forked from mindspore-Ecosystem/mindspore
763 lines
24 KiB
Python
763 lines
24 KiB
Python
# 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_cell_bprop """
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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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import mindspore.common.dtype as mstype
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import mindspore.nn as nn
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from mindspore import Parameter, ParameterTuple
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from mindspore import context
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from mindspore.common.initializer import initializer
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from mindspore.common.tensor import Tensor
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from mindspore.ops import composite as C
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from mindspore.ops import operations as P
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grad_all = C.GradOperation(get_all=True)
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class MulAdd(nn.Cell):
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def construct(self, x, y):
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return 2 * x + y
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def bprop(self, x, y, out, dout):
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# In this test case, The user defined bprop is wrong defined purposely to distinguish from ad result
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return 2 * dout, 2 * y
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@pytest.mark.level0
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_grad_mul_add():
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mul_add = MulAdd()
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x = Tensor(1, dtype=ms.int32)
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y = Tensor(2, dtype=ms.int32)
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assert grad_all(mul_add)(x, y) == (2, 4)
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class InlineMulADD(nn.Cell):
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def __init__(self):
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super(InlineMulADD, self).__init__()
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self.mul_add = MulAdd()
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self.param = 2
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def construct(self, x, y):
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return self.mul_add(x, y) + x + self.param * y
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@pytest.mark.level0
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_grad_inline_mul_add():
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inline_mul_add = InlineMulADD()
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x = Tensor(1, dtype=ms.int32)
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y = Tensor(2, dtype=ms.int32)
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assert grad_all(inline_mul_add)(x, y) == (3, 6)
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class WithParameter(nn.Cell):
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def __init__(self):
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super(WithParameter, self).__init__()
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self.param1 = Parameter(1, 'param1')
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self.param2 = Parameter(2, 'param2')
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def construct(self, x, y):
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return self.param1 * self.param2 * x + y
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def bprop(self, x, y, out, dout):
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# In this test case, The user defined bprop is wrong defined purposely to distinguish from ad result
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return self.param1 * self.param2 * dout, 2 * y
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@pytest.mark.level0
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_with_param():
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with_param = WithParameter()
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with pytest.raises(RuntimeError):
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grad_all(with_param)(1, 2)
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class WithNoBprop(nn.Cell):
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def construct(self, x, y):
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return 2 * x + y
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@pytest.mark.level0
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_with_no_bprop():
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with_no_bprop = WithNoBprop()
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x = Tensor(1, dtype=ms.int32)
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y = Tensor(2, dtype=ms.int32)
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assert grad_all(with_no_bprop)(x, y) == (2, 1)
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@pytest.mark.level0
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_grad_in_bprop_1():
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class GradInBprop_1(nn.Cell):
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def __init__(self):
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super(GradInBprop_1, self).__init__()
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self.relu = P.ReLU()
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def construct(self, x, y):
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return self.relu(x)
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class GradInBprop_2(nn.Cell):
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def __init__(self):
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super(GradInBprop_2, self).__init__()
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self.f = GradInBprop_1()
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def construct(self, x, y):
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return self.f(x, y), grad_all(self.f)(x, y)
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def bprop(self, x, y, out, dout):
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grads = grad_all(self.f)(x, y)
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return out[1][0], grads[1]
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class GradInBprop_3(nn.Cell):
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def __init__(self):
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super(GradInBprop_3, self).__init__()
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self.f = GradInBprop_2()
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def construct(self, x, y):
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return self.f(x, y)
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grad_in_bprop = GradInBprop_3()
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grads = grad_all(grad_in_bprop)(Tensor(np.ones([2, 2]).astype(np.float32)),
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Tensor(np.ones([2, 2]).astype(np.float32)))
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assert (grads[0].asnumpy() == np.ones([2, 2]).astype(np.float32)).all()
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assert (grads[1].asnumpy() == np.zeros([2, 2]).astype(np.float32)).all()
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@pytest.mark.level0
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_grad_in_bprop_2():
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class GradInBprop_1(nn.Cell):
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def __init__(self):
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super(GradInBprop_1, self).__init__()
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self.relu = P.ReLU()
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def construct(self, x, y):
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return self.relu(x)
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def bprop(self, x, y, out, dout):
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return x * y, y + x
