wehub-resource-sync
230 lines
7.7 KiB
Python
230 lines
7.7 KiB
Python
# Copyright (c) 2019 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 unittest
|
|
|
|
import numpy as np
|
|
|
|
import paddle
|
|
from paddle import base
|
|
from paddle.nn import Linear
|
|
|
|
|
|
class Test_Detach(unittest.TestCase):
|
|
def generate_Data(self):
|
|
data = np.array([[1, 8, 3, 9], [7, 20, 9, 6], [4, 6, 8, 10]]).astype(
|
|
'float32'
|
|
)
|
|
return data
|
|
|
|
def no_detach_multi(self):
|
|
data = self.generate_Data()
|
|
with base.dygraph.guard():
|
|
linear_w_param_attrs = paddle.ParamAttr(
|
|
initializer=paddle.nn.initializer.Constant(5.0)
|
|
)
|
|
linear_b_param_attrs = paddle.ParamAttr(
|
|
initializer=paddle.nn.initializer.Constant(6.0)
|
|
)
|
|
linear = Linear(
|
|
4,
|
|
10,
|
|
weight_attr=linear_w_param_attrs,
|
|
bias_attr=linear_b_param_attrs,
|
|
)
|
|
linear1_w_param_attrs = paddle.ParamAttr(
|
|
initializer=paddle.nn.initializer.Constant(7.0)
|
|
)
|
|
linear1_b_param_attrs = paddle.ParamAttr(
|
|
initializer=paddle.nn.initializer.Constant(8.0)
|
|
)
|
|
linear1 = Linear(
|
|
10,
|
|
1,
|
|
weight_attr=linear1_w_param_attrs,
|
|
bias_attr=linear1_b_param_attrs,
|
|
)
|
|
linear2_w_param_attrs = paddle.ParamAttr(
|
|
initializer=paddle.nn.initializer.Constant(9.0)
|
|
)
|
|
linear2_b_param_attrs = paddle.ParamAttr(
|
|
initializer=paddle.nn.initializer.Constant(10.0)
|
|
)
|
|
linear2 = Linear(
|
|
10,
|
|
1,
|
|
weight_attr=linear2_w_param_attrs,
|
|
bias_attr=linear2_b_param_attrs,
|
|
)
|
|
data = paddle.to_tensor(data)
|
|
x = linear(data)
|
|
x1 = linear1(x)
|
|
x2 = linear2(x)
|
|
loss = x1 + x2
|
|
# print(loss, loss.shape)
|
|
loss.backward()
|
|
return x.gradient()
|
|
|
|
def no_detach_single(self):
|
|
data = self.generate_Data()
|
|
with base.dygraph.guard():
|
|
linear_w_param_attrs = paddle.ParamAttr(
|
|
initializer=paddle.nn.initializer.Constant(5.0)
|
|
)
|
|
linear_b_param_attrs = paddle.ParamAttr(
|
|
initializer=paddle.nn.initializer.Constant(6.0)
|
|
)
|
|
linear = Linear(
|
|
4,
|
|
10,
|
|
weight_attr=linear_w_param_attrs,
|
|
bias_attr=linear_b_param_attrs,
|
|
)
|
|
linear1_w_param_attrs = paddle.ParamAttr(
|
|
initializer=paddle.nn.initializer.Constant(7.0)
|
|
)
|
|
linear1_b_param_attrs = paddle.ParamAttr(
|
|
initializer=paddle.nn.initializer.Constant(8.0)
|
|
)
|
|
linear1 = Linear(
|
|
10,
|
|
1,
|
|
weight_attr=linear1_w_param_attrs,
|
|
bias_attr=linear1_b_param_attrs,
|
|
)
|
|
data = paddle.to_tensor(data)
|
|
x = linear(data)
|
|
x.retain_grads()
|
|
x1 = linear1(x)
|
|
loss = x1
|
|
# print(loss, loss.shape)
|
|
loss.backward()
|
|
return x.gradient()
|
|
|
|
def detach_multi(self):
|
|
data = self.generate_Data()
|
|
with base.dygraph.guard():
|
|
linear_w_param_attrs = paddle.ParamAttr(
|
|
initializer=paddle.nn.initializer.Constant(5.0)
|
|
)
|
|
linear_b_param_attrs = base.ParamAttr(
|
|
initializer=paddle.nn.initializer.Constant(6.0)
|
|
)
|
|
linear = Linear(
|
|
4,
|
|
10,
|
|
