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('fn')
mod[gv] = fn
mod.entry_func = gv
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 = TupleValue(TensorValue(np.array(11)),
TensorValue(np.array(12)))
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,
[TensorValue(np.random.rand(1, 10).astype('float32')), nil_value],
prelude.cons, [relay.TensorType((1, 10), 'float32')])
ref_value = RefValue(TensorValue(np.random.rand(1, 10).astype('float32')))
tuple_value = TupleValue(*[
TensorValue(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.fields[i].asnumpy(),
tuple_value.fields[i].asnumpy())
def test_tensor_value():
x = relay.var("x", shape=(1, 10))
xx = np.ones((1, 10)).astype("float32")
check_eval(relay.Function([x], x), [TensorValue(xx)], xx)
def pytorch_to_relay(tensor):
#print(tensor.shape)
return TensorValue(
relay.const(tensor.detach().cpu().numpy().reshape((1, 300)),
dtype='float32').data)