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class GradInBprop_2(nn.Cell):
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def __init__(self):
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super(GradInBprop_2, self).__init__()
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self.f = GradInBprop_1()
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def construct(self, x, y):
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return self.f(x, y), grad_all(self.f)(x, y)
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def bprop(self, x, y, out, dout):
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grads = grad_all(self.f)(x, y)
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return out[1][0], grads[1]
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class GradInBprop_3(nn.Cell):
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def __init__(self):
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super(GradInBprop_3, self).__init__()
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self.f = GradInBprop_2()
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def construct(self, x, y):
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return self.f(x, y)
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grad_in_bprop = GradInBprop_3()
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grads = grad_all(grad_in_bprop)(Tensor(np.ones([2, 2]).astype(np.float32)),
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Tensor(np.ones([2, 2]).astype(np.float32)))
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assert (grads[0].asnumpy() == np.ones([2, 2]).astype(np.float32)).all()
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assert (grads[1].asnumpy() == np.array([[2, 2], [2, 2]]).astype(np.float32)).all()
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@pytest.mark.level0
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_grad_in_bprop_3():
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class GradInBprop_1(nn.Cell):
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def __init__(self):
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super(GradInBprop_1, self).__init__()
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self.relu = P.ReLU()
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def construct(self, x, y):
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return self.relu(x)
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class GradInBprop_2(nn.Cell):
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def __init__(self):
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super(GradInBprop_2, self).__init__()
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self.f = GradInBprop_1()
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def construct(self, x, y):
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return self.f(x, y), grad_all(self.f)(x, y)
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def bprop(self, x, y, out, dout):
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grads = grad_all(self.f)(x, y)
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return out[1][0], grads[1]
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class GradInBprop_3(nn.Cell):
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def __init__(self):
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super(GradInBprop_3, self).__init__()
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self.f = GradInBprop_2()
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def construct(self, x, y):
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return self.f(x, y)
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def bprop(self, x, y, out, dout):
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return x + y + y + out[0], x + x + y + y + dout[0]
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grad_in_bprop = GradInBprop_3()
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grads = grad_all(grad_in_bprop)(Tensor(np.ones([2, 2]).astype(np.float32)),
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Tensor(np.ones([2, 2]).astype(np.float32)))
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assert (grads[0].asnumpy() == np.array([[4, 4], [4, 4]]).astype(np.float32)).all()
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assert (grads[1].asnumpy() == np.array([[5, 5], [5, 5]]).astype(np.float32)).all()
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class OneInputBprop(nn.Cell):
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def __init__(self):
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super().__init__()
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self.op = P.ReLU()
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def construct(self, x):
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return self.op(x)
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def bprop(self, x, out, dout):
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return (5 * x,)
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@pytest.mark.level0
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_grad_one_input_bprop():
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net = OneInputBprop()
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input1 = Tensor(np.ones([2, 2]).astype(np.float32))
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grad = grad_all(net)(input1)
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assert (grad[0].asnumpy() == np.array([5, 5]).astype(np.float32)).all()
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class TwoInput(nn.Cell):
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def construct(self, x, y):
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return x * y
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class InlineBpropTwoInput(nn.Cell):
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def __init__(self):
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super().__init__()
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self.f = TwoInput()
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def construct(self, x, y):
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return self.f(x, y), grad_all(self.f)(x, y)
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def bprop(self, x, y, out, dout):
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grads = grad_all(self.f)(x, y)
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return grads[0] * 2, grads[1] * 2
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@pytest.mark.level0
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_grad_inline_bprop_two_input():
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net = InlineBpropTwoInput()
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input1 = Tensor(np.ones([2, 2]).astype(np.float32))
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input2 = Tensor(np.ones([2, 2]).astype(np.float32))
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grads = grad_all(net)(input1, input2)
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assert (grads[0].asnumpy() == np.array([2, 2]).astype(np.float32)).all()
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assert (grads[1].asnumpy() == np.array([2, 2]).astype(np.float32)).all()
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assert len(grads) == 2
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class TwoInputBprop(nn.Cell):
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def __init__(self):
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super().__init__()
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self.op = P.Mul()
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def construct(self, x, y):
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return self.op(x, y)
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def bprop(self, x, y, out, dout):
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return 5 * x, 8 * y
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class TwoInputWithParameter(nn.Cell):
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def __init__(self):
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super().__init__()
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self.op = P.Mul()
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self.inputdata = Parameter(initializer(1, (2, 2), mstype.float32), name="global_step")
