wehub-resource-sync
386 lines
12 KiB
Python
386 lines
12 KiB
Python
# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import copy
|
|
import unittest
|
|
|
|
import numpy as np
|
|
from op_test import OpTest, get_device_place, get_places, is_custom_device
|
|
|
|
import paddle
|
|
import paddle.base.dygraph as dg
|
|
import paddle.nn.functional as F
|
|
from paddle import base, nn
|
|
|
|
|
|
def silu(x):
|
|
y_ref = x * (1 / (1 + np.exp(-x)))
|
|
return y_ref.astype(x.dtype)
|
|
|
|
|
|
class TestSiluOpClass(unittest.TestCase):
|
|
def _test_case1_cpu(self):
|
|
x = np.random.uniform(-1, 1, size=(11, 17)).astype(np.float32)
|
|
y_ref = silu(x)
|
|
|
|
place = base.CPUPlace()
|
|
with dg.guard(place) as g:
|
|
x_var = paddle.to_tensor(x)
|
|
y_var1 = F.silu(x_var)
|
|
y_test1 = y_var1.numpy()
|
|
|
|
func = nn.Silu()
|
|
y_var2 = func(x_var)
|
|
y_test2 = y_var2.numpy()
|
|
np.testing.assert_allclose(y_ref, y_test1, rtol=1e-05, atol=1e-08)
|
|
np.testing.assert_allclose(y_ref, y_test2, rtol=1e-05, atol=1e-08)
|
|
|
|
def _test_case1_gpu(self):
|
|
x = np.random.uniform(-1, 1, size=(11, 17)).astype(np.float32)
|
|
y_ref = silu(x)
|
|
|
|
place = get_device_place()
|
|
with dg.guard(place) as g:
|
|
x_var = paddle.to_tensor(x)
|
|
y_var1 = F.silu(x_var)
|
|
y_test1 = y_var1.numpy()
|
|
|
|
func = nn.Silu()
|
|
y_var2 = func(x_var)
|
|
y_test2 = y_var2.numpy()
|
|
np.testing.assert_allclose(y_ref, y_test1, rtol=1e-05, atol=1e-08)
|
|
np.testing.assert_allclose(y_ref, y_test2, rtol=1e-05, atol=1e-08)
|
|
|
|
def test_cases(self):
|
|
self._test_case1_cpu()
|
|
if base.is_compiled_with_cuda() or is_custom_device():
|
|
self._test_case1_gpu()
|
|
|
|
def test_fast_math(self):
|
|
if not (paddle.is_compiled_with_cuda() or is_custom_device()):
|
|
return
|
|
|
|
def use_fast_math(enabled):
|
|
paddle.set_flags({'FLAGS_use_fast_math': enabled})
|
|
|
|
shape = [11, 17, 8]
|
|
x_np = np.random.uniform(-1, 1, size=shape).astype(np.float16)
|
|
y_g_np = np.random.uniform(-1, 1, size=shape).astype(np.float16)
|
|
|
|
def run_silu_op():
|
|
with dg.guard():
|
|
x = paddle.to_tensor(x_np)
|
|
x.stop_gradient = False
|
|
y = F.silu(x)
|
|
x_grad = paddle.grad([y], [x], [paddle.to_tensor(y_g_np)])[0]
|
|
return y.numpy(), x_grad.numpy()
|
|
|
|
def run_silu_class():
|
|
with dg.guard():
|
|
x = paddle.to_tensor(x_np)
|
|
x.stop_gradient = False
|
|
func = nn.Silu()
|
|
y = func(x)
|
|
x_grad = paddle.grad([y], [x], [paddle.to_tensor(y_g_np)])[0]
|
|
return y.numpy(), x_grad.numpy()
|
|
|
|
use_fast_math(True)
|
|
y_fast_math1, x_g_fast_math1 = run_silu_op()
|
|
