def func_empty_grad(self):
with fluid.dygraph.guard():
x = np.ones([2, 2], np.float32)
new_var = paddle.to_tensor(x)
self.assertIsNone(new_var.gradient())
# TODO(jiabin): Support clear_gradient in eager mode later and remove this if statement
if not _in_eager_mode():
try:
new_var.clear_gradient()
except Exception as e:
assert type(e) == core.EnforceNotMet
with fluid.dygraph.guard():
cur_program = fluid.Program()
cur_block = cur_program.current_block()
# Normally, we don't allow tensor with -1 shape being created in dygraph mode, this test is not good.
if not _in_eager_mode():
new_variable = cur_block.create_var(name="X",
shape=[-1, 23, 48],
dtype='float32')
else:
new_variable = cur_block.create_var(name="X",
shape=[1, 23, 48],
dtype='float32')
try:
new_variable.gradient()
except Exception as e:
assert type(e) == ValueError
def func_tensor_from_numpy(self):
data_np = np.array([[2, 3, 1]]).astype('float32')
with fluid.dygraph.guard(fluid.CPUPlace()):
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
var = fluid.dygraph.to_variable(data_np, zero_copy=True)
assert "Currently, zero_copy is not supported, and it will be discarded." in str(
w[-1].message)
# Temporally diable zero_copy
# var = fluid.dygraph.to_variable(data_np, zero_copy=True)
# self.assertTrue(np.array_equal(var.numpy(), data_np))
# data_np[0][0] = 4
# self.assertEqual(data_np[0][0], 4)
# self.assertEqual(var[0][0].numpy()[0], 4)
# self.assertTrue(np.array_equal(var.numpy(), data_np))
var2 = fluid.dygraph.to_variable(data_np, zero_copy=False)
self.assertTrue(np.array_equal(var2.numpy(), data_np))
data_np[0][0] = -1
self.assertEqual(data_np[0][0], -1)
if _in_eager_mode():
# eager_mode, var2 is EagerTensor, is not subscriptable
# TODO(wuweilong): to support slice in eager mode later
self.assertNotEqual(var2.numpy()[0][0], -1)
else:
self.assertNotEqual(var2[0][0].numpy()[0], -1)
self.assertFalse(np.array_equal(var2.numpy(), data_np))
def func_create_varbase(self):
x = np.ones([2, 2], np.float32)
y = np.zeros([3, 3], np.float32)
t = fluid.Tensor()
t.set(x, fluid.CPUPlace())
if _in_eager_mode():
# TODO(jiabin): Support Kwargs and uncomment these tests
# egr_tmp = fluid.core.eager.EagerTensor(value=x, place=fluid.core.CPUPlace())
egr_tmp2 = fluid.core.eager.EagerTensor(y, fluid.core.CPUPlace())
egr_tmp3 = paddle.to_tensor(x)
egr_tmp4 = fluid.core.eager.EagerTensor(y)
# egr_tmp5 = fluid.core.eager.EagerTensor(value=x)
# TODO(jiabin): Support it when we merge LoDTensor with DenseTensor
egr_tmp6 = fluid.core.eager.EagerTensor(t)
# self.assertTrue(np.array_equal(x, egr_tmp.numpy()))
self.assertTrue(np.array_equal(y, egr_tmp2.numpy()))
self.assertTrue(np.array_equal(x, egr_tmp3.numpy()))
self.assertTrue(np.array_equal(y, egr_tmp4.numpy()))
# self.assertTrue(np.array_equal(x, egr_tmp5.numpy()))
self.assertTrue(np.array_equal(x, egr_tmp6.numpy()))
else:
tmp = fluid.core.VarBase(value=x, place=fluid.core.CPUPlace())
tmp2 = fluid.core.VarBase(y, fluid.core.CPUPlace())
tmp3 = paddle.to_tensor(x)
tmp4 = fluid.core.VarBase(y)
tmp5 = fluid.core.VarBase(value=x)
tmp6 = fluid.core.VarBase(t)
self.assertTrue(np.array_equal(x, tmp.numpy()))
self.assertTrue(np.array_equal(y, tmp2.numpy()))
self.assertTrue(np.array_equal(x, tmp3.numpy()))
self.assertTrue(np.array_equal(y, tmp4.numpy()))
self.assertTrue(np.array_equal(x, tmp5.numpy()))
self.assertTrue(np.array_equal(x, tmp6.numpy()))
def test__test_eager_guard(self):
tracer = paddle.fluid.dygraph.tracer.Tracer()
with _test_eager_guard(tracer):
self.assertTrue(_in_eager_mode())