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def construct(self, x, y):
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x = self.inputdata + x
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return self.op(x, y)
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class TwoInputWithOnlyInitParameterBprop(nn.Cell):
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def __init__(self):
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super().__init__()
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self.op = P.Mul()
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self.inputdata = Parameter(initializer(1, (2, 2), mstype.float32), name="global_step")
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def construct(self, x, y):
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return self.op(x, y)
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def bprop(self, x, y, out, dout):
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return 5 * x, 8 * y
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class InlineMutilTwoInputParameterCell(nn.Cell):
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def __init__(self):
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super().__init__()
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self.f1 = TwoInputBprop()
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self.f2 = TwoInput()
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self.f3 = TwoInputWithParameter()
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self.f4 = TwoInputWithOnlyInitParameterBprop()
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def construct(self, x, y):
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output = self.f1(x, y) + self.f2(x, y) + self.f3(x, y) + self.f4(x, y)
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return output
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@pytest.mark.level0
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_grad_inline_bprop_multi_input():
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net = InlineMutilTwoInputParameterCell()
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input1 = Tensor(np.ones([2, 2]).astype(np.float32))
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input2 = Tensor(np.ones([2, 2]).astype(np.float32))
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net.init_parameters_data()
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grads = grad_all(net)(input1, input2)
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assert (grads[0].asnumpy() == np.array([[12, 12], [12, 12]]).astype(np.float32)).all()
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assert (grads[1].asnumpy() == np.array([[19, 19], [19, 19]]).astype(np.float32)).all()
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assert len(grads) == 2
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class MulAddWithParam(nn.Cell):
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def __init__(self):
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super(MulAddWithParam, self).__init__()
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self.mul_add = MulAdd()
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self.param = Parameter(Tensor(np.array([[3, 2]], np.float32)), 'param')
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def construct(self, x):
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return self.mul_add(self.param, x)
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@pytest.mark.level0
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_refkey_bprop():
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grad_by_list = C.GradOperation(get_all=True, get_by_list=True)
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class GradWrap(nn.Cell):
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def __init__(self, network):
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super(GradWrap, self).__init__()
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self.network = network
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self.weights = ParameterTuple(filter(lambda x: x.requires_grad, network.get_parameters()))
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def construct(self, x):
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weights = self.weights
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grads = grad_by_list(self.network, weights)(x)
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return grads
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network = GradWrap(MulAddWithParam())
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input_data = Tensor(np.array([2, 2], np.float32))
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grads = network(input_data)
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assert (grads[0][0].asnumpy() == np.array([4, 4]).astype(np.float32)).all()
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assert (grads[1][0].asnumpy() == np.array([2, 2]).astype(np.float32)).all()
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class MulAddWithWrongOutputNum(nn.Cell):
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def construct(self, x, y):
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return 2 * x + y
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def bprop(self, x, y, out, dout):
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return (2 * dout,)
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@pytest.mark.level0
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_grad_mul_add_with_wrong_output_num():
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context.set_context(check_bprop=True)
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mul_add = MulAddWithWrongOutputNum()
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with pytest.raises(TypeError):
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grad_all(mul_add)(1, 2)
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class MulAddWithWrongOutputType(nn.Cell):
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def construct(self, x, y):
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return 2 * x + y
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def bprop(self, x, y, out, dout):
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return 2 * dout, 2
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@pytest.mark.level0
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_grad_mul_add_with_wrong_output_type():
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context.set_context(check_bprop=True)
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mul_add = MulAddWithWrongOutputType()
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with pytest.raises(TypeError):
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grad_all(mul_add)(1, Tensor(np.ones([2, 2])))
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class MulAddWithWrongOutputShape(nn.Cell):
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def __init__(self):
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super(MulAddWithWrongOutputShape, self).__init__()
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self.ones = Tensor(np.ones([2,]))
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def construct(self, x, y):
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return 2 * x + y
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def bprop(self, x, y, out, dout):
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return 2, self.ones
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@pytest.mark.level0
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_grad_mul_add_with_wrong_output_shape():
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context.set_context(check_bprop=True)
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mul_add = MulAddWithWrongOutputShape()
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with pytest.raises(TypeError):
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grad_all(mul_add)(1, Tensor(np.ones([2, 2])))
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_forward_with_parameter():
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"""
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Feature: Custom cell bprop
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Description: Get the gradients of inputs when the forward net using Parameter.