weight_attr=linear_w_param_attrs,
|
|
bias_attr=linear_b_param_attrs,
|
|
)
|
|
linear1_w_param_attrs = paddle.ParamAttr(
|
|
initializer=paddle.nn.initializer.Constant(7.0)
|
|
)
|
|
linear1_b_param_attrs = base.ParamAttr(
|
|
initializer=paddle.nn.initializer.Constant(8.0)
|
|
)
|
|
linear1 = Linear(
|
|
10,
|
|
1,
|
|
weight_attr=linear1_w_param_attrs,
|
|
bias_attr=linear1_b_param_attrs,
|
|
)
|
|
linear2_w_param_attrs = paddle.ParamAttr(
|
|
initializer=paddle.nn.initializer.Constant(9.0)
|
|
)
|
|
linear2_b_param_attrs = paddle.ParamAttr(
|
|
initializer=paddle.nn.initializer.Constant(10.0)
|
|
)
|
|
linear2 = Linear(
|
|
10,
|
|
1,
|
|
weight_attr=linear2_w_param_attrs,
|
|
bias_attr=linear2_b_param_attrs,
|
|
)
|
|
data = paddle.to_tensor(data)
|
|
x = linear(data)
|
|
x.retain_grads()
|
|
x_detach = x.detach()
|
|
x1 = linear1(x)
|
|
x2 = linear2(x_detach)
|
|
loss = x1 + x2
|
|
# print(loss, loss.shape)
|
|
loss.backward()
|
|
return x.gradient()
|
|
|
|
def test_NoDetachMulti_DetachMulti(self):
|
|
array_no_detach_multi = self.no_detach_multi()
|
|
array_detach_multi = self.detach_multi()
|
|
|
|
assert not np.array_equal(array_no_detach_multi, array_detach_multi)
|
|
|
|
def test_NoDetachSingle_DetachMulti(self):
|
|
array_no_detach_single = self.no_detach_single()
|
|
array_detach_multi = self.detach_multi()
|
|
np.testing.assert_array_equal(
|
|
array_no_detach_single, array_detach_multi
|
|
)
|
|
|
|
def test_detach_keeps_tensor_name(self):
|
|
with base.dygraph.guard():
|
|
var = paddle.to_tensor(self.generate_Data())
|
|
detach_var = var.detach()
|
|
|
|
self.assertEqual(detach_var.name, var.name)
|
|
|
|
|
|
class TestInplace(unittest.TestCase):
|
|
def test_forward_version(self):
|
|
with paddle.base.dygraph.guard():
|
|
var = paddle.to_tensor(np.ones((4, 2, 3)).astype(np.float32))
|
|
self.assertEqual(var.inplace_version, 0)
|
|
detach_var_1 = var.detach()
|
|
self.assertEqual(detach_var_1.inplace_version, 0)
|
|
|
|
var[0] = 1.1
|
|
self.assertEqual(var.inplace_version, 1)
|
|
|
|
detach_var_2 = var.detach()
|
|
self.assertEqual(detach_var_2.inplace_version, 1)
|
|
|
|
var[0] = 3
|
|
self.assertEqual(detach_var_1.inplace_version, 2)
|
|
self.assertEqual(detach_var_2.inplace_version, 2)
|
|
|
|
def test_backward_error(self):
|
|
# It raises an error because the inplace operator will result
|
|
# in incorrect gradient computation.
|
|
with paddle.base.dygraph.guard():
|
|
var_a = paddle.ones(shape=[4, 2, 3], dtype="float32")
|
|
var_a.stop_gradient = False
|
|
|
|
var_b = var_a**2
|
|
|
|
# Here, the gradient computation will use the value of var_b
|
|
var_c = var_b**2
|
|
detach_var_b = var_b.detach()
|
|
detach_var_b[1:2] = 3.3 # var_b is modified inplace
|
|
|
|
var_d = var_b**2
|
|
|
|
loss = paddle.nn.functional.relu(var_c + var_d)
|
|
with self.assertRaisesRegex(
|
|
RuntimeError,
|
|
f"received tensor_version:{1} != wrapper_version_snapshot:{0}",
|
|
):
|
|
loss.backward()
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|