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397 lines
12 KiB
397 lines
12 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_cell_bprop """
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import numpy as np
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import pytest
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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
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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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from ....mindspore_test_framework.utils.bprop_util import bprop
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def setup_module(module):
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context.set_context(mode=context.PYNATIVE_MODE)
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class MulAdd(nn.Cell):
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def __init__(self):
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super(MulAdd, self).__init__()
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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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def test_grad_mul_add():
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mul_add = MulAdd()
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assert C.grad_all(mul_add)(1, 2) == (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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def test_grad_inline_mul_add():
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inline_mul_add = InlineMulADD()
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assert C.grad_all(inline_mul_add)(1, 2) == (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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def test_with_param():
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with_param = WithParameter()
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with pytest.raises(RuntimeError):
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C.grad_all(with_param)(1, 2)
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class WithNoBprop(nn.Cell):
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def __init__(self):
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super(WithNoBprop, self).__init__()
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def construct(self, x, y):
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return 2 * x + y
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def test_with_no_bprop():
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with_no_bprop = WithNoBprop()
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assert C.grad_all(with_no_bprop)(1, 2) == (2, 1)
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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), C.grad_all(self.f)(x, y)
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def bprop(self, x, y, out, dout):
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grads = C.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 = C.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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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), C.grad_all(self.f)(x, y)
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def bprop(self, x, y, out, dout):
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grads = C.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 = C.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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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), C.grad_all(self.f)(x, y)
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def bprop(self, x, y, out, dout):
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grads = C.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 = C.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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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 = C.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 __init__(self):
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super().__init__()
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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), C.grad_all(self.f)(x, y)
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def bprop(self, x, y, out, dout):
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grads = C.grad_all(self.f)(x, y)
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return grads[0] * 2, grads[1] * 2
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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 = C.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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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 = C.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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def test_refkey_bprop():
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net = MulAddWithParam()
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input_data = Tensor(np.array([2, 2], np.float32))
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grads = bprop(net, input_data,
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grads_wrt_outputs=(Tensor(np.ones([1, 2]).astype(np.float32))),
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wrt=['params', 'inputs'],
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params=net.trainable_params())
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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 __init__(self):
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super(MulAddWithWrongOutputNum, self).__init__()
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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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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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C.grad_all(mul_add)(1, 2)
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class MulAddWithWrongOutputType(nn.Cell):
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def __init__(self):
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super(MulAddWithWrongOutputType, self).__init__()
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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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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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C.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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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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C.grad_all(mul_add)(1, Tensor(np.ones([2, 2])))
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