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Expectation: Get the correct gradients.
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"""
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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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self.matmul = P.MatMul()
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self.z = Parameter(Tensor(np.array([1.0], np.float32)), name='z')
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def construct(self, x, y):
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x = x * self.z
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out = self.matmul(x, y)
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return out
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def bprop(self, x, y, out, dout):
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dx = x + x
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dy = y + y
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return dx, dy
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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):
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grad_f = grad_all(self.net)
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return grad_f(x, y)
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x = Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32)
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y = Tensor([[0.01, 0.3, 1.1], [0.1, 0.2, 1.3], [2.1, 1.2, 3.3]], dtype=mstype.float32)
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out = GradNet(Net())(x, y)
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expect_dx = np.array([[1.0, 1.2, 0.8],
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[2.4, 2.6, 2.2]]).astype(np.float32)
|
|
expect_dy = np.array([[0.02, 0.6, 2.2],
|
|
[0.2, 0.4, 2.6],
|
|
[4.2, 2.4, 6.6]]).astype(np.float32)
|
|
assert np.allclose(out[0].asnumpy(), expect_dx)
|
|
assert np.allclose(out[1].asnumpy(), expect_dy)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_forward_with_parameter_in_sub_cell():
|
|
"""
|
|
Feature: Custom cell bprop
|
|
Description: Get the gradients of inputs when the forward net using Parameter in the sub-cell.
|
|
Expectation: Get the correct gradients.
|
|
"""
|
|
|
|
class Net(nn.Cell):
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.net = Net1()
|
|
|
|
def construct(self, x, y):
|
|
return self.net(x, y)
|
|
|
|
class Net1(nn.Cell):
|
|
def __init__(self):
|
|
super(Net1, self).__init__()
|
|
self.matmul = P.MatMul()
|
|
self.z = Parameter(Tensor(np.array([1.0], np.float32)), name='z')
|
|
|
|
def construct(self, x, y):
|
|
x = x * self.z
|
|
out = self.matmul(x, y)
|
|
return out
|
|
|
|
def bprop(self, x, y, out, dout):
|
|
dx = x + x
|
|
dy = y + y
|
|
return dx, dy
|
|
|
|
class GradNet(nn.Cell):
|
|
def __init__(self, net):
|
|
super(GradNet, self).__init__()
|
|
self.net = net
|
|
|
|
def construct(self, x, y):
|
|
grad_f = grad_all(self.net)
|
|
return grad_f(x, y)
|
|
|
|
x = Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32)
|
|
y = Tensor([[0.01, 0.3, 1.1], [0.1, 0.2, 1.3], [2.1, 1.2, 3.3]], dtype=mstype.float32)
|
|
out = GradNet(Net())(x, y)
|
|
expect_dx = np.array([[1.0, 1.2, 0.8],
|
|
[2.4, 2.6, 2.2]]).astype(np.float32)
|
|
expect_dy = np.array([[0.02, 0.6, 2.2],
|
|
[0.2, 0.4, 2.6],
|
|
[4.2, 2.4, 6.6]]).astype(np.float32)
|
|
assert np.allclose(out[0].asnumpy(), expect_dx)
|
|
assert np.allclose(out[1].asnumpy(), expect_dy)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_forward_with_parameter_in_sub_cell_get_by_list():
|
|
"""
|
|
Feature: Custom cell bprop
|
|
Description: Get the gradients of inputs and Parameters when the forward net using Parameter in the sub-cell.
|
|
Expectation: Get the correct gradients.
