!4689 add check for user define bprop in Pynative mode
Merge pull request !4689 from zhangbuxue/add_check_for_user_define_bprop_in_Pynative_modepull/4689/MERGE
mindspore-ci-bot
5 years ago
committed by
Gitee
commit
24700afa2d
@ -0,0 +1,211 @@
|
||||
# Copyright 2020 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# 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.
|
||||
# ============================================================================
|
||||
""" test implicit conversion """
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
from mindspore import Tensor, nn, context, Parameter
|
||||
from mindspore import dtype as mstype
|
||||
from mindspore.ops import composite as C
|
||||
|
||||
|
||||
def test_user_define_bprop_check_ok():
|
||||
class Net(nn.Cell):
|
||||
def __init__(self):
|
||||
super(Net, self).__init__()
|
||||
self.grad = Tensor(np.array([[1.1, 2.2, 3.3], [2.0, 3.0, 4.0]], dtype=np.float32))
|
||||
|
||||
def construct(self, x):
|
||||
ret = x * 2
|
||||
return ret
|
||||
|
||||
def bprop(self, x, out, dout):
|
||||
return (self.grad * 3,)
|
||||
|
||||
class GradNet(nn.Cell):
|
||||
def __init__(self, net):
|
||||
super(GradNet, self).__init__()
|
||||
self.net = net
|
||||
|
||||
def construct(self, x, sens):
|
||||
return C.grad_all_with_sens(self.net)(x, sens)
|
||||
|
||||
x = Tensor(np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float32))
|
||||
sens = Tensor(np.array([[1.0, 2.0, 0.0], [0.0, 3.0, 4.0]], dtype=np.float32))
|
||||
context.set_context(mode=context.PYNATIVE_MODE, check_bprop=True)
|
||||
net = Net()
|
||||
grad_net = GradNet(net)
|
||||
ret = grad_net(x, sens)
|
||||
assert ret[0].shape == (2, 3)
|
||||
assert ret[0].dtype == mstype.float32
|
||||
assert (ret[0].asnumpy() == np.array([[1.1, 2.2, 3.3], [2.0, 3.0, 4.0]], np.float32) * 3).all()
|
||||
|
||||
|
||||
def test_user_define_bprop_no_check_dtype():
|
||||
class Net(nn.Cell):
|
||||
def __init__(self):
|
||||
super(Net, self).__init__()
|
||||
self.grad = Tensor(np.array([[1.1, 2.2, 3.3], [2.0, 3.0, 4.0]], dtype=np.float16))
|
||||
|
||||
def construct(self, x):
|
||||
ret = x * 2
|
||||
return ret
|
||||
|
||||
def bprop(self, x, out, dout):
|
||||
return (self.grad * 3,)
|
||||
|
||||
class GradNet(nn.Cell):
|
||||
def __init__(self, net):
|
||||
super(GradNet, self).__init__()
|
||||
self.net = net
|
||||
|
||||
def construct(self, x, sens):
|
||||
return C.grad_all_with_sens(self.net)(x, sens)
|
||||
|
||||
x = Tensor(np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float32))
|
||||
sens = Tensor(np.array([[1.0, 2.0, 0.0], [0.0, 3.0, 4.0]], dtype=np.float32))
|
||||
context.set_context(mode=context.PYNATIVE_MODE, check_bprop=False)
|
||||
net = Net()
|
||||
grad_net = GradNet(net)
|
||||
ret = grad_net(x, sens)
|
||||
assert ret[0].shape == (2, 3)
|
||||
assert ret[0].dtype == mstype.float16
|
||||
assert (ret[0].asnumpy() == np.array([[1.1, 2.2, 3.3], [2.0, 3.0, 4.0]], np.float16) * 3).all()
|
||||
|
||||
|
||||
def test_user_define_bprop_check_shape():
|
||||
class Net(nn.Cell):
|
||||
def __init__(self):
|
||||
super(Net, self).__init__()
|
||||
self.grad = Tensor(np.array([[1.1, 2.2], [2.0, 3.0]], dtype=np.float32))
|
||||
|
||||
def construct(self, x):
|
||||
ret = x * 2
|
||||
return ret
|
||||
|
||||
def bprop(self, x, out, dout):
|
||||
return (self.grad * 3,)
|
||||
|
||||
class GradNet(nn.Cell):
|
||||
def __init__(self, net):
|
||||
super(GradNet, self).__init__()
|
||||
self.net = net
|
||||
|
||||
def construct(self, x, sens):
|
||||
return C.grad_all_with_sens(self.net)(x, sens)
|
||||
|
||||
x = Tensor(np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float32))
|
||||
sens = Tensor(np.array([[1.0, 2.0, 0.0], [0.0, 3.0, 4.0]], dtype=np.float32))
|
||||
context.set_context(mode=context.PYNATIVE_MODE, check_bprop=True)
