cons(A, List[A]), Nil, } def @main[A]() -> fn(A, List[A]) -> List[A] { Cons } """) seq = tvm.transform.Sequential( [_transform.EtaExpand(expand_constructor=True)]) with tvm.transform.PassContext(opt_level=3): mod = seq(mod) expected = tvm.parser.fromtext(r""" #[version = "0.0.5"] type List[A] { Cons(A, List[A]), Nil, } def @main[A]() -> fn(A, List[A]) -> List[A] { fn [A](%x: A, %xs: List[A]) -> List[A] { Cons(%x, %xs) } } """) tvm.ir.assert_structural_equal(mod["main"], expected["main"], map_free_vars=True)
def test_eta_expand_global_var(): mod = tvm.parser.fromtext(r""" #[version = "0.0.5"] def @aux(%x: Tensor[(), int32]) -> Tensor[(), int32] { %x } def @main() -> fn(Tensor[(), int32]) -> Tensor[(), int32] { @aux } """) seq = tvm.transform.Sequential( [_transform.EtaExpand(expand_global_var=True)]) with tvm.transform.PassContext(opt_level=3): mod = seq(mod) expected = tvm.parser.fromtext(r""" #[version = "0.0.5"] def @aux(%x: Tensor[(), int32]) -> Tensor[(), int32] { %x } def @main() -> fn(Tensor[(), int32]) -> Tensor[(), int32] { fn (%x: Tensor[(), int32]) -> Tensor[(), int32] { @aux(%x) } } """) tvm.ir.assert_structural_equal(mod["main"], expected["main"], map_free_vars=True)
def test_eta_expand_constructor(): mod = relay.fromtext(r""" v0.0.4 type List[A] { Cons(A, List[A]), Nil, } def @main[A]() -> (fn(A, List[A]) -> List[A]) { Cons } """) seq = _transform.Sequential( [_transform.EtaExpand(expand_constructor=True)]) with _transform.PassContext(opt_level=3): mod = seq(mod) expected = relay.fromtext(r""" v0.0.4 type List[A] { Cons(A, List[A]), Nil, } def @main[A]() -> (fn(A, List[A]) -> List[A]) { fn [A](%x: A, %xs: List[A]) -> List[A] { Cons(%x, %xs) } } """) relay.analysis.assert_graph_equal(mod['main'], expected['main'])
def test_eta_expand_global_var(): mod = relay.fromtext(r""" v0.0.4 def @aux(%x: Tensor[(), int32]) -> Tensor[(), int32] { %x } def @main() -> (fn(Tensor[(), int32]) -> Tensor[(), int32]) { @aux } """) seq = _transform.Sequential([_transform.EtaExpand(expand_global_var=True)]) with _transform.PassContext(opt_level=3): mod = seq(mod) expected = relay.fromtext(r""" v0.0.4 def @aux(%x: Tensor[(), int32]) -> Tensor[(), int32] { %x } def @main() -> (fn(Tensor[(), int32]) -> Tensor[(), int32]) { fn (%x: Tensor[(), int32]) -> Tensor[(), int32] { @aux(%x) } } """) relay.analysis.assert_graph_equal(mod['main'], expected['main'])
def test_eta_expand_basic(): x = relay.var('x', 'int32') orig = relay.Function([x], x) mod = _module.Module.from_expr(orig) seq = _transform.Sequential([_transform.EtaExpand()]) with _transform.PassContext(opt_level=3): mod = seq(mod) got = mod[mod.entry_func.name_hint] y = relay.var('y', 'int32') expected = relay.Function([y], orig(y)) got = relay.ir_pass.infer_type(got, mod) expected = relay.ir_pass.infer_type(expected, mod) assert (relay.ir_pass.alpha_equal(got, expected))
def test_eta_expand_basic(): x = relay.var('x', 'int32') orig = relay.Function([x], x) mod = _module.Module.from_expr(orig) seq = _transform.Sequential([_transform.EtaExpand()]) with _transform.PassContext(opt_level=3): mod = seq(mod) got = mod["main"] y = relay.var('y', 'int32') expected = relay.Function([y], orig(y)) gv = relay.GlobalVar("gv") mod[gv] = expected mod = _transform.InferType()(mod) expected = mod["gv"] assert (relay.analysis.alpha_equal(got, expected))