!2010 fix operator issues for tuple_to_array and cast

Merge pull request !2010 from wangqiuliang/fix-tuple-to-array-issue

mindspore/common/parameter.py

@@ -15,7 +15,7 @@
 
 """Parameter for cell."""
 import numbers
-from copy import copy, deepcopy
+from copy import copy
 from mindspore import context
 from . import dtype as mstype
 from .initializer import initializer, Initializer
@@ -191,25 +191,16 @@ class Parameter:
         return self.default_input
 
     def __add__(self, other):
-        res = deepcopy(self)
-        res.default_input = res.default_input + other
-        return res
+        return self.default_input + other
 
     def __sub__(self, other):
-        res = deepcopy(self)
-        res.default_input = res.default_input - other
-        return res
+        return self.default_input - other
 
     def __mul__(self, other):
-        res = deepcopy(self)
-        default_input = res.default_input * other
-        res.default_input = Tensor(default_input.asnumpy().copy())
-        return res
+        return self.default_input * other
 
     def __truediv__(self, other):
-        res = deepcopy(self)
-        res.default_input = res.default_input / other
-        return res
+        return self.default_input / other
 
     def __setitem__(self, index, value):
         return self

mindspore/nn/cell.py

@@ -202,6 +202,7 @@ class Cell:
         if context.get_context("mode") == context.GRAPH_MODE:
             out = self.compile_and_run(*inputs)
             return out
+        self.init_parameters_data()
         orign_grad = []
         if self.requires_grad is True:
             _pynative_exec.set_grad_flag(True)
@@ -254,8 +255,11 @@ class Cell:
                 value.update_parameters_name(name + '.')
             cells[name] = value
         elif params and name in params:
-            if value is not None:
+            if isinstance(value, Tensor) and self._params[name] is not None:
+                self._params[name].set_parameter_data(value)
+            elif value is not None:
                 raise TypeError("Expected type in (Parameter, ParameterTuple), but got {}.".format(type(value)))
+            else:
                 self.insert_param_to_cell(name, None)
         elif cells and name in cells:
             if value is not None:

mindspore/ops/operations/array_ops.py

@@ -30,7 +30,7 @@ from ...common import dtype as mstype
 from ...common.tensor import Tensor
 from ..operations.math_ops import _infer_shape_reduce
 from .._utils import get_concat_offset
-from ..primitive import Primitive, PrimitiveWithInfer, prim_attr_register
+from ..primitive import Primitive, PrimitiveWithInfer, prim_attr_register, _run_op
 from ..._c_expression import signature_rw as sig_rw
 from ..._c_expression import signature_kind as sig_kind
 from ..._c_expression import signature_dtype as sig_dtype
@@ -983,9 +983,14 @@ class TupleToArray(PrimitiveWithInfer):
             ret = np.array(x, np.int32)
         else:
             ret = np.array(x, np.float32)
-
         return Tensor(ret)
 
+    def __call__(self, x):
+        args = list()
+        if isinstance(x, range):
+            args.append(tuple(x))
+        return _run_op(self, self.name, args)
+
 
 class ScalarToArray(PrimitiveWithInfer):
     """

tests/st/pynative/test_ops.py

@@ -0,0 +1,31 @@
+# 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.
+# ============================================================================
+
+import numpy as np
+
+import mindspore as ms
+import mindspore.ops.operations as P
+from mindspore import context, Tensor
+
+
+def test_cast():
+    """ tests cast for same dtype"""
+    context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
+    input_np = np.random.randn(2, 3, 4, 5).astype(np.float32)
+    input_x = Tensor(input_np)
+    type_dst = ms.float32
+    cast = P.Cast()
+    result = cast(input_x, type_dst)
+    assert result.dtype() == type_dst

tests/ut/python/nn/optim/test_optimizer.py

@@ -52,11 +52,11 @@ class TestAdam():
              use_nesterov=False, weight_decay=0.0, loss_scale=1.0)
 
     def test_construct(self):
-        with pytest.raises(TypeError):
+        with pytest.raises(RuntimeError):
             gradient = Tensor(np.zeros([1, 2, 3]))
             adam = Adam(params, learning_rate=1e-3, beta1=0.9, beta2=0.999, eps=1e-8, use_locking=False,
                         use_nesterov=False, weight_decay=0.0, loss_scale=1.0)
-            adam.construct(gradient)
+            adam(gradient)
 
 
 class TestSGD():

tests/ut/python/pynative_mode/nn/test_parameter_operation.py

@@ -0,0 +1,67 @@
+# 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_tensor_operation """
+import numpy as np
+
+import mindspore.nn as nn
+from mindspore import Tensor, Parameter
+from mindspore import context
+
+
+def setup_module(module):
+    context.set_context(mode=context.PYNATIVE_MODE)
+
+def test_parameter_add():
+    x = Parameter(Tensor(np.ones((3, 3)).astype(np.float32)), name="ref")
+    y = Tensor(np.ones((3, 3)).astype(np.float32))
+    expect = np.ones((3, 3)).astype(np.float32) * 2
+    z = x + y
+    assert np.allclose(z.asnumpy(), expect)
+
+def test_parameter_sub():
+    x = Parameter(Tensor(np.ones((3, 3)).astype(np.float32) * 2), name="ref")
+    y = Tensor(np.ones((3, 3)).astype(np.float32))
+    expect = np.ones((3, 3)).astype(np.float32)
+    z = x - y
+    assert np.allclose(z.asnumpy(), expect)
+
+def test_parameter_mul():
+    x = Parameter(Tensor(np.ones((3, 3)).astype(np.float32) * 2), name="ref")
+    y = Tensor(np.ones((3, 3)).astype(np.float32) * 2)
+    expect = np.ones((3, 3)).astype(np.float32) * 4
+    z = x * y
+    assert np.allclose(z.asnumpy(), expect)
+
+def test_parameter_div():
+    x = Parameter(Tensor(np.ones((3, 3)).astype(np.float32) * 8), name="ref")
+    y = Tensor(np.ones((3, 3)).astype(np.float32) * 2)
+    expect = np.ones((3, 3)).astype(np.float32) * 4
+    z = x / y
+    assert np.allclose(z.asnumpy(), expect)
+
+class ParameterNet(nn.Cell):
+    def __init__(self):
+        super(ParameterNet, self).__init__()
+        self.weight = Parameter(Tensor(np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], np.float32)), name="ref")
+
+    def construct(self, x):
+        self.weight = x
+
+def test_parameter_assign():
+    """test parameter assign with tensor"""
+    input_x = Tensor(np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 8.0]], np.float32))
+    net = ParameterNet()
+    net(input_x)
+    assert np.allclose(net.weight.data.asnumpy(), input_x.asnumpy())

tests/ut/python/pynative_mode/test_parse_method.py

@@ -31,6 +31,7 @@ from mindspore.common.api import ms_function
 from mindspore.common.tensor import Tensor
 from mindspore.ops.composite import core
 from mindspore.ops.primitive import constexpr
+from mindspore.ops import functional as F
 from ..ut_filter import non_graph_engine
 
 
@@ -427,3 +428,10 @@ def test_expr():
     def tuple_len(x):
         assert len(x) == 2
     tuple_len(a)
+
+
+def test_tuple_to_array():
+    """ test range tuple to array """
+    range_x = range(10)
+    res = F.tuple_to_array(range_x)
+    print(res)