forked from mindspore-Ecosystem/mindspore
424 lines
13 KiB
Python
424 lines
13 KiB
Python
# 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_cell_bprop """
|
|
import numpy as np
|
|
import pytest
|
|
|
|
import mindspore as ms
|
|
import mindspore.common.dtype as mstype
|
|
import mindspore.nn as nn
|
|
from mindspore import Parameter, ParameterTuple
|
|
from mindspore import context
|
|
from mindspore.common.initializer import initializer
|
|
from mindspore.common.tensor import Tensor
|
|
from mindspore.ops import composite as C
|
|
from mindspore.ops import operations as P
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
|
|
|
|
|
grad_all = C.GradOperation(get_all=True)
|
|
|
|
|
|
class MulAdd(nn.Cell):
|
|
def construct(self, x, y):
|
|
return 2 * x + y
|
|
|
|
def bprop(self, x, y, out, dout):
|
|
# In this test case, The user defined bprop is wrong defined purposely to distinguish from ad result
|
|
return 2 * dout, 2 * y
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_grad_mul_add():
|
|
mul_add = MulAdd()
|
|
x = Tensor(1, dtype=ms.int32)
|
|
y = Tensor(2, dtype=ms.int32)
|
|
assert grad_all(mul_add)(x, y) == (2, 4)
|
|
|
|
|
|
class InlineMulADD(nn.Cell):
|
|
def __init__(self):
|
|
super(InlineMulADD, self).__init__()
|
|
self.mul_add = MulAdd()
|
|
self.param = 2
|
|
|
|
def construct(self, x, y):
|
|
return self.mul_add(x, y) + x + self.param * y
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_grad_inline_mul_add():
|
|
inline_mul_add = InlineMulADD()
|
|
x = Tensor(1, dtype=ms.int32)
|
|
y = Tensor(2, dtype=ms.int32)
|
|
assert grad_all(inline_mul_add)(x, y) == (3, 6)
|
|
|
|
|
|
class WithParameter(nn.Cell):
|
|
def __init__(self):
|
|
super(WithParameter, self).__init__()
|
|
self.param1 = Parameter(1, 'param1')
|
|
self.param2 = Parameter(2, 'param2')
|
|
|
|
def construct(self, x, y):
|
|
return self.param1 * self.param2 * x + y
|
|
|
|
def bprop(self, x, y, out, dout):
|
|
# In this test case, The user defined bprop is wrong defined purposely to distinguish from ad result
|
|
return self.param1 * self.param2 * dout, 2 * y
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_with_param():
|
|
with_param = WithParameter()
|
|
with pytest.raises(RuntimeError):
|
|
grad_all(with_param)(1, 2)
|
|
|
|
|
|
class WithNoBprop(nn.Cell):
|
|
def construct(self, x, y):
|
|
return 2 * x + y
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_with_no_bprop():
|
|
with_no_bprop = WithNoBprop()
|
|
x = Tensor(1, dtype=ms.int32)
|
|
y = Tensor(2, dtype=ms.int32)
|
|
assert grad_all(with_no_bprop)(x, y) == (2, 1)
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_grad_in_bprop_1():
|
|
class GradInBprop_1(nn.Cell):
|
|
def __init__(self):
|
|
super(GradInBprop_1, self).__init__()
|
|
self.relu = P.ReLU()
|
|
|
|
def construct(self, x, y):
|
|
return self.relu(x)
|
|
|
|
class GradInBprop_2(nn.Cell):
|
|
def __init__(self):
|
|
super(GradInBprop_2, self).__init__()
|
|
self.f = GradInBprop_1()
|
|
|
|
def construct(self, x, y):
|
|
return self.f(x, y), grad_all(self.f)(x, y)
|
|
|
|
def bprop(self, x, y, out, dout):
|
|
grads = grad_all(self.f)(x, y)
|
|
return out[1][0], grads[1]
|
|
|
|
class GradInBprop_3(nn.Cell):
|
|
def __init__(self):
|
|
super(GradInBprop_3, self).__init__()
|
|
self.f = GradInBprop_2()
|
|
|
|
def construct(self, x, y):
|
|
return self.f(x, y)
|
|
|
|
grad_in_bprop = GradInBprop_3()
|
|
grads = grad_all(grad_in_bprop)(Tensor(np.ones([2, 2]).astype(np.float32)),
|
|
Tensor(np.ones([2, 2]).astype(np.float32)))
|
|
assert (grads[0].asnumpy() == np.ones([2, 2]).astype(np.float32)).all()
|
|
assert (grads[1].asnumpy() == np.zeros([2, 2]).astype(np.float32)).all()
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_grad_in_bprop_2():
|
|
class GradInBprop_1(nn.Cell):
|
|
def __init__(self):
|
|
super(GradInBprop_1, self).__init__()
|
|