|
||||
net = Net()
|
||||
grad_net = GradNet(net)
|
||||
with pytest.raises(ValueError) as ex:
|
||||
ret = grad_net(x, sens)
|
||||
assert "the gradient of the 0th arg should have the same shape and dtype as the 0th arg" in str(ex.value)
|
||||
|
||||
|
||||
def test_user_define_bprop_check_dtype():
|
||||
class Net(nn.Cell):
|
||||
def __init__(self):
|
||||
super(Net, self).__init__()
|
||||
self.grad = Tensor(np.array([[1.1, 2.2, 3.3], [2.0, 3.0, 4.0]], dtype=np.float16))
|
||||
|
||||
def construct(self, x):
|
||||
ret = x * 2
|
||||
return ret
|
||||
|
||||
def bprop(self, x, out, dout):
|
||||
return (self.grad * 3,)
|
||||
|
||||
class GradNet(nn.Cell):
|
||||
def __init__(self, net):
|
||||
super(GradNet, self).__init__()
|
||||
self.net = net
|
||||
|
||||
def construct(self, x, sens):
|
||||
return C.grad_all_with_sens(self.net)(x, sens)
|
||||
|
||||
x = Tensor(np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float32))
|
||||
sens = Tensor(np.array([[1.0, 2.0, 0.0], [0.0, 3.0, 4.0]], dtype=np.float32))
|
||||
context.set_context(mode=context.PYNATIVE_MODE, check_bprop=True)
|
||||
net = Net()
|
||||
grad_net = GradNet(net)
|
||||
with pytest.raises(ValueError) as ex:
|
||||
ret = grad_net(x, sens)
|
||||
assert "the gradient of the 0th arg should have the same shape and dtype as the 0th arg" in str(ex.value)
|
||||
|
||||
|
||||
def test_user_define_bprop_check_parameter():
|
||||
class Net(nn.Cell):
|
||||
def __init__(self):
|
||||
super(Net, self).__init__()
|
||||
self.par = Parameter(Tensor(np.array([[1.1, 2.2, 3.3], [2.0, 3.0, 4.0]], dtype=np.float32)), name="par")
|
||||
self.grad = Tensor(np.array([[1.1, 2.2, 3.3], [2.0, 3.0, 4.0]], dtype=np.float16))
|
||||
|
||||
def construct(self, x):
|
||||
ret = x * 2 + self.par
|
||||
return ret
|
||||
|
||||
def bprop(self, x, out, dout):
|
||||
return dout + x
|
||||
|
||||
class GradNet(nn.Cell):
|
||||
def __init__(self, net):
|
||||
super(GradNet, self).__init__()
|
||||
self.net = net
|
||||
|
||||
def construct(self, x, sens):
|
||||
return C.grad_all_with_sens(self.net)(x, sens)
|
||||
|
||||
x = Tensor(np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float32))
|
||||
sens = Tensor(np.array([[1.0, 2.0, 0.0], [0.0, 3.0, 4.0]], dtype=np.float32))
|
||||
context.set_context(mode=context.PYNATIVE_MODE, check_bprop=True)
|
||||
net = Net()
|
||||
grad_net = GradNet(net)
|
||||
with pytest.raises(RuntimeError) as ex:
|
||||
ret = grad_net(x, sens)
|
||||
assert "in scope Default does not support Parameter data type." in str(ex.value)
|
||||
|
||||
|
||||
def test_user_define_bprop_check_number():
|
||||
class Net(nn.Cell):
|
||||
def __init__(self):
|
||||
super(Net, self).__init__()
|
||||
self.grad = Tensor(np.array([[1.1, 2.2, 3.3], [2.0, 3.0, 4.0]], dtype=np.float32))
|
||||
|
||||
def construct(self, x, y):
|
||||
ret = x * 2 + y
|
||||
return ret
|
||||
|
||||
def bprop(self, x, y, out, dout):
|
||||
return (dout,)
|
||||
|
||||
class GradNet(nn.Cell):
|
||||
def __init__(self, net):
|
||||
super(GradNet, self).__init__()
|
||||
self.net = net
|
||||
|
||||
def construct(self, x, y, sens):
|
||||
return C.grad_all_with_sens(self.net)(x, y, sens)
|
||||
|
||||
x = Tensor(np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float32))
|
||||
y = Tensor(np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float32))
|
||||
sens = Tensor(np.array([[1.0, 2.0, 0.0], [0.0, 3.0, 4.0]], dtype=np.float32))
|
||||
context.set_context(mode=context.PYNATIVE_MODE, check_bprop=True)
|
||||
net = Net()
|
||||
grad_net = GradNet(net)
|
||||
with pytest.raises(ValueError) as ex:
|
||||
ret = grad_net(x, y, sens)
|
||||
assert "For user define net bprop, the gradients number: 1 is not equal to the args number: 2." in str(ex.value)
|
||||
Loading…
Reference in new issue