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289 lines
10 KiB
289 lines
10 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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""" test implicit conversion """
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import numpy as np
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import pytest
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import mindspore as ms
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from mindspore import Tensor, nn, Parameter
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from mindspore.ops import composite as C
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from mindspore.ops import functional as F
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grad_all_with_sens = C.GradOperation(get_all=True, sens_param=True)
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def test_float_tensor_and_int_add():
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x = Tensor(np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float32))
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y = 2
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ret_actual = x + y
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ret_expect = Tensor(np.array([[2.1, 2.2, 2.3], [2.4, 2.5, 2.6]], dtype=np.float32))
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assert ret_actual.dtype == ret_expect.dtype
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assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()
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def test_bool_tensor_and_float_add():
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x = Tensor(np.array([[True, False], [False, True]], dtype=np.bool_))
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y = 3.3
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ret_actual = x + y
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ret_expect = Tensor(np.array([[4.3, 3.3], [3.3, 4.3]], dtype=np.float32))
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assert ret_actual.dtype == ret_expect.dtype
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assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()
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def test_bool_tensor_and_int_add():
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x = Tensor(np.array([[True, False], [False, True]], dtype=np.bool_))
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y = 3
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ret_actual = x + y
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ret_expect = Tensor(np.array([[4, 3], [3, 4]], dtype=np.int64))
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assert ret_actual.dtype == ret_expect.dtype
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assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()
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def test_bool_and_int_tensor_add():
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x = True
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y = Tensor(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int32))
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ret_actual = x + y
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ret_expect = Tensor(np.array([[2, 3, 4], [5, 6, 7]], dtype=np.int32))
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assert ret_actual.dtype == ret_expect.dtype
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assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()
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def test_float_tensor_and_int_tensor_add():
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x = Tensor(np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float32))
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y = Tensor(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int32))
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ret_actual = x + y
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ret_expect = Tensor(np.array([[1.1, 2.2, 3.3], [4.4, 5.5, 6.6]], dtype=np.float32))
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assert ret_actual.dtype == ret_expect.dtype
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assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()
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def test_float_tensor_and_float_tensor_add():
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x = Tensor(np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float32))
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y = Tensor(np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], dtype=np.float16))
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ret_actual = x + y
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ret_expect = Tensor(np.array([[1.1, 2.2, 3.3], [4.4, 5.5, 6.6]], dtype=np.float32))
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assert ret_actual.dtype == ret_expect.dtype
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assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()
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def test_int_tensor_and_int_tensor_add():
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x = Tensor(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int8))
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y = Tensor(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int32))
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ret_actual = x + y
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ret_expect = Tensor(np.array([[2, 4, 6], [8, 10, 12]], dtype=np.int32))
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assert ret_actual.dtype == ret_expect.dtype
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assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()
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def test_float_tensor_and_bool_tensors_add():
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x = Tensor(np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float32))
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y = Tensor(np.array([[True, True, True], [False, False, False]], dtype=np.bool_))
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ret_actual = x + y
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ret_expect = Tensor(np.array([[1.1, 1.2, 1.3], [0.4, 0.5, 0.6]], dtype=np.float32))
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assert ret_actual.dtype == ret_expect.dtype
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assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()
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def test_int8_tensor_and_uint8_tensors_add():
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x = Tensor(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int8))
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y = Tensor(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.uint8))
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ret_actual = x + y
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ret_expect = Tensor(np.array([[2, 4, 6], [8, 10, 12]], dtype=np.int16))
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assert ret_actual.dtype == ret_expect.dtype
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assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()
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def test_float_tensor_and_str_add():
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x = Tensor(np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float32))
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y = "ok"
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with pytest.raises(TypeError) as er:
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ret = x + y
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assert "For 'Add', the 1th input is a not support implicit conversion type: str" in str(er.value)
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def test_float_tensor_and_tuple_add():
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x = Tensor(np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float32))
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y = (1, 2, 3)
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with pytest.raises(TypeError) as er:
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ret = x + y
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assert "For 'Add', the 1th input is a not support implicit conversion type: tuple" in str(er.value)
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def test_float_tensor_and_list_add():
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x = Tensor(np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float32))
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y = [1, 2, 3]
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with pytest.raises(TypeError) as er:
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ret = x + y
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assert "For 'Add', the 1th input is a not support implicit conversion type: list" in str(er.value)
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def test_float_tensor_and_bool_tensors_add_grad():
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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def construct(self, x, y):
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return x + y
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class GradNet(nn.Cell):
