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!4443 [bug]fix bugs in parameters and add ut cases 

Merge pull request !4443 from vlne-v1/I1RDCY-slice_shape_update_initializer
This commit is contained in:
mindspore-ci-bot 2020-08-14 18:03:20 +08:00 committed by Gitee
parent 180689c095 d18ce95714
commit 659ed37812
2 changed files with 59 additions and 16 deletions
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35
mindspore/common/parameter.py
View File

@ -280,15 +280,23 @@ class Parameter(MetaTensor):
Set `default_input` of current `Parameter`.
Args:
data (Union[Tensor, Initializer]): new data.
slice_shape (bool): If slice the Parameter. Default: False.
data (Union[Tensor, Initializer, int, float]): new data.
slice_shape (bool): If slice the Parameter, will not check if shape is match. Default: False.
Retruns:
Parameter, the parameter after set data.
"""
if not isinstance(data, (MetaTensor, Initializer)):
raise ValueError(f"Parameter data must be `Initializer` or a kind of `MetaTensor` "
f"(like `Tensor` or `MetaTensor`). But with type {type(data)}.")
def raise_type_error(incoming):
raise TypeError(f"Can not change the Parameter dtype. Current dtype is {self.set_dtype}"
f", and incoming is {incoming}. Use .set_dtype(xxx) to change the dtype.")
if not isinstance(data, (MetaTensor, Initializer, int, float)):
raise TypeError(f"Parameter data must be [`Initializer`, `int`, `float`] or a kind of `MetaTensor` "
f"(like `Tensor` or `MetaTensor`). But with type {type(data)}.")
if isinstance(data, (int, float)):
if self.dtype in mstype.int_type and isinstance(data, float):
raise_type_error(mstype.float_)
data = Tensor(data, self.dtype)
# both not init.
is_incoming_tensor = isinstance(data, Tensor)
is_current_tensor = isinstance(self, Tensor)
@ -300,25 +308,25 @@ class Parameter(MetaTensor):
"network, then call this method.")
if tuple(self.shape) != tuple(data.shape):
# If Slice create Parameter shape can be change.
if slice_shape:
self._update_tensor_data(data)
self.sliced = True
else:
if not slice_shape:
raise ValueError(f"Can not change the shape of Parameter which has been initialized."
f" Current shape is {self.shape}, and incoming is {data.shape}.")
if self.dtype != data.dtype:
raise ValueError(f"Can not change the Parameter dtype. Current dtype is {self.set_dtype}"
f", and incoming is {data.dtype}. Use .set_dtype(xxx) to change the dtype.")
raise_type_error(data.dtype)
if isinstance(data, Initializer):
# The parameter has been initializered, directly update by the data
if is_current_tensor:
self._update_tensor_data(data.to_tensor())
else:
# also update the related inited parameter data
if self.inited_param is not None:
self.inited_param.set_parameter_data(data)
self.init_mode = data
elif is_incoming_tensor or is_current_tensor:
self._update_tensor_data(data)
else:
raise ValueError(f"Not support to update the Parameter by {data}")
self.sliced = slice_shape
return self
def init_data(self, layout=None, set_sliced=False):
@ -340,8 +348,6 @@ class Parameter(MetaTensor):
"""
if self.init_mode is None:
return self
if self.inited_param is not None:
return self.inited_param
if layout is not None:
if not isinstance(layout, list):
raise TypeError("The layout should be list! layout is {}.".format(layout))
@ -362,8 +368,7 @@ class Parameter(MetaTensor):
if id(obj) != id(self):
self._inited_param = obj
obj.init_mode = None
if set_sliced:
obj.sliced = True
obj.sliced = set_sliced
return obj

40
tests/ut/python/nn/test_parameter.py
View File

@ -135,6 +135,40 @@ def test_check_str_by_regular():
with pytest.raises(ValueError):
_check_str_by_regular(str6)
def test_parameter_compute():
para_1 = Parameter(initializer('ones', [1, 2, 3], mstype.int32), 'test1')
para_2 = Parameter(initializer('ones', [1, 2, 3], mstype.int32), 'test2')
t3 = Tensor(np.ones((1, 2, 3)))
out = para_1 + para_2
assert np.array_equal(out.asnumpy(), np.ones((1, 2, 3)) * 2)
out = para_1 * para_2
assert np.array_equal(out.asnumpy(), np.ones((1, 2, 3)))
out = para_1 + t3
assert np.array_equal(out.asnumpy(), np.ones((1, 2, 3)) * 2)
out = para_1 * t3
assert np.array_equal(out.asnumpy(), np.ones((1, 2, 3)))
assert isinstance(para_1, Tensor)
def test_scalar_parameter_update():
fp = Parameter(0.5, 'fp')
fp.default_input = 0.8
assert np.array_equal(fp.default_input.asnumpy(), np.array(0.8, np.float32))
fp.default_input = 1
assert np.array_equal(fp.default_input.asnumpy(), np.array(1.0, np.float32))
int_ = Parameter(1, 'fp')
int_.default_input = 2
assert np.array_equal(int_.default_input.asnumpy(), np.array(2, np.int32))
with pytest.raises(TypeError):
int_.default_input = 1.2
def test_parameter_lazy_init():
# support lazy init in SEMI_AUTO_PARALLEL mode
context.reset_auto_parallel_context()
@ -155,7 +189,7 @@ def test_parameter_lazy_init():
# init then assign
para = para.init_data()
# check the type
with pytest.raises(ValueError):
with pytest.raises(TypeError):
para.default_input = Tensor(np.zeros((1, 2, 3)))
# check the shape
with pytest.raises(ValueError):
@ -170,4 +204,8 @@ def test_parameter_lazy_init():
# expect no effect.
para.init_data()
assert np.array_equal(para.default_input.asnumpy(), np.ones((1, 2, 3)))
para.set_parameter_data(Tensor(np.zeros((1, 2)).astype(np.float32)), slice_shape=True)
assert np.array_equal(para.default_input.asnumpy(), np.zeros((1, 2)))
para.set_parameter_data(initializer('ones', [1, 2], mstype.float32), slice_shape=True)
assert np.array_equal(para.default_input.asnumpy(), np.ones((1, 2)))
context.reset_auto_parallel_context()