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65 lines
2.4 KiB
65 lines
2.4 KiB
# Copyright 2019 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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import numpy as np
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from mindspore import Tensor
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from mindspore.common.api import ms_function
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from mindspore.ops import operations as P
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def test_nest_range_transpose():
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batch_size = 2
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num_layers = 5
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batch_tuple = tuple(Tensor(np.array(np.ones((2, 3)) * 0.01)) for i in range(batch_size))
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layers_tuple = tuple(Tensor(np.array(np.ones((3, 4)) * 0.02)) for i in range(num_layers))
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transpose1 = P.Transpose()
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@ms_function()
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def invoke_range():
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out1 = ()
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for m in range(num_layers):
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out1 += (transpose1(layers_tuple[m], (1, 0)),)
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# Both for loop will the same range symbol as phi node, when range primitive is converted
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# to DoSigature MetaFuncGraph, that MetaFuncGraph will take 2 and 5 as argument, so there is
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# 2 entries in that MetaFuncGraphEvaluator, that will make Specialier try to use AnyValue to
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# FindGeneralized for S-make_range MetaFuncGraph but it will fail as AnyValue is not constant.
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for i in range(batch_size):
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out1 += (transpose1(batch_tuple[i], (1, 0)),)
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for j in range(num_layers):
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out1 += (transpose1(layers_tuple[j], (1, 0)),)
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return out1
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print(invoke_range())
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def test_nest_range_simple():
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batch_size = 2
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num_layers = 5
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batch_tuple = tuple(Tensor(np.array(np.ones((2, 3)) * 0.01)) for i in range(batch_size))
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layers_tuple = tuple(Tensor(np.array(np.ones((3, 4)) * 0.02)) for i in range(num_layers))
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@ms_function()
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def invoke_range():
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out1 = ()
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for m in range(num_layers):
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out1 += (layers_tuple[m],)
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for i in range(batch_size):
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out1 += (batch_tuple[i],)
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for j in range(num_layers):
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out1 += (layers_tuple[j],)
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return out1
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print(invoke_range())
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