self.relu = P.ReLU()
|
|
|
|
def construct(self, x, y):
|
|
return self.relu(x)
|
|
|
|
def bprop(self, x, y, out, dout):
|
|
return x * y, y + x
|
|
|
|
class GradInBprop_2(nn.Cell):
|
|
def __init__(self):
|
|
super(GradInBprop_2, self).__init__()
|
|
self.f = GradInBprop_1()
|
|
|
|
def construct(self, x, y):
|
|
return self.f(x, y), grad_all(self.f)(x, y)
|
|
|
|
def bprop(self, x, y, out, dout):
|
|
grads = grad_all(self.f)(x, y)
|
|
return out[1][0], grads[1]
|
|
|
|
class GradInBprop_3(nn.Cell):
|
|
def __init__(self):
|
|
super(GradInBprop_3, self).__init__()
|
|
self.f = GradInBprop_2()
|
|
|
|
def construct(self, x, y):
|
|
return self.f(x, y)
|
|
|
|
grad_in_bprop = GradInBprop_3()
|
|
grads = grad_all(grad_in_bprop)(Tensor(np.ones([2, 2]).astype(np.float32)),
|
|
Tensor(np.ones([2, 2]).astype(np.float32)))
|
|
assert (grads[0].asnumpy() == np.ones([2, 2]).astype(np.float32)).all()
|
|
assert (grads[1].asnumpy() == np.array([[2, 2], [2, 2]]).astype(np.float32)).all()
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_grad_in_bprop_3():
|
|
class GradInBprop_1(nn.Cell):
|
|
def __init__(self):
|
|
super(GradInBprop_1, self).__init__()
|
|
self.relu = P.ReLU()
|
|
|
|
def construct(self, x, y):
|
|
return self.relu(x)
|
|
|
|
class GradInBprop_2(nn.Cell):
|
|
def __init__(self):
|
|
super(GradInBprop_2, self).__init__()
|
|
self.f = GradInBprop_1()
|
|
|
|
def construct(self, x, y):
|
|
return self.f(x, y), grad_all(self.f)(x, y)
|
|
|
|
def bprop(self, x, y, out, dout):
|
|
grads = grad_all(self.f)(x, y)
|
|
return out[1][0], grads[1]
|
|
|
|
class GradInBprop_3(nn.Cell):
|
|
def __init__(self):
|
|
super(GradInBprop_3, self).__init__()
|
|
self.f = GradInBprop_2()
|
|
|
|
def construct(self, x, y):
|
|
return self.f(x, y)
|
|
|
|
def bprop(self, x, y, out, dout):
|
|
return x + y + y + out[0], x + x + y + y + dout[0]
|
|
|
|
grad_in_bprop = GradInBprop_3()
|
|
grads = grad_all(grad_in_bprop)(Tensor(np.ones([2, 2]).astype(np.float32)),
|
|
Tensor(np.ones([2, 2]).astype(np.float32)))
|
|
assert (grads[0].asnumpy() == np.array([[4, 4], [4, 4]]).astype(np.float32)).all()
|
|
assert (grads[1].asnumpy() == np.array([[5, 5], [5, 5]]).astype(np.float32)).all()
|
|
|
|
|
|
class OneInputBprop(nn.Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.op = P.ReLU()
|
|
|
|
def construct(self, x):
|
|
return self.op(x)
|
|
|
|
def bprop(self, x, out, dout):
|
|
return (5 * x,)
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_grad_one_input_bprop():
|
|
net = OneInputBprop()
|
|
input1 = Tensor(np.ones([2, 2]).astype(np.float32))
|
|
grad = grad_all(net)(input1)
|
|
assert (grad[0].asnumpy() == np.array([5, 5]).astype(np.float32)).all()
|
|
|
|
|
|
class TwoInput(nn.Cell):
|
|
def construct(self, x, y):
|
|
return x * y
|
|
|
|
|
|
class InlineBpropTwoInput(nn.Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.f = TwoInput()
|
|
|
|
def construct(self, x, y):
|
|
return self.f(x, y), grad_all(self.f)(x, y)
|
|
|
|
def bprop(self, x, y, out, dout):
|
|
grads = grad_all(self.f)(x, y)
|
|
return grads[0] * 2, grads[1] * 2
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_grad_inline_bprop_two_input():
|
|
net = InlineBpropTwoInput()
|
|
input1 = Tensor(np.ones([2, 2]).astype(np.float32))
|
|
input2 = Tensor(np.ones([2, 2]).astype(np.float32))
|
|
grads = grad_all(net)(input1, input2)
|
|
assert (grads[0].asnumpy() == np.array([2, 2]).astype(np.float32)).all()
|
|
assert (grads[1].asnumpy() == np.array([2, 2]).astype(np.float32)).all()
|
|
assert len(grads) == 2
|
|
|
|
|
|
class TwoInputBprop(nn.Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.op = P.Mul()
|
|
|
|
def construct(self, x, y):
|
|
return self.op(x, y)
|
|
|
|
def bprop(self, x, y, out, dout):
|
|
return 5 * x, 8 * y
|
|
|
|
|
|
class TwoInputWithParameter(nn.Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.op = P.Mul()
|
|
