forked from mindspore-Ecosystem/mindspore
760 lines
27 KiB
Python
760 lines
27 KiB
Python
# Copyright 2022 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 getting gradient of mutable input"""
|
|
import numpy as np
|
|
import pytest
|
|
import mindspore.nn as nn
|
|
from mindspore import Tensor, context, Parameter, jit
|
|
from mindspore.ops.composite import GradOperation
|
|
from mindspore.ops import operations as P
|
|
from mindspore.common import dtype as mstype
|
|
from mindspore.common import mutable
|
|
|
|
|
|
def compare(a, b):
|
|
if isinstance(a, (list, tuple)):
|
|
if not a and b:
|
|
return False
|
|
for aa, bb in zip(a, b):
|
|
if not compare(aa, bb):
|
|
return False
|
|
return True
|
|
|
|
return np.allclose(a.asnumpy(), b)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_grad_mutable_tuple_tensor():
|
|
"""
|
|
Feature: Set Constants mutable.
|
|
Description: Get gradient with respect to tuple tensor input.
|
|
Expectation: Get the correct gradients.
|
|
"""
|
|
|
|
class Net(nn.Cell):
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.matmul = P.MatMul()
|
|
self.z = Parameter(Tensor(np.array([1.0], np.float32)), name='z')
|
|
|
|
def construct(self, t):
|
|
x = t[0]
|
|
y = t[1]
|
|
x = x * self.z
|
|
out = self.matmul(x, y)
|
|
return out
|
|
|
|
class GradNetWrtX(nn.Cell):
|
|
def __init__(self, net):
|
|
super(GradNetWrtX, self).__init__()
|
|
self.net = net
|
|
self.grad_op = GradOperation()
|
|
|
|
def construct(self, z):
|
|
gradient_function = self.grad_op(self.net)
|
|
return gradient_function(z)
|
|
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
t = mutable((Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32),
|
|
Tensor([[0.01, 0.3, 1.1], [0.1, 0.2, 1.3], [2.1, 1.2, 3.3]], dtype=mstype.float32)))
|
|
output = GradNetWrtX(Net())(t)
|
|
assert isinstance(output, tuple)
|
|
expect = [np.array([[1.4100001, 1.5999999, 6.6],
|
|
[1.4100001, 1.5999999, 6.6]]).astype(np.float32),
|
|
np.array([[1.7, 1.7, 1.7],
|
|
[1.9, 1.9, 1.9],
|
|
[1.5, 1.5, 1.5]]).astype(np.float32)]
|
|
assert compare(output, expect)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_grad_mutable_list_tensor():
|
|
"""
|
|
Feature: Set Constants mutable.
|
|
Description: Get gradient with respect to list tensor input.
|
|
Expectation: Get the correct gradients.
|
|
"""
|
|
|
|
class Net(nn.Cell):
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.matmul = P.MatMul()
|
|
self.z = Parameter(Tensor(np.array([1.0], np.float32)), name='z')
|
|
|
|
def construct(self, t):
|
|
x = t[0]
|
|
y = t[1]
|
|
x = x * self.z
|
|
out = self.matmul(x, y)
|
|
return out
|
|
|
|
class GradNetWrtX(nn.Cell):
|
|
def __init__(self, net):
|
|
super(GradNetWrtX, self).__init__()
|
|
self.net = net
|
|
self.grad_op = GradOperation()
|
|
|
|
def construct(self, z):
|
|
gradient_function = self.grad_op(self.net)
|
|
return gradient_function(z)
|
|
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
t = mutable([Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32),
|
|
Tensor([[0.01, 0.3, 1.1], [0.1, 0.2, 1.3], [2.1, 1.2, 3.3]], dtype=mstype.float32)])
|
|
output = GradNetWrtX(Net())(t)
|
|
assert isinstance(output, tuple)
|
|
expect = [np.array([[1.4100001, 1.5999999, 6.6],
|
|
[1.4100001, 1.5999999, 6.6]]).astype(np.float32),
|
|
np.array([[1.7, 1.7, 1.7],
|
|
[1.9, 1.9, 1.9],
|
|
[1.5, 1.5, 1.5]]).astype(np.float32)]
|
|
assert compare(output, expect)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_grad_mutable_dict_tensor():
|
|
"""
|
|
Feature: Set Constants mutable.