y_fast_math2, x_g_fast_math2 = run_silu_class()
|
|
use_fast_math(False)
|
|
|
|
y_ref1, x_g_ref1 = run_silu_op()
|
|
y_ref2, x_g_ref2 = run_silu_class()
|
|
np.testing.assert_allclose(
|
|
y_ref1, y_fast_math1, rtol=1e-05, atol=0.0005
|
|
)
|
|
|
|
np.testing.assert_allclose(
|
|
x_g_ref1, x_g_fast_math1, rtol=1e-05, atol=0.0005
|
|
)
|
|
|
|
np.testing.assert_allclose(
|
|
y_ref2, y_fast_math2, rtol=1e-05, atol=0.0005
|
|
)
|
|
|
|
np.testing.assert_allclose(
|
|
x_g_ref2, x_g_fast_math2, rtol=1e-05, atol=0.0005
|
|
)
|
|
|
|
|
|
class TestSiluOpClass_ZeroSize(unittest.TestCase):
|
|
def _test_case1_cpu(self):
|
|
x = np.random.uniform(-1, 1, size=(0, 17)).astype(np.float32)
|
|
y_ref = silu(x)
|
|
|
|
place = base.CPUPlace()
|
|
with dg.guard(place) as g:
|
|
x_var1 = paddle.to_tensor(x)
|
|
x_var2 = paddle.to_tensor(x)
|
|
|
|
x_var1.stop_gradient = False
|
|
x_var2.stop_gradient = False
|
|
|
|
y_var1 = F.silu(x_var1)
|
|
y_test1 = y_var1.numpy()
|
|
|
|
func = nn.Silu()
|
|
y_var2 = func(x_var2)
|
|
y_test2 = y_var2.numpy()
|
|
|
|
loss1 = paddle.sum(y_var1)
|
|
loss1.backward()
|
|
|
|
loss2 = paddle.sum(y_var2)
|
|
loss2.backward()
|
|
np.testing.assert_allclose(y_ref, y_test1, rtol=1e-05, atol=1e-08)
|
|
np.testing.assert_allclose(x_var1.grad.shape, x_var1.shape)
|
|
|
|
np.testing.assert_allclose(y_ref, y_test2, rtol=1e-05, atol=1e-08)
|
|
np.testing.assert_allclose(x_var2.grad.shape, x_var2.shape)
|
|
|
|
def _test_case1_gpu(self):
|
|
x = np.random.uniform(-1, 1, size=(0, 17)).astype(np.float32)
|
|
y_ref = silu(x)
|
|
|
|
place = get_device_place()
|
|
with dg.guard(place) as g:
|
|
x_var1 = paddle.to_tensor(x)
|
|
x_var2 = paddle.to_tensor(x)
|
|
|
|
x_var1.stop_gradient = False
|
|
x_var2.stop_gradient = False
|
|
|
|
y_var1 = F.silu(x_var1)
|
|
y_test1 = y_var1.numpy()
|
|
|
|
func = nn.Silu()
|
|
y_var2 = func(x_var2)
|
|
y_test2 = y_var2.numpy()
|
|
|
|
loss1 = paddle.sum(y_var1)
|
|
loss1.backward()
|
|
|
|
loss2 = paddle.sum(y_var2)
|
|
loss2.backward()
|
|
np.testing.assert_allclose(y_ref, y_test1, rtol=1e-05, atol=1e-08)
|
|
np.testing.assert_allclose(x_var1.grad.shape, x_var1.shape)
|
|
|
|
np.testing.assert_allclose(y_ref, y_test2, rtol=1e-05, atol=1e-08)
|
|
np.testing.assert_allclose(x_var2.grad.shape, x_var2.shape)
|
|
|
|
def test_cases(self):
|
|
self._test_case1_cpu()
|
|
if base.is_compiled_with_cuda() or is_custom_device():
|
|
self._test_case1_gpu()
|
|
|
|
|
|
class TestSiluOpClass_Inplace(unittest.TestCase):
|
|
def _test_case1_cpu(self):
|
|
x = np.random.uniform(-1, 1, size=(15, 17)).astype(np.float32)
|
|
y_ref = silu(x)
|
|
|
|
