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【UnitTestFix No.4】Fix unittest test_dropout_op #75729

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Merged
merged 7 commits into from
Oct 13, 2025
Merged

【UnitTestFix No.4】Fix unittest test_dropout_op #75729

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48fcca7
fix: using latest API
aztice Oct 10, 2025
fcf59f0
switch check_prim_pir ON
aztice Oct 10, 2025
ba1615d
fix: Code Style Issue
aztice Oct 10, 2025
15c7101
remove: useless whitelist.
aztice Oct 11, 2025
5fb0f59
fix: code-style issue.
aztice Oct 11, 2025
b425b3e
Update test/legacy_test/test_dropout_op.py
aztice Oct 11, 2025
02e3e74
fix: code-style issue.
aztice Oct 11, 2025
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90 changes: 39 additions & 51 deletions test/legacy_test/test_dropout_op.py
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Original file line number Diff line number Diff line change
Expand Up @@ -34,7 +34,6 @@
from paddle.base import Program, Scope, core, program_guard
from paddle.base.executor import scope_guard
from paddle.decomposition import decompose
from paddle.incubate.autograd import primapi


def dropout_wrapper(
Expand Down Expand Up @@ -478,71 +477,63 @@ def test_seed_cpu_place(self):
paddle.enable_static()
main_program = Program()
with program_guard(main_program):
paddle.seed(1)
seed_input_name = "tensor@SeedInput"
x_var_name = "tensor@X"
x_out_var = "tensor@XOut"

mask_var_name = "tensor@Mask"
seed_input_var = main_program.global_block().create_var(
seed_input_var = paddle.static.data(
name=seed_input_name,
shape=[1],
dtype='int32',
persistable=False,
stop_gradient=True,
)
x_out_var = main_program.global_block().create_var(
seed_input_var.persistable = False
seed_input_var.stop_gradient = True
x_out_var = paddle.static.data(
name=x_out_var,
shape=[40, 40],
dtype='float32',
persistable=False,
stop_gradient=True,
)
x_var = main_program.global_block().create_var(
x_out_var.persistable = False
x_out_var.stop_gradient = True
x_var = paddle.static.data(
name=x_var_name,
shape=[40, 40],
dtype='float32',
persistable=False,
stop_gradient=True,
)
mask_var = main_program.global_block().create_var(
x_var.persistable = False
x_var.stop_gradient = True
mask_var = paddle.static.data(
name=mask_var_name,
shape=[1],
dtype='int',
persistable=False,
stop_gradient=True,
)
mask_var.persistable = False
mask_var.stop_gradient = True

main_program.global_block().append_op(
type="fill_constant",
outputs={"Out": x_var_name},
attrs={
"shape": [40, 40],
"dtype": x_var.dtype,
"value": 1.0,
"place_type": 0,
},
)
main_program.global_block().append_op(
type='seed',
inputs={},
outputs={'Out': seed_input_var},
attrs={'seed': 1, 'force_cpu': True},
)
main_program.global_block().append_op(
type='dropout',
inputs={'X': x_var, 'Seed': seed_input_var},
attrs={'dropout_prob': 0.0},
outputs={'Out': x_out_var, 'Mask': mask_var},
x_var = paddle.full(shape=[40, 40], dtype='float32', fill_value=1.0)
x_out_var = paddle.static.data(
name='x_out', shape=[40, 40], dtype='float32'
)
x_out_var.persistable = True
tmp = paddle.nn.functional.dropout(x_var, p=0.0, training=False)
paddle.assign(tmp, output=x_out_var)

place = base.CPUPlace()
if core.is_compiled_with_cuda() or is_custom_device():
place = get_device_place()
exe = base.Executor(place)
x_out, mask_out = exe.run(
x_out = exe.run(
main_program,
feed={},
fetch_list=[x_out_var.name, mask_var.name],
)
feed={
'tensor@X': np.ones([40, 40], dtype=np.float32),
'tensor@XOut': np.ones([40, 40], dtype=np.float32),
'tensor@SeedInput': np.array([123], dtype=np.int32),
'tensor@Mask': np.array([123], dtype=np.int64),
},
fetch_list=[x_out_var],
)[0]
x_in_np = np.ones([40, 40]).astype("float32")
np.testing.assert_allclose(x_out, x_in_np, rtol=1e-05)

Expand Down Expand Up @@ -1423,27 +1414,23 @@ def setUp(self):
self.places = get_places()

def check_static_result(self, place):
from paddle.distributed.fleet.meta_parallel.parallel_layers.random import (
dropout,
)

with static.program_guard(static.Program(), static.Program()):
paddle.seed(0)
input = static.data(name="input", shape=[40, 40], dtype="float32")
res1 = dropout(
res1 = paddle.nn.functional.dropout(
input,
p=0.3,
training=True,
mode='upscale_in_train',
rng_name='seed0',
)
res2 = dropout(

res2 = paddle.nn.functional.dropout(
input,
p=0.3,
training=True,
mode='upscale_in_train',
rng_name='seed1',
)
res3 = dropout(input, p=0.3)
res3 = paddle.nn.functional.dropout(input, p=0.3)

in_np = np.random.random([40, 40]).astype("float32")

Expand Down Expand Up @@ -1489,8 +1476,8 @@ def init_info(self):
self.api = paddle.nn.functional.dropout

def api_case(self, x):
p = paddle.assign([0.5])
out = self.api(x=x, p=p, training=True)
p = 0.5
out = self.api(x, p, training=True)
return out

def run_static(self, x):
Expand Down Expand Up @@ -1859,7 +1846,8 @@ def test_static_comp(self):
mode=self.mode,
)
if core._is_fwd_prim_enabled():
primapi.to_prim(mp.blocks)
# primapi.to_prim(mp.blocks)
[output] = decompose(mp, [output])
grad = paddle.static.gradients(output, input_)[0]
if self.dtype == "bfloat16":
output = paddle.cast(output, "float32")
Expand All @@ -1874,7 +1862,7 @@ def test_static_comp(self):
mps.append(mp)
for i in range(len(self.places)):
self.assertTrue(
'dropout' not in [op.type for op in mps[i].block(0).ops]
'dropout' not in [op.name() for op in mps[i].global_block().ops]
)
np.testing.assert_allclose(
self.fwd_desire[i].sum(),
Expand Down
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