|
|
Description: Get gradient with respect to dict tensor input.
|
|
Expectation: Get the correct gradients.
|
|
"""
|
|
|
|
class Net(nn.Cell):
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.matmul = P.MatMul()
|
|
self.z = Parameter(Tensor(np.array([1.0], np.float32)), name='z')
|
|
|
|
def construct(self, t):
|
|
x = t['a']
|
|
y = t['b']
|
|
x = x * self.z
|
|
out = self.matmul(x, y)
|
|
return out
|
|
|
|
class GradNetWrtX(nn.Cell):
|
|
def __init__(self, net):
|
|
super(GradNetWrtX, self).__init__()
|
|
self.net = net
|
|
self.grad_op = GradOperation()
|
|
|
|
def construct(self, z):
|
|
gradient_function = self.grad_op(self.net)
|
|
return gradient_function(z)
|
|
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
t = mutable({'a': Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32),
|
|
'b': Tensor([[0.01, 0.3, 1.1], [0.1, 0.2, 1.3], [2.1, 1.2, 3.3]], dtype=mstype.float32)})
|
|
output = GradNetWrtX(Net())(t)
|
|
assert isinstance(output, tuple)
|
|
expect = [np.array([[1.4100001, 1.5999999, 6.6],
|
|
[1.4100001, 1.5999999, 6.6]]).astype(np.float32),
|
|
np.array([[1.7, 1.7, 1.7],
|
|
[1.9, 1.9, 1.9],
|
|
[1.5, 1.5, 1.5]]).astype(np.float32)]
|
|
assert compare(output, expect)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_grad_mutable_tuple_tuple_tensor():
|
|
"""
|
|
Feature: Set Constants mutable.
|
|
Description: Get gradient with respect to nested tuple tensor input.
|
|
Expectation: Get the correct gradients.
|
|
"""
|
|
|
|
class Net(nn.Cell):
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.matmul = P.MatMul()
|
|
self.z = Parameter(Tensor(np.array([1.0], np.float32)), name='z')
|
|
|
|
def construct(self, t):
|
|
x = t[0][0]
|
|
y = t[1]
|
|
x = x * self.z
|
|
out = self.matmul(x, y)
|
|
return out
|
|
|
|
class GradNetWrtX(nn.Cell):
|
|
def __init__(self, net):
|
|
super(GradNetWrtX, self).__init__()
|
|
self.net = net
|
|
self.grad_op = GradOperation()
|
|
|
|
def construct(self, z):
|
|
gradient_function = self.grad_op(self.net)
|
|
return gradient_function(z)
|
|
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
t = mutable(((Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32),
|
|
Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32)),
|
|
Tensor([[0.01, 0.3, 1.1], [0.1, 0.2, 1.3], [2.1, 1.2, 3.3]], dtype=mstype.float32)))
|
|
output = GradNetWrtX(Net())(t)
|
|
assert isinstance(output, tuple)
|
|
expect = [[np.array([[1.4100001, 1.5999999, 6.6],
|
|
[1.4100001, 1.5999999, 6.6]]).astype(np.float32), np.array([[0, 0, 0],
|
|
[0, 0, 0]]).astype(np.float32)],
|
|
np.array([[1.7, 1.7, 1.7],
|
|
[1.9, 1.9, 1.9],
|
|
[1.5, 1.5, 1.5]]).astype(np.float32)]
|
|
assert compare(output, expect)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_grad_mutable_tuple_list_tensor():
|
|
"""
|
|
Feature: Set Constants mutable.
|
|
Description: Get gradient with respect to nested tuple and list tensor input.
|
|
Expectation: Get the correct gradients.
