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Add 0d Tensor Test Cases for cond, case, switch_case (#49544)
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Add 0d Tensor Test Cases for cond, case, switch_case. Since the 3 APIs are control flow APIs, their support for 0d tensor relies on the underneath APIs. This PR just added test cases to prove that the 3 APIs have already handled 0d tensor well.
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@zhhsplendid
zhhsplendid authored Jan 5, 2023
1 parent 5feadc0 commit d5f1e30
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161 changes: 161 additions & 0 deletions python/paddle/fluid/tests/unittests/test_case.py
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Original file line number Diff line number Diff line change
Expand Up @@ -89,6 +89,67 @@ def fn_3():
np.testing.assert_allclose(res[3], 2, rtol=1e-05)
np.testing.assert_allclose(res[4], 2, rtol=1e-05)

def test_0d_tensor(self):
def fn_1():
return paddle.full(shape=[], dtype='int32', fill_value=1)

def fn_2():
return paddle.full(shape=[], dtype='int32', fill_value=2)

def fn_3():
return paddle.full(shape=[], dtype='int32', fill_value=3)

main_program = Program()
startup_program = Program()
with program_guard(main_program, startup_program):
x = paddle.full(shape=[], dtype='float32', fill_value=0.3)
y = paddle.full(shape=[], dtype='float32', fill_value=0.1)
z = paddle.full(shape=[], dtype='float32', fill_value=0.2)
pred_2 = paddle.less_than(x, y) # false: 0.3 < 0.1
pred_1 = paddle.less_than(z, x) # true: 0.2 < 0.3

# call fn_1
out_0 = paddle.static.nn.control_flow.case(
pred_fn_pairs=[(pred_1, fn_1), (pred_1, fn_2)], default=fn_3
)

# call fn_2
out_1 = paddle.static.nn.control_flow.case(
pred_fn_pairs=[(pred_2, fn_1), (pred_1, fn_2)], default=fn_3
)

# call default fn_3
out_2 = paddle.static.nn.control_flow.case(
pred_fn_pairs=((pred_2, fn_1), (pred_2, fn_2)), default=fn_3
)

# no default, call fn_2
out_3 = paddle.static.nn.control_flow.case(
pred_fn_pairs=[(pred_1, fn_2)]
)

# no default, call fn_2. but pred_2 is false
out_4 = paddle.static.nn.control_flow.case(
pred_fn_pairs=[(pred_2, fn_2)]
)

place = (
fluid.CUDAPlace(0)
if core.is_compiled_with_cuda()
else fluid.CPUPlace()
)
exe = fluid.Executor(place)

res = exe.run(
main_program, fetch_list=[out_0, out_1, out_2, out_3, out_4]
)

np.testing.assert_allclose(res[0], 1, rtol=1e-05)
np.testing.assert_allclose(res[1], 2, rtol=1e-05)
np.testing.assert_allclose(res[2], 3, rtol=1e-05)
np.testing.assert_allclose(res[3], 2, rtol=1e-05)
np.testing.assert_allclose(res[4], 2, rtol=1e-05)

def test_return_var_tuple(self):
def fn_1():
return layers.fill_constant(
Expand Down Expand Up @@ -236,6 +297,106 @@ def fn_3():
np.testing.assert_allclose(res[1], 2, rtol=1e-05)
np.testing.assert_allclose(res[2], 3, rtol=1e-05)

def test_nested_0d_tensor(self):
def fn_1(x=1):
var_5 = paddle.full(shape=[], dtype='int32', fill_value=5)
var_6 = paddle.full(shape=[], dtype='int32', fill_value=6)
out = paddle.static.nn.control_flow.case(
pred_fn_pairs=[
(
var_5 < var_6,
partial(
paddle.full,
shape=[],
dtype='int32',
fill_value=x,
),
),
(
var_5 == var_6,
partial(
paddle.full,
shape=[],
dtype='int32',
fill_value=x,
),
),
]
)
return out

def fn_2(x=2):
var_5 = paddle.full(shape=[], dtype='int32', fill_value=5)
var_6 = paddle.full(shape=[], dtype='int32', fill_value=6)
out = paddle.static.nn.control_flow.case(
pred_fn_pairs=[
(var_5 < var_6, partial(fn_1, x=x)),
(
var_5 == var_6,
partial(
paddle.full,
shape=[],
dtype='int32',
fill_value=x,
),
),
]
)
return out

def fn_3():
var_5 = paddle.full(shape=[], dtype='int32', fill_value=5)
var_6 = paddle.full(shape=[], dtype='int32', fill_value=6)
out = paddle.static.nn.control_flow.case(
pred_fn_pairs=[
(var_5 < var_6, partial(fn_2, x=3)),
(
var_5 == var_6,
partial(
paddle.full,
shape=[],
dtype='int32',
fill_value=7,
),
),
]
)
return out

main_program = Program()
startup_program = Program()
with program_guard(main_program, startup_program):
x = paddle.full(shape=[], dtype='float32', fill_value=0.3)
y = paddle.full(shape=[], dtype='float32', fill_value=0.1)
z = paddle.full(shape=[], dtype='float32', fill_value=0.2)
pred_2 = paddle.less_than(x, y) # false: 0.3 < 0.1
pred_1 = paddle.less_than(z, x) # true: 0.2 < 0.3

out_1 = paddle.static.nn.control_flow.case(
pred_fn_pairs=[(pred_1, fn_1), (pred_2, fn_2)], default=fn_3
)

out_2 = paddle.static.nn.control_flow.case(
pred_fn_pairs=[(pred_2, fn_1), (pred_1, fn_2)], default=fn_3
)

out_3 = paddle.static.nn.control_flow.case(
pred_fn_pairs=[(x == y, fn_1), (x == z, fn_2)], default=fn_3
)

