forked from huawei/mindspore2022
295 lines
7.2 KiB
Python
295 lines
7.2 KiB
Python
# Copyright 2021 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 graph fallback """
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import functools
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import pytest
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import numpy as np
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import mindspore.nn as nn
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from mindspore import Tensor, ms_function, context
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from mindspore.ops import operations as P
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from mindspore.ops import functional as F
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from mindspore.nn.probability import distribution
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import mindspore.common.dtype as mstype
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import mindspore.common._monad as monad
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import mindspore.scipy.linalg as alg
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context.set_context(mode=context.GRAPH_MODE)
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# `add_func` is defined in current file.
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def add_func(x, y):
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return x + y
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@ms_function
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def do_increment(i):
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add_1 = F.partial(add_func, 1)
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return add_1(i)
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def test_increment():
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a = do_increment(9)
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assert a == 10
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@ms_function
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def use_monad(x, y):
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res = P.Mul()(x, y)
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res = F.depend(res, monad.U)
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return res
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def test_use_monad():
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x = Tensor(1.0, mstype.float32)
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y = Tensor(1.0, mstype.float32)
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print(use_monad(x, y))
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@ms_function
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def use_tuple_of_tensor():
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me_x = (Tensor(1), Tensor(1))
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return me_x
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def test_tuple_of_tensor():
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"""
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Feature: JIT Fallback
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Description: Test tuple of tensor in graph mode.
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Expectation: No exception.
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"""
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print(use_tuple_of_tensor())
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@ms_function
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def use_list_of_tensor():
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me_x = [Tensor(1), Tensor(1)]
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return me_x
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def test_list_of_tensor():
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"""
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Feature: JIT Fallback
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Description: Test list of tensor in graph mode.
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Expectation: No exception.
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"""
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print(use_list_of_tensor())
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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.x = Tensor([2, 3, 4])
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def construct(self):
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x_len = len(self.x)
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for i in range(x_len):
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print(i)
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return x_len
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def test_builtins_len():
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net = Net()
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net()
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@ms_function
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def np_fallback_func():
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array_x = tuple([2, 3, 4, 5])
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np_x = np.array(array_x).astype(np.float32)
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me_x = Tensor(np_x)
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me_x = me_x + me_x
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return me_x
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def test_np_fallback_func():
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print(np_fallback_func())
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# Test `return` interpret node.
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@ms_function
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def div_mod_func1():
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x = 8
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y = 3
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a = divmod(x, y)
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return Tensor(a)
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def test_div_mod_func1():
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print(div_mod_func1()) # (2, 2)
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# Test interpret node with parameters as input.
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@ms_function
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def div_mod_func2(x, y):
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a = divmod(x, y)
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return Tensor(a)
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def test_div_mod_func2_scalar():
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"""
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Feature: JIT Fallback
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Description: Test divmod in graph.
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Expectation: No exception.
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"""
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print(div_mod_func2(8, 3)) # (2, 2)
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@pytest.mark.skip(reason='Not support in graph jit fallback feature yet')
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def test_div_mod_func2_tensor():
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"""
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Feature: JIT Fallback
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Description: Test divmod with Tensor input in graph. We'll support it in Tensor Input Fallback solution.
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Expectation: Not supported exception.
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"""
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with pytest.raises(RuntimeError) as err:
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print(div_mod_func2(Tensor(8), Tensor(3)))
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assert "Not support Tensor or variable type as input during running JIT Fallback, but got" in str(err.value)
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@ms_function
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def select_func(cond, x, y):
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if isinstance(cond, (tuple, list)):
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output = y
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elif isinstance(cond, Tensor):
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output = F.select(cond, x, y)
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else:
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output = x
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return output
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def test_select_func():
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cond = Tensor([True, False])
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x = Tensor([2, 3], mstype.float32)
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y = Tensor([1, 2], mstype.float32)
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print(select_func(cond, x, y))
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@ms_function
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def select_func2(cond, x, y):
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if isinstance(cond, (tuple, list)):
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output = y
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if isinstance(cond, Tensor):
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output = F.select(cond, x, y)
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else:
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output = x
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return output
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def test_select_func2():
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cond = Tensor([True, False])
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x = Tensor([2, 3], mstype.float32)
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y = Tensor([1, 2], mstype.float32)
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print(select_func2(cond, x, y))
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@ms_function
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def slice_func(a, b):
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a[1:3, ::] = b
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return a
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def test_slice_func():
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a = Tensor(np.arange(60).reshape(3, 4, 5), dtype=mstype.float32)
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b = Tensor([1], dtype=mstype.float32)
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print(slice_func(a, b))
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def test_context():
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"""
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Feature: JIT Fallback
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Description: Test context in graph.
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Expectation: No exception.
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"""
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class ContextNet(nn.Cell):
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def __init__(self):
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super(ContextNet, self).__init__()
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self.mode = context.get_context("mode")
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def construct(self):
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out = 1
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if self.mode == context.GRAPH_MODE:
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out = 2
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return out
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net = ContextNet()
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out = net()
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print(out)
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def test_scipy_module():
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"""
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Feature: JIT Fallback
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Description: Test scipy module in graph.
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Expectation: No exception.
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"""
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class Network(nn.Cell):
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def construct(self, x):
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return alg.eigh(x)
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net = Network()
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x = Tensor([[2, 0, 0, 0], [0, 1, 0, 0], [0, 0, 2, 0], [0, 0, 0, 1]])
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out = net(x)
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print(out)
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def test_probability_cauchy():
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"""
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Feature: JIT Fallback
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Description: NumPy method is called in probability cauchy.
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Expectation: No exception.
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"""
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class CauchyProb(nn.Cell):
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def __init__(self, loc, scale, seed=10, dtype=mstype.float32, name='Cauchy'):
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super().__init__()
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self.b = distribution.Cauchy(loc, scale, seed, dtype, name)
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def construct(self, value, loc=None, scale=None):
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out1 = self.b.prob(value, loc, scale)
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out2 = self.b.log_prob(value, loc, scale)
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out3 = self.b.cdf(value, loc, scale)
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out4 = self.b.log_cdf(value, loc, scale)
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out5 = self.b.survival_function(value, loc, scale)
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out6 = self.b.log_survival(value, loc, scale)
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return out1, out2, out3, out4, out5, out6
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loc = np.random.randn(1024, 512, 7, 7).astype(np.float32)
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scale = np.random.uniform(0.0001, 100, size=(1024, 512, 7, 7)).astype(np.float32)
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loc_a = np.random.randn(1024, 512, 7, 7).astype(np.float32)
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scale_a = np.random.uniform(0.0001, 100, size=(1024, 512, 7, 7)).astype(np.float32)
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value = np.random.randn(1024, 512, 7, 7).astype(np.float32)
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net = CauchyProb(loc, scale)
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net(Tensor(value), Tensor(loc_a), Tensor(scale_a))
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def test_third_party_module_functools():
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"""
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Feature: JIT Fallback
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Description: functools is a python built-in module and does not perform JIT Fallback.
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Expectation: No exception.
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"""
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class ModuleNet(nn.Cell):
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def construct(self, x, y):
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func = functools.partial(add_func, x)
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out = func(y)
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return out
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x = Tensor([1, 2, 3], mstype.int32)
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y = Tensor([4, 5, 6], mstype.int32)
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net = ModuleNet()
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out = net(x, y)
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print(out)
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