|
|
"""
|
|
|
|
class Net(nn.Cell):
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.matmul = P.MatMul()
|
|
self.z = Parameter(Tensor(np.array([1.0], np.float32)), name='z')
|
|
|
|
def construct(self, t):
|
|
x = t[0][0]
|
|
y = t[1]
|
|
x = x * self.z
|
|
out = self.matmul(x, y)
|
|
return out
|
|
|
|
class GradNetWrtX(nn.Cell):
|
|
def __init__(self, net):
|
|
super(GradNetWrtX, self).__init__()
|
|
self.net = net
|
|
self.grad_op = GradOperation()
|
|
|
|
def construct(self, z):
|
|
gradient_function = self.grad_op(self.net)
|
|
return gradient_function(z)
|
|
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
t = mutable(([Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32),
|
|
Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32)],
|
|
Tensor([[0.01, 0.3, 1.1], [0.1, 0.2, 1.3], [2.1, 1.2, 3.3]], dtype=mstype.float32)))
|
|
output = GradNetWrtX(Net())(t)
|
|
assert isinstance(output, tuple)
|
|
expect = [[np.array([[1.4100001, 1.5999999, 6.6],
|
|
[1.4100001, 1.5999999, 6.6]]).astype(np.float32), np.array([[0, 0, 0],
|
|
[0, 0, 0]]).astype(np.float32)],
|
|
np.array([[1.7, 1.7, 1.7],
|
|
[1.9, 1.9, 1.9],
|
|
[1.5, 1.5, 1.5]]).astype(np.float32)]
|
|
assert compare(output, expect)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_grad_mutable_list_tuple_tensor():
|
|
"""
|
|
Feature: Set Constants mutable.
|
|
Description: Get gradient with respect to nested list and tuple tensor input.
|
|
Expectation: Get the correct gradients.
|
|
"""
|
|
|
|
class Net(nn.Cell):
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.matmul = P.MatMul()
|
|
self.z = Parameter(Tensor(np.array([1.0], np.float32)), name='z')
|
|
|
|
def construct(self, t):
|
|
x = t[0][0]
|
|
y = t[1]
|
|
x = x * self.z
|
|
out = self.matmul(x, y)
|
|
return out
|
|
|
|
class GradNetWrtX(nn.Cell):
|
|
def __init__(self, net):
|
|
super(GradNetWrtX, self).__init__()
|
|
self.net = net
|
|
self.grad_op = GradOperation()
|
|
|
|
def construct(self, z):
|
|
gradient_function = self.grad_op(self.net)
|
|
return gradient_function(z)
|
|
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
t = mutable([(Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32),
|
|
Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32)),
|
|
Tensor([[0.01, 0.3, 1.1], [0.1, 0.2, 1.3], [2.1, 1.2, 3.3]], dtype=mstype.float32)])
|
|
output = GradNetWrtX(Net())(t)
|
|
assert isinstance(output, tuple)
|
|
expect = [[np.array([[1.4100001, 1.5999999, 6.6],
|
|
[1.4100001, 1.5999999, 6.6]]).astype(np.float32), np.array([[0, 0, 0],
|
|
[0, 0, 0]]).astype(np.float32)],
|
|
np.array([[1.7, 1.7, 1.7],
|
|
[1.9, 1.9, 1.9],
|
|
[1.5, 1.5, 1.5]]).astype(np.float32)]
|
|
assert compare(output, expect)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_grad_mutable_tuple_dict_tensor():
|
|
"""
|
|
Feature: Set Constants mutable.
|
|
Description: Get gradient with respect to nested tuple and dict tensor input.
|
|
Expectation: Get the correct gradients.
