forked from mindspore-Ecosystem/mindspore
325 lines
11 KiB
Python
325 lines
11 KiB
Python
# Copyright 2022 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 mutable"""
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import numpy as np
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import pytest
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from mindspore.ops.composite import GradOperation
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from mindspore.common import mutable
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from mindspore.common.api import _CellGraphExecutor
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from mindspore.ops import operations as P
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import mindspore.nn as nn
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import mindspore.common.dtype as mstype
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from mindspore import Tensor
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from mindspore._c_expression import Tensor as Tensor_
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from mindspore import Parameter
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from mindspore import jit
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@pytest.mark.skip(reason="No runtime support")
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def test_mutable_scalar_mul_grad_first():
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"""
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Feature: Set Constants mutable.
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Description: Get gradient with respect to the first scalar input.
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Expectation: Get the correct gradient.
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"""
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class Net(nn.Cell):
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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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self.grad_op = GradOperation()
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def construct(self, x, y):
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gradient_function = self.grad_op(self.net)
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return gradient_function(x, y)
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x = mutable(2)
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output = GradNet(Net())(x, 3)
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assert output == 3
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@pytest.mark.skip(reason="No runtime support")
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def test_mutable_scalar_mul_grad_all():
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"""
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Feature: Set Constants mutable.
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Description: Get gradient with respect to all scalar inputs.
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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 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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self.grad_op = GradOperation(get_all=True)
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def construct(self, x, y):
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gradient_function = self.grad_op(self.net)
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return gradient_function(x, y)
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x = mutable(2)
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y = mutable(3)
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output = GradNet(Net())(x, y)
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assert output == (3, 2)
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@pytest.mark.skip(reason="No runtime support")
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def test_mutable_tuple_or_list_scalar_mul_grad():
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"""
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Feature: Set Constants mutable.
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Description: Get gradient with respect to the tuple or list scalar input.
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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 construct(self, x):
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return x[0] * x[1]
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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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self.grad_op = GradOperation()
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def construct(self, x):
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gradient_function = self.grad_op(self.net)
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return gradient_function(x)
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x = mutable((2, 3))
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output = GradNet(Net())(x)
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assert output == (3, 2)
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x = mutable([2, 3])
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output = GradNet(Net())(x)
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assert output == (3, 2)
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@pytest.mark.skip(reason="No runtime support")
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def test_mutable_dict_scalar_mul_grad():
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"""
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Feature: Set Constants mutable.
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Description: Get gradient with respect to the dict scalar input.
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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 construct(self, x):
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return x['a'] * x['b']
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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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self.grad_op = GradOperation()
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def construct(self, x):
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gradient_function = self.grad_op(self.net)
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return gradient_function(x)
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x = mutable({'a': 2, 'b': 3})
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output = GradNet(Net())(x)
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assert output == (3, 2)
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@pytest.mark.skip(reason="No runtime support")
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def test_mutable_mix_scalar_mul_grad_all():
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"""
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Feature: Set Constants mutable.
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Description: Get gradient with respect to the mix scalar input including dict and tuple.
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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 construct(self, x, y):
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return x['a'] * x['b'] * y[0]
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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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self.grad_op = GradOperation(get_all=True)
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def construct(self, x, y):
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gradient_function = self.grad_op(self.net)
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return gradient_function(x, y)
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x = mutable({'a': 2, 'b': 3})
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y = mutable((4, 5))
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output = GradNet(Net())(x, y)
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assert output == ((12, 8), (6, 0))
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def test_tuple_inputs_compile_phase():
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"""
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Feature: Set Constants mutable.
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Description: Test whether the compilation phase for tuple(Tensor) input twice are the same.
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Expectation: The phases are the same.