|
|
"""
|
|
|
|
class Net(nn.Cell):
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.net = Net1()
|
|
|
|
def construct(self, x, y):
|
|
return self.net(x, y)
|
|
|
|
class Net1(nn.Cell):
|
|
def __init__(self):
|
|
super(Net1, self).__init__()
|
|
self.matmul = P.MatMul()
|
|
self.z = Parameter(Tensor(np.array([1.0], np.float32)), name='z')
|
|
|
|
def construct(self, x, y):
|
|
x = x * self.z
|
|
out = self.matmul(x, y)
|
|
return out
|
|
|
|
def bprop(self, x, y, out, dout):
|
|
dx = x + x
|
|
dy = y + y
|
|
return dx, dy
|
|
|
|
class GradNet(nn.Cell):
|
|
def __init__(self, net):
|
|
super(GradNet, self).__init__()
|
|
self.net = net
|
|
self.params = ParameterTuple(net.trainable_params())
|
|
self.grad_op = C.GradOperation(get_by_list=True, get_all=True)
|
|
|
|
def construct(self, x, y):
|
|
grad_f = self.grad_op(self.net, self.params)
|
|
return grad_f(x, y)
|
|
|
|
x = Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32)
|
|
y = Tensor([[0.01, 0.3, 1.1], [0.1, 0.2, 1.3], [2.1, 1.2, 3.3]], dtype=mstype.float32)
|
|
out = GradNet(Net())(x, y)
|
|
expect_dx = np.array([[1.0, 1.2, 0.8],
|
|
[2.4, 2.6, 2.2]]).astype(np.float32)
|
|
expect_dy = np.array([[0.02, 0.6, 2.2],
|
|
[0.2, 0.4, 2.6],
|
|
[4.2, 2.4, 6.6]]).astype(np.float32)
|
|
expect_dz = np.array([0.0]).astype(np.float32)
|
|
assert np.allclose(out[0][0].asnumpy(), expect_dx)
|
|
assert np.allclose(out[0][1].asnumpy(), expect_dy)
|
|
assert np.allclose(out[1][0].asnumpy(), expect_dz)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_pynative_forward_with_parameter():
|
|
"""
|
|
Feature: Custom cell bprop
|
|
Description: Get the gradients of inputs when the forward net using Parameter.
|
|
Expectation: Get the correct gradients.
|
|
"""
|
|
context.set_context(mode=context.PYNATIVE_MODE)
|
|
|
|
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, y):
|
|
x = x * self.z
|
|
out = self.matmul(x, y)
|
|
return out
|
|
|
|
def bprop(self, x, y, out, dout):
|
|
dx = x + x
|
|
dy = y + y
|
|
return dx, dy
|
|
|
|
class GradNet(nn.Cell):
|
|
def __init__(self, net):
|
|
super(GradNet, self).__init__()
|
|
self.net = net
|
|
|
|
def construct(self, x, y):
|
|
grad_f = grad_all(self.net)
|
|
return grad_f(x, y)
|
|
|
|
x = Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32)
|
|
y = Tensor([[0.01, 0.3, 1.1], [0.1, 0.2, 1.3], [2.1, 1.2, 3.3]], dtype=mstype.float32)
|
|
out = GradNet(Net())(x, y)
|
|
expect_dx = np.array([[1.0, 1.2, 0.8],
|
|
[2.4, 2.6, 2.2]]).astype(np.float32)
|
|
expect_dy = np.array([[0.02, 0.6, 2.2],
|
|
[0.2, 0.4, 2.6],
|
|
[4.2, 2.4, 6.6]]).astype(np.float32)
|
|
assert np.allclose(out[0].asnumpy(), expect_dx)
|
|
assert np.allclose(out[1].asnumpy(), expect_dy)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_pynative_forward_with_parameter_in_sub_cell():
|
|
"""
|
|
Feature: Custom cell bprop
|
|
Description: Get the gradients of inputs when the forward net using Parameter in the sub-cell.
|
|
Expectation: Get the correct gradients.