place = (
fluid.CUDAPlace(0)
if core.is_compiled_with_cuda()
else fluid.CPUPlace()
)
exe = fluid.Executor(place)

res = exe.run(main_program, fetch_list=[out_1, out_2, out_3])

np.testing.assert_allclose(res[0], 1, rtol=1e-05)
np.testing.assert_allclose(res[1], 2, rtol=1e-05)
np.testing.assert_allclose(res[2], 3, rtol=1e-05)


class TestAPICase_Error(unittest.TestCase):
def test_error(self):
Expand Down
173 changes: 173 additions & 0 deletions python/paddle/fluid/tests/unittests/test_cond.py
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Original file line number Diff line number Diff line change
Expand Up @@ -68,6 +68,115 @@ def false_func():
np.asarray(ret), np.full((3, 2), -1, np.int32), rtol=1e-05
)

def test_return_0d_tensor(self):
"""
pseudocode:
if 0.23 >= 0.1:
return 2
else:
return -1
"""

paddle.enable_static()

def true_func():
return paddle.full(shape=[], dtype='int32', fill_value=2)

def false_func():
return paddle.full(shape=[], dtype='int32', fill_value=-1)

main_program = Program()
startup_program = Program()
with program_guard(main_program, startup_program):
x = paddle.full(shape=[1], dtype='float32', fill_value=0.1)
y = paddle.full(shape=[1], dtype='float32', fill_value=0.23)
pred = paddle.greater_equal(y, x)
out = paddle.static.nn.cond(pred, true_func, false_func)
# out is one tensor

place = (
fluid.CUDAPlace(0)
if core.is_compiled_with_cuda()
else fluid.CPUPlace()
)
exe = fluid.Executor(place)
(ret,) = exe.run(main_program, fetch_list=[out.name])
np.testing.assert_allclose(np.asarray(ret), np.array(2), rtol=1e-05)

def test_0d_tensor_as_cond(self):
"""
pseudocode:
if 0.23 >= 0.1:
return 2
else:
return -1
"""

paddle.enable_static()

def true_func():
return paddle.full(shape=[3, 3], dtype='int32', fill_value=2)

def false_func():
return paddle.full(shape=[3, 3], dtype='int32', fill_value=-1)

main_program = Program()
startup_program = Program()
with program_guard(main_program, startup_program):
x = paddle.full(shape=[], dtype='float32', fill_value=0.1)
y = paddle.full(shape=[], dtype='float32', fill_value=0.23)
pred = paddle.greater_equal(y, x)
out = paddle.static.nn.cond(pred, true_func, false_func)
# out is one tensor

place = (
fluid.CUDAPlace(0)
if core.is_compiled_with_cuda()
else fluid.CPUPlace()
)
exe = fluid.Executor(place)
(ret,) = exe.run(main_program, fetch_list=[out.name])
np.testing.assert_allclose(
np.asarray(ret), np.full((3, 3), 2, np.int32), rtol=1e-05
)

def test_0d_tensor_backward(self):
"""
pseudocode:
a = -2.0
if a >= 0:
return a
else:
return -a
"""

paddle.enable_static()

main_program = Program()
startup_program = Program()
with program_guard(main_program, startup_program):
a = paddle.full(shape=[], dtype='float32', fill_value=-2.0)
a.stop_gradient = False
out = paddle.static.nn.cond(a >= 0, lambda: a, lambda: -a)
append_backward(out)

place = (
fluid.CUDAPlace(0)
if core.is_compiled_with_cuda()
else fluid.CPUPlace()
)
exe = fluid.Executor(place)
ret = exe.run(main_program, fetch_list=[out.name, a.grad_name])
np.testing.assert_allclose(
np.asarray(ret[0]), np.array(2.0), rtol=1e-05
)
np.testing.assert_allclose(
np.asarray(ret[1]), np.array(-1.0), rtol=1e-05
)

def test_return_var_tuple(self):
"""
pseudocode:
Expand Down Expand Up @@ -358,6 +467,70 @@ def greater_equal_branch(i, a):
self.assertEqual(ret[0][0], expected_ret)
self.assertEqual(ret[1][0], expected_a_grad)

def test_cond_inside_cond_0d_tensor(self):
"""
pseudocode:
i = 3.0
a = 2 * i
if i < 5:
if i >= 3:
return a + 1
else:
return 1 - a
else:
if i < 8:
return a * 2
else:
return a / 2
"""

paddle.enable_static()

def less_than_branch(i, a):
return paddle.static.nn.cond(
i >= 3.0,
lambda: a + 1,
lambda: 1 - a,
)

def greater_equal_branch(i, a):
return paddle.static.nn.cond(
i < 8.0,
lambda: a * 2,
lambda: a / 2,
)

main_program = Program()
startup_program = Program()
with program_guard(main_program, startup_program):
i = paddle.full(fill_value=3.0, shape=[], dtype='float32')
i.stop_gradient = False
a = 2.0 * i
out = paddle.static.nn.cond(
i < 5.0,
lambda: less_than_branch(i, a),
lambda: greater_equal_branch(i, a),
)
mean = paddle.mean(out)
append_backward(out)

place = (
fluid.CUDAPlace(0)
if core.is_compiled_with_cuda()
else fluid.CPUPlace()
)
exe = fluid.Executor(place)
ret = exe.run(
main_program,
fetch_list=[out.name, i.grad_name],
)
np.testing.assert_allclose(
np.asarray(ret[0]), np.array(7.0), rtol=1e-05
)
np.testing.assert_allclose(
np.asarray(ret[1]), np.array(2.0), rtol=1e-05
)

def test_cond_op_in_condition(self):
paddle.enable_static()
main_program = fluid.Program()
Expand Down
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