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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, tuple_input):
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x = tuple_input[0]
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y = tuple_input[1]
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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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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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p = Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32)
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q = 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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net = Net()
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_cell_graph_executor = _CellGraphExecutor()
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# tuple of Tensor
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phase1, _ = _cell_graph_executor.compile(net, (x, y))
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phase2, _ = _cell_graph_executor.compile(net, (p, q))
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assert phase1 != phase2
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phase1, _ = _cell_graph_executor.compile(net, mutable((x, y)))
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phase2, _ = _cell_graph_executor.compile(net, mutable((p, q)))
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assert phase1 == phase2
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# list of Tensor
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phase1, _ = _cell_graph_executor.compile(net, [x, y])
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phase2, _ = _cell_graph_executor.compile(net, [p, q])
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assert phase1 != phase2
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phase1, _ = _cell_graph_executor.compile(net, mutable([x, y]))
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phase2, _ = _cell_graph_executor.compile(net, mutable([p, q]))
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assert phase1 == phase2
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def test_dict_inputs_compile_phase():
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"""
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Feature: Set Constants mutable.
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Description: Test whether the compilation phase for dict(Tensor) input twice are the same.
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Expectation: The phases are the same.
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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, tuple_input):
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x = tuple_input['a']
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y = tuple_input['b']
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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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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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p = Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32)
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q = 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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net = Net()
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_cell_graph_executor = _CellGraphExecutor()
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phase1, _ = _cell_graph_executor.compile(net, {'a': x, 'b': y})
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phase2, _ = _cell_graph_executor.compile(net, {'a': p, 'b': q})
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assert phase1 != phase2
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phase1, _ = _cell_graph_executor.compile(net, mutable({'a': x, 'b': y}))
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phase2, _ = _cell_graph_executor.compile(net, mutable({'a': p, 'b': q}))
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assert phase1 == phase2
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def test_tensor_inputs_compile_phase():
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"""
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Feature: Set Constants mutable.
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Description: Test whether the compilation phase for Tensor input twice are the same.
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Expectation: The phases are the same.
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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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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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p = Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32)
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q = 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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net = Net()
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_cell_graph_executor = _CellGraphExecutor()
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# tuple of Tensor
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phase1, _ = _cell_graph_executor.compile(net, x, y)
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phase2, _ = _cell_graph_executor.compile(net, p, q)
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assert phase1 == phase2
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phase1, _ = _cell_graph_executor.compile(net, mutable(x), mutable(y))
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phase2, _ = _cell_graph_executor.compile(net, mutable(p), mutable(q))
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assert phase1 == phase2
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x = Tensor_(x)
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y = Tensor_(y)
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p = Tensor_(p)
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q = Tensor_(q)
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phase1, _ = _cell_graph_executor.compile(net, x, y)
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phase2, _ = _cell_graph_executor.compile(net, p, q)
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assert phase1 == phase2
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phase1, _ = _cell_graph_executor.compile(net, mutable(x), mutable(y))
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phase2, _ = _cell_graph_executor.compile(net, mutable(p), mutable(q))
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assert phase1 == phase2
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def test_check_mutable_value():
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"""
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Feature: Set Constants mutable.
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Description: Check the illegal mutable value.
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Expectation: Raise the correct error log.
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"""
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try:
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mutable(1)
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except TypeError as e:
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assert "For 'mutable', the 'input_data' should be one of (Tensor, tuple[Tensor], list[Tensor], dict[Tensor]) " \
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"or their nested structures, but got" in str(e)
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try:
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mutable([Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32), (2,)])
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except TypeError as e:
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assert "For 'mutable', the 'input_data' should be one of (Tensor, tuple[Tensor], list[Tensor], dict[Tensor]) " \
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"or their nested structures, but got" in str(e)
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try:
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mutable({'a': Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32), 'b': (2,)})
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except TypeError as e:
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assert "For 'mutable', the 'input_data' should be one of (Tensor, tuple[Tensor], list[Tensor], dict[Tensor]) " \
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"or their nested structures, but got" in str(e)
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@jit
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def net():
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x = mutable(2)
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return x
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try:
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net()
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except TypeError as e:
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assert "For mutable api in graph, the input arg should be one of (Tensor, tuple[Tensor], list[Tensor], " \
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"dict[Tensor]) or their nested structures, but got " in str(e)
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