|
|
"""
|
|
|
|
class Net(nn.Cell):
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.matmul = P.MatMul()
|
|
self.z = Parameter(Tensor(np.array([1.0], np.float32)), name='z')
|
|
|
|
def construct(self, t):
|
|
x = t[0]['a']
|
|
y = t[1]
|
|
x = x * self.z
|
|
out = self.matmul(x, y)
|
|
return out
|
|
|
|
class GradNetWrtX(nn.Cell):
|
|
def __init__(self, net):
|
|
super(GradNetWrtX, self).__init__()
|
|
self.net = net
|
|
self.grad_op = GradOperation()
|
|
|
|
def construct(self, z):
|
|
gradient_function = self.grad_op(self.net)
|
|
return gradient_function(z)
|
|
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
t = mutable(({'a': Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32),
|
|
'b': Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32)},
|
|
Tensor([[0.01, 0.3, 1.1], [0.1, 0.2, 1.3], [2.1, 1.2, 3.3]], dtype=mstype.float32)))
|
|
output = GradNetWrtX(Net())(t)
|
|
assert isinstance(output, tuple)
|
|
expect = [[np.array([[1.4100001, 1.5999999, 6.6],
|
|
[1.4100001, 1.5999999, 6.6]]).astype(np.float32), np.array([[0, 0, 0],
|
|
[0, 0, 0]]).astype(np.float32)],
|
|
np.array([[1.7, 1.7, 1.7],
|
|
[1.9, 1.9, 1.9],
|
|
[1.5, 1.5, 1.5]]).astype(np.float32)]
|
|
assert compare(output, expect)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_grad_mutable_dict_tuple_tensor():
|
|
"""
|
|
Feature: Set Constants mutable.
|
|
Description: Get gradient with respect to nested dict and tuple tensor input.
|
|
Expectation: Get the correct gradients.
|
|
"""
|
|
|
|
class Net(nn.Cell):
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.matmul = P.MatMul()
|
|
self.z = Parameter(Tensor(np.array([1.0], np.float32)), name='z')
|
|
|
|
def construct(self, t):
|
|
x = t['a'][0]
|
|
y = t['b']
|
|
x = x * self.z
|
|
out = self.matmul(x, y)
|
|
return out
|
|
|
|
class GradNetWrtX(nn.Cell):
|
|
def __init__(self, net):
|
|
super(GradNetWrtX, self).__init__()
|
|
self.net = net
|
|
self.grad_op = GradOperation()
|
|
|
|
def construct(self, z):
|
|
gradient_function = self.grad_op(self.net)
|
|
return gradient_function(z)
|
|
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
t = mutable({'a': (Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32),
|
|
Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32)),
|
|
'b': Tensor([[0.01, 0.3, 1.1], [0.1, 0.2, 1.3], [2.1, 1.2, 3.3]], dtype=mstype.float32)})
|
|
output = GradNetWrtX(Net())(t)
|
|
assert isinstance(output, tuple)
|
|
expect = [[np.array([[1.4100001, 1.5999999, 6.6],
|
|
[1.4100001, 1.5999999, 6.6]]).astype(np.float32), np.array([[0, 0, 0],
|
|
[0, 0, 0]]).astype(np.float32)],
|
|
np.array([[1.7, 1.7, 1.7],
|
|
[1.9, 1.9, 1.9],
|
|
[1.5, 1.5, 1.5]]).astype(np.float32)]
|
|
assert compare(output, expect)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_grad_mutable_list_dict_tensor():
|
|
"""
|
|
Feature: Set Constants mutable.
|
|
Description: Get gradient with respect to nested list and dict tensor input.
|
|
Expectation: Get the correct gradients.
|
|
"""
|
|
|
|
class Net(nn.Cell):
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.matmul = P.MatMul()
|
|
self.z = Parameter(Tensor(np.array([1.0], np.float32)), name='z')
|
|
|
|
def construct(self, t):
|
|
x = t[0]['a']
|
|
y = t[1]
|
|
x = x * self.z
|
|
out = self.matmul(x, y)
|
|
return out
|
|
|
|
class GradNetWrtX(nn.Cell):
|
|
def __init__(self, net):
|
|
super(GradNetWrtX, self).__init__()
|
|
self.net = net
|
|
self.grad_op = GradOperation()
|
|
|
|
def construct(self, z):
|
|
gradient_function = self.grad_op(self.net)
|
|
return gradient_function(z)
|
|
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
t = mutable([{'a': Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32),
|
|
'b': Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32)},
|
|
Tensor([[0.01, 0.3, 1.1], [0.1, 0.2, 1.3], [2.1, 1.2, 3.3]], dtype=mstype.float32)])
|
|
output = GradNetWrtX(Net())(t)
|
|
assert isinstance(output, tuple)
|
|
expect = [[np.array([[1.4100001, 1.5999999, 6.6],
|
|
[1.4100001, 1.5999999, 6.6]]).astype(np.float32), np.array([[0, 0, 0],
|
|
[0, 0, 0]]).astype(np.float32)],
|
|
np.array([[1.7, 1.7, 1.7],
|
|
[1.9, 1.9, 1.9],
|
|
[1.5, 1.5, 1.5]]).astype(np.float32)]
|
|
assert compare(output, expect)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_grad_mutable_dict_list_tensor():
|
|
"""
|
|
Feature: Set Constants mutable.