|
|
"""
|
|
context.set_context(mode=context.PYNATIVE_MODE)
|
|
|
|
class Net(nn.Cell):
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.net = Net1()
|
|
|
|
def construct(self, x, y):
|
|
return self.net(x, y)
|
|
|
|
class Net1(nn.Cell):
|
|
def __init__(self):
|
|
super(Net1, self).__init__()
|
|
self.matmul = P.MatMul()
|
|
self.z = Parameter(Tensor(np.array([1.0], np.float32)), name='z')
|
|
|
|
def construct(self, x, y):
|
|
x = x * self.z
|
|
out = self.matmul(x, y)
|
|
return out
|
|
|
|
def bprop(self, x, y, out, dout):
|
|
dx = x + x
|
|
dy = y + y
|
|
return dx, dy
|
|
|
|
class GradNet(nn.Cell):
|
|
def __init__(self, net):
|
|
super(GradNet, self).__init__()
|
|
self.net = net
|
|
|
|
def construct(self, x, y):
|
|
grad_f = grad_all(self.net)
|
|
return grad_f(x, y)
|
|
|
|
x = Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32)
|
|
y = Tensor([[0.01, 0.3, 1.1], [0.1, 0.2, 1.3], [2.1, 1.2, 3.3]], dtype=mstype.float32)
|
|
out = GradNet(Net())(x, y)
|
|
expect_dx = np.array([[1.0, 1.2, 0.8],
|
|
[2.4, 2.6, 2.2]]).astype(np.float32)
|
|
expect_dy = np.array([[0.02, 0.6, 2.2],
|
|
[0.2, 0.4, 2.6],
|
|
[4.2, 2.4, 6.6]]).astype(np.float32)
|
|
assert np.allclose(out[0].asnumpy(), expect_dx)
|
|
assert np.allclose(out[1].asnumpy(), expect_dy)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_pynative_forward_with_parameter_in_sub_cell_get_by_list():
|
|
"""
|
|
Feature: Custom cell bprop
|
|
Description: Get the gradients of inputs and Parameters when the forward net using Parameter in the sub-cell.
|
|
Expectation: Get the correct gradients.
|
|
"""
|
|
context.set_context(mode=context.PYNATIVE_MODE)
|
|
|
|
class Net(nn.Cell):
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.net = Net1()
|
|
|
|
def construct(self, x, y):
|
|
return self.net(x, y)
|
|
|
|
class Net1(nn.Cell):
|
|
def __init__(self):
|
|
super(Net1, self).__init__()
|
|
self.matmul = P.MatMul()
|
|
self.z = Parameter(Tensor(np.array([1.0], np.float32)), name='z')
|
|
|
|
def construct(self, x, y):
|
|
x = x * self.z
|
|
out = self.matmul(x, y)
|
|
return out
|
|
|
|
def bprop(self, x, y, out, dout):
|
|
dx = x + x
|
|
dy = y + y
|
|
return dx, dy
|
|
|
|
class GradNet(nn.Cell):
|
|
def __init__(self, net):
|
|
super(GradNet, self).__init__()
|
|
self.net = net
|
|
self.params = ParameterTuple(net.trainable_params())
|
|
self.grad_op = C.GradOperation(get_by_list=True, get_all=True)
|
|
|
|
def construct(self, x, y):
|
|
grad_f = self.grad_op(self.net, self.params)
|
|
return grad_f(x, y)
|
|
|
|
x = Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32)
|
|
y = Tensor([[0.01, 0.3, 1.1], [0.1, 0.2, 1.3], [2.1, 1.2, 3.3]], dtype=mstype.float32)
|
|
out = GradNet(Net())(x, y)
|
|
expect_dx = np.array([[1.0, 1.2, 0.8],
|
|
[2.4, 2.6, 2.2]]).astype(np.float32)
|
|
expect_dy = np.array([[0.02, 0.6, 2.2],
|
|
[0.2, 0.4, 2.6],
|
|
[4.2, 2.4, 6.6]]).astype(np.float32)
|
|
expect_dz = np.array([0.0]).astype(np.float32)
|
|
assert np.allclose(out[0][0].asnumpy(), expect_dx)
|
|
assert np.allclose(out[0][1].asnumpy(), expect_dy)
|
|
assert np.allclose(out[1][0].asnumpy(), expect_dz)
|