def test_tuple_passing():
x = relay.var('x',
type_annotation=relay.ty.TupleType([
relay.ty.TensorType((), 'int64'),
relay.ty.TensorType((), 'int64')
]))
fn = relay.Function([x], relay.expr.TupleGetItem(x, 0))
mod = relay.Module({})
gv = relay.GlobalVar('main')
mod[gv] = fn
mod = relay.transform.InferType()(mod)
ctx = tvm.cpu()
target = tvm.target.create('llvm')
exec = relay.create_executor(mod=mod, ctx=ctx, target=target)
f = exec.evaluate(gv)
# First use a Python tuple.
out = f((10, 8))
tvm.testing.assert_allclose(out.asnumpy(), np.array(10))
# Second use a tuple value.
value_tuple = container.tuple_object(
[nd.array(np.array(11)),
nd.array(np.array(12))])
out = f(value_tuple)
tvm.testing.assert_allclose(out.asnumpy(), np.array(11))
def test_tuple_value():
tv = container.tuple_object(
[relay.const(1), relay.const(2),
relay.const(3)])
np.testing.assert_allclose(tv[0].data.asnumpy(), 1)
np.testing.assert_allclose(tv[1].data.asnumpy(), 2)
np.testing.assert_allclose(tv[2].data.asnumpy(), 3)
def _convert(arg, cargs):
if isinstance(arg, _expr.Constant):
cargs.append(arg.data)
elif isinstance(arg, Object):
cargs.append(arg)
elif isinstance(arg, np.ndarray):
nd_arr = tvm.nd.array(arg, ctx=tvm.cpu(0))
cargs.append(nd_arr)
elif isinstance(arg, tvm.nd.NDArray):
cargs.append(arg)
elif isinstance(arg, (tuple, list)):
field_args = []
for field in arg:
_convert(field, field_args)
cargs.append(container.tuple_object(field_args))
elif isinstance(arg, (_base.numeric_types, bool)):
dtype = "int32" if isinstance(arg, (int, bool)) else "float32"
value = tvm.nd.array(np.array(arg, dtype=dtype), ctx=tvm.cpu(0))
cargs.append(value)
else:
raise TypeError("Unsupported type: %s" % (type(arg)))
def test_tuple_object():
x = relay.var('x',
type_annotation=relay.ty.TupleType([
relay.ty.TensorType((), 'int32'),
relay.ty.TensorType((), 'int32')
]))
fn = relay.Function([x], relay.expr.TupleGetItem(x, 0))
mod = relay.Module.from_expr(fn)
exe = relay.create_executor(kind="vm",
mod=mod,
ctx=nd.cpu(),
target="llvm")
f = exe.evaluate()
value_tuple = _container.tuple_object(
[nd.array(np.array(11)),
nd.array(np.array(12))])
# pass an ADT object to evaluate
out = f(value_tuple)
tvm.testing.assert_allclose(out.asnumpy(), np.array(11))
def test_function_taking_adt_ref_tuple():
mod = relay.Module()
prelude = relay.prelude.Prelude(mod)
intrp = create_executor("debug", mod)
nil_value = ConstructorValue(prelude.nil.tag, [], prelude.nil)
cons_value = ConstructorValue(
prelude.cons.tag,
[nd.array(np.random.rand(1, 10).astype('float32')), nil_value],
prelude.cons)
ref_value = RefValue(nd.array(np.random.rand(1, 10).astype('float32')))
tuple_value = container.tuple_object(
[nd.array(np.random.rand(1, 10).astype('float32')) for _ in range(10)])
id_func = intrp.evaluate(prelude.id)
res_nil = id_func(nil_value)
assert res_nil.tag == nil_value.tag
assert len(res_nil.fields) == 0
res_cons = id_func(cons_value)
assert res_cons.tag == cons_value.tag
assert len(res_cons.fields) == len(cons_value.fields)
tvm.testing.assert_allclose(res_cons.fields[0].asnumpy(),
cons_value.fields[0].asnumpy())
assert isinstance(res_cons.fields[1], ConstructorValue)
assert res_cons.fields[1].tag == prelude.nil.tag
assert len(res_cons.fields[1].fields) == 0
res_ref = id_func(ref_value)
tvm.testing.assert_allclose(res_ref.value.asnumpy(),
ref_value.value.asnumpy())
res_tuple = id_func(tuple_value)
for i in range(10):
tvm.testing.assert_allclose(res_tuple[i].asnumpy(),
tuple_value[i].asnumpy())