|
|
Description: Get gradient with respect to nested dict and list tensor input.
|
|
Expectation: Get the correct gradients.
|
|
"""
|
|
|
|
class Net(nn.Cell):
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.matmul = P.MatMul()
|
|
self.z = Parameter(Tensor(np.array([1.0], np.float32)), name='z')
|
|
|
|
def construct(self, t):
|
|
x = t['a'][0]
|
|
y = t['b']
|
|
x = x * self.z
|
|
out = self.matmul(x, y)
|
|
return out
|
|
|
|
class GradNetWrtX(nn.Cell):
|
|
def __init__(self, net):
|
|
super(GradNetWrtX, self).__init__()
|
|
self.net = net
|
|
self.grad_op = GradOperation()
|
|
|
|
def construct(self, z):
|
|
gradient_function = self.grad_op(self.net)
|
|
return gradient_function(z)
|
|
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
t = mutable({'a': [Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32),
|
|
Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32)],
|
|
'b': Tensor([[0.01, 0.3, 1.1], [0.1, 0.2, 1.3], [2.1, 1.2, 3.3]], dtype=mstype.float32)})
|
|
output = GradNetWrtX(Net())(t)
|
|
assert isinstance(output, tuple)
|
|
expect = [[np.array([[1.4100001, 1.5999999, 6.6],
|
|
[1.4100001, 1.5999999, 6.6]]).astype(np.float32), np.array([[0, 0, 0],
|
|
[0, 0, 0]]).astype(np.float32)],
|
|
np.array([[1.7, 1.7, 1.7],
|
|
[1.9, 1.9, 1.9],
|
|
[1.5, 1.5, 1.5]]).astype(np.float32)]
|
|
assert compare(output, expect)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_grad_mutable_tuple_tensor_jit_function():
|
|
"""
|
|
Feature: Set Constants mutable.
|
|
Description: Get gradient with respect to tuple tensor input.
|
|
Expectation: Get the correct gradients.
|
|
"""
|
|
|
|
@jit
|
|
def net(t):
|
|
x = t[0]
|
|
y = t[1]
|
|
out = P.MatMul()(x, y)
|
|
return out
|
|
|
|
z = mutable((Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32),
|
|
Tensor([[0.01, 0.3, 1.1], [0.1, 0.2, 1.3], [2.1, 1.2, 3.3]], dtype=mstype.float32)))
|
|
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
output = GradOperation()(net)(z)
|
|
assert isinstance(output, tuple)
|
|
expect = [np.array([[1.4100001, 1.5999999, 6.6],
|
|
[1.4100001, 1.5999999, 6.6]]).astype(np.float32),
|
|
np.array([[1.7, 1.7, 1.7],
|
|
[1.9, 1.9, 1.9],
|
|
[1.5, 1.5, 1.5]]).astype(np.float32)]
|
|
assert compare(output, expect)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_grad_mutable_list_tensor_jit_function():
|
|
"""
|
|
Feature: Set Constants mutable.
|
|
Description: Get gradient with respect to tuple tensor input.
|
|
Expectation: Get the correct gradients.