place = base.CPUPlace()
|
|
with dg.guard(place) as g:
|
|
x_var1 = paddle.to_tensor(x)
|
|
x_var2 = paddle.to_tensor(x)
|
|
|
|
y_var1 = F.silu(x_var1, True)
|
|
y_test1 = y_var1.numpy()
|
|
|
|
func = nn.Silu(True)
|
|
y_var2 = func(x_var2)
|
|
y_test2 = y_var2.numpy()
|
|
|
|
np.testing.assert_allclose(y_ref, y_test1, rtol=1e-05, atol=1e-08)
|
|
np.testing.assert_allclose(y_ref, y_test2, rtol=1e-05, atol=1e-08)
|
|
|
|
np.testing.assert_allclose(
|
|
y_ref, x_var1.numpy(), rtol=1e-05, atol=1e-08
|
|
)
|
|
np.testing.assert_allclose(
|
|
y_ref, x_var2.numpy(), rtol=1e-05, atol=1e-08
|
|
)
|
|
|
|
def _test_case1_gpu(self):
|
|
x = np.random.uniform(-1, 1, size=(15, 17)).astype(np.float32)
|
|
y_ref = silu(x)
|
|
|
|
place = get_device_place()
|
|
with dg.guard(place) as g:
|
|
x_var1 = paddle.to_tensor(x)
|
|
x_var2 = paddle.to_tensor(x)
|
|
|
|
y_var1 = F.silu(x_var1, True)
|
|
y_test1 = y_var1.numpy()
|
|
|
|
func = nn.Silu(True)
|
|
y_var2 = func(x_var2)
|
|
y_test2 = y_var2.numpy()
|
|
|
|
np.testing.assert_allclose(y_ref, y_test1, rtol=1e-05, atol=1e-08)
|
|
np.testing.assert_allclose(y_ref, y_test2, rtol=1e-05, atol=1e-08)
|
|
|
|
np.testing.assert_allclose(
|
|
y_ref, x_var1.numpy(), rtol=1e-05, atol=1e-08
|
|
)
|
|
np.testing.assert_allclose(
|
|
y_ref, x_var2.numpy(), rtol=1e-05, atol=1e-08
|
|
)
|
|
|
|
def test_cases(self):
|
|
self._test_case1_cpu()
|
|
if base.is_compiled_with_cuda() or is_custom_device():
|
|
self._test_case1_gpu()
|
|
|
|
|
|
class TestSiluParamDecorator(unittest.TestCase):
|
|
def setUp(self):
|
|
paddle.disable_static()
|
|
self.x_np = np.random.random((10, 3, 4)).astype("float64")
|
|
self.test_types = ["decorator"]
|
|
|
|
def do_test(self, test_type):
|
|
x = paddle.to_tensor(self.x_np, stop_gradient=False)
|
|
if test_type == 'raw':
|
|
result = F.silu(x, False)
|
|
result.mean().backward()
|
|
return result, x.grad
|
|
elif test_type == 'decorator':
|
|
result = F.silu(input=x, inplace=False)
|
|
result.mean().backward()
|
|
return result, x.grad
|
|
else:
|
|
raise ValueError(f"Unknown test type: {test_type}")
|
|
|
|
def test_all(self):
|
|
out_std, grad_x_std = self.do_test('raw')
|
|
for test_type in self.test_types:
|
|
out, grad_x = self.do_test(test_type)
|
|
np.testing.assert_allclose(out.numpy(), out_std.numpy(), rtol=1e-7)
|
|
np.testing.assert_allclose(
|
|
grad_x.numpy(), grad_x_std.numpy(), rtol=1e-7
|
|
)
|
|
|
|
|
|
class TestSiluPrint(unittest.TestCase):
|
|
def test_print(self):
|
|
print(nn.Silu())
|
|
print(nn.Silu(True))
|
|
print(nn.Silu(False))
|
|
print(nn.Silu(inplace=True))
|
|
print(nn.Silu(inplace=False))