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def __init__(self, net):
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super(GradNet, self).__init__()
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self.net = net
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def construct(self, x, y, sens):
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return grad_all_with_sens(self.net)(x, y, sens)
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x = Tensor(np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float32))
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y = Tensor(np.array([[True, True, True], [False, False, False]], dtype=np.bool_))
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sens = Tensor(np.array([[1.0, 2.0, 0.0], [0.0, 3.0, 4.0]], dtype=np.float32))
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net = Net()
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grad_net = GradNet(net)
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ret = grad_net(x, y, sens)
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assert ret[0].dtype == x.dtype
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assert ret[1].dtype == y.dtype
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assert (ret[0].asnumpy() == sens.asnumpy()).all()
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assert (ret[1].asnumpy() == sens.asnumpy().astype(np.bool_)).all()
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def test_float_tensor_and_int_tensors_sub_grad():
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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def construct(self, x, y):
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return x - y
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class GradNet(nn.Cell):
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def __init__(self, net):
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super(GradNet, self).__init__()
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self.net = net
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def construct(self, x, y, sens):
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return grad_all_with_sens(self.net)(x, y, sens)
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x = Tensor(np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float32))
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y = Tensor(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int32))
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sens = Tensor(np.array([[1.0, 2.0, 0.0], [0.0, 3.0, 4.0]], dtype=np.float32))
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net = Net()
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grad_net = GradNet(net)
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ret = grad_net(x, y, sens)
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assert ret[0].dtype == x.dtype
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assert ret[1].dtype == y.dtype
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assert (ret[0].asnumpy() == sens.asnumpy()).all()
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assert (ret[1].asnumpy() == sens.asnumpy() * -1).all()
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def test_float16_tensor_and_float32_tensors_sub_grad():
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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def construct(self, x, y):
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return x - y
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class GradNet(nn.Cell):
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def __init__(self, net):
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super(GradNet, self).__init__()
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self.net = net
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def construct(self, x, y, sens):
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return grad_all_with_sens(self.net)(x, y, sens)
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x = Tensor(np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.int32))
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y = Tensor(np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], dtype=np.float32))
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sens = Tensor(np.array([[1.0, 2.0, 0.0], [0.0, 3.0, 4.0]], dtype=np.float32))
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net = Net()
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grad_net = GradNet(net)
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ret = grad_net(x, y, sens)
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assert ret[0].dtype == x.dtype
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assert ret[1].dtype == y.dtype
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assert (ret[0].asnumpy() == sens.asnumpy()).all()
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assert (ret[1].asnumpy() == sens.asnumpy() * -1).all()
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def test_float_tensor_and_int_add_grad():
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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def construct(self, x):
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return x + 2
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class GradNet(nn.Cell):
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def __init__(self, net):
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super(GradNet, self).__init__()
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self.net = net
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def construct(self, x, sens):
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return grad_all_with_sens(self.net)(x, sens)
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x = Tensor(np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float32))
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sens = Tensor(np.array([[1.0, 2.0, 0.0], [0.0, 3.0, 4.0]], dtype=np.float32))
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net = Net()
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grad_net = GradNet(net)
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ret = grad_net(x, sens)
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assert ret[0].dtype == x.dtype
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assert (ret[0].asnumpy() == sens.asnumpy()).all()
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def test_int8_tensor_and_uint8_tensors_add_grad():
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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def construct(self, x, y):
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return x + y
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class GradNet(nn.Cell):
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def __init__(self, net):
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super(GradNet, self).__init__()
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self.net = net
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def construct(self, x, y, sens):
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return grad_all_with_sens(self.net)(x, y, sens)
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x = Tensor(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int8))
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y = Tensor(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.uint8))
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sens = Tensor(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int16))
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net = Net()
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grad_net = GradNet(net)
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ret = grad_net(x, y, sens)
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assert ret[0].dtype == x.dtype
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assert ret[1].dtype == y.dtype
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assert (ret[0].asnumpy() == sens.asnumpy()).all()
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assert (ret[1].asnumpy() == sens.asnumpy()).all()
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class AssignCheck(nn.Cell):
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""" NetWithNDarray definition """
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def __init__(self):
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super(AssignCheck, self).__init__()
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self.cov_step = Parameter(0.0, name="cov_step", requires_grad=False)
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def construct(self, x, y):
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F.assign(self.cov_step, y)
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F.assign(x, y)
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return x
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def test_assign_check_in_sig():
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net = AssignCheck()
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x = Tensor(2, ms.int8)
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y = Tensor(3, ms.uint8)
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with pytest.raises(TypeError) as e:
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net(x, y)
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assert "Parameter" in e.value.args[0]
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