|
|
"""
|
|
|
|
@jit
|
|
def net(t):
|
|
x = t[0]
|
|
y = t[1]
|
|
out = P.MatMul()(x, y)
|
|
return out
|
|
|
|
z = mutable([Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32),
|
|
Tensor([[0.01, 0.3, 1.1], [0.1, 0.2, 1.3], [2.1, 1.2, 3.3]], dtype=mstype.float32)])
|
|
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
output = GradOperation()(net)(z)
|
|
assert isinstance(output, tuple)
|
|
expect = [np.array([[1.4100001, 1.5999999, 6.6],
|
|
[1.4100001, 1.5999999, 6.6]]).astype(np.float32),
|
|
np.array([[1.7, 1.7, 1.7],
|
|
[1.9, 1.9, 1.9],
|
|
[1.5, 1.5, 1.5]]).astype(np.float32)]
|
|
assert compare(output, expect)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_grad_mutable_unused_tuple_tensor():
|
|
"""
|
|
Feature: Set Constants mutable.
|
|
Description: Get gradient with respect to tuple tensor input which is unused by backend nodes.
|
|
Expectation: Get the correct gradients.
|
|
"""
|
|
|
|
class Net(nn.Cell):
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.sub = P.Sub()
|
|
|
|
def construct(self, t):
|
|
x1 = t[0]
|
|
x2 = t[1]
|
|
output = x1 + self.sub(x1, x2)
|
|
return x1, x2, output
|
|
|
|
class GradNetWrtX(nn.Cell):
|
|
def __init__(self, net):
|
|
super(GradNetWrtX, self).__init__()
|
|
self.net = net
|
|
self.grad_op = GradOperation()
|
|
|
|
def construct(self, z):
|
|
gradient_function = self.grad_op(self.net)
|
|
return gradient_function(z)
|
|
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
t = mutable((Tensor([[4.0, 6.0, 6.0], [4.0, 6.0, 6.0]], dtype=mstype.float32),
|
|
Tensor([[2.0, 2.0, 2.0], [2.0, 2.0, 2.0]], dtype=mstype.float32),
|
|
Tensor([[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], dtype=mstype.float32)))
|
|
output = GradNetWrtX(Net())(t)
|
|
assert isinstance(output, tuple)
|
|
expect = [np.array([[3., 3., 3.],
|
|
[3., 3., 3.]]).astype(np.float32),
|
|
np.array([[0., 0., 0.],
|
|
[0., 0., 0.]]).astype(np.float32),
|
|
np.array([[0., 0., 0.],
|
|
[0., 0., 0.]]).astype(np.float32)]
|
|
assert compare(output, expect)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_grad_mutable_unused_list_tensor():
|
|
"""
|
|
Feature: Set Constants mutable.
|
|
Description: Get gradient with respect to dict tensor input which is unused by backend nodes.
|
|
Expectation: Get the correct gradients.
|
|
"""
|
|
|
|
class Net(nn.Cell):
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.sub = P.Sub()
|
|
|
|
def construct(self, t):
|
|
x1 = t[0]
|
|
t[1] = Tensor([[3.0, 6.0, 6.0], [3.0, 6.0, 6.0]], dtype=mstype.float32)
|
|
x2 = t[1]
|
|
output = x1 + self.sub(x1, x2)
|
|
return x1, x2, output
|
|
|
|
class GradNetWrtX(nn.Cell):
|
|
def __init__(self, net):
|
|
super(GradNetWrtX, self).__init__()
|
|
self.net = net
|
|
self.grad_op = GradOperation()
|
|
|
|
def construct(self, z):
|
|
gradient_function = self.grad_op(self.net)
|
|
return gradient_function(z)
|
|
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
t = mutable([Tensor([[4.0, 6.0, 6.0], [4.0, 6.0, 6.0]], dtype=mstype.float32),
|
|
Tensor([[2.0, 2.0, 2.0], [2.0, 2.0, 2.0]], dtype=mstype.float32),
|
|
Tensor([[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], dtype=mstype.float32)])
|
|
output = GradNetWrtX(Net())(t)
|
|
assert isinstance(output, tuple)
|
|
expect = [np.array([[3., 3., 3.],
|
|
[3., 3., 3.]]).astype(np.float32),
|
|
np.array([[0., 0., 0.],
|
|
[0., 0., 0.]]).astype(np.float32),
|
|
np.array([[0., 0., 0.],
|
|
[0., 0., 0.]]).astype(np.float32)]
|
|
assert compare(output, expect)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_grad_mutable_unused_dict_tensor():
|
|
"""
|
|
Feature: Set Constants mutable.