self.inputdata = Parameter(initializer(1, (2, 2), mstype.float32), name="global_step")
|
|
|
|
def construct(self, x, y):
|
|
x = self.inputdata + x
|
|
return self.op(x, y)
|
|
|
|
|
|
class TwoInputWithOnlyInitParameterBprop(nn.Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.op = P.Mul()
|
|
self.inputdata = Parameter(initializer(1, (2, 2), mstype.float32), name="global_step")
|
|
|
|
def construct(self, x, y):
|
|
return self.op(x, y)
|
|
|
|
def bprop(self, x, y, out, dout):
|
|
return 5 * x, 8 * y
|
|
|
|
|
|
class InlineMutilTwoInputParameterCell(nn.Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.f1 = TwoInputBprop()
|
|
self.f2 = TwoInput()
|
|
self.f3 = TwoInputWithParameter()
|
|
self.f4 = TwoInputWithOnlyInitParameterBprop()
|
|
|
|
def construct(self, x, y):
|
|
output = self.f1(x, y) + self.f2(x, y) + self.f3(x, y) + self.f4(x, y)
|
|
return output
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_grad_inline_bprop_multi_input():
|
|
net = InlineMutilTwoInputParameterCell()
|
|
input1 = Tensor(np.ones([2, 2]).astype(np.float32))
|
|
input2 = Tensor(np.ones([2, 2]).astype(np.float32))
|
|
net.init_parameters_data()
|
|
grads = grad_all(net)(input1, input2)
|
|
assert (grads[0].asnumpy() == np.array([[12, 12], [12, 12]]).astype(np.float32)).all()
|
|
assert (grads[1].asnumpy() == np.array([[19, 19], [19, 19]]).astype(np.float32)).all()
|
|
assert len(grads) == 2
|
|
|
|
|
|
class MulAddWithParam(nn.Cell):
|
|
def __init__(self):
|
|
super(MulAddWithParam, self).__init__()
|
|
self.mul_add = MulAdd()
|
|
self.param = Parameter(Tensor(np.array([[3, 2]], np.float32)), 'param')
|
|
|
|
def construct(self, x):
|
|
return self.mul_add(self.param, x)
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_refkey_bprop():
|
|
grad_by_list = C.GradOperation(get_all=True, get_by_list=True)
|
|
class GradWrap(nn.Cell):
|
|
def __init__(self, network):
|
|
super(GradWrap, self).__init__()
|
|
self.network = network
|
|
self.weights = ParameterTuple(filter(lambda x: x.requires_grad, network.get_parameters()))
|
|
def construct(self, x):
|
|
weights = self.weights
|
|
grads = grad_by_list(self.network, weights)(x)
|
|
return grads
|
|
network = GradWrap(MulAddWithParam())
|
|
input_data = Tensor(np.array([2, 2], np.float32))
|
|
grads = network(input_data)
|
|
assert (grads[0][0].asnumpy() == np.array([4, 4]).astype(np.float32)).all()
|
|
assert (grads[1][0].asnumpy() == np.array([2, 2]).astype(np.float32)).all()
|
|
|
|
|
|
class MulAddWithWrongOutputNum(nn.Cell):
|
|
def construct(self, x, y):
|
|
return 2 * x + y
|
|
|
|
def bprop(self, x, y, out, dout):
|
|
return (2 * dout,)
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_grad_mul_add_with_wrong_output_num():
|
|
context.set_context(check_bprop=True)
|
|
mul_add = MulAddWithWrongOutputNum()
|
|
with pytest.raises(TypeError):
|
|
grad_all(mul_add)(1, 2)
|
|
|
|
|
|
class MulAddWithWrongOutputType(nn.Cell):
|
|
def construct(self, x, y):
|
|
return 2 * x + y
|
|
|
|
def bprop(self, x, y, out, dout):
|
|
return 2 * dout, 2
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_grad_mul_add_with_wrong_output_type():
|
|
context.set_context(check_bprop=True)
|
|
mul_add = MulAddWithWrongOutputType()
|
|
with pytest.raises(TypeError):
|
|
grad_all(mul_add)(1, Tensor(np.ones([2, 2])))
|
|
|
|
|
|
class MulAddWithWrongOutputShape(nn.Cell):
|
|
def __init__(self):
|
|
super(MulAddWithWrongOutputShape, self).__init__()
|
|
self.ones = Tensor(np.ones([2,]))
|
|
|
|
def construct(self, x, y):
|
|
return 2 * x + y
|
|
|
|
def bprop(self, x, y, out, dout):
|
|
return 2, self.ones
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_grad_mul_add_with_wrong_output_shape():
|
|
context.set_context(check_bprop=True)
|
|
mul_add = MulAddWithWrongOutputShape()
|
|
with pytest.raises(TypeError):
|
|
grad_all(mul_add)(1, Tensor(np.ones([2, 2])))
|