|
|
|
|
|
|
class SiluOpDefaultTest(OpTest):
|
|
"""the base class of other op testcases"""
|
|
|
|
def setUp(self):
|
|
self.initTestCase()
|
|
self.python_api = F.silu
|
|
|
|
self.op_type = "silu"
|
|
self.inputs = {'X': self.X}
|
|
|
|
self.target = copy.deepcopy(self.X)
|
|
self.target = silu(self.target)
|
|
self.outputs = {'Out': (self.target)}
|
|
|
|
def test_check_output(self):
|
|
self.check_output(check_pir=True, check_symbol_infer=False)
|
|
|
|
def test_check_grad_normal(self):
|
|
self.check_grad(['X'], 'Out', check_pir=True)
|
|
|
|
def init_dtype(self):
|
|
self.dtype = np.float64
|
|
|
|
def initTestCase(self):
|
|
self.init_dtype()
|
|
self.X = np.arange(1, 101, dtype=self.dtype).reshape([10, -1])
|
|
if self.dtype == np.complex64 or self.dtype == np.complex128:
|
|
self.X = (
|
|
np.random.uniform(-1, 1, [10, 10])
|
|
+ 1j * np.random.uniform(-1, 1, [10, 10])
|
|
).astype(self.dtype)
|
|
|
|
|
|
class SiluOpDefaultTestFP16(SiluOpDefaultTest):
|
|
def init_dtype(self):
|
|
self.dtype = np.float16
|
|
|
|
|
|
class SiluOpDefaultTestComplex_64(SiluOpDefaultTest):
|
|
def init_dtype(self):
|
|
self.dtype = np.complex64
|
|
|
|
|
|
class SiluOpDefaultTestComplex_128(SiluOpDefaultTest):
|
|
def init_dtype(self):
|
|
self.dtype = np.complex128
|
|
|
|
|
|
class TestSiluAPI(unittest.TestCase):
|
|
def setUp(self):
|
|
np.random.seed(0)
|
|
self.shape = [10, 10]
|
|
self.x_np = np.random.random(self.shape).astype(np.float32)
|
|
self.place = get_places()
|
|
self.x_feed = copy.deepcopy(self.x_np)
|
|
|
|
def test_api_static(self):
|
|
paddle.enable_static()
|
|
|
|
def run(place, inplace):
|
|
with paddle.static.program_guard(paddle.static.Program()):
|
|
x = paddle.static.data('X', self.shape)
|
|
out = F.silu(x, inplace)
|
|
exe = paddle.static.Executor(self.place[0])
|
|
res = exe.run(
|
|
feed={
|
|
'X': self.x_feed,
|
|
},
|
|
fetch_list=[out],
|
|
)
|
|
target = copy.deepcopy(self.x_np)
|
|
out_ref = silu(target)
|
|
|
|
for out in res:
|
|
np.testing.assert_allclose(out, out_ref, rtol=0.001)
|
|
|
|
for place in self.place:
|
|
run(place, True)
|
|
run(place, False)
|
|
|
|
def test_api_dygraph(self):
|
|
def run(place, inplace):
|
|
paddle.disable_static(place)
|
|
x_tensor = paddle.to_tensor(self.x_np)
|
|
out = F.silu(x_tensor, inplace)
|
|
|
|
target = copy.deepcopy(self.x_np)
|
|
out_ref = silu(target)
|
|
|
|
np.testing.assert_allclose(out.numpy(), out_ref, rtol=0.001)
|
|
|
|
paddle.enable_static()
|
|
|
|
for place in self.place:
|
|
run(place, True)
|
|
run(place, False)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|