|
|
Description: Get gradient with respect to dict tensor input which is unused by backend nodes.
|
|
Expectation: Get the correct gradients.
|
|
"""
|
|
|
|
class Net(nn.Cell):
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.sub = P.Sub()
|
|
|
|
def construct(self, t):
|
|
x1 = t['x1']
|
|
t['x2'] = Tensor([[3.0, 6.0, 6.0], [3.0, 6.0, 6.0]], dtype=mstype.float32)
|
|
x2 = t['x2']
|
|
output = x1 + self.sub(x1, x2)
|
|
return x1, x2, output
|
|
|
|
class GradNetWrtX(nn.Cell):
|
|
def __init__(self, net):
|
|
super(GradNetWrtX, self).__init__()
|
|
self.net = net
|
|
self.grad_op = GradOperation()
|
|
|
|
def construct(self, z):
|
|
gradient_function = self.grad_op(self.net)
|
|
return gradient_function(z)
|
|
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
t = mutable({'x1': Tensor([[4.0, 6.0, 6.0], [4.0, 6.0, 6.0]], dtype=mstype.float32),
|
|
'x2': Tensor([[2.0, 2.0, 2.0], [2.0, 2.0, 2.0]], dtype=mstype.float32),
|
|
'x3': Tensor([[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], dtype=mstype.float32)})
|
|
output = GradNetWrtX(Net())(t)
|
|
assert isinstance(output, tuple)
|
|
expect = [np.array([[3., 3., 3.],
|
|
[3., 3., 3.]]).astype(np.float32),
|
|
np.array([[0., 0., 0.],
|
|
[0., 0., 0.]]).astype(np.float32),
|
|
np.array([[0., 0., 0.],
|
|
[0., 0., 0.]]).astype(np.float32)]
|
|
assert compare(output, expect)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_grad_mutable_single_element_dict_tensor():
|
|
"""
|
|
Feature: Set Constants mutable.
|
|
Description: Get gradient with respect to the dict tensor input which has only one element.
|
|
Expectation: Get the correct gradients.
|
|
"""
|
|
|
|
class Net(nn.Cell):
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.matmul = P.MatMul()
|
|
self.z = Parameter(Tensor(np.array([1.0], np.float32)), name='z')
|
|
|
|
def construct(self, x, t):
|
|
y = t['a']
|
|
x = x * self.z
|
|
out = self.matmul(x, y)
|
|
return out
|
|
|
|
class GradNetWrtX(nn.Cell):
|
|
def __init__(self, net):
|
|
super(GradNetWrtX, self).__init__()
|
|
self.net = net
|
|
self.grad_op = GradOperation(get_all=True)
|
|
|
|
def construct(self, x, t):
|
|
gradient_function = self.grad_op(self.net)
|
|
return gradient_function(x, t)
|
|
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
x = Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32)
|
|
y = mutable({'a': Tensor([[0.01, 0.3, 1.1], [0.1, 0.2, 1.3], [2.1, 1.2, 3.3]], dtype=mstype.float32)})
|
|
output = GradNetWrtX(Net())(x, y)
|
|
assert isinstance(output, tuple)
|
|
expect = [np.array([[1.4100001, 1.5999999, 6.6],
|
|
[1.4100001, 1.5999999, 6.6]]).astype(np.float32),
|
|
(np.array([[1.7, 1.7, 1.7],
|
|
[1.9, 1.9, 1.9],
|
|
[1.5, 1.5, 1.5]]).astype(np.float32),)]
|
|
assert compare(output, expect)
|